diff --git a/Disertationmainfile.ipynb b/Disertationmainfile.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..b3eb34b0cb364d536d1f9beb5bab172adb1db513 --- /dev/null +++ b/Disertationmainfile.ipynb @@ -0,0 +1,13658 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "from pyspark import SparkConf\n", + "from pyspark import SparkContext\n", + "from pyspark.sql import SparkSession\n", + "from pyspark.sql.functions import udf\n", + "from pyspark.sql.types import IntegerType\n", + "from pyspark.sql.types import LongType\n", + "from pyspark.sql.types import FloatType\n", + "from pyspark.rdd import RDD\n", + "from pyspark.sql.types import StringType\n", + "from pyspark.sql.functions import col\n", + "import pyspark.sql.functions as F\n", + "import csv\n", + "from datetime import datetime\n", + "from functools import reduce\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "# local[*]: run Spark in local-mode(parallel computing) with as many working processors as logical cores on your machine\n", + "master = \"local[*]\"\n", + "# The `appName` field is a name to be shown on the Spark cluster UI page\n", + "app_name = \"Big data Analysis of Road Crash Data\"\n", + "# Setup configuration parameters for Spark\n", + "spark_conf = SparkConf().setMaster(master).setAppName(app_name)\n", + "# creating a SparkContext object \n", + "spark = SparkSession.builder.config(conf=spark_conf).getOrCreate()\n", + "sc = spark.sparkContext\n", + "sc.setLogLevel('ERROR')" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " <div>\n", + " <p><b>SparkSession - in-memory</b></p>\n", + " \n", + " <div>\n", + " <p><b>SparkContext</b></p>\n", + "\n", + " <p><a href=\"http://10.77.206.160:4040\">Spark UI</a></p>\n", + "\n", + " <dl>\n", + " <dt>Version</dt>\n", + " <dd><code>v3.1.2</code></dd>\n", + " <dt>Master</dt>\n", + " <dd><code>local[*]</code></dd>\n", + " <dt>AppName</dt>\n", + " <dd><code>Big data Analysis of Road Crash Data</code></dd>\n", + " </dl>\n", + " </div>\n", + " \n", + " </div>\n", + " " + ], + "text/plain": [ + "<pyspark.sql.session.SparkSession at 0x1216b8908>" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "spark" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Uploading csv files to be read by Apache spark " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+--------------+--------------+---------------+--------------+---------------+-----------------+-------------------+----------+-----------+-------------------------------------------+----------------------------+-----------------------------------+---------+---------------------------+--------------------------+-------------------------+---------------------+----------------------+---------+-------------------------+--------------------+------------------+---------------------------------+---------------------------------------+-------------------+-----------------------+------------------+--------------------------+-----------+-----+-------------------+---------------------+----+----------+\n", + "|Accident_Index|1st_Road_Class|1st_Road_Number|2nd_Road_Class|2nd_Road_Number|Accident_Severity|Carriageway_Hazards|Date |Day_of_Week|Did_Police_Officer_Attend_Scene_of_Accident|Junction_Control |Junction_Detail |Latitude |Light_Conditions |Local_Authority_(District)|Local_Authority_(Highway)|Location_Easting_OSGR|Location_Northing_OSGR|Longitude|LSOA_of_Accident_Location|Number_of_Casualties|Number_of_Vehicles|Pedestrian_Crossing-Human_Control|Pedestrian_Crossing-Physical_Facilities|Police_Force |Road_Surface_Conditions|Road_Type |Special_Conditions_at_Site|Speed_limit|Time |Urban_or_Rural_Area|Weather_Conditions |Year|InScotland|\n", + "+--------------+--------------+---------------+--------------+---------------+-----------------+-------------------+----------+-----------+-------------------------------------------+----------------------------+-----------------------------------+---------+---------------------------+--------------------------+-------------------------+---------------------+----------------------+---------+-------------------------+--------------------+------------------+---------------------------------+---------------------------------------+-------------------+-----------------------+------------------+--------------------------+-----------+-----+-------------------+---------------------+----+----------+\n", + "|200501BS00022 |A |4 |Unclassified |0 |Serious |None |2005-01-08|Saturday |1 |Give way or uncontrolled |T or staggered junction |51.495498|Darkness - lights lit |Kensington and Chelsea |Kensington and Chelsea |526790 |178980 |-0.174925|E01002821 |1 |1 |0 |0 |Metropolitan Police|Dry |Single carriageway|None |30 |03:00|Urban |Fine no high winds |2005|No |\n", + "|200501BS00014 |A |3220 |A |308 |Slight |None |2005-01-25|Tuesday |1 |Auto traffic signal |Crossroads |51.484044|Darkness - lights lit |Kensington and Chelsea |Kensington and Chelsea |526170 |177690 |-0.184312|E01002912 |1 |2 |0 |5 |Metropolitan Police|Wet or damp |Single carriageway|None |30 |20:48|Urban |Fine no high winds |2005|No |\n", + "|200501BS00021 |B |302 |NA |0 |Slight |None |2005-01-21|Friday |1 |Data missing or out of range|Not at junction or within 20 metres|51.486552|Darkness - lights lit |Kensington and Chelsea |Kensington and Chelsea |527810 |178010 |-0.16059 |E01002901 |1 |2 |0 |0 |Metropolitan Police|Dry |Single carriageway|None |30 |21:16|Urban |Fine no high winds |2005|No |\n", + "|200501BS00007 |C |0 |Unclassified |0 |Slight |None |2005-01-13|Thursday |1 |Give way or uncontrolled |T or staggered junction |51.512695|Darkness - lights lit |Kensington and Chelsea |Kensington and Chelsea |524220 |180830 |-0.211277|E01002875 |1 |2 |0 |0 |Metropolitan Police|Dry |Single carriageway|None |30 |20:40|Urban |Fine no high winds |2005|No |\n", + "|200501BS00012 |A |4 |B |325 |Slight |None |2005-01-16|Sunday |1 |Auto traffic signal |Crossroads |51.494902|Darkness - lights lit |Kensington and Chelsea |Kensington and Chelsea |526240 |178900 |-0.182872|E01002835 |1 |1 |0 |5 |Metropolitan Police|Dry |Single carriageway|None |30 |00:42|Urban |Fine no high winds |2005|No |\n", + "|200501BS00017 |A |4 |NA |0 |Slight |None |2005-01-18|Tuesday |1 |Data missing or out of range|Not at junction or within 20 metres|51.495429|Daylight |Kensington and Chelsea |Kensington and Chelsea |526700 |178970 |-0.176224|E01002821 |2 |1 |0 |0 |Metropolitan Police|Dry |Dual carriageway |None |30 |11:15|Urban |Fine no high winds |2005|No |\n", + "|200501BS00020 |A |3218 |A |4 |Slight |None |2005-01-21|Friday |1 |Give way or uncontrolled |T or staggered junction |51.495811|Daylight |Kensington and Chelsea |Kensington and Chelsea |527000 |179020 |-0.171887|E01002821 |1 |2 |0 |0 |Metropolitan Police|Dry |Single carriageway|None |30 |09:15|Urban |Fine no high winds |2005|No |\n", + "|200501BS00003 |C |0 |NA |0 |Slight |None |2005-01-06|Thursday |1 |Data missing or out of range|Not at junction or within 20 metres|51.525301|Darkness - lights lit |Kensington and Chelsea |Kensington and Chelsea |524520 |182240 |-0.206458|E01002857 |1 |2 |0 |0 |Metropolitan Police|Dry |Single carriageway|None |30 |00:15|Urban |Fine no high winds |2005|No |\n", + "|200501BS00006 |Unclassified |0 |NA |0 |Slight |None |2005-01-11|Tuesday |1 |Data missing or out of range|Not at junction or within 20 metres|51.51554 |Daylight |Kensington and Chelsea |Kensington and Chelsea |524770 |181160 |-0.203238|E01002832 |1 |2 |0 |0 |Metropolitan Police|Wet or damp |Single carriageway|Oil or diesel |30 |12:40|Urban |Raining no high winds|2005|No |\n", + "|200501BS00010 |A |3212 |B |304 |Slight |None |2005-01-15|Saturday |1 |Auto traffic signal |Crossroads |51.48342 |Darkness - lights lit |Kensington and Chelsea |Kensington and Chelsea |527350 |177650 |-0.167342|E01002900 |2 |2 |0 |5 |Metropolitan Police|Dry |Single carriageway|None |30 |22:43|Urban |Fine no high winds |2005|No |\n", + "|200501BS00011 |B |450 |C |0 |Slight |None |2005-01-15|Saturday |1 |Give way or uncontrolled |T or staggered junction |51.512443|Daylight |Kensington and Chelsea |Kensington and Chelsea |524550 |180810 |-0.206531|E01002875 |5 |2 |0 |8 |Metropolitan Police|Dry |Single carriageway|None |30 |16:00|Urban |Fine no high winds |2005|No |\n", + "|200501BS00015 |Unclassified |0 |A |3220 |Slight |None |2005-01-11|Tuesday |1 |Give way or uncontrolled |T or staggered junction |51.491632|Daylight |Kensington and Chelsea |Kensington and Chelsea |525590 |178520 |-0.192366|E01002849 |1 |1 |0 |1 |Metropolitan Police|Wet or damp |One way street |None |30 |12:55|Urban |Raining no high winds|2005|No |\n", + "|200501BS00016 |A |3217 |A |3216 |Slight |None |2005-01-18|Tuesday |1 |Give way or uncontrolled |T or staggered junction |51.492622|Darkness - lights lit |Kensington and Chelsea |Kensington and Chelsea |527990 |178690 |-0.157753|E01002902 |1 |2 |0 |0 |Metropolitan Police|Wet or damp |One way street |None |30 |05:01|Urban |Raining no high winds|2005|No |\n", + "|200501BS00018 |A |3217 |Unclassified |0 |Slight |None |2005-01-18|Tuesday |1 |Give way or uncontrolled |T or staggered junction |51.481912|Daylight |Kensington and Chelsea |Kensington and Chelsea |526460 |177460 |-0.18022 |E01002840 |1 |1 |0 |1 |Metropolitan Police|Dry |Single carriageway|None |30 |10:50|Urban |Fine no high winds |2005|No |\n", + "|200501BS00019 |Unclassified |0 |Unclassified |0 |Serious |None |2005-01-20|Thursday |1 |Give way or uncontrolled |T or staggered junction |51.500191|Darkness - lights lit |Kensington and Chelsea |Kensington and Chelsea |524680 |179450 |-0.205139|E01002864 |1 |2 |0 |0 |Metropolitan Police|Dry |Single carriageway|None |30 |00:15|Urban |Fine no high winds |2005|No |\n", + "|200501BS00001 |A |3218 |NA |0 |Serious |None |2005-01-04|Tuesday |1 |Data missing or out of range|Not at junction or within 20 metres|51.489096|Daylight |Kensington and Chelsea |Kensington and Chelsea |525680 |178240 |-0.19117 |E01002849 |1 |1 |0 |1 |Metropolitan Police|Wet or damp |Single carriageway|None |30 |17:42|Urban |Raining no high winds|2005|No |\n", + "|200501BS00002 |B |450 |C |0 |Slight |None |2005-01-05|Wednesday |1 |Auto traffic signal |Crossroads |51.520075|Darkness - lights lit |Kensington and Chelsea |Kensington and Chelsea |524170 |181650 |-0.211708|E01002909 |1 |1 |0 |5 |Metropolitan Police|Dry |Dual carriageway |None |30 |17:36|Urban |Fine no high winds |2005|No |\n", + "|200501BS00004 |A |3220 |NA |0 |Slight |None |2005-01-07|Friday |1 |Data missing or out of range|Not at junction or within 20 metres|51.482442|Daylight |Kensington and Chelsea |Kensington and Chelsea |526900 |177530 |-0.173862|E01002840 |1 |1 |0 |0 |Metropolitan Police|Dry |Single carriageway|None |30 |10:35|Urban |Fine no high winds |2005|No |\n", + "|200501BS00005 |Unclassified |0 |NA |0 |Slight |None |2005-01-10|Monday |1 |Data missing or out of range|Not at junction or within 20 metres|51.495752|Darkness - lighting unknown|Kensington and Chelsea |Kensington and Chelsea |528060 |179040 |-0.156618|E01002863 |1 |1 |0 |0 |Metropolitan Police|Wet or damp |Single carriageway|None |30 |21:13|Urban |Fine no high winds |2005|No |\n", + "|200501BS00009 |A |315 |NA |0 |Slight |None |2005-01-14|Friday |1 |Data missing or out of range|Not at junction or within 20 metres|51.50226 |Daylight |Kensington and Chelsea |Kensington and Chelsea |525890 |179710 |-0.187623|E01002889 |2 |1 |0 |0 |Metropolitan Police|Dry |Dual carriageway |None |30 |17:35|Urban |Fine no high winds |2005|No |\n", + "+--------------+--------------+---------------+--------------+---------------+-----------------+-------------------+----------+-----------+-------------------------------------------+----------------------------+-----------------------------------+---------+---------------------------+--------------------------+-------------------------+---------------------+----------------------+---------+-------------------------+--------------------+------------------+---------------------------------+---------------------------------------+-------------------+-----------------------+------------------+--------------------------+-----------+-----+-------------------+---------------------+----+----------+\n", + "only showing top 20 rows\n", + "\n" + ] + } + ], + "source": [ + "Accident_Information_df = spark.read.format('csv')\\\n", + " .option('header',True).option('escape','\"')\\\n", + " .load('/Users/Asfandyar/Downloads/archive/Accident_Information.csv')\n", + "# changing the type of column(\"Year'\") to interger type\n", + "Accident_Information_df = Accident_Information_df.withColumn('Year',F.col('Year').cast(IntegerType()))\n", + "#Accident_Information_df=Accident_Information_df.filter(Accident_Information_df.Year<2017)\n", + "Accident_Information_df.sort(\"Year\").show(truncate=False)\n", + "Accident_Information_df\n", + "A2019 = spark.read.format('csv')\\\n", + " .option('header',True).option('escape','\"')\\\n", + " .load('/Users/Asfandyar/Downloads/Road Safety Data - Accidents 2019.csv')\n", + "# changing the type of column(\"Year'\") to interger type\n", + "A2019 = A2019.withColumn('Year',F.col('Year').cast(IntegerType()))\n", + "A2018 = spark.read.format('csv')\\\n", + " .option('header',True).option('escape','\"')\\\n", + " .load('/Users/Asfandyar/Downloads/dftRoadSafetyData_Accidents_2018.csv')\n", + "# changing the type of column(\"Year'\") to interger type\n", + "A2018 = A2018.withColumn('Year',F.col('Year').cast(IntegerType()))\n", + "#A2005 = spark.read.format('csv')\\\n", + "# .option('header',True).option('escape','\"')\\\n", + "# .load('/Users/Asfandyar/Downloads/Stats19_Data_2005-2014/Accidents0514.csv')\n", + "#A2005=A2005.withColumn('year', concat(A2005.Accident_Index.substr(1, 4)))\n", + "\n", + "# changing the type of column(\"Year'\") to interger type\n", + "#A2005 = A2005.withColumn('Year',F.col('Year').cast(IntegerType())) \n", + "\n", + "A2018 = A2018.union(A2019)\n", + "#A2018 = A2018.union(A2005)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "from pyspark.sql.functions import col, when\n", + "valueWhenTrue1 =\"M\"\n", + "valueWhenTrue2 =\"A\"\n", + "valueWhenTrue3 = \"A\"\n", + "valueWhenTrue4 = \"B\"\n", + "valueWhenTrue5 = \"C\"\n", + "valueWhenTrue6 = \"U\"\n", + "\n", + "A2018=A2018.withColumn(\n", + " \"Accident_Severity\",\n", + " when(\n", + " col(\"Accident_Severity\") == 1,\n", + " \"Fatal\"\n", + " ).\n", + " when(\n", + " col(\"Accident_Severity\") == 2,\n", + " \"Serious\"\n", + " ).\n", + " when(\n", + " col(\"Accident_Severity\") == 3,\n", + " \"Slight\"\n", + " ).otherwise(col(\"Accident_Severity\")),\n", + ")\n", + "A2018=A2018.withColumn(\n", + " \"1st_Road_Class\",\n", + " when(\n", + " col(\"1st_Road_Class\") == 1,\n", + " \"M\"\n", + " ).otherwise(col(\"1st_Road_Class\")),\n", + ")\n", + "A2018=A2018.withColumn(\n", + " \"1st_Road_Class\",\n", + " when(\n", + " col(\"1st_Road_Class\") == 2,\n", + " \"A\"\n", + " ).otherwise(col(\"1st_Road_Class\")),\n", + ")\n", + "A2018=A2018.withColumn(\n", + " \"1st_Road_Class\",\n", + " when(\n", + " col(\"1st_Road_Class\") == 3,\n", + " \"A\"\n", + " ).otherwise(col(\"1st_Road_Class\")),\n", + ")\n", + "A2018=A2018.withColumn(\n", + " \"1st_Road_Class\",\n", + " when(\n", + " col(\"1st_Road_Class\") == 4,\n", + " \"B\"\n", + " ).otherwise(col(\"1st_Road_Class\")),\n", + ")\n", + "A2018=A2018.withColumn(\n", + " \"1st_Road_Class\",\n", + " when(\n", + " col(\"1st_Road_Class\") == 5,\n", + " \"C\"\n", + " ).otherwise(col(\"1st_Road_Class\")),\n", + ")\n", + "A2018=A2018.withColumn(\n", + " \"1st_Road_Class\",\n", + " when(\n", + " col(\"1st_Road_Class\") == 6,\n", + " \"U\"\n", + " ).otherwise(col(\"1st_Road_Class\")),\n", + ")\n", + "A2018=A2018.withColumn(\n", + " \"Day_of_Week\",\n", + " when(\n", + " col(\"Day_of_Week\") == 1,\n", + " \"Monday\"\n", + " ).\n", + " when(\n", + " col(\"Day_of_Week\") == 2,\n", + " \"Tuesday\"\n", + " ).\n", + " when(\n", + " col(\"Day_of_Week\") == 3,\n", + " \"Wednesday\"\n", + " ).\n", + " when(\n", + " col(\"Day_of_Week\") == 4,\n", + " \"Thursday\"\n", + " ).\n", + " when(\n", + " col(\"Day_of_Week\") == 5,\n", + " \"Friday\"\n", + " ).\n", + " when(\n", + " col(\"Day_of_Week\") == 6,\n", + " \"Saturday\"\n", + " ).\n", + " when(\n", + " col(\"Day_of_Week\") == 7,\n", + " \"Sunday\"\n", + " ).otherwise(col(\"Day_of_Week\")),\n", + ")\n", + "A2018=A2018.withColumn(\n", + " \"Accident_Severity\",\n", + " when(\n", + " col(\"Accident_Severity\") == 1,\n", + " \"Fatal\"\n", + " ).\n", + " when(\n", + " col(\"Accident_Severity\") == 2,\n", + " \"Serious\"\n", + " ).\n", + " when(\n", + " col(\"Accident_Severity\") == 3,\n", + " \"Slight\"\n", + " ).otherwise(col(\"Accident_Severity\")),\n", + ")\n", + "\n", + "Accident_Information_df=Accident_Information_df.drop(\"InScotland\")\n", + "Accident_Information20052019_df = Accident_Information_df.unionByName(A2018)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+--------------+--------------+---------------+--------------+---------------+-----------------+-------------------+----------+-----------+-------------------------------------------+--------------------+--------------------+---------+--------------------+--------------------------+-------------------------+---------------------+----------------------+---------+-------------------------+--------------------+------------------+---------------------------------+---------------------------------------+-------------------+-----------------------+------------------+--------------------------+-----------+-----+-------------------+--------------------+----+\n", + "|Accident_Index|1st_Road_Class|1st_Road_Number|2nd_Road_Class|2nd_Road_Number|Accident_Severity|Carriageway_Hazards| Date|Day_of_Week|Did_Police_Officer_Attend_Scene_of_Accident| Junction_Control| Junction_Detail| Latitude| Light_Conditions|Local_Authority_(District)|Local_Authority_(Highway)|Location_Easting_OSGR|Location_Northing_OSGR|Longitude|LSOA_of_Accident_Location|Number_of_Casualties|Number_of_Vehicles|Pedestrian_Crossing-Human_Control|Pedestrian_Crossing-Physical_Facilities| Police_Force|Road_Surface_Conditions| Road_Type|Special_Conditions_at_Site|Speed_limit| Time|Urban_or_Rural_Area| Weather_Conditions|Year|\n", + "+--------------+--------------+---------------+--------------+---------------+-----------------+-------------------+----------+-----------+-------------------------------------------+--------------------+--------------------+---------+--------------------+--------------------------+-------------------------+---------------------+----------------------+---------+-------------------------+--------------------+------------------+---------------------------------+---------------------------------------+-------------------+-----------------------+------------------+--------------------------+-----------+-----+-------------------+--------------------+----+\n", + "| 200501BS00001| A| 3218| NA| 0| Serious| None|2005-01-04| Tuesday| 1|Data missing or o...|Not at junction o...|51.489096| Daylight| Kensington and Ch...| Kensington and Ch...| 525680| 178240| -0.19117| E01002849| 1| 1| 0| 1|Metropolitan Police| Wet or damp|Single carriageway| None| 30|17:42| Urban|Raining no high w...|2005|\n", + "| 200501BS00002| B| 450| C| 0| Slight| None|2005-01-05| Wednesday| 1| Auto traffic signal| Crossroads|51.520075|Darkness - lights...| Kensington and Ch...| Kensington and Ch...| 524170| 181650|-0.211708| E01002909| 1| 1| 0| 5|Metropolitan Police| Dry| Dual carriageway| None| 30|17:36| Urban| Fine no high winds|2005|\n", + "| 200501BS00003| C| 0| NA| 0| Slight| None|2005-01-06| Thursday| 1|Data missing or o...|Not at junction o...|51.525301|Darkness - lights...| Kensington and Ch...| Kensington and Ch...| 524520| 182240|-0.206458| E01002857| 1| 2| 0| 0|Metropolitan Police| Dry|Single carriageway| None| 30|00:15| Urban| Fine no high winds|2005|\n", + "| 200501BS00004| A| 3220| NA| 0| Slight| None|2005-01-07| Friday| 1|Data missing or o...|Not at junction o...|51.482442| Daylight| Kensington and Ch...| Kensington and Ch...| 526900| 177530|-0.173862| E01002840| 1| 1| 0| 0|Metropolitan Police| Dry|Single carriageway| None| 30|10:35| Urban| Fine no high winds|2005|\n", + "| 200501BS00005| Unclassified| 0| NA| 0| Slight| None|2005-01-10| Monday| 1|Data missing or o...|Not at junction o...|51.495752|Darkness - lighti...| Kensington and Ch...| Kensington and Ch...| 528060| 179040|-0.156618| E01002863| 1| 1| 0| 0|Metropolitan Police| Wet or damp|Single carriageway| None| 30|21:13| Urban| Fine no high winds|2005|\n", + "| 200501BS00006| Unclassified| 0| NA| 0| Slight| None|2005-01-11| Tuesday| 1|Data missing or o...|Not at junction o...| 51.51554| Daylight| Kensington and Ch...| Kensington and Ch...| 524770| 181160|-0.203238| E01002832| 1| 2| 0| 0|Metropolitan Police| Wet or damp|Single carriageway| Oil or diesel| 30|12:40| Urban|Raining no high w...|2005|\n", + "| 200501BS00007| C| 0| Unclassified| 0| Slight| None|2005-01-13| Thursday| 1|Give way or uncon...|T or staggered ju...|51.512695|Darkness - lights...| Kensington and Ch...| Kensington and Ch...| 524220| 180830|-0.211277| E01002875| 1| 2| 0| 0|Metropolitan Police| Dry|Single carriageway| None| 30|20:40| Urban| Fine no high winds|2005|\n", + "| 200501BS00009| A| 315| NA| 0| Slight| None|2005-01-14| Friday| 1|Data missing or o...|Not at junction o...| 51.50226| Daylight| Kensington and Ch...| Kensington and Ch...| 525890| 179710|-0.187623| E01002889| 2| 1| 0| 0|Metropolitan Police| Dry| Dual carriageway| None| 30|17:35| Urban| Fine no high winds|2005|\n", + "| 200501BS00010| A| 3212| B| 304| Slight| None|2005-01-15| Saturday| 1| Auto traffic signal| Crossroads| 51.48342|Darkness - lights...| Kensington and Ch...| Kensington and Ch...| 527350| 177650|-0.167342| E01002900| 2| 2| 0| 5|Metropolitan Police| Dry|Single carriageway| None| 30|22:43| Urban| Fine no high winds|2005|\n", + "| 200501BS00011| B| 450| C| 0| Slight| None|2005-01-15| Saturday| 1|Give way or uncon...|T or staggered ju...|51.512443| Daylight| Kensington and Ch...| Kensington and Ch...| 524550| 180810|-0.206531| E01002875| 5| 2| 0| 8|Metropolitan Police| Dry|Single carriageway| None| 30|16:00| Urban| Fine no high winds|2005|\n", + "| 200501BS00012| A| 4| B| 325| Slight| None|2005-01-16| Sunday| 1| Auto traffic signal| Crossroads|51.494902|Darkness - lights...| Kensington and Ch...| Kensington and Ch...| 526240| 178900|-0.182872| E01002835| 1| 1| 0| 5|Metropolitan Police| Dry|Single carriageway| None| 30|00:42| Urban| Fine no high winds|2005|\n", + "| 200501BS00014| A| 3220| A| 308| Slight| None|2005-01-25| Tuesday| 1| Auto traffic signal| Crossroads|51.484044|Darkness - lights...| Kensington and Ch...| Kensington and Ch...| 526170| 177690|-0.184312| E01002912| 1| 2| 0| 5|Metropolitan Police| Wet or damp|Single carriageway| None| 30|20:48| Urban| Fine no high winds|2005|\n", + "| 200501BS00015| Unclassified| 0| A| 3220| Slight| None|2005-01-11| Tuesday| 1|Give way or uncon...|T or staggered ju...|51.491632| Daylight| Kensington and Ch...| Kensington and Ch...| 525590| 178520|-0.192366| E01002849| 1| 1| 0| 1|Metropolitan Police| Wet or damp| One way street| None| 30|12:55| Urban|Raining no high w...|2005|\n", + "| 200501BS00016| A| 3217| A| 3216| Slight| None|2005-01-18| Tuesday| 1|Give way or uncon...|T or staggered ju...|51.492622|Darkness - lights...| Kensington and Ch...| Kensington and Ch...| 527990| 178690|-0.157753| E01002902| 1| 2| 0| 0|Metropolitan Police| Wet or damp| One way street| None| 30|05:01| Urban|Raining no high w...|2005|\n", + "| 200501BS00017| A| 4| NA| 0| Slight| None|2005-01-18| Tuesday| 1|Data missing or o...|Not at junction o...|51.495429| Daylight| Kensington and Ch...| Kensington and Ch...| 526700| 178970|-0.176224| E01002821| 2| 1| 0| 0|Metropolitan Police| Dry| Dual carriageway| None| 30|11:15| Urban| Fine no high winds|2005|\n", + "| 200501BS00018| A| 3217| Unclassified| 0| Slight| None|2005-01-18| Tuesday| 1|Give way or uncon...|T or staggered ju...|51.481912| Daylight| Kensington and Ch...| Kensington and Ch...| 526460| 177460| -0.18022| E01002840| 1| 1| 0| 1|Metropolitan Police| Dry|Single carriageway| None| 30|10:50| Urban| Fine no high winds|2005|\n", + "| 200501BS00019| Unclassified| 0| Unclassified| 0| Serious| None|2005-01-20| Thursday| 1|Give way or uncon...|T or staggered ju...|51.500191|Darkness - lights...| Kensington and Ch...| Kensington and Ch...| 524680| 179450|-0.205139| E01002864| 1| 2| 0| 0|Metropolitan Police| Dry|Single carriageway| None| 30|00:15| Urban| Fine no high winds|2005|\n", + "| 200501BS00020| A| 3218| A| 4| Slight| None|2005-01-21| Friday| 1|Give way or uncon...|T or staggered ju...|51.495811| Daylight| Kensington and Ch...| Kensington and Ch...| 527000| 179020|-0.171887| E01002821| 1| 2| 0| 0|Metropolitan Police| Dry|Single carriageway| None| 30|09:15| Urban| Fine no high winds|2005|\n", + "| 200501BS00021| B| 302| NA| 0| Slight| None|2005-01-21| Friday| 1|Data missing or o...|Not at junction o...|51.486552|Darkness - lights...| Kensington and Ch...| Kensington and Ch...| 527810| 178010| -0.16059| E01002901| 1| 2| 0| 0|Metropolitan Police| Dry|Single carriageway| None| 30|21:16| Urban| Fine no high winds|2005|\n", + "| 200501BS00022| A| 4| Unclassified| 0| Serious| None|2005-01-08| Saturday| 1|Give way or uncon...|T or staggered ju...|51.495498|Darkness - lights...| Kensington and Ch...| Kensington and Ch...| 526790| 178980|-0.174925| E01002821| 1| 1| 0| 0|Metropolitan Police| Dry|Single carriageway| None| 30|03:00| Urban| Fine no high winds|2005|\n", + "+--------------+--------------+---------------+--------------+---------------+-----------------+-------------------+----------+-----------+-------------------------------------------+--------------------+--------------------+---------+--------------------+--------------------------+-------------------------+---------------------+----------------------+---------+-------------------------+--------------------+------------------+---------------------------------+---------------------------------------+-------------------+-----------------------+------------------+--------------------------+-----------+-----+-------------------+--------------------+----+\n", + "only showing top 20 rows\n", + "\n" + ] + } + ], + "source": [ + "Accident_Information20052019_df.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+----+---------------+\n", + "|Year|Total accidents|\n", + "+----+---------------+\n", + "|2005| 198735|\n", + "|2006| 189161|\n", + "|2007| 182115|\n", + "|2008| 170591|\n", + "|2009| 163554|\n", + "|2010| 154414|\n", + "|2011| 151474|\n", + "|2012| 145571|\n", + "|2013| 138660|\n", + "|2014| 146322|\n", + "|2015| 140056|\n", + "|2016| 136621|\n", + "|2017| 129982|\n", + "|2018| 122635|\n", + "|2019| 117536|\n", + "+----+---------------+\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [] + } + ], + "source": [ + "YearAccident_df = Accident_Information20052019_df.groupby('Year').agg(F.count(Accident_Information20052019_df.Accident_Index).alias('Total accidents'))\n", + "YearAccident_df=YearAccident_df.sort(\"Year\")\n", + "YearAccident_df.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "YearAccident_df_df=YearAccident_df.toPandas()\n", + "YearAccident_df_df" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Uploading data set of vechile miles traveled yearly\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+----+--------------+---------------------------+----------------------+-----------+---------------+-------+------------------+-----------+\n", + "|Year|Cars and Taxis|Light Commercial,Vehicles 1|Heavy Goods Vehicles 2|Motorcycles|Buses & Coaches|Total 3|All motor vehicles|Pedal Cycle|\n", + "+----+--------------+---------------------------+----------------------+-----------+---------------+-------+------------------+-----------+\n", + "|1949| 12.6| 4| 7.8| 1.9| 2.5| 4.5| 28.9| null|\n", + "|1950| 15.9| 4.8| 7| 2.7| 2.5| 5.3| 33| null|\n", + "|1951| 18.2| 5.1| 7.3| 3.5| 2.6| 6.1| 36.7| null|\n", + "+----+--------------+---------------------------+----------------------+-----------+---------------+-------+------------------+-----------+\n", + "only showing top 3 rows\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+----+------------------+\n", + "|Year|All motor vehicles|\n", + "+----+------------------+\n", + "|2019| 356.5|\n", + "|2018| 349.5|\n", + "|2017| 345.2|\n", + "|2016| 338.2|\n", + "|2015| 329.6|\n", + "|2014| 322.2|\n", + "|2013| 311.9|\n", + "|2012| 309|\n", + "|2011| 308.2|\n", + "|2010| 305.8|\n", + "|2009| 308.1|\n", + "|2008| 311|\n", + "|2007| 314.1|\n", + "|2006| 311.4|\n", + "|2005| 306.9|\n", + "+----+------------------+\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [] + } + ], + "source": [ + "Billionvehiclemiles_df = spark.read.format('csv')\\\n", + " .option('header',True).option('escape','\"')\\\n", + " .load('/Users/Asfandyar/Downloads/archive/Billionvehiclemiles.csv')\n", + "Billionvehiclemiles_df.show(3)\n", + "Billionvehiclemiles_df = Billionvehiclemiles_df.withColumn('Year',F.col('Year').cast(IntegerType()))\n", + "Billionvehiclemiles2005_df=Billionvehiclemiles_df.filter(Billionvehiclemiles_df.Year>2004)\n", + "Billionvehiclemiles20052017_df=Billionvehiclemiles2005_df.filter(Billionvehiclemiles2005_df.Year<2020)\n", + "#Billionvehiclemiles20052017_df.show()\n", + "Milesoveryear = Billionvehiclemiles20052017_df.groupby('Year','All motor vehicles').agg(F.count(Billionvehiclemiles20052017_df['All motor vehicles']).alias('All_motor vehicles'))\n", + "Milesoveryear=Milesoveryear.drop(\"All_motor vehicles\")\n", + "Milesoveryear.sort(col('Year').desc()).show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+----+---------------+------------------+----------------------+\n", + "|Year|Total accidents|All motor vehicles|Accident_prob_overyear|\n", + "+----+---------------+------------------+----------------------+\n", + "|2005| 198735| 306.9| 6.475562072336266E-7|\n", + "|2006| 189161| 311.4| 6.074534360950545E-7|\n", + "|2007| 182115| 314.1| 5.797994269340974E-7|\n", + "|2008| 170591| 311| 5.48524115755627E-7|\n", + "|2009| 163554| 308.1| 5.308471275559883E-7|\n", + "|2010| 154414| 305.8| 5.049509483322433E-7|\n", + "|2011| 151474| 308.2| 4.914795587280987E-7|\n", + "|2012| 145571| 309| 4.711035598705501...|\n", + "|2013| 138660| 311.9| 4.445655658865020...|\n", + "|2014| 146322| 322.2| 4.541340782122905E-7|\n", + "|2015| 140056| 329.6| 4.249271844660194E-7|\n", + "|2016| 136621| 338.2| 4.039651094027203E-7|\n", + "|2017| 129982| 345.2| 3.765411355735805...|\n", + "|2018| 122635| 349.5| 3.508869814020028...|\n", + "|2019| 117536| 356.5| 3.296942496493688...|\n", + "+----+---------------+------------------+----------------------+\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [] + } + ], + "source": [ + "YearAccident_df_df=YearAccident_df.toPandas()\n", + "Milesoveryear_df=Milesoveryear.toPandas()\n", + "Normalizedaccidents=pd.merge(YearAccident_df_df, Milesoveryear_df, on='Year')\n", + "Normalizedaccidents_sparkDF=spark.createDataFrame(Normalizedaccidents) \n", + "Normalizedaccidents_sparkDF=Normalizedaccidents_sparkDF.withColumn('Accident_prob_overyear', Normalizedaccidents_sparkDF[1]/(Normalizedaccidents_sparkDF[2]*1000000000))\n", + "\n", + "Normalizedaccidents_sparkDF.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>Year</th>\n", + " <th>Total accidents</th>\n", + " <th>All motor vehicles</th>\n", + " <th>Accident_prob_overyear</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>2005</td>\n", + " <td>198735</td>\n", + " <td>306.9</td>\n", + " <td>6.475562e-07</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>2006</td>\n", + " <td>189161</td>\n", + " <td>311.4</td>\n", + " <td>6.074534e-07</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>2007</td>\n", + " <td>182115</td>\n", + " <td>314.1</td>\n", + " <td>5.797994e-07</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>2008</td>\n", + " <td>170591</td>\n", + " <td>311</td>\n", + " <td>5.485241e-07</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>2009</td>\n", + " <td>163554</td>\n", + " <td>308.1</td>\n", + " <td>5.308471e-07</td>\n", + " </tr>\n", + " <tr>\n", + " <th>5</th>\n", + " <td>2010</td>\n", + " <td>154414</td>\n", + " <td>305.8</td>\n", + " <td>5.049509e-07</td>\n", + " </tr>\n", + " <tr>\n", + " <th>6</th>\n", + " <td>2011</td>\n", + " <td>151474</td>\n", + " <td>308.2</td>\n", + " <td>4.914796e-07</td>\n", + " </tr>\n", + " <tr>\n", + " <th>7</th>\n", + " <td>2012</td>\n", + " <td>145571</td>\n", + " <td>309</td>\n", + " <td>4.711036e-07</td>\n", + " </tr>\n", + " <tr>\n", + " <th>8</th>\n", + " <td>2013</td>\n", + " <td>138660</td>\n", + " <td>311.9</td>\n", + " <td>4.445656e-07</td>\n", + " </tr>\n", + " <tr>\n", + " <th>9</th>\n", + " <td>2014</td>\n", + " <td>146322</td>\n", + " <td>322.2</td>\n", + " <td>4.541341e-07</td>\n", + " </tr>\n", + " <tr>\n", + " <th>10</th>\n", + " <td>2015</td>\n", + " <td>140056</td>\n", + " <td>329.6</td>\n", + " <td>4.249272e-07</td>\n", + " </tr>\n", + " <tr>\n", + " <th>11</th>\n", + " <td>2016</td>\n", + " <td>136621</td>\n", + " <td>338.2</td>\n", + " <td>4.039651e-07</td>\n", + " </tr>\n", + " <tr>\n", + " <th>12</th>\n", + " <td>2017</td>\n", + " <td>129982</td>\n", + " <td>345.2</td>\n", + " <td>3.765411e-07</td>\n", + " </tr>\n", + " <tr>\n", + " <th>13</th>\n", + " <td>2018</td>\n", + " <td>122635</td>\n", + " <td>349.5</td>\n", + " <td>3.508870e-07</td>\n", + " </tr>\n", + " <tr>\n", + " <th>14</th>\n", + " <td>2019</td>\n", + " <td>117536</td>\n", + " <td>356.5</td>\n", + " <td>3.296942e-07</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " Year Total accidents All motor vehicles Accident_prob_overyear\n", + "0 2005 198735 306.9 6.475562e-07\n", + "1 2006 189161 311.4 6.074534e-07\n", + "2 2007 182115 314.1 5.797994e-07\n", + "3 2008 170591 311 5.485241e-07\n", + "4 2009 163554 308.1 5.308471e-07\n", + "5 2010 154414 305.8 5.049509e-07\n", + "6 2011 151474 308.2 4.914796e-07\n", + "7 2012 145571 309 4.711036e-07\n", + "8 2013 138660 311.9 4.445656e-07\n", + "9 2014 146322 322.2 4.541341e-07\n", + "10 2015 140056 329.6 4.249272e-07\n", + "11 2016 136621 338.2 4.039651e-07\n", + "12 2017 129982 345.2 3.765411e-07\n", + "13 2018 122635 349.5 3.508870e-07\n", + "14 2019 117536 356.5 3.296942e-07" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Normalizedaccidents_sparkDFttest=Normalizedaccidents_sparkDF.toPandas()\n", + "Normalizedaccidents_sparkDFttest" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 6.475562e-07\n", + "1 6.074534e-07\n", + "2 5.797994e-07\n", + "3 5.485241e-07\n", + "4 5.308471e-07\n", + "5 5.049509e-07\n", + "6 4.914796e-07\n", + "7 4.711036e-07\n", + "8 4.445656e-07\n", + "9 4.541341e-07\n", + "10 4.249272e-07\n", + "11 4.039651e-07\n", + "12 3.765411e-07\n", + "13 3.508870e-07\n", + "14 3.296942e-07\n", + "Name: Accident_prob_overyear, dtype: float64" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Normalizedaccidents_sparkDFttest=Normalizedaccidents_sparkDFttest['Accident_prob_overyear']\n", + "mu=Normalizedaccidents_sparkDFttest.mean\n", + "Normalizedaccidents_sparkDFttest" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "numpy.float64" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "type(Normalizedaccidents_sparkDFttest[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "ename": "TypeError", + "evalue": "unsupported operand type(s) for -: 'float' and 'method'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/var/folders/v0/jqv1xcw13pn37fh0ppsl8b_w0000gp/T/ipykernel_1556/447502745.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mscipy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstats\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mttest_1samp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m21.5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m24.5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m18.5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m17.2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m14.5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m23.2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m22.1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m20.5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m19.4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m18.1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m24.1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m18.5\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mtscore\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mttest_1samp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mNormalizedaccidents_sparkDFttest\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"t Statistic: \"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtscore\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"P Value: \"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.9/site-packages/scipy/stats/stats.py\u001b[0m in \u001b[0;36mttest_1samp\u001b[0;34m(a, popmean, axis, nan_policy, alternative)\u001b[0m\n\u001b[1;32m 5648\u001b[0m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5649\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5650\u001b[0;31m \u001b[0md\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mpopmean\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5651\u001b[0m \u001b[0mv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mddof\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5652\u001b[0m \u001b[0mdenom\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqrt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for -: 'float' and 'method'" + ] + } + ], + "source": [ + "from scipy.stats import norm\n", + "from scipy import stats\n", + "#histogram and normal probability plot\n", + "sns.distplot(df1['age_of_driver'], fit=norm);\n", + "fig = plt.figure()\n", + "res = stats.probplot(df1['age_of_driver'], plot=plt)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>accident_index</th>\n", + " <th>accident_year</th>\n", + " <th>accident_reference</th>\n", + " <th>location_easting_osgr</th>\n", + " <th>location_northing_osgr</th>\n", + " <th>longitude</th>\n", + " <th>latitude</th>\n", + " <th>police_force</th>\n", + " <th>accident_severity</th>\n", + " <th>number_of_vehicles</th>\n", + " <th>...</th>\n", + " <th>pedestrian_crossing_physical_facilities</th>\n", + " <th>light_conditions</th>\n", + " <th>weather_conditions</th>\n", + " <th>road_surface_conditions</th>\n", + " <th>special_conditions_at_site</th>\n", + " <th>carriageway_hazards</th>\n", + " <th>urban_or_rural_area</th>\n", + " <th>did_police_officer_attend_scene_of_accident</th>\n", + " <th>trunk_road_flag</th>\n", + " <th>lsoa_of_accident_location</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>197901A11AD14</td>\n", + " <td>1979</td>\n", + " <td>01A11AD14</td>\n", + " <td>NaN</td>\n", + " <td>NaN</td>\n", + " <td>NaN</td>\n", + " <td>NaN</td>\n", + " <td>1</td>\n", + " <td>3</td>\n", + " <td>2</td>\n", + " <td>...</td>\n", + " <td>-1</td>\n", + " <td>1</td>\n", + " <td>8</td>\n", + " <td>1</td>\n", + " <td>-1</td>\n", + " <td>0</td>\n", + " <td>-1</td>\n", + " <td>-1</td>\n", + " <td>-1</td>\n", + " <td>-1</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>197901A1BAW34</td>\n", + " <td>1979</td>\n", + " <td>01A1BAW34</td>\n", + " <td>198460.0</td>\n", + " <td>894000.0</td>\n", + " <td>NaN</td>\n", + " <td>NaN</td>\n", + " <td>1</td>\n", + " <td>3</td>\n", + " <td>1</td>\n", + " <td>...</td>\n", + " <td>-1</td>\n", + " <td>4</td>\n", + " <td>8</td>\n", + " <td>3</td>\n", + " <td>-1</td>\n", + " <td>0</td>\n", + " <td>-1</td>\n", + " <td>-1</td>\n", + " <td>-1</td>\n", + " <td>-1</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>197901A1BFD77</td>\n", + " <td>1979</td>\n", + " <td>01A1BFD77</td>\n", + " <td>406380.0</td>\n", + " <td>307000.0</td>\n", + " <td>NaN</td>\n", + " <td>NaN</td>\n", + " <td>1</td>\n", + " <td>3</td>\n", + " <td>2</td>\n", + " <td>...</td>\n", + " <td>-1</td>\n", + " <td>4</td>\n", + " <td>8</td>\n", + " <td>3</td>\n", + " <td>-1</td>\n", + " <td>0</td>\n", + " <td>-1</td>\n", + " <td>-1</td>\n", + " <td>-1</td>\n", + " <td>-1</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>197901A1BGC20</td>\n", + " <td>1979</td>\n", + " <td>01A1BGC20</td>\n", + " <td>281680.0</td>\n", + " <td>440000.0</td>\n", + " <td>NaN</td>\n", + " <td>NaN</td>\n", + " <td>1</td>\n", + " <td>3</td>\n", + " <td>2</td>\n", + " <td>...</td>\n", + " <td>-1</td>\n", + " <td>4</td>\n", + " <td>8</td>\n", + " <td>3</td>\n", + " <td>-1</td>\n", + " <td>0</td>\n", + " <td>-1</td>\n", + " <td>-1</td>\n", + " <td>-1</td>\n", + " <td>-1</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>197901A1BGF95</td>\n", + " <td>1979</td>\n", + " <td>01A1BGF95</td>\n", + " <td>153960.0</td>\n", + " <td>795000.0</td>\n", + " <td>NaN</td>\n", + " <td>NaN</td>\n", + " <td>1</td>\n", + " <td>2</td>\n", + " <td>2</td>\n", + " <td>...</td>\n", + " <td>-1</td>\n", + " <td>4</td>\n", + " <td>3</td>\n", + " <td>3</td>\n", + " <td>-1</td>\n", + " <td>0</td>\n", + " <td>-1</td>\n", + " <td>-1</td>\n", + " <td>-1</td>\n", + " <td>-1</td>\n", + " </tr>\n", + " <tr>\n", + " <th>...</th>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " </tr>\n", + " <tr>\n", + " <th>8602819</th>\n", + " <td>2020991027064</td>\n", + " <td>2020</td>\n", + " <td>991027064</td>\n", + " <td>343034.0</td>\n", + " <td>731654.0</td>\n", + " <td>-2.926320</td>\n", + " <td>56.473539</td>\n", + " <td>99</td>\n", + " <td>2</td>\n", + " <td>2</td>\n", + " <td>...</td>\n", + " <td>0</td>\n", + " <td>1</td>\n", + " <td>1</td>\n", + " <td>1</td>\n", + " <td>0</td>\n", + " <td>0</td>\n", + " <td>1</td>\n", + " <td>1</td>\n", + " <td>-1</td>\n", + " <td>-1</td>\n", + " </tr>\n", + " <tr>\n", + " <th>8602820</th>\n", + " <td>2020991029573</td>\n", + " <td>2020</td>\n", + " <td>991029573</td>\n", + " <td>257963.0</td>\n", + " <td>658891.0</td>\n", + " <td>-4.267565</td>\n", + " <td>55.802353</td>\n", + " <td>99</td>\n", + " <td>3</td>\n", + " <td>1</td>\n", + " <td>...</td>\n", + " <td>0</td>\n", + " <td>1</td>\n", + " <td>1</td>\n", + " <td>1</td>\n", + " <td>0</td>\n", + " <td>0</td>\n", + " <td>1</td>\n", + " <td>2</td>\n", + " <td>-1</td>\n", + " <td>-1</td>\n", + " </tr>\n", + " <tr>\n", + " <th>8602821</th>\n", + " <td>2020991030297</td>\n", + " <td>2020</td>\n", + " <td>991030297</td>\n", + " <td>383664.0</td>\n", + " <td>810646.0</td>\n", + " <td>-2.271903</td>\n", + " <td>57.186317</td>\n", + " <td>99</td>\n", + " <td>2</td>\n", + " <td>2</td>\n", + " <td>...</td>\n", + " <td>0</td>\n", + " <td>1</td>\n", + " <td>1</td>\n", + " <td>1</td>\n", + " <td>0</td>\n", + " <td>0</td>\n", + " <td>2</td>\n", + " <td>1</td>\n", + " <td>-1</td>\n", + " <td>-1</td>\n", + " </tr>\n", + " <tr>\n", + " <th>8602822</th>\n", + " <td>2020991030900</td>\n", + " <td>2020</td>\n", + " <td>991030900</td>\n", + " <td>277161.0</td>\n", + " <td>674852.0</td>\n", + " <td>-3.968753</td>\n", + " <td>55.950940</td>\n", + " <td>99</td>\n", + " <td>3</td>\n", + " <td>2</td>\n", + " <td>...</td>\n", + " <td>0</td>\n", + " <td>1</td>\n", + " <td>1</td>\n", + " <td>1</td>\n", + " <td>0</td>\n", + " <td>0</td>\n", + " <td>1</td>\n", + " <td>2</td>\n", + " <td>-1</td>\n", + " <td>-1</td>\n", + " </tr>\n", + " <tr>\n", + " <th>8602823</th>\n", + " <td>2020991032575</td>\n", + " <td>2020</td>\n", + " <td>991032575</td>\n", + " <td>240402.0</td>\n", + " <td>681950.0</td>\n", + " <td>-4.561040</td>\n", + " <td>56.003843</td>\n", + " <td>99</td>\n", + " <td>3</td>\n", + " <td>1</td>\n", + " <td>...</td>\n", + " <td>0</td>\n", + " <td>1</td>\n", + " <td>1</td>\n", + " <td>1</td>\n", + " <td>0</td>\n", + " <td>2</td>\n", + " <td>1</td>\n", + " <td>1</td>\n", + " <td>-1</td>\n", + " <td>-1</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "<p>8602824 rows × 36 columns</p>\n", + "</div>" + ], + "text/plain": [ + " accident_index accident_year accident_reference \\\n", + "0 197901A11AD14 1979 01A11AD14 \n", + "1 197901A1BAW34 1979 01A1BAW34 \n", + "2 197901A1BFD77 1979 01A1BFD77 \n", + "3 197901A1BGC20 1979 01A1BGC20 \n", + "4 197901A1BGF95 1979 01A1BGF95 \n", + "... ... ... ... \n", + "8602819 2020991027064 2020 991027064 \n", + "8602820 2020991029573 2020 991029573 \n", + "8602821 2020991030297 2020 991030297 \n", + "8602822 2020991030900 2020 991030900 \n", + "8602823 2020991032575 2020 991032575 \n", + "\n", + " location_easting_osgr location_northing_osgr longitude latitude \\\n", + "0 NaN NaN NaN NaN \n", + "1 198460.0 894000.0 NaN NaN \n", + "2 406380.0 307000.0 NaN NaN \n", + "3 281680.0 440000.0 NaN NaN \n", + "4 153960.0 795000.0 NaN NaN \n", + "... ... ... ... ... \n", + "8602819 343034.0 731654.0 -2.926320 56.473539 \n", + "8602820 257963.0 658891.0 -4.267565 55.802353 \n", + "8602821 383664.0 810646.0 -2.271903 57.186317 \n", + "8602822 277161.0 674852.0 -3.968753 55.950940 \n", + "8602823 240402.0 681950.0 -4.561040 56.003843 \n", + "\n", + " police_force accident_severity number_of_vehicles ... \\\n", + "0 1 3 2 ... \n", + "1 1 3 1 ... \n", + "2 1 3 2 ... \n", + "3 1 3 2 ... \n", + "4 1 2 2 ... \n", + "... ... ... ... ... \n", + "8602819 99 2 2 ... \n", + "8602820 99 3 1 ... \n", + "8602821 99 2 2 ... \n", + "8602822 99 3 2 ... \n", + "8602823 99 3 1 ... \n", + "\n", + " pedestrian_crossing_physical_facilities light_conditions \\\n", + "0 -1 1 \n", + "1 -1 4 \n", + "2 -1 4 \n", + "3 -1 4 \n", + "4 -1 4 \n", + "... ... ... \n", + "8602819 0 1 \n", + "8602820 0 1 \n", + "8602821 0 1 \n", + "8602822 0 1 \n", + "8602823 0 1 \n", + "\n", + " weather_conditions road_surface_conditions \\\n", + "0 8 1 \n", + "1 8 3 \n", + "2 8 3 \n", + "3 8 3 \n", + "4 3 3 \n", + "... ... ... \n", + "8602819 1 1 \n", + "8602820 1 1 \n", + "8602821 1 1 \n", + "8602822 1 1 \n", + "8602823 1 1 \n", + "\n", + " special_conditions_at_site carriageway_hazards urban_or_rural_area \\\n", + "0 -1 0 -1 \n", + "1 -1 0 -1 \n", + "2 -1 0 -1 \n", + "3 -1 0 -1 \n", + "4 -1 0 -1 \n", + "... ... ... ... \n", + "8602819 0 0 1 \n", + "8602820 0 0 1 \n", + "8602821 0 0 2 \n", + "8602822 0 0 1 \n", + "8602823 0 2 1 \n", + "\n", + " did_police_officer_attend_scene_of_accident trunk_road_flag \\\n", + "0 -1 -1 \n", + "1 -1 -1 \n", + "2 -1 -1 \n", + "3 -1 -1 \n", + "4 -1 -1 \n", + "... ... ... \n", + "8602819 1 -1 \n", + "8602820 2 -1 \n", + "8602821 1 -1 \n", + "8602822 2 -1 \n", + "8602823 1 -1 \n", + "\n", + " lsoa_of_accident_location \n", + "0 -1 \n", + "1 -1 \n", + "2 -1 \n", + "3 -1 \n", + "4 -1 \n", + "... ... \n", + "8602819 -1 \n", + "8602820 -1 \n", + "8602821 -1 \n", + "8602822 -1 \n", + "8602823 -1 \n", + "\n", + "[8602824 rows x 36 columns]" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "A2018" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " </tr>\n", + " <tr>\n", + " <th>...</th>\n", + " </tr>\n", + " <tr>\n", + " <th>8602819</th>\n", + " </tr>\n", + " <tr>\n", + " <th>8602820</th>\n", + " </tr>\n", + " <tr>\n", + " <th>8602821</th>\n", + " </tr>\n", + " <tr>\n", + " <th>8602822</th>\n", + " </tr>\n", + " <tr>\n", + " <th>8602823</th>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "<p>8602824 rows × 0 columns</p>\n", + "</div>" + ], + "text/plain": [ + "Empty DataFrame\n", + "Columns: []\n", + "Index: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, ...]\n", + "\n", + "[8602824 rows x 0 columns]" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "A2005" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "'DataFrame' object has no attribute 'accident_year'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m<ipython-input-12-b6325cb1729b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0mA2005\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mA2018\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfilter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mA2018\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maccident_year\u001b[0m\u001b[0;34m>\u001b[0m\u001b[0;36m2004\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m \u001b[0maccidents\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mA2005\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfilter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mA2005\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maccident_year\u001b[0m\u001b[0;34m<\u001b[0m\u001b[0;36m2020\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 11\u001b[0m \u001b[0maccidents\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m 5139\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_can_hold_identifiers_and_holds_name\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5140\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5141\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5142\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5143\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'accident_year'" + ] + } + ], + "source": [ + "Accident_Information_df = pd.read_csv('/Users/Asfandyar/Downloads/dft-road-casualty-statistics-accident-1979-2020.csv')\n", + "# changing the type of column(\"Year'\") to interger type\n", + "#Accident_Information_df = Accident_Information_df.withColumn('Year',F.col('Year').cast(IntegerType()))\n", + "#Accident_Information_df=Accident_Information_df.filter(Accident_Information_df.Year<2017)\n", + "#Accident_Information_df.sort(\"Year\").show(truncate=False)\n", + "A2018 = Accident_Information_df\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "<Figure size 864x360 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# We import the usuals.\n", + "import pandas as pd\n", + "import numpy as np\n", + "import glob\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "mask = (A2018['accident_year'] > 2004) & (A2018['accident_year'] <= 2019)\n", + "\n", + "accidents=A2018.loc[mask]\n", + "\n", + "# suppressmoccasin all warnings\n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\")\n", + "accidents['date']= pd.to_datetime(accidents['date'], format=\"%d/%m/%Y\")\n", + "urban_rural_cmap = ['green','blue']\n", + "accidents.accident_severity.value_counts()\n", + "yearly_count = accidents['date'].dt.year.value_counts().sort_index(ascending=False)\n", + "\n", + "# prepare plot\n", + "sns.set_style('white')\n", + "fig, ax = plt.subplots(figsize=(12,5))\n", + "\n", + "# plot\n", + "ax.bar(yearly_count.index, yearly_count.values, color='#8c8df3')\n", + "ax.plot(yearly_count, linestyle='dotted', color='c')\n", + "ax.set_title('\\nAccidents per Year\\n', fontsize=14, fontweight='bold')\n", + "ax.set(ylabel='\\nTotal Accidents')\n", + "\n", + "# remove all spines\n", + "sns.despine(ax=ax, top=True, right=True, left=True, bottom=True);\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\\multicolumn{3}{c}\\textbf{" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "<Figure size 1080x432 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# prepare plot\n", + "sns.set_style('white')\n", + "fig, ax = plt.subplots(figsize=(15,6))\n", + "\n", + "# plot\n", + "accidents.set_index('date').resample('M').size().plot(label='Total per Month', color='grey', ax=ax)\n", + "accidents.set_index('date').resample('M').size().rolling(window=10).mean()\\\n", + " .plot(color='green', linewidth=5, label='10-Months Moving Average', ax=ax)\n", + "\n", + "ax.set_title('Accidents per Month', fontsize=14, fontweight='bold')\n", + "ax.set(ylabel='Total Accidents\\n', xlabel='')\n", + "ax.legend(bbox_to_anchor=(1.1, 1.1), frameon=False)\n", + "\n", + "# remove all spines\n", + "sns.despine(ax=ax, top=True, right=True, left=True, bottom=False);" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "<Figure size 720x576 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "<Figure size 720x576 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.figure(figsize=(10,8))\n", + "y_ans = [val for val in Normalizedaccidents_sparkDF.select('Accident_prob_overyear').collect()]\n", + "x_= [val for val in Normalizedaccidents_sparkDF.select('Year').collect()]\n", + "plt.plot(x_, y_ans,marker='o', markerfacecolor='green')\n", + "plt.ylabel('Accident probability per one mile travlled')\n", + "plt.xlabel('Year')\n", + "#plt.title('ASN values for time')\n", + "plt.legend(['Accident probability per one mile travlled'], loc='upper right')\n", + "plt.xticks(x_)\n", + "plt.show()\n", + "import matplotlib.pyplot as plt\n", + "plt.figure(figsize=(10,8))\n", + "y_ans_val = [val for val in YearAccident_df.select('Total accidents').collect()]\n", + "x_ts = [val for val in YearAccident_df.select('Year').collect()]\n", + "#plt.xticks(x_ts)\n", + "plt.plot(x_ts, y_ans_val,marker='o', markerfacecolor='blue')\n", + "plt.ylabel('Total accidents')\n", + "plt.xlabel('Year')\n", + "#plt.title('ASN values for time')\n", + "plt.legend(['Accidents'], loc='upper left')\n", + "plt.xticks(x_ts)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>Year</th>\n", + " <th>Total accidents</th>\n", + " <th>All motor vehicles</th>\n", + " <th>Accident_prob_overyear</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>2005</td>\n", + " <td>198735</td>\n", + " <td>306.9</td>\n", + " <td>647.556207</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>2006</td>\n", + " <td>189161</td>\n", + " <td>311.4</td>\n", + " <td>607.453436</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>2007</td>\n", + " <td>182115</td>\n", + " <td>314.1</td>\n", + " <td>579.799427</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>2008</td>\n", + " <td>170591</td>\n", + " <td>311</td>\n", + " <td>548.524116</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>2009</td>\n", + " <td>163554</td>\n", + " <td>308.1</td>\n", + " <td>530.847128</td>\n", + " </tr>\n", + " <tr>\n", + " <th>5</th>\n", + " <td>2010</td>\n", + " <td>154414</td>\n", + " <td>305.8</td>\n", + " <td>504.950948</td>\n", + " </tr>\n", + " <tr>\n", + " <th>6</th>\n", + " <td>2011</td>\n", + " <td>151474</td>\n", + " <td>308.2</td>\n", + " <td>491.479559</td>\n", + " </tr>\n", + " <tr>\n", + " <th>7</th>\n", + " <td>2012</td>\n", + " <td>145571</td>\n", + " <td>309</td>\n", + " <td>471.103560</td>\n", + " </tr>\n", + " <tr>\n", + " <th>8</th>\n", + " <td>2013</td>\n", + " <td>138660</td>\n", + " <td>311.9</td>\n", + " <td>444.565566</td>\n", + " </tr>\n", + " <tr>\n", + " <th>9</th>\n", + " <td>2014</td>\n", + " <td>146322</td>\n", + " <td>322.2</td>\n", + " <td>454.134078</td>\n", + " </tr>\n", + " <tr>\n", + " <th>10</th>\n", + " <td>2015</td>\n", + " <td>140056</td>\n", + " <td>329.6</td>\n", + " <td>424.927184</td>\n", + " </tr>\n", + " <tr>\n", + " <th>11</th>\n", + " <td>2016</td>\n", + " <td>136621</td>\n", + " <td>338.2</td>\n", + " <td>403.965109</td>\n", + " </tr>\n", + " <tr>\n", + " <th>12</th>\n", + " <td>2017</td>\n", + " <td>129982</td>\n", + " <td>345.2</td>\n", + " <td>376.541136</td>\n", + " </tr>\n", + " <tr>\n", + " <th>13</th>\n", + " <td>2018</td>\n", + " <td>122635</td>\n", + " <td>349.5</td>\n", + " <td>350.886981</td>\n", + " </tr>\n", + " <tr>\n", + " <th>14</th>\n", + " <td>2019</td>\n", + " <td>117536</td>\n", + " <td>356.5</td>\n", + " <td>329.694250</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " Year Total accidents All motor vehicles Accident_prob_overyear\n", + "0 2005 198735 306.9 647.556207\n", + "1 2006 189161 311.4 607.453436\n", + "2 2007 182115 314.1 579.799427\n", + "3 2008 170591 311 548.524116\n", + "4 2009 163554 308.1 530.847128\n", + "5 2010 154414 305.8 504.950948\n", + "6 2011 151474 308.2 491.479559\n", + "7 2012 145571 309 471.103560\n", + "8 2013 138660 311.9 444.565566\n", + "9 2014 146322 322.2 454.134078\n", + "10 2015 140056 329.6 424.927184\n", + "11 2016 136621 338.2 403.965109\n", + "12 2017 129982 345.2 376.541136\n", + "13 2018 122635 349.5 350.886981\n", + "14 2019 117536 356.5 329.694250" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Normalizedaccidents_sparkDF_df=Normalizedaccidents_sparkDF.toPandas()\n", + "Normalizedaccidents_sparkDF_df" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "from pyspark.sql.functions import concat, col, lit\n", + "\n", + "C2017 = spark.read.format('csv')\\\n", + " .option('header',True).option('escape','\"')\\\n", + " .load('/Users/Asfandyar/Downloads/Veh.csv')\n", + "# changing the type of column(\"Year'\") to interger type\n", + "C2017 = C2017.withColumn('Year',F.col('Year').cast(IntegerType()))\n", + "C2018 = spark.read.format('csv')\\\n", + " .option('header',True).option('escape','\"')\\\n", + " .load('/Users/Asfandyar/Downloads/dftRoadSafetyData_Vehicles_2018.csv')\n", + "# changing the type of column(\"Year'\") to interger type\n", + "C2018 = C2018.withColumn('Year',F.col('Year').cast(IntegerType())) \n", + "C2019 = spark.read.format('csv')\\\n", + " .option('header',True).option('escape','\"')\\\n", + " .load('/Users/Asfandyar/Downloads/Road Safety Data- Vehicles 2019.csv')\n", + "# changing the type of column(\"Year'\") to interger type\n", + "C2019 = C2019.withColumn('Year',F.col('Year').cast(IntegerType())) \n", + "C2016 = spark.read.format('csv')\\\n", + " .option('header',True).option('escape','\"')\\\n", + " .load('/Users/Asfandyar/Downloads/archive/Veh-3.csv')\n", + "# changing the type of column(\"Year'\") to interger type\n", + "C2016 = C2016.withColumn('Year',F.col('Year').cast(IntegerType())) \n", + "C2015 = spark.read.format('csv')\\\n", + " .option('header',True).option('escape','\"')\\\n", + " .load('/Users/Asfandyar/Downloads/archive/Vehicles_2015.csv')\n", + "# changing the type of column(\"Year'\") to interger type\n", + "C2015 = C2015.withColumn('Year',F.col('Year').cast(IntegerType())) \n", + "C2017 = C2017.union(C2018)\n", + "C2017 = C2017.union(C2019)\n", + "C2017 = C2017.union(C2016)\n", + "C2017 = C2017.union(C2015)\n", + "A20052014 = spark.read.format('csv')\\\n", + " .option('header',True).option('escape','\"')\\\n", + " .load('/Users/Asfandyar/Downloads/archive/Vehicles0514.csv')\n", + "A20052014=A20052014.withColumn('Year', concat(A20052014.Accident_Index.substr(1, 4)))\n", + "\n", + "#C2017aa = C2017.groupby('Vehicle_Type').agg(F.count(C2017.Accident_Index).alias('Total accidents'))\n", + "C2017 = C2017.withColumn('Vehicle_Type',F.col('Vehicle_Type').cast(IntegerType()))\n", + "#C2017aa.sort(\"Total accidents\").show(50)\n", + "#C2017.show()\n", + "C2017=C2017.drop(\"Vehicle_IMD_Decile\")\n", + "\n", + "V20052014 = A20052014.union(C2017)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "from pyspark.sql.functions import col, when\n", + "valueWhenTrue1 =\"Pedal cycle\"\n", + "valueWhenTrue2 =\"Motorcycle\"\n", + "valueWhenTrue3 = \"Motorcycle\"\n", + "valueWhenTrue4 = \"Motorcycle\"\n", + "valueWhenTrue5 = \"Motorcycle\"\n", + "valueWhenTrue8 = \"Car\"\n", + "valueWhenTrue9 =\"Car\"\n", + "valueWhenTrue10 =\"Bus\"\n", + "valueWhenTrue11 =\"Bus\"\n", + "valueWhenTrue16 =\"Ridden horse\"\n", + "valueWhenTrue17 =\"Agricultural vehicle\"\n", + "valueWhenTrue18 =\"Bus\"\n", + "valueWhenTrue19 =\"Goods\"\n", + "valueWhenTrue20 =\"Goods\"\n", + "valueWhenTrue21 =\"Goods\"\n", + "valueWhenTrue22 =\"Motorcycle\"\n", + "valueWhenTrue23 =\"Motorcycle\"\n", + "valueWhenTrue90 =\"Other vehicle\"\n", + "valueWhenTrue97 =\"Motorcycle\"\n", + "valueWhenTrue98 =\"Goods\"\n", + "valueWhenTrueo1 =\"Data missing or out of range\"\n", + "#C2017 = C2017df.withColumn(\"Vehicle_Type\", when(df.gender == \"M\",\"Male\")\n", + "# .when(df.gender == \"F\",\"Female\")\n", + "# .when(df.gender.isNull() ,\"\")\n", + "# .otherwise(df.gender))\n", + "V20052014=V20052014.withColumn(\n", + " \"Vehicle_Type\",\n", + " when(\n", + " col(\"Vehicle_Type\") == 1,\n", + " \"Pedal cycle\"\n", + " ).\n", + " when(\n", + " col(\"Vehicle_Type\") == 2,\n", + " valueWhenTrue2\n", + " ).\n", + " when(\n", + " col(\"Vehicle_Type\") == 3,\n", + " valueWhenTrue3\n", + " ).\n", + " when(\n", + " col(\"Vehicle_Type\") == 4,\n", + " valueWhenTrue4\n", + " ).\n", + " when(\n", + " col(\"Vehicle_Type\") == 5,\n", + " valueWhenTrue5\n", + " ).\n", + " when(\n", + " col(\"Vehicle_Type\") == 8,\n", + " valueWhenTrue8\n", + " ).\n", + " when(\n", + " col(\"Vehicle_Type\") == 9,\n", + " valueWhenTrue9\n", + " ).\n", + " when(\n", + " col(\"Vehicle_Type\") == 10,\n", + " valueWhenTrue10\n", + " ).\n", + " when(\n", + " col(\"Vehicle_Type\") == 11,\n", + " valueWhenTrue11\n", + " ).\n", + " when(\n", + " col(\"Vehicle_Type\") == 16,\n", + " valueWhenTrue16\n", + " ).\n", + " when(\n", + " col(\"Vehicle_Type\") == 17,\n", + " valueWhenTrue17\n", + " ).\n", + " when(\n", + " col(\"Vehicle_Type\") == 18,\n", + " valueWhenTrue18\n", + " ).\n", + " when(\n", + " col(\"Vehicle_Type\") == 19,\n", + " valueWhenTrue19\n", + " ).\n", + " when(\n", + " col(\"Vehicle_Type\") == 20,\n", + " valueWhenTrue20\n", + " ).\n", + " when(\n", + " col(\"Vehicle_Type\") == 21,\n", + " valueWhenTrue21\n", + " ).\n", + " when(\n", + " col(\"Vehicle_Type\") == 22,\n", + " valueWhenTrue22\n", + " ).\n", + " when(\n", + " col(\"Vehicle_Type\") == 23,\n", + " valueWhenTrue23\n", + " ).\n", + " when(\n", + " col(\"Vehicle_Type\") == 90,\n", + " valueWhenTrue90\n", + " ).\n", + " when(\n", + " col(\"Vehicle_Type\") == 97,\n", + " valueWhenTrue97\n", + " ).\n", + " when(\n", + " col(\"Vehicle_Type\") == 98,\n", + " valueWhenTrue98\n", + " ).\n", + " when(\n", + " col(\"Vehicle_Type\") == -1,\n", + " valueWhenTrueo1\n", + " ).otherwise(col(\"Vehicle_Type\"))\n", + ")\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "from pyspark.sql.functions import col, when\n", + "valueWhenTrue1 =\"Pedal cycle\"\n", + "valueWhenTrue2 =\"Motorcycle 50cc and under\"\n", + "valueWhenTrue3 = \"Motorcycle 125cc and under\"\n", + "valueWhenTrue4 = \"Motorcycle over 125cc and up to 500cc\"\n", + "valueWhenTrue5 = \"Motorcycle over 500cc\"\n", + "valueWhenTrue8 = \"Taxi/Private hire car\"\n", + "valueWhenTrue9 =\"Car\"\n", + "valueWhenTrue10 =\"Minibus (8 - 16 passenger seats)\"\n", + "valueWhenTrue11 =\"Bus or coach (17 or more pass seats)\"\n", + "valueWhenTrue16 =\"Ridden horse\"\n", + "valueWhenTrue17 =\"Agricultural vehicle\"\n", + "valueWhenTrue18 =\"Tram\"\n", + "valueWhenTrue19 =\"Van / Goods 3.5 tonnes mgw or under\"\n", + "valueWhenTrue20 =\"Goods over 3.5t. and under 7.5t\"\n", + "valueWhenTrue21 =\"Goods 7.5 tonnes mgw and over\"\n", + "valueWhenTrue22 =\"Mobility scooter Motorcycle\"\n", + "valueWhenTrue23 =\"Electric motorcycle\"\n", + "valueWhenTrue90 =\"Other vehicle\"\n", + "valueWhenTrue97 =\"Motorcycle - unknown cc\"\n", + "valueWhenTrue98 =\"Goods vehicle - unknown weight\"\n", + "valueWhenTrueo1 =\"Data missing or out of range\"\n", + "#C2017 = C2017df.withColumn(\"Vehicle_Type\", when(df.gender == \"M\",\"Male\")\n", + "# .when(df.gender == \"F\",\"Female\")\n", + "# .when(df.gender.isNull() ,\"\")\n", + "# .otherwise(df.gender))\n", + "\n", + "V20052014=V20052014.withColumn(\n", + " \"Age_Band_of_Driver\",\n", + " when(\n", + " col(\"Age_Band_of_Driver\") == 1,\n", + " \"Upto 20Y\"\n", + " ).\n", + " when(\n", + " col(\"Age_Band_of_Driver\") == 2,\n", + " \"Upto 20Y\"\n", + " ).\n", + " when(\n", + " col(\"Age_Band_of_Driver\") == 3,\n", + " \"Upto 20Y\"\n", + " ).\n", + " when(\n", + " col(\"Age_Band_of_Driver\") == 4,\n", + " \"Upto 20Y\"\n", + " ).\n", + " when(\n", + " col(\"Age_Band_of_Driver\") == 5,\n", + " \"20Y to 40Y\"\n", + " ).\n", + " when(\n", + " col(\"Age_Band_of_Driver\") == 6,\n", + " \"20Y to 40Y\"\n", + " ).\n", + " when(\n", + " col(\"Age_Band_of_Driver\") == 7,\n", + " \"20Y to 40Y\"\n", + " ).when(\n", + " col(\"Age_Band_of_Driver\") == 8,\n", + " \"40Y to 70Y\"\n", + " ).when(\n", + " col(\"Age_Band_of_Driver\") == 9,\n", + " \"40Y to 70Y\"\n", + " ).when(\n", + " col(\"Age_Band_of_Driver\") == 10,\n", + " \"40Y to 70Y\"\n", + " ).when(\n", + " col(\"Age_Band_of_Driver\") == 11,\n", + " \"Over 70\"\n", + " ).when(\n", + " col(\"Age_Band_of_Driver\") == -1,\n", + " \"Data missing or out of range\"\n", + " ).otherwise(col(\"Age_Band_of_Driver\")),\n", + ")\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+--------------------+---------------+\n", + "| Vehicle_Type|Total accidents|\n", + "+--------------------+---------------+\n", + "| Ridden horse| 1748|\n", + "|Data missing or o...| 1750|\n", + "|Agricultural vehicle| 8759|\n", + "| Other vehicle| 31295|\n", + "| Bus| 111647|\n", + "| Pedal cycle| 277086|\n", + "| Motorcycle| 317829|\n", + "| Goods| 319826|\n", + "| Car| 3126546|\n", + "+--------------------+---------------+\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [] + } + ], + "source": [ + "V20052014vech = V20052014.groupby('Vehicle_Type').agg(F.count(V20052014.Accident_Index).alias('Total accidents'))\n", + "V20052014vech.sort(\"Total accidents\").show(50)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+----+---------------+\n", + "|Year|Total accidents|\n", + "+----+---------------+\n", + "|2019| 216381|\n", + "|2018| 226409|\n", + "|2017| 238926|\n", + "|2016| 252500|\n", + "|2013| 252913|\n", + "|2015| 257845|\n", + "|2012| 265877|\n", + "|2014| 268527|\n", + "|2011| 276155|\n", + "|2010| 281401|\n", + "|2009| 298687|\n", + "|2008| 311604|\n", + "|2007| 334966|\n", + "|2006| 348059|\n", + "|2005| 366236|\n", + "+----+---------------+\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [] + } + ], + "source": [ + "V20052014year = V20052014.groupby('Year').agg(F.count(V20052014.Accident_Index).alias('Total accidents'))\n", + "V20052014year.sort(\"Total accidents\").show(50)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+------------+---------------+\n", + "|Vehicle_Type|Total accidents|\n", + "+------------+---------------+\n", + "| Car| 3126546|\n", + "+------------+---------------+\n", + "\n" + ] + } + ], + "source": [ + "CarAccident_df=V20052014.filter(V20052014.Vehicle_Type.contains(\"Car\"))\n", + "CarAccidentbytype_df = CarAccident_df.groupby('Vehicle_Type').agg(F.count(CarAccident_df.Accident_Index).alias('Total accidents'))\n", + "CarAccidentbytype_df.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 103, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+----+----------------------+\n", + "|Year|Total accidents of Car|\n", + "+----+----------------------+\n", + "|2005| 280583|\n", + "|2006| 266965|\n", + "|2007| 254885|\n", + "|2008| 235996|\n", + "|2009| 226447|\n", + "|2010| 211934|\n", + "|2011| 203978|\n", + "|2012| 196651|\n", + "|2013| 185174|\n", + "|2014| 194997|\n", + "|2015| 188374|\n", + "|2016| 184849|\n", + "|2017| 173686|\n", + "|2018| 164645|\n", + "|2019| 157382|\n", + "+----+----------------------+\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [] + } + ], + "source": [ + "CarAccident_df= CarAccident_df.withColumn('Year',F.col('Year').cast(IntegerType()))\n", + "CarAccidentovertheyeards_df= CarAccident_df.groupby('Year').agg(F.count(CarAccident_df.Accident_Index).alias('Total accidents of Car'))\n", + "CarAccidentovertheyeards_df=CarAccidentovertheyeards_df.sort(\"Year\")\n", + "CarAccidentovertheyeards_df.show()\n", + "#.groupby('Year').agg(F.count(MotorcycleAccident_df.Accident_Index).alias('Total accidents of Motorbikes'))\n", + "CarAccidenttotalovertheyeards_df=CarAccidentovertheyeards_df.groupby().agg(F.sum(\"Total accidents of Car\"))\n", + "CarAccidenttotalovertheyear=CarAccidenttotalovertheyeards_df.collect()[0][0]\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 104, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+------------+---------------+\n", + "|Vehicle_Type|Total accidents|\n", + "+------------+---------------+\n", + "| Bus| 111647|\n", + "+------------+---------------+\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+----+----------------------+\n", + "|Year|Total accidents of Bus|\n", + "+----+----------------------+\n", + "|2005| 11253|\n", + "|2006| 10185|\n", + "|2007| 9590|\n", + "|2008| 9325|\n", + "|2009| 8661|\n", + "|2010| 8237|\n", + "|2011| 7988|\n", + "|2012| 7070|\n", + "|2013| 6511|\n", + "|2014| 6705|\n", + "|2015| 5897|\n", + "|2016| 5478|\n", + "|2017| 5477|\n", + "|2018| 4937|\n", + "|2019| 4333|\n", + "+----+----------------------+\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "data": { + "text/plain": [ + "111647" + ] + }, + "execution_count": 104, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Analysis for Bus\n", + "BusAccident_df=V20052014.filter(V20052014.Vehicle_Type.contains(\"Bus\"))\n", + "BusAccidentbytype_df = BusAccident_df.groupby('Vehicle_Type').agg(F.count(BusAccident_df.Accident_Index).alias('Total accidents'))\n", + "BusAccidentbytype_df.show()\n", + "BusAccident_df= BusAccident_df.withColumn('Year',F.col('Year').cast(IntegerType()))\n", + "BusAccidentovertheyeards_df= BusAccident_df.groupby('Year').agg(F.count(BusAccident_df.Accident_Index).alias('Total accidents of Bus'))\n", + "BusAccidentovertheyeards_df= BusAccidentovertheyeards_df.sort(\"Year\")\n", + "BusAccidentovertheyeards_df.show()\n", + "#.groupby('Year').agg(F.count(MotorcycleAccident_df.Accident_Index).alias('Total accidents of Motorbikes'))\n", + "BusAccidenttotalovertheyeards_df=BusAccidentovertheyeards_df.groupby().agg(F.sum(\"Total accidents of Bus\"))\n", + "BusAccidenttotalovertheyear=BusAccidenttotalovertheyeards_df.collect()[0][0]\n", + "BusAccidenttotalovertheyear\n" + ] + }, + { + "cell_type": "code", + "execution_count": 105, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+------------+---------------+\n", + "|Vehicle_Type|Total accidents|\n", + "+------------+---------------+\n", + "| Goods| 319826|\n", + "+------------+---------------+\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+----+-------------------------------+\n", + "|Year|Total accidents of GoodsVechile|\n", + "+----+-------------------------------+\n", + "|2005| 28198|\n", + "|2006| 26929|\n", + "|2007| 25308|\n", + "|2008| 22661|\n", + "|2009| 20701|\n", + "|2010| 20481|\n", + "|2011| 20012|\n", + "|2012| 19310|\n", + "|2013| 19316|\n", + "|2014| 21182|\n", + "|2015| 20961|\n", + "|2016| 20032|\n", + "|2017| 18907|\n", + "|2018| 18020|\n", + "|2019| 17808|\n", + "+----+-------------------------------+\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "data": { + "text/plain": [ + "319826" + ] + }, + "execution_count": 105, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Analysis for Goods\n", + "#|Vehicle_Information_df.Vehicle_Type.contains(\"Bus\")\n", + "GoodsVechileAccident_df=V20052014.filter(V20052014.Vehicle_Type.contains(\"Goods\"))\n", + "GoodsVechileAccidentbytype_df = GoodsVechileAccident_df.groupby('Vehicle_Type').agg(F.count(GoodsVechileAccident_df.Accident_Index).alias('Total accidents'))\n", + "GoodsVechileAccidentbytype_df.show()\n", + "GoodsVechileAccident_df= GoodsVechileAccident_df.withColumn('Year',F.col('Year').cast(IntegerType()))\n", + "GoodsVechileAccidentovertheyeards_df= GoodsVechileAccident_df.groupby('Year').agg(F.count(GoodsVechileAccident_df.Accident_Index).alias('Total accidents of GoodsVechile'))\n", + "GoodsVechileAccidentovertheyeards_df= GoodsVechileAccidentovertheyeards_df.sort(\"Year\")\n", + "GoodsVechileAccidentovertheyeards_df.show()\n", + "GoodsVechileAccidenttotalovertheyeards_df=GoodsVechileAccidentovertheyeards_df.groupby().agg(F.sum(\"Total accidents of GoodsVechile\"))\n", + "GoodsVechileAccidenttotalovertheyear=GoodsVechileAccidenttotalovertheyeards_df.collect()[0][0]\n", + "GoodsVechileAccidenttotalovertheyear\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 107, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+--------------+-----------------+------------+-----------------------+-----------------+--------------------------------+-----------------+------------------------+-------------------------+---------------------------+--------------------------+-------------------+----------------------------+-------------------------+-------------+-------------+--------------------+--------------------+---------------+--------------+-----------------+---------------------+----+\n", + "|Accident_Index|Vehicle_Reference|Vehicle_Type|Towing_and_Articulation|Vehicle_Manoeuvre|Vehicle_Location-Restricted_Lane|Junction_Location|Skidding_and_Overturning|Hit_Object_in_Carriageway|Vehicle_Leaving_Carriageway|Hit_Object_off_Carriageway|1st_Point_of_Impact|Was_Vehicle_Left_Hand_Drive?|Journey_Purpose_of_Driver|Sex_of_Driver|Age_of_Driver| Age_Band_of_Driver|Engine_Capacity_(CC)|Propulsion_Code|Age_of_Vehicle|Driver_IMD_Decile|Driver_Home_Area_Type|Year|\n", + "+--------------+-----------------+------------+-----------------------+-----------------+--------------------------------+-----------------+------------------------+-------------------------+---------------------------+--------------------------+-------------------+----------------------------+-------------------------+-------------+-------------+--------------------+--------------------+---------------+--------------+-----------------+---------------------+----+\n", + "| 200501BS00005| 1| Motorcycle| 0| 18| 0| 0| 1| 10| 0| 0| 1| 1| 15| 1| 49| 40Y to 70Y| 85| 1| 10| -1| -1|2005|\n", + "| 200501BS00006| 2| Motorcycle| 0| 18| 0| 0| 0| 0| 0| 0| 0| 1| 15| 2| 30| 20Y to 40Y| 124| 1| 2| 1| 1|2005|\n", + "| 200501BS00007| 1| Motorcycle| 0| 18| 0| 1| 0| 4| 0| 0| 1| 1| 15| 1| 31| 20Y to 40Y| -1| -1| -1| -1| -1|2005|\n", + "| 200501BS00014| 2| Motorcycle| 0| 18| 0| 8| 0| 0| 0| 0| 1| 1| 15| 2| 20| Upto 20Y| 124| 1| 1| -1| -1|2005|\n", + "| 200501BS00017| 1| Motorcycle| 0| 18| 0| 0| 0| 0| 0| 0| 1| 1| 15| 1| 42| 20Y to 40Y| 1171| 1| 2| 8| 1|2005|\n", + "| 200501BS00019| 1| Motorcycle| 0| 18| 0| 8| 0| 0| 0| 0| 1| 1| 15| 1| 54| 40Y to 70Y| 599| 1| 6| 5| 1|2005|\n", + "| 200501BS00020| 2| Motorcycle| 0| 18| 0| 8| 0| 0| 0| 0| 1| 1| 15| 1| 26| 20Y to 40Y| -1| -1| -1| 2| 1|2005|\n", + "| 200501BS00021| 1| Motorcycle| 0| 13| 0| 0| 0| 0| 0| 0| 1| 1| 15| 1| 33| 20Y to 40Y| 599| 1| 1| 7| 1|2005|\n", + "| 200501BS00023| 1| Motorcycle| 0| 18| 0| 0| 0| 0| 0| 0| 1| 1| 1| 1| -1|Data missing or o...| -1| -1| -1| -1| -1|2005|\n", + "| 200501BS00025| 2| Motorcycle| 0| 18| 0| 8| 0| 0| 0| 0| 1| 1| 15| 1| 59| 40Y to 70Y| 740| 1| 11| 4| 1|2005|\n", + "| 200501BS00028| 1| Motorcycle| 0| 18| 0| 8| 0| 0| 0| 0| 3| 1| 15| 1| 40| 20Y to 40Y| 120| 1| 4| 10| 1|2005|\n", + "| 200501BS70001| 1| Motorcycle| 0| 13| 0| 2| 0| 0| 0| 0| 1| 1| 15| 2| 28| 20Y to 40Y| 398| 1| 14| 1| 1|2005|\n", + "| 200501BS70002| 2| Motorcycle| 0| 18| 0| 1| 0| 0| 0| 0| 1| 1| 15| 2| 33| 20Y to 40Y| 645| 1| 4| 5| 1|2005|\n", + "| 200501BS70003| 1| Motorcycle| 0| 18| 0| 8| 0| 0| 0| 0| 1| 1| 15| 1| 24| 20Y to 40Y| 124| 1| 3| 2| 1|2005|\n", + "| 200501BS70003| 2| Motorcycle| 0| 18| 0| 8| 0| 0| 0| 0| 4| 1| 15| 1| 28| 20Y to 40Y| -1| -1| -1| 3| 1|2005|\n", + "| 200501BS70005| 1| Motorcycle| 0| 18| 0| 1| 1| 0| 0| 0| 1| 1| 15| 1| 43| 20Y to 40Y| 244| 1| 1| 3| 1|2005|\n", + "| 200501BS70006| 1| Motorcycle| 0| 18| 0| 8| 0| 0| 0| 0| 4| 1| 15| 2| 23| 20Y to 40Y| 124| 1| 8| 2| 1|2005|\n", + "| 200501BS70012| 1| Motorcycle| 0| 18| 0| 0| 0| 0| 0| 0| 1| 1| 15| 1| 28| 20Y to 40Y| 100| 1| 4| -1| -1|2005|\n", + "| 200501BS70014| 1| Motorcycle| 0| 7| 0| 8| 0| 0| 0| 0| 1| 1| 15| 1| 39| 20Y to 40Y| 599| 1| 3| 6| 1|2005|\n", + "| 200501BS70018| 1| Motorcycle| 0| 15| 0| 0| 0| 0| 0| 0| 1| 1| 15| 1| 28| 20Y to 40Y| 599| 1| 1| 1| 1|2005|\n", + "+--------------+-----------------+------------+-----------------------+-----------------+--------------------------------+-----------------+------------------------+-------------------------+---------------------------+--------------------------+-------------------+----------------------------+-------------------------+-------------+-------------+--------------------+--------------------+---------------+--------------+-----------------+---------------------+----+\n", + "only showing top 20 rows\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+------------+---------------+\n", + "|Vehicle_Type|Total accidents|\n", + "+------------+---------------+\n", + "| Motorcycle| 317829|\n", + "+------------+---------------+\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+----+-----------------------------+\n", + "|Year|Total accidents of Motorcycle|\n", + "+----+-----------------------------+\n", + "|2005| 25870|\n", + "|2006| 24323|\n", + "|2007| 24381|\n", + "|2008| 22427|\n", + "|2009| 21590|\n", + "|2010| 19534|\n", + "|2011| 21069|\n", + "|2012| 20255|\n", + "|2013| 19694|\n", + "|2014| 21587|\n", + "|2015| 21218|\n", + "|2016| 20683|\n", + "|2017| 19440|\n", + "|2018| 18139|\n", + "|2019| 17619|\n", + "+----+-----------------------------+\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "data": { + "text/plain": [ + "317829" + ] + }, + "execution_count": 107, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#//Filter Motorcycle Accident condition\n", + "MotorcycleAccident_df=V20052014.filter(V20052014.Vehicle_Type.contains(\"Motor\"))\n", + "MotorcycleAccident_df.show()\n", + "MotorcycleAccidentbytype_df = MotorcycleAccident_df.groupby('Vehicle_Type').agg(F.count(MotorcycleAccident_df.Accident_Index).alias('Total accidents')).show()\n", + "\n", + "MotorcycleAccident_df= MotorcycleAccident_df.withColumn('Year',F.col('Year').cast(IntegerType()))\n", + "MotorcycleAccidentovertheyeards_df= MotorcycleAccident_df.groupby('Year').agg(F.count(MotorcycleAccident_df.Accident_Index).alias('Total accidents of Motorcycle'))\n", + "MotorcycleAccidentovertheyeards_df= MotorcycleAccidentovertheyeards_df.sort(\"Year\")\n", + "MotorcycleAccidentovertheyeards_df.show()\n", + "#.groupby('Year').agg(F.count(MotorcycleAccident_df.Accident_Index).alias('Total accidents of Motorbikes'))\n", + "MotorcycleAccidenttotaltheyeards_df=MotorcycleAccidentovertheyeards_df.groupby().agg(F.sum(\"Total accidents of Motorcycle\"))\n", + "MotorcycleAccidenttotaltheyeards_df=MotorcycleAccidenttotaltheyeards_df.collect()[0][0]\n", + "MotorcycleAccidenttotaltheyeards_df\n" + ] + }, + { + "cell_type": "code", + "execution_count": 108, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+--------------+-----------------+------------+-----------------------+-----------------+--------------------------------+-----------------+------------------------+-------------------------+---------------------------+--------------------------+-------------------+----------------------------+-------------------------+-------------+-------------+--------------------+--------------------+---------------+--------------+-----------------+---------------------+----+\n", + "|Accident_Index|Vehicle_Reference|Vehicle_Type|Towing_and_Articulation|Vehicle_Manoeuvre|Vehicle_Location-Restricted_Lane|Junction_Location|Skidding_and_Overturning|Hit_Object_in_Carriageway|Vehicle_Leaving_Carriageway|Hit_Object_off_Carriageway|1st_Point_of_Impact|Was_Vehicle_Left_Hand_Drive?|Journey_Purpose_of_Driver|Sex_of_Driver|Age_of_Driver| Age_Band_of_Driver|Engine_Capacity_(CC)|Propulsion_Code|Age_of_Vehicle|Driver_IMD_Decile|Driver_Home_Area_Type|Year|\n", + "+--------------+-----------------+------------+-----------------------+-----------------+--------------------------------+-----------------+------------------------+-------------------------+---------------------------+--------------------------+-------------------+----------------------------+-------------------------+-------------+-------------+--------------------+--------------------+---------------+--------------+-----------------+---------------------+----+\n", + "| 200501BS00024| 2| Pedal cycle| 0| 17| 0| 8| 0| 0| 0| 0| 3| 1| 15| 2| 25| 20Y to 40Y| -1| -1| -1| 7| 1|2005|\n", + "| 200501BS00032| 1| Pedal cycle| 0| 18| 0| 0| 0| 0| 0| 0| 3| 1| 15| 1| 47| 40Y to 70Y| -1| -1| -1| -1| -1|2005|\n", + "| 200501BS70004| 1| Pedal cycle| 0| 16| 0| 1| 0| 0| 0| 0| 0| 1| 15| 1| 31| 20Y to 40Y| -1| -1| -1| 4| 1|2005|\n", + "| 200501BS70009| 2| Pedal cycle| 0| 18| 0| 0| 0| 0| 0| 0| 3| 1| 15| 1| 37| 20Y to 40Y| -1| -1| -1| 2| 1|2005|\n", + "| 200501BS70010| 1| Pedal cycle| 0| 18| 0| 0| 0| 0| 0| 0| 3| 1| 15| 1| 50| 40Y to 70Y| -1| -1| -1| 4| 1|2005|\n", + "| 200501BS70020| 1| Pedal cycle| 0| 9| 0| 8| 0| 0| 0| 0| 3| 1| 15| 2| 65| 40Y to 70Y| -1| -1| -1| -1| -1|2005|\n", + "| 200501BS70027| 1| Pedal cycle| 0| 3| 0| 1| 0| 0| 0| 0| 2| 1| 15| 1| -1|Data missing or o...| -1| -1| -1| 4| 1|2005|\n", + "| 200501BS70030| 2| Pedal cycle| 0| 18| 0| 1| 0| 0| 0| 0| 3| 1| 15| 2| 36| 20Y to 40Y| -1| -1| -1| 7| 1|2005|\n", + "| 200501BS70034| 1| Pedal cycle| 0| 18| 0| 8| 0| 0| 0| 0| 3| 1| 15| 1| 14| Upto 20Y| -1| -1| -1| -1| -1|2005|\n", + "| 200501BS70040| 2| Pedal cycle| 0| 18| 0| 0| 0| 0| 0| 0| 2| 1| 15| 2| 59| 40Y to 70Y| -1| -1| -1| 4| 1|2005|\n", + "| 200501BS70050| 3| Pedal cycle| 0| 9| 0| 8| 0| 0| 0| 0| 1| 1| 15| 1| 26| 20Y to 40Y| -1| -1| -1| 4| 1|2005|\n", + "| 200501BS70054| 2| Pedal cycle| 0| 15| 0| 2| 0| 0| 0| 0| 3| 1| 15| 1| 29| 20Y to 40Y| -1| -1| -1| 7| 1|2005|\n", + "| 200501BS70058| 2| Pedal cycle| 0| 18| 0| 8| 0| 0| 0| 0| 4| 1| 15| 1| 54| 40Y to 70Y| -1| -1| -1| -1| -1|2005|\n", + "| 200501BS70065| 2| Pedal cycle| 0| 18| 0| 8| 0| 0| 0| 0| 3| 1| 15| 1| 52| 40Y to 70Y| -1| -1| -1| 4| 1|2005|\n", + "| 200501BS70067| 2| Pedal cycle| 0| 18| 0| 8| 0| 0| 0| 0| 1| 1| 15| 2| 26| 20Y to 40Y| -1| -1| -1| 4| 1|2005|\n", + "| 200501BS70073| 2| Pedal cycle| 0| 18| 0| 1| 0| 0| 0| 0| 2| 1| 15| 1| 74| 40Y to 70Y| -1| -1| -1| 7| 1|2005|\n", + "| 200501BS70076| 1| Pedal cycle| 0| 12| 0| 1| 0| 0| 0| 0| 1| 1| 15| 1| 29| 20Y to 40Y| -1| -1| -1| 5| 1|2005|\n", + "| 200501BS70078| 2| Pedal cycle| 0| 9| 0| 8| 0| 0| 0| 0| 1| 1| 15| 1| 34| 20Y to 40Y| -1| -1| -1| 2| 1|2005|\n", + "| 200501BS70084| 1| Pedal cycle| 0| 18| 0| 8| 0| 0| 0| 0| 1| 1| 15| 1| 40| 20Y to 40Y| -1| -1| -1| 3| 1|2005|\n", + "| 200501BS70089| 2| Pedal cycle| 0| 7| 0| 8| 0| 0| 0| 0| 2| 1| 15| 2| 44| 20Y to 40Y| -1| -1| -1| 4| 1|2005|\n", + "+--------------+-----------------+------------+-----------------------+-----------------+--------------------------------+-----------------+------------------------+-------------------------+---------------------------+--------------------------+-------------------+----------------------------+-------------------------+-------------+-------------+--------------------+--------------------+---------------+--------------+-----------------+---------------------+----+\n", + "only showing top 20 rows\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+------------+---------------+\n", + "|Vehicle_Type|Total accidents|\n", + "+------------+---------------+\n", + "| Pedal cycle| 277086|\n", + "+------------+---------------+\n", + "\n" + ] + } + ], + "source": [ + "#//Filter Motorcycle Accident condition\n", + "cycleAccident_df=V20052014.filter(V20052014.Vehicle_Type.contains(\"Pedal\"))\n", + "cycleAccident_df.show()\n", + "cycleAccidentbytype_df = cycleAccident_df.groupby('Vehicle_Type').agg(F.count(cycleAccident_df.Accident_Index).alias('Total accidents'))\n", + "cycleAccidentbytype_df.show()\n", + "cycleAccident_df= cycleAccident_df.withColumn('Year',F.col('Year').cast(IntegerType()))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 109, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+----+------------------------------+\n", + "|Year|Total accidents of Pedal Cycle|\n", + "+----+------------------------------+\n", + "|2005| 17039|\n", + "|2006| 16611|\n", + "|2007| 16607|\n", + "|2008| 16797|\n", + "|2009| 17599|\n", + "|2010| 17811|\n", + "|2011| 19883|\n", + "|2012| 19708|\n", + "|2013| 20049|\n", + "|2014| 21979|\n", + "|2015| 19440|\n", + "|2016| 19047|\n", + "|2017| 18954|\n", + "|2018| 18125|\n", + "|2019| 17437|\n", + "+----+------------------------------+\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [] + } + ], + "source": [ + "cycleAccidentovertheyeards_df= cycleAccident_df.groupby('Year').agg(F.count(cycleAccident_df.Accident_Index).alias('Total accidents of Pedal Cycle'))\n", + "cycleVechileAccidentovertheyeards_df= cycleAccidentovertheyeards_df.sort(\"Year\")\n", + "cycleVechileAccidentovertheyeards_df.show()\n", + "#.groupby('Year').agg(F.count(MotorcycleAccident_df.Accident_Index).alias('Total accidents of Motorbikes'))\n", + "cycleAccidenttotaltheyeards_df=cycleAccidentovertheyeards_df.groupby().agg(F.sum(\"Total accidents of Pedal Cycle\"))\n", + "cycleAccidenttotaltheyeards_df=cycleAccidenttotaltheyeards_df.collect()[0][0]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 110, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "45.49999999999999" + ] + }, + "execution_count": 110, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#MOTORCYCLE MILES OVER 11 YEARS\n", + "MotorcycleMILES=Billionvehiclemiles20052017_df.groupby().agg(F.sum(\"Motorcycles\")).collect()[0][0]\n", + "MotorcycleMILES\n" + ] + }, + { + "cell_type": "code", + "execution_count": 111, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "46.2" + ] + }, + "execution_count": 111, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Cars and Taxis\n", + "CarMILES=Billionvehiclemiles20052017_df.groupby().agg(F.sum(\"Cars and Taxis\")).collect()[0][0]\n", + "CarMILES\n", + "BusMILES=Billionvehiclemiles20052017_df.groupby().agg(F.sum(\"Buses & Coaches\")).collect()[0][0]\n", + "BusMILES\n", + "Pedalcycle=Billionvehiclemiles20052017_df.groupby().agg(F.sum(\"Pedal Cycle\")).collect()[0][0]\n", + "Pedalcycle\n" + ] + }, + { + "cell_type": "code", + "execution_count": 112, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "936.4" + ] + }, + "execution_count": 112, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Goods1MILES=Billionvehiclemiles20052017_df.groupby().agg(F.sum(\"Light Commercial,Vehicles 1\")).collect()[0][0]\n", + "#Goods1MILES\n", + "Goods2MILES=Billionvehiclemiles20052017_df.groupby().agg(F.sum(\"Heavy Goods Vehicles 2\")).collect()[0][0]\n", + "#Goods2MILES\n", + "Goods=Goods1MILES+Goods2MILES\n", + "Goods\n" + ] + }, + { + "cell_type": "code", + "execution_count": 113, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2.549018264840183e-06" + ] + }, + "execution_count": 113, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# FINDING ACCIDENT PER MILES \n", + "Accidentsofbyclerperbillionvechilemiles=cycleAccidenttotaltheyeards_df/(Pedalcycle*1000000000)\n", + "Accidentsofbyclerperbillionvechilemiles\n", + "\n", + "AccidentsofMotorbyclerperbillionvechilemiles=MotorcycleAccidenttotaltheyeards_df/(MotorcycleMILES*1000000000)\n", + "AccidentsofMotorbyclerperbillionvechilemiles\n", + "AccidentsofCarrperbillionvechilemiles=CarAccidenttotalovertheyear/(CarMILES*1000000000)\n", + "AccidentsofCarrperbillionvechilemiles\n", + "AccidentsofBusrperbillionvechilemiles=BusAccidenttotalovertheyear/(BusMILES*1000000000)\n", + "AccidentsofBusrperbillionvechilemiles\n" + ] + }, + { + "cell_type": "code", + "execution_count": 114, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3.415484835540367e-07" + ] + }, + "execution_count": 114, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#GoodsVechileAccidenttotalovertheyear Goods\n", + "BusAccidenttotalovertheyear\n", + "AccidentsofGoodsrperbillionvechilemiles=GoodsVechileAccidenttotalovertheyear/(Goods*1000000000)\n", + "AccidentsofGoodsrperbillionvechilemiles\n" + ] + }, + { + "cell_type": "code", + "execution_count": 115, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bycycle accident per mile= 5.997532467532468e-06\n", + "Motor Cycle accident per mile= 6.985252747252748e-06\n", + "Car accident per mile= 8.224073440829101e-07\n", + "Bus accident per mile= 2.549018264840183e-06\n", + "Goods accident per mile= 3.415484835540367e-07\n" + ] + } + ], + "source": [ + "print(\"Bycycle accident per mile=\",Accidentsofbyclerperbillionvechilemiles)\n", + "print(\"Motor Cycle accident per mile=\",AccidentsofMotorbyclerperbillionvechilemiles)\n", + "print(\"Car accident per mile=\",AccidentsofCarrperbillionvechilemiles)\n", + "print(\"Bus accident per mile=\",AccidentsofBusrperbillionvechilemiles)\n", + "print(\"Goods accident per mile=\",AccidentsofGoodsrperbillionvechilemiles)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>lab</th>\n", + " <th>val</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>A</td>\n", + " <td>10</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>B</td>\n", + " <td>30</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>C</td>\n", + " <td>20</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " lab val\n", + "0 A 10\n", + "1 B 30\n", + "2 C 20" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = pd.DataFrame({'lab':['A', 'B', 'C'], 'val':[10, 30, 20]})\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 126, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "<AxesSubplot:xlabel='vehicle'>" + ] + }, + "execution_count": 126, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": [ + "<Figure size 1440x216 with 0 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "<Figure size 720x216 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "x=[Accidentsofbyclerperbillionvechilemiles,AccidentsofMotorbyclerperbillionvechilemiles,AccidentsofCarrperbillionvechilemiles,AccidentsofBusrperbillionvechilemiles,AccidentsofGoodsrperbillionvechilemiles]\n", + "y=[\"Bicycle \",\n", + "\"Motor Cycle\",\n", + "\"Car \",\n", + "\"Bus \",\n", + "\"Goods \"]\n", + "plt.figure(figsize=(20, 3))\n", + "df = pd.DataFrame({'vehicle':y, 'Accidents per mile travelled':x})\n", + "df.plot.bar(x='vehicle', y='Accidents per mile travelled', rot=0,figsize=(10, 3))\n", + "#plt.plot(x, y)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "Accident_Information20052019_df=Accident_Information20052019_df.withColumn(\n", + " \"Day_of_Week\",\n", + " when(\n", + " col(\"Day_of_Week\") == 1,\n", + " \"Sunday\"\n", + " ).\n", + " when(\n", + " col(\"Day_of_Week\") == 2,\n", + " \"Monday\"\n", + " ).\n", + " when(\n", + " col(\"Day_of_Week\") == 3,\n", + " \"Tuesday\"\n", + " ).\n", + " when(\n", + " col(\"Day_of_Week\") == 4,\n", + " \"Wednesday\"\n", + " ).\n", + " when(\n", + " col(\"Day_of_Week\") == 5,\n", + " \"Thursday\"\n", + " ).\n", + " when(\n", + " col(\"Day_of_Week\") == 6,\n", + " \"Friday\"\n", + " ).\n", + " when(\n", + " col(\"Day_of_Week\") == 7,\n", + " \"Saturday\"\n", + " ).otherwise(col(\"Day_of_Week\")),\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DataFrame[Accident_Index: string, 1st_Road_Class: string, 1st_Road_Number: string, 2nd_Road_Class: string, 2nd_Road_Number: string, Accident_Severity: string, Carriageway_Hazards: string, Date: string, Day_of_Week: string, Did_Police_Officer_Attend_Scene_of_Accident: string, Junction_Control: string, Junction_Detail: string, Latitude: string, Light_Conditions: string, Local_Authority_(District): string, Local_Authority_(Highway): string, Location_Easting_OSGR: string, Location_Northing_OSGR: string, Longitude: string, LSOA_of_Accident_Location: string, Number_of_Casualties: string, Number_of_Vehicles: string, Pedestrian_Crossing-Human_Control: string, Pedestrian_Crossing-Physical_Facilities: string, Police_Force: string, Road_Surface_Conditions: string, Road_Type: string, Special_Conditions_at_Site: string, Speed_limit: string, Time: string, Urban_or_Rural_Area: string, Weather_Conditions: string, Year: int]" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Accident_Information20052019_df" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+-----------------+-----------+---------------+\n", + "|Accident_Severity|Day_of_Week|Total accidents|\n", + "+-----------------+-----------+---------------+\n", + "| Serious| Friday| 52828|\n", + "| Slight| Sunday| 208376|\n", + "| Fatal| Thursday| 3946|\n", + "| Fatal| Saturday| 4999|\n", + "| Slight| Thursday| 292268|\n", + "| Serious| Wednesday| 47344|\n", + "| Slight| Wednesday| 292966|\n", + "| Serious| Monday| 44440|\n", + "| Serious| Saturday| 49372|\n", + "| Serious| Thursday| 48198|\n", + "| Serious| Tuesday| 46919|\n", + "| Fatal| Monday| 3872|\n", + "| Slight| Saturday| 257858|\n", + "| Serious| Sunday| 43825|\n", + "| Slight| Tuesday| 289922|\n", + "| Fatal| Friday| 4477|\n", + "| Slight| Friday| 314507|\n", + "| Fatal| Tuesday| 3834|\n", + "| Fatal| Sunday| 4752|\n", + "| Slight| Monday| 268906|\n", + "+-----------------+-----------+---------------+\n", + "only showing top 20 rows\n", + "\n" + ] + } + ], + "source": [ + "DayAccidentwrtseverity_df = Accident_Information20052019_df.groupby('Accident_Severity','Day_of_Week').agg(F.count(Accident_Information20052019_df.Accident_Index).alias('Total accidents'))\n", + "#DayAccident_df.sort(\"Year\").show(truncate=False)\n", + "DayAccidentwrtseverity_df.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+-----------+---------------+\n", + "|Day_of_Week|Total accidents|\n", + "+-----------+---------------+\n", + "| Wednesday| 344128|\n", + "| Tuesday| 340675|\n", + "| Friday| 371812|\n", + "| Thursday| 344412|\n", + "| Saturday| 312229|\n", + "| Monday| 317218|\n", + "| Sunday| 256953|\n", + "+-----------+---------------+\n", + "\n" + ] + } + ], + "source": [ + "#Day of week accidents\n", + "DayAccident_df = Accident_Information20052019_df.groupby('Day_of_Week').agg(F.count(Accident_Information20052019_df.Accident_Index).alias('Total accidents'))\n", + "#DayAccident_df.sort(\"Year\").show(truncate=False)\n", + "DayAccident_df.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DataFrame[Accident_Index: string, 1st_Road_Class: string, 1st_Road_Number: string, 2nd_Road_Class: string, 2nd_Road_Number: string, Accident_Severity: string, Carriageway_Hazards: string, Date: string, Day_of_Week: string, Did_Police_Officer_Attend_Scene_of_Accident: string, Junction_Control: string, Junction_Detail: string, Latitude: string, Light_Conditions: string, Local_Authority_(District): string, Local_Authority_(Highway): string, Location_Easting_OSGR: string, Location_Northing_OSGR: string, Longitude: string, LSOA_of_Accident_Location: string, Number_of_Casualties: string, Number_of_Vehicles: string, Pedestrian_Crossing-Human_Control: string, Pedestrian_Crossing-Physical_Facilities: string, Police_Force: string, Road_Surface_Conditions: string, Road_Type: string, Special_Conditions_at_Site: string, Speed_limit: string, Time: string, Urban_or_Rural_Area: string, Weather_Conditions: string, Year: int]" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Accident_Information20052019_df" + ] + }, + { + "cell_type": "code", + "execution_count": 146, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+--------------+--------------+---------------+--------------+---------------+-----------------+-------------------+----------+-----------+-------------------------------------------+--------------------+--------------------+---------+--------------------+--------------------------+-------------------------+---------------------+----------------------+---------+-------------------------+--------------------+------------------+---------------------------------+---------------------------------------+-------------------+-----------------------+------------------+--------------------------+-----------+-----+-------------------+--------------------+----+-------------------+----+\n", + "|Accident_Index|1st_Road_Class|1st_Road_Number|2nd_Road_Class|2nd_Road_Number|Accident_Severity|Carriageway_Hazards| Date|Day_of_Week|Did_Police_Officer_Attend_Scene_of_Accident| Junction_Control| Junction_Detail| Latitude| Light_Conditions|Local_Authority_(District)|Local_Authority_(Highway)|Location_Easting_OSGR|Location_Northing_OSGR|Longitude|LSOA_of_Accident_Location|Number_of_Casualties|Number_of_Vehicles|Pedestrian_Crossing-Human_Control|Pedestrian_Crossing-Physical_Facilities| Police_Force|Road_Surface_Conditions| Road_Type|Special_Conditions_at_Site|Speed_limit| Time|Urban_or_Rural_Area| Weather_Conditions|Year| timestamp|hour|\n", + "+--------------+--------------+---------------+--------------+---------------+-----------------+-------------------+----------+-----------+-------------------------------------------+--------------------+--------------------+---------+--------------------+--------------------------+-------------------------+---------------------+----------------------+---------+-------------------------+--------------------+------------------+---------------------------------+---------------------------------------+-------------------+-----------------------+------------------+--------------------------+-----------+-----+-------------------+--------------------+----+-------------------+----+\n", + "| 200501BS00001| A| 3218| NA| 0| Serious| None|2005-01-04| Tuesday| 1|Data missing or o...|Not at junction o...|51.489096| Daylight| Kensington and Ch...| Kensington and Ch...| 525680| 178240| -0.19117| E01002849| 1| 1| 0| 1|Metropolitan Police| Wet or damp|Single carriageway| None| 30|17:42| Urban|Raining no high w...|2005|2021-12-20 17:42:00| 17|\n", + "| 200501BS00002| B| 450| C| 0| Slight| None|2005-01-05| Wednesday| 1| Auto traffic signal| Crossroads|51.520075|Darkness - lights...| Kensington and Ch...| Kensington and Ch...| 524170| 181650|-0.211708| E01002909| 1| 1| 0| 5|Metropolitan Police| Dry| Dual carriageway| None| 30|17:36| Urban| Fine no high winds|2005|2021-12-20 17:36:00| 17|\n", + "| 200501BS00003| C| 0| NA| 0| Slight| None|2005-01-06| Thursday| 1|Data missing or o...|Not at junction o...|51.525301|Darkness - lights...| Kensington and Ch...| Kensington and Ch...| 524520| 182240|-0.206458| E01002857| 1| 2| 0| 0|Metropolitan Police| Dry|Single carriageway| None| 30|00:15| Urban| Fine no high winds|2005|2021-12-20 00:15:00| 0|\n", + "| 200501BS00004| A| 3220| NA| 0| Slight| None|2005-01-07| Friday| 1|Data missing or o...|Not at junction o...|51.482442| Daylight| Kensington and Ch...| Kensington and Ch...| 526900| 177530|-0.173862| E01002840| 1| 1| 0| 0|Metropolitan Police| Dry|Single carriageway| None| 30|10:35| Urban| Fine no high winds|2005|2021-12-20 10:35:00| 10|\n", + "| 200501BS00005| U| 0| NA| 0| Slight| None|2005-01-10| Monday| 1|Data missing or o...|Not at junction o...|51.495752|Darkness - lighti...| Kensington and Ch...| Kensington and Ch...| 528060| 179040|-0.156618| E01002863| 1| 1| 0| 0|Metropolitan Police| Wet or damp|Single carriageway| None| 30|21:13| Urban| Fine no high winds|2005|2021-12-20 21:13:00| 21|\n", + "| 200501BS00006| U| 0| NA| 0| Slight| None|2005-01-11| Tuesday| 1|Data missing or o...|Not at junction o...| 51.51554| Daylight| Kensington and Ch...| Kensington and Ch...| 524770| 181160|-0.203238| E01002832| 1| 2| 0| 0|Metropolitan Police| Wet or damp|Single carriageway| Oil or diesel| 30|12:40| Urban|Raining no high w...|2005|2021-12-20 12:40:00| 12|\n", + "| 200501BS00007| C| 0| Unclassified| 0| Slight| None|2005-01-13| Thursday| 1|Give way or uncon...|T or staggered ju...|51.512695|Darkness - lights...| Kensington and Ch...| Kensington and Ch...| 524220| 180830|-0.211277| E01002875| 1| 2| 0| 0|Metropolitan Police| Dry|Single carriageway| None| 30|20:40| Urban| Fine no high winds|2005|2021-12-20 20:40:00| 20|\n", + "| 200501BS00009| A| 315| NA| 0| Slight| None|2005-01-14| Friday| 1|Data missing or o...|Not at junction o...| 51.50226| Daylight| Kensington and Ch...| Kensington and Ch...| 525890| 179710|-0.187623| E01002889| 2| 1| 0| 0|Metropolitan Police| Dry| Dual carriageway| None| 30|17:35| Urban| Fine no high winds|2005|2021-12-20 17:35:00| 17|\n", + "| 200501BS00010| A| 3212| B| 304| Slight| None|2005-01-15| Saturday| 1| Auto traffic signal| Crossroads| 51.48342|Darkness - lights...| Kensington and Ch...| Kensington and Ch...| 527350| 177650|-0.167342| E01002900| 2| 2| 0| 5|Metropolitan Police| Dry|Single carriageway| None| 30|22:43| Urban| Fine no high winds|2005|2021-12-20 22:43:00| 22|\n", + "| 200501BS00011| B| 450| C| 0| Slight| None|2005-01-15| Saturday| 1|Give way or uncon...|T or staggered ju...|51.512443| Daylight| Kensington and Ch...| Kensington and Ch...| 524550| 180810|-0.206531| E01002875| 5| 2| 0| 8|Metropolitan Police| Dry|Single carriageway| None| 30|16:00| Urban| Fine no high winds|2005|2021-12-20 16:00:00| 16|\n", + "| 200501BS00012| A| 4| B| 325| Slight| None|2005-01-16| Sunday| 1| Auto traffic signal| Crossroads|51.494902|Darkness - lights...| Kensington and Ch...| Kensington and Ch...| 526240| 178900|-0.182872| E01002835| 1| 1| 0| 5|Metropolitan Police| Dry|Single carriageway| None| 30|00:42| Urban| Fine no high winds|2005|2021-12-20 00:42:00| 0|\n", + "| 200501BS00014| A| 3220| A| 308| Slight| None|2005-01-25| Tuesday| 1| Auto traffic signal| Crossroads|51.484044|Darkness - lights...| Kensington and Ch...| Kensington and Ch...| 526170| 177690|-0.184312| E01002912| 1| 2| 0| 5|Metropolitan Police| Wet or damp|Single carriageway| None| 30|20:48| Urban| Fine no high winds|2005|2021-12-20 20:48:00| 20|\n", + "| 200501BS00015| U| 0| A| 3220| Slight| None|2005-01-11| Tuesday| 1|Give way or uncon...|T or staggered ju...|51.491632| Daylight| Kensington and Ch...| Kensington and Ch...| 525590| 178520|-0.192366| E01002849| 1| 1| 0| 1|Metropolitan Police| Wet or damp| One way street| None| 30|12:55| Urban|Raining no high w...|2005|2021-12-20 12:55:00| 12|\n", + "| 200501BS00016| A| 3217| A| 3216| Slight| None|2005-01-18| Tuesday| 1|Give way or uncon...|T or staggered ju...|51.492622|Darkness - lights...| Kensington and Ch...| Kensington and Ch...| 527990| 178690|-0.157753| E01002902| 1| 2| 0| 0|Metropolitan Police| Wet or damp| One way street| None| 30|05:01| Urban|Raining no high w...|2005|2021-12-20 05:01:00| 5|\n", + "| 200501BS00017| A| 4| NA| 0| Slight| None|2005-01-18| Tuesday| 1|Data missing or o...|Not at junction o...|51.495429| Daylight| Kensington and Ch...| Kensington and Ch...| 526700| 178970|-0.176224| E01002821| 2| 1| 0| 0|Metropolitan Police| Dry| Dual carriageway| None| 30|11:15| Urban| Fine no high winds|2005|2021-12-20 11:15:00| 11|\n", + "| 200501BS00018| A| 3217| Unclassified| 0| Slight| None|2005-01-18| Tuesday| 1|Give way or uncon...|T or staggered ju...|51.481912| Daylight| Kensington and Ch...| Kensington and Ch...| 526460| 177460| -0.18022| E01002840| 1| 1| 0| 1|Metropolitan Police| Dry|Single carriageway| None| 30|10:50| Urban| Fine no high winds|2005|2021-12-20 10:50:00| 10|\n", + "| 200501BS00019| U| 0| Unclassified| 0| Serious| None|2005-01-20| Thursday| 1|Give way or uncon...|T or staggered ju...|51.500191|Darkness - lights...| Kensington and Ch...| Kensington and Ch...| 524680| 179450|-0.205139| E01002864| 1| 2| 0| 0|Metropolitan Police| Dry|Single carriageway| None| 30|00:15| Urban| Fine no high winds|2005|2021-12-20 00:15:00| 0|\n", + "| 200501BS00020| A| 3218| A| 4| Slight| None|2005-01-21| Friday| 1|Give way or uncon...|T or staggered ju...|51.495811| Daylight| Kensington and Ch...| Kensington and Ch...| 527000| 179020|-0.171887| E01002821| 1| 2| 0| 0|Metropolitan Police| Dry|Single carriageway| None| 30|09:15| Urban| Fine no high winds|2005|2021-12-20 09:15:00| 9|\n", + "| 200501BS00021| B| 302| NA| 0| Slight| None|2005-01-21| Friday| 1|Data missing or o...|Not at junction o...|51.486552|Darkness - lights...| Kensington and Ch...| Kensington and Ch...| 527810| 178010| -0.16059| E01002901| 1| 2| 0| 0|Metropolitan Police| Dry|Single carriageway| None| 30|21:16| Urban| Fine no high winds|2005|2021-12-20 21:16:00| 21|\n", + "| 200501BS00022| A| 4| Unclassified| 0| Serious| None|2005-01-08| Saturday| 1|Give way or uncon...|T or staggered ju...|51.495498|Darkness - lights...| Kensington and Ch...| Kensington and Ch...| 526790| 178980|-0.174925| E01002821| 1| 1| 0| 0|Metropolitan Police| Dry|Single carriageway| None| 30|03:00| Urban| Fine no high winds|2005|2021-12-20 03:00:00| 3|\n", + "+--------------+--------------+---------------+--------------+---------------+-----------------+-------------------+----------+-----------+-------------------------------------------+--------------------+--------------------+---------+--------------------+--------------------------+-------------------------+---------------------+----------------------+---------+-------------------------+--------------------+------------------+---------------------------------+---------------------------------------+-------------------+-----------------------+------------------+--------------------------+-----------+-----+-------------------+--------------------+----+-------------------+----+\n", + "only showing top 20 rows\n", + "\n", + "root\n", + " |-- Accident_Severity: string (nullable = true)\n", + " |-- hour: integer (nullable = true)\n", + " |-- Total accidents: long (nullable = false)\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "data": { + "text/plain": [ + "Row(Accident_Severity='Slight', hour=None, Total accidents=185)" + ] + }, + "execution_count": 146, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from pyspark.sql.functions import *\n", + "#Timestamp String to DateType\n", + "Accident_Information20052019_dfff=Accident_Information20052019_df.withColumn(\"timestamp\",to_timestamp(\"Time\"))\n", + "Accident_Information20052019_dfff\n", + "TimeAccident_dfhour = Accident_Information20052019_dfff.withColumn('hour',hour(Accident_Information20052019_dfff.timestamp))\n", + "TimeAccident_dfhour.show()\n", + "#Time of week accidents\n", + "TimeAccident_df = TimeAccident_dfhour.groupby(\"Accident_Severity\",'hour').agg(F.count(Accident_Information20052019_dfff.Accident_Index).alias('Total accidents'))\n", + "#TimeAccident_df= TimeAccident_df.withColumn('Time',F.col('Time').cast(IntegerType()))\n", + "TimeAccident_df.printSchema()\n", + "TimeAccident_df=TimeAccident_df.sort(\"hour\")\n", + "TimeAccident_df.head()\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 147, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+-----------------+----+---------------+\n", + "|Accident_Severity|hour|Total accidents|\n", + "+-----------------+----+---------------+\n", + "| Slight|null| 185|\n", + "| Fatal|null| 4|\n", + "| Serious|null| 43|\n", + "| Serious| 0| 6799|\n", + "| Slight| 0| 26893|\n", + "| Fatal| 0| 1052|\n", + "| Serious| 1| 5124|\n", + "| Slight| 1| 19407|\n", + "| Fatal| 1| 809|\n", + "| Slight| 2| 15119|\n", + "| Serious| 2| 4228|\n", + "| Fatal| 2| 708|\n", + "| Fatal| 3| 624|\n", + "| Serious| 3| 3362|\n", + "| Slight| 3| 12324|\n", + "| Serious| 4| 2652|\n", + "| Fatal| 4| 499|\n", + "| Slight| 4| 9838|\n", + "| Fatal| 5| 626|\n", + "| Serious| 5| 3674|\n", + "+-----------------+----+---------------+\n", + "only showing top 20 rows\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [] + } + ], + "source": [ + "TimeAccident_df.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+-----------------+----+---------------+\n", + "|Accident_Severity|hour|Total accidents|\n", + "+-----------------+----+---------------+\n", + "| Slight|null| 185|\n", + "| Fatal|null| 4|\n", + "| Serious|null| 43|\n", + "| Serious| 0| 6799|\n", + "| Fatal| 0| 1052|\n", + "| Slight| 0| 26893|\n", + "| Slight| 1| 19407|\n", + "| Fatal| 1| 809|\n", + "| Serious| 1| 5124|\n", + "| Slight| 2| 15119|\n", + "| Serious| 2| 4228|\n", + "| Fatal| 2| 708|\n", + "| Slight| 3| 12324|\n", + "| Fatal| 3| 624|\n", + "| Serious| 3| 3362|\n", + "| Fatal| 4| 499|\n", + "| Serious| 4| 2652|\n", + "| Slight| 4| 9838|\n", + "| Serious| 5| 3674|\n", + "| Slight| 5| 14928|\n", + "+-----------------+----+---------------+\n", + "only showing top 20 rows\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [] + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "y_ans_val = [val for val in TimeAccident_df.select('Total accidents').collect()]\n", + "x_ts = [val for val in TimeAccident_df.select('hour').collect()]\n", + "\n", + "plt.plot(x_ts, y_ans_val)\n", + "\n", + "plt.ylabel('Total accidents')\n", + "plt.xlabel('Hour')\n", + "plt.legend(['Accidents'], loc='upper left')\n", + "plt.show()\n", + "TimeAccident_df.show()\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "data": { + "text/plain": [ + "<AxesSubplot:xlabel='hour'>" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYwAAAETCAYAAAAlCTHcAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8rg+JYAAAACXBIWXMAAAsTAAALEwEAmpwYAAAtFUlEQVR4nO3deZxU5ZX/8c8RUcSFfRQFhSSoUdAGcZlR4xpFzS9qxmR0NOAyYqJmmUlUkknEmOhgxkkyZjFDRtxiFKOJEoMiKsZkEpEWWhbR0CBIoyIBFQVBwfP74zkFt8vq7tvV1Qvwfb9e91X1nHruvc+tuvc5d6sqc3dERESasl17N0BERLYMShgiIpKLEoaIiOSihCEiIrkoYYiISC5KGCIiksv27d2ASuvdu7cPGDCgvZshIrJFefbZZ//m7n0aq7PVJYwBAwZQXV3d3s0QEdmimNmSpurolJSIiOTSZMIwswlm9rqZzc3EJppZTQyLzawm4gPM7N3Maz/PjHOImc0xs1ozu8nMLOI9zWyqmS2Ixx4Rt6hXa2azzWxYxZdeRERyy3OEcRswIhtw939y9yp3rwLuB36TeXlh4TV3/0ImfjNwMTAohsI0xwCPu/sg4PEoA5ySqTs6xhcRkXbS5DUMd3/KzAaUei2OEj4HHN/YNMysL7Cbuz8d5TuAM4CHgdOBY6Pq7cCTwFURv8PTj109bWbdzayvu7/a5FIVef/996mrq2PdunXNHVXaSZcuXejXrx+dO3du76aISGjpRe+jgeXuviATG2hms4DVwLfc/Y/AXkBdpk5dxAB2zySB14Dd4/lewNIS4zQ7YdTV1bHrrrsyYMAA4kyYdGDuzsqVK6mrq2PgwIHt3RwRCS296H0OcHem/Cqwt7sPBf4N+JWZ7ZZ3YnE00eyfzzWz0WZWbWbVK1as+NDr69ato1evXkoWWwgzo1evXjoiFOlgyk4YZrY98BlgYiHm7uvdfWU8fxZYCOwLLAP6ZUbvFzGA5XHKqnDq6vWILwP6NzBOPe4+3t2Hu/vwPn1K30asZLFl0ecl0vG05AjjROAFd990qsnM+phZp3j+EdIF60Vxymm1mR0R1z1GAg/GaJOAUfF8VFF8ZNwtdQTwVjnXLzqClStXUlVVRVVVFXvssQd77bXXpvJ7771Xr+6PfvQj1q5d2+Q0jz322Fb7vsnVV1/NY4899qH4k08+yac+9amyp3v99de3pFki0s6avIZhZneTLkr3NrM6YKy73wKcTf3TUQCfAK41s/eBD4AvuPuqeO1S0h1XO5Eudj8c8XHAvWZ2EbCEdBEdYDJwKlALrAUuKGP5Show5veVmhQAi8ed1ujrvXr1oqamBoBrrrmGXXbZha9//esl6/7oRz/ivPPOo2vXrhVtY3Nce+21rTLd66+/nm9+85utMm3ZuhS20aa2LWlbTR5huPs57t7X3Tu7e79IFrj7+e7+86K697v7gXFL7TB3/13mtWp3H+zuH3X3y+N6Be6+0t1PcPdB7n5iIcF4clnUH+LuW9XXtx9//HGGDh3KkCFDuPDCC1m/fj033XQTr7zyCscddxzHHXccAF/84hcZPnw4Bx54IGPHjm1yutdeey2HHnoogwcPZvTo0RT+UbG2tpYTTzyRgw8+mGHDhrFw4UIAbrjhBoYMGcLBBx/MmDHpjubzzz+f++67D4BHHnmE/fffn2HDhvGb32y+e3rNmjVceOGFHHbYYQwdOpQHH0wHhrfddhuf+cxnGDFiBIMGDeLKK68EYMyYMbz77rtUVVVx7rnnsmbNGk477TQOPvhgBg8ezMSJm85sikgHpW96t4N169Zx/vnnM3HiRObMmcOGDRu4+eab+fKXv8yee+7JtGnTmDZtGgDXXXcd1dXVzJ49mz/84Q/Mnj270WlffvnlzJgxg7lz5/Luu+/y0EMPAXDuuedy2WWX8dxzz/HnP/+Zvn378vDDD/Pggw8yffp0nnvuuU2de7adF198Mb/73e949tlnee211za9dt1113H88cfzzDPPMG3aNK644grWrFkDQE1NzaZlmzhxIkuXLmXcuHHstNNO1NTUcNddd/HII4+w55578txzzzF37lxGjKj3VR8R6YCUMNrBxo0bGThwIPvuuy8Ao0aN4qmnnipZ995772XYsGEMHTqUefPm8fzzzzc67WnTpnH44YczZMgQnnjiCebNm8fbb7/NsmXLOPPMM4H0HYeuXbvy2GOPccEFF2w6/dWzZ89603rhhRcYOHAggwYNwsw477zzNr326KOPMm7cOKqqqjj22GNZt24dL7/8MgAnnHAC3bp1o0uXLhxwwAEsWfLhn6gZMmQIU6dO5aqrruKPf/wj3bp1y/nuiUh72ep+fHBr8tJLL3HjjTcyY8YMevTowfnnn9/orabr1q3j0ksvpbq6mv79+3PNNde02q2p7s7999/PfvvtVy8+ffp0dtxxx03lTp06sWHDhg+Nv++++zJz5kwmT57Mt771LU444QSuvvrqVmmriFSGjjDaQadOnVi8eDG1tbUA3HnnnRxzzDEA7Lrrrrz99tsArF69mp133plu3bqxfPlyHn744QanCWxKDr179+add97ZdB1i1113pV+/fjzwwAMArF+/nrVr1/LJT36SW2+9ddNdWatWrao3vf3335/Fixdvut5x992b73E4+eST+fGPf7zpGsmsWbOaXO7OnTvz/vvvA/DKK6/QtWtXzjvvPK644gpmzpzZ5Pgi0r50hNEOunTpwq233spnP/tZNmzYwKGHHsoXvpB+dmv06NGMGDFi07WMoUOHsv/++9O/f3+OPPLIRqfbvXt3Lr74YgYPHswee+zBoYceuum1O++8k0suuYSrr76azp078+tf/5oRI0ZQU1PD8OHD2WGHHTj11FPr3frapUsXxo8fz2mnnUbXrl05+uijNyWzb3/723z1q1/loIMO4oMPPmDgwIGbrpc0ZPTo0Rx00EEMGzaMkSNHcsUVV7DddtvRuXNnbr5ZPxUm0tFZYQ9xazF8+HAv/n7C/Pnz+fjHP95OLZJy6XPbdum22rZnZs+6+/DG6uiUlIiI5KKEISIiuShhiIhILttMwtjartVs7fR5iXQ820TC6NKlCytXrlQntIUo/B9Gly5d2rspIpKxTdxW269fP+rq6ij1XxnSMRX+cU9EOo5tImF07txZ/9wmItJC28QpKRERaTklDBERyUUJQ0REclHCEBGRXJQwRKRVDRjz+4r/LbK0DyUMERHJRQlDRERyUcIQEZFcmkwYZjbBzF43s7mZ2DVmtszMamI4NfPaN8ys1sxeNLOTM/EREas1szGZ+EAzmx7xiWa2Q8R3jHJtvD6gYkstIiLNlucI4zZgRIn4D929KobJAGZ2AHA2cGCM8zMz62RmnYCfAqcABwDnRF2AG2JaHwPeAC6K+EXAGxH/YdQTEZF20mTCcPengFVN1QunA/e4+3p3fwmoBQ6LodbdF7n7e8A9wOlmZsDxwH0x/u3AGZlp3R7P7wNOiPoiItIOWnIN43Izmx2nrHpEbC9gaaZOXcQaivcC3nT3DUXxetOK19+K+iIi0g7KTRg3Ax8FqoBXgf+qVIPKYWajzazazKr1i7QiraPwfQp9p2LbVVbCcPfl7r7R3T8AfkE65QSwDOifqdovYg3FVwLdzWz7oni9acXr3aJ+qfaMd/fh7j68T58+5SySiIg0oayEYWZ9M8UzgcIdVJOAs+MOp4HAIOAZYAYwKO6I2oF0YXySp380mgacFeOPAh7MTGtUPD8LeML1D0giIu2myf/DMLO7gWOB3mZWB4wFjjWzKsCBxcAlAO4+z8zuBZ4HNgCXufvGmM7lwBSgEzDB3efFLK4C7jGz7wGzgFsifgtwp5nVki66n93ShRURkfI1mTDc/ZwS4VtKxAr1rwOuKxGfDEwuEV/E5lNa2fg64LNNtU9ERNqGvuktWy1doBWpLCUMERHJRQlDRERyUcIQEZFclDBERCQXJQwREclFCUNERHJRwhARkVyUMEREJBclDBERyUUJQ0REclHCEBGRXJQwREQkFyUMERHJRQlDRLZa+sXiylLCEBGRXJQwREQkFyUMERHJRQlDRERyUcIQEZFclDBERCSXJhOGmU0ws9fNbG4m9p9m9oKZzTaz35pZ94gPMLN3zawmhp9nxjnEzOaYWa2Z3WRmFvGeZjbVzBbEY4+IW9SrjfkMq/jSi4hIbnmOMG4DRhTFpgKD3f0g4K/ANzKvLXT3qhi+kInfDFwMDIqhMM0xwOPuPgh4PMoAp2Tqjo7xRUSknTSZMNz9KWBVUexRd98QxaeBfo1Nw8z6Aru5+9Pu7sAdwBnx8unA7fH89qL4HZ48DXSP6YiISDuoxDWMC4GHM+WBZjbLzP5gZkdHbC+gLlOnLmIAu7v7q/H8NWD3zDhLGxhHRETa2PYtGdnM/h3YANwVoVeBvd19pZkdAjxgZgfmnZ67u5l5Ge0YTTptxd57793c0UVEJIeyjzDM7HzgU8C5cZoJd1/v7ivj+bPAQmBfYBn1T1v1ixjA8sKppnh8PeLLgP4NjFOPu4939+HuPrxPnz7lLpKIiDSirIRhZiOAK4FPu/vaTLyPmXWK5x8hXbBeFKecVpvZEXF31EjgwRhtEjAqno8qio+Mu6WOAN7KnLoSEZE21uQpKTO7GzgW6G1mdcBY0l1ROwJT4+7Yp+OOqE8A15rZ+8AHwBfcvXDB/FLSHVc7ka55FK57jAPuNbOLgCXA5yI+GTgVqAXWAhe0ZEFFRKRlmkwY7n5OifAtDdS9H7i/gdeqgcEl4iuBE0rEHbisqfaJiEjb0De9RUQkFyUMERHJRQlDRERyUcIQEZFclDBEtgHl/Le1/g9biilhiIhILkoYIiKSixKGSAem00LSkShhiIhILkoYIiKSixKGiIjkooQhIiK5KGGIiEguShgiIpKLEoaIiOSihCEiIrkoYYiISC5KGCIikosShoiI5KKEISIiuShhiIhILrkShplNMLPXzWxuJtbTzKaa2YJ47BFxM7ObzKzWzGab2bDMOKOi/gIzG5WJH2Jmc2Kcm8zMGpuHiIi0vbxHGLcBI4piY4DH3X0Q8HiUAU4BBsUwGrgZUucPjAUOBw4DxmYSwM3AxZnxRjQxDxERaWO5Eoa7PwWsKgqfDtwez28HzsjE7/DkaaC7mfUFTgamuvsqd38DmAqMiNd2c/en3d2BO4qmVWoeIiLSxlpyDWN3d381nr8G7B7P9wKWZurVRayxeF2JeGPzEBGRNlaRi95xZOCVmFY58zCz0WZWbWbVK1asaM1miFSM/k1PtjQtSRjL43QS8fh6xJcB/TP1+kWssXi/EvHG5lGPu4939+HuPrxPnz4tWCQRkQ9Tck9akjAmAYU7nUYBD2biI+NuqSOAt+K00hTgJDPrERe7TwKmxGurzeyIuDtqZNG0Ss1DRETa2PZ5KpnZ3cCxQG8zqyPd7TQOuNfMLgKWAJ+L6pOBU4FaYC1wAYC7rzKz7wIzot617l64kH4p6U6snYCHY6CReYi0isJe5OJxp7VzS0Q6nlwJw93PaeClE0rUdeCyBqYzAZhQIl4NDC4RX1lqHiIi0vb0TW8REclFCUNERHJRwhARkVyUMEREJBclDBERyUUJQ0REclHCEBGRXJQwREQkFyUMERHJRQlDRERyUcIQaSb9cqlsq5QwREQkFyUMERHJRQlDRERyUcIQEZFclDBERCQXJQzpcHQXkkjHpIQhIiK5KGGIiEguShgiIpKLEoaIiORSdsIws/3MrCYzrDazr5rZNWa2LBM/NTPON8ys1sxeNLOTM/EREas1szGZ+EAzmx7xiWa2Q/mLKiIiLVF2wnD3F929yt2rgEOAtcBv4+UfFl5z98kAZnYAcDZwIDAC+JmZdTKzTsBPgVOAA4Bzoi7ADTGtjwFvABeV214REWmZSp2SOgFY6O5LGqlzOnCPu69395eAWuCwGGrdfZG7vwfcA5xuZgYcD9wX498OnFGh9oqISDNVKmGcDdydKV9uZrPNbIKZ9YjYXsDSTJ26iDUU7wW86e4biuIiItIOWpww4rrCp4FfR+hm4KNAFfAq8F8tnUeONow2s2ozq16xYkVrz05EZJtUiSOMU4CZ7r4cwN2Xu/tGd/8A+AXplBPAMqB/Zrx+EWsovhLobmbbF8U/xN3Hu/twdx/ep0+fCiySiIgUq0TCOIfM6Sgz65t57UxgbjyfBJxtZjua2UBgEPAMMAMYFHdE7UA6vTXJ3R2YBpwV448CHqxAe0VEpAzbN12lYWa2M/BJ4JJM+PtmVgU4sLjwmrvPM7N7geeBDcBl7r4xpnM5MAXoBExw93kxrauAe8zse8As4JaWtFdERMrXooTh7mtIF6ezsc83Uv864LoS8cnA5BLxRWw+pSUi0mEVfjBz8bjT2rklrUff9BYRkVyUMEREJBclDBERyUUJQ0REclHCEBGRXJQwREQkFyUMERHJRQlDRERyUcIQEZFclDBERCQXJQwREclFCUNERHJRwhARkVyUMEREJBclDBERyUUJQ0REclHCEBGRXJQwRETayIAxv9/0z3xbIiUMERHJRQlDRERyUcIQEZFcWpwwzGyxmc0xsxozq45YTzObamYL4rFHxM3MbjKzWjObbWbDMtMZFfUXmNmoTPyQmH5tjGstbbOIiDRfpY4wjnP3KncfHuUxwOPuPgh4PMoApwCDYhgN3AwpwQBjgcOBw4CxhSQTdS7OjDeiQm0WEZFmaK1TUqcDt8fz24EzMvE7PHka6G5mfYGTganuvsrd3wCmAiPitd3c/Wl3d+COzLRERKQNVSJhOPComT1rZqMjtru7vxrPXwN2j+d7AUsz49ZFrLF4XYm4iIi0se0rMI2j3H2Zmf0dMNXMXsi+6O5uZl6B+TQoEtVogL333rs1ZyUiss1q8RGGuy+Lx9eB35KuQSyP00nE4+tRfRnQPzN6v4g1Fu9XIl7chvHuPtzdh/fp06eliyQiIiW0KGGY2c5mtmvhOXASMBeYBBTudBoFPBjPJwEj426pI4C34tTVFOAkM+sRF7tPAqbEa6vN7Ii4O2pkZloiIkLbfYO8paekdgd+G3e6bg/8yt0fMbMZwL1mdhGwBPhc1J8MnArUAmuBCwDcfZWZfReYEfWudfdV8fxS4DZgJ+DhGEREpI21KGG4+yLg4BLxlcAJJeIOXNbAtCYAE0rEq4HBLWmniIi0nL7pLSIiuShhiIhILkoYIiKSixKGiIjkooQhIiK5KGGIiEguShgiIpKLEoaIiOSihCEiIrkoYYiIbAMq8XtTShgiIpKLEoaIiOSihCEiIrkoYYiISC5KGCIikksl/tNbpKTsHRmLx53Wji0RkUrQEYaIiOSihCEiIrkoYYiISC5KGCIikosShoiI5KKEISJbhEr8FpK0TNkJw8z6m9k0M3vezOaZ2Vcifo2ZLTOzmhhOzYzzDTOrNbMXzezkTHxExGrNbEwmPtDMpkd8opntUG57RUSkZVpyhLEB+Jq7HwAcAVxmZgfEaz9096oYJgPEa2cDBwIjgJ+ZWScz6wT8FDgFOAA4JzOdG2JaHwPeAC5qQXubpD0YkfahbW/LUHbCcPdX3X1mPH8bmA/s1cgopwP3uPt6d38JqAUOi6HW3Re5+3vAPcDpZmbA8cB9Mf7twBnltldERFqmItcwzGwAMBSYHqHLzWy2mU0wsx4R2wtYmhmtLmINxXsBb7r7hqJ4qfmPNrNqM6tesWJFJRZJRHLQkcG2pcUJw8x2Ae4Hvuruq4GbgY8CVcCrwH+1dB5Ncffx7j7c3Yf36dOntWcnIrJNatFvSZlZZ1KyuMvdfwPg7sszr/8CeCiKy4D+mdH7RYwG4iuB7ma2fRxlZOuLiJSlcESk3zdrvpbcJWXALcB8d/9BJt43U+1MYG48nwScbWY7mtlAYBDwDDADGBR3RO1AujA+yd0dmAacFeOPAh4st70iItIyLTnCOBL4PDDHzGoi9k3SXU5VgAOLgUsA3H2emd0LPE+6w+oyd98IYGaXA1OATsAEd58X07sKuMfMvgfMIiUoERFpB2UnDHf/E2AlXprcyDjXAdeViE8uNZ67LyLdRSUiIu1M3/QWEZFclDDagG49lI5G66SUQ/+4J1uFbfnOl2152VuD/imyYUoYIh2EOirp6HRKSkREclHCEGkFukYgWyOdkuqgdHpCRDoaHWFILtpjFhEdYUiHV6m7gFrrbiIdDUopW+N6oSMMERHJRUcYWxndky+y5djSjkJ0hCEiIrkoYYiIbGHa6yYUJQzRHVAikosShojIVqg1dgSVMEREJBcljEboVE3z6P0S2bopYYiISC5KGC2kvWoR2Vboi3vtYEv7so6ICOgIQxqgIycRKdbhE4aZjTCzF82s1szGtHd7OpJyO3UlAxEpR4c+JWVmnYCfAp8E6oAZZjbJ3Z9v35aJSCl5TrdW6pSsfjet7XXohAEcBtS6+yIAM7sHOB1ot4ShlbSyyu082vInzysxr7bsJMtt79awbpezDC0Zp7njldOe1vxcmjvtjp4w9gKWZsp1wOFNjdRWnUBj023utFurzVvicopIx2Tu3t5taJCZnQWMcPd/ifLngcPd/fKieqOB0VHcD3gR6A38rWiSxbH2rNPR29eWdTp6+ypVp6O3ry3rdPT2VapOR29fNraPu/ehMe7eYQfg74EpmfI3gG/kHLe6qVh71uno7dN7oeXUe7FtLmdjQ0e/S2oGMMjMBprZDsDZwKR2bpOIyDapQ1/DcPcNZnY5MAXoBExw93nt3CwRkW1Sh04YAO4+GZhcxqjjc8Tas05Hb19b1uno7atUnY7evras09HbV6k6Hb19DcVK6tAXvUVEpOPo6NcwRESkg1DCEBGRXJQwADPraWY9G4uVqtOW8rSnqTp5xunotoTPSmRrtc1ewzCzvYHvAycAbwIGdAfWALsAq4DOwB4xyqvARmA34AlgjLsvbuY8dyd9ex1gmbsvL47F416Z0a7MtLFUe5pqs+UYx2K5/g/436iXp31tVWcP4GLgKMr7rG4EPmjOvN19OSIZZtYNGEH9dfQvpO+LFWKF7alHps4i4PhMnfficYdMnTnAkBbWWQm8nWly3vZNcfc3G1n0TbaqhGFmnwFuAP6O9KYY6XbcZZlYZ9Ib7KQ3fCPg7r6bmf0FeIb0bfIjovzfMd5XItYJ+CxwBakTg6Y7vH2jftdM/KNs/oblQlLHd3C8Npv0wR9J+jmUc9y9uoH2NNrmWM6mxqkCfg70j3bMyNm+tqqzHyk5LAUudfeZOT+rYcBEoB8pGeaZN1H/beB+Nm+UpTa04o3RSUmqV6ZOJTqCUuM8AXyExjuCPJ1FR+rwyp1uOe9Fcz+r/YFDgEeBwq39xwNHA08B04Aq0g+lAkwFaoBPAUOBB4DfRfkTUecp4CHgXOBY4EngrjLrvEnqlxx4Fvhjzvb1i9h33P0OmpL3G35bwgDUAh9vLFYoAwtKjL+g1GNRrAp4mrQyPRbDS6QO5qUoPw28G8P0eG1xxIfFdGqAC4HnMuXDgSMysQXF5abaWlwn5zg1pI63ONZU+9qkTqZNH3ovmvisCtMpLjc474iNJCXqvwHfAu4D3orhvog9AbwPPB7lKVF/JSn5ngc8TFoHHo7yPcArMdyTs06pcSYC60nrU972lVvn6ZjXxBYsQ7nLmWe65bwX5XxWrwLXkNaXMbGevAjsA/w1U+5OSlSF2F+BPkXlwk7rgkysa1G5WXUy886O02T7Il6v3Ggf296dfIUTxv81FSuUYyX4GakD2TOGR4G5pE5/T9JewX0xTIrYX6N8b2aaNZTf4dWW6PBqi9q4tJH2NNXmPOMsivb+rLBcOdvXJnWKPquXmvFZvVxqmRqbd9GGVVtUznYExRtjYQMu7ixa2hGUGudFUie0oBntK7dOW3V45U63nPei2Z9VlLuVaF//onK3GAqxF4DBReV9YngxE/uHonKz6mTmnR2nyfZFvF65saHDf3GvmarNbCKp81gfsTeKYm+Y2Z9IexZHx2NPYDWbD0t7kvZCLKZhpEO9KaTD1R8Bt2Tmu7O7TzCzb2bK0wHMbGdgspn9HrgD6GVm/wDUmtnrwMwozzKzmTF+TcQmkX4/64NG2tNUm8kxzm6kw+VJwE+b0b42qUPq9EdGGzvlXO4p8b59AviPZswbYFfSnusjRdP0ovl8kCl/QEpSbxbFqth8/aRQh6JYY3W8xDgG7F5Ubqp95db5IOZVvJzNWYZylzPPdMt5L8r5rK4HZgJ/BrrHtl5H2tn6Y5SfI+2kAEyN2EJgFjDfzMZH/cLfM/whYu+QTjU9V24dYCfSUdD7wG+a0b69SaekvksOW9s1jFtLhI8knb8uOIq0MmVj7u4X5pzHTaTz3new+afXv006vzkTuBb4KvCxeG0R8APg/wH/TFr55pI6vGWkBFQ4Z7odaQXeGOVlwCRP33ZvVWZ2Cum/RrLXYPK0ry3rNPu9KLFceea9M+m05QOkz3gocFK8NpX0OZ9I2uH4I+koZxDwGdJe6TOkvblhwEGkDXUW6XzxMTGdP5A26KbqUGKcI6J9U0gdWJ72lVvnyIjNJ532KWcZyl3OPNMt570o97P6COloYRJpr35ZvCdHsHndeTMeu8fjMtLR8L6ZOq+Q1sG+mTrVwPAW1llNuh63Z2acPO2b4u5vkMNWlTAqxcw+5e4PNRSLTuhLtEGHl7eNTbW53HE6unKWO+d0ewAn0/iGVrwxvkI6N75bpk4lOoJS4ywg7Rk2p33l1mmrDq/c6ZbzXpT7Wc1w98I2ve0p51pBRx2ALsBlpFMYE2K4vSh2O2lPJFtnQtF0vlNi2t9pqk6ZbR7dVKyBOk22p6k6Occpt31tUqfcz6qceWvQUGoAxjcVa6DOQ42VK1Wn3PaVGra2L+7dSboX/2TSYWQ/0oWibOxk0nnwk0mHokcBw8zsJjO7ysxOBdZG+SYzu9HMbgR6ZOp83N3HFs88/sip0ViJOsaHbYqZ2WHAgHh+gJn90My+4u5jo/xvZvZV0q13ZGKnFtpoZncANFRuKJanfW1Rx8x2MLORZnZilP/ZzH5mZoXkj5l93symxinD70Wdn5jZZcD3KtQ+4vxxg+UG6nzo6KY4Vk6dBsYpp33l1mnxMuSp04LpttVn9T/F45SIlapzcRPlStUpt30fslWdkjKzWe4+1Mxmu/tBZtYZeNPdd87EZpEufu8DLAfuJd3hdA357ofuR/pfjnvcfVzR/C9x9/8pFTOz/UmHuge6+03x2v7AGcBP3P2dTJ1d3f0BMxsLnEK6sHs/8E+k867dSedmN5KuxxxKOn95J/AvpNsF+wCvk+4Eyf7L1oyYZqH8DKljPI50+yGkLzQeBsx190ejrUdlY1E+C3gy2roT8ONo/2zSdZtLSfeCV8f7+x7wG9KvJM8Ernf3t8zsy8Bv3X1pzKu4fFeM05V0umEX0oXOvhF/Mj63wmnB96INvyFdv9iHlFg2ku7Rd9L3cjaSdhr+TDrv3T8T+5W7r6aImR3i7s82VG6gTl93f7WoTr1YOXUaGKec9pVbp8XL0ILlbK33oqx5bTNa81CtrQfgmXh8inRxqjewrig2k3Qhei3projewKKo0+RtfBGvV87ELygVA75Mukj2AKmjPj0Te5P0HY1fZOqsjzpzSEdDs0gJYV6Ue7L5m8xzSBdp50SdGuCXpA5wYSzDu6Qv8X2Z1LG+Tzov/RXShb35pDssjgH+M6ZRR7oxYAxpj2UNMDZi90ed1zN1xpMS1TFR7yXS3WQvAd8hdd7jY9n+BiyJ5epDumf+FdJFyUtjObLlefFebk9K8p1ICcHicTkwO1Pn/ajz5VjO10hJ4SnSZ/866caDn5Iulr5DuuvtzxG7jnQHyrHtvU638vbyd03FStUpc169moqVqtNRBtKtp+NIt7OuinX9RdIXAv8asVVs/n7HG/E4P8brXmKaD5cqk7br/yDtANYUxV4k3TyTrTOVdBbl5lh/byHtoM0n3ZZ+K+kLijdGm2qBA6LOHNJOc99c70N7fxAV/lD/hbRH/YlMx3BHUWw18K/RYb0cdS6J8Zu8Hzri9cqZ+MulYvGh7BLlwsW1V0h7yrNIp5zWAldEnXlRpy7KswpDZrprC6/FY03U2S6Wb2qsXNvFSjIVqIq6izJ1qmK8QtKcQerEZ7E5Ec0A5sTrO0db+8QKWagzE5hfon3zM+2bmWnjSdGuFfGZnE9KkreQOvxHSMnrDmADqWP/IulLkD1JHf6usYxvR1t2IG04G6POHNIOwPx4fIeUSPaO9jwZdQZEu7pGrBvpGtc6Gu4IijuLBjsHijqGBjqHZZTuCOqVY5xC5zCHhjuC4s7iBlISfIB0V1Ev0ro5kHT3T0/S3X/ZWC9SMu4Rr3eLz2U28CvSnYC3kNanX5GO+rqR7jSaF7GT4vU3SHecHUO6sPxmxJcAo0k7EivjfTgmlnMm6Yt2Hy1Vjthw0reYf0k6QpxOWl/+RvoZj6nxua8Gfh91nojP9l3SurOCdKT9YLT7rYjVRXveIh21vkz6FYHCvKeR/qvnyShPId2e+l3g0Yh9kvQLBH8h3Yn1zzGcG/MYVqL8eHxuX4t23g/8lrRuLSTdpbWMtH2cEe/la6Rtekws11WkdfnXpM99Nmmd/XYs51tRpz/pBp4Ht8WEsWO8+d8k7eWOJe3dZmPXxpt4a6ygC0id8/h4fI/UQY6PlWFNDJMj9nas3IviQ5jN5m91f1BULsTWRXxOjLtLTOcHbN6DmE/aGH9A6sx2iRXhpihvR9oYupI2ypkx3nRSBzIT2C7zXnyctJH+hLSi94vl/gmR2DKxv5E25l7E//uSNvoe0e7qTLkXm5PBr0lHULPi/XwsyvuSVv7hUWdMvKe3Ai/EuPtGrDNpI7gbWJHpGD5diJE2nNfic3iHtEH9JT6rN0lHEn8ldQrvxWf1OKkjn0v63HuQEt2OMY/nY7nmkNabuVGnmrThX5Vpa6mOoLizGEbpzqG4Y2ioc1hF6Y6guHx/tOdL8VpDHcGT1O8sPiCtByvjfXyJdGru/RgK5VKxl0jr+/+SjkT3IXVOi0nXh+ZG+YGo83qmzgrSKdM5pM+8Ot672sx6UPgO1I2kHak1Me7LEXuZ1KGvJCXKQvlfSeveKcA5pHV4QZS/R9rWzoq6Y0jryNIon086xbs43rvHgD+RTqv+G2l7vCrew8dI28gg0k0z10fbXyz1WBTbSEpQa2O5nbRdvhGvZWOF8tvxOI3Uh/w7ab3vxebt/lXS0X0v0vbySqb8XomdyZepv8P5XlHfWbMtJoxHSF+4upLUyXyNdJSQjb0Qb+CVwNdJG/htwD+SbrvbPh7/MYa/Jx11FMqrSBv6PplhBXAqaW+sUD6FdA/78vggTyHtyb6S6XB+B2yM8hOk73LckYl1LSoXOrvewJBMktxUzrwXvUm/i3NaYQWPeL1yxJbHClvoGPqSNqSX2Jwcl8bj4oj1JSWuX0Z5OqmTeTtW8jls7nRei/GnkzaORaQbEA4uWrG7ZstFsT1j6Eq6hnNWvOd7xuvdSafOTs2UJ5A6kF/E534XqfO8k7QBX0A6LTePtAG+ELHCt4efaqQjKH4sdAzT+HDnUNwxlOoc3m2gIygu1+s8aKAj4MOdxddI28cQNu+krCzEovy1aPuQzPTWZzsVosOKcqHNMzOv15A62+0jtiYe55O2rTmkW1yzddYWTXch6Qjv/XhvRpO+R7GCtC5NI11nK67T1HsxK+pkfwZmFmnH5TnSTllhJ6GwU7Qdab14i9Rn7JGp8yRpp6GwnjxK2iH9LvBYZrm/nynPBQbF86XZWKY8n9j5y8ReIa2nSzJ1LijEov3nR/nNqFP4VYk5pASaXe7Xi/qAObn62Pbu5Cs5kC7INhorVaeZ87gFOKpUjHShtF4d0mF5P2CPQjke+8XKd2S2HM+PLJr+kS1pcwuWtSswsLFYlIeQftTvENJpid2y5ahXiJ1ZiGWmsW9j5RYuw4Gk5LJ/qXIDdR6NzmH3TLm4I3iS+p3FXNKOxlXFnUNxxxDP63UOReVsR1CvHHVezrxesiOgqLPIrGO/JnW8u5ISdyH2g4gtKSpvIO1xfy3q12XK60nXkQqxV6LOl+I9O560M/LfMSyM+DXx/s0g7bE/RzoqOYZ0RuDOaO9M0mmlWzPlTkWxv5DOHkyLtr9AOg02Ntp3RtT5Wsy/UOeomN8CNn/p7yjSzsKnSUm9sA3XkY4Eb4hxCzdPvEhKfn9l845A4dTlm1GnjrTD0jOmdRawXzw/IxvLlL8PnFhU5/uko6QF2TrxXiwgrZ+7FMpR51rSlxHvy5R3IZ1KvC+zztQrN7o9tUdH1Iod3Hg+vKddL1aqjgYN2YF0aqrQOawq6gjeKNFZrCIdKSyM8XrEdIo7glKdRWHDLy4XOoJ65ahzbXSE2XK9jqC4s8gs28dIRyxPA69l4p/OxjLlt9l8endsLN9YNt8c0aeoPDZix5JOka0iJa3JpNNL95L26gtHGjOi/GbUGc3mI497itp+T4nP6mBSh/8w6Rdl72Lzke7ZpES1mnRU9peo80tSItxISlb7xns1k3S08yfSLzM8E+1aGHX2J51l+Hpm/vuTOvJdMuVNn2cjdU6oYJ0zS5XzTjfiI3JtG+29cVZ4Q3+edA77RTZfM1hXFFtHOp+brTO7vduuYcsYKLoTrrjclnVaMl3Sbw8NztYpjpWqs6UtZwXfr3LvdCynzvgSdb7URJ1yxjk9s4wzi5e75PrfFhtZWw3Uv65QGP6hRLk4tk97t13DljFQdCdccbkt67TnvLe15aT8Ox3bqk650/1KlGflWf+3Zyvi7ktKhItjpeqIbGJms4tCgzLPd4zXBzVQrnSdd9tx3lrOiJG+6PlnMzPSDSX7kW5IuJb0BejFZrYEOMHM+pJOix3bhnXKne59ZrYPpX/p4MPyZBUNGralgXShtor6d8Fl73orxBq7M65Sddpz3lrOlt/p2CZ1WjDd7bPlpoat6ghDpEIeIp1+qAEws0nA2+7+f2b2uLsvidhqIFuueB3iy2XtMW8tZ732XQlscPfXzOzJWE8+T7p4/tEoj4w6I83sf9q4zpJypuvuG4DsOI3aqn5LSkREWs927d0AERHZMihhiIhILkoYIi1kZgPMbG57t0OktSlhiHRAZqYbUqTDUcIQqYxOZvYLM5tnZo+a2U5mVmVmT5vZbDP7raX/CcfMnjSz4fG8t5ktjufnm9kkM3uC9NtFIh2KEoZIZQwCfuruB5J+iuEfSfe3X+XuB5G+KTw2x3SGAWe5+zGt1VCRcilhiFTGS4XvbQDPku597+7uf4jY7Wz+u9/GTHX3Va3QPpEWU8IQqYz1mecbSf/H0ZANbN72uhS9tqaCbRKpKCUMkdbxFvCGmR0d5c+T/jQK0i+HHhLPz2rjdomUTXdiiLSeUcDPzawr6Y+FLoj4jcC9Zjaa9D/TIlsE/TSIiIjkolNSIiKSixKGiIjkooQhIiK5KGGIiEguShgiIpKLEoaIiOSihCEiIrkoYYiISC7/H/OR4Tuuu88nAAAAAElFTkSuQmCC", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "df = TimeAccident_df.toPandas()\n", + "#df.plot()\n", + "#display(plt.show())\n", + "df.plot.bar(x='hour', y='Total accidents')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+--------------------+---------------+\n", + "| Age_Band_of_Driver|Total accidents|\n", + "+--------------------+---------------+\n", + "| Over 70| 96482|\n", + "| Upto 20Y| 416735|\n", + "|Data missing or o...| 476400|\n", + "| 40Y to 70Y| 1115355|\n", + "| 20Y to 40Y| 2091514|\n", + "+--------------------+---------------+\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [] + } + ], + "source": [ + "V20052014\n", + "Age_df = V20052014.groupby('Age_Band_of_Driver').agg(F.count(V20052014.Accident_Index).alias('Total accidents')).sort(\"Total accidents\")\n", + "Age_df.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DataFrame[Accident_Index: string, Vehicle_Reference: string, Vehicle_Type: string, Towing_and_Articulation: string, Vehicle_Manoeuvre: string, Vehicle_Location-Restricted_Lane: string, Junction_Location: string, Skidding_and_Overturning: string, Hit_Object_in_Carriageway: string, Vehicle_Leaving_Carriageway: string, Hit_Object_off_Carriageway: string, 1st_Point_of_Impact: string, Was_Vehicle_Left_Hand_Drive?: string, Journey_Purpose_of_Driver: string, Sex_of_Driver: string, Age_of_Driver: string, Age_Band_of_Driver: string, Engine_Capacity_(CC): string, Propulsion_Code: string, Age_of_Vehicle: string, Driver_IMD_Decile: string, Driver_Home_Area_Type: string, Year: string]" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "V20052014" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+--------------+--------------------+---------------+\n", + "|Accident_Index| Vehicle_Type|Total accidents|\n", + "+--------------+--------------------+---------------+\n", + "| 201998QC01004| Motorcycle| 1|\n", + "| 201997UD71205| Car| 1|\n", + "| 201997UD71005| Car| 3|\n", + "| 201997UD70905| Car| 2|\n", + "| 201997UD70905| Motorcycle| 1|\n", + "| 201997UD70901| Bus| 1|\n", + "| 201997UD70901| Car| 1|\n", + "| 201997UD70805| Car| 2|\n", + "| 201997UD70803| Car| 2|\n", + "| 201997UD70801| Car| 2|\n", + "| 201997UD70706| Motorcycle| 1|\n", + "| 201997UD70705| Car| 2|\n", + "| 201997UD70606| Car| 1|\n", + "| 201997UD70605| Car| 1|\n", + "| 201997UD70605| Pedal cycle| 1|\n", + "| 201997UD70603| Car| 1|\n", + "| 201997UD70506| Car| 1|\n", + "| 201997UD70506|Agricultural vehicle| 1|\n", + "| 201997UD70505| Car| 1|\n", + "| 201997UD70503| Pedal cycle| 1|\n", + "+--------------+--------------------+---------------+\n", + "only showing top 20 rows\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [] + } + ], + "source": [ + "\n", + "VECHTYPE = V20052014.groupby('Accident_Index','Vehicle_Type').agg(F.count(V20052014.Accident_Index).alias('Total accidents')).sort(\"Total accidents\")\n", + "VECHTYPE.sort(col('Accident_Index').desc()).show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DataFrame[Accident_Index: string, Vehicle_Reference: string, Vehicle_Type: string, Towing_and_Articulation: string, Vehicle_Manoeuvre: string, Vehicle_Location-Restricted_Lane: string, Junction_Location: string, Skidding_and_Overturning: string, Hit_Object_in_Carriageway: string, Vehicle_Leaving_Carriageway: string, Hit_Object_off_Carriageway: string, 1st_Point_of_Impact: string, Was_Vehicle_Left_Hand_Drive?: string, Journey_Purpose_of_Driver: string, Sex_of_Driver: string, Age_of_Driver: string, Age_Band_of_Driver: string, Engine_Capacity_(CC): string, Propulsion_Code: string, Age_of_Vehicle: string, Driver_IMD_Decile: string, Driver_Home_Area_Type: string, Year: string]" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "V20052014" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [], + "source": [ + "V20052014=V20052014.withColumn(\n", + " \"Sex_of_Driver\",\n", + " when(\n", + " col(\"Sex_of_Driver\") == 1,\n", + " \"Male\"\n", + " ).\n", + " when(\n", + " col(\"Sex_of_Driver\") == 2,\n", + " \"Female\"\n", + " ).\n", + " when(\n", + " col(\"Sex_of_Driver\") == 3,\n", + " \"Unkown\"\n", + " ).\n", + " when(\n", + " col(\"Sex_of_Driver\") == -1,\n", + " \"Data missing or out of range\"\n", + " ).otherwise(col(\"Sex_of_Driver\")),\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+-----------------+----+---------------+\n", + "|Accident_Severity|hour|Total accidents|\n", + "+-----------------+----+---------------+\n", + "| Slight|null| 185|\n", + "| Fatal|null| 4|\n", + "| Serious|null| 43|\n", + "| Slight| 0| 26893|\n", + "| Fatal| 0| 1052|\n", + "| Serious| 0| 6799|\n", + "| Slight| 1| 19407|\n", + "| Serious| 1| 5124|\n", + "| Fatal| 1| 809|\n", + "| Fatal| 2| 708|\n", + "| Serious| 2| 4228|\n", + "| Slight| 2| 15119|\n", + "| Fatal| 3| 624|\n", + "| Serious| 3| 3362|\n", + "| Slight| 3| 12324|\n", + "| Serious| 4| 2652|\n", + "| Fatal| 4| 499|\n", + "| Slight| 4| 9838|\n", + "| Serious| 5| 3674|\n", + "| Slight| 5| 14928|\n", + "+-----------------+----+---------------+\n", + "only showing top 20 rows\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [] + } + ], + "source": [ + "TimeAccident_df.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 145, + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'TimeAccident_df' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/var/folders/v0/jqv1xcw13pn37fh0ppsl8b_w0000gp/T/ipykernel_21452/77352620.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mTimeAccident_df\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mNameError\u001b[0m: name 'TimeAccident_df' is not defined" + ] + } + ], + "source": [ + "TimeAccident_df" + ] + }, + { + "cell_type": "code", + "execution_count": 179, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "TimeAccident_dfwithbrack=TimeAccident_df.withColumn(\n", + " \"hour\",\n", + " when(\n", + " col(\"hour\") == 0,\n", + " \"Off peak\"\n", + " ). when(\n", + " col(\"hour\") == 1,\n", + " \"Off peak\"\n", + " ). when(\n", + " col(\"hour\") == 2,\n", + " \"Off peak\"\n", + " ). when(\n", + " col(\"hour\") == 3,\n", + " \"Off peak\"\n", + " ). when(\n", + " col(\"hour\") == 4,\n", + " \"Off peak\"\n", + " ). when(\n", + " col(\"hour\") == 5,\n", + " \"Off peak\"\n", + " ). when(\n", + " col(\"hour\") == 6,\n", + " \"Off peak\"\n", + " ). when(\n", + " col(\"hour\") == 7,\n", + " \"07:00-10:00 Rush Hour\"\n", + " ).\n", + " when(\n", + " col(\"hour\") == 8,\n", + " \"07:00-10:00 Rush Hour\"\n", + " ).\n", + " when(\n", + " col(\"hour\") == 9,\n", + " \"07:00-10:00 Rush Hour\"\n", + " ). when(\n", + " col(\"hour\") == 10,\n", + " \"07:00-10:00 Rush Hour\"\n", + " ). when(\n", + " col(\"hour\") == 11,\n", + " \"Off peak\"\n", + " ). when(\n", + " col(\"hour\") == 12,\n", + " \"Off peak\"\n", + " ). when(\n", + " col(\"hour\") == 13,\n", + " \"Off peak\"\n", + " ). when(\n", + " col(\"hour\") == 14,\n", + " \"Off peak\"\n", + " ). when(\n", + " col(\"hour\") == 15,\n", + " \"Off peak\"\n", + " ). when(\n", + " col(\"hour\") == 16,\n", + " \"16:00-19:00 Rush Hour\"\n", + " ). when(\n", + " col(\"hour\") == 17,\n", + " \"16:00-19:00 Rush Hour\"\n", + " ). when(\n", + " col(\"hour\") == 18,\n", + " \"16:00-19:00 Rush Hour\"\n", + " ). when(\n", + " col(\"hour\") == 19,\n", + " \"16:00-19:00 Rush Hour\"\n", + " ). when(\n", + " col(\"hour\") == 20,\n", + " \"Off peak\"\n", + " ). when(\n", + " col(\"hour\") == 21,\n", + " \"Off peak\"\n", + " ). when(\n", + " col(\"hour\") == 22,\n", + " \"Off peak\"\n", + " ). when(\n", + " col(\"hour\") == 23,\n", + " \"Off peak\"\n", + " ).\n", + " when(\n", + " col(\"hour\") == -1,\n", + " \"Data missing or out of range\"\n", + " ).otherwise(col(\"hour\")))" + ] + }, + { + "cell_type": "code", + "execution_count": 172, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+-----------------+--------+---------------+\n", + "|Accident_Severity| hour|Total accidents|\n", + "+-----------------+--------+---------------+\n", + "| Slight| null| 185|\n", + "| Fatal| null| 4|\n", + "| Serious| null| 43|\n", + "| Fatal|Off peak| 1052|\n", + "| Serious|Off peak| 6799|\n", + "| Slight|Off peak| 26893|\n", + "| Slight|Off peak| 19407|\n", + "| Serious|Off peak| 5124|\n", + "| Fatal|Off peak| 809|\n", + "| Fatal|Off peak| 708|\n", + "| Slight|Off peak| 15119|\n", + "| Serious|Off peak| 4228|\n", + "| Fatal|Off peak| 624|\n", + "| Serious|Off peak| 3362|\n", + "| Slight|Off peak| 12324|\n", + "| Serious|Off peak| 2652|\n", + "| Fatal|Off peak| 499|\n", + "| Slight|Off peak| 9838|\n", + "| Fatal|Off peak| 626|\n", + "| Serious|Off peak| 3674|\n", + "+-----------------+--------+---------------+\n", + "only showing top 20 rows\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [] + } + ], + "source": [ + "TimeAccident_dfwithbrack.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 181, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>Accident_Severity</th>\n", + " <th>hour</th>\n", + " <th>Total_accidents</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>Fatal</td>\n", + " <td>None</td>\n", + " <td>4</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>Serious</td>\n", + " <td>None</td>\n", + " <td>43</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>Slight</td>\n", + " <td>None</td>\n", + " <td>185</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>Fatal</td>\n", + " <td>07:00-10:00 Rush Hour</td>\n", + " <td>4600</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>Fatal</td>\n", + " <td>16:00-19:00 Rush Hour</td>\n", + " <td>7220</td>\n", + " </tr>\n", + " <tr>\n", + " <th>5</th>\n", + " <td>Fatal</td>\n", + " <td>Off peak</td>\n", + " <td>17874</td>\n", + " </tr>\n", + " <tr>\n", + " <th>6</th>\n", + " <td>Serious</td>\n", + " <td>07:00-10:00 Rush Hour</td>\n", + " <td>60337</td>\n", + " </tr>\n", + " <tr>\n", + " <th>7</th>\n", + " <td>Serious</td>\n", + " <td>16:00-19:00 Rush Hour</td>\n", + " <td>97439</td>\n", + " </tr>\n", + " <tr>\n", + " <th>8</th>\n", + " <td>Serious</td>\n", + " <td>Off peak</td>\n", + " <td>175107</td>\n", + " </tr>\n", + " <tr>\n", + " <th>9</th>\n", + " <td>Slight</td>\n", + " <td>07:00-10:00 Rush Hour</td>\n", + " <td>414871</td>\n", + " </tr>\n", + " <tr>\n", + " <th>10</th>\n", + " <td>Slight</td>\n", + " <td>16:00-19:00 Rush Hour</td>\n", + " <td>561996</td>\n", + " </tr>\n", + " <tr>\n", + " <th>11</th>\n", + " <td>Slight</td>\n", + " <td>Off peak</td>\n", + " <td>947751</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " Accident_Severity hour Total_accidents\n", + "0 Fatal None 4\n", + "1 Serious None 43\n", + "2 Slight None 185\n", + "3 Fatal 07:00-10:00 Rush Hour 4600\n", + "4 Fatal 16:00-19:00 Rush Hour 7220\n", + "5 Fatal Off peak 17874\n", + "6 Serious 07:00-10:00 Rush Hour 60337\n", + "7 Serious 16:00-19:00 Rush Hour 97439\n", + "8 Serious Off peak 175107\n", + "9 Slight 07:00-10:00 Rush Hour 414871\n", + "10 Slight 16:00-19:00 Rush Hour 561996\n", + "11 Slight Off peak 947751" + ] + }, + "execution_count": 181, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "TimeAccident_dfwithbrack=TimeAccident_dfwithbrack.toPandas()\n", + "TimeAccident_dfwithbrack" + ] + }, + { + "cell_type": "code", + "execution_count": 182, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>Accident_Severity</th>\n", + " <th>hour</th>\n", + " <th>Total_accidents</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>Fatal</td>\n", + " <td>None</td>\n", + " <td>4</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>Serious</td>\n", + " <td>None</td>\n", + " <td>43</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>Slight</td>\n", + " <td>None</td>\n", + " <td>185</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>Fatal</td>\n", + " <td>07:00-10:00 Rush Hour</td>\n", + " <td>4600</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>Fatal</td>\n", + " <td>16:00-19:00 Rush Hour</td>\n", + " <td>7220</td>\n", + " </tr>\n", + " <tr>\n", + " <th>5</th>\n", + " <td>Fatal</td>\n", + " <td>Off peak</td>\n", + " <td>17874</td>\n", + " </tr>\n", + " <tr>\n", + " <th>6</th>\n", + " <td>Serious</td>\n", + " <td>07:00-10:00 Rush Hour</td>\n", + " <td>60337</td>\n", + " </tr>\n", + " <tr>\n", + " <th>7</th>\n", + " <td>Serious</td>\n", + " <td>16:00-19:00 Rush Hour</td>\n", + " <td>97439</td>\n", + " </tr>\n", + " <tr>\n", + " <th>8</th>\n", + " <td>Serious</td>\n", + " <td>Off peak</td>\n", + " <td>175107</td>\n", + " </tr>\n", + " <tr>\n", + " <th>9</th>\n", + " <td>Slight</td>\n", + " <td>07:00-10:00 Rush Hour</td>\n", + " <td>414871</td>\n", + " </tr>\n", + " <tr>\n", + " <th>10</th>\n", + " <td>Slight</td>\n", + " <td>16:00-19:00 Rush Hour</td>\n", + " <td>561996</td>\n", + " </tr>\n", + " <tr>\n", + " <th>11</th>\n", + " <td>Slight</td>\n", + " <td>Off peak</td>\n", + " <td>947751</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " Accident_Severity hour Total_accidents\n", + "0 Fatal None 4\n", + "1 Serious None 43\n", + "2 Slight None 185\n", + "3 Fatal 07:00-10:00 Rush Hour 4600\n", + "4 Fatal 16:00-19:00 Rush Hour 7220\n", + "5 Fatal Off peak 17874\n", + "6 Serious 07:00-10:00 Rush Hour 60337\n", + "7 Serious 16:00-19:00 Rush Hour 97439\n", + "8 Serious Off peak 175107\n", + "9 Slight 07:00-10:00 Rush Hour 414871\n", + "10 Slight 16:00-19:00 Rush Hour 561996\n", + "11 Slight Off peak 947751" + ] + }, + "execution_count": 182, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "TimeAccident_dfwithbrack = TimeAccident_dfwithbrack[TimeAccident_dfwithbrack.hour != \"None\"]\n", + "TimeAccident_dfwithbrack" + ] + }, + { + "cell_type": "code", + "execution_count": 183, + "metadata": {}, + "outputs": [], + "source": [ + "TimeAccident_dfwithbrack = TimeAccident_dfwithbrack.iloc[3:]" + ] + }, + { + "cell_type": "code", + "execution_count": 184, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>Accident_Severity</th>\n", + " <th>hour</th>\n", + " <th>Total_accidents</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>Fatal</td>\n", + " <td>07:00-10:00 Rush Hour</td>\n", + " <td>4600</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>Fatal</td>\n", + " <td>16:00-19:00 Rush Hour</td>\n", + " <td>7220</td>\n", + " </tr>\n", + " <tr>\n", + " <th>5</th>\n", + " <td>Fatal</td>\n", + " <td>Off peak</td>\n", + " <td>17874</td>\n", + " </tr>\n", + " <tr>\n", + " <th>6</th>\n", + " <td>Serious</td>\n", + " <td>07:00-10:00 Rush Hour</td>\n", + " <td>60337</td>\n", + " </tr>\n", + " <tr>\n", + " <th>7</th>\n", + " <td>Serious</td>\n", + " <td>16:00-19:00 Rush Hour</td>\n", + " <td>97439</td>\n", + " </tr>\n", + " <tr>\n", + " <th>8</th>\n", + " <td>Serious</td>\n", + " <td>Off peak</td>\n", + " <td>175107</td>\n", + " </tr>\n", + " <tr>\n", + " <th>9</th>\n", + " <td>Slight</td>\n", + " <td>07:00-10:00 Rush Hour</td>\n", + " <td>414871</td>\n", + " </tr>\n", + " <tr>\n", + " <th>10</th>\n", + " <td>Slight</td>\n", + " <td>16:00-19:00 Rush Hour</td>\n", + " <td>561996</td>\n", + " </tr>\n", + " <tr>\n", + " <th>11</th>\n", + " <td>Slight</td>\n", + " <td>Off peak</td>\n", + " <td>947751</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " Accident_Severity hour Total_accidents\n", + "3 Fatal 07:00-10:00 Rush Hour 4600\n", + "4 Fatal 16:00-19:00 Rush Hour 7220\n", + "5 Fatal Off peak 17874\n", + "6 Serious 07:00-10:00 Rush Hour 60337\n", + "7 Serious 16:00-19:00 Rush Hour 97439\n", + "8 Serious Off peak 175107\n", + "9 Slight 07:00-10:00 Rush Hour 414871\n", + "10 Slight 16:00-19:00 Rush Hour 561996\n", + "11 Slight Off peak 947751" + ] + }, + "execution_count": 184, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "TimeAccident_dfwithbrack" + ] + }, + { + "cell_type": "code", + "execution_count": 185, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead tr th {\n", + " text-align: left;\n", + " }\n", + "\n", + " .dataframe thead tr:last-of-type th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr>\n", + " <th></th>\n", + " <th colspan=\"3\" halign=\"left\">Total_accidents</th>\n", + " </tr>\n", + " <tr>\n", + " <th>Accident_Severity</th>\n", + " <th>Fatal</th>\n", + " <th>Serious</th>\n", + " <th>Slight</th>\n", + " </tr>\n", + " <tr>\n", + " <th>hour</th>\n", + " <th></th>\n", + " <th></th>\n", + " <th></th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>07:00-10:00 Rush Hour</th>\n", + " <td>4600</td>\n", + " <td>60337</td>\n", + " <td>414871</td>\n", + " </tr>\n", + " <tr>\n", + " <th>16:00-19:00 Rush Hour</th>\n", + " <td>7220</td>\n", + " <td>97439</td>\n", + " <td>561996</td>\n", + " </tr>\n", + " <tr>\n", + " <th>Off peak</th>\n", + " <td>17874</td>\n", + " <td>175107</td>\n", + " <td>947751</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " Total_accidents \n", + "Accident_Severity Fatal Serious Slight\n", + "hour \n", + "07:00-10:00 Rush Hour 4600 60337 414871\n", + "16:00-19:00 Rush Hour 7220 97439 561996\n", + "Off peak 17874 175107 947751" + ] + }, + "execution_count": 185, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "TimeAccident_dfwithbrack=TimeAccident_dfwithbrack.pivot(index ='hour', columns ='Accident_Severity')\n", + "#TimeAccident_dfwithbrack = TimeAccident_dfwithbrack.drop(labels=['None'], axis=0)\n", + "TimeAccident_dfwithbrack" + ] + }, + { + "cell_type": "code", + "execution_count": 186, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([0, 1, 2]),\n", + " [Text(0, 0, '07:00-10:00 Rush Hour'),\n", + " Text(1, 0, '16:00-19:00 Rush Hour'),\n", + " Text(2, 0, 'Off peak')])" + ] + }, + "execution_count": 186, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "<Figure size 1440x720 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "TimeAccident_dfwithbrack.plot.bar(stacked=True,rot=15, title=\"Accidents Time \",figsize=(20, 10))\n", + "plt.xticks(fontsize=20)" + ] + }, + { + "cell_type": "code", + "execution_count": 144, + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'TimeAccident_dfwithbrack' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/var/folders/v0/jqv1xcw13pn37fh0ppsl8b_w0000gp/T/ipykernel_21452/826514350.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mTimeAccident_dfwithbrack\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mNameError\u001b[0m: name 'TimeAccident_dfwithbrack' is not defined" + ] + } + ], + "source": [ + "TimeAccident_dfwithbrack=TimeAccident_dfwithbrack.toPandas()\n", + "Accident_Information20052019_dfroadsurface = Accident_Information20052019_dfroadsurface.drop(labels=['Data missing or out of range'], axis=0)\n", + "Accident_Information20052019_dfroadsurface\n", + "Accident_Information20052019_dfroadsurface.plot.bar(stacked=True,rot=15, title=\"Accidents Road Surface \",figsize=(20, 10))\n", + "plt.xticks(fontsize=20)" + ] + }, + { + "cell_type": "code", + "execution_count": 180, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+-----------------+--------------------+---------------+\n", + "|Accident_Severity| hour|Total_accidents|\n", + "+-----------------+--------------------+---------------+\n", + "| Fatal| null| 4|\n", + "| Serious| null| 43|\n", + "| Slight| null| 185|\n", + "| Fatal|07:00-10:00 Rush ...| 4600|\n", + "| Fatal|16:00-19:00 Rush ...| 7220|\n", + "| Fatal| Off peak| 17874|\n", + "| Serious|07:00-10:00 Rush ...| 60337|\n", + "| Serious|16:00-19:00 Rush ...| 97439|\n", + "| Serious| Off peak| 175107|\n", + "| Slight|07:00-10:00 Rush ...| 414871|\n", + "| Slight|16:00-19:00 Rush ...| 561996|\n", + "| Slight| Off peak| 947751|\n", + "+-----------------+--------------------+---------------+\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [] + } + ], + "source": [ + "TimeAccident_dfwithbrack = TimeAccident_dfwithbrack.groupby('Accident_Severity','hour').agg(F.sum(TimeAccident_dfwithbrack['Total accidents']).alias('Total_accidents')).sort(\"Total_accidents\")\n", + "TimeAccident_dfwithbrack.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+-----------------+-----------+---------------+\n", + "|Accident_Severity|Day_of_Week|Total accidents|\n", + "+-----------------+-----------+---------------+\n", + "| Serious| Friday| 52828|\n", + "| Slight| Sunday| 208376|\n", + "| Fatal| Thursday| 3946|\n", + "| Fatal| Saturday| 4999|\n", + "| Slight| Thursday| 292268|\n", + "| Serious| Wednesday| 47344|\n", + "| Slight| Wednesday| 292966|\n", + "| Serious| Monday| 44440|\n", + "| Serious| Saturday| 49372|\n", + "| Serious| Thursday| 48198|\n", + "| Serious| Tuesday| 46919|\n", + "| Fatal| Monday| 3872|\n", + "| Slight| Saturday| 257858|\n", + "| Serious| Sunday| 43825|\n", + "| Slight| Tuesday| 289922|\n", + "| Fatal| Friday| 4477|\n", + "| Slight| Friday| 314507|\n", + "| Fatal| Tuesday| 3834|\n", + "| Fatal| Sunday| 4752|\n", + "| Slight| Monday| 268906|\n", + "+-----------------+-----------+---------------+\n", + "only showing top 20 rows\n", + "\n" + ] + } + ], + "source": [ + "DayAccidentwrtseverity_df.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+-----------------+-----------+---------------+\n", + "|Accident_Severity|Day_of_Week|Total accidents|\n", + "+-----------------+-----------+---------------+\n", + "| Serious| Friday| 52828|\n", + "| Slight| Sunday| 208376|\n", + "| Fatal| Thursday| 3946|\n", + "| Fatal| Saturday| 4999|\n", + "| Slight| Thursday| 292268|\n", + "| Serious| Wednesday| 47344|\n", + "| Slight| Wednesday| 292966|\n", + "| Serious| Monday| 44440|\n", + "| Serious| Saturday| 49372|\n", + "| Serious| Thursday| 48198|\n", + "| Serious| Tuesday| 46919|\n", + "| Fatal| Monday| 3872|\n", + "| Slight| Saturday| 257858|\n", + "| Serious| Sunday| 43825|\n", + "| Slight| Tuesday| 289922|\n", + "| Fatal| Friday| 4477|\n", + "| Slight| Friday| 314507|\n", + "| Fatal| Tuesday| 3834|\n", + "| Fatal| Sunday| 4752|\n", + "| Slight| Monday| 268906|\n", + "+-----------------+-----------+---------------+\n", + "only showing top 20 rows\n", + "\n" + ] + } + ], + "source": [ + "DayAccidentwrtseverity_df.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [], + "source": [ + "DayAccidentwrtseverity_dfupdate=DayAccidentwrtseverity_df.withColumn(\n", + " \"Day_of_Week\",\n", + " when(\n", + " col(\"Day_of_Week\") == \"Sunday\",\n", + " \"Weekend\"\n", + " ).\n", + " when(\n", + " col(\"Day_of_Week\") == \"Monday\",\n", + " \"Week Day\"\n", + " ).\n", + " when(\n", + " col(\"Day_of_Week\") == \"Tuesday\",\n", + " \"Week Day\"\n", + " ).\n", + " when(\n", + " col(\"Day_of_Week\") == \"Wednesday\",\n", + " \"Week Day\"\n", + " ).\n", + " when(\n", + " col(\"Day_of_Week\") == \"Thursday\",\n", + " \"Week Day\"\n", + " ).\n", + " when(\n", + " col(\"Day_of_Week\") == \"Friday\",\n", + " \"Week Day\"\n", + " ).\n", + " when(\n", + " col(\"Day_of_Week\") == \"Saturday\",\n", + " \"Weekend\"\n", + " ).otherwise(col(\"Day_of_Week\")),\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+-----------------+-----------+---------------+\n", + "|Accident_Severity|Day_of_Week|Total_accidents|\n", + "+-----------------+-----------+---------------+\n", + "| Fatal| Weekend| 9751|\n", + "| Fatal| Week Day| 19947|\n", + "| Serious| Weekend| 93197|\n", + "| Serious| Week Day| 239729|\n", + "| Slight| Weekend| 466234|\n", + "| Slight| Week Day| 1458569|\n", + "+-----------------+-----------+---------------+\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [] + } + ], + "source": [ + "\n", + "weekAccident_dfwithbrack = DayAccidentwrtseverity_dfupdate.groupby('Accident_Severity','Day_of_Week').agg(F.sum(DayAccidentwrtseverity_dfupdate['Total accidents']).alias('Total_accidents')).sort(\"Total_accidents\")\n", + "weekAccident_dfwithbrack.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "Accident_Information20052019_dfjunction=Accident_Information20052019_df.withColumn(\n", + " \"Junction_Detail\",\n", + " when(\n", + " col(\"Junction_Detail\") == 0,\n", + " \"Not at junction or within 20 metres\"\n", + " ).\n", + " when(\n", + " col(\"Junction_Detail\") == 1,\n", + " \"Roundabout\"\n", + " ).\n", + " when(\n", + " col(\"Junction_Detail\") == 2,\n", + " \"Mini-roundabout\"\n", + " ).\n", + " when(\n", + " col(\"Junction_Detail\") == 3,\n", + " \"T or staggered junction\"\n", + " ).\n", + " when(\n", + " col(\"Junction_Detail\") == 5,\n", + " \"Slip road\"\n", + " ).\n", + " when(\n", + " col(\"Junction_Detail\") == 6,\n", + " \"Crossroads\"\n", + " ).\n", + " when(\n", + " col(\"Junction_Detail\") == 7,\n", + " \"More than 4 arms (not roundabout)\"\n", + " ).when(\n", + " col(\"Junction_Detail\") == 8,\n", + " \"Private drive or entrance\"\n", + " )\n", + " .when(\n", + " col(\"Junction_Detail\") == 9,\n", + " \"Other junction\"\n", + " ).when(\n", + " col(\"Junction_Detail\") == -1,\n", + " \"Data missing or out of range\"\n", + " ).otherwise(col(\"Junction_Detail\"))\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DataFrame[Accident_Index: string, 1st_Road_Class: string, 1st_Road_Number: string, 2nd_Road_Class: string, 2nd_Road_Number: string, Accident_Severity: string, Carriageway_Hazards: string, Date: string, Day_of_Week: string, Did_Police_Officer_Attend_Scene_of_Accident: string, Junction_Control: string, Junction_Detail: string, Latitude: string, Light_Conditions: string, Local_Authority_(District): string, Local_Authority_(Highway): string, Location_Easting_OSGR: string, Location_Northing_OSGR: string, Longitude: string, LSOA_of_Accident_Location: string, Number_of_Casualties: string, Number_of_Vehicles: string, Pedestrian_Crossing-Human_Control: string, Pedestrian_Crossing-Physical_Facilities: string, Police_Force: string, Road_Surface_Conditions: string, Road_Type: string, Special_Conditions_at_Site: string, Speed_limit: string, Time: string, Urban_or_Rural_Area: string, Weather_Conditions: string, Year: int]" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Accident_Information20052019_dfjunction" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Accident_Information20052019_dfweather_dffindex=Accident_Information20052019_dfweather_df.set_index('Weather_Conditions')\n", + "Accident_Information20052019_dfweather_dffindex3=Accident_Information20052019_dfweather_dffindex\n", + "Accident_Information20052019_dfweather_dffindex3\n", + "\n", + "grouped = Accident_Information20052019_dfweather_dffindex3.groupby(Accident_Information20052019_dfweather_dffindex3.Accident_Severity)\n", + "Serious = grouped.get_group(\"Serious\")\n", + "Serious=Serious[\"Total accidents\"]\n", + "Serious=Serious.reset_index(drop=True)\n", + "Fatal = grouped.get_group(\"Fatal\")\n", + "Fatal=Fatal[\"Total accidents\"]\n", + "Fatal=Fatal.reset_index(drop=True)\n", + "Slight = grouped.get_group(\"Slight\")\n", + "Slight=Slight[\"Total accidents\"]\n", + "Slight=Slight.reset_index(drop=True)\n", + "Slight\n", + "Casulaty = pd.DataFrame({'period': ['Fine no high winds', 'Raining no high winds', 'Snowing no high winds', 'Fine + high winds', 'Raining + high winds', 'Snowing + high winds', 'Fog or mist', 'Unknown','Unknown','Unknown'],\n", + " 'Serious': Serious,\n", + " 'Fatal': Fatal,\n", + " 'Slight': Slight})\n", + "Casulaty\n", + "dflist=['Serious','Fatal','Slight']\n", + "Casulaty['Total_casualties']=Casulaty[dflist].sum(axis=1)\n", + "\n", + "Casulaty_spark=spark.createDataFrame(Casulaty)\n", + "Casulaty_spark=Casulaty_spark.withColumn('KSI', Casulaty_spark[2]+Casulaty_spark[1])\n", + "Casulaty_spark=Casulaty_spark.withColumn('\"%\"KSI', (Casulaty_spark[5]/Casulaty_spark[4])*100)\n", + "Casulaty_spark_df=Casulaty_spark.toPandas()\n", + "\n", + "Casulaty_spark_df" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+--------------+-------------+---------------+\n", + "|Accident_Index|Sex_of_Driver|Total accidents|\n", + "+--------------+-------------+---------------+\n", + "| 201998QC01004| Male| 1|\n", + "| 201997UD71205| Male| 1|\n", + "| 201997UD71005| Female| 1|\n", + "| 201997UD71005| Male| 2|\n", + "| 201997UD70905| Male| 2|\n", + "| 201997UD70905| Female| 1|\n", + "| 201997UD70901| Male| 2|\n", + "| 201997UD70805| Male| 2|\n", + "| 201997UD70803| Male| 2|\n", + "| 201997UD70801| Male| 1|\n", + "| 201997UD70801| Female| 1|\n", + "| 201997UD70706| Male| 1|\n", + "| 201997UD70705| Male| 2|\n", + "| 201997UD70606| Female| 1|\n", + "| 201997UD70605| Male| 2|\n", + "| 201997UD70603| Male| 1|\n", + "| 201997UD70506| Male| 1|\n", + "| 201997UD70506| Female| 1|\n", + "| 201997UD70505| Female| 1|\n", + "| 201997UD70503| Male| 2|\n", + "+--------------+-------------+---------------+\n", + "only showing top 20 rows\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [] + } + ], + "source": [ + "\n", + "DvrSex = V20052014.groupby('Accident_Index','Sex_of_Driver').agg(F.count(V20052014.Accident_Index).alias('Total accidents')).sort(\"Total accidents\")\n", + "DvrSex.sort(col('Accident_Index').desc()).show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+--------------+------------------+---------------+\n", + "|Accident_Index|Age_Band_of_Driver|Total accidents|\n", + "+--------------+------------------+---------------+\n", + "| 201998QC01004| 40Y to 70Y| 1|\n", + "| 201997UD71205| 40Y to 70Y| 1|\n", + "| 201997UD71005| 20Y to 40Y| 1|\n", + "| 201997UD71005| 40Y to 70Y| 2|\n", + "| 201997UD70905| 40Y to 70Y| 3|\n", + "| 201997UD70901| 20Y to 40Y| 1|\n", + "| 201997UD70901| 40Y to 70Y| 1|\n", + "| 201997UD70805| Over 70| 2|\n", + "| 201997UD70803| Over 70| 1|\n", + "| 201997UD70803| 20Y to 40Y| 1|\n", + "| 201997UD70801| Upto 20Y| 1|\n", + "| 201997UD70801| 20Y to 40Y| 1|\n", + "| 201997UD70706| 40Y to 70Y| 1|\n", + "| 201997UD70705| 20Y to 40Y| 1|\n", + "| 201997UD70705| 40Y to 70Y| 1|\n", + "| 201997UD70606| 40Y to 70Y| 1|\n", + "| 201997UD70605| 20Y to 40Y| 1|\n", + "| 201997UD70605| 40Y to 70Y| 1|\n", + "| 201997UD70603| 20Y to 40Y| 1|\n", + "| 201997UD70506| Upto 20Y| 1|\n", + "+--------------+------------------+---------------+\n", + "only showing top 20 rows\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [] + } + ], + "source": [ + "\n", + "DvrAge = V20052014.groupby('Accident_Index','Age_Band_of_Driver').agg(F.count(V20052014.Accident_Index).alias('Total accidents')).sort(\"Total accidents\")\n", + "DvrAge.sort(col('Accident_Index').desc()).show()" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+--------------+-----------------+---------------+\n", + "|Accident_Index|Accident_Severity|Total accidents|\n", + "+--------------+-----------------+---------------+\n", + "| 201998QC01004| Serious| 1|\n", + "| 201997UD71205| Slight| 1|\n", + "| 201997UD71005| Slight| 1|\n", + "| 201997UD70905| Serious| 1|\n", + "| 201997UD70901| Slight| 1|\n", + "| 201997UD70805| Slight| 1|\n", + "| 201997UD70803| Slight| 1|\n", + "| 201997UD70801| Slight| 1|\n", + "| 201997UD70706| Serious| 1|\n", + "| 201997UD70705| Slight| 1|\n", + "| 201997UD70606| Serious| 1|\n", + "| 201997UD70605| Serious| 1|\n", + "| 201997UD70603| Fatal| 1|\n", + "| 201997UD70506| Slight| 1|\n", + "| 201997UD70505| Serious| 1|\n", + "| 201997UD70503| Slight| 1|\n", + "| 201997UD70501| Slight| 1|\n", + "| 201997UD70406| Serious| 1|\n", + "| 201997UD70403| Slight| 1|\n", + "| 201997UD70306| Slight| 1|\n", + "+--------------+-----------------+---------------+\n", + "only showing top 20 rows\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [] + } + ], + "source": [ + "\n", + "Accidensev = Accident_Information20052019_df.groupby('Accident_Index','Accident_Severity').agg(F.count(Accident_Information20052019_df.Accident_Index).alias('Total accidents')).sort(\"Total accidents\")\n", + "Accidensev.sort(col('Accident_Index').desc()).show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DataFrame[Accident_Index: string, 1st_Road_Class: string, 1st_Road_Number: string, 2nd_Road_Class: string, 2nd_Road_Number: string, Accident_Severity: string, Carriageway_Hazards: string, Date: string, Day_of_Week: string, Did_Police_Officer_Attend_Scene_of_Accident: string, Junction_Control: string, Junction_Detail: string, Latitude: string, Light_Conditions: string, Local_Authority_(District): string, Local_Authority_(Highway): string, Location_Easting_OSGR: string, Location_Northing_OSGR: string, Longitude: string, LSOA_of_Accident_Location: string, Number_of_Casualties: string, Number_of_Vehicles: string, Pedestrian_Crossing-Human_Control: string, Pedestrian_Crossing-Physical_Facilities: string, Police_Force: string, Road_Surface_Conditions: string, Road_Type: string, Special_Conditions_at_Site: string, Speed_limit: string, Time: string, Urban_or_Rural_Area: string, Weather_Conditions: string, Year: int]" + ] + }, + "execution_count": 99, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Accident_Information20052019_df" + ] + }, + { + "cell_type": "code", + "execution_count": 102, + "metadata": {}, + "outputs": [], + "source": [ + "Accident_Information20052019_dfweather=Accident_Information20052019_df.withColumn(\n", + " \"Weather_Conditions\",\n", + " when(\n", + " col(\"Weather_Conditions\") == 1,\n", + " \"Fine no high winds\"\n", + " ).\n", + " when(\n", + " col(\"Weather_Conditions\") == 2,\n", + " \"Raining no high winds\"\n", + " ).\n", + " when(\n", + " col(\"Weather_Conditions\") == 3,\n", + " \"Snowing no high winds\"\n", + " ).\n", + " when(\n", + " col(\"Weather_Conditions\") == 4,\n", + " \"Fine + high winds\"\n", + " ).\n", + " when(\n", + " col(\"Weather_Conditions\") == 5,\n", + " \"Raining + high winds\"\n", + " ).\n", + " when(\n", + " col(\"Weather_Conditions\") == 6,\n", + " \"Snowing + high winds\"\n", + " ).\n", + " when(\n", + " col(\"Weather_Conditions\") == 7,\n", + " \"Fog or mist\"\n", + " ).when(\n", + " col(\"Weather_Conditions\") == 8,\n", + " \"Unknown\"\n", + " )\n", + " .when(\n", + " col(\"Weather_Conditions\") == 9,\n", + " \"Unknown\"\n", + " ).when(\n", + " col(\"Weather_Conditions\") == -1,\n", + " \"Unknown\"\n", + " ).otherwise(col(\"Weather_Conditions\"))\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 103, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+-----------------+--------------------+---------------+\n", + "|Accident_Severity| Weather_Conditions|Total accidents|\n", + "+-----------------+--------------------+---------------+\n", + "| Slight|Snowing no high w...| 13123|\n", + "| Fatal| Fog or mist| 278|\n", + "| Slight| Fine no high winds| 1533207|\n", + "| Fatal| Fine + high winds| 540|\n", + "| Fatal| Fine no high winds| 24527|\n", + "| Serious| Fog or mist| 1818|\n", + "| Slight|Data missing or o...| 157|\n", + "| Fatal| Unknown| 465|\n", + "| Serious|Raining no high w...| 35321|\n", + "| Slight| Fog or mist| 9815|\n", + "| Slight| Unknown| 48872|\n", + "| Slight| Fine + high winds| 23207|\n", + "| Slight|Raining no high w...| 229164|\n", + "| Serious|Data missing or o...| 13|\n", + "| Serious| Fine no high winds| 273898|\n", + "| Serious|Snowing no high w...| 1565|\n", + "| Fatal|Data missing or o...| 5|\n", + "| Fatal|Snowing + high winds| 17|\n", + "| Slight| Other| 38627|\n", + "| Fatal|Raining no high w...| 2879|\n", + "+-----------------+--------------------+---------------+\n", + "only showing top 20 rows\n", + "\n" + ] + } + ], + "source": [ + "\n", + "Accident_Information20052019_dfweather = Accident_Information20052019_dfweather.groupby('Accident_Severity','Weather_Conditions').agg(F.count(Accident_Information20052019_dfweather.Accident_Index).alias('Total accidents'))\n", + "Accident_Information20052019_dfweather.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 109, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>Accident_Severity</th>\n", + " <th>Weather_Conditions</th>\n", + " <th>Total accidents</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>Slight</td>\n", + " <td>Snowing no high winds</td>\n", + " <td>13123</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>Fatal</td>\n", + " <td>Fog or mist</td>\n", + " <td>278</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>Slight</td>\n", + " <td>Fine no high winds</td>\n", + " <td>1533207</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>Fatal</td>\n", + " <td>Fine + high winds</td>\n", + " <td>540</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>Fatal</td>\n", + " <td>Fine no high winds</td>\n", + " <td>24527</td>\n", + " </tr>\n", + " <tr>\n", + " <th>5</th>\n", + " <td>Serious</td>\n", + " <td>Fog or mist</td>\n", + " <td>1818</td>\n", + " </tr>\n", + " <tr>\n", + " <th>6</th>\n", + " <td>Slight</td>\n", + " <td>Data missing or out of range</td>\n", + " <td>157</td>\n", + " </tr>\n", + " <tr>\n", + " <th>7</th>\n", + " <td>Fatal</td>\n", + " <td>Unknown</td>\n", + " <td>465</td>\n", + " </tr>\n", + " <tr>\n", + " <th>8</th>\n", + " <td>Serious</td>\n", + " <td>Raining no high winds</td>\n", + " <td>35321</td>\n", + " </tr>\n", + " <tr>\n", + " <th>9</th>\n", + " <td>Slight</td>\n", + " <td>Fog or mist</td>\n", + " <td>9815</td>\n", + " </tr>\n", + " <tr>\n", + " <th>10</th>\n", + " <td>Slight</td>\n", + " <td>Unknown</td>\n", + " <td>48872</td>\n", + " </tr>\n", + " <tr>\n", + " <th>11</th>\n", + " <td>Slight</td>\n", + " <td>Fine + high winds</td>\n", + " <td>23207</td>\n", + " </tr>\n", + " <tr>\n", + " <th>12</th>\n", + " <td>Slight</td>\n", + " <td>Raining no high winds</td>\n", + " <td>229164</td>\n", + " </tr>\n", + " <tr>\n", + " <th>13</th>\n", + " <td>Serious</td>\n", + " <td>Data missing or out of range</td>\n", + " <td>13</td>\n", + " </tr>\n", + " <tr>\n", + " <th>14</th>\n", + " <td>Serious</td>\n", + " <td>Fine no high winds</td>\n", + " <td>273898</td>\n", + " </tr>\n", + " <tr>\n", + " <th>15</th>\n", + " <td>Serious</td>\n", + " <td>Snowing no high winds</td>\n", + " <td>1565</td>\n", + " </tr>\n", + " <tr>\n", + " <th>16</th>\n", + " <td>Fatal</td>\n", + " <td>Data missing or out of range</td>\n", + " <td>5</td>\n", + " </tr>\n", + " <tr>\n", + " <th>17</th>\n", + " <td>Fatal</td>\n", + " <td>Snowing + high winds</td>\n", + " <td>17</td>\n", + " </tr>\n", + " <tr>\n", + " <th>18</th>\n", + " <td>Slight</td>\n", + " <td>Other</td>\n", + " <td>38627</td>\n", + " </tr>\n", + " <tr>\n", + " <th>19</th>\n", + " <td>Fatal</td>\n", + " <td>Raining no high winds</td>\n", + " <td>2879</td>\n", + " </tr>\n", + " <tr>\n", + " <th>20</th>\n", + " <td>Serious</td>\n", + " <td>Snowing + high winds</td>\n", + " <td>365</td>\n", + " </tr>\n", + " <tr>\n", + " <th>21</th>\n", + " <td>Serious</td>\n", + " <td>Unknown</td>\n", + " <td>5951</td>\n", + " </tr>\n", + " <tr>\n", + " <th>22</th>\n", + " <td>Fatal</td>\n", + " <td>Raining + high winds</td>\n", + " <td>462</td>\n", + " </tr>\n", + " <tr>\n", + " <th>23</th>\n", + " <td>Serious</td>\n", + " <td>Other</td>\n", + " <td>5058</td>\n", + " </tr>\n", + " <tr>\n", + " <th>24</th>\n", + " <td>Slight</td>\n", + " <td>Snowing + high winds</td>\n", + " <td>2547</td>\n", + " </tr>\n", + " <tr>\n", + " <th>25</th>\n", + " <td>Slight</td>\n", + " <td>Raining + high winds</td>\n", + " <td>26084</td>\n", + " </tr>\n", + " <tr>\n", + " <th>26</th>\n", + " <td>Fatal</td>\n", + " <td>Snowing no high winds</td>\n", + " <td>127</td>\n", + " </tr>\n", + " <tr>\n", + " <th>27</th>\n", + " <td>Serious</td>\n", + " <td>Raining + high winds</td>\n", + " <td>4504</td>\n", + " </tr>\n", + " <tr>\n", + " <th>28</th>\n", + " <td>Fatal</td>\n", + " <td>Other</td>\n", + " <td>398</td>\n", + " </tr>\n", + " <tr>\n", + " <th>29</th>\n", + " <td>Serious</td>\n", + " <td>Fine + high winds</td>\n", + " <td>4433</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " Accident_Severity Weather_Conditions Total accidents\n", + "0 Slight Snowing no high winds 13123\n", + "1 Fatal Fog or mist 278\n", + "2 Slight Fine no high winds 1533207\n", + "3 Fatal Fine + high winds 540\n", + "4 Fatal Fine no high winds 24527\n", + "5 Serious Fog or mist 1818\n", + "6 Slight Data missing or out of range 157\n", + "7 Fatal Unknown 465\n", + "8 Serious Raining no high winds 35321\n", + "9 Slight Fog or mist 9815\n", + "10 Slight Unknown 48872\n", + "11 Slight Fine + high winds 23207\n", + "12 Slight Raining no high winds 229164\n", + "13 Serious Data missing or out of range 13\n", + "14 Serious Fine no high winds 273898\n", + "15 Serious Snowing no high winds 1565\n", + "16 Fatal Data missing or out of range 5\n", + "17 Fatal Snowing + high winds 17\n", + "18 Slight Other 38627\n", + "19 Fatal Raining no high winds 2879\n", + "20 Serious Snowing + high winds 365\n", + "21 Serious Unknown 5951\n", + "22 Fatal Raining + high winds 462\n", + "23 Serious Other 5058\n", + "24 Slight Snowing + high winds 2547\n", + "25 Slight Raining + high winds 26084\n", + "26 Fatal Snowing no high winds 127\n", + "27 Serious Raining + high winds 4504\n", + "28 Fatal Other 398\n", + "29 Serious Fine + high winds 4433" + ] + }, + "execution_count": 109, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Accident_Information20052019_dfweather_df=Accident_Information20052019_dfweather.toPandas()\n", + "Accident_Information20052019_dfweather_df" + ] + }, + { + "cell_type": "code", + "execution_count": 115, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>period</th>\n", + " <th>Serious</th>\n", + " <th>Fatal</th>\n", + " <th>Slight</th>\n", + " <th>Total_casualties</th>\n", + " <th>KSI</th>\n", + " <th>\"%\"KSI</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>Fine no high winds</td>\n", + " <td>1818</td>\n", + " <td>278</td>\n", + " <td>13123</td>\n", + " <td>15219</td>\n", + " <td>2096</td>\n", + " <td>13.772258</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>Raining no high winds</td>\n", + " <td>35321</td>\n", + " <td>540</td>\n", + " <td>1533207</td>\n", + " <td>1569068</td>\n", + " <td>35861</td>\n", + " <td>2.285497</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>Snowing no high winds</td>\n", + " <td>13</td>\n", + " <td>24527</td>\n", + " <td>157</td>\n", + " <td>24697</td>\n", + " <td>24540</td>\n", + " <td>99.364295</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>Fine + high winds</td>\n", + " <td>273898</td>\n", + " <td>465</td>\n", + " <td>9815</td>\n", + " <td>284178</td>\n", + " <td>274363</td>\n", + " <td>96.546179</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>Raining + high winds</td>\n", + " <td>1565</td>\n", + " <td>5</td>\n", + " <td>48872</td>\n", + " <td>50442</td>\n", + " <td>1570</td>\n", + " <td>3.112486</td>\n", + " </tr>\n", + " <tr>\n", + " <th>5</th>\n", + " <td>Snowing + high winds</td>\n", + " <td>365</td>\n", + " <td>17</td>\n", + " <td>23207</td>\n", + " <td>23589</td>\n", + " <td>382</td>\n", + " <td>1.619399</td>\n", + " </tr>\n", + " <tr>\n", + " <th>6</th>\n", + " <td>Fog or mist</td>\n", + " <td>5951</td>\n", + " <td>2879</td>\n", + " <td>229164</td>\n", + " <td>237994</td>\n", + " <td>8830</td>\n", + " <td>3.710178</td>\n", + " </tr>\n", + " <tr>\n", + " <th>7</th>\n", + " <td>Unknown</td>\n", + " <td>5058</td>\n", + " <td>462</td>\n", + " <td>38627</td>\n", + " <td>44147</td>\n", + " <td>5520</td>\n", + " <td>12.503681</td>\n", + " </tr>\n", + " <tr>\n", + " <th>8</th>\n", + " <td>Unknown</td>\n", + " <td>4504</td>\n", + " <td>127</td>\n", + " <td>2547</td>\n", + " <td>7178</td>\n", + " <td>4631</td>\n", + " <td>64.516578</td>\n", + " </tr>\n", + " <tr>\n", + " <th>9</th>\n", + " <td>Unknown</td>\n", + " <td>4433</td>\n", + " <td>398</td>\n", + " <td>26084</td>\n", + " <td>30915</td>\n", + " <td>4831</td>\n", + " <td>15.626718</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " period Serious Fatal Slight Total_casualties KSI \\\n", + "0 Fine no high winds 1818 278 13123 15219 2096 \n", + "1 Raining no high winds 35321 540 1533207 1569068 35861 \n", + "2 Snowing no high winds 13 24527 157 24697 24540 \n", + "3 Fine + high winds 273898 465 9815 284178 274363 \n", + "4 Raining + high winds 1565 5 48872 50442 1570 \n", + "5 Snowing + high winds 365 17 23207 23589 382 \n", + "6 Fog or mist 5951 2879 229164 237994 8830 \n", + "7 Unknown 5058 462 38627 44147 5520 \n", + "8 Unknown 4504 127 2547 7178 4631 \n", + "9 Unknown 4433 398 26084 30915 4831 \n", + "\n", + " \"%\"KSI \n", + "0 13.772258 \n", + "1 2.285497 \n", + "2 99.364295 \n", + "3 96.546179 \n", + "4 3.112486 \n", + "5 1.619399 \n", + "6 3.710178 \n", + "7 12.503681 \n", + "8 64.516578 \n", + "9 15.626718 " + ] + }, + "execution_count": 115, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "\n", + "\n", + "\n", + "Accident_Information20052019_dfweather_dffindex=Accident_Information20052019_dfweather_df.set_index('Weather_Conditions')\n", + "Accident_Information20052019_dfweather_dffindex3=Accident_Information20052019_dfweather_dffindex\n", + "Accident_Information20052019_dfweather_dffindex3\n", + "\n", + "grouped = Accident_Information20052019_dfweather_dffindex3.groupby(Accident_Information20052019_dfweather_dffindex3.Accident_Severity)\n", + "Serious = grouped.get_group(\"Serious\")\n", + "Serious=Serious[\"Total accidents\"]\n", + "Serious=Serious.reset_index(drop=True)\n", + "Fatal = grouped.get_group(\"Fatal\")\n", + "Fatal=Fatal[\"Total accidents\"]\n", + "Fatal=Fatal.reset_index(drop=True)\n", + "Slight = grouped.get_group(\"Slight\")\n", + "Slight=Slight[\"Total accidents\"]\n", + "Slight=Slight.reset_index(drop=True)\n", + "Slight\n", + "Casulaty = pd.DataFrame({'period': ['Fine no high winds', 'Raining no high winds', 'Snowing no high winds', 'Fine + high winds', 'Raining + high winds', 'Snowing + high winds', 'Fog or mist', 'Unknown','Unknown','Unknown'],\n", + " 'Serious': Serious,\n", + " 'Fatal': Fatal,\n", + " 'Slight': Slight})\n", + "Casulaty\n", + "dflist=['Serious','Fatal','Slight']\n", + "Casulaty['Total_casualties']=Casulaty[dflist].sum(axis=1)\n", + "\n", + "Casulaty_spark=spark.createDataFrame(Casulaty)\n", + "Casulaty_spark=Casulaty_spark.withColumn('KSI', Casulaty_spark[2]+Casulaty_spark[1])\n", + "Casulaty_spark=Casulaty_spark.withColumn('\"%\"KSI', (Casulaty_spark[5]/Casulaty_spark[4])*100)\n", + "Casulaty_spark_df=Casulaty_spark.toPandas()\n", + "\n", + "Casulaty_spark_df\n" + ] + }, + { + "cell_type": "code", + "execution_count": 127, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DataFrame[Accident_Index: string, 1st_Road_Class: string, 1st_Road_Number: string, 2nd_Road_Class: string, 2nd_Road_Number: string, Accident_Severity: string, Carriageway_Hazards: string, Date: string, Day_of_Week: string, Did_Police_Officer_Attend_Scene_of_Accident: string, Junction_Control: string, Junction_Detail: string, Latitude: string, Light_Conditions: string, Local_Authority_(District): string, Local_Authority_(Highway): string, Location_Easting_OSGR: string, Location_Northing_OSGR: string, Longitude: string, LSOA_of_Accident_Location: string, Number_of_Casualties: string, Number_of_Vehicles: string, Pedestrian_Crossing-Human_Control: string, Pedestrian_Crossing-Physical_Facilities: string, Police_Force: string, Road_Surface_Conditions: string, Road_Type: string, Special_Conditions_at_Site: string, Speed_limit: string, Time: string, Urban_or_Rural_Area: string, Weather_Conditions: string, Year: int]" + ] + }, + "execution_count": 127, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Accident_Information20052019_df" + ] + }, + { + "cell_type": "code", + "execution_count": 138, + "metadata": {}, + "outputs": [], + "source": [ + "Accident_Information20052019_dfroadsurface=Accident_Information20052019_df.withColumn(\n", + " \"Road_Surface_Conditions\",\n", + " when(\n", + " col(\"Road_Surface_Conditions\") == 1,\n", + " \"Dry\"\n", + " ).when(\n", + " col(\"Road_Surface_Conditions\") == 2,\n", + " \"Wet or damp\"\n", + " ).when(\n", + " col(\"Road_Surface_Conditions\") == 3,\n", + " \"Snow\"\n", + " ).\n", + " when(\n", + " col(\"Road_Surface_Conditions\") == 4,\n", + " \"Frost or ice\"\n", + " ).\n", + " when(\n", + " col(\"Road_Surface_Conditions\") == 5,\n", + " \"Flood over 3cm. deep\"\n", + " ).\n", + " when(\n", + " col(\"Road_Surface_Conditions\") == 6,\n", + " \"Oil or diesel\"\n", + " ).\n", + " when(\n", + " col(\"Road_Surface_Conditions\") == 7,\n", + " \"Mud\"\n", + " ).when(\n", + " col(\"Road_Surface_Conditions\") == -1,\n", + " \"Data missing or out of range\"\n", + " ).otherwise(col(\"Road_Surface_Conditions\"))\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 139, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+-----------------+-----------------------+---------------+\n", + "|Accident_Severity|Road_Surface_Conditions|Total accidents|\n", + "+-----------------+-----------------------+---------------+\n", + "| Serious| Frost or ice| 5313|\n", + "| Slight| Data missing or o...| 7036|\n", + "| Slight| Dry| 1335619|\n", + "| Serious| Data missing or o...| 659|\n", + "| Serious| Snow| 1429|\n", + "| Serious| Flood over 3cm. deep| 456|\n", + "| Serious| Dry| 235406|\n", + "| Fatal| Wet or damp| 9007|\n", + "| Slight| Frost or ice| 37160|\n", + "| Fatal| Data missing or o...| 22|\n", + "| Slight| Wet or damp| 530367|\n", + "| Slight| Flood over 3cm. deep| 2638|\n", + "| Serious| Wet or damp| 89663|\n", + "| Slight| Snow| 11983|\n", + "| Fatal| Flood over 3cm. deep| 63|\n", + "| Fatal| Dry| 20057|\n", + "| Fatal| Snow| 99|\n", + "| Fatal| Frost or ice| 450|\n", + "+-----------------+-----------------------+---------------+\n", + "\n" + ] + } + ], + "source": [ + "\n", + "Accident_Information20052019_dfroadsurface = Accident_Information20052019_dfroadsurface.groupby('Accident_Severity','Road_Surface_Conditions').agg(F.count(Accident_Information20052019_dfroadsurface.Accident_Index).alias('Total accidents'))\n", + "Accident_Information20052019_dfroadsurface.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 140, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [] + } + ], + "source": [ + "Accident_Information20052019_dfroadsurface=Accident_Information20052019_dfroadsurface.toPandas()\n", + "Accident_Information20052019_dfroadsurface=Accident_Information20052019_dfroadsurface.pivot(index ='Road_Surface_Conditions', columns ='Accident_Severity')" + ] + }, + { + "cell_type": "code", + "execution_count": 141, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead tr th {\n", + " text-align: left;\n", + " }\n", + "\n", + " .dataframe thead tr:last-of-type th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr>\n", + " <th></th>\n", + " <th colspan=\"3\" halign=\"left\">Total accidents</th>\n", + " </tr>\n", + " <tr>\n", + " <th>Accident_Severity</th>\n", + " <th>Fatal</th>\n", + " <th>Serious</th>\n", + " <th>Slight</th>\n", + " </tr>\n", + " <tr>\n", + " <th>Road_Surface_Conditions</th>\n", + " <th></th>\n", + " <th></th>\n", + " <th></th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>Data missing or out of range</th>\n", + " <td>22</td>\n", + " <td>659</td>\n", + " <td>7036</td>\n", + " </tr>\n", + " <tr>\n", + " <th>Dry</th>\n", + " <td>20057</td>\n", + " <td>235406</td>\n", + " <td>1335619</td>\n", + " </tr>\n", + " <tr>\n", + " <th>Flood over 3cm. deep</th>\n", + " <td>63</td>\n", + " <td>456</td>\n", + " <td>2638</td>\n", + " </tr>\n", + " <tr>\n", + " <th>Frost or ice</th>\n", + " <td>450</td>\n", + " <td>5313</td>\n", + " <td>37160</td>\n", + " </tr>\n", + " <tr>\n", + " <th>Snow</th>\n", + " <td>99</td>\n", + " <td>1429</td>\n", + " <td>11983</td>\n", + " </tr>\n", + " <tr>\n", + " <th>Wet or damp</th>\n", + " <td>9007</td>\n", + " <td>89663</td>\n", + " <td>530367</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " Total accidents \n", + "Accident_Severity Fatal Serious Slight\n", + "Road_Surface_Conditions \n", + "Data missing or out of range 22 659 7036\n", + "Dry 20057 235406 1335619\n", + "Flood over 3cm. deep 63 456 2638\n", + "Frost or ice 450 5313 37160\n", + "Snow 99 1429 11983\n", + "Wet or damp 9007 89663 530367" + ] + }, + "execution_count": 141, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Accident_Information20052019_dfroadsurface" + ] + }, + { + "cell_type": "code", + "execution_count": 143, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([0, 1, 2, 3, 4]),\n", + " [Text(0, 0, 'Dry'),\n", + " Text(1, 0, 'Flood over 3cm. deep'),\n", + " Text(2, 0, 'Frost or ice'),\n", + " Text(3, 0, 'Snow'),\n", + " Text(4, 0, 'Wet or damp')])" + ] + }, + "execution_count": 143, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAABIEAAAKeCAYAAAAyZgu1AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8rg+JYAAAACXBIWXMAAAsTAAALEwEAmpwYAACDrklEQVR4nOzdeZyVZf3/8deHRQQXFsVcCy1FdIARAXfFDTVLLS0tv7miZWpm6Ve/9fsmLRalZWGLUiFpimuaX5c0SkVNFLQRFxQ33EAFTEFFZbl+f1z3GQ7DDDPA6ID36/l4zOMw577PfV9nOOfc93nfn+u6IqWEJEmSJEmSPtratXUDJEmSJEmS9MEzBJIkSZIkSSoBQyBJkiRJkqQSMASSJEmSJEkqAUMgSZIkSZKkEjAEkiRJkiRJKgFDIEmS9IGIiMciYkgTy4ZExEsfbovaTkT0iogUER0+pP19LCLGR8TciPj5h7FPSZK06jMEkiRJ9SLizoj4T0R0WtltpZS2TSnd2QrNWqaImBYR+6zEY+dFxFsR8UpEjImItVu7jS1ox64R8a+IeDMiXo+IeyNi0Eps8kRgFrBuSunbrdRMSZK0mjMEkiRJQK5WAXYDEnBQ27bmQ/XZlNLaQC2wHfA/H+bOI2Jd4CbgQqAHsAnwfeC9FdhWREQ74BPA4yml1JptlSRJqzdDIEmSVHEUMAEYAxxdvSAiNouIv0TEzIiYHRG/rlp2QkRMKboePR4RA4r76yt0IqJzUWXzn4h4HBjUYPsbR8R1xfafi4hvVC0bHhFXR8SlxT4ei4iBxbLLgI8D/1dU8/x3RKwZEX8u2vlGREyMiI819+RTSq8At5HDoMq+Dyr290ZRJdWnatnZEfFM1fP+XNWy9hFxfkTMiohngQOXseutiv2PTSktTCnNSyndnlKaXPX8/1y17SW6lhXtOjci7gXeAS4l///9d/E32SciBkfEfcXzmBERv46INaq2uW1E/L2oQno1Ir5T3N+u6nnOLv4fejT3t5QkSasmQyBJklRxFHB58bNfJTiJiPbkSpXngV7kSpUri2VfAIYXj12XXEE0u5FtnwN8svjZj6qQqahc+T/g4WLbewPfjIj9qh5/ULHPbsCNwK8BUkpfAV6gqOZJKf2s2HZXYDNgPeBrwLzmnnxEbAocADxd/L4VMBb4JtATuIUcNlXCk2fIlVNdyZU7f46IjYplJwCfIVcWDQQOW8aupwILI+JPEXFARHRvrq2N+Aq5C9g6wLHk/8OfFX+TccBC4HRgfWAn8t/468XzXAcYB/wN2Bj4FPCPYrunAocAexTL/gP8ZgXaJ0mSVgFtGgJFxOiIeC0iHm3h+l8srrQ9FhFXfNDtkySpLCJiV3IXoqtTSg+SA44vF4sHkwOAM1NKb6eU3k0p3VMsG0YOGyam7OmU0vON7OKLwLkppddTSi8CI6uWDQJ6ppR+kFJ6P6X0LPB74Iiqde5JKd2SUloIXAb0X8bTmU8Ofz5VVNY8mFKas4z1b4iIucCLwGvkwArgcODmlNLfU0rzgfOBzsDOACmla1JK01NKi1JKVwFPFX+ryvP9ZUrpxZTS68BPmtp50bZdyd3wfg/MjIgbW1K9VGVMSumxlNKCoq0N9/FgSmlCsXwacDE52IEcVr2SUvp58X87N6V0f7Hsa8B3U0ovpZTeIwd+h8WHNMC1JElqXW1dCTQG2L8lK0bEluQ++ruklLYlX5WTJEmt42jg9pTSrOL3K1hcrbMZ8HxKaUEjj9uMHBg1Z2NyyFJRHRR9Ati46Kr0RkS8AXwHqA5BXqn69zvAmssIIi4jd+u6MiKmR8TPIqLjMtp2SEppHWAIsDW5WqbS5vp2ppQWFc9hE4CIOCoi6qraXNPgsU0936WklKaklI5JKW1abGdj4JfLekwDLy5rYURsFRE3RR78eg7w46q2Luv/8BPA9VXPcQq5qmh5AipJkrSKaNMQKKU0Hni9+r6I+GRE/C0iHoyIuyNi62LRCcBvUkr/KR772ofcXEmSPpIiojO5cmWPIiR4hdx1qH9E9CcHDB9vInR5kdzFqzkzyGFDxccbbOO5lFK3qp91UkqfbuFTWGLw45TS/JTS91NK25Crdj5D7q627I2kdBf5AtX5xV3TySEIkAddLp7DyxHxCXLVzinAeimlbsCjQLTg+TbXjieKdtQUd70NdKlaZcPGHtbMZn8HPAFsmVJalxyyVdr6IrBFE497ETigwf/Nmimll5t/JpIkaVXT1pVAjRkFnJpS2h44A/htcf9WwFaRp0ydEBEtqiCSJEnNOoRc3bENeVDkWqAPcDc5PHmAHGqMiIi1ioGXdyke+wfgjIjYPrJPFQFJQ1cD/xMR3Yuxd06tWvYAMDcizoo8gHT7iKiJlk+R/ipVIUZE7BkRfYuxjOaQu4ctauG2fgnsW4RfVwMHRsTeRSXRt8kzdv0LWIscvMws9nksi0ObyvP9RkRsWozxc3ZTO4yIrSPi28XfhYjYDPgSeZBugDpg94j4eER0ZcVmL1uH/Ld4q7jAdlLVspuAjSLimxHRKSLWiYgdimUXAedW/k8jomdEHLwC+5ckSauAVSoEioi1yVfsromIOnJ/9coAix2ALcml2l8Cfh8R3T78VkqS9JFzNHBJSumFlNIrlR/y4MtHkitGPkseMPgF4CXyeDmklK4BziV3H5sL3ECe5ryh75O7RD0H3E7uskWxjYXkap3aYvkscrjUtYXt/wnw/4ouS2eQK2WuJYceU4C7qve3LCmlmeTZtb6XUnoS+C/y1O2zyH+DzxbjFj0O/By4jxxC9QXurdrU78ld0h4GHgL+sozdzgV2AO6PiLfJ4c+j5NCJlNLfgauAycCD5NBmeZ1BHuNpbtG2q6qe81xg3+L5vUIe22jPYvGvyANx316MmzShaKskSVoNRUrNVQ9/wA2I6AXclFKqiYh1gSdTShs1st5FwP0ppUuK3/8BnJ1SmvihNliSJEmSJGk1tEpVAhWzYzxXTDdLUVZemf3jBnIVEBGxPrl72LNt0ExJkiRJkqTVTltPET+WXEbdOyJeiojjyWXnx0fEw8BjQKXf+W3A7Ih4HLiDPE3t7LZotyRJkiRJ0uqmzbuDSZIkSZIk6YO3SnUHkyRJkiRJ0gejQ1vteP3110+9evVqq91LkiRJkiR95Dz44IOzUko9G1vWZiFQr169mDRpUlvtXpIkSZIk6SMnIp5vapndwSRJkiRJkkrAEEiSJEmSJKkEDIEkSZIkSZJKoM3GBJIkSZIkaXU2f/58XnrpJd599922bopKaM0112TTTTelY8eOLX6MIZAkSZIkSSvgpZdeYp111qFXr15ERFs3RyWSUmL27Nm89NJLbL755i1+nN3BJEmSJElaAe+++y7rrbeeAZA+dBHBeuutt9xVaIZAkiRJkiStIAMgtZUVee0ZAkmSJEmSJJWAIZAkSZIkSVIJGAJJkiRJkrQKiQi+/e1v1/9+/vnnM3z48A98v3V1dUQEf/vb31bo8dOnT+ewww5rdNmQIUOYNGnSCm33zjvv5F//+tcy13nyyScZMmQItbW19OnThxNPPHGF9rU8dt55ZwCmTZvGFVdc8YHvrzUYAkmSJEmStArp1KkTf/nLX5g1a9aHut+xY8ey6667Mnbs2BV6/MYbb8y1117byq1qWQj0jW98g9NPP526ujqmTJnCqaee2urtqFiwYAFAfZsMgSRJkiRJ0grp0KEDJ554IhdccMFSy6ZNm8Zee+1Fv3792HvvvXnhhRcAOOaYY/jGN77BzjvvzBZbbLFEGHPeeecxaNAg+vXrxznnnNPoPlNKXHPNNYwZM4a///3vS8w69dOf/pS+ffvSv39/zj77bACefvpp9tlnH/r378+AAQN45plnmDZtGjU1NQDMmzePI444gj59+vC5z32OefPm1W/v9ttvZ6eddmLAgAF84Qtf4K233gKgV69enHPOOQwYMIC+ffvyxBNPMG3aNC666CIuuOACamtrufvuuxtt/4wZM9h0003rf+/bty8ACxcu5Mwzz6x//hdffDEARxxxBDfffHP9+scccwzXXnttk+vfeeed7Lbbbhx00EFss802AKy99toAnH322dx9993U1tZywQUXsPvuu1NXV1e/7V133ZWHH3640XZ/2AyBJEmSJElaxZx88slcfvnlvPnmm0vcf+qpp3L00UczefJkjjzySL7xjW/UL5sxYwb33HMPN910U31Yc/vtt/PUU0/xwAMPUFdXx4MPPsj48eOX2t+//vUvNt98cz75yU8yZMiQ+oDk1ltv5a9//Sv3338/Dz/8MP/93/8NwJFHHsnJJ5/Mww8/zL/+9S822mijJbb3u9/9ji5dujBlyhS+//3v8+CDDwIwa9YsfvSjHzFu3DgeeughBg4cyC9+8Yv6x62//vo89NBDnHTSSZx//vn06tWLr33ta/VVPrvttlujf6/TTz+dvfbaiwMOOIALLriAN954A4A//vGPdO3alYkTJzJx4kR+//vf89xzz3H44Ydz9dVXA/D+++/zj3/8gwMPPLDJ9QEeeughfvWrXzF16tQl9j1ixAh222036urqOP300zn++OMZM2YMAFOnTuXdd9+lf//+TfxPf7gMgSRJkiRJWsWsu+66HHXUUYwcOXKJ+++77z6+/OUvA/CVr3yFe+65p37ZIYccQrt27dhmm2149dVXgRwC3X777Wy33XYMGDCAJ554gqeeemqp/Y0dO5YjjjgCyFUylS5h48aN49hjj6VLly4A9OjRg7lz5/Lyyy/zuc99DoA111yzfnnF+PHj+a//+i8A+vXrR79+/QCYMGECjz/+OLvssgu1tbX86U9/4vnnn69/3Oc//3kAtt9+e6ZNm9biv9exxx7LlClT+MIXvsCdd97JjjvuyHvvvcftt9/OpZdeSm1tLTvssAOzZ8/mqaee4oADDuCOO+7gvffe49Zbb2X33Xenc+fOTa4PMHjwYDbffPNm2/KFL3yBm266ifnz5zN69GiOOeaYFj+PD1qHtm6AJEmSJEla2je/+U0GDBjAscce26L1O3XqVP/vlFL97f/8z//w1a9+tcnHLVy4kOuuu46//vWvnHvuuaSUmD17NnPnzl25J9CIlBL77rtvk+MOVZ5D+/bt68feaamNN96Y4447juOOO46amhoeffRRUkpceOGF7LfffkutP2TIEG677Tauuuqq+gCsqfXvvPNO1lprrRa1o0uXLuy777789a9/5eqrr66vgloVWAkkSZIkSdIqqEePHnzxi1/kj3/8Y/19O++8M1deeSUAl19+eZPdoyr2228/Ro8eXT/uzssvv8xrr70GwN57783LL7/MP/7xD/r168eLL77ItGnTeP755zn00EO5/vrr2Xfffbnkkkt45513AHj99ddZZ5112HTTTbnhhhsAeO+99+qXV+y+++71gyU/+uijTJ48GYAdd9yRe++9l6effhqAt99+e6nuVQ2ts846zQZSf/vb35g/fz4Ar7zyCrNnz2aTTTZhv/3243e/+139sqlTp/L2228DcPjhh3PJJZdw9913s//++9f/vZpaf3naN2zYML7xjW8waNAgunfvvszHf5gMgSRJkiRJWkV9+9vfXmKWsAsvvJBLLrmEfv36cdlll/GrX/1qmY8fOnQoX/7yl9lpp53o27cvhx12GHPnzmXRokU8/fTT9OjRg7Fjx9Z37ao49NBDGTt2LPvvvz8HHXQQAwcOpLa2lvPPPx+Ayy67jJEjR9KvXz923nlnXnnllSUef9JJJ/HWW2/Rp08fvve977H99tsD0LNnT8aMGcOXvvQl+vXrx0477cQTTzyxzOfw2c9+luuvv36ZA0Pffvvt1NTU0L9/f/bbbz/OO+88NtxwQ4YNG8Y222zDgAEDqKmp4atf/Wp9hdHQoUO566672GeffVhjjTUAlrl+U/r160f79u3p379//WDe22+/Peuuu26Lq7g+LFEpEfuwDRw4ME2aNKlN9i1JkiRJ0sqaMmUKffr0aetmrJBHH32U0aNHLzEos1rP9OnTGTJkCE888QTt2n1w9TeNvQYj4sGU0sDG1m+2JRExOiJei4hHl7HOkIioi4jHIuKu5W61JEmSJEn60NTU1BgAfUAuvfRSdthhB84999wPNABaES0ZGHoM8Gvg0sYWRkQ34LfA/imlFyJig1ZrnSRJkiRJUuHcc8/lmmuuWeK+L3zhC3z3u99toxYt7aijjuKoo45q62Y0qtkQKKU0PiJ6LWOVLwN/SSm9UKz/Wiu1TZIkSZIkqd53v/vdVSrwWd20Rl3SVkD3iLgzIh6MiCbjrog4MSImRcSkmTNntsKuJUmSJEmS1BKtEQJ1ALYHDgT2A/43IrZqbMWU0qiU0sCU0sCePXu2wq4lSZIkSZLUEi0ZE6g5LwGzU0pvA29HxHigPzC1FbYtSZIkSZKkVtAaIdBfgV9HRAdgDWAH4IJW2O5qre+f+rZ1E7QSHjn6kbZugiRJkqTVTK+zb27V7U0bcWCrbk9qyRTxY4H7gN4R8VJEHB8RX4uIrwGklKYAfwMmAw8Af0gpNTmdvCRJkiRJah3z5s1jjz324OGHH6a2tpba2lp69OjB5ptvTm1tLfvss0+jj/vxj3/cou336tWLWbNmtWaT6w0bNozHH398qfvHjBnDKaecskLbfOONN/jtb3+7wm065phj6v92tbW1jBw5ssl1x4wZw/Tp01u0zWuvvRaAI444gqeeemqF27eyWjI72JdasM55wHmt0iJJkiRJktQio0eP5vOf/zz9+/enrq4OyKHDZz7zGQ477LAmH/fjH/+Y73znOx9SKxv3hz/8odW3WQmBvv71r6/wNs4777xl/u0qxowZQ01NDRtvvHGLt33SSSfxs5/9jN///vcr3L6V0RoDQ0uSJEmSpDZw+eWXc/DBBze5fOzYsfTt25eamhrOOussAM4++2zmzZtHbW0tRx55JACHHHII22+/Pdtuuy2jRo1qdr8nnXQSAwcOZNttt+Wcc86pv3/ixInsvPPO9O/fn8GDBzN37lwWLlzIGWecQU1NDf369ePCCy8EYMiQIUyaNAmASy65hK222orBgwdz77331m9v5syZHHrooQwaNIhBgwbVLxs+fDjHHXccQ4YMYYsttqiv2Dn77LN55plnqK2t5cwzz2TGjBnsvvvu1NbWUlNTw9133708f14AfvCDHzBo0CBqamo48cQTSSlx7bXXMmnSJI488khqa2uZN29eo+s1tNtuuzFu3DgWLFiw3O1oDYZAkiRJkiStht5//32effZZevXq1ejy6dOnc9ZZZ/HPf/6Turo6Jk6cyA033MCIESPo3LkzdXV1XH755UCuKHrwwQeZNGkSI0eOZPbs2cvc97nnnsukSZOYPHkyd911F5MnT+b999/n8MMP51e/+hUPP/ww48aNo3PnzowaNYpp06ZRV1fH5MmT64OnihkzZnDOOedw7733cs899yzRRey0007j9NNPZ+LEiVx33XUMGzasftkTTzzBbbfdxgMPPMD3v/995s+fz4gRI/jkJz9JXV0d5513HldccQX77bcfdXV19V3mmnPmmWfWdwd75JFHOOWUU5g4cSKPPvoo8+bN46abbuKwww5j4MCBXH755dTV1dG5c+dG12uoXbt2fOpTn+Lhhx9uth0fhNYYGFqSJEmSJH3IZs2aRbdu3ZpcPnHiRIYMGULPnj0BOPLIIxk/fjyHHHLIUuuOHDmS66+/HoAXX3yRp556ivXWW6/JbV999dWMGjWKBQsWMGPGDB5//HEigo022ohBgwYBsO666wIwbtw4vva1r9GhQ44gevToscS27r///iXaefjhhzN16tT6x1aHQnPmzOGtt94C4MADD6RTp0506tSJDTbYgFdffXWpdg4aNIjjjjuO+fPnc8ghh7QoBGrYHey6667jZz/7Ge+88w6vv/462267LZ/97GeXetwdd9zRovU22GADpk+fzvbbb99sW1qbIZAkSZIkSauhzp078+677670du68807GjRvHfffdR5cuXRgyZMgyt/vcc89x/vnnM3HiRLp3784xxxzTKu1ozKJFi5gwYQJrrrnmUss6depU/+/27ds32sVq9913Z/z48dx8880cc8wxfOtb3+Koo45q8f7fffddvv71rzNp0iQ222wzhg8f3uhzbel6lXU7d+7c4ja0JkMgSZIkSZJawYc9pXv37t1ZuHAh7777bqMhyeDBg/nGN77BrFmz6N69O2PHjuXUU08FoGPHjsyfP5+OHTvy5ptv0r17d7p06cITTzzBhAkTlrnfOXPmsNZaa9G1a1deffVVbr31VoYMGULv3r2ZMWMGEydOZNCgQcydO5fOnTuz7777cvHFF7PnnnvSoUMHXn/99SWqgXbYYQdOO+00Zs+ezbrrrss111xD//79ARg6dCgXXnghZ555JgB1dXXLrOZZZ511mDt3bv3vzz//PJtuuiknnHAC7733Hg899BBHHXUURx11FKeccgqDBw9e5nOtBDnrr78+b731Ftdee219lVD1vpa1XkNTp06lpqZmmfv9oBgCSZIkSZK0mho6dCj33HNPo1PBb7TRRowYMYI999yTlBIHHnhg/SDSJ554Iv369WPAgAGMHj2aiy66iD59+tC7d2923HHHZe6zf//+bLfddmy99dZsttlm7LLLLgCsscYaXHXVVZx66qnMmzePzp07M27cOIYNG8bUqVPp168fHTt25IQTTlhiCviNNtqI4cOHs9NOO9GtW7clQp6RI0dy8skn069fPxYsWMDuu+/ORRdd1GTb1ltvPXbZZRdqamo44IADqKmp4bzzzqNjx46svfbaXHrppQBMnjy5RbN6devWjRNOOIGamho23HDD+q5ukGdh+9rXvkbnzp257777mlyv2quvvkrnzp3ZcMMNm933ByEaG636wzBw4MBUGQX8o6jvn/q2dRO0Eh45+pG2boIkSZKkVdyUKVPo06dPm7bhoYce4oILLuCyyy5r03asTubMmcPxxx/PNddc86Hv+4ILLmDdddfl+OOPb5XtNfYajIgHU0oDG1vf2cEkSZIkSVpNDRgwgD333JOFCxe2dVNWG5UuZ22hW7duHH300W2yb7A7mCRJkiRJq7XjjjuurZugFjr22GPbdP9WAkmSJEmSJJWAIZAkSZIkSVIJGAJJkiRJkiSVgGMCSZIkSZLUGoZ3beXtvdm621PpWQkkSZIkSdJqat68eeyxxx48/PDD1NbWUltbS48ePdh8882pra1ln332afRxP/7xj1u0/V69ejFr1qzWbHK9YcOG8fjjjy91/5gxYzjllFNWaJtvvPEGv/3tb1e4TRMmTGCHHXagtraWPn36MHz48OV6/PTp0znssMNWeP+NOeOMM/jnP//ZKtsyBJIkSZIkaTU1evRoPv/5z9O/f3/q6uqoq6vjoIMO4rzzzqOuro5x48Y1+riWhkAfpD/84Q9ss802rbrNlQ2Bjj76aEaNGkVdXR2PPvooX/ziF1v82AULFrDxxhtz7bXXrvD+G3PqqacyYsSIVtmWIZAkSZIkSaupyy+/nIMPPrjJ5WPHjqVv377U1NRw1llnAXD22Wczb948amtrOfLIIwE45JBD2H777dl2220ZNWpUs/s96aSTGDhwINtuuy3nnHNO/f0TJ05k5513pn///gwePJi5c+eycOFCzjjjDGpqaujXrx8XXnghAEOGDGHSpEkAXHLJJWy11VYMHjyYe++9t357M2fO5NBDD2XQoEEMGjSoftnw4cM57rjjGDJkCFtssQUjR46sf27PPPMMtbW1nHnmmcyYMYPdd9+d2tpaampquPvuu5f5vF577TU22mgjANq3b18fUr399tscd9xxDB48mO22246//vWvQK5aOuigg9hrr73Ye++9mTZtGjU1NQC8++67HHvssfTt25ftttuOO+64o/4x1ZVOn/nMZ7jzzjtZuHAhxxxzDDU1NfTt25cLLrgAgE984hPMnj2bV155pdn/l+Y4JpAkSZIkSauh999/n2effZZevXo1unz69OmcddZZPPjgg3Tv3p2hQ4dyww03MGLECH79619TV1dXv+7o0aPp0aMH8+bNY9CgQRx66KGst956Te773HPPpUePHixcuJC9996byZMns/XWW3P44Ydz1VVXMWjQIObMmUPnzp0ZNWoU06ZNo66ujg4dOvD6668vsa0ZM2Zwzjnn8OCDD9K1a1f23HNPtttuOwBOO+00Tj/9dHbddVdeeOEF9ttvP6ZMmQLAE088wR133MHcuXPp3bs3J510EiNGjODRRx+tf24///nP2W+//fjud7/LwoULeeedd5b5Nz399NPp3bs3Q4YMYf/99+foo49mzTXX5Nxzz2WvvfZi9OjRvPHGGwwePLi+q91DDz3E5MmT6dGjB9OmTavf1m9+8xsigkceeYQnnniCoUOHMnXq1Cb3XVdXx8svv8yjjz4K5KqmigEDBnDvvfdy6KGHLrP9zTEEkiRJkiRpNTRr1iy6devW5PKJEycyZMgQevbsCcCRRx7J+PHjOeSQQ5Zad+TIkVx//fUAvPjiizz11FPLDIGuvvpqRo0axYIFC5gxYwaPP/44EcFGG23EoEGDAFh33XUBGDduHF/72tfo0CFHED169FhiW/fff/8S7Tz88MPrw5Jx48YtMW7QnDlzeOuttwA48MAD6dSpE506dWKDDTbg1VdfXaqdgwYN4rjjjmP+/Pkccsgh1NbWNvmcAL73ve9x5JFHcvvtt3PFFVcwduxY7rzzTm6//XZuvPFGzj//fCBX+bzwwgsA7Lvvvks9J4B77rmHU089FYCtt96aT3ziE8sMgbbYYgueffZZTj31VA488ECGDh1av2yDDTZg+vTpy2x7S9gdTJIkSZKk1VDnzp159913V3o7d955J+PGjeO+++7j4YcfZrvttlvmdp977jnOP/98/vGPfzB58mQOPPDAVmlHYxYtWsSECRPqxzt6+eWXWXvttQHo1KlT/Xrt27dnwYIFSz1+9913Z/z48WyyySYcc8wxXHrppc3u85Of/CQnnXQS//jHP3j44YeZPXs2KSWuu+66+na88MIL9OnTB4C11lpruZ5Thw4dWLRoUf3vlb9d9+7defjhhxkyZAgXXXQRw4YNW2Kdzp07L9d+GmMIJEmSJElSaxj+Zuv+NKN79+4sXLiwyQBm8ODB3HXXXcyaNYuFCxcyduxY9thjDwA6duzI/PnzAXjzzTfp3r07Xbp04YknnmDChAnL3O+cOXNYa6216Nq1K6+++iq33norAL1792bGjBlMnDgRgLlz57JgwQL23XdfLr744vqQpmF3sB122IG77rqL2bNnM3/+fK655pr6ZUOHDq0fQwhYogtbY9ZZZx3mzp1b//vzzz/Pxz72MU444QSGDRvGQw89BMBRRx3FAw88sNTjb775ZlJKADz11FO0b9+ebt26sd9++3HhhRfWL/v3v/+9zHYA7Lbbblx++eUATJ06lRdeeIHevXvTq1cv6urqWLRoES+++GJ9O2bNmsWiRYs49NBD+dGPflTf1srjK2MNrQy7g0mSJEmStJoaOnQo99xzT6NTwW+00UaMGDGCPffck5QSBx54YP0g0ieeeCL9+vVjwIABjB49mosuuog+ffrQu3dvdtxxx2Xus3///my33XZsvfXWbLbZZuyyyy4ArLHGGlx11VWceuqpzJs3j86dOzNu3DiGDRvG1KlT6devHx07duSEE05YYmDkjTbaiOHDh7PTTjvRrVu3JbpsjRw5kpNPPpl+/fqxYMECdt99dy666KIm27beeuuxyy67UFNTwwEHHEBNTQ3nnXceHTt2ZO21166vBJo8eTIbb7zxUo+/7LLLOP300+nSpQsdOnTg8ssvp3379vzv//4v3/zmN+nXrx+LFi1i880356abblrm3+nrX/86J510En379qVDhw6MGTOGTp06scsuu7D55puzzTbb0KdPHwYMGADAyy+/zLHHHltfJfSTn/wEgPnz5/P0008zcODAZe6vJaKSYn3YBg4cmCqjgH8U9f1T37ZuglbCI0c/0tZNkCRJkrSKmzJlSn2XoLby0EMPccEFF3DZZZe1aTtWJ3PmzOH4449fouJoVXb99dfz0EMP8cMf/nCpZY29BiPiwZRSo4mR3cEkSZIkSVpNDRgwgD333JOFCxe2dVNWG+uuu+5qEwABLFiwgG9/+9utsi27g0mSJEmStBo77rjj2roJ+gB94QtfaLVtWQkkSZIkSZJUAoZAkiRJkiRJJWAIJEmSJEmSVAKOCSRJkiRJUito7VmiWzJr8bx589h///0ZOXIkRx99NAAvvPACXbt2pWvXrqy//vqMGzduqcf9+Mc/5jvf+U6z2+/VqxeTJk1i/fXXX/4n0Ixhw4bxrW99i2222WaJ+8eMGcOkSZP49a9/vdzbfOONN7jiiiv4+te/vkJtmjBhAqeddhrvvfce7733HocffjjDhw9fok0XXXQRXbp04aijjmpyO8t6DtV/+/fff5999tmHf/7zn3To8MFHNFYCSZIkSZK0mho9ejSf//zn6d+/P3V1ddTV1XHQQQdx3nnnUVdX12gABDmIaGt/+MMflgqAVtYbb7zBb3/72xV+/NFHH82oUaOoq6vj0Ucf5Ytf/OJS63zta19bZgDUnOq//RprrMHee+/NVVddtcLbWx6GQJIkSZIkraYuv/xyDj744CaXjx07lr59+1JTU8NZZ50FwNlnn828efOora3lyCOPBOCQQw5h++23Z9ttt2XUqFHN7vekk05i4MCBbLvttpxzzjn190+cOJGdd96Z/v37M3jwYObOncvChQs544wzqKmpoV+/flx44YUADBkyhEmTJgFwySWXsNVWWzF48GDuvffe+u3NnDmTQw89lEGDBjFo0KD6ZcOHD+e4445jyJAhbLHFFowcObL+uT3zzDPU1tZy5plnMmPGDHbffXdqa2upqanh7rvvXubzeu2119hoo40AaN++faMh1fDhwzn//PPrn2+/fv3q91dTU1O/3vTp09l///3Zcsst+e///u9l/u0vv/zyZv/mrcHuYJIkSZIkrYbef/99nn32WXr16tXo8unTp3PWWWfx4IMP0r17d4YOHcoNN9zAiBEj+PWvf01dXV39uqNHj6ZHjx7MmzePQYMGceihh7Leeus1ue9zzz2XHj16sHDhQvbee28mT57M1ltvzeGHH85VV13FoEGDmDNnDp07d2bUqFFMmzaNuro6OnTowOuvv77EtmbMmME555zDgw8+SNeuXdlzzz3ZbrvtADjttNM4/fTT2XXXXXnhhRfYb7/9mDJlCgBPPPEEd9xxB3PnzqV3796cdNJJjBgxgkcffbT+uf385z9nv/3247vf/S4LFy7knXfeWebf9PTTT6d3794MGTKE/fffn6OPPpo111yzyfWPPfZYfv/737PTTjtx9tlnL7Gsrq6Of//733Tq1InevXtz6qmnNvq3r6mpYeLEictsV2uxEkiSJEmSpNXQrFmz6NatW5PLJ06cyJAhQ+jZsycdOnTgyCOPZPz48Y2uO3LkSPr378+OO+7Iiy++yFNPPbXMfV999dUMGDCA7bbbjscee4zHH3+cJ598ko022ohBgwYBsO6669KhQwfGjRvHV7/61foxb3r06LHEtu6///76dq6xxhocfvjh9cvGjRvHKaecQm1tLQcddBBz5szhrbfeAuDAAw+kU6dOrL/++mywwQa8+uqrS7Vz0KBBXHLJJQwfPpxHHnmEddZZZ5nP63vf+x6TJk1i6NChXHHFFey///5NrvvGG28wd+5cdtppJwC+/OUvL7F87733pmvXrqy55ppss802PP/8841up3379qyxxhrMnTt3mW1rDVYCSZIkSZK0GurcuTPvvvvuSm/nzjvvZNy4cdx333106dKFIUOGLHO7zz33HOeffz4TJ06ke/fuHHPMMa3SjsYsWrSICRMmNFqN06lTp/p/t2/fngULFiy1zu6778748eO5+eabOeaYY/jWt77V7Hg+n/zkJznppJM44YQT6NmzJ7Nnz16htrekfRXvvffeMiuOWouVQJIkSZIkrYa6d+/OwoULmwxgBg8ezF133cWsWbNYuHAhY8eOZY899gCgY8eOzJ8/H4A333yT7t2706VLF5544gkmTJiwzP3OmTOHtdZai65du/Lqq69y6623AtC7d29mzJhR37Vp7ty5LFiwgH333ZeLL764PgRp2B1shx124K677mL27NnMnz+fa665pn7Z0KFD68cQApboRtWYddZZZ4mKmueff56PfexjnHDCCQwbNoyHHnoIgKOOOooHHnhgqcfffPPNpJQAeOqpp2jfvn2T1VbdunVjnXXW4f777wfgyiuvXGbbKqr/9gCzZ89m/fXXp2PHji16/MqwEkiSJEmSpFbQkindW9vQoUO555572GeffZZattFGGzFixAj23HNPUkoceOCB9YNIn3jiifTr148BAwYwevRoLrroIvr06UPv3r3Zcccdl7nP/v37s91227H11luz2WabscsuuwB5pqurrrqKU089lXnz5tG5c2fGjRvHsGHDmDp1Kv369aNjx46ccMIJnHLKKUu0c/jw4ey0005069aN2tra+mUjR47k5JNPpl+/fixYsIDdd9+diy66qMm2rbfeeuyyyy7U1NRwwAEHUFNTw3nnnUfHjh1Ze+21ufTSSwGYPHkyG2+88VKPv+yyyzj99NPp0qULHTp04PLLL6d9+/ZN7u+Pf/wjJ5xwAu3atWOPPfaga9euy/zbwZJ/+8svv5w77riDAw88sNnHtYaoJFwftoEDB6bKKOAfRX3/1Letm6CV0BYf3pIkSZJWL1OmTKFPnz5t2oaHHnqICy64gMsuu6xN27E6mTNnDscff/wSFUcr6q233mLttdcGYMSIEcyYMYNf/epXy7WNz3/+84wYMYKtttpqufff2GswIh5MKQ1sbH0rgSRJkiRJWk0NGDCAPffck4ULFy6zYkWLrbvuuq0SAEHuPvaTn/yEBQsW8IlPfIIxY8Ys1+Pff/99DjnkkBUKgFaEIZAkSZIkSaux4447rq2bUFqHH374ErOZLa811lij2YGqW5MDQ0uSJEmStILaaogVaUVee4ZAkiRJkiStgDXXXJPZs2cbBOlDl1Ji9uzZyz2tvN3BJEmSJElaAZtuuikvvfQSM2fObOumqITWXHNNNt100+V6jCGQJEmSJEkroGPHjmy++eZt3QypxewOJkmSJEmSVAKGQJIkSZIkSSVgCCRJkiRJklQChkCSJEmSJEklYAgkSZIkSZJUAoZAkiRJkiRJJWAIJEmSJEmSVAKGQJIkSZIkSSVgCCRJkiRJklQChkCSJEmSJEklYAgkSZIkSZJUAoZAkiRJkiRJJWAIJEmSJEmSVAKGQJIkSZIkSSXQbAgUEaMj4rWIeLSZ9QZFxIKIOKz1midJkiRJkqTW0JJKoDHA/staISLaAz8Fbm+FNkmSJEmSJKmVNRsCpZTGA683s9qpwHXAa63RKEmSJEmSJLWulR4TKCI2AT4H/G7lmyNJkiRJkqQPQmsMDP1L4KyU0qLmVoyIEyNiUkRMmjlzZivsWpIkSZIkSS3RoRW2MRC4MiIA1gc+HRELUko3NFwxpTQKGAUwcODA1Ar7liRJkiRJUgusdAiUUtq88u+IGAPc1FgAJEmSJEmSpLbTbAgUEWOBIcD6EfEScA7QESCldNEH2jpJkiRJkiS1imZDoJTSl1q6sZTSMSvVGkmSJEmSJH0gWmNgaEmSJEmSJK3iDIEkSZIkSZJKwBBIkiRJkiSpBAyBJEmSJEmSSsAQSJIkSZIkqQQMgSRJkiRJkkrAEEiSJEmSJKkEDIEkSZIkSZJKwBBIkiRJkiSpBAyBJEmSJEmSSsAQSJIkSZIkqQQMgSRJkiRJkkrAEEiSJEmSJKkEDIEkSZIkSZJKwBBIkiRJkiSpBAyBJEmSJEmSSsAQSJIkSZIkqQQMgSRJkiRJkkrAEEiSJEmSJKkEDIEkSZIkSZJKwBBIkiRJkiSpBAyBJEmSJEmSSsAQSJIkSZIkqQQMgSRJkiRJkkrAEEiSJEmSJKkEDIEkSZIkSZJKwBBIkiRJkiSpBAyBJEmSJEmSSsAQSJIkSZIkqQQMgSRJkiRJkkrAEEiSJEmSJKkEDIEkSZIkSZJKwBBIkiRJkiSpBAyBJEmSJEmSSsAQSJIkSZIkqQQMgSRJkiRJkkrAEEiSJEmSJKkEDIEkSZIkSZJKwBBIkiRJkiSpBAyBJEmSJEmSSsAQSJIkSZIkqQQMgSRJkiRJkkrAEEiSJEmSJKkEDIEkSZIkSZJKwBBIkiRJkiSpBAyBJEmSJEmSSsAQSJIkSZIkqQQMgSRJkiRJkkrAEEiSJEmSJKkEDIEkSZIkSZJKwBBIkiRJkiSpBAyBJEmSJEmSSsAQSJIkSZIkqQQMgSRJkiRJkkrAEEiSJEmSJKkEDIEkSZIkSZJKoNkQKCJGR8RrEfFoE8uPjIjJEfFIRPwrIvq3fjMlSZIkSZK0MlpSCTQG2H8Zy58D9kgp9QV+CIxqhXZJkiRJkiSpFXVoboWU0viI6LWM5f+q+nUCsGkrtEuSJEmSJEmtqLXHBDoeuLWVtylJkiRJkqSV1GwlUEtFxJ7kEGjXZaxzInAiwMc//vHW2rUkSZIkSZKa0SqVQBHRD/gDcHBKaXZT66WURqWUBqaUBvbs2bM1di1JkiRJkqQWWOkQKCI+DvwF+EpKaerKN0mSJEmSJEmtrdnuYBExFhgCrB8RLwHnAB0BUkoXAd8D1gN+GxEAC1JKAz+oBkuSJEmSJGn5tWR2sC81s3wYMKzVWiRJkiRJkqRW19qzg0mSJEmSJGkVZAgkSZIkSZJUAoZAkiRJkiRJJWAIJEmSJEmSVAKGQJIkSZIkSSVgCCRJkiRJklQChkCSJEmSJEklYAgkSZIkSZJUAoZAkiRJkiRJJWAIJEmSJEmSVAKGQJIkSZIkSSVgCCRJkiRJklQChkCSJEmSJEklYAgkSZIkSZJUAoZAkiRJkiRJJWAIJEmSJEmSVAKGQJIkSZIkSSVgCCRJkiRJklQChkCSJEmSJEklYAgkSZIkSZJUAoZAkiRJkiRJJWAIJEmSJEmSVAKGQJIkSZIkSSVgCCRJkiRJklQChkCSJEmSJEklYAgkSZIkSZJUAoZAkiRJkiRJJWAIJEmSJEmSVAKGQJIkSZIkSSVgCCRJkiRJklQChkCSJEmSJEklYAgkSZIkSZJUAoZAkiRJkiRJJWAIJEmSJEmSVAKGQJIkSZIkSSVgCCRJkiRJklQChkCSJEmSJEklYAgkSZIkSZJUAoZAkiRJkiRJJWAIJEmSJEmSVAKGQJIkSZIkSSVgCCRJkiRJklQChkCSJEmSJEklYAgkSZIkSZJUAoZAkiRJkiRJJWAIJEmSJEmSVAKGQJIkSZIkSSVgCCRJkiRJklQChkCSJEmSJEklYAgkSZIkSZJUAoZAkiRJkiRJJWAIJEmSJEmSVAKGQJIkSZIkSSVgCCRJkiRJklQChkCSJEmSJEklYAgkSZIkSZJUAs2GQBExOiJei4hHm1geETEyIp6OiMkRMaD1mylJkiRJkqSV0ZJKoDHA/stYfgCwZfFzIvC7lW+WJEmSJEmSWlOzIVBKaTzw+jJWORi4NGUTgG4RsVFrNVCSJEmSJEkrrzXGBNoEeLHq95eK+yRJkiRJkrSK+FAHho6IEyNiUkRMmjlz5oe5a0mSJEmSpFJrjRDoZWCzqt83Le5bSkppVEppYEppYM+ePVth15IkSZIkSWqJ1giBbgSOKmYJ2xF4M6U0oxW2K0mSJEmSpFbSobkVImIsMARYPyJeAs4BOgKklC4CbgE+DTwNvAMc+0E1VpIkSZIkSSum2RAopfSlZpYn4ORWa5EkSZIkSZJa3Yc6MLQkSZIkSZLahiGQJEmSJElSCRgCSZIkSZIklYAhkCRJkiRJUgkYAkmSJEmSJJWAIZAkSZIkSVIJGAJJkiRJkiSVgCGQJEmSJElSCRgCSZIkSZIklYAhkCRJkiRJUgkYAkmSJEmSJJWAIZAkSZIkSVIJGAJJkiRJkiSVgCGQJEmSJElSCRgCSZIkSZIklYAhkCRJkiRJUgkYAkmSJEmSJJWAIZAkSZIkSVIJGAJJkiRJkiSVgCGQJEmSJElSCRgCSZIkSZIklYAhkCRJkiRJUgkYAkmSJEmSJJWAIZAkSZIkSVIJGAJJkiRJkiSVgCGQJEmSJElSCRgCSZIkSZIklYAhkCRJkiRJUgkYAkmSJEmSJJWAIZAkSZIkSVIJGAJJkiRJkiSVgCGQJEmSJElSCRgCSZIkSZIklYAhkCRJkiRJUgkYAkmSJEmSJJWAIZAkSZIkSVIJGAJJkiRJkiSVgCGQJEmSJElSCRgCSZIkSZIklYAhkCRJkiRJUgkYAkmSJEmSJJWAIZAkSZIkSVIJGAJJkiRJkiSVgCGQJEmSJElSCRgCSZIkSZIklYAhkCRJkiRJUgkYAkmSJEmSJJWAIZAkSZIkSVIJGAJJkiRJkiSVgCGQJEmSJElSCRgCSZIkSZIklYAhkCRJkiRJUgkYAkmSJEmSJJWAIZAkSZIkSVIJGAJJkiRJkiSVgCGQJEmSJElSCbQoBIqI/SPiyYh4OiLObmT5xyPijoj4d0RMjohPt35TJUmSJEmStKKaDYEioj3wG+AAYBvgSxGxTYPV/h9wdUppO+AI4Let3VBJkiRJkiStuJZUAg0Gnk4pPZtSeh+4Eji4wToJWLf4d1dgeus1UZIkSZIkSSurQwvW2QR4ser3l4AdGqwzHLg9Ik4F1gL2aZXWSZIkSZIkqVW01sDQXwLGpJQ2BT4NXBYRS207Ik6MiEkRMWnmzJmttGtJkiRJkiQ1pyUh0MvAZlW/b1rcV+144GqAlNJ9wJrA+g03lFIalVIamFIa2LNnzxVrsSRJkiRJkpZbS0KgicCWEbF5RKxBHvj5xgbrvADsDRARfcghkKU+kiRJkiRJq4hmQ6CU0gLgFOA2YAp5FrDHIuIHEXFQsdq3gRMi4mFgLHBMSil9UI2WJEmSJEnS8mnJwNCklG4Bbmlw3/eq/v04sEvrNk2SJEmSJEmtpbUGhpYkSZIkSdIqzBBIkiRJkiSpBAyBJEmSJEmSSsAQSJIkSZIkqQQMgSRJkiRJkkrAEEiSJEmSJKkEDIEkSZIkSZJKwBBIkiRJkiSpBAyBJEmSJEmSSsAQSJIkSZIkqQQMgSRJkiRJkkrAEEiSJEmSJKkEDIEkSZIkSZJKwBBIkiRJkiSpBAyBJEmSJEmSSsAQSJIkSZIkqQQMgSRJkiRJkkrAEEiSJEmSJKkEDIEkSZIkSZJKwBBIkiRJkiSpBAyBJEmSJEmSSsAQSJIkSZIkqQQMgSRJkiRJkkrAEEiSJEmSJKkEDIEkSZIkSZJKwBBIkiRJkiSpBAyBJEmSJEmSSsAQSJIkSZIkqQQMgSRJkiRJkkrAEEiSJEmSJKkEDIEkSZIkSZJKwBBIkiRJkiSpBAyBJEmSJEmSSsAQSJIkSZIkqQQMgSRJkiRJkkrAEEiSJEmSJKkEDIEkSZIkSZJKwBBIkiRJkiSpBAyBJEmSJEmSSsAQSJIkSZIkqQQMgSRJkiRJkkrAEEiSJEmSJKkEDIEkSZIkSZJKwBBIkiRJkiSpBAyBJEmSJEmSSsAQSJIkSZIkqQQMgSRJkiRJkkrAEEiSJEmSJKkEDIEkSZIkSZJKwBBIkiRJkiSpBDq0dQMkSZIkSVrd9P1T37ZuglbQI0c/0tZNaDNWAkmSJEmSJJWAIZAkSZIkSVIJGAJJkiRJkiSVgCGQJEmSJElSCRgCSZIkSZIklYAhkCRJkiRJUgm0KASKiP0j4smIeDoizm5inS9GxOMR8VhEXNG6zZQkSZIkSdLK6NDcChHRHvgNsC/wEjAxIm5MKT1etc6WwP8Au6SU/hMRG3xQDZYkSZIkSdLya0kl0GDg6ZTSsyml94ErgYMbrHMC8JuU0n8AUkqvtW4zJUmSJEmStDJaEgJtArxY9ftLxX3VtgK2ioh7I2JCROzf2IYi4sSImBQRk2bOnLliLZYkSZIkSdJya62BoTsAWwJDgC8Bv4+Ibg1XSimNSikNTCkN7NmzZyvtWpIkSZIkSc1pSQj0MrBZ1e+bFvdVewm4MaU0P6X0HDCVHApJkiRJkiRpFdCSEGgisGVEbB4RawBHADc2WOcGchUQEbE+uXvYs63XTEmSJEmSJK2MZkOglNIC4BTgNmAKcHVK6bGI+EFEHFSsdhswOyIeB+4Azkwpzf6gGi1JkiRJkqTl0+wU8QAppVuAWxrc972qfyfgW8WPJEmSJEmSVjGtNTC0JEmSJEmSVmGGQJIkSZIkSSVgCCRJkiRJklQChkCSJEmSJEklYAgkSZIkSZJUAoZAkiRJkiRJJWAIJEmSJEmSVAKGQJIkSZIkSSVgCCRJkiRJklQChkCSJEmSJEklYAgkSZIkSZJUAoZAkiRJkiRJJWAIJEmSJEmSVAKGQJIkSZIkSSVgCCRJkiRJklQChkCSJEmSJEklYAgkSZIkSZJUAoZAkiRJkiRJJWAIJEmSJEmSVAKGQJIkSZIkSSVgCCRJkiRJklQChkCSJEmSJEklYAgkSZIkSZJUAoZAkiRJkiRJJWAIJEmSJEmSVAKGQJIkSZIkSSVgCCRJkiRJklQChkCSJEmSJEklYAgkSZIkSZJUAoZAkiRJkiRJJWAIJEmSJEmSVAKGQJIkSZIkSSVgCCRJkiRJklQChkCSJEmSJEklYAgkSZIkSZJUAoZAkiRJkiRJJWAIJEmSJEmSVAKGQJIkSZIkSSVgCCRJkiRJklQChkCSJEmSJEklYAgkSZIkSZJUAoZAkiRJkiRJJWAIJEmSJEmSVAKGQJIkSZIkSSVgCCRJkiRJklQChkCSJEmSJEklYAgkSZIkSZJUAoZAkiRJkiRJJWAIJEmSJEmSVAKGQJIkSZIkSSVgCCRJkiRJklQChkCSJEmSJEklYAgkSZIkSZJUAoZAkiRJkiRJJWAIJEmSJEmSVAKGQJIkSZIkSSXQohAoIvaPiCcj4umIOHsZ6x0aESkiBrZeEyVJkiRJkrSymg2BIqI98BvgAGAb4EsRsU0j660DnAbc39qNlCRJkiRJ0sppSSXQYODplNKzKaX3gSuBgxtZ74fAT4F3W7F9kiRJkiRJagUtCYE2AV6s+v2l4r56ETEA2CyldPOyNhQRJ0bEpIiYNHPmzOVurCRJkiRJklbMSg8MHRHtgF8A325u3ZTSqJTSwJTSwJ49e67sriVJkiRJktRCLQmBXgY2q/p90+K+inWAGuDOiJgG7Ajc6ODQkiRJkiRJq46WhEATgS0jYvOIWAM4ArixsjCl9GZKaf2UUq+UUi9gAnBQSmnSB9JiSZIkSZIkLbdmQ6CU0gLgFOA2YApwdUrpsYj4QUQc9EE3UJIkSZIkSSuvQ0tWSindAtzS4L7vNbHukJVvliRJkiRJklrTSg8MLUmSJEmSpFWfIZAkSZIkSVIJGAJJkiRJkiSVgCGQJEmSJElSCRgCSZIkSZIklYAhkCRJkiRJUgkYAkmSJEmSJJWAIZAkSZIkSVIJGAJJkiRJkiSVgCGQJEmSJElSCRgCSZIkSZIklYAhkCRJkiRJUgkYAkmSJEmSJJWAIZAkSZIkSVIJGAJJkiRJkiSVgCGQJEmSJElSCRgCSZIkSZIklYAhkCRJkiRJUgkYAkmSJEmSJJWAIZAkSZIkSVIJGAJJkiRJkiSVgCGQJEmSJElSCRgCSZIkSZIklYAhkCRJkiRJUgkYAkmSJEmSJJWAIZAkSZIkSVIJGAJJkiRJkiSVgCGQJEmSJElSCRgCSZIkSZIklYAhkCRJkiRJUgkYAkmSJEmSJJWAIZAkSZIkSVIJGAJJkiRJkiSVgCGQJEmSJElSCRgCSZIkSZIklYAhkCRJkiRJUgkYAkmSJEmSJJWAIZAkSZIkSVIJGAJJkiRJkiSVgCGQJEmSJElSCRgCSZIkSZIklUCHtm7AR9Ujz73Q1k2QJEmSJEmqZyWQJEmSJElSCRgCSZIkSZIklYAhkCRJkiRJUgkYAkmSJEmSJJWAIZAkSZIkSVIJGAJJkiRJkiSVgCGQJEmSJElSCRgCSZIkSZIklYAhkCRJkiRJUgkYAkmSJEmSJJWAIZAkSZIkSVIJGAJJkiRJkiSVgCGQJEmSJElSCRgCSZIkSZIklUCLQqCI2D8inoyIpyPi7EaWfysiHo+IyRHxj4j4ROs3VZIkSZIkSSuq2RAoItoDvwEOALYBvhQR2zRY7d/AwJRSP+Ba4Get3VBJkiRJkiStuJZUAg0Gnk4pPZtSeh+4Eji4eoWU0h0ppXeKXycAm7ZuMyVJkiRJkrQyWhICbQK8WPX7S8V9TTkeuLWxBRFxYkRMiohJM2fObHkrJUmSJEmStFJadWDoiPgvYCBwXmPLU0qjUkoDU0oDe/bs2Zq7liRJkiRJ0jJ0aME6LwObVf2+aXHfEiJiH+C7wB4ppfdap3mSJEmSJElqDS2pBJoIbBkRm0fEGsARwI3VK0TEdsDFwEEppddav5mSJEmSJElaGc2GQCmlBcApwG3AFODqlNJjEfGDiDioWO08YG3gmoioi4gbm9icJEmSJEmS2kBLuoORUroFuKXBfd+r+vc+rdwuSZIkSZIktaJWHRhakiRJkiRJqyZDIEmSJEmSpBIwBJIkSZIkSSoBQyBJkiRJkqQSMASSJEmSJEkqAUMgSZIkSZKkEjAEkiRJkiRJKgFDIEmSJEmSpBIwBJIkSZIkSSoBQyBJkiRJkqQSMASSJEmSJEkqAUMgSZIkSZKkEjAEkiRJkiRJKgFDIEmSJEmSpBIwBJIkSZIkSSoBQyBJkiRJkqQSMASSJEmSJEkqAUMgSZIkSZKkEjAEkiRJkiRJKgFDIEmSJEmSpBIwBJIkSZIkSSoBQyBJkiRJkqQSMASSJEmSJEkqAUMgSZIkSZKkEjAEkiRJkiRJKgFDIEmSJEmSpBIwBJIkSZIkSSqBDm3dAEmSJEmSVjePPPdCWzdBWm5WAkmSJEmSJJWAIZAkSZIkSVIJGAJJkiRJkiSVgCGQJEmSJElSCRgCSZIkSZIklYAhkCRJkiRJUgkYAkmSJEmSJJWAIZAkSZIkSVIJGAJJkiRJkiSVgCGQJEmSJElSCRgCSZIkSZIklYAhkCRJkiRJUgkYAkmSJEmSJJWAIZAkSZIkSVIJGAJJkiRJkiSVgCGQJEmSJElSCRgCSZIkSZIklUCHtm6AJEla/fX9U9+2boJWwiNHP9LWTZAkSR8CK4EkSZIkSZJKwBBIkiRJkiSpBAyBJEmSJEmSSsAQSJIkSZIkqQQMgSRJkiRJkkrAEEiSJEmSJKkEDIEkSZIkSZJKoENbN+Cjqte7V7R1E7QSprV1AyRJkiRJamVWAkmSJEmSJJWAlUCSJEnSaqzvn/q2dRO0gh45+pG2boKkkmlRCBQR+wO/AtoDf0gpjWiwvBNwKbA9MBs4PKU0rXWbKkmSJEnSqsEhQFZf09q6AW2o2RAoItoDvwH2BV4CJkbEjSmlx6tWOx74T0rpUxFxBPBT4PAPosGSJGnV88hzL7R1EyRJktSMllQCDQaeTik9CxARVwIHA9Uh0MHA8OLf1wK/johIKaVWbKskNcuS+NWbZfGSJEnSB6clIdAmwItVv78E7NDUOimlBRHxJrAeMKt6pYg4ETix+PWtiHhyRRqtVcL6NPj//SiJn7Z1C6QmfbTfe8dEWzdBaspH+r3H933vaZX1kX7vedzTKu4j+/4rwfe9TzS14EMdGDqlNAoY9WHuUx+MiJiUUhrY1u2Qysb3ntQ2fO9JbcP3ntR2fP99NLVkiviXgc2qft+0uK/RdSKiA9CVPEC0JEmSJEmSVgEtCYEmAltGxOYRsQZwBHBjg3VuBI4u/n0Y8E/HA5IkSZIkSVp1NNsdrBjj5xTgNvIU8aNTSo9FxA+ASSmlG4E/ApdFxNPA6+SgSB9tduuT2obvPalt+N6T2obvPant+P77CAoLdiRJkiRJkj76WtIdTJIkSZIkSas5QyBJkiRJkqQSMASSJEmSJEkqAUMgSZIkSZKkEjAE0ociItpFRLOz0UmSJEkfpIhoHxHt27odktQWnB1MbSIiIvnik7Qaioh25OPnwrZui9SU4nXaDljo8VaSpKzq+LgopbSordvTFqwEUquIrMkrKhHRIyL+HhHXAnhCKml1UZws1EspLWoYADVcR2prxet0QUopRcRaEbFmW7dJ+rA1dW4aEb0jYmxEHPRht0nSh6vh99Sq4+Oisp6/lfJJq3VUl9KmbFlXxTcG9gamW34raXVSfZWo+NzbJyJ+HhGXRMRREbFxmU8k1HYiIpq6LyIGRMQvIuJRYDIwtni9dmnqsdLqqviSt9RrOqW0sPjc7lGsVzkH3RM4HNi6uN/Pb+kjquH31IjoHxE/iYg7gcsj4oiIWLftWvjhc4wWtVhV4LOw+rZYtglwEvAq8PuU0rsNHr5TcftocUBuV9byO0mrnmV9JkVEf2DblNIVwB+Bg4D5QA/gaODxiPhSSumRD63B+siJiI7AUODVlNKkJtZpB7RLKS2Axqtqi8qfzwC/BroAk4D3ge2Ag4F9gKOsyNVHSWOv5+K89SzgR8BfI+IrKaW3isUvFreV8Mf3g7QaW1ZX/YhYBzgBmAY8AVwFdAbmADuSA+GbIuLIlNLcD63RbcjUWwBERJeI6BURaze1TkppYeWNFRGdIuLLEXF3RPwN2BX4AvArYP+q7VaCxjUabq51n4EkLZ/i6nA7WLLap8E6awBjgT9HxDnkL+k/BA4EegNjgG2A30TEVsVjrLDQitgL+D/g9IhYq7EVKiXskC++RMSuEbFl8Xv74rYW+AvwNnAicFxK6ZCUUi9yiPlfxfHbc0CtVopJRprq3rV+RHwxIvpU3d0Z2Lz49w7AkVXLFhS3b4HDFEirq8o5V2Nd9at8DDgf+AZwG/A48F/AAcCngFuAzwD/LyK6f+CNXgVYCVRSxcnfXuQvMjsA65JDwdkRMRf4JXBXSum9qsf0BX4O/AII4DzgJXKZ+b3kk83rgXMi4oWU0kPAwmJfG5IPuHXgwVZS26sKtbuTqxXXBP5euQpUVAe9HxHjgF7A/wBHppSuq2wjIr5JrrI4Efgi+YpzO8BBo9UiVRMlPEMOHCfQxOsnIvoBxwKfB7oD75K/xG5RdfL7LfL53ZdSSpOrHrsWcBPwWfJFm3uB5z+I5yS1pkql5jLC+vbkgOcC4PfAV4tFbwPrA4+Qz3O/GhF/SSnNBHoW67xTbMMJS6RVWBH2tCN/jVxUua+ofl2D/L12J2AmcFlK6c2qhz8LPADsRj7GDkspvV617e+Tg6L/AsYDN38IT6lNGQKVTER8HfgSOfjpQA5xpgAvk78AbUp+A+0P/DwizkspvVY8fHNyGfkW5DfYWGAU8HxK6b2ImAmcDlwC/CQi9i8OqCkitgPaAzM+nGcqScsWEUOBM8knDvOLu9+OiP8FLkkpzSvuewA4DphOLiOu7h47JyIuJodAXyKHQHZ1VYtVffF8JqV0ZFPrRURv8hfcPsDtwKPAG8C6xaDP7xXb6g88RH4tfxzYllytNoB87N8AqCFfnDEE0iqv6gvfHuTK8wXkq/lTUkrvFQHoryJiGHBcRPwipfRk8eWwG/mz+xpyQHoicC6LP6c3KG4N76VVTES0rxqGJNHgPVq8x2vI3Z93ZHHPk4Mi4uyU0kMR0SGltKC4oDcYeDKl9Hpx3Hy/+HyZTO4i9jPyZ4whkD5yNgd2Af5KPpmcSQ5xXoPczYt8Nfvn5JK59uSDJsD9wMPkE8znUkpnVG+4qBr6U0TsT+5beVpEjE4pzQG6kgOgTh/s05P0UVRUFB4CkFL6S2Nj+EQjU2I3dXU3InYFLiaH3+eTS4O7AIeRTyY2Bv5fsfr9wJvkY+bzRRvqT0RSSv+OiJeBPhGxXkppdms9b5VH1RfW44EngZuL+4L8Or2CHN58A7iywVVOACLiE+TKhs2Ac4r1+5CPva8A48jB5+0ppbc/8CclNaO5LrnFOkeTKzG3Al4nj8f2E/I55/9LKb1crHoh8DvgjIj4TlHx8yqwCXA5eRDokyPiSuC54jFrFvs3AJJWMQ3Gn90Y+Bw5pJkGXEke2+snwHrkgPdlYF/gv4HvkM/pKtsYX9y3VrHt+vFri2KGSeTq2k9GxDof9bGBDIHK5xLg28DbKaVbqhcUSel7wGUR8RZwHbl09vyU0vSU0qsRMZUcAl1fPKb+i1jVv88hH6BHAK9GxPXkctxpKaWnHRRa0groBFwLPBYRN1bGRalWfK5UPo82JF/hfYLcXau6bLgn8FPy59JewKSq0OhK8hWgkyPiVymlmSmlpyLiEfJ4QOtSjCFRrF+5SvUE+YvGNsDdH8hfQKulIsRpDzTZnaVKf3JX6yvJJ6xzitfs58kDO/82pXRx1bbrB7UtXsPvALPIFT+fI3f5upgcKL3UoF0ei9VmmuviVbXeZ8hB/RvAKeQr9h2Lfx8NtIuIb6eUZpE/u68AjgDuJAc/7wObpZSei4ifkrtCnl0sWwS8aVcwqW1UjefT2MW6TuT369PAVOBGctd8yN8zK92auwE7Vi5sRMS9QF/ggIjYKqU0tXjMv8nnbz0joltK6Y0Gu1xAvuC3Jvkz5iPNQQHL56nidm9YckrMqsEm26eUrid3gegMfCYWD/B8X3FbKZ+NqscvKm6nkq+gLyAnsTuS37Spej1JaoniM2keMJHcZ7t3cX+74rYyJfZWkaf8fI58lfdW4P8i4isNNvkpcrfXX6SUJlYqMIrxVnYkf9noChwaecYmyNVAkMdTqUxH3B5YFHnWifbkSot5qPQiokNVhUNKKS1IKS2Kpge1rRxLnwXuIFc8bFC1yibF7VPF+pXuiIuKn8rxdSZFl0XgtJTS/imlixsJgNYnB5ZSq4qIbSLiyIg4NiL2bmq94v3QOSI+HRHfiojti3E96qd7Lz5/TwHWBk5OKf02pXRPSukOcpAznlwhelCxzZfJ3TnWAr5VVUU3LyK6pjyD41hyxfvngPcoulFWvQclrYSI2CUibiy6aTW2vH3VMaxyAaMx2wOXkt+z5xX3DQOGkN/zvYCvAA+klN6unJcVFT43kb/D7lN1LJ5JPo8cSO4eXZnoqHoSo48V677+Uf9MMAQqmZTSfIovUhHxqeIg3PBFXvm9Mvjp7izuxjWBfNDctZn9TCQPzNefXJ7bCfhX1RcqSWpWcaJQOUDfTq7eGVy1vFLdswkwEjgNeIw8QOhN5O4wF0XEl6o2u0Nx+0JE7Bl5cOffkMeMuIk8JtqL5G4EFRPIQfaREdG3OG+pdDvbnHxS8lJqYmpvlUsl9AGIiHUj4psR8QwwOiK6NrJ+5SR4Fvlq5dbk8fcqs2yuS379zSkqKBqbArdy7L4ZeIHcJXvvquXtI88Ceiy5a/fPW+npqsQiYo2I2CsiroiI/5AvIP6SPBPdRRGxWYP1Ny9C0mPIXTpuIlf6PEAe12eNqi+GW5ErMB9JKd3eIFx9ijwuZRdg36JqgCLo+TO5cm4/Fk8DXZnx5/fkoPTkYtmGxf1+J5JWQtUxaGfyTFv7NrZeWnK26W0iYu+I2LSR7Uwmn3ttTL5At1tK6YaU0qMppZuAPxTrPVFsN7F49ulHyBfmdqPo8lm4jRwqH1885r2qyvIhxe0tVdv7yLI7WDldBAwiHxyfJl/Bru5aUanUuaO4HcjiYOgR8hesXWHZfahTSpdHxF7kwVIBXk4pzbcEXVJLFZ8xlc+Z64Dvkk8GLmFxFxjIXzj2IQ9Of1VaPM7ZBuRy4R+RB9J9hMUnBMPJJcWdgdeAf5CrGG9LeSyzanXkcVp2Bf4QEZUx0T5F7mP+H/IXH5VA8UU0mjoGRsTu5DFMRgEfJ4+t9xI5XFyjsccApJTmRR6XYE2gb0TcURw3Xycfhzcvlr3ToC3174WU0p0R8RPy2Ch/joi/kEvc1wb6kccIegZfr1oJVedyXyZX37Qjjzf5IDnMnE2+aDi/WL8H+ar+QBYH9jcBd5HfE8OAY8ivzfOL3VRe5+9HMbhrg2Y8QA6StiaPg/V0cf8o8nHie+TX/VzyewDyl8rfAaOL39cGxwSSWkGQQ5h7yQHMnuQLcotXyGHtrsBR5KCoK/k76IsRcSkwojjmRUrprYh4jPxe/hv5c6Ad0L4oariN3CV0u6pdVM4JnyOf8+1Avnj4QnH/XcXtYcUFlr8Uv+8BfI18sfHalf1DrA4Mgcrp78XtZ8hXv5dIOqsCmifIb8ytyEERRbndJGBAROyYUprQWKhTdd+Pyf0q/4vF4dJHOlmV1DqKLiufIYfWT5APzO8BA6Nq0L6IGEyuDrokpXRh1eM3IX/eTCBPH3wYOQR6nPx5tCb5avDdKaVnGtn/2imltwBSSjMi4nHyl4255NBpDfIVqrnkL0FXtvbfQKuWSuVZYxcyGhwLP0a+0LIGuaT9bPIX3pdbcHVxKnkihR3I1QuvkSt3XiJ3R/w/YGJEdEwpza+qOKoeyHIUuevYueTuMl2KtkwmD6J5Q0pp2vL/BaSsqCQ/iHxh8WXgBGBianow1XfIs9YNJZ8T/hr4cVVFwN3kbreHR8SYlMf4WYvFX942BaZV3oPFffPJ74+e5Nd4xQPkL5+/IV9EeD6l9J/isQsj4s/AWeQp5G9d2b+FVGZF5U6lq/Mi8jHsSWCnohtm9SQGJ5MHcE7ADcV6HcnHtu+TB37/TdV7/F/kqp32xXu3unDhEXJovEfRDWxh1eNeJX/e7A1syeLPkSnkgGoW+SLe/yMHyGuTg6azi8+ejzxDoHKqzKIwBJZ59WMz8rSalS86lTfxfeQ38GfIX67a0WBK5KoT4RfJYxzMJ18d+siX10laeRFxKPAL8on/v8kBzhfJnzcbkbua3lOsXkseGPDJyLMr7UUeN2iHYr1PFOt1K27Hk08cNgGuSCm938j+uwNnR57h8Mni7gnA58lh1JXkKsdngDsb24ZWPVE13eyKKLoerkE+sdyLfHwcD/wrpTSjatXx5LGl9gTOTSldtBy7eZlcebYduavKa+T3wDXkSrezIuK4lNKcoi2fAD4N/CAiNk7F4JgppX+ST8L7kAPPx1Oe/EFqLQPI74E/FK83AKq6/i+oqlB7NyL+TQ5ePglcVvWlrn1K6cmI+Ad5rJ7tyBcs55AvAAwgf5ZPAzpExKLifTyffIzoRA73Kfb1fkSMIlfjbQp0jYjuRRDULuXpovs2UlkkaTkV7/EFVb/PioiJ5OqaWuCuqmPvXuT36i+AyZWq64j4MTmQPSki7kspPVRs7gFypfXuVburfI+cRj42Hkn+TKkMAE3xHn+YfOFwUETcWYREcyLifvJM2YeSZ3zdBrgvNTLj5keZ/V9LqHjzXAd0Kq6gLzFAdHFSCfnq5ceBf6eUpsTigbMeICeoB1c2uYx9vU8+eHdkcfgkSU2KiFrgh8A65KtGXyFXJI4mz+zwMRaP6wP5SzLkq8v/Aq4mV0D0JV/Z2Q/omFI6DSDlGSEuJn9xuCsiBhbjpWwWEUMi4n/Jn3PHs3hAXoptLwQOSim9kVL6XUrp9uILR7uqfuxaRa1sl48inHyIPEvJEeQLItcAt1SOp8V+XmXxYOIPFo9tshtYA5VxVT5BPrElpfQ6+Srpw+Qg8t7IM9n9htzF5izy63OdYv3643JKaUpK6d8GQPoAbEi+CPhG9Z1Fhdr8Ri76PU0+F3yCxReiF7G4y+8Nxe2Oxe1McuXb+uSuYpVtV9bfrPipXBAAlgh7f0x+fxxPrtisnsTEAEhqBRHxsYj4RkT8MyLuj4jTyN/73iOHPtW+nFL6fMoDvM+JiO6Rx2y8kjx+zybk758VT5ArWGsjoktV5WsU7+F/F+vVj1Vb9Z32SfL7/vMU3T4Lt5E/UwaklF5IKf0tpfRmcR5XmmykNE9US7mkuD2guK2vCiu+0PQgz8iwkKJvZNUBcyr5IN4rIjo1dVIdedaH/uT+3/eQPwwkqVFVB9+9yN2ufpFSuqj4EvsmeerfM8j9zgdXPfQV8lWobcgH/a8AG6SUPplSOiml9PfKFefK4KHkrgI/JF9xfoD8ufYX4CryrIYvAcex5HTvj5D7mO9RtDdi8SClixr5wqNVSESsFRHfj4i/Fv93HSKiY0vDu8jj/Iwgz9p1OrliYVvyFc3+wAURMajqIZXXTq/itkVfOouLJ3XkL8fbxeIBb+eQ3xs/JIehu5NPbt8G/hc4KqX0Skv2IbWS8eQZEU+NiGERsVtE9I+IPSLi+Ig4PCK2iIi1ivVfI3+G9qQYqLn43KxUj99e3A4uls0D/kS+Wn9wRJwbEdsW+ziW/Hk9B7igOtSpnJcWx49jUko3GvpILbccx8X1yBcjfkmuCnyGPN7P4eQLbTvDEu/JSuXPZyPiJnK3rcvJx9C7yBcy+kdE56rHPVDsbq/ise1ZPFZtHXn8seqwqXIu9jx58Og/s+TMreOL29qommW2OI8rz5i1KSV/SvhDPvguAu5vcH8P8vgBE4rlY4AOVcujuP1YM9v/GPmL1AvkfuDHVD/eH3/8+ej8kC8otGulbXUhhz3zyFeD2lV/bpC7tcwkj3eyYXHfhuQTgZeX9dlE7sJTW/V7B/IJyv8jX22+jVxBNKiRx1Y++y4qPhsHVZ57W//9/Wnxa2tt8uDfi4BNluNx7Yrbi4vHfqXB8h7kcGgRMLLq/l2L+365Am3tQw4dbwE2rm5H8e91gC3a+m/qT7l/yGP2/Kl4nS8iBzLzq35fQL4SP4Y8w107cpC/ADiiwbYqn7HPkEP4XlXL9iR/EVxEHjPkJeD9Yt3D2vrv4I8/H5Wf5TmnIQcx36kc+8gzWlbex8OqPhM2bvC43clVga+QxwY7lNyFfwD5gty9wKeq1v9csa2Li987VO1nY/LFv/nAWi1sd0fyJEcPV46jlPD7qWMClVTK/aIr/SS3AdYjv/l2JZfhzgdOSSn9tsHjKn27X4WlBsJsaFdyGd/FlGS6PalMKgN0LuMzYLmllN6JPKBzJ2BuyoOPRrG/9imPK3EXuQKiFvhbSumVYqDPnwG/i4jTUkovRsSa5IBoJ3JVzy7kLjx1xb4WkLvQ/KuxsWIafL61I1dGPlb8PhSYiAPdr9Iqr1GAlGcauZr8hXJIRLwMfIE8ns4ZKaXrGjumFa/Bj5NL1V8Ebi6uRKaUrxy+HhEXky98fCYivp3yzCWPkAPLARHRM6U0czma/lqxr13JZevTq9uV8uC7TQ3AK30oUp4s5BvkAH1n8rlj5bNyLjkk+iq5MuBl4Bxy98jKAP83ppQqM4BVBny9jTxLz3bkgaA7pJTuiIiDyeen+xWPv5M8k2P9THmSVk5a3N1qB3IRwL2NnR8VugDfIFfinF15Lxbr/yEiPk0uLBgI3Fh1PP4+eWzHL6SUbqlsLCJ6kcOejcmDOVdm+3uQPP5PpdpnYdVxfXrk2TDnAe829pyiwcyCKc8+9hhwILlo4dkyfj81BCq33wHfJE/p+THyVdJp5PLaqyj6WS4r6FnG/a8CB7V6iyW1ieJLL9UnApWDZjGGz07kL643NHGy0KJ9FI99ivyFexD5qlA78kG/st1HySHQzuQxfyB3cd2SfPVpUERMIY9TsQn5CtPr5Onl6wcvrZZyd7F2xb4WpaXLgisnCFeRBwF+sDpg0Kqh8n9YOeGreo22J09F+2lyBcGl5KuYL5LDmiXGC2nETPIYee8Bc6pf48Ux8rnIg95uR56G/cGUxxi4jxzk9AZmNnPhpNrrwLeBN9KSA05Lq5S0uKvuFZVuGtVfuCLPKHs++cvgGPLFwafJn+/rsXga+Mpn6f+RQ6ADgOspuooV74Prix9JrSgiNkkpvRwRh5Erorckv9fuXcY53XrkLOERcpX2O8VFu8p7+Rry+LH7ADemlFJReNALeCCldEuD86hPkMd/fIt8zLwVIKX0QkRMB3aOiI9VChEqUkpjlvXcUuNdQU8GvrSi56sfBY4JVG43FLdPkK/OfCqltEVK6YyU0sSqk+jy9I+UBCzdHzzlWRUaVspsHhH/BCaxeIDamyLP0LUyKuNCfKGqOe2Lf7QDaor7qwfinZ1S+ip5rJb7yVeZdiBfkf4BMDSldEEqpnxvTBH8LGjsMy8tHkz0tZSSMx2uoir/hwARsVFE7BIRfYrX7u7kaWjfIQeER5G/mB6dUrq9iU1WqonmkS+SrEPuqlU/hlXV62Uy+eT341UP/zu5u1i/ShNb+DxSymNhGQBptRCLp2heUIy51blY9AAwgxzCrsPigc97kwd1Bpa4wHAHuTvIncX9noNKK6Bq7LvqyX+iwTodI+IS4MWI+CI5sH2KXMH3u8q5VxM2IR9PF5CrtyvnRZXj3GRyldDusXjGwBnkixz9i0AnRcTaEbEXMIpcmLA2sFfkWVorzgZ2Sim92th4Rc20cykppZllDoDASqCyuyeltFQQWF3m3gZtkrQKqA44ImJtcvenHckH7zHkL9F/JI/z8E3yjIFHkCsAvxkRP0nLORtR1QH57+QThaERcUBK6daq1TYlfwl/F9gyIrZKKU2t+gIyMiJ+Qx4baPpyPm2txooTwx3Js8R9mvyFcyG52mcgcBN5UO+TgS+Sx8R7qrntVr0X7iQPPr43+cpnh4hYUHQXW5fFJ8TV27yzuN0pIn5ncKiPqqKasgOLu2pUBmLtQB7f7SnyRcf3yTPsHQ8MjjwddPVsdu+SK0ElrYTifVW5KNIR2Cil9EJleXGBY35EvEA+p/oluUDgzJTS201tt6p6ZyY55PkkeazZGVX7hcWV3P3IVT5PpzwcyVhy9/37iu797ckX7Z4ndxWbWzz2/arnck/Vv5c6jpY90FkRhkAllhb3+6ykp4uKq4++kaSPuOILc6WSYan3fETsBkwnD6B3EbnqZo3iMV8mz67wH+CElNIzxWMmkK/2fok8hfZDK9JlqjhJ+BF5sN2xEfE94Dlydc9Z5BLlLclVHVsBU9OS3dQWFm2vnkVi4fK2Q6uGxroiNtGt6tMsHrh7AvAs8CbwXuTp2eenlO4uytG/SA42n2ruNVq1/Crg68CxEfGnlNJ/qlbrQR6v4OFivxVPkwPSnsU6s5f7DyCtJqqq8NqTP693JVeavw9clhaPGTIJ+C0w3s9laWmt0d08IjYkn499iRzCvB4RdcCvU0r3Vm3/IfIsXZsCN6c81ld7iu+FDbdbdd/z5PF6jgX2BR5vsOrm5HHCIIc8lTF+Lihu/ws4jDx49G3kGWEfJVfpNvZ87ILfisK/pSStvhrpkrLMA2VTy6oHzos8mN9N5LClO7nf90/IQcxQclluZ+BnKaWzI6JjcTWpI3lMnt8AJ6eUfreSz+004EzyIIGQqzrGk6eAnwt0r76qpVVTETgGrHzXjojYlHzu8mKD+zcjBz/zgVPI4xj8p8E67YqqnT2A64C7U0qfW879X0eeqWQ8i7/cbkWeIWUD4PiU0l+q32cR0SOl9PoKPF1ptRARnyVPLtKefLGgC9CXPHj/POD8lNIv2qyB0mqo6FK5SUrp6WZXXvqx3chjv36OfGx8hhzKDCFX8AyvnKNFxBbkixybAnullKYsx34+S54AaD7wxZTS/cX96xb734lcMX5vSungBsfGDYBODY/nxbLqqkJ9AKwEkqTVWFVFXwdyV5WFKaXHGlu3wcF3Q/LMCEPIM7iMj4grUkqvkcc+uYs8oN/9wM6V0uCImEzu9jKM3M2GlGdCqsy48AD5pH+7iFhrWSXFTam0M6X0q8izOR1E/nJxb0rp4apVnR1pFVWpNCu66NWPEVCElmulPLtV9fqNhpnk//ddgRPJlTYdgWci4h7ydOzTitX7kysPfpRSuqmxNlVtewr5iuVOEbFOw7Y09XyK53EqOfg5nNxt8W3y+AXTybOD3VDsq7p7iwGQPupeJ78nNiZ3iQzgBeDnwDUppSfbsG3SaiciepCPK/+OiM+klFpcRVocO88kvyd/QT5WvlAsO4I8kPt3I+KGlMedm0aecGN7coDbYiml/4uIAeSLIn8pLpS8Rg5/NiYPMv01oFMsPUvXa1VtXmIoktT4YM5qRYZAkrSaijyd5mfJY/H0Jn9BnhkRs4H/TQ0Gu00ppSIsOpM8js/a5KtDa5CrGMaTD94vkKdC3wN4vCgNrnxJnxcRt5PLfzeMiE4ppfeqviQ/T/6CPYAcFk1tED51A95LeaDdRjX4Aj2DfJWp+nm3dIYltZHi/3AhQERsRw7yDiZXzQwDroyqaWerwswuLJ4SfUFEHAj8mvza/iuLxxf5JrBNRHw35YG6XyGfMB8Teer3N4qfII8N9EBK6fliX69FxEPkcKkfcG9zZebFe6ddytPRHk8+sd6fHFLdB/zDk1aV2ARyd8yNyMHolMrFAUnLpwhx3gAmAhuSz9GIiE7AtsALKaVZyzhubQB8ixzunF1V5d0upXRlUe39X8ARkceqe7foJnY0efy8B1vazmL/55MvCp5OHnMvyOeB56WU/hQRlzb3eZAciuRDZwgkSauhiDiK3C2rG3kMkj+RKxQ2J493ckFEfDOl9PcGJwpnk7uuTCZfpX0EeDXlcXgqY6+8FRGPk8dWea5y9aY4MQF4knxyMYDcXewV8kE/kcdguRc4DvgUebyeVJy8fIvclet/yd1xluf51vdPNwBatUXEJuRug4cDu5Arzf4D1JFn/3gMFp/0Fa+rLcldDj9Hnlb2KxGxNblrYQfymAYPppTmRB6o/Jfk19ibxX4eBr5XbKOxboivRcRJwP8VJ8T/Lu7fm1xhloqgs1NTAWXVFcp3yCfnE1fgzyN95BTv5WnFjyRafsGqYZhTqZ6NiL8BPwR+ExGVGU9nkGdOnbWMCxfrk8/HHicHSJULFJX1ryNXgu8GXFls82HycXpfGlx4a0pl/0XF9+iIuJF8QfLZVDWzZVEl3q54iN27VhGGQJK0eupDnpLzLOAe4KXKlZYiwBlOLsf9e1UVzqbk7izTgX1Sg+nSU57dpXIy8jh5QNs+5GPFAhYHPTPIAwnuRq72eaW4v3Kwv7fYzzbALcX970VEf2BrYIuGZcHN8SrRauVf5KmfnwL+QO5+9Sh5TKnXqsKfr5FnCPk+ubpnK3LVz0Ry5c8O5HLyC1JKd1Rt/x3gNHJg9IWIOD3lmeBGR8Q/gUHk1+tr5MGYdyUP6Pw/5Eq1h8iB1MPA8UXl0Gzy+CUBnGO1mSRpRRXnUosiT0rQI6X0StW4dJWJOVJKaanBl4vHnEQ+Ni4CDiBXnP6MfOxqrnvlxuSKvPfI4ze+02D50+Tj45ZV91W6Se/S0m7SDaWUZpHPG5foEl4s83i6ijEEkqTV049SSv/TxLJKP+s3Gtx/OHmWoguKap+OwIImrsw8S57OdxD5i/T0qoP4m8AD5Iqj3uQKjeptPEKuStonIi6p6sv+I+DHKaXJLX2SWn1UBSdjyePj3JRS+vYyHvIKuUviEeSBI78IPFLVNWzHYr1bit8rAWW7oovi1cBXgX2AS4v9T2PpaoT/izzw5Z7AmsV9j5OriX5NnlGsHTkAGh8Ra6SU3keSpCYsq7qlqC49iDxO3BXk7leVCRKqu0uvQ+7iNSel9Hix/P2iW/8fgCPJoc1pKaVHmmlP9dTtb5CrsbtSzEpZ1c7nivu7kKt/SCm9WnST3qVoz4Tl/4ssMaZj/XPUqqldWzdAkrRYZO2bWy8tHqi5ft2I6FJU25xIvvIzvri/Evh3LG4rB+alZl6o+n0GuV/4J4uf6nUWkruTzQFqi3FcKld+IH+5vxq4m3wlqvK4Rw2APtIqr52bi9tdYPGgzxVVr9l/kQPF7YGfpJQeLq6SVl6nle1tUNy2a3D/3cVtJSxaSkR0jIi9yFVr7cgBJSml+SmlP5HHKTqdXBrfJaU0xABIktScShVPRKzfxCqVc69KCFMf/ETE0RFxX7Hsn8AtEXF5RGxVPOYv5PF1/k6uuO5cPLZD1blWw/ZUjo3PkM/ftiZ3eabBY9YnHw+fY/GFEcjndZCPiyvE7l6rDyuBJKmNVXc9Wd6rJ0UXrk+QB6ndhlzt0508RXtdsVple5VpODctHttkeW7RrauOfOVqh4i4t1LGXLRxGvnk5WDgt1RVX6SU3iAPMNjYc13mALxafVX9v95HDmoGVwZ/joiPAR8nv0Y/HhF/Tik9F3k2uX3JoWP92E/Fdh4gzyqyB7m6qHISWx1UQh6cmeL1uWfx+8eA9cjl7geSA8vTGpa4p5T+ST4BlySpXhGcRGPnSsWyvclhTceIGJCWnlq9K/n8a26Dyuth5DHsppMHVZ5LHmPxcGCjiDg1FbO8Fl2cDwEGk4+JzU6bnlKaGxHXA/sBZ0bEfSmlR4vtrUcel3ED8sDNb1R1z38QuJQ8rqM+4gyBJKmNpSWnxd6UfCIwH7gspfSfFmyiN3kw3LeAqeQxT86NiG8BpxRfdCH3I3+XPDV297R4MOgl+qRXdYd5knylaGdyl5m3WDwu0GvkrjTzyJU/S10BKiqQljhhMQD6aCsCzQURMZ4c3pwTEW+Rp3fvR57p5D/k1+hzwJ3kEOhTwG3k11blNTKuuD0sIk6nqCqrer/sX9zeV9WEL5JPsN8iXzl9F/gHeUDqv7fmc5UkfXQV5yupsYtXRQVQLblLM8DJEfG9lNLrsXjmyy7kixIL0+IxGw8kT8rxT3LV9otVyyrTt3+NPK4iwF3FbW1Vm1rS9quKLtWnkauMbmXx1O27AJeQz+Hqp2MvLhwe06I/jlZ7dgeTpA9Y0cVrqfLd4u61IuLwiPhERAwkf2H9KfkL9MdauIu7gF1SSuumlAaSZ+A6llwR9NfIsyxBHpz3XvIUoAdBriSqnFRExEaRZx37VLH+i+Rqi4MqbamqWJqbUvplSunilNK7jTUqpdTUeEP66Lu1uD0LGEGuAroc2C2ltF5KqdJlbHxxuyUsLq8v/v0SeRaTHsAF5IHQK6X0u5LHWHgFmFS1358BXyafQO+WUuqWUjo0pXTbsirfJEmqFhEbRJ6ha2rk2bkq522V789dyWMnPgp8lsUXJipFFjOL2w2Lx65VrPc2cFJK6dmi6rprRGxDHsfnTeDIiOhRPPYp8hiNAyLi48V2ljlkQNX55jnkcfNeI18g+Ta5EvyHwPdSSu819tiqIQT0EeZ/siR9AIqThKgOWaqW1Q8OGBGfI5ff3kz+ojyVPKju4+QDf7OKA/l9lf2mPBDzLRExhvwl/EDgiZTSuxExkjyGysgiHKp8ya50JduUXHoM+Qv2T8mVGc818TwrV7wkWFzFcys5/JmVUtqk4UpVXSAnk09Qt4+IDVJKrzVY/mOgG/mK6cCIeJBcXn9Isa+TU0qPVl6HKaXnaOK1KklSS6WUXouIheRxEY8Ezi/O2yrdlmeTg6AR5IsSpwFXVIUrcxps7+2I2Jdc1To/IoaQK2S3J3f3+iT5u/kU8oW314vztvHkaeFrgBeaO+equpAyB/h9RFxBvtDyfHPV5cVjWzxzq1ZfVgJJ0gegqGioDALYLyIOLUqHKwfZyufv34rb7clfmD+bUvprSumpFQlXinFRKgP93V/cblO1yt/IV4TeBM4gzwBxLbk8uTP5ytGUSjtTSjenlG5pqorCAEjVqk4+HyFf1dyoMmhmRKxRuYJaXVFGHiB6G3K3xoZjZP2bPFVuZQavw8mv33+Rx526qVjP16EkqbWNIIc9w4oKVFg8bt3r5ItodwG/BwZFxFFVlTg9yYHKfyKic3HfLPKFjb+SL/79Evg0edKCY4D1UkrbppSmVG3nVmAtYFeAiOgeEVtXqpOak1J6O6VUVwwBEJEHlzYDKDkrgSRpBTUzaOAmwNeB48hXdN4F3ikqGY5JKc0ASCnNiogpQB9yBRCx7KnbG2tHwy/Wle5Zny9uK33KKfqe3xoRg4EdgO3IY7T8ozJwYGPbtyuNWqrq9fIP4FByifyfyeMiNPY6+ju5sqcfecavhmMvPA18PSJ65V/T8x9c6yVJqjcJuBj4Drlr1T1Vx7F1yd2U30sp/SEiTgO+QR5g+TFycNMBIKU0rwiCHgQGkcdXPIN87jW14U4jYq1UzAJLDovmAKcX40Z2Io/V+MeI+GlKaV5Ln4yVPqowBZSkFVRUyiyKiLWr7y8qcf6XHOo8TC4RPpk8i8TuwGWxeBpQWDx+yjrF7aKWBkBFOxZVTkoionNE9I+IXwFfIl9dur1B+9qnlF4pKo6Gp5R+lRbPHNG+4fhFBkBaTpXX7k3F7YHFbaPT2rJ4XKC9igBpqdd+cf80AyBJ0oelCFh+R552/ciI2KlqcRfyBb5PFr//ijwt+/HF768VtxsWt++z+Hh3V0rpd00EQFsDn42ILkUb3iFXxP6LXDU0oPj3LcsTAEnVrASSpCYUYUj7VMyc0MQ61wGfi4gtU0rPFHefQh7DZCTwP1UH6UuKqp+fk/uPf6+4/xbgW8C2xe8tDl0iogboC2xCnqViM3Lf8q3J4dI3U0qvRCye3aK660zxHNtRBE92q9HKqgpxKrN77VPc39T76CnyCe1j5CucS53UGkRKkj5sxbnTyxFxMXnigdMiYlpRzT2bfP5UOeb9H3na+KMi4rfkrl9QVN6klBZGxNXkgZlPjojJwB+KcYY2JHeLPgD4Jvmi4d+Bd4rHjo2Iv5PP1V7/wJ+4PvIMgSSpCdVls9UhSvF7pcvLJuQvr+8X93cjf+l9AfjvlNL7RfeuTYD1yJ+7iTx7USUEuru47R+Lp2dfpqr2rE+uOPoUecDc+cBE4CfArUUf8Ca7cxXbMPhRq6o6cX4a+FREbJ1SeqKx12Lxet+18S1JktTmrgWGkitbxwO/Bd4in9O9BpBSejUifgF8jtwt7OFinTcjYg1gflE9fhrwfXI3szMiYjr5vHBroGOxr5+kPMlHvZTSLFh8gTLf5YU7rRhDIEmlVXUgXdRYSBIR65EHSq4FzgTur5qFaFFEbEGehv31lNKLxcPWIo+18zKwcUTsQh7rZIfitht5DJ6JRanvuylPEfoA0J98JaiuuXF4qgKp+8jjDq1PnjXiyUbWtYpCH7Z25HDxAXJAuT95Kt0Wd3OUJKktVVVQTytmV92LPDbPGGBNqiqBIqJDSmliRNwAHEyebXUNoGNxQbA9eWy8WyLiSeAocteuXuRuZX8GrkkpTWxBmxzXRyvFEEhSaTWo9FkT6JpSerVqlbfIMxztSh7A7wtFOW+lCuctYHNgZtU2Xy7CpU+Su7hsSO7e9RB5FogbU0p1lfWLKqFF5IH/BpMHDKxf3oLn8B7w7+r7ihMNZ0xSW6oEjz8GrgFualhNJ0nS6iKldFNE3AQcRJ7MYG1yFfhm5HO8yli7vyHPdnkAeSy8DSqbqNrWM8A5EdGVfDGwMq08sHT1udTaHBha0motIjaNiMsi4rQiUGnJY6K47RUR34mIfwPPAbdExM8joh/UByw/Jk+r/rmI+Hpxf+XAvBb56s3jsXhadsjdsTqRp1/fGeiSUhqcUvpBgwCoa8qzdcHiQXQHF7fLdfAvpv2Mon0LDYDUlqqunj5eDEC+0BNaSdLqqHJxjTxI9FvACeRZXd9j8ffpyvncROAy8nlgRyAVoc4SVdlFxfebKaX3IqJdVE3d7vFSHzRDIEmru4+Rr8oMJXe1alYxCN9GwB/I3b1eA24jVy+cDvwtIvoW675Lnhr0ReDHETGwalP9yVeCXkkpvVsVQlUCnX+nlCY0NsZPRHyaPDh0pU2Vap4DImLd5T0BKAZ19qRBkiSpFVVdWLuXXOmzB3l8oDVYPCZQ5eLHu+TzyyuB4eTxIZc6P6sOhVKe5XWB3ff1YTEEkrS6e54c4PQhD77cUn8m9+3+PnA0cHxKaRB5QOUNgfMiojdAUb3zU2Bd4P9FRK9iG+0bbLNy8L4KeBb4fkScXFzh6R4RfSLiqGJ2iJuAXhGxZuXKDzkUOiilNGc5nockSZI+YCmlt8hTwc/g/7d378F6VeUdx7+/JAipULSIQEARAVFu4SJiQREatGq9IMUCWqtSi7YoWhWxLRa1VlSUTi0og5cB8a4V6wVQFKEIFBUCBAuCYBEwRANRowKB5Okfa73J5pCTCyC5nO9nhtnv2XvtvdY+ZyYkz3nW88CWtGyfSbAky7yP+3VVvaRngN+01IdJq5A1gSStcXpabnrL6Xm0vdgvArZlBerpJHkKrUjzucB7x2ydOpFW5+cQYD9gVGj587Q6P2+idf46Epjar10HS35TVK0l+2HACcB/0H4TdBOtiOBjaW1FjwNO6r8xGu3//vTKfSckSZL0UOjNQW5NchpwKK05xy2w9C1cSabQikGbqa3ViplAktY4vb7IsDPCJbR92bsnmTrObQwybraltWu/uhd6ftjg2bNpKbzQ0n1H52+nZQ39GHhtDyRN75d/0p8/qjWUqvpv2l8QjqBl/fwauBJ4DbBbVf1TVf1s8PxRdwn/XJYkSVr9jDK+315VW/VsnxvGG9y3eBkA0mrHTCBJa5Qk69Nab/4lra3mebQsm5tprTY3Bn46TmeF0de39ONGAEup2XM1MB94TJJHVtW83vrzN0mOAU4GjqG1h7+Dtk1sySRL9oVfB1yX5ONL6/wwHDu41/3gkiRJq5nB3+8WwOJMn7IZh9Y0BoEkrXI9IDIZWLQCQZA30oo5/xg4H9gG2Bd4Aq0V52OBn45ThG907nZad4ctkmxUVbeNGTqVtmXrLpbU/Rn9D/4LtH3g7+1f39bXMl4qcEYBoGHrdn8zJEmStOYak5UurTHcdiBpleuNre6pqkW9iPJ6SxuXZH9afZ3vAgcBr6uqZwIHAnNoNXu2XYEpbwJmAk8D9uzPftjg+ka0LKN5VTV3mFXUj6cA3+zP+Vw/jvtug8+2bpckSZK0yhgEkrTKJdkqydFJzqcVef5YklcuZehf0fZjv7OqZlXVXb1I39XAu2nZOk9OssGy5quqebTuYAB/n2RaVS3oAagtgb/r1z7Xx4/dsvUr4MCq2rKqXltVd9zPV5ckSZKkh4zbwSTdL0n+qBdLHu/6JNr2rEVVVePU6CHJHsCpwGOAK4BfAc8FDk2yD/D6Qcv07WjB69n93sksKdJ3Tr//KbS6QPPHm7P7XJ/nhcA5Sc6h1QHah5YhdBxwxnjvV1W/G2xjc3uXJEmSpNWemUCSxpVm8phzf5xkPvDB8e6BVuB4VPsmyaRRIGjM2E2B02lbr94IvKSqnkELxJwHvBx4ZR87BfgZvWBzf8SiQfBlDnAtrUbQVn0N4wZmemDpYOBDwD3AK4CjaMWe3wi8f3l7vQfb2AwASZIkSVrtmQkkabEepBkGcYpeEDnJelV1J60Q8k+B6b1j1j3D+wetzvcCDqDV6bkxydm0wM6CHhRaBLyYVtD5mKr66Og5VTUrybuAJwOvSPI14EbgBuB5wOP7uGG9nXlJ1gE2BLZPct6y6u/0tS6gtXvfhBZYunaQdSRJkiRJaxUzgSQt1jNbFvUCzY9MckiSLya5FvjTPux64FJgB1rWzb3uTzItyem0gM+htLbtrwHOBt6XZItBB7DH9uMt0LJ9BtlClwJfBp4ETO/Bpotpbd5f2AM+DMYDbNGPOwCPWN67Dj7PqaofVNWve12gKWOeK0mSJElrPINAkoBWwyfJPklOSnIdLePnk8D2tIDMTb0I80Ja8WZotXOGz3g48DbgpcAnaJk+TwVm0AI4RwL/2MeuT2vFvoDWrp3h1qpefPlq4GG0luzQAkk/AJ5F3ybWA0/rJ3kLsAlwDS1gNQoIrej7DzOg3OIlSZIkaa3jdjBJIzcCmwO/oLVgP41WaPla4GdVNX+QHTMq4PxM4KOD7V07Aq8GvltVrxo8e06SQ2gBmsOTHFtVv0hyOy3Is9lwa9ngeaOtWRsCVNVvkhwPnAScnOS5/ZnTaMGmT9L+XHshrc7PCjPoI0mSJGltZyaQNMENCj9/ux8/UlV/Drynqr5aVT+qqvmj4f14DS1LZ+8kDx9s79q+H8/KvU2uqptoHbkm0er6AMyiFXp+Dr3Y82ibVze1HxeMTlTVGcDzge8ATwfeAuwHfBp4H3BsVe1UVT+8v98TSZIkSVobGQSSNArgfLkfp/fjfTJjRsGeqpoNzKRl4OwwGDKqETR3sK1r+Jzv9OPe/XgZ8C3a9q6X9fF395pE2wOHAXfRspKG67i0qmYA+wNbVdVjqurtVfW7qlrYA0/++SZJkiRJA24Hkya4QZDm/H6c0c8v7DV+pgHbApsBX66q2/q4mf24L/C9/vnGftypH0MLJo0CTbP6cet+vAH4F+AFwNuTbEzLMvpDWvv2acDrquqWsevu3b1mDr6eTG8Z39/J7V2SJEmSNGAQSNKoBs+8JLOAnZK8mpaBsw+wO63Wz2+Bm4Fv9NuuAG6lZeO8r5+7iBZ8eXaSDXodoUksCchs0o8/TrJOVd0NXJ7kmcCxtMyfybQ6QVcBRwOnL23Ng1b06XGfcdvBS5IkSZIMAkm6t2/QsnjeDzycVvz5fODfgDOr6ueDos3X0TJ79kzyqKqaW1VXJTmbVuPn8CQnDOoFAYyKRZ9ZVXePnlVV5yW5qM+9MXB5Vd26Igu2oLMkSZIkrRiDQJJgSabOmcCbgbnANlU1ZzioZ92M6gL9MslltA5huwHf7MM+AGwHHA9smORCWkDpxbSuXacB5wyf1Z+3gNaKfvFctLpliwz0SJIkSdIDF/9tJWkkyRTaNrBU1aR+bh1aIGbhYNykXrz5L2hduT5QVUcPrj+XVutnV2AhrSbQQuBTwHFVdcMy1jBpTPaQJEmSJOlBYBBIEnCvwM65tGLPf1ZVZ/X27gvHjE1VVe/g9SXg9qraa3S+j9mQ1gXsicD1wLmDVvOSJEmSpIeYLZQljfX1fnxOP2bsgMH2rBuAnwBPTfK44batqvpVVZ1ZVSdU1X+NikTbul2SJEmSVg3/MSZpZBTAObsfR0GgpXbd6lk/d9K2gx0D3D7OuMWBn14E2q1ekiRJkrQKuB1M0n0k+TnwKGDzqpo93OYlSZIkSVozmQkkabHBVq1Rl65njy4t577JvZuXJEmSJGk1ZSaQpMUGxaFnAHsCp1TV3FW9LkmSJEnSA2cQSJIkSZIkaQJwO5ikpXKLlyRJkiStXcwEkiRJkiRJmgDMBJIkSZIkSZoADAJJkiRJkiRNAAaBJEmSJEmSJgCDQJIkSZIkSROAQSBJkiRJkqQJwCCQJEmSJEnSBGAQSJIkrbAkC5NcnuSqJF9N8ogH6bm/Wca1SUk+2OecleT7SbZayeevm+Rbfe0HP/AVL3OuTZN8Nsn1SS5NcmaSJzwIz903ydf65xckeWv/fECS7Qfj3plk/wc6nyRJWvtMWdULkCRJa5Q7qmoXgCSnAUcA//p7nvNgYBqwc1UtSrIF8NsVvTnJFGBXgNHaf1+SBDgDOK2qDunnpgObANc+WPNU1VeAr/QvDwC+Bvxvv/bPD9Y8kiRp7WImkCRJur8uBjYHSLJLkv9JcmWSM5I8sp//m565c0WS/0zyB/38Vkku7pk971rOPJsBs6tqEUBV3VxV8/pzFmcQJTkoyan986lJTk5yCXAK8Elgj54JtHWSf+7ruirJKT14Q5JtesbQFUkuS7J1P39UH39lkncsY637AXdX1cmjE1V1RVVdkOb4QUbTwf3Z+yY5L8kXk1yT5FOD9Ty7n7sMOHDwrq9IcmKSvYAXAMcP3u3UJAf1cTOSzOzzfTzJuv38/yV5R3/HWUme2M8/oz/n8n7fBsv52UiSpDWIQSBJkrTSkkwGZrAkG+UTwNFVtTMwCzi2n/9SVe1RVdOBq4G/7uf/HfhwVe0EzF7OdJ8Hnt8DEx9IsusKLnMLYK+qOgx4FXBBVe1SVdcDJ/Z17QhMBZ7X7/kUcFJf717A7CTPArYFngLsAuyeZJ9x5twRuHScawf2+6cD+9MCN5v1a7sCbwC2Bx4P7J1kPeAjwPOB3YFNxz6wqi6i/QyOGrwbAP3+U4GD+/d5CvC3g9vnVtVuwIeBN/dzbwaO6BlTTwfuGOddJEnSGsggkCRJWhlTk1wO3Erb4nROkg2BR1TV+X3MacAoSLJjkguSzAJeCuzQz+8NfKZ/Pn1ZE1bVzcB2wD8Ai4BvJ5mxAmv9QlUtHOfafkku6ev6E2CHnvWyeVWd0ee9s6p+Bzyr/zcTuAx4Ii0otLKeBnymqhZW1RzgfGCPfu17PcNpEXA58Lg+z0+q6rqqKlo208rYrt8/2oY2/LkAfKkfL+3zAVwInJDkSNrP9J6VnFOSJK3GDAJJkqSVMaoJtCUQWk2gZTkVeG3PRHkHsN7gWq3opFV1V1WdVVVHAe+m1cEZ+4z1xty21LpBPUPmQ8BBfV0fWcq997oFOK5n2uxSVdtU1cfGGftDWtbOyrpr8HkhD03dxtGci+erqvfQsqamAheOtolJkqS1g0EgSZK00nqGzJHAm2jBlnlJnt4vv4yW5QKwAW1L1Tq0TKCRC4FD+ufh+ftIsluSaf3zJGBn4MZ+eU6SJ/XzL1rB5Y8CPnOTrA8c1N9pPnBzkgP6XOv2GkbfAA7rY0myeZJHj/Psc4F1kxw+WP/O/XtzAXBwkslJNqZl5XxvGeu8BnjcqC4RcOg44+bTvs9j/ajfv03/evhzWaokW1fVrKp6L/B9WjaSJElaSxgEkiRJ90tVzQSupAUnXk6rcXMlre7NO/uwtwGX0II+1wxufz1wRN+Otflypno08NUkV/X57gFO7NfeSuuMdRHLry00Wvcvadk/V9ECPN8fXH4ZcGR/j4uATavqm8CngYv7er/I0oMu9G1bLwL2T2sR/0PgONr2uTP6+q+gBYveUlW3LmOddwKHA1/vhaF/Ps7QzwJH9ULOW4+5/5XAF/q6FwEnj/OMkTf0wtVXAncDZy1nvCRJWoOk/V1FkiRJkiRJazMzgSRJkiRJkiaAh6LooCRJ0nIl2Yn7dgq7q6r2XBXrWZYkGwHfXsqlGVV120O9HkmSpBXhdjBJkiRJkqQJwO1gkiRJkiRJE4BBIEmSJEmSpAnAIJAkSZIkSdIEYBBIkiRJkiRpAvh/ngoiBv8yYHYAAAAASUVORK5CYII=", + "text/plain": [ + "<Figure size 1440x720 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "Accident_Information20052019_dfroadsurface = Accident_Information20052019_dfroadsurface.drop(labels=['Data missing or out of range'], axis=0)\n", + "Accident_Information20052019_dfroadsurface\n", + "Accident_Information20052019_dfroadsurface.plot.bar(stacked=True,rot=15, title=\"Accidents Road Surface \",figsize=(20, 10))\n", + "plt.xticks(fontsize=20)" + ] + }, + { + "cell_type": "code", + "execution_count": 116, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DataFrame[Accident_Index: string, 1st_Road_Class: string, 1st_Road_Number: string, 2nd_Road_Class: string, 2nd_Road_Number: string, Accident_Severity: string, Carriageway_Hazards: string, Date: string, Day_of_Week: string, Did_Police_Officer_Attend_Scene_of_Accident: string, Junction_Control: string, Junction_Detail: string, Latitude: string, Light_Conditions: string, Local_Authority_(District): string, Local_Authority_(Highway): string, Location_Easting_OSGR: string, Location_Northing_OSGR: string, Longitude: string, LSOA_of_Accident_Location: string, Number_of_Casualties: string, Number_of_Vehicles: string, Pedestrian_Crossing-Human_Control: string, Pedestrian_Crossing-Physical_Facilities: string, Police_Force: string, Road_Surface_Conditions: string, Road_Type: string, Special_Conditions_at_Site: string, Speed_limit: string, Time: string, Urban_or_Rural_Area: string, Weather_Conditions: string, Year: int]" + ] + }, + "execution_count": 116, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Accident_Information20052019_df" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [], + "source": [ + "Accident_Information20052019_dfLight_Conditions=Accident_Information20052019_df.withColumn(\n", + " \"Light_Conditions\",\n", + " when(\n", + " col(\"Light_Conditions\") == 1,\n", + " \"Daylight\"\n", + " ).\n", + " when(\n", + " col(\"Light_Conditions\") == 4,\n", + " \"Darkness - lights lit\"\n", + " ).\n", + " when(\n", + " col(\"Light_Conditions\") == 5,\n", + " \"Darkness - lights unlit\"\n", + " ).\n", + " when(\n", + " col(\"Light_Conditions\") == 6,\n", + " \"Darkness - no lighting\"\n", + " ).\n", + " when(\n", + " col(\"Light_Conditions\") == 7,\n", + " \"Darkness - lighting unknown\"\n", + " ).when(\n", + " col(\"Light_Conditions\") == -1,\n", + " \"Data missing or out of range\"\n", + " ).otherwise(col(\"Light_Conditions\"))\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+-----------------+--------------------+---------------+\n", + "|Accident_Severity| Light_Conditions|Total accidents|\n", + "+-----------------+--------------------+---------------+\n", + "| Fatal|Darkness - lights...| 234|\n", + "| Slight|Darkness - lights...| 375595|\n", + "| Fatal|Darkness - lights...| 6149|\n", + "| Slight|Data missing or o...| 14|\n", + "| Serious| Daylight| 229762|\n", + "| Fatal|Darkness - no lig...| 5521|\n", + "| Serious|Darkness - no lig...| 25689|\n", + "| Slight|Darkness - no lig...| 93646|\n", + "| Slight|Darkness - lighti...| 25134|\n", + "| Serious|Darkness - lights...| 71553|\n", + "| Serious|Darkness - lights...| 1947|\n", + "| Fatal|Data missing or o...| 1|\n", + "| Fatal| Daylight| 17429|\n", + "| Serious|Darkness - lighti...| 3975|\n", + "| Slight| Daylight| 1420876|\n", + "| Slight|Darkness - lights...| 9538|\n", + "| Fatal|Darkness - lighti...| 364|\n", + "+-----------------+--------------------+---------------+\n", + "\n" + ] + } + ], + "source": [ + "\n", + "Accident_Information20052019_dfLight_Conditions = Accident_Information20052019_dfLight_Conditions.groupby('Accident_Severity','Light_Conditions').agg(F.count(Accident_Information20052019_dfLight_Conditions.Accident_Index).alias('Total accidents'))\n", + "Accident_Information20052019_dfLight_Conditions.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead tr th {\n", + " text-align: left;\n", + " }\n", + "\n", + " .dataframe thead tr:last-of-type th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr>\n", + " <th></th>\n", + " <th colspan=\"3\" halign=\"left\">Total accidents</th>\n", + " </tr>\n", + " <tr>\n", + " <th>Accident_Severity</th>\n", + " <th>Fatal</th>\n", + " <th>Serious</th>\n", + " <th>Slight</th>\n", + " </tr>\n", + " <tr>\n", + " <th>Light_Conditions</th>\n", + " <th></th>\n", + " <th></th>\n", + " <th></th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>Darkness - lighting unknown</th>\n", + " <td>364.0</td>\n", + " <td>3975.0</td>\n", + " <td>25134.0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>Darkness - lights lit</th>\n", + " <td>6149.0</td>\n", + " <td>71553.0</td>\n", + " <td>375595.0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>Darkness - lights unlit</th>\n", + " <td>234.0</td>\n", + " <td>1947.0</td>\n", + " <td>9538.0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>Darkness - no lighting</th>\n", + " <td>5521.0</td>\n", + " <td>25689.0</td>\n", + " <td>93646.0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>Data missing or out of range</th>\n", + " <td>1.0</td>\n", + " <td>NaN</td>\n", + " <td>14.0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>Daylight</th>\n", + " <td>17429.0</td>\n", + " <td>229762.0</td>\n", + " <td>1420876.0</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " Total accidents \n", + "Accident_Severity Fatal Serious Slight\n", + "Light_Conditions \n", + "Darkness - lighting unknown 364.0 3975.0 25134.0\n", + "Darkness - lights lit 6149.0 71553.0 375595.0\n", + "Darkness - lights unlit 234.0 1947.0 9538.0\n", + "Darkness - no lighting 5521.0 25689.0 93646.0\n", + "Data missing or out of range 1.0 NaN 14.0\n", + "Daylight 17429.0 229762.0 1420876.0" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Accident_Information20052019_dfLight_Conditions=Accident_Information20052019_dfLight_Conditions.toPandas()\n", + "Accident_Information20052019_dfLight_Conditions=Accident_Information20052019_dfLight_Conditions.pivot(index ='Light_Conditions', columns ='Accident_Severity')\n", + "Accident_Information20052019_dfLight_Conditions" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead tr th {\n", + " text-align: left;\n", + " }\n", + "\n", + " .dataframe thead tr:last-of-type th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr>\n", + " <th></th>\n", + " <th colspan=\"3\" halign=\"left\">Total accidents</th>\n", + " </tr>\n", + " <tr>\n", + " <th>Accident_Severity</th>\n", + " <th>Fatal</th>\n", + " <th>Serious</th>\n", + " <th>Slight</th>\n", + " </tr>\n", + " <tr>\n", + " <th>Light_Conditions</th>\n", + " <th></th>\n", + " <th></th>\n", + " <th></th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>Darkness - lighting unknown</th>\n", + " <td>364.0</td>\n", + " <td>3975.0</td>\n", + " <td>25134.0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>Darkness - lights lit</th>\n", + " <td>6149.0</td>\n", + " <td>71553.0</td>\n", + " <td>375595.0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>Darkness - lights unlit</th>\n", + " <td>234.0</td>\n", + " <td>1947.0</td>\n", + " <td>9538.0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>Darkness - no lighting</th>\n", + " <td>5521.0</td>\n", + " <td>25689.0</td>\n", + " <td>93646.0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>Daylight</th>\n", + " <td>17429.0</td>\n", + " <td>229762.0</td>\n", + " <td>1420876.0</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " Total accidents \n", + "Accident_Severity Fatal Serious Slight\n", + "Light_Conditions \n", + "Darkness - lighting unknown 364.0 3975.0 25134.0\n", + "Darkness - lights lit 6149.0 71553.0 375595.0\n", + "Darkness - lights unlit 234.0 1947.0 9538.0\n", + "Darkness - no lighting 5521.0 25689.0 93646.0\n", + "Daylight 17429.0 229762.0 1420876.0" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Accident_Information20052019_dfLight_Conditions = Accident_Information20052019_dfLight_Conditions.drop(labels=['Data missing or out of range'], axis=0)\n", + "Accident_Information20052019_dfLight_Conditions" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([0, 1, 2, 3, 4]),\n", + " [Text(0, 0, 'Darkness - lighting unknown'),\n", + " Text(1, 0, 'Darkness - lights lit'),\n", + " Text(2, 0, 'Darkness - lights unlit'),\n", + " Text(3, 0, 'Darkness - no lighting'),\n", + " Text(4, 0, 'Daylight')])" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "<Figure size 1440x720 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "Accident_Information20052019_dfLight_Conditions.plot.bar(stacked=True,rot=15, title=\"Accidents Light Condition \",figsize=(20, 10))\n", + "plt.xticks(fontsize=20)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 120, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>period</th>\n", + " <th>Serious</th>\n", + " <th>Fatal</th>\n", + " <th>Slight</th>\n", + " <th>Total_casualties</th>\n", + " <th>KSI</th>\n", + " <th>\"%\"KSI</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>Daylight</td>\n", + " <td>229762.0</td>\n", + " <td>234</td>\n", + " <td>375595</td>\n", + " <td>605591.0</td>\n", + " <td>229996.0</td>\n", + " <td>37.978768</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>Darkness - lights lit</td>\n", + " <td>25689.0</td>\n", + " <td>6149</td>\n", + " <td>14</td>\n", + " <td>31852.0</td>\n", + " <td>31838.0</td>\n", + " <td>99.956047</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>Darkness - lights unlit</td>\n", + " <td>71553.0</td>\n", + " <td>5521</td>\n", + " <td>93646</td>\n", + " <td>170720.0</td>\n", + " <td>77074.0</td>\n", + " <td>45.146439</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>Darkness - no lighting</td>\n", + " <td>1947.0</td>\n", + " <td>1</td>\n", + " <td>25134</td>\n", + " <td>27082.0</td>\n", + " <td>1948.0</td>\n", + " <td>7.192970</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>Darkness - lighting unknown</td>\n", + " <td>3975.0</td>\n", + " <td>17429</td>\n", + " <td>1420876</td>\n", + " <td>1442280.0</td>\n", + " <td>21404.0</td>\n", + " <td>1.484039</td>\n", + " </tr>\n", + " <tr>\n", + " <th>5</th>\n", + " <td>Data missing or out of range</td>\n", + " <td>NaN</td>\n", + " <td>364</td>\n", + " <td>9538</td>\n", + " <td>9902.0</td>\n", + " <td>NaN</td>\n", + " <td>NaN</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " period Serious Fatal Slight Total_casualties \\\n", + "0 Daylight 229762.0 234 375595 605591.0 \n", + "1 Darkness - lights lit 25689.0 6149 14 31852.0 \n", + "2 Darkness - lights unlit 71553.0 5521 93646 170720.0 \n", + "3 Darkness - no lighting 1947.0 1 25134 27082.0 \n", + "4 Darkness - lighting unknown 3975.0 17429 1420876 1442280.0 \n", + "5 Data missing or out of range NaN 364 9538 9902.0 \n", + "\n", + " KSI \"%\"KSI \n", + "0 229996.0 37.978768 \n", + "1 31838.0 99.956047 \n", + "2 77074.0 45.146439 \n", + "3 1948.0 7.192970 \n", + "4 21404.0 1.484039 \n", + "5 NaN NaN " + ] + }, + "execution_count": 120, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "Accident_Information20052019_dfLight_Conditions_df=Accident_Information20052019_dfLight_Conditions.toPandas()\n", + "\n", + "\n", + "Accident_Information20052019_dfLight_Conditions_dfindex=Accident_Information20052019_dfLight_Conditions_df.set_index('Light_Conditions')\n", + "Accident_Information20052019_dfLight_Conditions_dfindex3=Accident_Information20052019_dfLight_Conditions_dfindex\n", + "Accident_Information20052019_dfLight_Conditions_dfindex3\n", + "\n", + "grouped = Accident_Information20052019_dfLight_Conditions_dfindex3.groupby(Accident_Information20052019_dfLight_Conditions_dfindex3.Accident_Severity)\n", + "Serious = grouped.get_group(\"Serious\")\n", + "Serious=Serious[\"Total accidents\"]\n", + "Serious=Serious.reset_index(drop=True)\n", + "Fatal = grouped.get_group(\"Fatal\")\n", + "Fatal=Fatal[\"Total accidents\"]\n", + "Fatal=Fatal.reset_index(drop=True)\n", + "Slight = grouped.get_group(\"Slight\")\n", + "Slight=Slight[\"Total accidents\"]\n", + "Slight=Slight.reset_index(drop=True)\n", + "Slight\n", + "Casulaty = pd.DataFrame({'period': ['Daylight', 'Darkness - lights lit', 'Darkness - lights unlit', 'Darkness - no lighting', 'Darkness - lighting unknown', 'Data missing or out of range'],\n", + " 'Serious': Serious,\n", + " 'Fatal': Fatal,\n", + " 'Slight': Slight})\n", + "Casulaty\n", + "dflist=['Serious','Fatal','Slight']\n", + "Casulaty['Total_casualties']=Casulaty[dflist].sum(axis=1)\n", + "\n", + "Casulaty_spark=spark.createDataFrame(Casulaty)\n", + "Casulaty_spark=Casulaty_spark.withColumn('KSI', Casulaty_spark[2]+Casulaty_spark[1])\n", + "Casulaty_spark=Casulaty_spark.withColumn('\"%\"KSI', (Casulaty_spark[5]/Casulaty_spark[4])*100)\n", + "Casulaty_spark_df=Casulaty_spark.toPandas()\n", + "\n", + "Casulaty_spark_df" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DataFrame[Accident_Index: string, 1st_Road_Class: string, 1st_Road_Number: string, 2nd_Road_Class: string, 2nd_Road_Number: string, Accident_Severity: string, Carriageway_Hazards: string, Date: string, Day_of_Week: string, Did_Police_Officer_Attend_Scene_of_Accident: string, Junction_Control: string, Junction_Detail: string, Latitude: string, Light_Conditions: string, Local_Authority_(District): string, Local_Authority_(Highway): string, Location_Easting_OSGR: string, Location_Northing_OSGR: string, Longitude: string, LSOA_of_Accident_Location: string, Number_of_Casualties: string, Number_of_Vehicles: string, Pedestrian_Crossing-Human_Control: string, Pedestrian_Crossing-Physical_Facilities: string, Police_Force: string, Road_Surface_Conditions: string, Road_Type: string, Special_Conditions_at_Site: string, Speed_limit: string, Time: string, Urban_or_Rural_Area: string, Weather_Conditions: string, Year: int]" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Accident_Information20052019_df" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "Accident_Information20052019_dfroadtype=Accident_Information20052019_df.withColumn(\n", + " \"Road_Type\",\n", + " when(\n", + " col(\"Road_Type\") == 1,\n", + " \"Roundabout\"\n", + " ).when(\n", + " col(\"Road_Type\") == 2,\n", + " \"One way street\"\n", + " ).when(\n", + " col(\"Road_Type\") == 3,\n", + " \"Dual carriageway\"\n", + " ).when(\n", + " col(\"Road_Type\") == 6,\n", + " \"Single carriageway\"\n", + " ).when(\n", + " col(\"Road_Type\") == 7,\n", + " \"Slip road\"\n", + " ).when(\n", + " col(\"Road_Type\") == 9,\n", + " \"Unknown\"\n", + " ).when(\n", + " col(\"Road_Type\") == 12,\n", + " \"One way street/Slip road\"\n", + " ).when(\n", + " col(\"Road_Type\") == -1,\n", + " \"Data missing or out of range\"\n", + " ).otherwise(col(\"Road_Type\"))\n", + ")\n", + "/Users/Asfandyar/Desktop/uk/from1997.ipynb" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+-----------------+--------------------+---------------+\n", + "|Accident_Severity| Road_Type|Total accidents|\n", + "+-----------------+--------------------+---------------+\n", + "| Fatal| Slip road| 205|\n", + "| Serious| Slip road| 2494|\n", + "| Slight| Single carriageway| 1415946|\n", + "| Slight| Dual carriageway| 291262|\n", + "| Slight|Data missing or o...| 1|\n", + "| Fatal| Unknown| 116|\n", + "| Slight| Unknown| 16850|\n", + "| Fatal| Roundabout| 471|\n", + "| Slight| Roundabout| 136061|\n", + "| Slight| One way street| 42377|\n", + "| Serious| Roundabout| 14675|\n", + "| Slight| Slip road| 22306|\n", + "| Serious| Unknown| 1965|\n", + "| Fatal| One way street| 328|\n", + "| Fatal| Single carriageway| 22595|\n", + "| Serious| Dual carriageway| 44225|\n", + "| Fatal| Dual carriageway| 5983|\n", + "| Serious| One way street| 6583|\n", + "| Serious| Single carriageway| 262984|\n", + "+-----------------+--------------------+---------------+\n", + "\n" + ] + } + ], + "source": [ + "dangeorusroadtype = Accident_Information20052019_dfroadtype.groupby('Accident_Severity','Road_Type').agg(F.count(Accident_Information20052019_dfroadtype.Accident_Index).alias('Total accidents'))\n", + "dangeorusroadtype.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>period</th>\n", + " <th>Serious</th>\n", + " <th>Fatal</th>\n", + " <th>Slight</th>\n", + " <th>Total_casualties</th>\n", + " <th>KSI</th>\n", + " <th>\"%\"KSI</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>Slip road</td>\n", + " <td>2494.0</td>\n", + " <td>205.0</td>\n", + " <td>1415946</td>\n", + " <td>1418645.0</td>\n", + " <td>2699.0</td>\n", + " <td>0.190252</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>One way street</td>\n", + " <td>14675.0</td>\n", + " <td>116.0</td>\n", + " <td>291262</td>\n", + " <td>306053.0</td>\n", + " <td>14791.0</td>\n", + " <td>4.832823</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>Single carriageway</td>\n", + " <td>1965.0</td>\n", + " <td>471.0</td>\n", + " <td>1</td>\n", + " <td>2437.0</td>\n", + " <td>2436.0</td>\n", + " <td>99.958966</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>Dual carriageway</td>\n", + " <td>44225.0</td>\n", + " <td>328.0</td>\n", + " <td>16850</td>\n", + " <td>61403.0</td>\n", + " <td>44553.0</td>\n", + " <td>72.558344</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>Roundabout</td>\n", + " <td>6583.0</td>\n", + " <td>22595.0</td>\n", + " <td>136061</td>\n", + " <td>165239.0</td>\n", + " <td>29178.0</td>\n", + " <td>17.658059</td>\n", + " </tr>\n", + " <tr>\n", + " <th>5</th>\n", + " <td>Unknown</td>\n", + " <td>262984.0</td>\n", + " <td>5983.0</td>\n", + " <td>42377</td>\n", + " <td>311344.0</td>\n", + " <td>268967.0</td>\n", + " <td>86.389010</td>\n", + " </tr>\n", + " <tr>\n", + " <th>6</th>\n", + " <td>Data missing or out of range</td>\n", + " <td>NaN</td>\n", + " <td>NaN</td>\n", + " <td>22306</td>\n", + " <td>22306.0</td>\n", + " <td>NaN</td>\n", + " <td>NaN</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " period Serious Fatal Slight Total_casualties \\\n", + "0 Slip road 2494.0 205.0 1415946 1418645.0 \n", + "1 One way street 14675.0 116.0 291262 306053.0 \n", + "2 Single carriageway 1965.0 471.0 1 2437.0 \n", + "3 Dual carriageway 44225.0 328.0 16850 61403.0 \n", + "4 Roundabout 6583.0 22595.0 136061 165239.0 \n", + "5 Unknown 262984.0 5983.0 42377 311344.0 \n", + "6 Data missing or out of range NaN NaN 22306 22306.0 \n", + "\n", + " KSI \"%\"KSI \n", + "0 2699.0 0.190252 \n", + "1 14791.0 4.832823 \n", + "2 2436.0 99.958966 \n", + "3 44553.0 72.558344 \n", + "4 29178.0 17.658059 \n", + "5 268967.0 86.389010 \n", + "6 NaN NaN " + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "dangerousroad_df=dangeorusroadtype.toPandas()\n", + "\n", + "\n", + "dangerousroad_dfindex=dangerousroad_df.set_index('Road_Type')\n", + "dangerousroad_dfindex3=dangerousroad_dfindex\n", + "dangerousroad_dfindex3\n", + "\n", + "grouped = dangerousroad_dfindex3.groupby(dangerousroad_dfindex3.Accident_Severity)\n", + "Serious = grouped.get_group(\"Serious\")\n", + "Serious=Serious[\"Total accidents\"]\n", + "Serious=Serious.reset_index(drop=True)\n", + "Fatal = grouped.get_group(\"Fatal\")\n", + "Fatal=Fatal[\"Total accidents\"]\n", + "Fatal=Fatal.reset_index(drop=True)\n", + "Slight = grouped.get_group(\"Slight\")\n", + "Slight=Slight[\"Total accidents\"]\n", + "Slight=Slight.reset_index(drop=True)\n", + "Slight\n", + "Casulaty = pd.DataFrame({'period': ['Slip road', 'One way street', 'Single carriageway','Dual carriageway', 'Roundabout','Unknown','Data missing or out of range'],\n", + " 'Serious': Serious,\n", + " 'Fatal': Fatal,\n", + " 'Slight': Slight})\n", + "Casulaty\n", + "dflist=['Serious','Fatal','Slight']\n", + "Casulaty['Total_casualties']=Casulaty[dflist].sum(axis=1)\n", + "\n", + "Casulaty_spark=spark.createDataFrame(Casulaty)\n", + "Casulaty_spark=Casulaty_spark.withColumn('KSI', Casulaty_spark[2]+Casulaty_spark[1])\n", + "Casulaty_spark=Casulaty_spark.withColumn('\"%\"KSI', (Casulaty_spark[5]/Casulaty_spark[4])*100)\n", + "Casulaty_spark_df=Casulaty_spark.toPandas()\n", + "\n", + "Casulaty_spark_df" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "Accident_Information20052019_df=Accident_Information20052019_df.withColumn(\n", + " \"1st_Road_Class\",\n", + " when(\n", + " col(\"1st_Road_Class\") == 'Unclassified',\n", + " \"U\"\n", + " ).when(\n", + " col(\"1st_Road_Class\") == 'M',\n", + " \"Motorway\"\n", + " ).otherwise(col(\"1st_Road_Class\"))\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+-----------------+--------------+---------------+\n", + "|Accident_Severity|1st_Road_Class|Total accidents|\n", + "+-----------------+--------------+---------------+\n", + "| Slight| C| 159036|\n", + "| Fatal| A(M)| 115|\n", + "| Serious| U| 100648|\n", + "| Fatal| A| 16630|\n", + "| Slight| Motorway| 75254|\n", + "| Serious| B| 45662|\n", + "| Serious| A| 149873|\n", + "| Slight| A(M)| 4717|\n", + "| Serious| C| 26768|\n", + "| Slight| A| 866728|\n", + "| Fatal| Motorway| 1534|\n", + "| Slight| B| 237083|\n", + "| Serious| A(M)| 657|\n", + "| Slight| U| 581985|\n", + "| Serious| Motorway| 9318|\n", + "| Fatal| B| 4079|\n", + "| Fatal| C| 2221|\n", + "| Fatal| U| 5119|\n", + "+-----------------+--------------+---------------+\n", + "\n" + ] + } + ], + "source": [ + "Accident_Information20052019_df\n", + "dangerousroad = Accident_Information20052019_df.groupby('Accident_Severity','1st_Road_Class').agg(F.count(Accident_Information20052019_df.Accident_Index).alias('Total accidents'))\n", + "dangerousroad.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead tr th {\n", + " text-align: left;\n", + " }\n", + "\n", + " .dataframe thead tr:last-of-type th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr>\n", + " <th></th>\n", + " <th colspan=\"3\" halign=\"left\">Total accidents</th>\n", + " </tr>\n", + " <tr>\n", + " <th>Accident_Severity</th>\n", + " <th>Fatal</th>\n", + " <th>Serious</th>\n", + " <th>Slight</th>\n", + " </tr>\n", + " <tr>\n", + " <th>1st_Road_Class</th>\n", + " <th></th>\n", + " <th></th>\n", + " <th></th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>A</th>\n", + " <td>16630</td>\n", + " <td>149873</td>\n", + " <td>866728</td>\n", + " </tr>\n", + " <tr>\n", + " <th>A(M)</th>\n", + " <td>115</td>\n", + " <td>657</td>\n", + " <td>4717</td>\n", + " </tr>\n", + " <tr>\n", + " <th>B</th>\n", + " <td>4079</td>\n", + " <td>45662</td>\n", + " <td>237083</td>\n", + " </tr>\n", + " <tr>\n", + " <th>C</th>\n", + " <td>2221</td>\n", + " <td>26768</td>\n", + " <td>159036</td>\n", + " </tr>\n", + " <tr>\n", + " <th>Motorway</th>\n", + " <td>1534</td>\n", + " <td>9318</td>\n", + " <td>75254</td>\n", + " </tr>\n", + " <tr>\n", + " <th>U</th>\n", + " <td>5119</td>\n", + " <td>100648</td>\n", + " <td>581985</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " Total accidents \n", + "Accident_Severity Fatal Serious Slight\n", + "1st_Road_Class \n", + "A 16630 149873 866728\n", + "A(M) 115 657 4717\n", + "B 4079 45662 237083\n", + "C 2221 26768 159036\n", + "Motorway 1534 9318 75254\n", + "U 5119 100648 581985" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#dangerousroad=dangerousroad.toPandas()\n", + "dangerousroad=dangerousroad.pivot(index ='1st_Road_Class', columns ='Accident_Severity')\n", + "dangerousroad\n" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "ename": "KeyError", + "evalue": "'Accident_Severity'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/usr/local/lib/python3.9/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m 3360\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3361\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3362\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.9/site-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.9/site-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", + "\u001b[0;31mKeyError\u001b[0m: 'Accident_Severity'", + "\nThe above exception was the direct cause of the following exception:\n", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/var/folders/v0/jqv1xcw13pn37fh0ppsl8b_w0000gp/T/ipykernel_21452/2942835424.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdangerousroadd\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'total'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdangerousroad\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Accident_Severity'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Slight'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mdangerousroadd\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.9/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 3452\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mis_single_key\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3453\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3454\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3455\u001b[0m \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3456\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.9/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m_getitem_multilevel\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 3503\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3504\u001b[0m \u001b[0;31m# self.columns is a MultiIndex\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3505\u001b[0;31m \u001b[0mloc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3506\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mslice\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3507\u001b[0m \u001b[0mnew_columns\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.9/site-packages/pandas/core/indexes/multi.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method)\u001b[0m\n\u001b[1;32m 2920\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2921\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2922\u001b[0;31m \u001b[0mloc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_level_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2923\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0m_maybe_to_slice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2924\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.9/site-packages/pandas/core/indexes/multi.py\u001b[0m in \u001b[0;36m_get_level_indexer\u001b[0;34m(self, key, level, indexer)\u001b[0m\n\u001b[1;32m 3202\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3203\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3204\u001b[0;31m \u001b[0midx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_loc_single_level_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlevel_index\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3205\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3206\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlevel\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_lexsort_depth\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.9/site-packages/pandas/core/indexes/multi.py\u001b[0m in \u001b[0;36m_get_loc_single_level_index\u001b[0;34m(self, level_index, key)\u001b[0m\n\u001b[1;32m 2853\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2854\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2855\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mlevel_index\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2856\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2857\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.9/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m 3361\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3362\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3363\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3364\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3365\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mis_scalar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0misna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhasnans\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mKeyError\u001b[0m: 'Accident_Severity'" + ] + } + ], + "source": [ + "dangerousroadd['total'] = dangerousroad['Accident_Severity']['Slight'].sum() \n", + "dangerousroadd" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([0, 1, 2, 3, 4, 5]),\n", + " [Text(0, 0, 'A'),\n", + " Text(1, 0, 'A(M)'),\n", + " Text(2, 0, 'B'),\n", + " Text(3, 0, 'C'),\n", + " Text(4, 0, 'Motorway'),\n", + " Text(5, 0, 'U')])" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "<Figure size 1440x720 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Draw a vertical bar chart\n", + "\n", + "dangerousroad.plot.bar(stacked=True,rot=15, title=\"Accidents Time \",figsize=(20, 10))\n", + "plt.xticks(fontsize=20)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>period</th>\n", + " <th>Serious</th>\n", + " <th>Fatal</th>\n", + " <th>Slight</th>\n", + " <th>Total_casualties</th>\n", + " <th>KSI</th>\n", + " <th>\"%\"KSI</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>A(M)</td>\n", + " <td>100648</td>\n", + " <td>115</td>\n", + " <td>159036</td>\n", + " <td>259799</td>\n", + " <td>100763</td>\n", + " <td>38.784984</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>A</td>\n", + " <td>45662</td>\n", + " <td>16630</td>\n", + " <td>75254</td>\n", + " <td>137546</td>\n", + " <td>62292</td>\n", + " <td>45.288122</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>B</td>\n", + " <td>149873</td>\n", + " <td>1534</td>\n", + " <td>4717</td>\n", + " <td>156124</td>\n", + " <td>151407</td>\n", + " <td>96.978684</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>C</td>\n", + " <td>26768</td>\n", + " <td>4079</td>\n", + " <td>866728</td>\n", + " <td>897575</td>\n", + " <td>30847</td>\n", + " <td>3.436704</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>U</td>\n", + " <td>657</td>\n", + " <td>2221</td>\n", + " <td>237083</td>\n", + " <td>239961</td>\n", + " <td>2878</td>\n", + " <td>1.199362</td>\n", + " </tr>\n", + " <tr>\n", + " <th>5</th>\n", + " <td>Motorway</td>\n", + " <td>9318</td>\n", + " <td>5119</td>\n", + " <td>581985</td>\n", + " <td>596422</td>\n", + " <td>14437</td>\n", + " <td>2.420602</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " period Serious Fatal Slight Total_casualties KSI \"%\"KSI\n", + "0 A(M) 100648 115 159036 259799 100763 38.784984\n", + "1 A 45662 16630 75254 137546 62292 45.288122\n", + "2 B 149873 1534 4717 156124 151407 96.978684\n", + "3 C 26768 4079 866728 897575 30847 3.436704\n", + "4 U 657 2221 237083 239961 2878 1.199362\n", + "5 Motorway 9318 5119 581985 596422 14437 2.420602" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "dangerousroad_df=dangerousroad.toPandas()\n", + "\n", + "\n", + "dangerousroad_dfindex=dangerousroad_df.set_index('1st_Road_Class')\n", + "dangerousroad_dfindex3=dangerousroad_dfindex\n", + "dangerousroad_dfindex3\n", + "\n", + "grouped = dangerousroad_dfindex3.groupby(dangerousroad_dfindex3.Accident_Severity)\n", + "Serious = grouped.get_group(\"Serious\")\n", + "Serious=Serious[\"Total accidents\"]\n", + "Serious=Serious.reset_index(drop=True)\n", + "Fatal = grouped.get_group(\"Fatal\")\n", + "Fatal=Fatal[\"Total accidents\"]\n", + "Fatal=Fatal.reset_index(drop=True)\n", + "Slight = grouped.get_group(\"Slight\")\n", + "Slight=Slight[\"Total accidents\"]\n", + "Slight=Slight.reset_index(drop=True)\n", + "Slight\n", + "Casulaty = pd.DataFrame({'period': ['A(M)', 'A', 'B','C','U','Motorway'],\n", + " 'Serious': Serious,\n", + " 'Fatal': Fatal,\n", + " 'Slight': Slight})\n", + "Casulaty\n", + "dflist=['Serious','Fatal','Slight']\n", + "Casulaty['Total_casualties']=Casulaty[dflist].sum(axis=1)\n", + "\n", + "Casulaty_spark=spark.createDataFrame(Casulaty)\n", + "Casulaty_spark=Casulaty_spark.withColumn('KSI', Casulaty_spark[2]+Casulaty_spark[1])\n", + "Casulaty_spark=Casulaty_spark.withColumn('\"%\"KSI', (Casulaty_spark[5]/Casulaty_spark[4])*100)\n", + "Casulaty_spark_df=Casulaty_spark.toPandas()\n", + "\n", + "Casulaty_spark_df\n", + "#comherelater" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>period</th>\n", + " <th>Serious</th>\n", + " <th>Fatal</th>\n", + " <th>Slight</th>\n", + " <th>Total_casualties</th>\n", + " <th>KSI</th>\n", + " <th>\"%\"KSI</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>A(M)</td>\n", + " <td>100648</td>\n", + " <td>115</td>\n", + " <td>159036</td>\n", + " <td>259799</td>\n", + " <td>100763</td>\n", + " <td>38.784984</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>A</td>\n", + " <td>1238</td>\n", + " <td>16630</td>\n", + " <td>68635</td>\n", + " <td>86503</td>\n", + " <td>17868</td>\n", + " <td>20.655931</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>B</td>\n", + " <td>45662</td>\n", + " <td>1356</td>\n", + " <td>4717</td>\n", + " <td>51735</td>\n", + " <td>47018</td>\n", + " <td>90.882381</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>C</td>\n", + " <td>149873</td>\n", + " <td>178</td>\n", + " <td>866728</td>\n", + " <td>1016779</td>\n", + " <td>150051</td>\n", + " <td>14.757484</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>M</td>\n", + " <td>26768</td>\n", + " <td>4079</td>\n", + " <td>237083</td>\n", + " <td>267930</td>\n", + " <td>30847</td>\n", + " <td>11.513082</td>\n", + " </tr>\n", + " <tr>\n", + " <th>5</th>\n", + " <td>U</td>\n", + " <td>657</td>\n", + " <td>2221</td>\n", + " <td>6619</td>\n", + " <td>9497</td>\n", + " <td>2878</td>\n", + " <td>30.304307</td>\n", + " </tr>\n", + " <tr>\n", + " <th>6</th>\n", + " <td>Motorway</td>\n", + " <td>8080</td>\n", + " <td>5119</td>\n", + " <td>581985</td>\n", + " <td>595184</td>\n", + " <td>13199</td>\n", + " <td>2.217634</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " period Serious Fatal Slight Total_casualties KSI \"%\"KSI\n", + "0 A(M) 100648 115 159036 259799 100763 38.784984\n", + "1 A 1238 16630 68635 86503 17868 20.655931\n", + "2 B 45662 1356 4717 51735 47018 90.882381\n", + "3 C 149873 178 866728 1016779 150051 14.757484\n", + "4 M 26768 4079 237083 267930 30847 11.513082\n", + "5 U 657 2221 6619 9497 2878 30.304307\n", + "6 Motorway 8080 5119 581985 595184 13199 2.217634" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "dangerousroad_df=dangerousroad.toPandas()\n", + "\n", + "\n", + "dangerousroad_dfindex=dangerousroad_df.set_index('1st_Road_Class')\n", + "dangerousroad_dfindex3=dangerousroad_dfindex\n", + "dangerousroad_dfindex3\n", + "\n", + "grouped = dangerousroad_dfindex3.groupby(dangerousroad_dfindex3.Accident_Severity)\n", + "Serious = grouped.get_group(\"Serious\")\n", + "Serious=Serious[\"Total accidents\"]\n", + "Serious=Serious.reset_index(drop=True)\n", + "Fatal = grouped.get_group(\"Fatal\")\n", + "Fatal=Fatal[\"Total accidents\"]\n", + "Fatal=Fatal.reset_index(drop=True)\n", + "Slight = grouped.get_group(\"Slight\")\n", + "Slight=Slight[\"Total accidents\"]\n", + "Slight=Slight.reset_index(drop=True)\n", + "Slight\n", + "Casulaty = pd.DataFrame({'period': ['A(M)', 'A', 'B','C', 'M','U','Motorway'],\n", + " 'Serious': Serious,\n", + " 'Fatal': Fatal,\n", + " 'Slight': Slight})\n", + "Casulaty\n", + "dflist=['Serious','Fatal','Slight']\n", + "Casulaty['Total_casualties']=Casulaty[dflist].sum(axis=1)\n", + "\n", + "Casulaty_spark=spark.createDataFrame(Casulaty)\n", + "Casulaty_spark=Casulaty_spark.withColumn('KSI', Casulaty_spark[2]+Casulaty_spark[1])\n", + "Casulaty_spark=Casulaty_spark.withColumn('\"%\"KSI', (Casulaty_spark[5]/Casulaty_spark[4])*100)\n", + "Casulaty_spark_df=Casulaty_spark.toPandas()\n", + "\n", + "Casulaty_spark_df\n", + "#comherelater" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+-----------------+--------------------+---------------+\n", + "|Accident_Severity| Age_Band_of_Driver|Total accidents|\n", + "+-----------------+--------------------+---------------+\n", + "| Slight|Data missing or o...| 304989|\n", + "| Serious| 40Y to 70Y| 101012|\n", + "| Fatal| 40Y to 70Y| 10720|\n", + "| Slight| 40Y to 70Y| 620252|\n", + "| Slight| Upto 20Y| 280929|\n", + "| Serious| Upto 20Y| 45292|\n", + "| Fatal| Upto 20Y| 3828|\n", + "| Serious|Data missing or o...| 32055|\n", + "| Fatal|Data missing or o...| 1560|\n", + "| Fatal| Over 70| 1859|\n", + "| Slight| 20Y to 40Y| 1074279|\n", + "| Slight| Over 70| 56391|\n", + "| Serious| 20Y to 40Y| 159200|\n", + "| Serious| Over 70| 11763|\n", + "| Fatal| 20Y to 40Y| 15533|\n", + "+-----------------+--------------------+---------------+\n", + "\n" + ] + } + ], + "source": [ + "DvrAge\n", + "resultage = Accidensev.join(DvrAge,on=['Accident_Index'])\n", + "resultage = resultage.groupby('Accident_Severity','Age_Band_of_Driver').agg(F.count(resultage.Accident_Index).alias('Total accidents'))\n", + "resultage.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+-----------------+--------------------+---------------+\n", + "|Accident_Severity| Vehicle_Type|Total accidents|\n", + "+-----------------+--------------------+---------------+\n", + "| Slight| Goods| 192757|\n", + "| Fatal|Agricultural vehicle| 309|\n", + "| Fatal| Ridden horse| 48|\n", + "| Slight| Pedal cycle| 171809|\n", + "| Slight|Data missing or o...| 469|\n", + "| Fatal| Pedal cycle| 1441|\n", + "| Slight| Other vehicle| 21436|\n", + "| Serious| Car| 202353|\n", + "| Fatal| Motorcycle| 5117|\n", + "| Slight| Motorcycle| 174054|\n", + "| Serious|Data missing or o...| 59|\n", + "| Fatal| Car| 18530|\n", + "| Slight| Ridden horse| 1031|\n", + "| Fatal|Data missing or o...| 29|\n", + "| Serious| Bus| 10917|\n", + "| Serious| Ridden horse| 299|\n", + "| Fatal| Bus| 1157|\n", + "| Slight|Agricultural vehicle| 5212|\n", + "| Serious| Pedal cycle| 32844|\n", + "| Fatal| Goods| 5259|\n", + "+-----------------+--------------------+---------------+\n", + "only showing top 20 rows\n", + "\n" + ] + } + ], + "source": [ + "result = Accidensev.join(VECHTYPE,on=['Accident_Index'])\n", + "result = result.groupby('Accident_Severity','Vehicle_Type').agg(F.count(result.Accident_Index).alias('Total accidents'))\n", + "result.show()\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+-----------------+------------+---------------+\n", + "|Accident_Severity|Vehicle_Type|Total accidents|\n", + "+-----------------+------------+---------------+\n", + "| Slight| Goods| 192757|\n", + "| Slight| Pedal cycle| 171809|\n", + "| Fatal| Pedal cycle| 1441|\n", + "| Serious| Car| 202353|\n", + "| Fatal| Motorcycle| 5117|\n", + "| Slight| Motorcycle| 174054|\n", + "| Fatal| Car| 18530|\n", + "| Serious| Bus| 10917|\n", + "| Fatal| Bus| 1157|\n", + "| Serious| Pedal cycle| 32844|\n", + "| Fatal| Goods| 5259|\n", + "| Serious| Motorcycle| 61906|\n", + "| Slight| Car| 1388224|\n", + "| Slight| Bus| 80324|\n", + "| Serious| Goods| 29198|\n", + "+-----------------+------------+---------------+\n", + "\n" + ] + } + ], + "source": [ + "Car_AS=result.filter(result.Vehicle_Type.contains(\"Pedal cycle\")|result.Vehicle_Type.contains(\"Motorcycle\")|result.Vehicle_Type.contains(\"Car\")|result.Vehicle_Type.contains(\"Bus\")|result.Vehicle_Type.contains(\"Goods\"))\n", + "Car_AS.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+-----------------+--------------------+---------------+\n", + "|Accident_Severity| Sex_of_Driver|Total accidents|\n", + "+-----------------+--------------------+---------------+\n", + "| Fatal| Female| 6322|\n", + "| Slight|Data missing or o...| 30|\n", + "| Serious|Data missing or o...| 4|\n", + "| Slight| Male| 1272839|\n", + "| Serious| Unkown| 20700|\n", + "| Fatal|Data missing or o...| 2|\n", + "| Fatal| Male| 21417|\n", + "| Slight| Unkown| 159819|\n", + "| Serious| Female| 83216|\n", + "| Serious| Male| 210294|\n", + "| Fatal| Unkown| 1078|\n", + "| Slight| Female| 701888|\n", + "+-----------------+--------------------+---------------+\n", + "\n" + ] + } + ], + "source": [ + "result2 = Accidensev.join(DvrSex,on=['Accident_Index'])\n", + "result2 = result2.groupby('Accident_Severity','Sex_of_Driver').agg(F.count(result2.Accident_Index).alias('Total accidents'))\n", + "result2.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "ename": "ValueError", + "evalue": "array length 1 does not match index length 9", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/var/folders/v0/jqv1xcw13pn37fh0ppsl8b_w0000gp/T/ipykernel_532/3087850080.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 20\u001b[0m \u001b[0mSlight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mSlight\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreset_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 21\u001b[0m \u001b[0mSlight\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 22\u001b[0;31m Casulaty1 = pd.DataFrame({'period': ['Car'],\n\u001b[0m\u001b[1;32m 23\u001b[0m \u001b[0;34m'Serious'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mSerious\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 24\u001b[0m \u001b[0;34m'Fatal'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mFatal\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.9/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, data, index, columns, dtype, copy)\u001b[0m\n\u001b[1;32m 612\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 613\u001b[0m \u001b[0;31m# GH#38939 de facto copy defaults to False only in non-dict cases\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 614\u001b[0;31m \u001b[0mmgr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdict_to_mgr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtyp\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmanager\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 615\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMaskedArray\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 616\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmrecords\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mmrecords\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.9/site-packages/pandas/core/internals/construction.py\u001b[0m in \u001b[0;36mdict_to_mgr\u001b[0;34m(data, index, columns, dtype, typ, copy)\u001b[0m\n\u001b[1;32m 460\u001b[0m \u001b[0;31m# TODO: can we get rid of the dt64tz special case above?\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 461\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 462\u001b[0;31m return arrays_to_mgr(\n\u001b[0m\u001b[1;32m 463\u001b[0m \u001b[0marrays\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata_names\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtyp\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtyp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconsolidate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 464\u001b[0m )\n", + "\u001b[0;32m/usr/local/lib/python3.9/site-packages/pandas/core/internals/construction.py\u001b[0m in \u001b[0;36marrays_to_mgr\u001b[0;34m(arrays, arr_names, index, columns, dtype, verify_integrity, typ, consolidate)\u001b[0m\n\u001b[1;32m 115\u001b[0m \u001b[0;31m# figure out the index, if necessary\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 116\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mindex\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 117\u001b[0;31m \u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_extract_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marrays\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 118\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 119\u001b[0m \u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mensure_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.9/site-packages/pandas/core/internals/construction.py\u001b[0m in \u001b[0;36m_extract_index\u001b[0;34m(data)\u001b[0m\n\u001b[1;32m 635\u001b[0m \u001b[0;34mf\"length {len(index)}\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 636\u001b[0m )\n\u001b[0;32m--> 637\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 638\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 639\u001b[0m \u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mibase\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdefault_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlengths\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mValueError\u001b[0m: array length 1 does not match index length 9" + ] + } + ], + "source": [ + "Vehicle_factors=result.toPandas()\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "Vechile_Severity=Vehicle_factors[\"Accident_Severity\"]\n", + "Total_accidents=Vehicle_factors[\"Total accidents\"]\n", + "\n", + "Vehicle_factorsindex=Vehicle_factors.set_index('Accident_Severity')\n", + "Vehicle_factorsindex3=Vehicle_factorsindex[:3]\n", + "\n", + "#Accident_Severitydfindex3.plot.bar(rot=4)\n", + "grouped = Vehicle_factors.groupby(Vehicle_factors.Accident_Severity)\n", + "Serious = grouped.get_group(\"Serious\")\n", + "Serious=Serious[\"Total accidents\"]\n", + "Serious=Serious.reset_index(drop=True)\n", + "Fatal = grouped.get_group(\"Fatal\")\n", + "Fatal=Fatal[\"Total accidents\"]\n", + "Fatal=Fatal.reset_index(drop=True)\n", + "Slight = grouped.get_group(\"Slight\")\n", + "Slight=Slight[\"Total accidents\"]\n", + "Slight=Slight.reset_index(drop=True)\n", + "Slight\n", + "Casulaty1 = pd.DataFrame({'period': ['Car'],\n", + " 'Serious': Serious,\n", + " 'Fatal': Fatal,\n", + " 'Slight': Slight})\n", + "Casulaty1\n", + "dflist=['Serious','Fatal','Slight']\n", + "Casulaty1['Total_casualties']=Casulaty1[dflist].sum(axis=1)\n", + "\n", + "Casulaty_spark1=spark.createDataFrame(Casulaty1)\n", + "Casulaty_spark1.show()\n", + "#Casulaty_spark1=Casulaty_spark1.withColumn('KSI', Casulaty_spark1[2]+Casulaty_spark1[1])\n", + "#Casulaty_spark_df=Casulaty_spark.toPandas()\n", + "\n", + "#Casulaty_spark_df\n" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [], + "source": [ + "Accident_Severity_Vech_df = Accident_Information20052019_df.groupby('Accident_Severity').agg(F.count(Accident_Information20052019_df.Accident_Index).alias('Total accidents'))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+-----------------+---------------+\n", + "|Accident_Severity|Total accidents|\n", + "+-----------------+---------------+\n", + "| Slight| 1924803|\n", + "| Fatal| 29698|\n", + "| Serious| 332926|\n", + "+-----------------+---------------+\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "Accident_SeverityKSI_df = Accident_Information20052019_df.groupby('Accident_Severity').agg(F.count(Accident_Information20052019_df.Accident_Index).alias('Total accidents'))\n", + "#Accident_Severity_df.sort(\"Year\").show(truncate=False)\n", + "Accident_SeverityKSI_df.show()\n", + "df2 = Accident_SeverityKSI_df.toPandas()\n", + "#Creating Visualization\n", + "fig = plt.pie(df2['Total accidents'], autopct='%1.1f%%', startangle=140,labels=df2['Accident_Severity'])\n", + "#plt.title('No of age group where lstat < 2')\n", + "plt.show()\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+-----------------+----+---------------+\n", + "|Accident_Severity|Year|Total accidents|\n", + "+-----------------+----+---------------+\n", + "| Serious|2005| 25029|\n", + "| Fatal|2005| 2913|\n", + "| Slight|2005| 170793|\n", + "| Serious|2006| 24946|\n", + "| Slight|2006| 161289|\n", + "| Fatal|2006| 2926|\n", + "| Serious|2007| 24322|\n", + "| Fatal|2007| 2714|\n", + "| Slight|2007| 155079|\n", + "| Serious|2008| 23121|\n", + "| Slight|2008| 145129|\n", + "| Fatal|2008| 2341|\n", + "| Slight|2009| 139500|\n", + "| Serious|2009| 21997|\n", + "| Fatal|2009| 2057|\n", + "| Fatal|2010| 1731|\n", + "| Serious|2010| 20440|\n", + "| Slight|2010| 132243|\n", + "| Serious|2011| 20986|\n", + "| Fatal|2011| 1797|\n", + "| Slight|2011| 128691|\n", + "| Fatal|2012| 1637|\n", + "| Slight|2012| 123033|\n", + "| Serious|2012| 20901|\n", + "| Fatal|2013| 1608|\n", + "| Serious|2013| 19624|\n", + "| Slight|2013| 117428|\n", + "| Serious|2014| 20676|\n", + "| Slight|2014| 123988|\n", + "| Fatal|2014| 1658|\n", + "| Serious|2015| 20038|\n", + "| Slight|2015| 118402|\n", + "| Fatal|2015| 1616|\n", + "| Serious|2016| 21725|\n", + "| Fatal|2016| 1695|\n", + "| Slight|2016| 113201|\n", + "| Fatal|2017| 1676|\n", + "| Serious|2017| 22534|\n", + "| Slight|2017| 105772|\n", + "| Serious|2018| 23165|\n", + "+-----------------+----+---------------+\n", + "only showing top 40 rows\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [] + } + ], + "source": [ + "Accident_Severity_df = Accident_Information20052019_df.groupby('Accident_Severity','Year').agg(F.count(Accident_Information20052019_df.Accident_Index).alias('Total accidents'))\n", + "Accident_Severity_df=Accident_Severity_df.sort(\"Year\")\n", + "Accident_Severity_df.show(40)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>period</th>\n", + " <th>Serious</th>\n", + " <th>Fatal</th>\n", + " <th>Slight</th>\n", + " <th>Total_casualties</th>\n", + " <th>KSI</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>2005</td>\n", + " <td>25029</td>\n", + " <td>2913</td>\n", + " <td>170793</td>\n", + " <td>198735</td>\n", + " <td>27942</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>2006</td>\n", + " <td>24946</td>\n", + " <td>2926</td>\n", + " <td>161289</td>\n", + " <td>189161</td>\n", + " <td>27872</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>2007</td>\n", + " <td>24322</td>\n", + " <td>2714</td>\n", + " <td>155079</td>\n", + " <td>182115</td>\n", + " <td>27036</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>2008</td>\n", + " <td>23121</td>\n", + " <td>2341</td>\n", + " <td>145129</td>\n", + " <td>170591</td>\n", + " <td>25462</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>2009</td>\n", + " <td>21997</td>\n", + " <td>2057</td>\n", + " <td>139500</td>\n", + " <td>163554</td>\n", + " <td>24054</td>\n", + " </tr>\n", + " <tr>\n", + " <th>5</th>\n", + " <td>2010</td>\n", + " <td>20440</td>\n", + " <td>1731</td>\n", + " <td>132243</td>\n", + " <td>154414</td>\n", + " <td>22171</td>\n", + " </tr>\n", + " <tr>\n", + " <th>6</th>\n", + " <td>2011</td>\n", + " <td>20986</td>\n", + " <td>1797</td>\n", + " <td>128691</td>\n", + " <td>151474</td>\n", + " <td>22783</td>\n", + " </tr>\n", + " <tr>\n", + " <th>7</th>\n", + " <td>2012</td>\n", + " <td>20901</td>\n", + " <td>1637</td>\n", + " <td>123033</td>\n", + " <td>145571</td>\n", + " <td>22538</td>\n", + " </tr>\n", + " <tr>\n", + " <th>8</th>\n", + " <td>2013</td>\n", + " <td>19624</td>\n", + " <td>1608</td>\n", + " <td>117428</td>\n", + " <td>138660</td>\n", + " <td>21232</td>\n", + " </tr>\n", + " <tr>\n", + " <th>9</th>\n", + " <td>2014</td>\n", + " <td>20676</td>\n", + " <td>1658</td>\n", + " <td>123988</td>\n", + " <td>146322</td>\n", + " <td>22334</td>\n", + " </tr>\n", + " <tr>\n", + " <th>10</th>\n", + " <td>2015</td>\n", + " <td>20038</td>\n", + " <td>1616</td>\n", + " <td>118402</td>\n", + " <td>140056</td>\n", + " <td>21654</td>\n", + " </tr>\n", + " <tr>\n", + " <th>11</th>\n", + " <td>2016</td>\n", + " <td>21725</td>\n", + " <td>1695</td>\n", + " <td>113201</td>\n", + " <td>136621</td>\n", + " <td>23420</td>\n", + " </tr>\n", + " <tr>\n", + " <th>12</th>\n", + " <td>2017</td>\n", + " <td>22534</td>\n", + " <td>1676</td>\n", + " <td>105772</td>\n", + " <td>129982</td>\n", + " <td>24210</td>\n", + " </tr>\n", + " <tr>\n", + " <th>13</th>\n", + " <td>2018</td>\n", + " <td>23165</td>\n", + " <td>1671</td>\n", + " <td>97799</td>\n", + " <td>122635</td>\n", + " <td>24836</td>\n", + " </tr>\n", + " <tr>\n", + " <th>14</th>\n", + " <td>2019</td>\n", + " <td>23422</td>\n", + " <td>1658</td>\n", + " <td>92456</td>\n", + " <td>117536</td>\n", + " <td>25080</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " period Serious Fatal Slight Total_casualties KSI\n", + "0 2005 25029 2913 170793 198735 27942\n", + "1 2006 24946 2926 161289 189161 27872\n", + "2 2007 24322 2714 155079 182115 27036\n", + "3 2008 23121 2341 145129 170591 25462\n", + "4 2009 21997 2057 139500 163554 24054\n", + "5 2010 20440 1731 132243 154414 22171\n", + "6 2011 20986 1797 128691 151474 22783\n", + "7 2012 20901 1637 123033 145571 22538\n", + "8 2013 19624 1608 117428 138660 21232\n", + "9 2014 20676 1658 123988 146322 22334\n", + "10 2015 20038 1616 118402 140056 21654\n", + "11 2016 21725 1695 113201 136621 23420\n", + "12 2017 22534 1676 105772 129982 24210\n", + "13 2018 23165 1671 97799 122635 24836\n", + "14 2019 23422 1658 92456 117536 25080" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "Accident_Severitydf = Accident_Severity_df.toPandas()\n", + "#df.plot()\n", + "#display(plt.show())\n", + "Year=Accident_Severitydf[\"Year\"]\n", + "Accident_Severity=Accident_Severitydf[\"Accident_Severity\"]\n", + "Total_accidents=Accident_Severitydf[\"Total accidents\"]\n", + "dddd=Accident_Severitydf[[\"Year\",\"Accident_Severity\"]]\n", + "dddd\n", + "##Accident_Severitydf.plot.bar(x='Year', y='Total accidents')\n", + "#dff =(dddd, index=Year)\n", + "#Accident_Severitydf.plot.bar(rot=0)\n", + "\n", + "Accident_Severitydfindex=Accident_Severitydf.set_index('Year')\n", + "Accident_Severitydfindex3=Accident_Severitydfindex[:3]\n", + "Accident_Severitydfindex3\n", + "Accident_Severitydf\n", + "#Accident_Severitydfindex3.plot.bar(rot=4)\n", + "grouped = Accident_Severitydf.groupby(Accident_Severitydf.Accident_Severity)\n", + "Serious = grouped.get_group(\"Serious\")\n", + "Serious=Serious[\"Total accidents\"]\n", + "Serious=Serious.reset_index(drop=True)\n", + "Fatal = grouped.get_group(\"Fatal\")\n", + "Fatal=Fatal[\"Total accidents\"]\n", + "Fatal=Fatal.reset_index(drop=True)\n", + "Slight = grouped.get_group(\"Slight\")\n", + "Slight=Slight[\"Total accidents\"]\n", + "Slight=Slight.reset_index(drop=True)\n", + "Slight\n", + "Casulaty = pd.DataFrame({'period': [2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012,2013,2014,2015,2016,2017,2018,2019],\n", + " 'Serious': Serious,\n", + " 'Fatal': Fatal,\n", + " 'Slight': Slight})\n", + "Casulaty\n", + "dflist=['Serious','Fatal','Slight']\n", + "Casulaty['Total_casualties']=Casulaty[dflist].sum(axis=1)\n", + "\n", + "Casulaty_spark=spark.createDataFrame(Casulaty)\n", + "Casulaty_spark=Casulaty_spark.withColumn('KSI', Casulaty_spark[2]+Casulaty_spark[1])\n", + "Casulaty_spark_df=Casulaty_spark.toPandas()\n", + "\n", + "Casulaty_spark_df\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0, 'Year ')" + ] + }, + "execution_count": 63, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": [ + "<Figure size 648x216 with 0 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "<Figure size 1440x432 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "<Figure size 648x216 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "plt.figure(figsize=(9, 3))\n", + "Casulaty_spark_df[['period', 'Serious','Slight', 'Fatal','KSI','Total_casualties']].plot(x='period', kind='line',figsize=(20,6))\n", + "plt.ylabel('Number of Casualties ')\n", + "plt.xlabel('Year ')\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "plt.figure(figsize=(9, 3))\n", + "#, kind='line',figsize=(20,6)\n", + "Casulaty_spark_df['Fatal'].plot(x='period')\n", + "plt.ylabel('Number of Casualties ')\n", + "plt.xlabel('Year ')\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DataFrame[Accident_Index: string, 1st_Road_Class: string, 1st_Road_Number: string, 2nd_Road_Class: string, 2nd_Road_Number: string, Accident_Severity: string, Carriageway_Hazards: string, Date: string, Day_of_Week: string, Did_Police_Officer_Attend_Scene_of_Accident: string, Junction_Control: string, Junction_Detail: string, Latitude: string, Light_Conditions: string, Local_Authority_(District): string, Local_Authority_(Highway): string, Location_Easting_OSGR: string, Location_Northing_OSGR: string, Longitude: string, LSOA_of_Accident_Location: string, Number_of_Casualties: string, Number_of_Vehicles: string, Pedestrian_Crossing-Human_Control: string, Pedestrian_Crossing-Physical_Facilities: string, Police_Force: string, Road_Surface_Conditions: string, Road_Type: string, Special_Conditions_at_Site: string, Speed_limit: string, Time: string, Urban_or_Rural_Area: string, Weather_Conditions: string, Year: int]" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Accident_Information20052019_df" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "Accident_Information20052019_df=Accident_Information20052019_df.withColumn(\n", + " \"Junction_Detail\",\n", + " when(\n", + " col(\"Junction_Detail\") == 0,\n", + " \"Not at or within 20 metres of junction\"\n", + " ).when(\n", + " col(\"Junction_Detail\") == 1,\n", + " \"Roundabout\"\n", + " ).when(\n", + " col(\"Junction_Detail\") == 2,\n", + " \"Mini-roundabout\"\n", + " ).\n", + " when(\n", + " col(\"Junction_Detail\") == 3,\n", + " \"T or staggered junction\"\n", + " ).\n", + " when(\n", + " col(\"Junction_Detail\") == 5,\n", + " \"Slip road\"\n", + " ).\n", + " when(\n", + " col(\"Junction_Detail\") == 6,\n", + " \"Crossroads\"\n", + " ).\n", + " when(\n", + " col(\"Junction_Detail\") == 7,\n", + " \"More than 4 arms (not roundabout)\"\n", + " ).\n", + " when(\n", + " col(\"Junction_Detail\") == 8,\n", + " \"Private drive or entrance\"\n", + " ).\n", + " when(\n", + " col(\"Junction_Detail\") == 9,\n", + " \"Other junction\"\n", + " ).\n", + " when(\n", + " col(\"Junction_Detail\") == -1,\n", + " \"Data missing or out of range\"\n", + " ).otherwise(col(\"Junction_Detail\")),\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+--------------------------------------+---------------+\n", + "|Junction_Detail |Total accidents|\n", + "+--------------------------------------+---------------+\n", + "|Data missing or out of range |2903 |\n", + "|Mini-roundabout |25668 |\n", + "|More than 4 arms (not roundabout) |28617 |\n", + "|Slip road |33401 |\n", + "|Other junction |70008 |\n", + "|Private drive or entrance |78274 |\n", + "|Not at or within 20 metres of junction|101069 |\n", + "|Roundabout |196371 |\n", + "|Crossroads |218926 |\n", + "|T or staggered junction |704967 |\n", + "|Not at junction or within 20 metres |827223 |\n", + "+--------------------------------------+---------------+\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [] + } + ], + "source": [ + "Junction_Detail_df= Accident_Information20052019_df.groupby('Junction_Detail').agg(F.count(Accident_Information20052019_df.Accident_Index).alias('Total accidents'))\n", + "Junction_Detail_df.sort(\"Total accidents\").show(truncate=False)\n", + "\n", + "#Junction_Detail_df.show\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DataFrame[Accident_Index: string, 1st_Road_Class: string, 1st_Road_Number: string, 2nd_Road_Class: string, 2nd_Road_Number: string, Accident_Severity: string, Carriageway_Hazards: string, Date: string, Day_of_Week: string, Did_Police_Officer_Attend_Scene_of_Accident: string, Junction_Control: string, Junction_Detail: string, Latitude: string, Light_Conditions: string, Local_Authority_(District): string, Local_Authority_(Highway): string, Location_Easting_OSGR: string, Location_Northing_OSGR: string, Longitude: string, LSOA_of_Accident_Location: string, Number_of_Casualties: string, Number_of_Vehicles: string, Pedestrian_Crossing-Human_Control: string, Pedestrian_Crossing-Physical_Facilities: string, Police_Force: string, Road_Surface_Conditions: string, Road_Type: string, Special_Conditions_at_Site: string, Speed_limit: string, Time: string, Urban_or_Rural_Area: string, Weather_Conditions: string, Year: int]" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Accident_Information20052019_df" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+--------------------------------------+-----------------+---------------+\n", + "|Junction_Detail |Accident_Severity|Total accidents|\n", + "+--------------------------------------+-----------------+---------------+\n", + "|Data missing or out of range |Fatal |1 |\n", + "|Mini-roundabout |Fatal |96 |\n", + "|Data missing or out of range |Serious |158 |\n", + "|More than 4 arms (not roundabout) |Fatal |177 |\n", + "|Slip road |Fatal |480 |\n", + "|Roundabout |Fatal |669 |\n", + "|Other junction |Fatal |690 |\n", + "|Private drive or entrance |Fatal |770 |\n", + "|Crossroads |Fatal |1636 |\n", + "|Not at or within 20 metres of junction|Fatal |2118 |\n", + "|Mini-roundabout |Serious |2678 |\n", + "|Data missing or out of range |Slight |2744 |\n", + "|More than 4 arms (not roundabout) |Serious |3551 |\n", + "|Slip road |Serious |3786 |\n", + "|T or staggered junction |Fatal |6170 |\n", + "|Other junction |Serious |9562 |\n", + "|Private drive or entrance |Serious |11266 |\n", + "|Not at junction or within 20 metres |Fatal |16891 |\n", + "|Roundabout |Serious |18574 |\n", + "|Not at or within 20 metres of junction|Serious |21490 |\n", + "+--------------------------------------+-----------------+---------------+\n", + "only showing top 20 rows\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [] + } + ], + "source": [ + "Junction_Detailyearly_df= Accident_Information20052019_df.groupby('Junction_Detail','Accident_Severity',).agg(F.count(Accident_Information20052019_df.Accident_Index).alias('Total accidents'))\n", + "Junction_Detailyearly_df.sort(\"Total accidents\").show(truncate=False)\n", + "\n", + "#Junction_Detail_df.show\n" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead tr th {\n", + " text-align: left;\n", + " }\n", + "\n", + " .dataframe thead tr:last-of-type th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr>\n", + " <th></th>\n", + " <th colspan=\"3\" halign=\"left\">Total accidents</th>\n", + " </tr>\n", + " <tr>\n", + " <th>Accident_Severity</th>\n", + " <th>Fatal</th>\n", + " <th>Serious</th>\n", + " <th>Slight</th>\n", + " </tr>\n", + " <tr>\n", + " <th>Junction_Detail</th>\n", + " <th></th>\n", + " <th></th>\n", + " <th></th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>Crossroads</th>\n", + " <td>1636</td>\n", + " <td>28086</td>\n", + " <td>189204</td>\n", + " </tr>\n", + " <tr>\n", + " <th>Data missing or out of range</th>\n", + " <td>1</td>\n", + " <td>158</td>\n", + " <td>2744</td>\n", + " </tr>\n", + " <tr>\n", + " <th>Mini-roundabout</th>\n", + " <td>96</td>\n", + " <td>2678</td>\n", + " <td>22894</td>\n", + " </tr>\n", + " <tr>\n", + " <th>More than 4 arms (not roundabout)</th>\n", + " <td>177</td>\n", + " <td>3551</td>\n", + " <td>24889</td>\n", + " </tr>\n", + " <tr>\n", + " <th>Not at junction or within 20 metres</th>\n", + " <td>16891</td>\n", + " <td>133816</td>\n", + " <td>676516</td>\n", + " </tr>\n", + " <tr>\n", + " <th>Not at or within 20 metres of junction</th>\n", + " <td>2118</td>\n", + " <td>21490</td>\n", + " <td>77461</td>\n", + " </tr>\n", + " <tr>\n", + " <th>Other junction</th>\n", + " <td>690</td>\n", + " <td>9562</td>\n", + " <td>59756</td>\n", + " </tr>\n", + " <tr>\n", + " <th>Private drive or entrance</th>\n", + " <td>770</td>\n", + " <td>11266</td>\n", + " <td>66238</td>\n", + " </tr>\n", + " <tr>\n", + " <th>Roundabout</th>\n", + " <td>669</td>\n", + " <td>18574</td>\n", + " <td>177128</td>\n", + " </tr>\n", + " <tr>\n", + " <th>Slip road</th>\n", + " <td>480</td>\n", + " <td>3786</td>\n", + " <td>29135</td>\n", + " </tr>\n", + " <tr>\n", + " <th>T or staggered junction</th>\n", + " <td>6170</td>\n", + " <td>99959</td>\n", + " <td>598838</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " Total accidents \n", + "Accident_Severity Fatal Serious Slight\n", + "Junction_Detail \n", + "Crossroads 1636 28086 189204\n", + "Data missing or out of range 1 158 2744\n", + "Mini-roundabout 96 2678 22894\n", + "More than 4 arms (not roundabout) 177 3551 24889\n", + "Not at junction or within 20 metres 16891 133816 676516\n", + "Not at or within 20 metres of junction 2118 21490 77461\n", + "Other junction 690 9562 59756\n", + "Private drive or entrance 770 11266 66238\n", + "Roundabout 669 18574 177128\n", + "Slip road 480 3786 29135\n", + "T or staggered junction 6170 99959 598838" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Junction_Detailwithbrack=Junction_Detailyearly_df.toPandas()\n", + "Junction_Detailwithbrack=Junction_Detailwithbrack.pivot(index ='Junction_Detail', columns ='Accident_Severity')\n", + "Junction_Detailwithbrack" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]),\n", + " [Text(0, 0, 'Crossroads'),\n", + " Text(1, 0, 'Data missing or out of range'),\n", + " Text(2, 0, 'Mini-roundabout'),\n", + " Text(3, 0, 'More than 4 arms (not roundabout)'),\n", + " Text(4, 0, 'Not at junction or within 20 metres'),\n", + " Text(5, 0, 'Not at or within 20 metres of junction'),\n", + " Text(6, 0, 'Other junction'),\n", + " Text(7, 0, 'Private drive or entrance'),\n", + " Text(8, 0, 'Roundabout'),\n", + " Text(9, 0, 'Slip road'),\n", + " Text(10, 0, 'T or staggered junction')])" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "<Figure size 1440x720 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "Junction_Detailwithbrack.plot.bar(stacked=True,rot=90, title=\"Accidents Time \",figsize=(20, 10))\n", + "plt.xticks(fontsize=20)" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "ename": "AttributeError", + "evalue": "'numpy.int32' object has no attribute '_get_object_id'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/var/folders/v0/jqv1xcw13pn37fh0ppsl8b_w0000gp/T/ipykernel_532/1185095021.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0mTotal_accidents\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mAccident_Severitydf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Total accidents\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0mJunction_Detailyearly_dff\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mJunction_Detailyearly_df\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtoPandas\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0mgrouped\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mJunction_Detailyearly_dff\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mJunction_Detailyearly_df\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mJunction_Detail\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 9\u001b[0m \u001b[0mJ0\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgrouped\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Not at junction or within 20 metres\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0mJ0\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mJ0\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Total accidents\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.9/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mgroupby\u001b[0;34m(self, by, axis, level, as_index, sort, group_keys, squeeze, observed, dropna)\u001b[0m\n\u001b[1;32m 7624\u001b[0m \u001b[0;31m# error: Argument \"squeeze\" to \"DataFrameGroupBy\" has incompatible type\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7625\u001b[0m \u001b[0;31m# \"Union[bool, NoDefault]\"; expected \"bool\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 7626\u001b[0;31m return DataFrameGroupBy(\n\u001b[0m\u001b[1;32m 7627\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7628\u001b[0m \u001b[0mkeys\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mby\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.9/site-packages/pandas/core/groupby/groupby.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, obj, keys, axis, level, grouper, exclusions, selection, as_index, sort, group_keys, squeeze, observed, mutated, dropna)\u001b[0m\n\u001b[1;32m 886\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgrouper\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mget_grouper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 887\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 888\u001b[0;31m grouper, exclusions, obj = get_grouper(\n\u001b[0m\u001b[1;32m 889\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 890\u001b[0m \u001b[0mkeys\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.9/site-packages/pandas/core/groupby/grouper.py\u001b[0m in \u001b[0;36mget_grouper\u001b[0;34m(obj, key, axis, level, sort, observed, mutated, validate, dropna)\u001b[0m\n\u001b[1;32m 841\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mgpr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 842\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 843\u001b[0;31m \u001b[0;32mif\u001b[0m \u001b[0mis_in_obj\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgpr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# df.groupby(df['name'])\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 844\u001b[0m \u001b[0min_axis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 845\u001b[0m \u001b[0mexclusions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgpr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.9/site-packages/pandas/core/groupby/grouper.py\u001b[0m in \u001b[0;36mis_in_obj\u001b[0;34m(gpr)\u001b[0m\n\u001b[1;32m 833\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 834\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 835\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mgpr\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mgpr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 836\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mKeyError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mIndexError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 837\u001b[0m \u001b[0;31m# IndexError reached in e.g. test_skip_group_keys when we pass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.9/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 3418\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__getitem__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3419\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitem_from_zerodim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3420\u001b[0;31m \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_if_callable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3421\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3422\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mis_hashable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.9/site-packages/pandas/core/common.py\u001b[0m in \u001b[0;36mapply_if_callable\u001b[0;34m(maybe_callable, obj, **kwargs)\u001b[0m\n\u001b[1;32m 352\u001b[0m \"\"\"\n\u001b[1;32m 353\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcallable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmaybe_callable\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 354\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mmaybe_callable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 355\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 356\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mmaybe_callable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/Cellar/apache-spark/3.1.2/libexec/python/pyspark/sql/column.py\u001b[0m in \u001b[0;36malias\u001b[0;34m(self, *alias, **kwargs)\u001b[0m\n\u001b[1;32m 747\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mColumn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"as\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0malias\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mjmeta\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 748\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 749\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mColumn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"as\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0malias\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 750\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 751\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mmetadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.9/site-packages/py4j/java_gateway.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m 1294\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1295\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1296\u001b[0;31m \u001b[0margs_command\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtemp_args\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_build_args\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1297\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1298\u001b[0m \u001b[0mcommand\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mproto\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mCALL_COMMAND_NAME\u001b[0m \u001b[0;34m+\u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.9/site-packages/py4j/java_gateway.py\u001b[0m in \u001b[0;36m_build_args\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m 1258\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_build_args\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1259\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconverters\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconverters\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1260\u001b[0;31m \u001b[0;34m(\u001b[0m\u001b[0mnew_args\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtemp_args\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_args\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1261\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1262\u001b[0m \u001b[0mnew_args\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.9/site-packages/py4j/java_gateway.py\u001b[0m in \u001b[0;36m_get_args\u001b[0;34m(self, args)\u001b[0m\n\u001b[1;32m 1245\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mconverter\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgateway_client\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconverters\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1246\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mconverter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcan_convert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1247\u001b[0;31m \u001b[0mtemp_arg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconverter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconvert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgateway_client\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1248\u001b[0m \u001b[0mtemp_args\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtemp_arg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1249\u001b[0m \u001b[0mnew_args\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtemp_arg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.9/site-packages/py4j/java_collections.py\u001b[0m in \u001b[0;36mconvert\u001b[0;34m(self, object, gateway_client)\u001b[0m\n\u001b[1;32m 521\u001b[0m \u001b[0mjava_map\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mHashMap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 522\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 523\u001b[0;31m \u001b[0mjava_map\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 524\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mjava_map\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 525\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.9/site-packages/py4j/java_collections.py\u001b[0m in \u001b[0;36m__setitem__\u001b[0;34m(self, key, value)\u001b[0m\n\u001b[1;32m 80\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 81\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__setitem__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 82\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mput\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 83\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 84\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__len__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.9/site-packages/py4j/java_gateway.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m 1294\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1295\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1296\u001b[0;31m \u001b[0margs_command\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtemp_args\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_build_args\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1297\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1298\u001b[0m \u001b[0mcommand\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mproto\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mCALL_COMMAND_NAME\u001b[0m \u001b[0;34m+\u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.9/site-packages/py4j/java_gateway.py\u001b[0m in \u001b[0;36m_build_args\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m 1258\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_build_args\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1259\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconverters\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconverters\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1260\u001b[0;31m \u001b[0;34m(\u001b[0m\u001b[0mnew_args\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtemp_args\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_args\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1261\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1262\u001b[0m \u001b[0mnew_args\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.9/site-packages/py4j/java_gateway.py\u001b[0m in \u001b[0;36m_get_args\u001b[0;34m(self, args)\u001b[0m\n\u001b[1;32m 1245\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mconverter\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgateway_client\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconverters\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1246\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mconverter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcan_convert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1247\u001b[0;31m \u001b[0mtemp_arg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconverter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconvert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgateway_client\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1248\u001b[0m \u001b[0mtemp_args\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtemp_arg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1249\u001b[0m \u001b[0mnew_args\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtemp_arg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.9/site-packages/py4j/java_collections.py\u001b[0m in \u001b[0;36mconvert\u001b[0;34m(self, object, gateway_client)\u001b[0m\n\u001b[1;32m 521\u001b[0m \u001b[0mjava_map\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mHashMap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 522\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 523\u001b[0;31m \u001b[0mjava_map\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 524\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mjava_map\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 525\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.9/site-packages/py4j/java_collections.py\u001b[0m in \u001b[0;36m__setitem__\u001b[0;34m(self, key, value)\u001b[0m\n\u001b[1;32m 80\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 81\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__setitem__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 82\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mput\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 83\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 84\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__len__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.9/site-packages/py4j/java_gateway.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m 1294\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1295\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1296\u001b[0;31m \u001b[0margs_command\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtemp_args\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_build_args\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1297\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1298\u001b[0m \u001b[0mcommand\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mproto\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mCALL_COMMAND_NAME\u001b[0m \u001b[0;34m+\u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.9/site-packages/py4j/java_gateway.py\u001b[0m in \u001b[0;36m_build_args\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m 1264\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1265\u001b[0m args_command = \"\".join(\n\u001b[0;32m-> 1266\u001b[0;31m [get_command_part(arg, self.pool) for arg in new_args])\n\u001b[0m\u001b[1;32m 1267\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1268\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0margs_command\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtemp_args\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.9/site-packages/py4j/java_gateway.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 1264\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1265\u001b[0m args_command = \"\".join(\n\u001b[0;32m-> 1266\u001b[0;31m [get_command_part(arg, self.pool) for arg in new_args])\n\u001b[0m\u001b[1;32m 1267\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1268\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0margs_command\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtemp_args\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.9/site-packages/py4j/protocol.py\u001b[0m in \u001b[0;36mget_command_part\u001b[0;34m(parameter, python_proxy_pool)\u001b[0m\n\u001b[1;32m 296\u001b[0m \u001b[0mcommand_part\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;34m\";\"\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0minterface\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 297\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 298\u001b[0;31m \u001b[0mcommand_part\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mREFERENCE_TYPE\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mparameter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_object_id\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 299\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 300\u001b[0m \u001b[0mcommand_part\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;34m\"\\n\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mAttributeError\u001b[0m: 'numpy.int32' object has no attribute '_get_object_id'" + ] + } + ], + "source": [ + "\n", + "#Junction_Detailyearly_df = Junction_Detailyearly_df.toPandas()\n", + "#df.plot()\n", + "#display(plt.show())\n", + "Year=Junction_Detailyearly_df[\"Year\"]\n", + "Junction_Detail=Junction_Detailyearly_df[\"Junction_Detail\"]\n", + "Total_accidents=Accident_Severitydf[\"Total accidents\"]\n", + "Junction_Detailyearly_dff=Junction_Detailyearly_df.toPandas()\n", + "grouped = Junction_Detailyearly_dff.groupby(Junction_Detailyearly_df.Junction_Detail)\n", + "J0 = grouped.get_group(\"Not at junction or within 20 metres\")\n", + "J0=J0[\"Total accidents\"]\n", + "J0=J0.reset_index(drop=True)\n", + "J1 = grouped.get_group(\"Roundabout\")\n", + "J1=J1[\"Total accidents\"]\n", + "J1=J1.reset_index(drop=True)\n", + "J2 = grouped.get_group(\"Mini-roundabout\")\n", + "J2=J2[\"Total accidents\"]\n", + "J2=J2.reset_index(drop=True)\n", + "J3 = grouped.get_group(\"T or staggered junction\")\n", + "J3=J3[\"Total accidents\"]\n", + "J3=J3.reset_index(drop=True)\n", + "J5= grouped.get_group(\"Slip road\")\n", + "J5=J5[\"Total accidents\"]\n", + "J5=J5.reset_index(drop=True)\n", + "J6 = grouped.get_group(\"Crossroads\")\n", + "J6=J6[\"Total accidents\"]\n", + "J6=J6.reset_index(drop=True)\n", + "J7 = grouped.get_group(\"More than 4 arms (not roundabout)\")\n", + "J7=J7[\"Total accidents\"]\n", + "J7=J7.reset_index(drop=True)\n", + "J8 = grouped.get_group(\"Private drive or entrance\")\n", + "J8=J8[\"Total accidents\"]\n", + "J8=J8.reset_index(drop=True)\n", + "J9 = grouped.get_group(\"Other junction\")\n", + "J9=J9[\"Total accidents\"]\n", + "J9=J9.reset_index(drop=True)\n", + "J11 = grouped.get_group(\"Data missing or out of range\")\n", + "J11=J11[\"Total accidents\"]\n", + "J11=J11.reset_index(drop=True)\n", + "JUNCTION = pd.DataFrame({'period': [2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012,2013,2014,2015,2016,2017,2018,2019],\n", + " 'Not at junction or within 20 metres': J0,\n", + " 'Roundabout': J1,\n", + " 'Mini-roundabout': J2,\n", + " 'T or staggered junction': J3,\n", + " 'Slip road': J5,\n", + " 'Crossroads': J6,\n", + " 'not roundabout': J7,\n", + " 'Private drive or entrance': J8,\n", + " 'Other junction': J9,\n", + " 'Data missing or out of range': J11})\n", + "JUNCTION\n", + "dflist=['Not at junction or within 20 metres',\n", + " 'Roundabout',\n", + " 'Mini-roundabout',\n", + " 'T or staggered junction',\n", + " 'Slip road',\n", + " 'Crossroads',\n", + " 'not roundabout',\n", + " 'Private drive or entrance',\n", + " 'Other junction',\n", + " 'Data missing or out of range']\n", + "#JUNCTION[]=JUNCTION[dflist].sum(axis=1)\n", + "\n", + "JUNCTION_spark=spark.createDataFrame(JUNCTION)\n", + "#JUNCTION_spark=JUNCTION_spark.withColumn('KSI', Casulaty_spark[2]+Casulaty_spark[1])\n", + "JUNCTION_spark=JUNCTION_spark.toPandas()\n", + "\n", + "JUNCTION_spark" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+----------+----+---------------+\n", + "| Date|Year|Total accidents|\n", + "+----------+----+---------------+\n", + "|01/01/2018|2018| 234|\n", + "|01/01/2019|2019| 231|\n", + "|01/02/2018|2018| 408|\n", + "|01/02/2019|2019| 318|\n", + "|01/03/2018|2018| 231|\n", + "|01/03/2019|2019| 305|\n", + "|01/04/2018|2018| 225|\n", + "|01/04/2019|2019| 312|\n", + "|01/05/2018|2018| 342|\n", + "|01/05/2019|2019| 309|\n", + "|01/06/2018|2018| 330|\n", + "|01/06/2019|2019| 320|\n", + "|01/07/2018|2018| 322|\n", + "|01/07/2019|2019| 329|\n", + "|01/08/2018|2018| 313|\n", + "|01/08/2019|2019| 351|\n", + "|01/09/2018|2018| 328|\n", + "|01/09/2019|2019| 268|\n", + "|01/10/2018|2018| 382|\n", + "|01/10/2019|2019| 372|\n", + "+----------+----+---------------+\n", + "only showing top 20 rows\n", + "\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "Junction_Detaildf = Junction_Detail_df.toPandas()\n", + "#df.plot()\n", + "#display(plt.show())\n", + "Junction_Detaildf.plot.bar(x='Junction_Detail', y='Total accidents')\n", + "Accident_SeverityDATEYEAR_df = Accident_Information20052019_df.groupby('Date','Year').agg(F.count(Accident_Information20052019_df.Accident_Index).alias('Total accidents')).sort('Date')\n", + "#Accident_SeverityDATEYEARrrrr_df=Accident_SeverityDATEYEAR_df.withColumn(\"timestamp\",to_timestamp(\"Date\"))\n", + "#Accident_SeverityDATEYEARrrrrmm_df = Accident_SeverityDATEYEARrrrr_df.withColumn('month',hour(Accident_Information_df.timestamp))\n", + "\n", + "\n", + "#Accident_SeverityDATEYEARrrrrmm_df.show(50)\n", + "Accident_SeverityDATEYEAR_df.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+--------------+--------------+---------------+--------------+---------------+-----------------+-------------------+----------+-----------+-------------------------------------------+--------------------+--------------------+---------+--------------------+--------------------------+-------------------------+---------------------+----------------------+---------+-------------------------+--------------------+------------------+---------------------------------+---------------------------------------+-------------------+-----------------------+------------------+--------------------------+-----------+-----+-------------------+--------------------+----+-------------------+-----+\n", + "|Accident_Index|1st_Road_Class|1st_Road_Number|2nd_Road_Class|2nd_Road_Number|Accident_Severity|Carriageway_Hazards| Date|Day_of_Week|Did_Police_Officer_Attend_Scene_of_Accident| Junction_Control| Junction_Detail| Latitude| Light_Conditions|Local_Authority_(District)|Local_Authority_(Highway)|Location_Easting_OSGR|Location_Northing_OSGR|Longitude|LSOA_of_Accident_Location|Number_of_Casualties|Number_of_Vehicles|Pedestrian_Crossing-Human_Control|Pedestrian_Crossing-Physical_Facilities| Police_Force|Road_Surface_Conditions| Road_Type|Special_Conditions_at_Site|Speed_limit| Time|Urban_or_Rural_Area| Weather_Conditions|Year| timestamp|month|\n", + "+--------------+--------------+---------------+--------------+---------------+-----------------+-------------------+----------+-----------+-------------------------------------------+--------------------+--------------------+---------+--------------------+--------------------------+-------------------------+---------------------+----------------------+---------+-------------------------+--------------------+------------------+---------------------------------+---------------------------------------+-------------------+-----------------------+------------------+--------------------------+-----------+-----+-------------------+--------------------+----+-------------------+-----+\n", + "| 200501BS00001| A| 3218| NA| 0| Serious| None|2005-01-04| Tuesday| 1|Data missing or o...|Not at junction o...|51.489096| Daylight| Kensington and Ch...| Kensington and Ch...| 525680| 178240| -0.19117| E01002849| 1| 1| 0| 1|Metropolitan Police| Wet or damp|Single carriageway| None| 30|17:42| Urban|Raining no high w...|2005|2005-01-04 00:00:00| 1|\n", + "| 200501BS00002| B| 450| C| 0| Slight| None|2005-01-05| Wednesday| 1| Auto traffic signal| Crossroads|51.520075|Darkness - lights...| Kensington and Ch...| Kensington and Ch...| 524170| 181650|-0.211708| E01002909| 1| 1| 0| 5|Metropolitan Police| Dry| Dual carriageway| None| 30|17:36| Urban| Fine no high winds|2005|2005-01-05 00:00:00| 1|\n", + "| 200501BS00003| C| 0| NA| 0| Slight| None|2005-01-06| Thursday| 1|Data missing or o...|Not at junction o...|51.525301|Darkness - lights...| Kensington and Ch...| Kensington and Ch...| 524520| 182240|-0.206458| E01002857| 1| 2| 0| 0|Metropolitan Police| Dry|Single carriageway| None| 30|00:15| Urban| Fine no high winds|2005|2005-01-06 00:00:00| 1|\n", + "| 200501BS00004| A| 3220| NA| 0| Slight| None|2005-01-07| Friday| 1|Data missing or o...|Not at junction o...|51.482442| Daylight| Kensington and Ch...| Kensington and Ch...| 526900| 177530|-0.173862| E01002840| 1| 1| 0| 0|Metropolitan Police| Dry|Single carriageway| None| 30|10:35| Urban| Fine no high winds|2005|2005-01-07 00:00:00| 1|\n", + "| 200501BS00005| U| 0| NA| 0| Slight| None|2005-01-10| Monday| 1|Data missing or o...|Not at junction o...|51.495752|Darkness - lighti...| Kensington and Ch...| Kensington and Ch...| 528060| 179040|-0.156618| E01002863| 1| 1| 0| 0|Metropolitan Police| Wet or damp|Single carriageway| None| 30|21:13| Urban| Fine no high winds|2005|2005-01-10 00:00:00| 1|\n", + "| 200501BS00006| U| 0| NA| 0| Slight| None|2005-01-11| Tuesday| 1|Data missing or o...|Not at junction o...| 51.51554| Daylight| Kensington and Ch...| Kensington and Ch...| 524770| 181160|-0.203238| E01002832| 1| 2| 0| 0|Metropolitan Police| Wet or damp|Single carriageway| Oil or diesel| 30|12:40| Urban|Raining no high w...|2005|2005-01-11 00:00:00| 1|\n", + "| 200501BS00007| C| 0| Unclassified| 0| Slight| None|2005-01-13| Thursday| 1|Give way or uncon...|T or staggered ju...|51.512695|Darkness - lights...| Kensington and Ch...| Kensington and Ch...| 524220| 180830|-0.211277| E01002875| 1| 2| 0| 0|Metropolitan Police| Dry|Single carriageway| None| 30|20:40| Urban| Fine no high winds|2005|2005-01-13 00:00:00| 1|\n", + "| 200501BS00009| A| 315| NA| 0| Slight| None|2005-01-14| Friday| 1|Data missing or o...|Not at junction o...| 51.50226| Daylight| Kensington and Ch...| Kensington and Ch...| 525890| 179710|-0.187623| E01002889| 2| 1| 0| 0|Metropolitan Police| Dry| Dual carriageway| None| 30|17:35| Urban| Fine no high winds|2005|2005-01-14 00:00:00| 1|\n", + "| 200501BS00010| A| 3212| B| 304| Slight| None|2005-01-15| Saturday| 1| Auto traffic signal| Crossroads| 51.48342|Darkness - lights...| Kensington and Ch...| Kensington and Ch...| 527350| 177650|-0.167342| E01002900| 2| 2| 0| 5|Metropolitan Police| Dry|Single carriageway| None| 30|22:43| Urban| Fine no high winds|2005|2005-01-15 00:00:00| 1|\n", + "| 200501BS00011| B| 450| C| 0| Slight| None|2005-01-15| Saturday| 1|Give way or uncon...|T or staggered ju...|51.512443| Daylight| Kensington and Ch...| Kensington and Ch...| 524550| 180810|-0.206531| E01002875| 5| 2| 0| 8|Metropolitan Police| Dry|Single carriageway| None| 30|16:00| Urban| Fine no high winds|2005|2005-01-15 00:00:00| 1|\n", + "| 200501BS00012| A| 4| B| 325| Slight| None|2005-01-16| Sunday| 1| Auto traffic signal| Crossroads|51.494902|Darkness - lights...| Kensington and Ch...| Kensington and Ch...| 526240| 178900|-0.182872| E01002835| 1| 1| 0| 5|Metropolitan Police| Dry|Single carriageway| None| 30|00:42| Urban| Fine no high winds|2005|2005-01-16 00:00:00| 1|\n", + "| 200501BS00014| A| 3220| A| 308| Slight| None|2005-01-25| Tuesday| 1| Auto traffic signal| Crossroads|51.484044|Darkness - lights...| Kensington and Ch...| Kensington and Ch...| 526170| 177690|-0.184312| E01002912| 1| 2| 0| 5|Metropolitan Police| Wet or damp|Single carriageway| None| 30|20:48| Urban| Fine no high winds|2005|2005-01-25 00:00:00| 1|\n", + "| 200501BS00015| U| 0| A| 3220| Slight| None|2005-01-11| Tuesday| 1|Give way or uncon...|T or staggered ju...|51.491632| Daylight| Kensington and Ch...| Kensington and Ch...| 525590| 178520|-0.192366| E01002849| 1| 1| 0| 1|Metropolitan Police| Wet or damp| One way street| None| 30|12:55| Urban|Raining no high w...|2005|2005-01-11 00:00:00| 1|\n", + "| 200501BS00016| A| 3217| A| 3216| Slight| None|2005-01-18| Tuesday| 1|Give way or uncon...|T or staggered ju...|51.492622|Darkness - lights...| Kensington and Ch...| Kensington and Ch...| 527990| 178690|-0.157753| E01002902| 1| 2| 0| 0|Metropolitan Police| Wet or damp| One way street| None| 30|05:01| Urban|Raining no high w...|2005|2005-01-18 00:00:00| 1|\n", + "| 200501BS00017| A| 4| NA| 0| Slight| None|2005-01-18| Tuesday| 1|Data missing or o...|Not at junction o...|51.495429| Daylight| Kensington and Ch...| Kensington and Ch...| 526700| 178970|-0.176224| E01002821| 2| 1| 0| 0|Metropolitan Police| Dry| Dual carriageway| None| 30|11:15| Urban| Fine no high winds|2005|2005-01-18 00:00:00| 1|\n", + "| 200501BS00018| A| 3217| Unclassified| 0| Slight| None|2005-01-18| Tuesday| 1|Give way or uncon...|T or staggered ju...|51.481912| Daylight| Kensington and Ch...| Kensington and Ch...| 526460| 177460| -0.18022| E01002840| 1| 1| 0| 1|Metropolitan Police| Dry|Single carriageway| None| 30|10:50| Urban| Fine no high winds|2005|2005-01-18 00:00:00| 1|\n", + "| 200501BS00019| U| 0| Unclassified| 0| Serious| None|2005-01-20| Thursday| 1|Give way or uncon...|T or staggered ju...|51.500191|Darkness - lights...| Kensington and Ch...| Kensington and Ch...| 524680| 179450|-0.205139| E01002864| 1| 2| 0| 0|Metropolitan Police| Dry|Single carriageway| None| 30|00:15| Urban| Fine no high winds|2005|2005-01-20 00:00:00| 1|\n", + "| 200501BS00020| A| 3218| A| 4| Slight| None|2005-01-21| Friday| 1|Give way or uncon...|T or staggered ju...|51.495811| Daylight| Kensington and Ch...| Kensington and Ch...| 527000| 179020|-0.171887| E01002821| 1| 2| 0| 0|Metropolitan Police| Dry|Single carriageway| None| 30|09:15| Urban| Fine no high winds|2005|2005-01-21 00:00:00| 1|\n", + "| 200501BS00021| B| 302| NA| 0| Slight| None|2005-01-21| Friday| 1|Data missing or o...|Not at junction o...|51.486552|Darkness - lights...| Kensington and Ch...| Kensington and Ch...| 527810| 178010| -0.16059| E01002901| 1| 2| 0| 0|Metropolitan Police| Dry|Single carriageway| None| 30|21:16| Urban| Fine no high winds|2005|2005-01-21 00:00:00| 1|\n", + "| 200501BS00022| A| 4| Unclassified| 0| Serious| None|2005-01-08| Saturday| 1|Give way or uncon...|T or staggered ju...|51.495498|Darkness - lights...| Kensington and Ch...| Kensington and Ch...| 526790| 178980|-0.174925| E01002821| 1| 1| 0| 0|Metropolitan Police| Dry|Single carriageway| None| 30|03:00| Urban| Fine no high winds|2005|2005-01-08 00:00:00| 1|\n", + "+--------------+--------------+---------------+--------------+---------------+-----------------+-------------------+----------+-----------+-------------------------------------------+--------------------+--------------------+---------+--------------------+--------------------------+-------------------------+---------------------+----------------------+---------+-------------------------+--------------------+------------------+---------------------------------+---------------------------------------+-------------------+-----------------------+------------------+--------------------------+-----------+-----+-------------------+--------------------+----+-------------------+-----+\n", + "only showing top 20 rows\n", + "\n" + ] + } + ], + "source": [ + "from pyspark.sql.functions import *\n", + "#Timestamp String to DateType\n", + "Accident_Information_df=Accident_Information20052019_df.withColumn(\"timestamp\",to_timestamp(\"Date\"))\n", + "#Accident_Information_df\n", + "TimeAccident_dfmonth = Accident_Information_df.withColumn('month',month(Accident_Information_df.timestamp))\n", + "TimeAccident_dfmonth.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "TimeAccident_dfmonth_weath=TimeAccident_dfmonth.withColumn(\n", + " \"month\",\n", + " when(\n", + " col(\"month\") == 1,\n", + " \"Winter\"\n", + " ).\n", + " when(\n", + " col(\"month\") == 2,\n", + " \"Winter\"\n", + " ).\n", + " when(\n", + " col(\"month\") == 12,\n", + " \"Winter\"\n", + " ).\n", + " when(\n", + " col(\"month\") == 3,\n", + " \"Spring\"\n", + " ).\n", + " when(\n", + " col(\"month\") == 4,\n", + " \"Spring\"\n", + " ).\n", + " when(\n", + " col(\"month\") == 5,\n", + " \"Spring\"\n", + " ).\n", + " when(\n", + " col(\"month\") == 6,\n", + " \"Summer\"\n", + " ).\n", + " when(\n", + " col(\"month\") == 7,\n", + " \"Summer\"\n", + " ).\n", + " when(\n", + " col(\"month\") == 8,\n", + " \"Summer\"\n", + " ).\n", + " when(\n", + " col(\"month\") == 9,\n", + " \"Fall\"\n", + " ).\n", + " when(\n", + " col(\"month\") == 11,\n", + " \"Winter\"\n", + " ).\n", + " when(\n", + " col(\"month\") == 10,\n", + " \"Fall\"\n", + " ).otherwise(col(\"month\")),\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DataFrame[Accident_Index: string, 1st_Road_Class: string, 1st_Road_Number: string, 2nd_Road_Class: string, 2nd_Road_Number: string, Accident_Severity: string, Carriageway_Hazards: string, Date: string, Day_of_Week: string, Did_Police_Officer_Attend_Scene_of_Accident: string, Junction_Control: string, Junction_Detail: string, Latitude: string, Light_Conditions: string, Local_Authority_(District): string, Local_Authority_(Highway): string, Location_Easting_OSGR: string, Location_Northing_OSGR: string, Longitude: string, LSOA_of_Accident_Location: string, Number_of_Casualties: string, Number_of_Vehicles: string, Pedestrian_Crossing-Human_Control: string, Pedestrian_Crossing-Physical_Facilities: string, Police_Force: string, Road_Surface_Conditions: string, Road_Type: string, Special_Conditions_at_Site: string, Speed_limit: string, Time: string, Urban_or_Rural_Area: string, Weather_Conditions: string, Year: int, timestamp: timestamp, month: string]" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "TimeAccident_dfmonth_weath" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+-----------------+------+----+---------------+\n", + "|Accident_Severity| month|Year|Total_accidents|\n", + "+-----------------+------+----+---------------+\n", + "| Serious|Winter|2007| 7655|\n", + "| Fatal|Winter|2012| 552|\n", + "| Fatal|Winter|2010| 502|\n", + "| Fatal| Fall|2013| 299|\n", + "| Serious| Fall|2014| 3672|\n", + "| Fatal|Summer|2017| 416|\n", + "| Serious|Spring|2007| 6019|\n", + "| Serious| Fall|2015| 3573|\n", + "| Slight|Spring|2007| 38315|\n", + "| Slight|Spring|2016| 27459|\n", + "| Serious|Spring|2006| 5781|\n", + "| Slight| Fall|2007| 26115|\n", + "| Slight|Summer|2015| 30531|\n", + "| Slight|Spring|2017| 25553|\n", + "| Fatal| Fall|2012| 274|\n", + "| Slight|Summer|2016| 28134|\n", + "| Fatal|Spring|2011| 433|\n", + "| Fatal|Summer|2014| 419|\n", + "| Fatal|Winter|2007| 885|\n", + "| Fatal|Spring|2007| 673|\n", + "+-----------------+------+----+---------------+\n", + "only showing top 20 rows\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [] + } + ], + "source": [ + "TimeAccident_dfmonth_weath = TimeAccident_dfmonth_weath.groupby('Accident_Severity','month','Year').agg(F.count(TimeAccident_dfmonth_weath.Accident_Index).alias('Total_accidents'))\n", + "TimeAccident_dfmonth_weath.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+------+---------------+\n", + "| month|Total_accidents|\n", + "+------+---------------+\n", + "| null| 190255|\n", + "|Spring| 418586|\n", + "|Summer| 437905|\n", + "| Fall| 304152|\n", + "|Winter| 573905|\n", + "+------+---------------+\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+------+---------------+\n", + "| month|Total_accidents|\n", + "+------+---------------+\n", + "| null| 49916|\n", + "|Spring| 75851|\n", + "|Summer| 82871|\n", + "| Fall| 55863|\n", + "|Winter| 98123|\n", + "+------+---------------+\n", + "\n" + ] + } + ], + "source": [ + "\n", + "\n", + "TimeAccident_dfmonth_weathslight=TimeAccident_dfmonth_weath.filter(TimeAccident_dfmonth_weath.Accident_Severity.contains(\"Slight\"))\n", + "TimeAccident_dfmonth_weathslight = TimeAccident_dfmonth_weathslight.groupby('month').agg(F.count(TimeAccident_dfmonth_weathslight.Accident_Index).alias('Total_accidents'))\n", + "\n", + "TimeAccident_dfmonth_weathslight.show()\n", + "\n", + "TimeAccident_dfmonth_weathKSI=TimeAccident_dfmonth_weath.filter(TimeAccident_dfmonth_weath.Accident_Severity.contains(\"Fatal\")|TimeAccident_dfmonth_weath.Accident_Severity.contains(\"Serious\"))\n", + "TimeAccident_dfmonth_weathKSI = TimeAccident_dfmonth_weathKSI.groupby('month').agg(F.count(TimeAccident_dfmonth_weathKSI.Accident_Index).alias('Total_accidents'))\n", + "\n", + "TimeAccident_dfmonth_weathKSI.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Total_accidents %')" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX8AAAEmCAYAAACKxZBYAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8rg+JYAAAACXBIWXMAAAsTAAALEwEAmpwYAAAeeklEQVR4nO3de5xVdb3/8dfbYeQiN7lkKBpa4o27DOTlEER4r+z2M495iYQ0LfB28tQjtY6WJy+HRFOwQrPsqNk5djIr5eAlK+QSgoqJFho6IuJRMBWZ4fP7Y62BAQb2nmHWXjOs9/PxmMfs9d17r/2ZzfCe7/6u7/ouRQRmZlYsu+RdgJmZVZ7D38ysgBz+ZmYF5PA3Mysgh7+ZWQF1yLuAcvXp0ycGDBiQdxlmZu3KggULXo2Ivlu2t5vwHzBgAPPnz8+7DDOzdkXS8021e9jHzKyAHP5mZgXk8DczK6B2M+bflPXr17NixQreeeedvEuxjHXq1In+/ftTXV2ddylmO4V2Hf4rVqygW7duDBgwAEl5l2MZiQhWr17NihUr2HffffMux2yn0K6Hfd555x169+7t4N/JSaJ3797+hGfWitp1+AMO/oLwv7NZ62r34W9mZs3Xrsf8tzTg4ntbdX/Lrzy+VfdnZtZW7FThX2mrV69m/PjxALz88stUVVXRt29yFvVjjz3GrrvuuvGx06ZNY/LkyXTp0mW7+xw7dixXX301I0eOzKzuM888k/PPP5+DDz54s/ZbbrmF+fPnc/311zd7n6+//jq33347X/rSl8p+zimnnMKSJUs44YQT+Pa3vw3A5ZdfzqBBgzjxxBObXYNZa2ntjmRLZdkB9bDPDujduzeLFi1i0aJFnHXWWZx33nkbtxsHPyTh/9Zbb+VU6eZ+8IMfbBX8O+r111/n+9//ftmPX7x4MZ07d2bx4sXMmzePN954g9raWubOnevgN6sAh38rmz17NsOHD2fw4MFMnDiRdevWcd111/HSSy8xbtw4xo0bB8DZZ5/NyJEjOeSQQ7j00kvL3v+2njdv3jwOP/xwhg4dyqhRo1i7di319fVceOGFDBo0iCFDhjB9+nQg+XTRsE7SrFmzGDhwIKNGjeLRRx/duL9Vq1bxqU99ipqaGmpqajbed9lllzFx4kTGjh3Lfvvtx3XXXQfAxRdfzHPPPcewYcO46KKLqK2tZcyYMQwbNoxBgwbxyCOPbPZzVFdX8/bbb7NhwwbWr19PVVUVl1xyCd/85jdb8K6bWXN52KcVvfPOO5xxxhnMnj2bgQMHctppp3HjjTcydepUrr32WubMmUOfPn0AuOKKK+jVqxf19fWMHz+exYsXM2TIkJKv0dTzDjzwQE466STuuOMOampqWLNmDZ07d2bmzJksX76cRYsW0aFDB1577bXN9lVbW8ull17KggUL6NGjB+PGjWP48OEATJkyhfPOO48jjzySF154gaOPPpqlS5cC8PTTTzNnzhzWrl3LAQccwNlnn82VV17JE088waJFiwC45pprOProo/n6179OfX39Vp96DjroIPr27cuIESM49dRTefbZZ9mwYQMjRozY0X8GMyuDw78V1dfXs++++zJw4EAATj/9dG644QamTp261WPvvPNOZs6cSV1dHbW1tTz11FNlhX9Tz5NEv379qKmpAaB79+4APPDAA5x11ll06JD8M/fq1Wuzfc2dO5exY8duPE5x0kkn8cwzz2x87lNPPbXxsWvWrOHNN98E4Pjjj6djx4507NiR97znPaxcuXKrOmtqapg4cSLr16/nxBNPZNiwYVs9Ztq0aRtvf/SjH2XGjBlcccUVPP7440yYMIFJkyaVfD/MrGU87JODv/3tb1x99dXMnj2bxYsXc/zxx5d1AlNLn9cSGzZs4E9/+tPGYxgvvvgiXbt2BaBjx44bH1dVVUVdXd1Wzx8zZgwPP/wwe+21F2eccQY//vGPt/la99xzD4ceeihvvvkmzz33HHfeeSc///nP28wxErOd0U7V8897amZVVRXLly/n2Wef5QMf+AC33XYbH/rQhwDo1q0ba9eupU+fPqxZs4bddtuNHj16sHLlSu677z7Gjh1bcv/bet4BBxxAbW0t8+bNo6amhrVr19K5c2cmTJjAjBkzGDdu3MZhn8a9/9GjRzNlyhRWr15N9+7dueuuuxg6dCgARx11FNOnT+eiiy4CYNGiRU323hs0/HwNnn/+efr378+kSZNYt24dCxcu5LTTTtvqeevXr2fatGnce++9LFu2bOPJXPX19bz77rslZ0eZWcvsVOGft06dOjFr1iw+85nPUFdXR01NDWeddRYAkydP5phjjmHPPfdkzpw5DB8+nAMPPJC9996bI444oqz9Dx06tMnn7brrrtxxxx18+ctf5u2336Zz58488MADnHnmmTzzzDMMGTKE6upqJk2axLnnnrtxf/369eOyyy7jsMMOo2fPnpuF+3XXXcc555zDkCFDqKurY8yYMdx0003brK13794cccQRDBo0iGOPPZZBgwZx1VVXUV1dTdeuXbfZ87/hhhs4/fTT6dKlC0OGDOGtt95i8ODBHHfccfTs2bOs98XMmk8RkXcNZRk5cmRseSWvpUuXctBBB+VUkVWa/72tUnamef6SFkTEVicOeczfzKyAPOzTRo0ePZp169Zt1nbbbbcxePDgnCoys51Juw//iNgpV3ycO3du3iW0Ke1leNKsvch02EfS3pLmSHpK0pOSpqTtl0l6UdKi9Ou4luy/U6dOrF692sGwk2u4mEunTp3yLsVsp5F1z78OuCAiFkrqBiyQdH96339ExNU7svP+/fuzYsUKVq1atcOFWtvWcBlHM2sdmYZ/RNQCtenttZKWAnu11v6rq6t9WT8zsxao2GwfSQOA4UDDYPa5khZL+pGk3bfxnMmS5kua7969mVnrqUj4S+oK3A1MjYg1wI3A+4FhJJ8MrmnqeRExMyJGRsTIhvVnzMxsx2Ue/pKqSYL/pxHxC4CIWBkR9RGxAbgZGJV1HWZmtknWs30E/BBYGhHXNmrv1+hhnwCeyLIOMzPbXNazfY4ATgWWSFqUtn0NOFnSMCCA5cAXM67DzMwayXq2z++Bps7A+nWWr2tmZtvntX3MzArI4W9mVkAOfzOzAnL4m5kVkMPfzKyAHP5mZgXk8DczKyCHv5lZATn8zcwKyOFvZlZADn8zswJq9xdwN9tRAy6+N+8SWH7l8XmXYAXjnr+ZWQE5/M3MCsjhb2ZWQA5/M7MCcvibmRWQw9/MrIAc/mZmBeTwNzMrIIe/mVkBOfzNzArI4W9mVkAOfzOzAnL4m5kVkMPfzKyAvKRzQXkZY7Nic8/fzKyAHP5mZgXk8DczK6BMw1/S3pLmSHpK0pOSpqTtvSTdL2lZ+n33LOswM7PNZd3zrwMuiIiDgQ8C50g6GLgYmB0R+wOz020zM6uQTMM/ImojYmF6ey2wFNgL+Dhwa/qwW4ETs6zDzMw2V7Exf0kDgOHAXGCPiKhN73oZ2GMbz5ksab6k+atWrapMoWZmBdDs8JfUSVL3Zj6nK3A3MDUi1jS+LyICiKaeFxEzI2JkRIzs27dvc0s1M7NtaFb4SzoT+G/gbknfLvM51STB/9OI+EXavFJSv/T+fsArzanDzMx2zHbDX9LHtmj6SEQcExETgJKnZ0oS8ENgaURc2+iuXwKnp7dPB+4pv2QzM9tRpXr+gyXdI2lYur1Y0g8k3Qw8Wcb+jwBOBT4saVH6dRxwJTBB0jLgI+m2mZlVyHbX9omIKyS9F/hW2ov/BtAN6BwRi0vtPCJ+D2gbd49vbrFmZtY6ylnY7R/AVGB/YCYwH/huhjWZmVnGSo35X05ysPZXwLiI+BiwCPi1pNOyL8/MzLJQasz/hIg4imSI5jSAiPglcBTgJRnMzNqpUsM+T0iaCXQGHmpojIg64HtZFmZmZtkpdcD3c5IGA+sj4ukK1WRmZhkrecA3IpZUohAzM6scr+dvZlZADn8zswIqK/wlHSFpt/T25yRdK+l92ZZmZmZZKbfnfyPwlqShwAXAc8CPM6vKzMwyVW7416VLL38cuD4ibiBZ5sHMzNqhcpZ3AFgr6V+BzwFjJO0CVGdXlpmZZancnv9JwDrgCxHxMtAfuCqzqszMLFPl9vzPi4ivNmxExAuSDsmoJjMzy1i5Pf8JTbQd25qFmJlZ5Wy35y/pbOBLwH6SGq/f3w34Q5aFmZlZdkoN+9wO3Ad8B7i4UfvaiHgts6rMzCxTpRZ2ewN4AzhZUhWwR/qcrpK6RsQLFajRzMxaWVkHfCWdC1wGrAQ2pM0BDMmmLDMzy1K5s32mAgdExOoMazEzswopd7bP30mGf8zMbCdQbs//r8CDku4lOdkLgIi4NpOqzMwsU+WG/wvp167pl5mZtWNlhX9EfBNAUpeIeCvbkszMLGvlrud/mKSngKfT7aGSvp9pZWZmlplyD/hOA44GVgNExOPAmIxqMjOzjJV9GceI+PsWTfWtXIuZmVVIuQd8/y7pcCAkVQNTgKXZlWVmZlkqt+d/FnAOsBfwIjAs3TYzs3ao3Nk+rwKnNHfnkn4EnAC8EhGD0rbLgEnAqvRhX4uIXzd332bWugZcfG/eJQCw/Mrj8y6hEEot6TydZA2fJkXEV0rs/xbgera+2Pt/RMTV5RRoZmatr9Swz3xgAdAJGAEsS7+GUcbJXhHxMOCln83M2phSSzrfChsv6nJkRNSl2zcBj+zA654r6TSSPy4XRMT/NfUgSZOByQD77LPPDrycmZk1Vu4B392B7o22u6ZtLXEj8H6STw+1wDXbemBEzIyIkRExsm/fvi18OTMz21K5Uz2vBP4saQ4gkhO8LmvJC0bEyobbkm4GftWS/ZiZWcuVO9tnlqT7gNFp01cj4uWWvKCkfhFRm25+AniiJfsxM7OWKzXb58CIeFrSiLSp4SzfPSXtGRELSzz/Z8BYoI+kFcClwFhJw0hmES0Hvtjy8s3MrCVK9fzPJzng2tS4fAAf3t6TI+LkJpp/WF5pZmaWlVKzfSan38dVphwzM6uEcpd0PkdSz0bbu0v6UmZVmZlZpsqd6jkpIl5v2Ejn5U/KpCIzM8tcuVM9qyQpIgJAUhXt8HKObWHtEq9bYmZtQbnh/xvgDkkz0u0vpm1mZtYOlRv+XyWZ9XN2un0/8INMKjIzs8yVG/6dgZsj4ibYOOzTEfDF3M3M2qFyD/jOJvkD0KAz8EDrl2NmZpVQbvh3iog3GzbS212yKcnMzLJWbvj/o9ESD0g6FHg7m5LMzCxr5Y75TwXukvQSyaqe7wVOyqooMzPLVrmres6TdCBwQNr0l4hYn11ZZmaWpXJ7/pAE/8Gkl3SURERseW1eMzNrB8oKf0mXkizNfDDwa+BY4PdsfWF2MzNrB8o94PtpYDzwckR8HhgK9MisKjMzy1S54f92RGwA6iR1B14B9s6uLDMzy1K5Y/7z0yWdbwYWAG8Cf8yqKDMzy1a5s30a1u6/SdJvgO4RsbjhfkmHRMSTWRRoZmatr9xhn40iYnnj4E/d1kr1mJlZBTQ7/LdBrbQfMzOrgNYK/2il/ZiZWQW0VvibmVk70lrh/24r7cfMzCpgu7N9Gq/k2ZSIWJh+/2BrFmVmZtkqNdXzmu3cF8CHW7EWMzOrkO2Gf0SMq1QhZmZWOWWv6ilpEJtW9QTwqp5mZu2UV/U0Mysgr+ppZlZAma7qKelHkl6R9ESjtl6S7pe0LP2+e8tKNzOzlio3/Ldc1XMh5a3qeQtwzBZtFwOzI2J/YHa6bWZmFdQqq3pu53kPSxqwRfPHSY4fANwKPAh8tZw6zMysdZTV85c0u+F2w6qejduaaY+IqE1vvwzssZ3XnSxpvqT5q1atauHLmZnZlrYb/pI6SeoF9JG0ezpe3yvtze+1oy8eEcF2FoWLiJkRMTIiRvbt23dHX87MzFKlhn2+CEwF9iQZ52+wBri+ha+5UlK/iKiV1I/k4LGZmVXQdnv+EfG9iNgXuDAi9m30NTQiWhr+vwROT2+fDtzTwv2YmVkLlXuG7wxJXwHGpNsPAjMiYv32niTpZyQHd/tIWgFcClwJ3CnpC8DzwP9rQd1mZrYDyg3/7wPV6XeAU4EbgTO396SIOHkbd40v83XNzCwDpZZ07hARdUBNRAxtdNf/Sno829LMzCwrpaZ6PpZ+r5f0/oZGSfsB9ZlVZWZmmSo17NNwYfYLgTmS/ppuDwA+n1VRZmaWrVLh31fS+entGUBVerseGA7MyaowMzPLTqnwrwK6sukTQOPndcukIjMzy1yp8K+NiG9VpBIzM6uYUgd8t+zxm5nZTqBU+Hs+vpnZTqjU8g6vVaoQMzOrnHIv5mJmZjsRh7+ZWQE5/M3MCsjhb2ZWQA5/M7MCcvibmRWQw9/MrIAc/mZmBeTwNzMrIIe/mVkBOfzNzArI4W9mVkAOfzOzAnL4m5kVkMPfzKyAHP5mZgXk8DczKyCHv5lZATn8zcwKyOFvZlZAHfJ6YUnLgbVAPVAXESPzqsXMrGhyC//UuIh4NecazMwKx8M+ZmYFlGf4B/A7SQskTW7qAZImS5ovaf6qVasqXJ6Z2c4rz/A/MiJGAMcC50gas+UDImJmRIyMiJF9+/atfIVmZjup3MI/Il5Mv78C/BcwKq9azMyKJpfwl7SbpG4Nt4GjgCfyqMXMrIjymu2zB/BfkhpquD0ifpNTLWZmhZNL+EfEX4Gheby2mZl5qqeZWSE5/M3MCsjhb2ZWQA5/M7MCcvibmRWQw9/MrIAc/mZmBeTwNzMrIIe/mVkBOfzNzArI4W9mVkAOfzOzAnL4m5kVkMPfzKyAHP5mZgXk8DczKyCHv5lZATn8zcwKyOFvZlZADn8zswJy+JuZFZDD38ysgBz+ZmYF5PA3Mysgh7+ZWQE5/M3MCsjhb2ZWQA5/M7MCcvibmRWQw9/MrIByC39Jx0j6i6RnJV2cVx1mZkWUS/hLqgJuAI4FDgZOlnRwHrWYmRVRXj3/UcCzEfHXiHgX+E/g4znVYmZWOIqIyr+o9GngmIg4M90+FRgdEedu8bjJwOR08wDgLxUtdGt9gFdzrqGt8Huxid+LTfxebNJW3ov3RUTfLRs75FFJuSJiJjAz7zoaSJofESPzrqMt8Huxid+LTfxebNLW34u8hn1eBPZutN0/bTMzswrIK/znAftL2lfSrsBngV/mVIuZWeHkMuwTEXWSzgV+C1QBP4qIJ/OopZnazBBUG+D3YhO/F5v4vdikTb8XuRzwNTOzfPkMXzOzAnL4m5kVkMPfzKyAHP5mzaTE3qUfadZ2+YBvGSQdCewfEbMk9QW6RsTf8q6r0iR9sonmN4AlEfFKpevJk6QlETE47zryJGkJ0FSACIiIGFLhknKVrln2ZEQcmHct5WjTZ/i2BZIuBUaSLC8xC6gGfgIckWddOfkCcBgwJ90eCywA9pX0rYi4La/CcrBQUk1EzMu7kBydkHcBbUlE1KcrFe8TES/kXU8pDv/SPgEMBxYCRMRLkrrlW1JuOgAHRcRKAEl7AD8GRgMPA0UK/9HAKZKeB/5BAXu7EfF83jW0QbsDT0p6jOT3AoCI+Fh+JTXN4V/auxERkgJA0m55F5SjvRuCP/VK2vaapPV5FZWTo/MuIG+S1rL9YZ/uFS6pLfhG3gWUy+Ff2p2SZgA9JU0CJgI351xTXh6U9CvgrnT7U2nbbsDruVWVg4h4vqljQXnXVUkRUdRPwNsUEQ9Jeh/J78UDkrqQrGLQ5viAbxkkTQCOIunR/DYi7s+5pFxIEkngNxzveBS4Owr4S9T4WFBEDJS0J3BXRBTxWBAAkt4DdGrYbg/j3q0t7SBOBnpFxPsl7Q/cFBHjcy5tKw5/sxaQtIj0WFBEDE/bFhdpzL+BpI8B1wB7kgwFvg9YGhGH5FpYDtLfi1HA3Ea/F21yZpjn+Zcg6ZOSlkl6Q9IaSWslrcm7rjz4vdjMu+knHh8Lgn8DPgg8ExH7AuOBP+VbUm7WpVcnBEBSB5o+LpI7h39p3wU+FhE9IqJ7RHQr6IEs8HvR2JbHgh6guMeC1kfEamAXSbtExBySIbEiekjS14DO6XDxXcD/5FxTkzzsU4KkR4s8jtuY34vN+VhQQtIDwInAd0guXfgKUBMRh+dZVx4k7UJyPkzj34s22Slw+Jcg6XvAe4H/BtY1tEfEL/KqKS9+L7YmqTuNZs1FxGs5llNRDSczpUNeb5OMJJwC9AB+mn4aKBRJUyLie6Xa2gKHfwmSZjXRHBExseLF5MzvxSaSvgh8E3gH2MCmue375VpYBUlaGBEj0tt3R8Sn8q4pb43fk0Ztf244+NuWOPzNWkDSMuCwiHg171ry0jjU2mrAVYqkk4F/Bo4EHml0VzdgQ1uc6umTvEqQ1B+Yzqa57Y8AUyJiRX5VVZakf4mI70qaThMzFyLiKzmUlbfngLfyLiJnsY3bRfQHoJbkmMc1jdrXAotzqagEh39ps4Dbgc+k259L2ybkVlHlLU2/z8+1irblX4E/SJrL5sc/ivSHcGg61Vcks1sapv0WbnmHdJ2j50kWPmwXPOxTgqRFETGsVNvOLl2u9t8j4sK8a2kL0oW7fg8sIRnzByAibs2tKMtduuz5vwPvIfkj2Gb/ELrnX9pqSZ8DfpZunwwUbhZDulytp3luUh0R5+ddhLU53wU+GhFLSz4yZ+75l5Au0jSd5ONckIztfaWg65bcCOxFcuJK4+VqCzfVU9K3geUkJ/A0HvYpzFRP21p7OhfG4W9l81TPTSQ1dSW3Qk31tK21p3NhHP7bIOmS7dwdEfFvFSvGzNqF9tRBcvhvg6QLmmjejeTU7d4RUai12wEk7Qd8j2QRrwD+CEyNYl7PuAo4HhjA5mf4XptXTWbN4QO+2xARG+fqppdtnAJ8HvhPNp/HWyS3AzeQXNoS4LMk78fo3CrKz/+QnN272WwfK6b2eC6Mw387JPUCzidZr+RWYERE/F++VeWqS2x+kfafSLoot2ry1b+Ia/fbNnWUNAp4HHiXZIpnm+bw3wZJVwGfBGYCgyPizZxLagvuk3QxSW8/gJOAX6d/JIs20+U+SUdFxO/yLsTahB7ANOAgkjN6HyWZGfiHtvr/wmP+2yBpA8nR+jo2/xjXZk/ayFqjGS4N70fj3k2hZrpI+gTwE5KVLNdT4N8L20TSriTXMjicZHr4YcDrEXFwroU1wT3/bYgIX+gmJakG+Ht6lSYknU5yLd/lwGVttWeTsWtJ/mMvKeI1jG2bOgPdST4J9ABeIjku1Oa4528lSVoIfCQiXpM0hmTY58vAMOCgiPh0nvXlQdLDwNiI8MFeQ9JM4BCShdzmklzG8k9t+Rihe/5WjqpGvfuTgJkRcTdwd3rB6iL6K/CgpPvY/GQeT/Uspn2AjsAy4EVgBfB6ngWV4vC3clRJ6hARdSQX557c6L6i/g79Lf3aNf2yAouIYySJpPd/OHABMEjSa8AfI+LSXAtsgod9rCRJXweOA14l6eGMiIiQ9AHg1vaylolZJaTXADmC5I/ACSQnhfbMtagmOPytLJI+CPQDfhcR/0jbBgJdI2JhrsXlQNIcmj6Z58M5lGM5k/QVkrA/nGT21x8afS1pi8eGHP5mLSDp0EabnUhmP9VFxL/kVJLlSNK1pHP7I6I273rK4fA3ayWSHouIUXnXYVaOoh6sM9shDWc1p3YhObGnR07lmDWbw9+sZRawacy/juSEty/kVo1ZMzn8zZqhxNnOT+VYmlmzeAkDs+aZQbJqI+nZzt8hWfH1DZJFAM3aBff8zZrHZzvbTsE9f7PmqZLU0GkaD/xvo/vcmbJ2w7+sZs3zM+AhSa8CbwOPAKRnO7+RZ2FmzeF5/mbN5LOdbWfg8DczKyCP+ZuZFZDD38ysgBz+VniSvi7pSUmLJS2SNDrvmsyy5tk+VmiSDiNZc31ERKyT1AdfnMUKwD1/K7p+wKsRsQ4gIl6NiJckHSrpIUkLJP1WUj8ASZMkzZP0uKS7JXVJ2z8j6Ym0/eG0rZOkWZKWSPqzpHFp+xmSfiHpN5KWSfpuTj+7FZhn+1ihSeoK/B7oAjwA3EFyAY6HgI9HxCpJJwFHR8RESb0jYnX63MuBlRExXdIS4JiIeFFSz4h4XdIFwCHp8w4EfgcMBD4LXAIMJ7n+71+AIyPi7xX94a3QPOxjhRYRb6YXZvknYBxJ+F8ODALuTy7LShXQcIGOQWno9wS6Ar9N2x8FbpF0J/CLtO1IYHr6Ok9Lep4k/AFmR8QbAJKeAt4HOPytYhz+VngRUQ88CDyY9uDPAZ6MiMOaePgtwIkR8bikM4Cx6T7OSg8UHw8s2OJKX01Z1+h2Pf6/aBXmMX8rNEkHSNq/UdMwYCnQNz0YjKRqSYek93cDaiVVA6c02s/7I2JuRFwCrAL2Jln64ZT0/oHAPiRDPGa5c2/Diq4rMF1ST5KLsjwLTCZZnvk6ST1I/p9MA54EvgHMJQn4uSR/DACuSv+ICJgNPA48DdyYfpqoA85IZxRV5icz2w4f8DUzKyAP+5iZFZDD38ysgBz+ZmYF5PA3Mysgh7+ZWQE5/M3MCsjhb2ZWQP8fq2jEWJfVl+wAAAAASUVORK5CYII=", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "TimeAccident_dfmonth_weathslight = TimeAccident_dfmonth_weathslight.toPandas()\n", + "TimeAccident_dfmonth_weathslight['Total_accidents %'] = (TimeAccident_dfmonth_weathslight['Total_accidents'] / TimeAccident_dfmonth_weathslight['Total_accidents'].sum()) * 100\n", + "TimeAccident_dfmonth_weathslight.plot.bar(x='month', y='Total_accidents %')\n", + "plt.xlabel(\"Speed_limit of Non Serious Accident\")\n", + "plt.ylabel(\"Total_accidents %\")\n", + "\n", + "TimeAccident_dfmonth_weathKSI = TimeAccident_dfmonth_weathKSI.toPandas()\n", + "TimeAccident_dfmonth_weathKSI['Total_accidents %'] = (TimeAccident_dfmonth_weathKSI['Total_accidents'] / TimeAccident_dfmonth_weathKSI['Total_accidents'].sum()) * 100\n", + "\n", + "TimeAccident_dfmonth_weathKSI.plot.bar(x='month', y='Total_accidents %')\n", + "plt.xlabel(\"Season\")\n", + "plt.ylabel(\"Total_accidents %\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DataFrame[Accident_Index: string, 1st_Road_Class: string, 1st_Road_Number: string, 2nd_Road_Class: string, 2nd_Road_Number: string, Accident_Severity: string, Carriageway_Hazards: string, Date: string, Day_of_Week: string, Did_Police_Officer_Attend_Scene_of_Accident: string, Junction_Control: string, Junction_Detail: string, Latitude: string, Light_Conditions: string, Local_Authority_(District): string, Local_Authority_(Highway): string, Location_Easting_OSGR: string, Location_Northing_OSGR: string, Longitude: string, LSOA_of_Accident_Location: string, Number_of_Casualties: string, Number_of_Vehicles: string, Pedestrian_Crossing-Human_Control: string, Pedestrian_Crossing-Physical_Facilities: string, Police_Force: string, Road_Surface_Conditions: string, Road_Type: string, Special_Conditions_at_Site: string, Speed_limit: string, Time: string, Urban_or_Rural_Area: string, Weather_Conditions: string, Year: int, timestamp: timestamp, month: int]" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "TimeAccident_dfmonth" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "root\n", + " |-- month: integer (nullable = true)\n", + " |-- Accident_Severity: string (nullable = true)\n", + " |-- Total accidents: long (nullable = false)\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+-----+-----------------+---------------+\n", + "|month|Accident_Severity|Total accidents|\n", + "+-----+-----------------+---------------+\n", + "| null| Fatal| 3329|\n", + "| null| Slight| 190255|\n", + "| null| Serious| 46587|\n", + "| 1| Serious| 21716|\n", + "| 1| Fatal| 2140|\n", + "| 1| Slight| 141148|\n", + "| 2| Slight| 130153|\n", + "| 2| Serious| 20035|\n", + "| 2| Fatal| 1875|\n", + "| 3| Fatal| 1991|\n", + "| 3| Serious| 22307|\n", + "| 3| Slight| 139827|\n", + "| 4| Fatal| 2032|\n", + "| 4| Slight| 132877|\n", + "| 4| Serious| 22409|\n", + "| 5| Serious| 24934|\n", + "| 5| Fatal| 2178|\n", + "| 5| Slight| 145882|\n", + "| 6| Serious| 25183|\n", + "| 6| Slight| 146027|\n", + "+-----+-----------------+---------------+\n", + "only showing top 20 rows\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [] + } + ], + "source": [ + "TimeAccident_dfmonthly = TimeAccident_dfmonth.groupby('month','Accident_Severity').agg(F.count(TimeAccident_dfmonth.Accident_Index).alias('Total accidents'))\n", + "#TimeAccident_df= TimeAccident_df.withColumn('Time',F.col('Time').cast(IntegerType()))\n", + "TimeAccident_dfmonthly.printSchema()\n", + "TimeAccident_dfmonthly=TimeAccident_dfmonthly.sort(\"month\")\n", + "TimeAccident_dfmonthly.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "TimeAccident_dfmonthly_new=TimeAccident_dfmonthly.withColumn(\n", + " \"month\",\n", + " when(\n", + " col(\"month\") == 1,\n", + " \"Jan\"\n", + " ).\n", + " when(\n", + " col(\"month\") == 2,\n", + " \"Feb\"\n", + " ).\n", + " when(\n", + " col(\"month\") == 3,\n", + " \"March\"\n", + " ).\n", + " when(\n", + " col(\"month\") == 4,\n", + " \"April\"\n", + " ).\n", + " when(\n", + " col(\"month\") == 5,\n", + " \"May\"\n", + " ).\n", + " when(\n", + " col(\"month\") == 6,\n", + " \"June\"\n", + " ).\n", + " when(\n", + " col(\"month\") == 7,\n", + " \"July\"\n", + " ).\n", + " when(\n", + " col(\"month\") == 8,\n", + " \"August\"\n", + " ).\n", + " when(\n", + " col(\"month\") == 9,\n", + " \"September\"\n", + " ).\n", + " when(\n", + " col(\"month\") == 10,\n", + " \"October\"\n", + " ).\n", + " when(\n", + " col(\"month\") == 11,\n", + " \"November\"\n", + " ).\n", + " when(\n", + " col(\"month\") == 12,\n", + " \"December\"\n", + " ).otherwise(col(\"month\")),\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead tr th {\n", + " text-align: left;\n", + " }\n", + "\n", + " .dataframe thead tr:last-of-type th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr>\n", + " <th></th>\n", + " <th colspan=\"3\" halign=\"left\">Total accidents</th>\n", + " </tr>\n", + " <tr>\n", + " <th>Accident_Severity</th>\n", + " <th>Fatal</th>\n", + " <th>Serious</th>\n", + " <th>Slight</th>\n", + " </tr>\n", + " <tr>\n", + " <th>month</th>\n", + " <th></th>\n", + " <th></th>\n", + " <th></th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>NaN</th>\n", + " <td>3329</td>\n", + " <td>46587</td>\n", + " <td>190255</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1.0</th>\n", + " <td>2140</td>\n", + " <td>21716</td>\n", + " <td>141148</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2.0</th>\n", + " <td>1875</td>\n", + " <td>20035</td>\n", + " <td>130153</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3.0</th>\n", + " <td>1991</td>\n", + " <td>22307</td>\n", + " <td>139827</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4.0</th>\n", + " <td>2032</td>\n", + " <td>22409</td>\n", + " <td>132877</td>\n", + " </tr>\n", + " <tr>\n", + " <th>5.0</th>\n", + " <td>2178</td>\n", + " <td>24934</td>\n", + " <td>145882</td>\n", + " </tr>\n", + " <tr>\n", + " <th>6.0</th>\n", + " <td>2139</td>\n", + " <td>25183</td>\n", + " <td>146027</td>\n", + " </tr>\n", + " <tr>\n", + " <th>7.0</th>\n", + " <td>2236</td>\n", + " <td>26248</td>\n", + " <td>151367</td>\n", + " </tr>\n", + " <tr>\n", + " <th>8.0</th>\n", + " <td>2367</td>\n", + " <td>24698</td>\n", + " <td>140511</td>\n", + " </tr>\n", + " <tr>\n", + " <th>9.0</th>\n", + " <td>2293</td>\n", + " <td>25538</td>\n", + " <td>148185</td>\n", + " </tr>\n", + " <tr>\n", + " <th>10.0</th>\n", + " <td>2390</td>\n", + " <td>25642</td>\n", + " <td>155967</td>\n", + " </tr>\n", + " <tr>\n", + " <th>11.0</th>\n", + " <td>2362</td>\n", + " <td>25278</td>\n", + " <td>159843</td>\n", + " </tr>\n", + " <tr>\n", + " <th>12.0</th>\n", + " <td>2366</td>\n", + " <td>22351</td>\n", + " <td>142761</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " Total accidents \n", + "Accident_Severity Fatal Serious Slight\n", + "month \n", + "NaN 3329 46587 190255\n", + "1.0 2140 21716 141148\n", + "2.0 1875 20035 130153\n", + "3.0 1991 22307 139827\n", + "4.0 2032 22409 132877\n", + "5.0 2178 24934 145882\n", + "6.0 2139 25183 146027\n", + "7.0 2236 26248 151367\n", + "8.0 2367 24698 140511\n", + "9.0 2293 25538 148185\n", + "10.0 2390 25642 155967\n", + "11.0 2362 25278 159843\n", + "12.0 2366 22351 142761" + ] + }, + "execution_count": 96, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "TimeAccident_dfmonthly_new=TimeAccident_dfmonthly.toPandas()\n", + "TimeAccident_dfmonthly_new=TimeAccident_dfmonthly_new.pivot(index ='month', columns ='Accident_Severity')\n", + "TimeAccident_dfmonthly_new" + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead tr th {\n", + " text-align: left;\n", + " }\n", + "\n", + " .dataframe thead tr:last-of-type th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr>\n", + " <th></th>\n", + " <th colspan=\"3\" halign=\"left\">Total accidents</th>\n", + " <th>month1</th>\n", + " </tr>\n", + " <tr>\n", + " <th>Accident_Severity</th>\n", + " <th>Fatal</th>\n", + " <th>Serious</th>\n", + " <th>Slight</th>\n", + " <th></th>\n", + " </tr>\n", + " <tr>\n", + " <th>month</th>\n", + " <th></th>\n", + " <th></th>\n", + " <th></th>\n", + " <th></th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>NaN</th>\n", + " <td>3329</td>\n", + " <td>46587</td>\n", + " <td>190255</td>\n", + " <td>Null</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1.0</th>\n", + " <td>2140</td>\n", + " <td>21716</td>\n", + " <td>141148</td>\n", + " <td>January</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2.0</th>\n", + " <td>1875</td>\n", + " <td>20035</td>\n", + " <td>130153</td>\n", + " <td>February</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3.0</th>\n", + " <td>1991</td>\n", + " <td>22307</td>\n", + " <td>139827</td>\n", + " <td>March</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4.0</th>\n", + " <td>2032</td>\n", + " <td>22409</td>\n", + " <td>132877</td>\n", + " <td>April</td>\n", + " </tr>\n", + " <tr>\n", + " <th>5.0</th>\n", + " <td>2178</td>\n", + " <td>24934</td>\n", + " <td>145882</td>\n", + " <td>May</td>\n", + " </tr>\n", + " <tr>\n", + " <th>6.0</th>\n", + " <td>2139</td>\n", + " <td>25183</td>\n", + " <td>146027</td>\n", + " <td>June</td>\n", + " </tr>\n", + " <tr>\n", + " <th>7.0</th>\n", + " <td>2236</td>\n", + " <td>26248</td>\n", + " <td>151367</td>\n", + " <td>July</td>\n", + " </tr>\n", + " <tr>\n", + " <th>8.0</th>\n", + " <td>2367</td>\n", + " <td>24698</td>\n", + " <td>140511</td>\n", + " <td>August</td>\n", + " </tr>\n", + " <tr>\n", + " <th>9.0</th>\n", + " <td>2293</td>\n", + " <td>25538</td>\n", + " <td>148185</td>\n", + " <td>September</td>\n", + " </tr>\n", + " <tr>\n", + " <th>10.0</th>\n", + " <td>2390</td>\n", + " <td>25642</td>\n", + " <td>155967</td>\n", + " <td>October</td>\n", + " </tr>\n", + " <tr>\n", + " <th>11.0</th>\n", + " <td>2362</td>\n", + " <td>25278</td>\n", + " <td>159843</td>\n", + " <td>November</td>\n", + " </tr>\n", + " <tr>\n", + " <th>12.0</th>\n", + " <td>2366</td>\n", + " <td>22351</td>\n", + " <td>142761</td>\n", + " <td>December</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " Total accidents month1\n", + "Accident_Severity Fatal Serious Slight \n", + "month \n", + "NaN 3329 46587 190255 Null\n", + "1.0 2140 21716 141148 January\n", + "2.0 1875 20035 130153 February\n", + "3.0 1991 22307 139827 March\n", + "4.0 2032 22409 132877 April\n", + "5.0 2178 24934 145882 May\n", + "6.0 2139 25183 146027 June\n", + "7.0 2236 26248 151367 July\n", + "8.0 2367 24698 140511 August\n", + "9.0 2293 25538 148185 September\n", + "10.0 2390 25642 155967 October\n", + "11.0 2362 25278 159843 November\n", + "12.0 2366 22351 142761 December" + ] + }, + "execution_count": 97, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "TimeAccident_dfmonthly_new['month1'] = month_name\n", + "TimeAccident_dfmonthly_new" + ] + }, + { + "cell_type": "code", + "execution_count": 101, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]),\n", + " [Text(0, 0, 'Null'),\n", + " Text(1, 0, 'January'),\n", + " Text(2, 0, 'February'),\n", + " Text(3, 0, 'March'),\n", + " Text(4, 0, 'April'),\n", + " Text(5, 0, 'May'),\n", + " Text(6, 0, 'June'),\n", + " Text(7, 0, 'July'),\n", + " Text(8, 0, 'August'),\n", + " Text(9, 0, 'September'),\n", + " Text(10, 0, 'October'),\n", + " Text(11, 0, 'November'),\n", + " Text(12, 0, 'December')])" + ] + }, + "execution_count": 101, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "<Figure size 1440x720 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "TimeAccident_dfmonthly_new.plot.bar(stacked=True,rot=15, title=\"Accidents Time \",figsize=(20, 10),label=month_name)\n", + "plt.xticks(fontsize=20,label='month_name')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead tr th {\n", + " text-align: left;\n", + " }\n", + "\n", + " .dataframe thead tr:last-of-type th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr>\n", + " <th></th>\n", + " <th colspan=\"3\" halign=\"left\">Total accidents</th>\n", + " </tr>\n", + " <tr>\n", + " <th>Accident_Severity</th>\n", + " <th>Fatal</th>\n", + " <th>Serious</th>\n", + " <th>Slight</th>\n", + " </tr>\n", + " <tr>\n", + " <th>month</th>\n", + " <th></th>\n", + " <th></th>\n", + " <th></th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>NaN</th>\n", + " <td>3329</td>\n", + " <td>46587</td>\n", + " <td>190255</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1.0</th>\n", + " <td>2140</td>\n", + " <td>21716</td>\n", + " <td>141148</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2.0</th>\n", + " <td>1875</td>\n", + " <td>20035</td>\n", + " <td>130153</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3.0</th>\n", + " <td>1991</td>\n", + " <td>22307</td>\n", + " <td>139827</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4.0</th>\n", + " <td>2032</td>\n", + " <td>22409</td>\n", + " <td>132877</td>\n", + " </tr>\n", + " <tr>\n", + " <th>5.0</th>\n", + " <td>2178</td>\n", + " <td>24934</td>\n", + " <td>145882</td>\n", + " </tr>\n", + " <tr>\n", + " <th>6.0</th>\n", + " <td>2139</td>\n", + " <td>25183</td>\n", + " <td>146027</td>\n", + " </tr>\n", + " <tr>\n", + " <th>7.0</th>\n", + " <td>2236</td>\n", + " <td>26248</td>\n", + " <td>151367</td>\n", + " </tr>\n", + " <tr>\n", + " <th>8.0</th>\n", + " <td>2367</td>\n", + " <td>24698</td>\n", + " <td>140511</td>\n", + " </tr>\n", + " <tr>\n", + " <th>9.0</th>\n", + " <td>2293</td>\n", + " <td>25538</td>\n", + " <td>148185</td>\n", + " </tr>\n", + " <tr>\n", + " <th>10.0</th>\n", + " <td>2390</td>\n", + " <td>25642</td>\n", + " <td>155967</td>\n", + " </tr>\n", + " <tr>\n", + " <th>11.0</th>\n", + " <td>2362</td>\n", + " <td>25278</td>\n", + " <td>159843</td>\n", + " </tr>\n", + " <tr>\n", + " <th>12.0</th>\n", + " <td>2366</td>\n", + " <td>22351</td>\n", + " <td>142761</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " Total accidents \n", + "Accident_Severity Fatal Serious Slight\n", + "month \n", + "NaN 3329 46587 190255\n", + "1.0 2140 21716 141148\n", + "2.0 1875 20035 130153\n", + "3.0 1991 22307 139827\n", + "4.0 2032 22409 132877\n", + "5.0 2178 24934 145882\n", + "6.0 2139 25183 146027\n", + "7.0 2236 26248 151367\n", + "8.0 2367 24698 140511\n", + "9.0 2293 25538 148185\n", + "10.0 2390 25642 155967\n", + "11.0 2362 25278 159843\n", + "12.0 2366 22351 142761" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "TimeAccident_dfmonthly" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['Null',\n", + " 'January',\n", + " 'February',\n", + " 'March',\n", + " 'April',\n", + " 'May',\n", + " 'June',\n", + " 'July',\n", + " 'August',\n", + " 'September',\n", + " 'October',\n", + " 'November',\n", + " 'December']" + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "month_name=['Null','January', 'February', 'March', 'April', 'May', 'June', 'July', 'August', 'September', 'October', 'November', 'December']\n", + "month_name" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>month</th>\n", + " <th>Accident_Severity</th>\n", + " <th>Total accidents</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>NaN</td>\n", + " <td>Slight</td>\n", + " <td>190255</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>NaN</td>\n", + " <td>Fatal</td>\n", + " <td>3329</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>NaN</td>\n", + " <td>Serious</td>\n", + " <td>46587</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>1.0</td>\n", + " <td>Slight</td>\n", + " <td>141148</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>1.0</td>\n", + " <td>Fatal</td>\n", + " <td>2140</td>\n", + " </tr>\n", + " <tr>\n", + " <th>5</th>\n", + " <td>1.0</td>\n", + " <td>Serious</td>\n", + " <td>21716</td>\n", + " </tr>\n", + " <tr>\n", + " <th>6</th>\n", + " <td>2.0</td>\n", + " <td>Serious</td>\n", + " <td>20035</td>\n", + " </tr>\n", + " <tr>\n", + " <th>7</th>\n", + " <td>2.0</td>\n", + " <td>Slight</td>\n", + " <td>130153</td>\n", + " </tr>\n", + " <tr>\n", + " <th>8</th>\n", + " <td>2.0</td>\n", + " <td>Fatal</td>\n", + " <td>1875</td>\n", + " </tr>\n", + " <tr>\n", + " <th>9</th>\n", + " <td>3.0</td>\n", + " <td>Fatal</td>\n", + " <td>1991</td>\n", + " </tr>\n", + " <tr>\n", + " <th>10</th>\n", + " <td>3.0</td>\n", + " <td>Serious</td>\n", + " <td>22307</td>\n", + " </tr>\n", + " <tr>\n", + " <th>11</th>\n", + " <td>3.0</td>\n", + " <td>Slight</td>\n", + " <td>139827</td>\n", + " </tr>\n", + " <tr>\n", + " <th>12</th>\n", + " <td>4.0</td>\n", + " <td>Slight</td>\n", + " <td>132877</td>\n", + " </tr>\n", + " <tr>\n", + " <th>13</th>\n", + " <td>4.0</td>\n", + " <td>Fatal</td>\n", + " <td>2032</td>\n", + " </tr>\n", + " <tr>\n", + " <th>14</th>\n", + " <td>4.0</td>\n", + " <td>Serious</td>\n", + " <td>22409</td>\n", + " </tr>\n", + " <tr>\n", + " <th>15</th>\n", + " <td>5.0</td>\n", + " <td>Slight</td>\n", + " <td>145882</td>\n", + " </tr>\n", + " <tr>\n", + " <th>16</th>\n", + " <td>5.0</td>\n", + " <td>Fatal</td>\n", + " <td>2178</td>\n", + " </tr>\n", + " <tr>\n", + " <th>17</th>\n", + " <td>5.0</td>\n", + " <td>Serious</td>\n", + " <td>24934</td>\n", + " </tr>\n", + " <tr>\n", + " <th>18</th>\n", + " <td>6.0</td>\n", + " <td>Slight</td>\n", + " <td>146027</td>\n", + " </tr>\n", + " <tr>\n", + " <th>19</th>\n", + " <td>6.0</td>\n", + " <td>Serious</td>\n", + " <td>25183</td>\n", + " </tr>\n", + " <tr>\n", + " <th>20</th>\n", + " <td>6.0</td>\n", + " <td>Fatal</td>\n", + " <td>2139</td>\n", + " </tr>\n", + " <tr>\n", + " <th>21</th>\n", + " <td>7.0</td>\n", + " <td>Serious</td>\n", + " <td>26248</td>\n", + " </tr>\n", + " <tr>\n", + " <th>22</th>\n", + " <td>7.0</td>\n", + " <td>Fatal</td>\n", + " <td>2236</td>\n", + " </tr>\n", + " <tr>\n", + " <th>23</th>\n", + " <td>7.0</td>\n", + " <td>Slight</td>\n", + " <td>151367</td>\n", + " </tr>\n", + " <tr>\n", + " <th>24</th>\n", + " <td>8.0</td>\n", + " <td>Slight</td>\n", + " <td>140511</td>\n", + " </tr>\n", + " <tr>\n", + " <th>25</th>\n", + " <td>8.0</td>\n", + " <td>Fatal</td>\n", + " <td>2367</td>\n", + " </tr>\n", + " <tr>\n", + " <th>26</th>\n", + " <td>8.0</td>\n", + " <td>Serious</td>\n", + " <td>24698</td>\n", + " </tr>\n", + " <tr>\n", + " <th>27</th>\n", + " <td>9.0</td>\n", + " <td>Slight</td>\n", + " <td>148185</td>\n", + " </tr>\n", + " <tr>\n", + " <th>28</th>\n", + " <td>9.0</td>\n", + " <td>Serious</td>\n", + " <td>25538</td>\n", + " </tr>\n", + " <tr>\n", + " <th>29</th>\n", + " <td>9.0</td>\n", + " <td>Fatal</td>\n", + " <td>2293</td>\n", + " </tr>\n", + " <tr>\n", + " <th>30</th>\n", + " <td>10.0</td>\n", + " <td>Fatal</td>\n", + " <td>2390</td>\n", + " </tr>\n", + " <tr>\n", + " <th>31</th>\n", + " <td>10.0</td>\n", + " <td>Slight</td>\n", + " <td>155967</td>\n", + " </tr>\n", + " <tr>\n", + " <th>32</th>\n", + " <td>10.0</td>\n", + " <td>Serious</td>\n", + " <td>25642</td>\n", + " </tr>\n", + " <tr>\n", + " <th>33</th>\n", + " <td>11.0</td>\n", + " <td>Fatal</td>\n", + " <td>2362</td>\n", + " </tr>\n", + " <tr>\n", + " <th>34</th>\n", + " <td>11.0</td>\n", + " <td>Serious</td>\n", + " <td>25278</td>\n", + " </tr>\n", + " <tr>\n", + " <th>35</th>\n", + " <td>11.0</td>\n", + " <td>Slight</td>\n", + " <td>159843</td>\n", + " </tr>\n", + " <tr>\n", + " <th>36</th>\n", + " <td>12.0</td>\n", + " <td>Slight</td>\n", + " <td>142761</td>\n", + " </tr>\n", + " <tr>\n", + " <th>37</th>\n", + " <td>12.0</td>\n", + " <td>Serious</td>\n", + " <td>22351</td>\n", + " </tr>\n", + " <tr>\n", + " <th>38</th>\n", + " <td>12.0</td>\n", + " <td>Fatal</td>\n", + " <td>2366</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " month Accident_Severity Total accidents\n", + "0 NaN Slight 190255\n", + "1 NaN Fatal 3329\n", + "2 NaN Serious 46587\n", + "3 1.0 Slight 141148\n", + "4 1.0 Fatal 2140\n", + "5 1.0 Serious 21716\n", + "6 2.0 Serious 20035\n", + "7 2.0 Slight 130153\n", + "8 2.0 Fatal 1875\n", + "9 3.0 Fatal 1991\n", + "10 3.0 Serious 22307\n", + "11 3.0 Slight 139827\n", + "12 4.0 Slight 132877\n", + "13 4.0 Fatal 2032\n", + "14 4.0 Serious 22409\n", + "15 5.0 Slight 145882\n", + "16 5.0 Fatal 2178\n", + "17 5.0 Serious 24934\n", + "18 6.0 Slight 146027\n", + "19 6.0 Serious 25183\n", + "20 6.0 Fatal 2139\n", + "21 7.0 Serious 26248\n", + "22 7.0 Fatal 2236\n", + "23 7.0 Slight 151367\n", + "24 8.0 Slight 140511\n", + "25 8.0 Fatal 2367\n", + "26 8.0 Serious 24698\n", + "27 9.0 Slight 148185\n", + "28 9.0 Serious 25538\n", + "29 9.0 Fatal 2293\n", + "30 10.0 Fatal 2390\n", + "31 10.0 Slight 155967\n", + "32 10.0 Serious 25642\n", + "33 11.0 Fatal 2362\n", + "34 11.0 Serious 25278\n", + "35 11.0 Slight 159843\n", + "36 12.0 Slight 142761\n", + "37 12.0 Serious 22351\n", + "38 12.0 Fatal 2366" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "month=TimeAccident_dfmonthly.toPandas()\n", + "month" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "y_ans_val = [val for val in TimeAccident_dfmonthly.select('Total accidents').collect()]\n", + "x_ts = [val for val in TimeAccident_dfmonthly.select('month').collect()]\n", + "\n", + "plt.plot(x_ts, y_ans_val)\n", + "\n", + "plt.ylabel('Total accidents')\n", + "plt.xlabel('Month')\n", + "#plt.title('ASN values for time')\n", + "plt.legend(['Accidents'], loc='upper left')\n", + "\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+--------------+--------------+---------------+--------------+---------------+-----------------+-------------------+----------+-----------+-------------------------------------------+--------------------+--------------------+---------+--------------------+--------------------------+-------------------------+---------------------+----------------------+---------+-------------------------+--------------------+------------------+---------------------------------+---------------------------------------+-------------------+-----------------------+------------------+--------------------------+-----------+-----+-------------------+--------------------+----+-------------------+----+\n", + "|Accident_Index|1st_Road_Class|1st_Road_Number|2nd_Road_Class|2nd_Road_Number|Accident_Severity|Carriageway_Hazards| Date|Day_of_Week|Did_Police_Officer_Attend_Scene_of_Accident| Junction_Control| Junction_Detail| Latitude| Light_Conditions|Local_Authority_(District)|Local_Authority_(Highway)|Location_Easting_OSGR|Location_Northing_OSGR|Longitude|LSOA_of_Accident_Location|Number_of_Casualties|Number_of_Vehicles|Pedestrian_Crossing-Human_Control|Pedestrian_Crossing-Physical_Facilities| Police_Force|Road_Surface_Conditions| Road_Type|Special_Conditions_at_Site|Speed_limit| Time|Urban_or_Rural_Area| Weather_Conditions|Year| timestamp|hour|\n", + "+--------------+--------------+---------------+--------------+---------------+-----------------+-------------------+----------+-----------+-------------------------------------------+--------------------+--------------------+---------+--------------------+--------------------------+-------------------------+---------------------+----------------------+---------+-------------------------+--------------------+------------------+---------------------------------+---------------------------------------+-------------------+-----------------------+------------------+--------------------------+-----------+-----+-------------------+--------------------+----+-------------------+----+\n", + "| 200501BS00001| A| 3218| NA| 0| Serious| None|2005-01-04| Tuesday| 1|Data missing or o...|Not at junction o...|51.489096| Daylight| Kensington and Ch...| Kensington and Ch...| 525680| 178240| -0.19117| E01002849| 1| 1| 0| 1|Metropolitan Police| Wet or damp|Single carriageway| None| 30|17:42| Urban|Raining no high w...|2005|2021-10-04 17:42:00| 17|\n", + "| 200501BS00002| B| 450| C| 0| Slight| None|2005-01-05| Wednesday| 1| Auto traffic signal| Crossroads|51.520075|Darkness - lights...| Kensington and Ch...| Kensington and Ch...| 524170| 181650|-0.211708| E01002909| 1| 1| 0| 5|Metropolitan Police| Dry| Dual carriageway| None| 30|17:36| Urban| Fine no high winds|2005|2021-10-04 17:36:00| 17|\n", + "| 200501BS00003| C| 0| NA| 0| Slight| None|2005-01-06| Thursday| 1|Data missing or o...|Not at junction o...|51.525301|Darkness - lights...| Kensington and Ch...| Kensington and Ch...| 524520| 182240|-0.206458| E01002857| 1| 2| 0| 0|Metropolitan Police| Dry|Single carriageway| None| 30|00:15| Urban| Fine no high winds|2005|2021-10-04 00:15:00| 0|\n", + "| 200501BS00004| A| 3220| NA| 0| Slight| None|2005-01-07| Friday| 1|Data missing or o...|Not at junction o...|51.482442| Daylight| Kensington and Ch...| Kensington and Ch...| 526900| 177530|-0.173862| E01002840| 1| 1| 0| 0|Metropolitan Police| Dry|Single carriageway| None| 30|10:35| Urban| Fine no high winds|2005|2021-10-04 10:35:00| 10|\n", + "| 200501BS00005| Unclassified| 0| NA| 0| Slight| None|2005-01-10| Monday| 1|Data missing or o...|Not at junction o...|51.495752|Darkness - lighti...| Kensington and Ch...| Kensington and Ch...| 528060| 179040|-0.156618| E01002863| 1| 1| 0| 0|Metropolitan Police| Wet or damp|Single carriageway| None| 30|21:13| Urban| Fine no high winds|2005|2021-10-04 21:13:00| 21|\n", + "| 200501BS00006| Unclassified| 0| NA| 0| Slight| None|2005-01-11| Tuesday| 1|Data missing or o...|Not at junction o...| 51.51554| Daylight| Kensington and Ch...| Kensington and Ch...| 524770| 181160|-0.203238| E01002832| 1| 2| 0| 0|Metropolitan Police| Wet or damp|Single carriageway| Oil or diesel| 30|12:40| Urban|Raining no high w...|2005|2021-10-04 12:40:00| 12|\n", + "| 200501BS00007| C| 0| Unclassified| 0| Slight| None|2005-01-13| Thursday| 1|Give way or uncon...|T or staggered ju...|51.512695|Darkness - lights...| Kensington and Ch...| Kensington and Ch...| 524220| 180830|-0.211277| E01002875| 1| 2| 0| 0|Metropolitan Police| Dry|Single carriageway| None| 30|20:40| Urban| Fine no high winds|2005|2021-10-04 20:40:00| 20|\n", + "| 200501BS00009| A| 315| NA| 0| Slight| None|2005-01-14| Friday| 1|Data missing or o...|Not at junction o...| 51.50226| Daylight| Kensington and Ch...| Kensington and Ch...| 525890| 179710|-0.187623| E01002889| 2| 1| 0| 0|Metropolitan Police| Dry| Dual carriageway| None| 30|17:35| Urban| Fine no high winds|2005|2021-10-04 17:35:00| 17|\n", + "| 200501BS00010| A| 3212| B| 304| Slight| None|2005-01-15| Saturday| 1| Auto traffic signal| Crossroads| 51.48342|Darkness - lights...| Kensington and Ch...| Kensington and Ch...| 527350| 177650|-0.167342| E01002900| 2| 2| 0| 5|Metropolitan Police| Dry|Single carriageway| None| 30|22:43| Urban| Fine no high winds|2005|2021-10-04 22:43:00| 22|\n", + "| 200501BS00011| B| 450| C| 0| Slight| None|2005-01-15| Saturday| 1|Give way or uncon...|T or staggered ju...|51.512443| Daylight| Kensington and Ch...| Kensington and Ch...| 524550| 180810|-0.206531| E01002875| 5| 2| 0| 8|Metropolitan Police| Dry|Single carriageway| None| 30|16:00| Urban| Fine no high winds|2005|2021-10-04 16:00:00| 16|\n", + "| 200501BS00012| A| 4| B| 325| Slight| None|2005-01-16| Sunday| 1| Auto traffic signal| Crossroads|51.494902|Darkness - lights...| Kensington and Ch...| Kensington and Ch...| 526240| 178900|-0.182872| E01002835| 1| 1| 0| 5|Metropolitan Police| Dry|Single carriageway| None| 30|00:42| Urban| Fine no high winds|2005|2021-10-04 00:42:00| 0|\n", + "| 200501BS00014| A| 3220| A| 308| Slight| None|2005-01-25| Tuesday| 1| Auto traffic signal| Crossroads|51.484044|Darkness - lights...| Kensington and Ch...| Kensington and Ch...| 526170| 177690|-0.184312| E01002912| 1| 2| 0| 5|Metropolitan Police| Wet or damp|Single carriageway| None| 30|20:48| Urban| Fine no high winds|2005|2021-10-04 20:48:00| 20|\n", + "| 200501BS00015| Unclassified| 0| A| 3220| Slight| None|2005-01-11| Tuesday| 1|Give way or uncon...|T or staggered ju...|51.491632| Daylight| Kensington and Ch...| Kensington and Ch...| 525590| 178520|-0.192366| E01002849| 1| 1| 0| 1|Metropolitan Police| Wet or damp| One way street| None| 30|12:55| Urban|Raining no high w...|2005|2021-10-04 12:55:00| 12|\n", + "| 200501BS00016| A| 3217| A| 3216| Slight| None|2005-01-18| Tuesday| 1|Give way or uncon...|T or staggered ju...|51.492622|Darkness - lights...| Kensington and Ch...| Kensington and Ch...| 527990| 178690|-0.157753| E01002902| 1| 2| 0| 0|Metropolitan Police| Wet or damp| One way street| None| 30|05:01| Urban|Raining no high w...|2005|2021-10-04 05:01:00| 5|\n", + "| 200501BS00017| A| 4| NA| 0| Slight| None|2005-01-18| Tuesday| 1|Data missing or o...|Not at junction o...|51.495429| Daylight| Kensington and Ch...| Kensington and Ch...| 526700| 178970|-0.176224| E01002821| 2| 1| 0| 0|Metropolitan Police| Dry| Dual carriageway| None| 30|11:15| Urban| Fine no high winds|2005|2021-10-04 11:15:00| 11|\n", + "| 200501BS00018| A| 3217| Unclassified| 0| Slight| None|2005-01-18| Tuesday| 1|Give way or uncon...|T or staggered ju...|51.481912| Daylight| Kensington and Ch...| Kensington and Ch...| 526460| 177460| -0.18022| E01002840| 1| 1| 0| 1|Metropolitan Police| Dry|Single carriageway| None| 30|10:50| Urban| Fine no high winds|2005|2021-10-04 10:50:00| 10|\n", + "| 200501BS00019| Unclassified| 0| Unclassified| 0| Serious| None|2005-01-20| Thursday| 1|Give way or uncon...|T or staggered ju...|51.500191|Darkness - lights...| Kensington and Ch...| Kensington and Ch...| 524680| 179450|-0.205139| E01002864| 1| 2| 0| 0|Metropolitan Police| Dry|Single carriageway| None| 30|00:15| Urban| Fine no high winds|2005|2021-10-04 00:15:00| 0|\n", + "| 200501BS00020| A| 3218| A| 4| Slight| None|2005-01-21| Friday| 1|Give way or uncon...|T or staggered ju...|51.495811| Daylight| Kensington and Ch...| Kensington and Ch...| 527000| 179020|-0.171887| E01002821| 1| 2| 0| 0|Metropolitan Police| Dry|Single carriageway| None| 30|09:15| Urban| Fine no high winds|2005|2021-10-04 09:15:00| 9|\n", + "| 200501BS00021| B| 302| NA| 0| Slight| None|2005-01-21| Friday| 1|Data missing or o...|Not at junction o...|51.486552|Darkness - lights...| Kensington and Ch...| Kensington and Ch...| 527810| 178010| -0.16059| E01002901| 1| 2| 0| 0|Metropolitan Police| Dry|Single carriageway| None| 30|21:16| Urban| Fine no high winds|2005|2021-10-04 21:16:00| 21|\n", + "| 200501BS00022| A| 4| Unclassified| 0| Serious| None|2005-01-08| Saturday| 1|Give way or uncon...|T or staggered ju...|51.495498|Darkness - lights...| Kensington and Ch...| Kensington and Ch...| 526790| 178980|-0.174925| E01002821| 1| 1| 0| 0|Metropolitan Police| Dry|Single carriageway| None| 30|03:00| Urban| Fine no high winds|2005|2021-10-04 03:00:00| 3|\n", + "+--------------+--------------+---------------+--------------+---------------+-----------------+-------------------+----------+-----------+-------------------------------------------+--------------------+--------------------+---------+--------------------+--------------------------+-------------------------+---------------------+----------------------+---------+-------------------------+--------------------+------------------+---------------------------------+---------------------------------------+-------------------+-----------------------+------------------+--------------------------+-----------+-----+-------------------+--------------------+----+-------------------+----+\n", + "only showing top 20 rows\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+--------------------+---------------+\n", + "| Road_Type|Total accidents|\n", + "+--------------------+---------------+\n", + "|Data missing or o...| 1|\n", + "| 7| 3447|\n", + "| 9| 4535|\n", + "| 2| 6030|\n", + "| Unknown| 14396|\n", + "| 1| 14453|\n", + "| Slip road| 21558|\n", + "| 3| 38063|\n", + "| One way street| 43258|\n", + "| Roundabout| 136754|\n", + "| 6| 173643|\n", + "| Dual carriageway| 303407|\n", + "| Single carriageway| 1527882|\n", + "+--------------------+---------------+\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [] + } + ], + "source": [ + "from pyspark.sql.functions import *\n", + "#Timestamp String to DateType\n", + "Accident_Information_df=Accident_Information_df.withColumn(\"timestamp\",to_timestamp(\"Time\"))\n", + "#Accident_Information_df\n", + "TimeAccident_dfhour = Accident_Information_df.withColumn('hour',hour(Accident_Information_df.timestamp))\n", + "TimeAccident_dfhour.show()\n", + "# Using Cast to convert TimestampType to DateType\n", + "#TimeAccident_df.withColumn('timestamp_string', \\\n", + "# to_timestamp('Time').cast('string')) \\\n", + "# .show(truncate=False)\n", + "Roadtype_df = Accident_Information_df.groupby('Road_Type').agg(F.count(Accident_Information_df.Accident_Index).alias('Total accidents')).sort(\"Total accidents\")\n", + "Roadtype_df.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAaAAAADnCAYAAABYHII5AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8rg+JYAAAACXBIWXMAAAsTAAALEwEAmpwYAABKzklEQVR4nO3dd3gc1fXw8e/ZplV3k3u3wbisbXDBGDcIBIKB/EiBAAGZEkogdAhvaAqEHlJoIQkBRO+EkgAB997LuvfeLatrpS33/WNGtiyr2pJGks/nefRod/bOzNm1tWdumXvFGINSSinV0FxOB6CUUurEpAlIKaWUIzQBKaWUcoQmIKWUUo7QBKSUUsoRmoCUUko5QhOQUkopR2gCUkop5QhNQEoppRyhCUgppZQjNAEppZRyhCYgpZRSjtAEpJRSyhGagJRSSjlCE5BSSilHaAJSSinlCE1ASimlHKEJSCmllCM0ASmllHKEJiCllFKO0ASklFLKEZqAlFJKOUITkFJKKUdoAlJKKeUITUBKKaUcoQlIKaWUIzQBKaWUcoTH6QCUai4CmYEEoB3QtpqfNMAHhICiMj9ln+cDu4BtwPYyv/cE04Omwd6UUvVIjNH/y0rVViAz0BEYDgyzfw8BWjbAqQuBVcAKYBmwBFgcTA9mNcC5lapTmoCUqkYgMxAPnImVaEqTTkdHgzraGmAqMAWYGkwP7nQ2HKWqpwlIqQoEMgPdgfHABcBZQLyjAdXeeuB74BNgcjA9GHU4HqWOoglIKVsgMzAQuBz4P+AUZ6OpU/uBfwMfAxOD6cGIs+EoZdEEpE5odl/OVfZPf4fDaQhZwOfAR8D3wfRg2OF41AlME5A64QQyAwKcB/zG/u12NiLH7ANeAF4MpgcPOh2MOvFoAlInDHswwdXA7UBfh8NpTPKBvwN/0sELqiFpAlLNXiAz0Aa4C7gRaOVwOI1ZCfAW8EwwPbjW6WBU86cJSDVbgcxAElbiuRtIcTicpiQGfAY8GUwPLnQ6GNV8aQJSzU4gM+DFqu08hDXzgDp2E4EngunBSU4HopofTUCq2bAHF1wBPAb0cDic5ubfwG+C6cHtTgeimg9NQKpZCGQGzgb+BAxyOpZmLA+rVvlCMD0YczoY1fRpAlJNWiAz4AeeAW4FxOFwThQLgBuD6cFFTgeimjZNQKrJCmQGTgXeBvo5HcsJKAo8DzwUTA8WOB2Mapo0AakmJ5AZcAH3AY8CXofDOdFtA64JpgcnOh2Iano0AakmxZ4k9E1gtMOhqMNiwJPAIzrpqaoNTUCqyQhkBq7GmjpG7+lpnGYAVwTTg9ucDkQ1DZqAVKMXyAy0wpoq5mdOx6KqlYXVJPeF04Goxs/ldABKVSWQGQgAS9Hk01S0Av4dyAw87HQgqvHTGpBqtAKZgbFYSwekOh2LOiYfAhOC6cEipwNRjVO1NSARiYrIEhFZISJLReRuEalyPxHpLiJX1F2YRxz7YhG5/xj2m1Uf8dQXEZkgIrVa9llETrH/rRaLSK/6iq0hBDIDPwO+RZNPU3YpMCmQGUhzOhDVONWkCa7IGDPYGNMfOBf4EfBINft0x5oSpc4ZY74wxjx1DPuNrI94KiIinjo4zASgVgkIayXPj40xpxpjNlRWSETqdP0bEeljJ77Sn1wRueNYjxfIDPwG+ACIq7MglVNGAHMCmYHeTgeiGp9a9QEZY/YCNwC3iqW7iEwXkUX2T+mX/FPAaPvL6M4qyh1il1ktIm+IyFoReUdEzhGRmSKyTkSG2+UmiMiL9uOfi8hyu2Y2zd7WX0Tm2edeJiIn2dvz7d/jRGSKiHxsn+8dERH7tQvsbQtF5HkR+aqCOP0i8rqIBO2axlll4vpCRCZhTeBYfr+77FiXl3452+95eZky94hIhoj8DBgKvGO/j/hyxxosInPs9/eZiLQUkQuAO4CbRWRyBefPF5HnRGQpcIaIPCwi8+14/lHmM5giIk/bn+FaERltb08QkQ9FZKV9zrkiMtQ+/O3AyUAf+3kR1mzKtRLIDEggM/AU1g2O2j/ZfPQEpgYyA7oGkzpCrf/IjTEbsVaQbAvsBc41xpwGXIb1xQFwPzDdrjn9uYpy5fUGngNOsX+uAEYB9wC/q6D8w8B5xphBwMX2tpuAvxpjBmN9iVc0eeKpWF/W/bD+OM4UET/WSKsfGWOGAJU1G9xifQwmAFwOZNr7ApwG/MwYM7bsDiIyBLgGOB3rivBXInJqJcfHGPMx1nQnV9qfYfk29DeB3xpjBgJB4BFjzH+BV4A/G2POquCwicBcY8wgY8wM4EVjzDBjzAAgHriwTFmPMWa4/RmV1nZ/DRw0xvTDmg9sCNBORBZhfeZbsf6N0oCoMWZLZe+vIoHMgAfIBH5bm/1Uk9ERKwkNdDoQ1Xgc71WmF/iniASx1pivbEqUmpbbZIwJGmNiwApgorFGSQSxmvXKmwm8ISK/4vCyyrOB34nIb4FuFXx5A8wzxmy3z7PEPvYpwEZjzCa7zHuVxDgKa/oXjDGrgS1YV/8A3xljsirZ5zNjTIExJh/4lGO8kVJEUoEWxpip9qZMYEwNdo0Cn5R5fpZdiwkCZwP9y7z2qf17IYc/91HA+wDGmOXAMiCC9fmV2D9PAq3txzVmz2zwNnBVbfZTTU4aMDmQGRhabUl1Qqh1AhKRnlhfZnuBO4E9WDMQDwV8lexW03LFZR7HyjyPAUf1qxhjbgIeBLoAC0WktTHmXazaUBHwXxE5u5rzRCs69jGq7ZxYEY78N/CD1TQHDCtb0G6au6eyA4nIBOCCKs4VMsZE7bJ+4GWs2loA+GfpuW2ln091n80BYD5WwjkLq+nRA3xfxT5HsJdQ+BdWzVg1f62AiYHMwLBqS6pmr1YJSETSsJp5XrRrJqnALrsmcRWHayF5QHKZXSsrd1xEpJcxZq4x5mFgH9DFTpAbjTHPYw3hrWmVfw3Q0/7yh8q/EKcDV9rnPxnoau9blenA/9n9KInAJfa2PUBbEWktInEc2QwW5cjPEABjTA5wsLRvBuvznFq+XDVKk81+EUmiZvfYzMQa1YSI9AMC9vaPgTAwC2uQyh5ql9BfwhpwoU4cKcB/dGCCqkkCirc7wldgXdn+D/i9/drLQLrdsX0Kh2sAy4CoPTjgzirKHa9n7cEAy7G+AJdifUkuF5ElwACs/pJq2U11vwa+EZGFWEk0p4KiLwMuu+nqA2CCMaa4gnJlj70IeAOYB8wFXjXGLDbGhLEm1JwHfAesLrNbNvBK+UEIIjIFWAR8LSLFwA/sY1CmzHgRmS0ibcQa1PE81r/jRhH5mTEmG6vWswsrYXTkcBPcSRxu0svEalLD/jzGicg6YA6Qi9VM+Q5wHdAZKxElY9U+qxXIDDwL3FyTsqrZSQO+CWQGdMXaE5jeiFqGiCQZY/LtEWEvAevsQRQNHUd34Ct7gEDptgwgH6uWtNAYc7c98u0uY8w5dhPcUKxmsLuAi40xB0XkDawBCJdhJf8vjDG9ReSnWIMHzgfaYDWlnQ6MBYYYY+4VkXlAzBgzwj7Ox8DVwM+xmg8TsRJQf+yBEVg10eF2kqtUIDNwD/DscXxMqnmYD5ylSzqcmHSo65F+ZdecVmA1G/7doTgquyoo3V7RIAGwBhP8FhhvjDlYZvu/jTExY8xKoJ29bRTwnjEmaozZg9WMNwyraXC03cy2EtgjIh2AkVg1reFYNZ0d9u+eWLWfq7FGwt1cg+TzE6xF5JQaBnxoj4JUJxhNQGUYY/5sD3vuZ4y50hhT6FAoB4CW5ba1AvbbjysbJLABqwnsZI5UtomwylVDjTE7gBZYNaNpWAnpUiDXHkY/DtiEVRs7E2voeRHWQARDNTePrn6izeDx+QU3oTVvddgFWH3L6gSjCagRsodq7yodwScirbASwoxqdt0C/BR4U0T6V1N2OnCZiLjtwSVjsPqiwOrjuYPDCege+3f5OGcA9wJ/sxP3YPt+pArNOa1v2lWt272dFIv57svKnk3FQ+TViem6QGYgw+kgVMPSBNSIiMgDYs25twyrf+UZu0lwK/CmPb1OAEiq7Bj2vUlXAh9J1fPBfYY1WGQpMAm4zxiz235tOtbNqOuxBjy0AqbbfVPfHst7e+mmSZ6kkO+df74Q800PJ3X/Iikx9rc9e9e6rNk1lAJ4OJAZONfpIFTD0UEIjYSInAH8CRhnjCkWkTaAzxiz0x75do8xZsFxnsNjjIkcx/7dga+wBkIcMUiiOi/dNOlZV7T4ouELnoqPL9qb8vzFrvXLTsH38u69nl91aOsOuVx9qj+KOgHsAALB9ODBakuqJk9rQI1HB2B/6ZBuY8x+Y8zO8oVEZLM9vLp07rx3RGSVWHPbJVRQfoqI/EVEFgC3i8gPxJrDLigir9n3H1HF3HBD7OH0S7GmIaq1l26aNB64O+aO6zNn+MOpu9uPWHvbF7HTfvm1yZrQsV3qX/bsC3UMR+Yey7FVs9MJ+JvTQaiGoQmo8fgf1o20a0XkZREZW+0e1uSfLxtj+mLdl/PrSsr5jDFDsYaWvwFcZs+A4OHwfTiVzQ33OvAbe749sEa8zQb6iMh2EbmuqgCfu+zC9rHogccpHfwgkrqqzy+HBQfcMG1ckDHP/SO65fZWaR2vzs2NjiksmlKD96yav8sCmYF6mU1fNS6agBoJe+DBEKzZxvcBH9j39lRlmzFmpv34bayh1RX5wP7dB2u+vbX287LzyB01N5yItMCad26aXeYtYLsxpoMxxmuM6WyM+Vc1Mb5ekpvZI1I0b+ahLSKyv82gcTNHPrG4TV5Sn1efjx18gxad44yRWw5mT8eYWs0lp5qllwKZgc5OB6HqlyagRsS+J2eKMeYR4FasEW1V7lLN81JV3uRXg7nhjslzl114I9bovZRIaMaZxTlvzDKx0KHZJUp8KUNmjHyiuCDp5PBf/xFLztvqT/48KdH77N79y8SYA8d7ftWktQDesOcKVM2UJqBGQqxF3U4qs2kw1rDqqnS1By+AtXRFdcO01wDdRaR0Dq7SeeQqnBvOvqE0W0RKa1ZXVvc+Sm2/f3rXHklHNqOYWNbI4pxX8qLhTcsObRR3xyWDbjtpfa9Lgnf+Ozbogu9dxQ+ltU56cc++LXGx2Pqank81Sz/AWmtKNVOagBqPJKy1hVbaw7D7ARnV7LMGuEVEVmHduFpl560xJoS1LtFHdlNbDHilzNxwy7GGWS/CmhViKdZw8I/t4eC1uRp9ZXjaBWPO73TtTJ/LX2ZEU6xzOP+z/iX5X04xJhYFQMS3rcs5Y+YNe2DOWcu8w554NZZ1d6s2bX+/LysrLRI5rpF/qsl7MpAZqGz5FtXE6TDsJqqi+eLq8NgCJNrz4nmxala3G2Pm1GT/7fdP/znwYelzY2L7Fh74bsOGvCUjjjyRL+hLviLV5W7VtXSTK1qybtjCp73u8O7Qvde64i6SvK1L/XHMj/fXZFCGap4WA6cH04NhpwNRdUtrQOooxpJvP/XaPzW6Utl+//Qk4C9lt4m40oa2OW/EBZ1vmON3J+47fKKSQEnuGy0jRXMPDVCIuX0nzR32YKsDbUZl//XvsZRle5NTDJCenTuV47iHSTVpp2JNsKuaGU1ATZQxZnN91H5K2VP0LMFaePA7Y0yN7tPJJ3Q31vIOR0n2thxxcZdb3Keknj6z7OZIaOaRAxREUtb0uXzEssCvl9/1qek3aJqXr5ISkx46kDVXqpnoVDVb9wcyA+XnR1RNnDbBqSrZQ7E/w7oXaHlVZTMyMnpgWNop1mrRD8KBU314UiorWxjJnTdx59tdCqN5Hcqcbbs36f+y3N4ehxYR9JbkLxm+4PFOe1Ny1z5ytavN73Kysh5Ja90hfHjhQHXieDqYHrzf6SBU3dEakKqSPUBhMtZw6uo8hZC8w5019s24qcULPBumG0ysooIJnpThF3a5OWFAi9FlRu4Ze4DCF1OMiUUAwr6kwTPPeDyaEO2b8vILsdgLnlat7zxwcFvLaHTx8b871cTcFsgMdKi+mGoqtAakjmLPjh02xmTbq7H+D3jaGPNVZftkZGScBiyg3Eg5j3GtOSs8oKhbLG1wZfuGogWLJu58u21+JPvwjYfiW+5LviLl0AAFY8KddkydddL6j/o/9xPX1k4dC3PW+7ye5XFxoys7rmqWXgmmB3UV3WZCE5A6iogMxJolwY1VS/7QGPNoVftkZGR8BYyv7PXkWPyc88ODO6WahC4VvW6MyV+bu2DRkqxJozmcxPI8/jOXeeJPP7O0XHzhnllDFz7d7/vBJUtnjo26BxSXhD9NThyDiLuWb1M1TRGgbzA9qPeINQOagNRxy8jIGMbhtYQqZyjuGmsz+6xw/6FePBUuKVEcLVo6adc7qbnhA91Lt4mr5Sxf8uX9xeVPBXDFwhuGLHxWshJ37HzmShIuL8zPe75l6qmIVNrnpJqV94LpQZ0rrhk4ofqARKSziHwuIutEZIOI/FVEfE7HdSzs2bDr7I/wOI/3SM1OQtxW9/5xb8ZNLVjk3jTDVLAsapw7ftD5na5rN7TN+VMFiQKY2MGRxTl/OzSDwvLti3tduH51j6vmbRva86F9nT8hpeW9WQcXuY3ZDhALx9j68lbW3reWDY9uoGSfNbVcwboC1j24jvUZ6ynebS0SGy2IsunZTZiYXog1Ib8IZAYGVV9MNXYnTA3IvrlyLtbqna+L1WTzDyDLGHOvs9HVnoiMw1oj6MIKXqv1uj9VHa8qGRkZAayF7WrNY9yrfhAOhLvEWg+s6PVwrHjF5F3vxR0s2VM6dVB08fbIyndnfxcQBL8vATHRksxO7XP+c7l/88C2ebnPTQ713fntgY6x4hjiE7wtvPja++jwiw5s+P0GTNTgSnSR2CeRLjd0Yc29a3Anuun1YC9ccSfU9VhT93YwPXiV00Go43Mi/cWdDYSMMa+DNfEncCdwrYgkiMgEEflURL6xa0jPlO4oIj8UkdkiskhEPrLnS6PM621FZKH9eJCIGBHpaj/fYB//Inu26cUi8r2ItBMRl32uNLusS0TWlz4vc/yxIrLE/lksIsnAU8Boe9uddvxfiMgkYKKIJNrr/cyz9/mxfSy3iDxrr/2zTERutE9zxPFq+qGKRI95rq6IRPt+61sy8CPf7Fm5UrSj/OteV1z/czumdx2RdtFUQSKxmHF/Nn9SwJgYyfGpUbfLA+L1/dnTU279KqVjeEGSJ2FXUdSEYzFvmhdPigdcULCigD2f7iEWjuFOduNOchPaHGL3J7uJ5kZpfW5rTT5Nz6W1HREnZVYctv+fn25vf1VEjmm6H7vloMrbE+qTiAwVkeedOv/xOpH+6voDC8tuMMbkYi13XXqFPRi4DGvZ68tEpItYK5M+CJxjjCkd6XVXuePsBfxi9UGMtsuMFpFuwF5jTCHWdDYjjDGnAu9jLYEdw1pGoXSSz3OApcaYfRzpHuAWY8xg+/hFwP3AdGPMYGPMn+1yp2HNaD0WeACYZIwZDpwFPCsiicB1QI4xZhgwDGvOtx6VHK9KEyf1anfmqHcvHjDg+6l+f95RCaSmclyFIz/0zWr1vXfZlDDRwrKviYivW1K/sT/pdseGWWv37C0MhxHg9O6tI9FYqCQ+LokdOTvazB7xmO/0jQNSx+xPKAQkriRW6I53EzkYwRhD7uJcxC3EimOEd4fxtvFy4OsDeNp4aDlK729sgnwcXsuqWvakvRcCpxljBmL9rW0DMMZcb4xZWS9R1iER8ZR/boxZYIy5zamYjteJlIBqYqIxJseetHMl0A0YgTUx6Ex7ZoB0e3t5s4AzsdbXecL+PRqYbr/eGfjWngT0XqyECPAacLX9+FqsBeDKmwn8SURuw1qfp7Lmte+MMVn24x8C99sxT8Ga8bqrvf1qe/tcoDVw0lFHqpkbRUhr2WrX2KHD/t1uyNDPZ6W22LXimI4kxG927xv3ZtyUnKXuzTPL9w9N3bSwzxfBRW0B4r1eE9yxO65zi0RfQdG+cChcEENcac+bNoE3Nu0/yR0Ff5u44laxaJYxBqIgIriT3cQKY4hfKFxbCC5oe1Fbtr2yjW1/30YkR2f6aWJuCmQG4mpYttIVh8VaNXio/ThfRB4XaxXgOSLSzt7ey34eFJE/iEh++RNU0bpQvtzV9utLReQte9tRLST29gwReUtEZgJvVfB8nIh8ZZcdbrfULBaRWSLSx96eICIfijXR8Wf2eUrf71GtOyIyTEQ+tV//sYgUiYhPRPwistHe/iv7fS4VkU/scySLyCax5o9ERFLKPq/IiZSAVmIt+HaIXWPpCpQO6Swu83IUa8VQwfpiH2z/9DPGVLQK6DSshNMN+BwYhLVAXGkCegFr1dEAcCP2EgjGmG3AHhE5GxgOfF3+wMaYp4DrsVYqnSkip1TyHsuu+yPAT8vE3dUYs8re/psy23sYY/5XyfEqNXFSL7f9PqyTCZ6EhNyRAwd+3/+Mke8HO3ZcPQfs2a5rwQgd5ns3nPlm3NSVO11ZKwCisSi//eZZ0hJb0yIumeJITPqkdd5fVBImZmJeDzmR7Xunr/p28Xvu/xtxkys1MS1UuKEoddeOcEsfJowb0+aiNsQKY7iT3KQMTcGEDf6efvZ8sofCtYWU7Clhz2d7ahuuclYa1jIkNVHTFYcTgTn2CsDTgF/Z2/8K/NX++91eyb6VtS4cIiL9sVpUzrbPUdqEfVQLSZnd+mG1wFxeyfNSq4HR9jEexroQBmul5IPGmH7AQ9jfg1W07izGag0C6zttuf1+Tse6aAX41F5BeRCwCrjOGJOHdbFbejvGL+xylU4ieyIloIlAgohcDdbVCvAc8IbdRFaZOcCZYq+hY/etnFxBuenAL4F1dtNaFnABh9foSQVKm6nSy+37KlZT3Ed239QRRKSXMSZojHkamA+cAuQByVXE/S3wGxER+xinltl+c5mrlJPtprnqjlfej6hkzjePJxzo1Xv+iDNHvburd+85U93uktxaHBeAsET7/9e7uN8nvjkzp+9YtL91fCptE1vhcrkQAYmmtA6VSFE4GqVHm5a+2SvfPMXvJf9gwb5YOBaNLzK44sUdcxuXaZ3myc2fl1skbqHrb7qSPS0b4sDXykc0L4qnpQdjDAWrq1y3TzVOFdYyyqvFisMlQOkN1wuB7vbjM4CP7MfvVnKamrQunI31d77fjqu0xaKyFhKAL4wxRVU8L5WKtdTKcuDPZY4xCiupYU+nVTpoqMLWHbuFZYOI9MW6KP4TR7foDBCR6Xa8V5Y516tYS75g/66oReeQEyYBGWu43yXAz0VkHbAWCAG/q2a/fcAE4D2x1umZjZUAypfbjFW7KF2+egaQbYwpXQsnA+s/x0Jgf7ndv8BaD6iyf6w7RGS5ff4wVi1pGRC1q8AVDRp4DGsW62UissJ+DtZ/kJXAIvs/6t+xanrVHa+8a6sr4HKZzh06rht7xsgPJDDwf1P98bnbanDcwwSZv3HpmTd/8UjrtVlbYvklRbHCcBFxbh+zti6SfQXZ8R7xxLbszy+csW6TZOXnJE1b8Qn5oRxAKIxF3YUlMZ8nG29c1JQMubnjki1/3QIxSDkthfyl+YhPKNlbQmhrCG+rSlsKVON1eiAzUFmLwBFquOJw2BweGlzaClJTx9O6UGELia38lVFlV0qPAZPtSYovovpVjatq3ZmGdZEZBr7HSmJlW3TeAG614/09h1t0ZmItejkOcFc3f2RtPtwmz27uuqiS197A+lBLn19Y5vEkrCpodcfvUubxExyuAmOM+Ryraa4ig7AGH6yu5Li/qWS/s8s9f6PMPkVUcHVo185+R8WJt/zxKjRxUq9WVDHrQXkiJLdosWfs0KGfR0OhpDkb1g9POHiwU4VDr8uKxWL897//ZfTo0bJ+/XrZvHmz8bo8YUG8xdEwgtAxpa3rpFbdE3KKlpYUR0PeWCzmwkSPWDuiIBbn+b5dj7W/nLm9ZawgZtypbgltDoELXD4XkewILr+Ltpe0relbUo1LOvD/qipg94fEjDHr7E2DqX7F4bLmYCWsD7CalipS2rowyRgTtltKdhhjyiaMScBnIvInY8wBEWll14KqaiGpqbLHmFBm+0zgUmCyWKP9AmXe00si0tsYs95uCelkjFmLlWjeBN40xuwTkdZAO6zmOLBaS3bZLSlXljkv9n7vcviit1InTA2osRKR+4FPqOYPqJG5DGsUUq2I4I6Pzx8xIDBp4Bkj31vZqdPKWRCrtOd/x44dtGrVig4dOhCNRnG73dKtV3dvWKJRl4iZcNpP8Lm9dGnRgcEd+vl+1u+C/HA0RlpKEi4RBMwvx91VcHKXYb7p/X/n2zknr7M3TsL9+8Vv9HeMi7b+YWtiRTFcfhe9Hu5F4kmJx/WhKMdcHsgMVLda77GsOFzWHcBd9r69gZwKylTWunCIMWYF8DgwVawVh/9kv5RB5S0kNfUM8KSILC533peBNBFZCfwBWIHVV1VV685crIRT2qKzDAiWqR0+ZJeZidX3VNY7WCs0v1ddwCfMjaiq7kyc1GsKUCcrlBoju/bs6bl208YhgyORuNTS7Z9//jmrVq1CRLj33nt54YUX6NGjBytXriQSidCxY0cTKigqzMnJSYz3xJFbXMCQjv1ZsHM5HpcnFolFXMn+OFLj/SWXjrpl078m/r1PSaQ49vbgcxc/sX9iWotB7oMzZhf0D2dHPN3u7qbJp+k7M5genFVfBxeRBKDIGGNE5BfA5caYH9fX+eqS3d/tNcaERKQXVpNaH2NMST2d72fAj40x1d4orDUgVSsTJ/VKw+qMrBMipkP79hvGjjjjQ8/AQd9MS0jI3gwwePBgRo+2TuNyubjgggtYu3YtAGPGjOHaa6+VvgP6Jf7wR+eFklNScuO9cRRFinGJ0Do+1fX0D+/OaZvUKu+Oc0f5Nu/4/KSCUHb0rh//NfZUVu5p+QdbJAW/zu8dF44V9b61y1ZNPs3CpfV8/CHAErum8Gvg7no+X11KAGbYNa7PgF/XY/J5Aeum9mqb3+AE6wNSx+8Drjj7dGZv6s6mXnV5XBESU1P3jTltyJex4uLEea1aD/MVFLQcHI1agwJPOukkTj/9dIBDiemss84C8G/evNk/cMigrOv6/XT9NS/dMfzOUddwwSlnpX6xZjLntJ8wK+PzK8+47ZyRrqlLntg3rPflsfbDr2v38bTnDrzYgfVvtc3NXh+J7Nrn8Zxel+9HNbgLsJrJ6oUxZjpWX22TYw+PHtpA56qsv7pCWgNStfKF/PSyB+SPva7m/S1P8vDURQxZGsNV6/t9KiOCy+8vGN6//5TB5/5w3oZoNGKysw+Eo9EoK1asoE+fPkeUz8nJYf369QwYGGj153lvDD9lQN89n6z+X8EVH9zFrSOu4i8z3h85sF3/8LiuZ88SYmkm9H1yceH8eXj8rYODn2h/5WcntRi83uT2Ky7+vq7eg3LESYHMQJ1eFKn6p31AqsbaT17iBQ5Q7n4hMbGszmxbeQ7fekYxJeCnuE7atHbvDnPXnbtwu4mGQu7w4MFDzZgx58RPnjyZjh070qdPH7799lsWLlxIcnIy3bp144ILLsDj9sTSTMrMYdnd+533zwmtv5nwGn+dlcnqfetztuVtS/K6Xe4LBw8LDjz57q4icb5uW/4zd127r8NrR4TDM+L951LFnduqUbstmB58wekgVM1pAlI11n7ykrOxbuitnDHFLclaNoqpoR/y9cmtyGp3rOfbvTvMgw/s5tV/dbEPTVFeXusF69ed0amgoGVPgA8++IB+/foRCASO2v/rr78uHt1tyOrWuQkD/G6fe3yfcVz76f+LPHDeT+euzJ51JsgOb9KP97u9PQcl526enrT/L8WTLyoITU2OH2lEWh1r3MoxXwfTgxc4HYSqOe0DUrVxXrUlROIO0nrYl/yEL80lxk/RyiEs2Hs+X3bqycYq55x79tm9zJ1TSIsW7kNJp6z8/Gj8c39cMXrnzqXEYlJw0UVjt23evPmU8847j9dee41QKMTZZ5/NKaecwoEDB1i5cmXcqFGjBk1aujTrFNPloIj0CkcjnkDL0Wf2Th684Ptd73QszP93IOrtNSU3+aJRhQlPrb3oo2f2xy7ePmt2G3/fsDVi6IQRLYiy4/UdhLaHEBE6XdeJhN4JHPjuAAcmHkBcQvKgZNpf1v6offd/u5+DUw+CgL+zn07XdcLlc7HtlW2EtodIHpxM+59Z++39Yi/+Tn5ShtT5+oHjApmB+GB6sKJZAlQjpDUgVWPtJy+ZijUlxzFxm8jWPqzadB7/TT2VBQPcxI64AFq2rIh4v4unn95Ljx4+li4NkZMTpWVLN+npLZk8pQCvV3j88fZs3VpCxiN76NTJl5fWtstet+vkrn37DvC+8847TJgwgddee4327dtzwQUXUFBQwPvvv09hQWHorjOvOZje/8cdwOqcXZ0zd8myg1NHgW+lL+WKJJerZXKPDW8tnTp8VuGs3p622W53tTcgNxfb/7mdhJMTaDW2FbFIDFNsKNpaxL4v99Htzm64vC4iuRFrmYsywgfDbHx8Iyc9cRIun4utL20leWAy8d3jOfDdATpd24lNz26i6y1diZXE2Pn6TrrdWdF8vnVidDA9OKP6Yqox0BqQqpH2k5e4sJZ7OGZR8XRdSaDrSgJgzMFObFv1A76TMUweEE9R8sCB8ezebc1b+MCDR7fcTZ9RwC8us24V6trVRzhsuOuu1snTZ+xPjsX25XXtdmCuSPT0WCzm9Xg8nHvuuQAkJiZy3XXXAfjDhrZfmgXTzikZOCBefK36thgxumfywCUTd73TMi/3jVYe/8ilm3pdNfbUtQOmdN756t5vRsUmbvV6f3A877spiBZGKVhTQKfrOwHg8rjAA1mTskgbn4bLa41XKp98SpmYIVYSQ9yCKTF4WnrADbGSGCZmMBEDLtj76d76nnFiKIfnX1SNnI6CUzXVF+tu8roh0nKHdB35plx3xvW8FXcL/1z4LldN2x9rubeyXXr1jGP6DGtWk9WrQ+zZE2Hf/ihnn53EvHmFyX96bv6om26OsG7dvzYMHtx1j9dbwVgCwb3HlTPmnbjprlmeNVNjxCJx7oTBP+p0fdpprc9dFAnNGlmc+8ac/a36DfHLE23T/50aDhQWf0UtV5htakr2leBJ9rDj1R2sf3g9O17bQaw4RsnuEgrWFrDh0Q1sfHIjhRuPnrfX29JLm/PbsPbutay+YzWueBfJA5Lxd/TjSfaw4ZENpAxOoWRPCcYY4rvH1+dbOWFqrM2BNsGpGmk/eckEqpnZti5Ed+8k+96bise/ed+cH/FV+96sOzTuuqAgxssv7Wf9+hJ69PCxdVuYu+5qQ+/eh5eEycuL8tije/n9o+149pms/fv2JbrPOOP8Fl26dK1wqha3cW0YHe57sHes/VCAkmgoOGnXu4k54f1x3sQf7/d4urXutOXllf89b3nhrFZxZyGSWtFxmrqiTUVseGwDPR/oSUKvBHa9swuX30XuolwST0mkwy87ULSpiG0vb+PkZ0/GnmQdsPqOtr64lS43d8Gd4GbrS1tJHZZKi5EtjjjHlj9voeOEjhycfpDQthBJ/ZNoNa7Ox3qsDaYH+1RfTDUGWgNSNdVgV5bG64ubI6PGPiJP9bmKD7c/xqNT5zFikT/RE7n3vrb8/R+d+e39aeRkR+nQ4chazttvZXPFlS2YNCmfESPi2jzzbFzLBQveC3Xvvmi6yxU5qnM6KrFeU3wrhr4bN2N+luRv8rn9gfM6XdNpeJsfrY8UfN63uPCr9du6/Xrk2dN+Hn/+xpLJHmvW82bH09KDt6WXhF4JAKQMTaFoSxHell5ShqYgIiT0TACBaN6Rt33lr8jH28ZaAl08QsrQFArXH1lTyl2Ui7+736pV7Suh6y1dyV2QS6w4Vtdv5aRAZqBZXiQ0R9oHpGrKkaaNmLg7r6Z/59X0J5abk9sx7sCKH/imUvjlR4MDA/3xiYmHr6G2bw+zb3+EwYPj2bihBF+KIALGEN+l64rRnbusyMo60Hnu+vWn9y0pSTiik6lQiod96psb7hBrOe2ccGBQj+TA2M6JfVZN2f1+h6ycv23Zm3bFKf02nbKl/Z5nl344Inaw0OU69ahgmzBvCy/e1l6KdxUT1yGO/JX5+Dv68bX1UbCqgKS+SRTvLsZEDe5k95H7tvZStKGIWHEM8QkFKwuOaGYzEcOB/x2g253dKN5zeM3HQ31DNV3TtGYEa9qcSXV6VFUvtAmuEiISBYJYSXoTcJUxJrsezzcBGGqMubWKMhlAvjHmj3VwvjuAf1SzGB8A7Scv8WEtWFfrGbBrI/ux+wkvXUgsJxtXy1YkTbgJE7G6XhIu/jklK5aS+/TDgODp1sN0vOv2ZWNaBLPP47+927K306OP7uHaa1vRubOXgwejPPLwbgoKYqRPaMmYMYe7r4yhpLCgxbx160ak5eWlHd1cY8gKRLsuHxbpfaYYYtsL186as/fLgMs/fKXPd1paYvYf17x+3s6k/V5PjZavaCqKthSx4/UdmIjBl+aj8/WdkThhx792ENoaQjxC+8vak9QvifDBMDte30H3u7oDsOezPeTMzUHcgr+rn07Xdjo0cGH/t/txJ7hpObolxhi2v7Kd0I4QyQOTaX/p0UO668D9wfTg0/VxYFW3NAFVQkTyjTFJ9uNMYK0x5vF6PN8EGjYBbbbPV+3U7+0nLzkVWHS856xPcSa0ZhCLdp3Pf9r3YXWNFigDCIfjlmzaeFrJnj29hoEc0U/kNq51Y8P98nrG2p0WiYXXTdvzUXh/SV6OL+kXndsc+HLpx2dPMWuSvOMR0absxuWTYHrwZ04Hoaqnfzg1MxvoBCAig0VkjogsE5HPRKSlvX2KiAy1H7exv+ARkQki8qmIfCMi60TkmdKDisg19hr184Azy2y/SETmishiEfleRMo2Fw0Skdn2sX5llxcRedZeNTUoIpfZ28eJyFdljvuiHc9tWMtpTxaRyTV4/43+hsxi8feZJyPHPSqPn3IVH+78PX+YNpszF0VwV7oePYDXWzz45D6zh5856t0tPXoumOZyhQ8tHhaV2EmTfMtPey9u5rw8d4nvrPaX9xmddm5JJO+1yN4WQzuPn31r3Kjtkc+wlntWjUf/6ouoxkD7gKphr6XxA+Bf9qY3sZbdnSoijwKPUP0svIOBU4FiYI09ZXkEaynbIViLW00GFtvlZwAj7LVHrgfu4/D07wOx1nJPBBaLyH+w1qsfjDVbbxtgvoiULiR1FGPM8yJyF3BWTWpAQI8alGk0YuLuuJa+HdfSF4zJbc+uBWfxfewsvh+QSEGFHdQuV6x7586runfqtCr74MGOU9evO/3k4uKkDgAFEhr+iW9OSadYqxk/kMCpl3T7zYEZez5lr7jiBm18IjUt68lvPh9YNCIm0rlh36mqhC5t20RoDahy8SKyBNiNtTLgd2INwW1hjJlql8mkZjMDTDTG5BhjQlgrJnYDTgemGGP22WtzfFCmfGfgWxEJAvdy5BXd58aYIjtxTAaGY63V/p695v0eYCp1O2igex0eq2GJpOyWjme8J1efeQOZiTfx2uJMrp26m/bbKy5Oi1atdo4dNvyztCFDP5+Vmrp7pfUCvh3urLFvxk0tXuzdsn10u58NGNv6dIqLPvK2y76j5/XTT57ji5llDfreVGVaBjIDenHdBGgCqlyRMWYwVrIQ4JZqykc4/Hn6y71WXOZxlOprni8ALxpjAsCN5Y5XvtOuqk68sjFVFFdN1du8KQCx/DyyM+5hf/ol7J/wE0pWLD3idWMMuS88zf5fXsyB6y8lvHYVAJGtmzlw4xUcuP7SQ/uYaISD99yICVUwHZiIJ09ST/2fjB97t7zU+RreXftn7pu6in4rTbnPUQRPQkLuyIGDvus34owPgh06rJkDJoaQtsSzefSb/qkbihL9qZd0vTEtjSXFscipaTdN/snmlsWxqUefWDUwwWoJUI2cJqBq2KPEbsNqAisADopI6YqgV2HVNgA2YzWnAdSkA3QuMFZEWos1/f/Py7yWCuywH6eX2+/HIuIXkdbAOGA+MB24TETcIpKGVSubB2wB+olInIi0wGpKLJVHuWUVqtCxhuWOSd6Lz+AbNpI2mZ/R+p8f4OnW84jXS+bOILpjK63f+pzkux4k9y9PAFD01cck33ovLZ58gcIP37S2ff4R/nPGI/7q77YvkbiTF8jpY/8gj/W7mg93P8IT02YyekEEzxGrRXq9JYHeJ80bceaod3b06jVvmtsdzotIrM93vmWDP4qbu6N/u3M6n9Wqj6s4tiflitm3Ffc+yFfo6B6npTkdgKqeVlNrwBiz2F6K93KshPCKvUb8RuAau9gfgQ9F5AbgPzU45i57VNtsIBtYUublDOAjETmIdT9D2T6YZVhNb22Ax4wxO0XkM6x+oKVYV/L3GWN2A4jIh8ByrKHki8sc5x/ANyKy0xhzVjXh1stYWbBqPyXLFpHy20cBEK8XKTeFTvGsqfjPvRARwddvICY/j+iBfeDxYkIhTCgEHg+x/DyKZ0+jxdMv1T4OcXdYT58O6+nDy+b2vHbsXjiOibGz+L5fMnktAVwu06VjpzVdOnRck5ud3X7q+nUjeuWFGPGRb3ZxV0+bDef7ft4leGD6lh8sTpe2vb74dFbXrB9h/T9RDU8TUBOgw7BVlexJSIupp4uV8Po15D73GJ7uPYlsWIvn5L6k3HIfEn+4BnPwd7eRePk1+ALWvZ8H776RpBtuw9WiFTlPPQThEpLvfJDQ/74k7oyx+AbX4erDxkSSyFsxglnZ5/OfHh3Y2fXwS0RDoeT569cNT8zO7hgQw55TIz3X9ShKNLP2zc/f2nZ73peBJWOMSL0lcFWpXwTTgx9UX0w5SWtAqjqtqM//J9EIkXWrSbntt3j7Bsh98RkK3nuNpGur63IDd7sOtPrzqwBEdmwlum8v7q49yHniQUwkTNI1v8bT5Ti7r0Q8+aQM+p7z+Z7z8ZqS9QGWbj+fr9L6yfJ+8fF5IwIDJxKJeFds2TIoZ9GO2Iigx7t2jG+Iu/PBtiZ5btrMD4Z91zfiln7HF4iqJR0J1wRoAlLVOdaBCzXiSmuHK60t3r7Wiqb+MedQ8N6Rc56627Qlunf3oefRfXtwtTny+yX/Xy+RdO2vKfrsPeLH/x/udh3J/9eLpD7wRJ3GGxZf70UM672IYYiJ7enBhrXn8o1/hGdmoFevBf6ePRfu2L27174pG4sGJflarBxdMNSTOtO/+q3TvzmQFxcZXf0ZVB3RJrgmQAchqOrU60WKu1Ub3G3bE9m6GYCSRfOOGoQQN3Isoe+sfv2SlcuQxCTcrQ9/v5QsXYC7dRqezt2s/iBxgctlPa5HRlztNspJo/8uvxl2De9F7+DlOf+Wn25K7LAncMbIDzzdBn8WmZG2oLu3Q/uU3y2+Zm+XgynV9g2qOqMJqAnQPiBVpfaTl/QG1tXnOcLr15D7x99DJIK7QydS7vs9ocnfAtYccMYY8p5/ipJ5sxC/n5T7MvD2sW6NMsaQfd/NpD70NK6UVCJbNpLz+AMQi5J8x+/wDRhcn6FXzJhoIvnLhzMn+zzzVZe04oP7tmwYXpy0LXBwWsqUkrmd112MSN1OwanK0+l4mgBNQKpK7Scv6Yt186w6Rl5TsqE/we2jS6aXdN9RWLR8R4uCr7rOPgfRq/R6NCWYHqxudKdymPYBqero/5HjFBZfryUM6bUkbgjSI7avY7cdy/vu6TpxTfijwTGJ1XjiVFUrVc4BqBoH/XJR1dH/I7URiRVISSybUDRXQtFCCUWLJRSNSHHUSHHURUnMuz/iadWVZM8lbWT9Jy3QBFQ/KpgKQzU2+uWiqnPi/h+JmhAl0WwpjuVKKFogoWhIQpGwhGJGiqNCScwrkZifiEkkZlKAFmJNEpuIPXt6efEUF77s/cv8ca6lI2/ztpkJep9qPal2nSvlvBP3y0XVlLv6Ik1AzIQpiR2U4mjO4WQSDUsoGqM4iljJJI6ISSBmUjCHkkl76mgmiJ+6ps1/yvvPDl6JjgVYGedLrIvjqgppDagJ0ASkqtP4/pCNiVnJJJYjxdF8CUWLJBQtOZRMiqMeCcd8djJJtpNJCtbNiQ1+g2JH9u96z/eHLd1ce0eU3b7f7dblG+qP1oCaAE1AqjrZ9Xp0Ywxhkysl0WwJRfMkFC3C6jeJSnEUKY65rWQSiydqkjC0AFIFWmP9NFouYtFHPJkzr3Z/d6oIRySfgy5XVkykg1OxnQA0ATUBmoBUdbJrVTocyxOr3ySPsp3woShSYnXCS8TEE40lESMVq98kFeun2ThdVq58zfesJEpxhetFLfTHbcGa5kjVj8ZXc1dH0QSkqpMr2cUrpSgasvtNIhKKGimJCSVRr4RNPNFDnfAtxVrioabLPDQ7SRTmvu57dslQWTNKpPKZRmbH+3MbMq4TkNaAmgBNQKpKu88abLrf/5826OSO1brG/fXsBz1v93SLqXaV3MX+uOYxuKPxOuh0AKp6moBUTexEE1ClesjOre/7/rC3nWSfUdN9tnk8OgtC/drqdACqepqAVE3sAgY7HURj4yESfsb7j5mXuGacLkLX6vewhCEcEulRfUl1HLY5HYCqniYgVRO7nA6gsTnbtWjpy96/JvklPK62+66K821C5OR6CEsdpgmoCdAEpGpCE5CtJblZb/ueXNlPtpwpghzLMeb4/XsBTUD1JzeYHtRBHk2AJiBVE2vr+wThA9vZ98XTh55HsnfTYtQvSRn24yPKhbYuI2viPyEaxZWQQvsrniJamMO+Tx8nVpxPi9FXkXCy1RWz95PHaPXDX+NJrpvbhW53fzLjds+n/VxiRh3PcebH+6N1EpCqjNZ+mghNQKomFtb3CbytO9PxmhcAMLEo219OP5RISsVC+WT972+0vfT3eFLaEi3IBqBg5VSSTv0RCSefwd6PMkg4+QwK18/F165nnSSfvrJlw7u+x/NbSv5xJZ5Sa3zelLo4jqrUaqcDUDWjCUjVxGqs+yoaZObM0JaleFt0wJN65MC7gpVTiT95JJ4Ua7s7sQUA4vZgwsWYaARxuTCxKHkLPiftpw8fVxx+iote8L447xzXwjNE8B3XwcrIdrm61dWxVIVWOB2AqhldkltVa/NT46PAkoY6X8GqaST0PfpWmnDWDmKhfHa/ez+73rid/OUTAUjsN5aidXPY+8GDpIy4lLxF/yGx/9m4vP5jjuEi16wFwbjr953rXji2LpPPLrd7lxHRGRDq13KnA1A1ozUgVVMLgZH1fRITDVO0fh4tx6ZX9CIlu9fT7hePYyLF7H77HuI6noK3VSfa/jwDgGgon9w5H5P2kwc48PXzxEL5pAy/hLhOfWt0/vZk7XnX94eNPV27a3xPT23Mj4/bDugccPVrsdMBqJrRGpCqqXrvBwIo2rgQX7teuBNbHvWaO7kN8T1Ow+Xz405IJa7zAEr2bjqiTM7M90gdeSkFK6cS17k/rcffRfaMd6s9rxCLPeh5e9rsuFv99ZV8AOb443WKmPqVC2xwOghVM5qAVE01SAIqWDmVxAqa3wASeo+geMcKTCxKLByiZNcavK0Pr2gQztpBNO8A/q4DMZFiEAEBEymp8pynydrVwbjrV1/v+e8YkfqdFHWZ31dnzXmqQouC6UHjdBCqZrQJTtXUKup5IEKsJERo8xJan3/roW15i/8LQPKpF+Bt0wV/jyHseu1WECFp4Hn40rofKps97S1ajLkKgMS+Y9n36R/InfMxqaOvrPB8iRTlver946IRrlWjRBpm4b2dHk+dLG6nKjXN6QBUzYkxerGgaqb7/f+ZSQP0AzWEq9zfzcnwZHZzS6zB+mOKRAqHd+vsR0RbHurP2cH04GSng1A1ozUgVRsNMhChPnWVPds/8D22q4Nkjai+dN1aZk3B07+hz3sCKQZmOx2Eqjm9ElO1McPpAI6Vm2jkGc/fp0713dmqg2QNcyKGOfH+LCfOewKZG0wPhpwOQtWc1oBUbXwDhAGv04HUxmjXsuA/vc/5/RIe62QcC/x+be+uX1OcDkDVjtaAVI1tfmp8Lk3ojzyV/OwvfA9Mf9P71AC/hE9yOp4NXu/RY8tVXfrK6QBU7WgCUrX1hdMB1MSv3Z/PXBx3Y2Sga9PoY521ui4ZMHkuXQOoHm0OpgfnOx2Eqh1NQCc4EWkhIh+LyGoRWSUi1d2E2agT0MmybdPCuBsX3+f94EyXmDZOx1Nqs9ezFZEkp+Noxj5yOgBVe5qA1F+Bb4wxpwCDsO73qdTmp8ZvBRY0RGC14SNc/LL3L1O+9f22Y2vJO9XpeMqb5/frmkr1SxNQE6SDEE5gIpIKjAEmABhjSoCqpw2wvAcMrb/IaucC19xFf/G+1MonkXFOx1KZOfH+YqdjaMa0+a2J0gR0YusB7ANeF5FBWPf53G6MKahmvw+AZ3G4Bt2Wg/ve8T2+9iTXzjOdjKMmVsT54p2OoRnT2k8TpU1wJzYPcBrwN2PMqUABcH91O21+avwOHJ3yxJjfet6bPjfuFl9TSD4Ae93ujk7H0IxpAmqitAZ0YtsObDfGzLWff0wNEpDtXWBcfQRVlUGyfu1bvidLUqRodEOf+1jluCQnKtK5+pLqGGjzWxOmNaATmDFmN7BNRPrYm34ArKzh7u8DOfUSWAUSCBW86X1y6r99D/dMkaIBDXXeurA4zr/Z6RiasVedDkAdO01A6jfAOyKyDBgMPFGTnTY/NT4P+Fs9xnXIL9yT5i6Luz5njDs4VqTp1dpnx/uznY6hmcoDXnI6CHXsmtwfs6pbxpglHPuItr8CdwDHvvZ1FTrLvp3v+x7b1ln2n14fx28oi/1xeqFXP/4WTA9mOx2EOnb6h6GO2eanxu8GMuv6uG6ikSc8r06d7rs9taknH4DNXk+a0zE0QyHgz04HoY6P1oDU8foj8Cvq6GJmpGv5ile9f/QkSImjE4fWlQhEikSn4KkHrwfTg7udDkIdH60BqeOy+anx64FPjvc4KeTnfOp7ePo73if6JkhJn+r3aBrW+rybEYlzOo5mJop1H5pq4jQBqbrw9PHs/Cv3f2Ytjrux5DTX+tEizev/5Nx4/x6nY2iG3g+mBzc5HYQ6fs3qj105Y/NT4xcCE2u7Xy/ZsWV+3M0LH/C+M9Itpk76Sa79vIi2z+Yx4OX8Q9semhRi4N/yGfxKPj98q4CdebGj9tuSHeO0v1tl+r+czysLrBmJiiOG898uYMDL+bw8//AsRTd8WcSiXdFq45nr90fq4G2pwwzwlNNBqLqhCUjVlRrXgrxESp73vjDle9+97dIkZ0hdBjFhsJdvfplwxLZ7z4xj2c1JLLkpiQtP9vDo1KOnZeuQLMy+LpElNyUx9/pEnppRzM68GN9uiDCqq4dlNyfy1rIwAEt3R4nG4LQO7mrjWePz6QzYdeurYHpwudNBqLqhCUjVic1Pjf8OmFtduXNdC5Ysj7t2x8Xu2eNE6n749phuHlrFH7n8T0rc4ecFJVS4OJDPLcR5rFeKI4aYvXap1wWFYUM4Csbe9tDkYh47u2bdOgfcrq61fhOqKk86HYCqOzoKTtWl24A5VPAd35qc/e/4nlh9imvbqIYPCx6YGOLNZWFS44TJ6QkVltmWE2P8u4Wsz4rx7Ll+Oia7aJsovLUszIh/FXDvyDi+WBPmtA4uOiZXf+22z+3aZ0R0CHbd+TaYHpztdBCq7ogxuky9qjvd7//Pv4BrD28x5m7PRzNucf97gEtokCWpN2fHuPDdQpb/+ujWryenFxOKGH5/VuWVr515Mf7v/UK+vDyBdkmHE004ajjv7UI+/0UCj0wpZmtOjKsHebm4j7fC4/w3MWHhb9u2qdMmxhNYETAgmB7c6HQgqu5oE5yqa/cD2QD9ZdP6JXE3BH/j+ffohko+1blyoJdPVlU9LqBjsosBbd1M33rkIIOX55dw9SAvc7ZHSY0TPvhZPM/Nrnz5pDnx/rw6CVoBPKrJp/nRBKTq1Oanxu9LoeCB17zPTPnK90C3FlIw0OmY1h04nEg+Xx3hlDZH/7ffnhujKGy1BhwsMszYGqVP68PlDhYZvloX4epBXgrDBpeACIf2qcjSuLiKq0aqtoJYNzyrZkb7gFSdW+b/1d+Ba4AG/wK+/JNCpmyOsr/Q0PlPefx+XBz/XR9hzf4YLoFuLVy8Mt5qfluwM8orC0p49eJ4Vu2Lcff/QohYgw3uGekj0O7wKLdHpxbzwOg4XCKc19vDS/MLCfwtzE1DfJXGssPjblfvb7j5iwE3BNODOpy9GdI+IFU/MlIHAgtwIAk1BsVCaGi3Lh5E9CLv+LwUTA/e6nQQqn5oE5yqHxk5y4BnnA7DKct9cZs0+Ry3ncDvnA5C1R9NQKo+PUbNF7hrVubE+/c7HUMzcEswPZjrdBCq/mgCUvUnI6cYuBQocDqUhjbfH6dt28fnlWB68N9OB6HqlyYgVb8yclZgLddwQlnv86Y6HUMTthS40+kgVP3TBKTqX0bOe8CLTofRkHJcru5Ox9BE5QOXBdODIacDUfVPE5BqKHdhTdPT7G3zeLYjojWgY3NzMD24xukgVMPQBKQaRkZOGPg5sM/pUOrbPH/cTqdjaKL+HkwPvu10EKrhaAJSDScjZzvwC6wVLZutOfH+QqdjaIImA3q/zwlGE5BqWBk5k4CHnA6jPi2Pi6vzZSaauXXAz3S2gxOPJiDV8DJyngRedjqM+rLb4+7gdAxNyH7gR8H0YJbTgaiGpwlIOeVW4A2ng6hr+SJ5EdBF6GqmELg4mB7c4HQgyhmagJQzMnIMcD3wodOh1KUl/rhNiFS06Ko6Ugi4SBeYO7FpAlLOyciJAr8EvnQ6lLoyJ96f7XQMTUAJcEkwPTjJ6UCUszQBKWcdHp79vdOh1IWF/jit/VQtDPw8mB78xulAlPM0ASnnWXPG/RiY4XQox2uT19vK6RgasTBwZTA9+IXTgajGQROQahwycgqB8cA0p0M5VjGIFYj0cDqORioXuCCYHvzI6UBU46EJSDUeGTm5wLlAptOhHIv1Xu8WRBKcjqMR2gmMCaYHm0Uzq6o7moBU45KRU0JGzgSshcia1JIGc+P9u52OoRFaCZwRTA8udToQ1fhoAlKNk3Wz6qVAkdOh1NTc+Lhip2NoZKYBZwbTg1udDkQ1TpqAVOOVkfMxMBZoEjWLVT5fotMxNCIfAj8MpgeznQ5ENV6agFTjlpEzHzgdWOZ0KNXZ73Z3djqGRiACPAL8Ipge1BqhqpIY06Sa2dWJKiM1GXgFuMLpUCqS5XIdGNutc2un43DYGuCqYHpwvtOBqKZBa0CqacjIySMj50rgSiDH6XDKW+iP2+J0DA4ywAvAqZp8VG1oAlJNS0bOu8AgYLrToZQ1O96f53QMDtmO1ddzWzA92GQGjKjGQROQanoycrYA44A7sWZUdtwSf5zb6Rgc8A4Q0Pt71LHSPiDVtGWk9gJexUpIjhnWrfPakMt1spMxNKAs4Cad1UAdL60BqaYtI2cDcDbwKxwarl0CJaETZwqe/wADNPmouqAJqA6JSFRElojIChFZKiJ3i8gxf8YiMkVEhtZljJWcZ1Z9n6NeZeQYMnJeBXoDDwMN2h+zOs63CRFvQ57TAXOBs4PpwQuD6cFdTgejmgeP0wE0M0XGmMEAItIWeBdIwbovwnEi4jHGRMo/N8aMdDKuOpORUwA8RkbqK8CDwE2Ar75PO9vv3w/0qe/zOGQ58GAwPfi504Go5kdrQPXEGLMXuAG4VSwTROTF0tdF5CsRGWc//puILLBrTr+v7tgiMkxEZtm1rHkikiwi3UVkuogssn9G2mXH2du/AFaWf26Xybd/J4nIRHv/oIj8uMw5HxKRNSIyQ0TeE5F77O29ROQbEVloH/cUEXGLyCb7fbewa4Zj7PLTROQkERkuIrNFZLH9XvqUeX1wmfPOEJFBtfrwM3L2kZFzO9AXeI96nlNufrw/Un2pJmcTcBUwSJOPqi9aA6pHxpiNIuIG2lZT9AFjTJZddqKIDDTGVHjnv4j4gA+Ay4wx80UkBWu+tL3AucaYkIichPXFW9p8dxowwBizyU56h56XO3wIuMQYkysibYA5dqIaCvwUa/izF1gELLT3+QdwkzFmnYicDrxsjDlbRNYA/YAedvnRIjIX6GKXTQFGG2MiInIO8IR9jn8BE4A7RORkwG+MObaJLDNyNgJXkJH6LPAH4EdAnS8Yt9bnTanrYzpoB9a/xT+D6cGw08Go5k0TUONwqYjcgPXv0QHri7uyqWf6ALuMMfMBjDG5ACKSCLxo1x6iQNkRWfPKJZvyz0sJ8IRdW4kBnYB2wJnA58aYEBASkS/tcyYBI4GPRA59r8fZv6cDY7AS0JNYgwSmAqU3KqYCmXayNFiJDeAj4CERuRe4Fnijks+h5jJyFgPjyUjtDdwIXAPU2awF2S5Xt7o6loNmAc8DnwTTg82xRqcaIU1A9UhEemIlg71Yc2SVbfL022V6APcAw4wxB0XkjdLXaulOYA9WLcWFVZspVVCubPnnpa4E0oAhxpiwiGyuJhYXkF3a71XONOBmoCPWwIB7sYZKl95A+hgw2RhziYh0B6YAGGMKReQ7rBVSLwWGVHH+2snIWQ/cS0bqQ/axf401z9wx2+l27zYi7esiPAeUYE0a+tdgenCB08GoE4/2AdUTEUnDmrvsRWPdbLUZGCwiLhHpAgy3i6ZgJYQcEWmH1UxUlTVABxEZZp8nWUQ8WDWKXcaYGFbb/bHcGJkK7LWTz1lA6ZX9TOAiEfHbtZ4L4VDta5OI/NyORcr018zDqh3F7JrTEqzax7Qy59phP55QLo5Xsa7G5xtjDh7D+6haRk6IjJw3ycgZgdUc+U8qT8pVmh/v31ansTWMRcDtQOdgevAqTT7KKVoDqlvxIrIEqzkpArwF/Ml+bSZWx+5KYBXWlwDGmKUishhYDWyzy1XKGFMiIpcBL4hIPFb/zznAy8AnInI18A3H9oX6DvCliASBBXZM2H1NX2A1C+4Bghyej+1K4G8i8qD9vt8HlhpjikVkGzDHLjcduNzeF+AZrCa4B7HuLSn7HheKSC7w+jG8h9qxmuduICP1HqwlwccD51PDJro58f5jSlwOWI3Vd/h+MD242ulglAKdCUHVkIgkGWPyxVpyehpwgzFmUT2dqyNWk9wpdo2uYWWkuoERWDW98UCgsqIXdu4we4vXe0ZDhVYLRVif4dfAN8H04Dpnw1HqaJqAVI2IyLtYgyP8QKYx5sl6Os/VwOPAXcaYxnG3fUZqV6xE9COsptN2pS+d1r3LpnDjmAUhD6tWPR/4HpgaTA+Gqt5FKWdpAlKqtjJSOwNDY3DaoO5dBmH1ezXkSLgCYDFWM2npz9pgelD/mFWToglIqToQyAykYA0571rupxOQBCQAifbvBI4eXRgBDgD7gP1lfu8vt20HsCaYHmz4pkml6pgmIKUcEMgMuDicjMJAttZg1IlGE5BSSilH6H1ASimlHKEJSCmllCM0ASmllHKEJiCllFKO0ASklFLKEZqAlFJKOUITkFJKKUdoAlJKKeUITUBKKaUcoQlIKaWUIzQBKaWUcoQmIKWUUo7QBKSUUsoRmoCUUko5QhOQUkopR2gCUkop5QhNQEoppRyhCUgppZQjNAEppZRyhCYgpZRSjtAEpJRSyhGagJRSSjlCE5BSSilHaAJSSinlCE1ASimlHKEJSCmllCM0ASmllHLE/wcd5Hi7Y+hLtgAAAABJRU5ErkJggg==", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "Roadtype = Roadtype_df.toPandas()\n", + "#Creating Visualization\n", + "fig = plt.pie(Roadtype['Total accidents'], autopct='%1.1f%%', startangle=140,labels=Roadtype['Road_Type'])\n", + "#plt.title('No of age group where lstat < 2')\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+-----------+---------------+\n", + "|Speed_limit|Total accidents|\n", + "+-----------+---------------+\n", + "| 0| 1|\n", + "| 10| 19|\n", + "| 15| 16|\n", + "| 20| 38399|\n", + "| 30| 1306174|\n", + "| 40| 168357|\n", + "| 50| 69479|\n", + "| 60| 317469|\n", + "| 70| 147305|\n", + "| NA| 37|\n", + "+-----------+---------------+\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Total accidents')" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "Speed_df = Accident_Information_df.groupby('Speed_limit').agg(F.count(Accident_Information_df.Accident_Index).alias('Total accidents')).sort(\"Total accidents\")\n", + "Speed_df=Speed_df.sort(\"Speed_limit\")\n", + "Speed_df.show()\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "Speed_df = Speed_df.toPandas()\n", + "#df.plot()\n", + "#display(plt.show())\n", + "Speed_df.plot.bar(x='Speed_limit', y='Total accidents')\n", + "plt.xlabel(\"Speed_limit of Serious Accident\")\n", + "plt.ylabel(\"Total accidents\")\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+-----------------+-----------+----+---------------+\n", + "|Accident_Severity|Speed_limit|Year|Total_accidents|\n", + "+-----------------+-----------+----+---------------+\n", + "| Slight| 70|2005| 13084|\n", + "| Fatal| 50|2005| 118|\n", + "| Fatal| 10|2005| 1|\n", + "| Slight| 30|2005| 110399|\n", + "| Fatal| 70|2005| 394|\n", + "| Slight| 20|2005| 841|\n", + "| Fatal| 20|2005| 7|\n", + "| Fatal| 40|2005| 239|\n", + "| Fatal| 30|2005| 943|\n", + "| Slight| 60|2005| 28573|\n", + "| Serious| 10|2005| 1|\n", + "| Fatal| 60|2005| 1211|\n", + "| Serious| 50|2005| 664|\n", + "| Slight| 40|2005| 13643|\n", + "| Serious| 60|2005| 6136|\n", + "| Serious| 20|2005| 124|\n", + "| Slight| 50|2005| 4250|\n", + "| Serious| 70|2005| 1822|\n", + "| Serious| 40|2005| 1935|\n", + "| Serious| 30|2005| 14347|\n", + "| Slight| 10|2005| 3|\n", + "| Serious| 40|2006| 1987|\n", + "| Fatal| 70|2006| 384|\n", + "| Slight| 40|2006| 13065|\n", + "| Serious| 70|2006| 1665|\n", + "| Slight| 50|2006| 4544|\n", + "| Fatal| 30|2006| 909|\n", + "| Fatal| 20|2006| 15|\n", + "| Serious| 60|2006| 6066|\n", + "| Fatal| 60|2006| 1204|\n", + "| Slight| 60|2006| 26643|\n", + "| Serious| 50|2006| 709|\n", + "| Slight| 30|2006| 103336|\n", + "| Slight| 70|2006| 12874|\n", + "| Slight| 20|2006| 824|\n", + "| Slight| 15|2006| 3|\n", + "| Serious| 20|2006| 154|\n", + "| Serious| 30|2006| 14365|\n", + "| Fatal| 50|2006| 121|\n", + "| Fatal| 40|2006| 293|\n", + "| Serious| 30|2007| 14278|\n", + "| Fatal| 70|2007| 369|\n", + "| Slight| 10|2007| 3|\n", + "| Serious| 50|2007| 756|\n", + "| Serious| 40|2007| 1916|\n", + "| Fatal| 60|2007| 1109|\n", + "| Slight| 20|2007| 1002|\n", + "| Slight| 50|2007| 4385|\n", + "| Slight| 70|2007| 12225|\n", + "| Slight| 60|2007| 25344|\n", + "| Serious| 20|2007| 135|\n", + "| Slight| 15|2007| 4|\n", + "| Serious| 10|2007| 1|\n", + "| Slight| 40|2007| 12966|\n", + "| Serious| 60|2007| 5605|\n", + "| Fatal| 20|2007| 9|\n", + "| Serious| 70|2007| 1631|\n", + "| Fatal| 50|2007| 119|\n", + "| Fatal| 30|2007| 864|\n", + "| Fatal| 40|2007| 244|\n", + "+-----------------+-----------+----+---------------+\n", + "only showing top 60 rows\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+-----------+---------------+\n", + "|Speed_limit|Total_accidents|\n", + "+-----------+---------------+\n", + "| 0| 1|\n", + "| 10| 14|\n", + "| 15| 15|\n", + "| 20| 32568|\n", + "| 30| 1130677|\n", + "| 40| 142139|\n", + "| 50| 57313|\n", + "| 60| 246181|\n", + "| 70| 125606|\n", + "| NA| 34|\n", + "+-----------+---------------+\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+-----------+---------------+\n", + "|Speed_limit|Total_accidents|\n", + "+-----------+---------------+\n", + "| 10| 5|\n", + "| 15| 1|\n", + "| 20| 5831|\n", + "| 30| 175497|\n", + "| 40| 26218|\n", + "| 50| 12166|\n", + "| 60| 71288|\n", + "| 70| 21699|\n", + "| NA| 3|\n", + "+-----------+---------------+\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [] + } + ], + "source": [ + "Accident_Speed_Severity_df = Accident_Information_df.groupby('Accident_Severity','Speed_limit','Year').agg(F.count(Accident_Information_df.Accident_Index).alias('Total_accidents'))\n", + "\n", + "Accident_Speed_Severity_df=Accident_Speed_Severity_df.sort(\"Speed_limit\")\n", + "Accident_Speed_Severity_df=Accident_Speed_Severity_df.sort(\"Year\")\n", + "Accident_Speed_Severity_df.show(60)\n", + "#Accident_Severitydf\n", + "\n", + "CarAccidentfatal_dfslight=Accident_Information_df.filter(Accident_Information_df.Accident_Severity.contains(\"Slight\"))\n", + "CarAccidentfatal_dfslight = CarAccidentfatal_dfslight.groupby('Speed_limit').agg(F.count(CarAccidentfatal_dfslight.Accident_Index).alias('Total_accidents')).sort(\"Speed_limit\")\n", + "\n", + "CarAccidentfatal_dfslight.show()\n", + "\n", + "CarAccidentfatal_dfKSI=Accident_Information_df.filter(Accident_Information_df.Accident_Severity.contains(\"Fatal\")|Accident_Information_df.Accident_Severity.contains(\"Serious\"))\n", + "CarAccidentfatal_dfKSI = CarAccidentfatal_dfKSI.groupby('Speed_limit').agg(F.count(CarAccidentfatal_dfKSI.Accident_Index).alias('Total_accidents')).sort(\"Speed_limit\")\n", + "\n", + "CarAccidentfatal_dfKSI.show()\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Total_accidents %')" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX8AAAEMCAYAAAAs8rYIAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8rg+JYAAAACXBIWXMAAAsTAAALEwEAmpwYAAAgWklEQVR4nO3de5xVZd338c/XAQEFRRAJRcMO4gFh1EHzEIGE58ynNPIxxRRJMxPNkvvV/aRWelOpqakpWYo+t+WhTO/OiqAdEVDEA4pgaCgSoSgkIgO/+491zbgZ5rAHZu89M+v7fr32a691rb2u9Zu94Levda21rqWIwMzM8mWrSgdgZmbl5+RvZpZDTv5mZjnk5G9mlkNO/mZmOeTkb2aWQ10qHUCxdtxxxxg0aFClwzAz61DmzJnzr4jo17C8wyT/QYMGMXv27EqHYWbWoUh6qbFyd/uYmeWQk7+ZWQ45+ZuZ5VCH6fM3s9ZZt24dS5Ys4Z133ql0KFYG3bt3Z+DAgXTt2rWozzv5m3VSS5YsoVevXgwaNAhJlQ7HSigiWLFiBUuWLGH33Xcvah13+5h1Uu+88w59+/Z14s8BSfTt27dVR3lO/madmBN/frR2Xzv5m5nlkPv8rd0YNOnXbVLP4snHtkk9nU1bfb91WvqeV6xYwejRowF47bXXqKqqol+/7EbTxx57jK233rr+s9dccw0TJkxgm222abbOkSNHcuWVV1JTU7OF0Tdt/PjxXHjhhey9994bld92223Mnj2b66+/vtV1rly5kjvvvJMvfvGLRa9zyimn8NRTT3HcccdxxRVXAPDtb3+bIUOGcMIJJ7Q6hobc8jezkujbty9z585l7ty5nH322VxwwQX184WJH7Lk//bbb1co0o3dcsstmyT+LbVy5UpuvPHGoj8/b948evTowbx585g1axZvvvkmS5cuZebMmW2S+MHJ38zKaNq0aey3337su+++nHHGGaxdu5brrruOV199lVGjRjFq1CgAzjnnHGpqathnn3245JJLiq6/qfVmzZrFIYccwrBhwzjwwANZtWoV69ev56KLLmLIkCEMHTqUH/zgB0B2dFE3lMytt97KHnvswYEHHsif//zn+vqWL1/Opz/9aYYPH87w4cPrl1166aWcccYZjBw5kg984ANcd911AEyaNIlFixZRXV3NV7/6VZYuXcqIESOorq5myJAh/PGPf9zo7+jatStr1qxhw4YNrFu3jqqqKr7xjW9w2WWXbca33jh3+5hZWbzzzjucfvrpTJs2jT322IPTTjuNH/7wh0ycOJGrr76a6dOns+OOOwJw+eWX06dPH9avX8/o0aOZN28eQ4cObXEbja235557MnbsWO666y6GDx/OW2+9RY8ePZgyZQqLFy9m7ty5dOnShddff32jupYuXcoll1zCnDlz2H777Rk1ahT77bcfAOeffz4XXHABhx12GC+//DJHHnkk8+fPB+C5555j+vTprFq1isGDB3POOecwefJknn76aebOnQvAVVddxZFHHsnXv/511q9fv8lRz1577UW/fv3Yf//9OfXUU1m4cCEbNmxg//3339LdUM/J38zKYv369ey+++7sscceAIwbN44bbriBiRMnbvLZu+++mylTplBbW8vSpUt59tlni0r+ja0niQEDBjB8+HAAtttuOwAeeughzj77bLp0ydJgnz59Nqpr5syZjBw5sv48xdixY1mwYEH9us8++2z9Z9966y1Wr14NwLHHHku3bt3o1q0bO+20E8uWLdskzuHDh3PGGWewbt06TjjhBKqrqzf5zDXXXFM//YlPfIKbb76Zyy+/nCeffJIxY8Zw1llntfh9NMfdPmbWrvz973/nyiuvZNq0acybN49jjz22qOvXN3e9zbFhwwb+9re/1Z/DeOWVV+jZsycA3bp1q/9cVVUVtbW1m6w/YsQIHn30UXbZZRdOP/10br/99ia3df/993PAAQewevVqFi1axN1338299967xedInPzNrCyqqqpYvHgxCxcuBOCOO+7gYx/7GAC9evVi1apVQNaK3nbbbdl+++1ZtmwZv/3tb4uqv6n1Bg8ezNKlS5k1axYAq1atora2ljFjxnDzzTfXJ+eG3T4HHXQQjzzyCCtWrGDdunXcc8899cuOOOKI+nMEQH13TlMK/z6Al156if79+3PWWWcxfvx4Hn/88UbXW7duHddccw1f+9rXWLNmTf21/OvXr+fdd98t6ntpirt9zHKi0pfAdu/enVtvvZWTTjqJ2tpahg8fztlnnw3AhAkTOOqoo9h5552ZPn06++23H3vuuSe77rorhx56aFH1Dxs2rNH1tt56a+666y7OO+881qxZQ48ePXjooYcYP348CxYsYOjQoXTt2pWzzjqLL33pS/X1DRgwgEsvvZSDDz6Y3r17b9Q1c91113HuuecydOhQamtrGTFiBDfddFOTsfXt25dDDz2UIUOGcPTRRzNkyBC+973v0bVrV3r27Nlky/+GG25g3LhxbLPNNgwdOpS3336bfffdl2OOOYbevXsX9b00RRGxRRWUS01NTfhhLp2br/NvW/Pnz2evvfaqdBhWRo3tc0lzImKTGyPc7WNmlkPu9jGzDueggw5i7dq1G5Xdcccd7LvvvhWKqONx8jezDmfmzJmVDqHDc7ePWSfWUc7p2ZZr7b528jfrpLp3786KFSv8A5ADdQ9z6d69e9HruNvHrJMaOHAgS5YsYfny5ZUOxcqg7jGOxXLyN+ukunbtWvQj/Sx/3O1jZpZDTv5mZjnk5G9mlkMl7/OXtBhYBawHaiOiRlIf4C5gELAY+ExEvFHqWMzMLFOulv+oiKguGF9iEjAtIj4MTEvzZmZWJpXq9vkkMDVNTwVOqFAcZma5VI7kH8AfJM2RNCGV9Y+IpWn6NaB/GeIwM7OkHNf5HxYRr0jaCXhQ0nOFCyMiJDV6C2L6sZgAsNtuu5U+UjOznCh5yz8iXknv/wTuAw4ElkkaAJDe/9nEulMioiYiauqeo2lmZluupMlf0raSetVNA0cATwMPAOPSx8YB95cyDjMz21ipu336A/el5052Ae6MiN9JmgXcLelM4CXgMyWOw8zMCpQ0+UfEi8CwRspXAKNLuW0zM2ua7/A1M8shJ38zsxxy8jczyyEnfzOzHHLyNzPLISd/M7MccvI3M8shJ38zsxxy8jczyyEnfzOzHHLyNzPLISd/M7MccvI3M8shJ38zsxxy8jczyyEnfzOzHHLyNzPLISd/M7MccvI3M8shJ38zsxxy8jczyyEnfzOzHHLyNzPLISd/M7MccvI3M8shJ38zsxxy8jczy6FWJ39J3SVtV4pgzMysPFqV/CWNB34J/FzSFa1Yr0rSE5J+leZ3lzRT0kJJd0naulVRm5nZFmk2+Us6vkHRxyPiqIgYAxzbiu2cD8wvmP8O8P2I+BDwBnBmK+oyM7Mt1FLLf19J90uqTvPzJN0i6UfAM8VsQNJAsh+KW9K8gMOBe9NHpgIntDJuMzPbAl2aWxgRl0t6H/DNlLT/H9AL6BER84rcxjXA19J6AH2BlRFRm+aXALu0NnAzM9t8xfT5/xuYCFwPTAFOBhYUU7mk44B/RsSczQlO0gRJsyXNXr58+eZUYWZmjWipz//bwM+BXwGjIuJ4YC7wG0mnFVH/ocDxkhYDPyPr7rkW6C2p7qhjIPBKYytHxJSIqImImn79+hWxOTMzK0ZLLf/jIuIIYDRwGkBEPAAcAezQUuUR8R8RMTAiBgGfBR6OiFOA6cCJ6WPjgPs3L3wzM9scLSX/pyVNAW4HHqkrjIjaiLh2C7Z7MXChpIVk5wB+vAV1mZlZK7V0wvdzkvYF1kXEc1uyoYiYAcxI0y8CB25JfWZmtvmaTf4AEfFUOQIxM7Py8dg+ZmY55ORvZpZDRSV/SYdK2jZNf07S1ZLeX9rQzMysVIpt+f8QeFvSMOArwCKyK4DMzKwDKjb510ZEAJ8Ero+IG3hvuAYzM+tgWrzaJ1kl6T+AzwEjJG0FdC1dWGZmVkrFtvzHAmuBMyPiNbIhGb5XsqjMzKykim35XxARF9fNRMTLkvYpUUxmZlZixbb8xzRSdnRbBmJmZuXTbMtf0jnAF4EPSCocv78X8JdSBmZmZqXTUrfPncBvgf8CJhWUr4qI10sWlZmZlVRLA7u9CbwJnCypCuif1ukpqWdEvFyGGM3MrI0VdcJX0peAS4FlwIZUHMDQ0oRlZmalVOzVPhOBwRGxooSxmJlZmRR7tc8/yLp/zMysEyi25f8iMEPSr8lu9gIgIq4uSVRmZlZSxSb/l9Nr6/QyM7MOrKjkHxGXAUjaJiLeLm1IZmZWasWO53+wpGeB59L8MEk3ljQyMzMrmWJP+F4DHAmsAIiIJ4ERJYrJzMxKrOjHOEbEPxoUrW/jWMzMrEyKPeH7D0mHACGpK3A+ML90YZmZWSkV2/I/GzgX2AV4BahO82Zm1gEVe7XPv4BTShyLmZmVSUtDOv+AbAyfRkXEl9s8IjMzK7mWun1mA3OA7sD+wAvpVY1v9jIz67BaGtJ5KtQ/1OWwiKhN8zcBfyx9eGZmVgrFnvDdAdiuYL5nKmuWpO6SHpP0pKRnJNXdKby7pJmSFkq6S5KPIszMyqjY5D8ZeELSbZKmAo8DVxSx3lrg8IgYRtZVdJSkjwDfAb4fER8C3gDObHXkZma22YpK/hFxK3AQcB/wC+Dgui6hFtaLiFidZrumVwCHA/em8qnACa0L28zMtkSzyV/Snul9f2BnsnH9/wHsnMpaJKlK0lzgn8CDwCJgZd35A2AJ2f0DZmZWJi1d538hMAG4qpFldS34ZkXEeqBaUm+yI4c9iw1O0oS0fXbbbbdiVzMzsxa0dLXPhPQ+aks3FBErJU0HDgZ6S+qSWv8Dye4abmydKcAUgJqamibvNzAzs9Ypdkjnc1PLvW5+B0lfLGK9fnXrSeoBjCEbE2g6cGL62Djg/taFbWZmW6LYq33OioiVdTMR8QZwVhHrDQCmS5oHzAIejIhfARcDF0paCPQFftyqqM3MbIsUO6pnlSRFREB2Epci7vCNiHnAfo2Uvwgc2JpAzcys7RSb/H8H3CXp5jT/hVRmZmYdULHJ/2Kyq27OSfMPAreUJCIzMyu5YpN/D+BHEXET1Hf7dAP8MHczsw6o2BO+08h+AOr0AB5q+3DMzKwcik3+3QuGaSBNb1OakMzMrNSKTf7/LhzOQdIBwJrShGRmZqVWbJ//ROAeSa8CAt4HjC1VUGZmVlrFPsN3VhrkbXAqej4i1pUuLDMzK6ViW/6QJf69SY90lERE3F6asMzMrJSKSv6SLgFGkiX/3wBHA38CnPzNzDqgYk/4ngiMBl6LiM8Dw4DtSxaVmZmVVLHJf01EbABqJW1H9mCWXUsXlpmZlVKxff6z09DMPwLmAKuBv5YqKDMzK61ir/apG7v/Jkm/A7ZLI3YCIGmfiHimFAGamVnbK7bbp15ELC5M/MkdbRSPmZmVQauTfxPURvWYmVkZtFXy9/N1zcw6kLZK/mZm1oG0VfJ/t43qMTOzMmj2ap/CkTwbExGPp/ePtGVQZmZWWi1d6nlVM8sCOLwNYzEzszJpNvlHxKhyBWJmHdegSb9us7oWTz62zeqyphU9qqekIbw3qieAR/U0M+ugPKqnmVkOeVRPM7Mc8qieZmY55FE9zcxyqE1G9TQzs46lqG4fSdPqputG9Swsa2a9XSVNl/SspGcknZ/K+0h6UNIL6X2Hzf8TzMystZpN/pK6S+oD7Chph5S0+0gaBOxSRP21wFciYm/gI8C5kvYGJgHTIuLDwLQ0b2ZmZdJSt88XgInAzsDjBeVvAde3VHlELAWWpulVkuaT/Wh8kuzSUYCpwAzg4uLDNjOzLdHSHb7XAtdKOi8ifrAlG0pHC/sBM4H+6YcB4DWg/5bUbWZmrVPs1T43S/oyMCLNzwBujoh1xawsqSfwc2BiRLwlvffsl4gISY0+D0DSBGACwG677VZkqGZm1pJir/O/ETggvddN/7CYFSV1JUv8/x0Rv0jFyyQNSMsHkN03sImImBIRNRFR069fvyJDNTOzlrQ0pHOXiKgFhkfEsIJFD0t6sqXKlTXxfwzMj4irCxY9AIwDJqf3+1sduZmZbbaWWv6Ppff1kj5YVyjpA8D6Iuo/FDgVOFzS3PQ6hizpj5H0AvDxNG9mZmXSUp9/Xef8RcB0SS+m+UHA51uqPCL+RNMPdx9dTIBmZtb2Wkr+/SRdmKZvBqrS9HqyK3emlyowMzMrnZaSfxXQk01b712AXiWJyMzMSq6l5L80Ir5ZlkjMzKxsWjrh21R/vZmZdWAtJX+flDUz64SaTf4R8Xq5AjEzs/Ip9g5fMzPrRJz8zcxyyMnfzCyHnPzNzHLIyd/MLIec/M3McsjJ38wsh5z8zcxyyMnfzCyHnPzNzHLIyd/MLIec/M3McsjJ38wsh5z8zcxyyMnfzCyHnPzNzHLIyd/MLIec/M3McsjJ38wsh5z8zcxyyMnfzCyHnPzNzHKopMlf0k8k/VPS0wVlfSQ9KOmF9L5DKWMwM7NNlbrlfxtwVIOyScC0iPgwMC3Nm5lZGZU0+UfEo8DrDYo/CUxN01OBE0oZg5mZbaoSff79I2Jpmn4N6F+BGMzMcq2iJ3wjIoBoarmkCZJmS5q9fPnyMkZmZta5VSL5L5M0ACC9/7OpD0bElIioiYiafv36lS1AM7POrhLJ/wFgXJoeB9xfgRjMzHKt1Jd6/hT4KzBY0hJJZwKTgTGSXgA+nubNzKyMupSy8og4uYlFo0u5XTMza57v8DUzy6GStvzNOrpBk37dJvUsnnxsm9Rj1lbc8jczyyEnfzOzHHLyNzPLISd/M7Mc8glfM+uU2upkPXTOE/Zu+ZuZ5ZCTv5lZDjn5m5nlkJO/mVkOOfmbmeWQk7+ZWQ45+ZuZ5ZCTv5lZDjn5m5nlkJO/mVkOeXgHsw7GwxZYW3DL38wsh5z8zcxyyMnfzCyHnPzNzHLIyd/MLIec/M3McsjJ38wsh3ydfw75OnEzc8vfzCyHnPzNzHKoYslf0lGSnpe0UNKkSsVhZpZHFUn+kqqAG4Cjgb2BkyXtXYlYzMzyqFIt/wOBhRHxYkS8C/wM+GSFYjEzy51KJf9dgH8UzC9JZWZmVgaKiPJvVDoROCoixqf5U4GDIuJLDT43AZiQZgcDz7fB5ncE/tUG9bS19hiXYyqOYypee4yrs8f0/ojo17CwUtf5vwLsWjA/MJVtJCKmAFPacsOSZkdETVvW2RbaY1yOqTiOqXjtMa68xlSpbp9ZwIcl7S5pa+CzwAMVisXMLHcq0vKPiFpJXwJ+D1QBP4mIZyoRi5lZHlVseIeI+A3wmwpsuk27kdpQe4zLMRXHMRWvPcaVy5gqcsLXzMwqy8M7mJnlkJO/mVkOOfmbmbVTkvqXqm4nf0PS9pImS3pO0uuSVkian8p6VzCuLpK+IOl3kual128lnS2pq2Oqj6nd7b/2GFN7jquQpN6SzpQ0DXiiVNvp1Mm/ve5oSUc1iPHHKYncWcpf+mbcDbwBjIyIPhHRFxiVyu6uQDx17gCqgUuBY9LrMmAY8P8dU732uP/aY0ztNi5JPSR9VtIDwFPAVcC3yG6ALc02O/PVPpJ+DzwMTI2I11LZ+4BxwOiIOKJCcT0eEfun6VuA14AfAZ8CPhYRJ5Q5nucjYnBrl5WapAURsUdrl+Uwpna3/9pjTC1tu4Lf1Z3AR4E/kA1y+TDZwJe7l3K7nbrlDwyKiO/UJX6AiHgtIr4DvL+CcRWqiYj/jIiXIuL7wKAKxPCSpK8VHnVI6i/pYjYegK/cXpd0kqT6f6eStpI0lqyl5pgy7XH/tceY2mtce5P925kPzI+I9UDJW+WdPfm3xx0NsJOkCyV9BdhOkgqWVWKfjAX6Ao9IekPS68AMoA/wmQrEU+ezwInAa5IWSFpAdpT0qbSskjEtSzG90A5iao/7rz3G1C7jiojqtO1ewEOS/gT0KnUXcGfv9tkBmET2rICdUvEysnGEJkdERVpqki5pUHRjRCxPXVLfjYjTKhDTnmT9i3+LiNUF5UdFxO/KHU/B9g8iawUtAvYEDgaeTXeIV5Skvmny2oj4XEWDKSDpo2TPzHgqIv5QoRgOAp6LiDclbUP2/3B/4Bngioh4s0JxfRm4LyIq2fhrlqQDgP8LnAQsiYhDSrKdzpz8myPp8xFxa6XjaKgScaX/EOeSHXZWA+dHxP1pWf35iXJLP5JHkw1D8iBZQpsBjAF+HxGXVyCmxgYgPJysn5aIOL68EYGkxyLiwDQ9nmxf/hI4AvifiJhcgZieAYalcbymAP8Gfg6MTuWfKndMKa43UyyLgDuBeyKivQ3nDEDqEfhoRDxakvpznPxfjojdKh1HQ5WIS9JTwMERsVrSIOBe4I6IuFbSExGxXznjaRBXNdCNrGtlYES8JakHMDMihlYgpseBZ4FbyI5IBPyU1OUTEY9UIKb6fSRpFnBMOpLcluxIbt8KxDQ/IvZK0xs1ICTNTV0dZSfpCeAA4ONkXUDHA3PI9uEvImJVBWL6RnPLI+KbpdhuxQZ2KwdJ85paBFTiksps4+0vrq3qunoiYrGkkcC9kt6fYqqU2nTy621JiyLirRTjGkkbKhRTDXA+8HXgqxExV9KaSiT9AlulLs6tyBp0ywEi4t+SaisU09MFR7FPSqqJiNmS9gDWVSgmgIiIDWRX1vxB2b0ZRwMnA1cCmzz0pAz+3UjZtsCZZOcnnPw3Q3/gSDa9CkPAX8ofTr32FtcySdURMRcgHQEcB/wEKHurscC7kraJiLfJWmtAdm8EUJHknxLH9yXdk96XUfn/R9uTtV4FhKQBEbFUUk8q9+M9HrhW0n+SPZHqr5L+QXahxfgKxQQNvo+IWEd2DvCBdG6i7CLiqrppSb3IGhefJ7vs86qm1ttSlf5HW2q/AnrWJbVCkmaUPZr3tLe4TgM2aiFGRC1wmqSbKxBPnRERsTbFU5jsu5Ldq1ExEbEEOEnSscBbFY5lUBOLNgD/p4yh1EsndE+XtB2wO1muWRIRyyoRT4GxTS1IjYyKkNQHuBA4BZgK7F/qC1Jy2+dvZtYeSPoe2aXCU4AbCq+2K+l2nfzNzConnb9aS3b0XZiQRXaOYruSbNfJ38wsfzr7Hb5mZtYIJ38zsxxy8jczyyEnf6sn6euSnlH2bIG5aXyWUm5vhqSaZpYvlrRjmm7V/Q/KHq5yWpo+XdLOrVx/z/QdPCHpgw2WnSHpqfQ9PS3pk62s+3hJk1qzTmtJ2lHSOklnb0EdjX7nkm6TdOJm1lkt6ZjNjcnaTme/zt+KJOlg4Diy64vXpqS7dYXDqtfawa0i4qaC2dOBp4FXW1HFCcC9EfHtwkJJA8nu7t0/DVrWk1bcFSqpS0Q8QHZjUSmdBPyN7M7Vm1r4bKNKNKBYNdld0hUfmC/v3PK3OgOAfxXcVPWviHgV6lvg302t3cckfSiV95P0c0mz0uvQVL6tpJ+kzz5R1zJW9rSinyl7mtp9QI9ig5O0Or2PlPSIpPslvajsqWynpG09VddKl3SppItSC7UG+O/Uku/RoN5qSX9Lrfj7JO2QWqYTgXMkTW8Qyk7AKqBuOIzVEfH3VNcHlT3ecY6kPyobKbWupXyTpJnAd9ORyPVp2SBJD6ftT5O0W8E69a3rgr9/gKRH09/ytLIRPBtzMvAVYJf0g1VXz2lpW09KuiOV9U9/+5PpdUiDbUrS9ZKel/QQ742Qi6QD0v6YI+n3kgak8hmSvpP2ywJJH5W0NdlQBWNT/E3ecGVlEBF++QXQE5gLLABuJHuiWN2yxcDX0/RpwK/S9J3AYWl6N7IHUQBcAXwuTfdOdW5LdgfjT1L5ULLrmmuaiWkxsGOaXp3eRwIryX6sugGvAJelZecD16TpS4GL0vSMprYDzKv7W8kS0ybrN/h8FfB74GXgVuATBcumAR9O0wcBD6fp28ju6q5K86cD16fp/wHGpekzgF8WrHNiQd11f/9XCvZFFdCrkRh3BV4o2BdfSdP7pH1R9532Se93ARML6ty+wTY/RTaqahWwc/r+TyS70/ovQL/0ubEF+3cGcFWaPgZ4qOHf7ldlX+72MaB+PJ8DyB4nNwq4S9KkiLgtfeSnBe/fT9MfB/bWe8+i2S51gxwBHC/polTenezHYQRwXdrePDU9wF1LZkXEUgBJi8gG6YLs2aejiq1E2RhBveO9QdmmAvc0t05ErFf2DObhZMMTfz99b1cChwD3FHwf3QpWvSeyQeoaOpgsuUL2fODvthD2LOAnygYk+2U0MkQIWRKuex7tz8jGaLqKbOjp+iGMI+L19JnDyX7USTE2HGt/BPDTtOxVSQ+n8sHAEODB9DdXAUsL1vtFep9DZZ5QZ81w8rd66T/3DGCGsuGUx5G1QGHjOw/rprcCPhIR7xTWoywTfDoinm9Q3lahri2Y3lAwv4Ey/JuOrAn7GPCYpAfJjgCuBlZG00MVNzZyY3NqSd2yyh4ZuXXa9qOSRgDHArdJujoibm+w7snA+ySdkuZ3lvThVm6/GAKeiYiDm1het1/W41zT7rjP3wCQNLhBgqgGXiqYH1vw/tc0/QfgvII6qtPk74Hz0o8AkuqeB/Ao2ROKkDSErOunHFaRPSJvI5ENPvZGQb/5qUCzQzNL2llS4cNtqoGXIhtu+u+STkqfk6RhRcT2F957/OMpwB/T9GLeG8n0eLIuFpQNs70sIn5E9kyBjR60o2zI5J4RsUtEDIps0Lf/IvtBeJhsMLq+6bN90mrTgHNSWVU6Iir0KFk/fVXq0687unoe6KfsYgEkdZW0Twt/b6P7wsrPyd/q9ASmSno2dcfsTdbvXWeHVH4+cEEq+zJQk04gPgvUXVb4LbJkNU/ZE52+lcp/CPSUNJ+sf31OKf+gArcBNzV2wpfs6OZ76W+rpuWx07sCV0p6TtJcsh/D89OyU4AzJT1J9rjCYi4BPQ/4fNr+qQV1/Qj4WKrrYN47chhJNj7+E2nb1zao72TgvgZlPwdOjohngMvJnl/7JNnRCmmbo9LR3hyyfV/oPuAFsofY3E768Y+Id8n6/r+T6ptL1vXVnOlkXYU+4VthHtvHWiRpMdkJ03b5uDszaz23/M3Mcsgtf6s4Zde/d2tQfGpEPFWJeMzywMnfzCyH3O1jZpZDTv5mZjnk5G9mlkNO/mZmOeTkb2aWQ/8LoG6X7gRLqlkAAAAASUVORK5CYII=", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "CarAccidentfatal_dfslight = CarAccidentfatal_dfslight.toPandas()\n", + "CarAccidentfatal_dfslight['Total_accidents %'] = (CarAccidentfatal_dfslight['Total_accidents'] / CarAccidentfatal_dfslight['Total_accidents'].sum()) * 100\n", + "CarAccidentfatal_dfslight.plot.bar(x='Speed_limit', y='Total_accidents %')\n", + "plt.xlabel(\"Speed_limit of Non Serious Accident\")\n", + "plt.ylabel(\"Total_accidents %\")\n", + "\n", + "CarAccidentfatal_dfKSI = CarAccidentfatal_dfKSI.toPandas()\n", + "CarAccidentfatal_dfKSI['Total_accidents %'] = (CarAccidentfatal_dfKSI['Total_accidents'] / CarAccidentfatal_dfKSI['Total_accidents'].sum()) * 100\n", + "\n", + "CarAccidentfatal_dfKSI.plot.bar(x='Speed_limit', y='Total_accidents %')\n", + "plt.xlabel(\"Speed_limit of Serious Accident\")\n", + "plt.ylabel(\"Total_accidents %\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Total_accidents %')" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "CarAccidentfatal_dfKSI.plot.bar(x='Speed_limit', y='Total_accidents %')\n", + "plt.xlabel(\"Speed_limit of Serious Accident\")\n", + "plt.ylabel(\"Total_accidents %\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DataFrame[Accident_Index: string, 1st_Road_Class: string, 1st_Road_Number: string, 2nd_Road_Class: string, 2nd_Road_Number: string, Accident_Severity: string, Carriageway_Hazards: string, Date: string, Day_of_Week: string, Did_Police_Officer_Attend_Scene_of_Accident: string, Junction_Control: string, Junction_Detail: string, Latitude: string, Light_Conditions: string, Local_Authority_(District): string, Local_Authority_(Highway): string, Location_Easting_OSGR: string, Location_Northing_OSGR: string, Longitude: string, LSOA_of_Accident_Location: string, Number_of_Casualties: string, Number_of_Vehicles: string, Pedestrian_Crossing-Human_Control: string, Pedestrian_Crossing-Physical_Facilities: string, Police_Force: string, Road_Surface_Conditions: string, Road_Type: string, Special_Conditions_at_Site: string, Speed_limit: string, Time: string, Urban_or_Rural_Area: string, Weather_Conditions: string, Year: int, timestamp: timestamp]" + ] + }, + "execution_count": 140, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Accident_Information_df" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+-----------------+-----------+----+---------------+\n", + "|Accident_Severity|Speed_limit|Year|Total_accidents|\n", + "+-----------------+-----------+----+---------------+\n", + "| Serious| 20|2005| 124|\n", + "| Serious| 50|2005| 664|\n", + "| Slight| 70|2005| 13084|\n", + "| Fatal| 50|2005| 118|\n", + "| Fatal| 30|2005| 943|\n", + "| Fatal| 40|2005| 239|\n", + "| Fatal| 10|2005| 1|\n", + "| Serious| 70|2005| 1822|\n", + "| Fatal| 70|2005| 394|\n", + "| Slight| 40|2005| 13643|\n", + "| Fatal| 60|2005| 1211|\n", + "| Slight| 20|2005| 841|\n", + "| Fatal| 20|2005| 7|\n", + "| Serious| 10|2005| 1|\n", + "| Slight| 30|2005| 110399|\n", + "| Slight| 60|2005| 28573|\n", + "| Slight| 10|2005| 3|\n", + "| Serious| 60|2005| 6136|\n", + "| Serious| 40|2005| 1935|\n", + "| Serious| 30|2005| 14347|\n", + "| Slight| 50|2005| 4250|\n", + "| Slight| 50|2006| 4544|\n", + "| Slight| 30|2006| 103336|\n", + "| Serious| 30|2006| 14365|\n", + "| Fatal| 20|2006| 15|\n", + "| Serious| 40|2006| 1987|\n", + "| Slight| 15|2006| 3|\n", + "| Slight| 60|2006| 26643|\n", + "| Fatal| 40|2006| 293|\n", + "| Slight| 40|2006| 13065|\n", + "| Slight| 70|2006| 12874|\n", + "| Serious| 60|2006| 6066|\n", + "| Slight| 20|2006| 824|\n", + "| Fatal| 30|2006| 909|\n", + "| Fatal| 60|2006| 1204|\n", + "| Serious| 70|2006| 1665|\n", + "| Serious| 20|2006| 154|\n", + "| Fatal| 50|2006| 121|\n", + "| Serious| 50|2006| 709|\n", + "| Fatal| 70|2006| 384|\n", + "| Slight| 70|2007| 12225|\n", + "| Fatal| 30|2007| 864|\n", + "| Slight| 10|2007| 3|\n", + "| Serious| 10|2007| 1|\n", + "| Serious| 30|2007| 14278|\n", + "| Serious| 20|2007| 135|\n", + "| Slight| 20|2007| 1002|\n", + "| Serious| 50|2007| 756|\n", + "| Serious| 40|2007| 1916|\n", + "| Slight| 40|2007| 12966|\n", + "| Fatal| 60|2007| 1109|\n", + "| Slight| 60|2007| 25344|\n", + "| Slight| 15|2007| 4|\n", + "| Serious| 70|2007| 1631|\n", + "| Fatal| 70|2007| 369|\n", + "| Slight| 50|2007| 4385|\n", + "| Fatal| 40|2007| 244|\n", + "| Slight| 30|2007| 99150|\n", + "| Fatal| 20|2007| 9|\n", + "| Fatal| 50|2007| 119|\n", + "+-----------------+-----------+----+---------------+\n", + "only showing top 60 rows\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+-----------------+-----------+----+---------------+\n", + "|Accident_Severity|Speed_limit|Year|Total_accidents|\n", + "+-----------------+-----------+----+---------------+\n", + "| Fatal| 40|2005| 239|\n", + "| Fatal| 70|2005| 394|\n", + "| Fatal| 10|2005| 1|\n", + "| Fatal| 20|2005| 7|\n", + "| Fatal| 30|2005| 943|\n", + "| Fatal| 60|2005| 1211|\n", + "| Fatal| 50|2005| 118|\n", + "| Fatal| 30|2006| 909|\n", + "| Fatal| 50|2006| 121|\n", + "| Fatal| 70|2006| 384|\n", + "| Fatal| 60|2006| 1204|\n", + "| Fatal| 20|2006| 15|\n", + "| Fatal| 40|2006| 293|\n", + "| Fatal| 40|2007| 244|\n", + "| Fatal| 60|2007| 1109|\n", + "| Fatal| 50|2007| 119|\n", + "| Fatal| 20|2007| 9|\n", + "| Fatal| 30|2007| 864|\n", + "| Fatal| 70|2007| 369|\n", + "| Fatal| 30|2008| 796|\n", + "+-----------------+-----------+----+---------------+\n", + "only showing top 20 rows\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+-----------+---------------+\n", + "|Speed_limit|Total_accidents|\n", + "+-----------+---------------+\n", + "| 10| 3|\n", + "| 20| 304|\n", + "| 30| 9838|\n", + "| 40| 2828|\n", + "| 50| 1792|\n", + "| 60| 11106|\n", + "| 70| 3827|\n", + "+-----------+---------------+\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [] + } + ], + "source": [ + "Accident_Sex_Severity_df = Accident_Information_df.groupby('Accident_Severity','Year').agg(F.count(Accident_Information_df.Accident_Index).alias('Total_accidents'))\n", + "\n", + "Accident_Speed_Severity_df=Accident_Speed_Severity_df.sort(\"Speed_limit\")\n", + "Accident_Speed_Severity_df=Accident_Speed_Severity_df.sort(\"Year\")\n", + "Accident_Speed_Severity_df.show(60)\n", + "#Accident_Severitydf\n", + "CarAccidentfatal_df=Accident_Speed_Severity_df.filter(Accident_Speed_Severity_df.Accident_Severity.contains(\"Fatal\")).show()\n", + "#CarAccidentfatal_df = CarAccidentfatal_df.groupby('Accident_Severity','Speed_limit','Year').agg(F.count(CarAccidentfatal_df.Accident_Index).alias('Total_accidents'))\n", + "\n", + "\n", + "CarAccidentfatal_df=Accident_Information_df.filter(Accident_Information_df.Accident_Severity.contains(\"Fatal\"))\n", + "CarAccidentfatal_df = CarAccidentfatal_df.groupby('Speed_limit').agg(F.count(CarAccidentfatal_df.Accident_Index).alias('Total_accidents')).sort(\"Speed_limit\")\n", + "\n", + "CarAccidentfatal_df.show()\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+----+----------------------+--------------+--------------------------+\n", + "|Year|Total accidents of Car|Cars and Taxis|Accidents per billion mile|\n", + "+----+----------------------+--------------+--------------------------+\n", + "|2005| 280583| 244| 1149.9303278688524|\n", + "|2006| 266965| 246.9| 1081.2677197245848|\n", + "|2007| 254885| 247.3| 1030.671249494541|\n", + "|2008| 235996| 245.4| 961.6788916055419|\n", + "|2009| 226447| 244.8| 925.0285947712418|\n", + "|2010| 211934| 241.9| 876.1223646134766|\n", + "|2011| 203978| 244.3| 834.9488334015555|\n", + "|2012| 196651| 245.5| 801.0224032586558|\n", + "|2013| 185174| 246.6| 750.9083536090835|\n", + "|2014| 194997| 253.5| 769.2189349112426|\n", + "|2015| 188374| 258.1| 729.8488957768307|\n", + "|2016| 184849| 263.9| 700.450928381963|\n", + "|2017| 173686| 269| 645.6728624535316|\n", + "|2018| 164645| 272.3| 604.6456114579507|\n", + "|2019| 157382| 278.2| 565.7153127246586|\n", + "+----+----------------------+--------------+--------------------------+\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "data": { + "text/plain": [ + "<AxesSubplot:xlabel='Year'>" + ] + }, + "execution_count": 79, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "Billionvehiclemiles20052017Car_df=Billionvehiclemiles20052017_df.select(col(\"Year\"),col(\"Cars and Taxis\"))\n", + "resultcarperbill = CarAccidentovertheyeards_df.join(Billionvehiclemiles20052017Car_df, on=['Year'], how='left_outer').sort('Year')\n", + "resultcarperbill=resultcarperbill.withColumn('Accidents per billion mile', resultcarperbill[1]/resultcarperbill[2])\n", + "resultcarperbill.show()\n", + "\n", + "resultcarperbillp = resultcarperbill.toPandas()\n", + "resultcarperbillp.plot.bar(x=\"Year\", y=\"Accidents per billion mile\")\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+----+----------------------+---------------+--------------------------+\n", + "|Year|Total accidents of Bus|Buses & Coaches|Accidents per billion mile|\n", + "+----+----------------------+---------------+--------------------------+\n", + "|2005| 11253| 3.2| 3516.5625|\n", + "|2006| 10185| 3.3| 3086.3636363636365|\n", + "|2007| 9590| 3.4| 2820.5882352941176|\n", + "|2008| 9325| 3.1| 3008.064516129032|\n", + "|2009| 8661| 3.1| 2793.8709677419356|\n", + "|2010| 8237| 3.2| 2574.0625|\n", + "|2011| 7988| 3| 2662.6666666666665|\n", + "|2012| 7070| 2.8| 2525.0|\n", + "|2013| 6511| 2.9| 2245.1724137931037|\n", + "|2014| 6705| 2.9| 2312.0689655172414|\n", + "|2015| 5897| 2.8| 2106.071428571429|\n", + "|2016| 5478| 2.6| 2106.9230769230767|\n", + "|2017| 5477| 2.6| 2106.5384615384614|\n", + "|2018| 4937| 2.5| 1974.8|\n", + "|2019| 4333| 2.4| 1805.4166666666667|\n", + "+----+----------------------+---------------+--------------------------+\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "data": { + "text/plain": [ + "<AxesSubplot:xlabel='Year'>" + ] + }, + "execution_count": 80, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "Billionvehiclemiles20052017Bus_df=Billionvehiclemiles20052017_df.select(col(\"Year\"),col(\"Buses & Coaches\"))\n", + "resultBusperbill=BusAccidentovertheyeards_df.join(Billionvehiclemiles20052017Bus_df, on=['Year'], how='left_outer').sort('Year')\n", + "#resultBusperbill.show()\n", + "resultBusperbill=resultBusperbill.withColumn('Accidents per billion mile', resultBusperbill[1]/resultBusperbill[2])\n", + "resultBusperbill.show()\n", + "resultBusperbillp = resultBusperbill.toPandas()\n", + "resultBusperbillp.plot.bar(x=\"Year\", y=\"Accidents per billion mile\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+----+-----------------------------+-----------+\n", + "|Year|Total accidents of Motorcycle|Motorcycles|\n", + "+----+-----------------------------+-----------+\n", + "|2005| 25870| 3.3|\n", + "|2006| 24323| 3.2|\n", + "|2007| 24381| 3.4|\n", + "|2008| 22427| 3.1|\n", + "|2009| 21590| 3.2|\n", + "|2010| 19534| 2.9|\n", + "|2011| 21069| 2.9|\n", + "|2012| 20255| 2.9|\n", + "|2013| 19694| 2.8|\n", + "|2014| 21587| 2.9|\n", + "|2015| 21218| 2.9|\n", + "|2016| 20683| 3|\n", + "|2017| 19440| 3|\n", + "|2018| 18139| 3|\n", + "|2019| 17619| 3|\n", + "+----+-----------------------------+-----------+\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+----+-----------------------------+-----------+--------------------------+\n", + "|Year|Total accidents of Motorcycle|Motorcycles|Accidents per billion mile|\n", + "+----+-----------------------------+-----------+--------------------------+\n", + "|2005| 25870| 3.3| 7839.39393939394|\n", + "|2006| 24323| 3.2| 7600.9375|\n", + "|2007| 24381| 3.4| 7170.882352941177|\n", + "|2008| 22427| 3.1| 7234.516129032258|\n", + "|2009| 21590| 3.2| 6746.875|\n", + "|2010| 19534| 2.9| 6735.862068965517|\n", + "|2011| 21069| 2.9| 7265.172413793103|\n", + "|2012| 20255| 2.9| 6984.48275862069|\n", + "|2013| 19694| 2.8| 7033.571428571429|\n", + "|2014| 21587| 2.9| 7443.793103448276|\n", + "|2015| 21218| 2.9| 7316.551724137931|\n", + "|2016| 20683| 3| 6894.333333333333|\n", + "|2017| 19440| 3| 6480.0|\n", + "|2018| 18139| 3| 6046.333333333333|\n", + "|2019| 17619| 3| 5873.0|\n", + "+----+-----------------------------+-----------+--------------------------+\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "data": { + "text/plain": [ + "<AxesSubplot:xlabel='Year'>" + ] + }, + "execution_count": 81, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#MotorcycleAccidentovertheyeards_df\n", + "Billionvehiclemiles20052017Bycycle_df=Billionvehiclemiles20052017_df.select(col(\"Year\"),col(\"Motorcycles\")).sort(\"Year\")\n", + "resultMCperbill=MotorcycleAccidentovertheyeards_df.join(Billionvehiclemiles20052017Bycycle_df, on=['Year'], how='left_outer').sort('Year')\n", + "resultMCperbill.show()\n", + "resultMCperbill=resultMCperbill.withColumn('Accidents per billion mile', resultMCperbill[1]/resultMCperbill[2])\n", + "resultMCperbill.show()\n", + "resultMCperbillp = resultMCperbill.toPandas()\n", + "resultMCperbillp.plot.bar(x=\"Year\", y=\"Accidents per billion mile\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+----+---------------------------+----------------------+------------------+\n", + "|Year|Light Commercial,Vehicles 1|Heavy Goods Vehicles 2|Total billion mile|\n", + "+----+---------------------------+----------------------+------------------+\n", + "|2005| 38.4| 18| 56.4|\n", + "|2006| 39.9| 18| 57.9|\n", + "|2007| 41.9| 18.2|60.099999999999994|\n", + "|2008| 41.6| 17.8|59.400000000000006|\n", + "|2009| 40.7| 16.3| 57.0|\n", + "|2010| 41.4| 16.4| 57.8|\n", + "|2011| 42| 16| 58.0|\n", + "|2012| 42.2| 15.6|57.800000000000004|\n", + "|2013| 43.8| 15.8|59.599999999999994|\n", + "|2014| 46.6| 16.2| 62.8|\n", + "|2015| 48.9| 16.8| 65.7|\n", + "|2016| 51.7| 17| 68.7|\n", + "|2017| 53.4| 17.2| 70.6|\n", + "|2018| 54.4| 17.3| 71.7|\n", + "|2019| 55.5| 17.4| 72.9|\n", + "+----+---------------------------+----------------------+------------------+\n", + "\n" + ] + } + ], + "source": [ + "#Billionvehiclemiles20052017_df.show()\n", + "Totalmilesofgoods=Billionvehiclemiles20052017_df.select(\"Year\",\"Light Commercial,Vehicles 1\",\"Heavy Goods Vehicles 2\")\n", + "Totalmilesofgoods=Totalmilesofgoods.withColumn('Total billion mile', Totalmilesofgoods[1]+Totalmilesofgoods[2])\n", + "Totalmilesofgoods.show()\n", + "\n", + "\n", + "\n", + "\n", + "Billionvehiclemiles20052017Goods_df=Totalmilesofgoods\n", + "resultGoodsperbilll=GoodsVechileAccidentovertheyeards_df.join(Billionvehiclemiles20052017Goods_df, on=['Year'], how='left_outer').sort('Year')\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+----+-------------------------------+---------------------------+----------------------+------------------+\n", + "|Year|Total accidents of GoodsVechile|Light Commercial,Vehicles 1|Heavy Goods Vehicles 2|Total billion mile|\n", + "+----+-------------------------------+---------------------------+----------------------+------------------+\n", + "|2005| 28198| 38.4| 18| 56.4|\n", + "|2006| 26929| 39.9| 18| 57.9|\n", + "|2007| 25308| 41.9| 18.2|60.099999999999994|\n", + "|2008| 22661| 41.6| 17.8|59.400000000000006|\n", + "|2009| 20701| 40.7| 16.3| 57.0|\n", + "|2010| 20481| 41.4| 16.4| 57.8|\n", + "|2011| 20012| 42| 16| 58.0|\n", + "|2012| 19310| 42.2| 15.6|57.800000000000004|\n", + "|2013| 19316| 43.8| 15.8|59.599999999999994|\n", + "|2014| 21182| 46.6| 16.2| 62.8|\n", + "|2015| 20961| 48.9| 16.8| 65.7|\n", + "|2016| 20032| 51.7| 17| 68.7|\n", + "|2017| 18907| 53.4| 17.2| 70.6|\n", + "|2018| 18020| 54.4| 17.3| 71.7|\n", + "|2019| 17808| 55.5| 17.4| 72.9|\n", + "+----+-------------------------------+---------------------------+----------------------+------------------+\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [] + } + ], + "source": [ + "resultGoodsperbilll.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+----+-------------------------------+---------------------------+----------------------+------------------+--------------------------+\n", + "|Year|Total accidents of GoodsVechile|Light Commercial,Vehicles 1|Heavy Goods Vehicles 2|Total billion mile|Accidents per billion mile|\n", + "+----+-------------------------------+---------------------------+----------------------+------------------+--------------------------+\n", + "|2005| 28198| 38.4| 18| 56.4| 499.9645390070922|\n", + "|2006| 26929| 39.9| 18| 57.9| 465.09499136442145|\n", + "|2007| 25308| 41.9| 18.2|60.099999999999994| 421.09816971713815|\n", + "|2008| 22661| 41.6| 17.8|59.400000000000006| 381.49831649831646|\n", + "|2009| 20701| 40.7| 16.3| 57.0| 363.17543859649123|\n", + "|2010| 20481| 41.4| 16.4| 57.8| 354.34256055363323|\n", + "|2011| 20012| 42| 16| 58.0| 345.0344827586207|\n", + "|2012| 19310| 42.2| 15.6|57.800000000000004| 334.08304498269894|\n", + "|2013| 19316| 43.8| 15.8|59.599999999999994| 324.09395973154363|\n", + "|2014| 21182| 46.6| 16.2| 62.8| 337.2929936305733|\n", + "|2015| 20961| 48.9| 16.8| 65.7| 319.04109589041093|\n", + "|2016| 20032| 51.7| 17| 68.7| 291.58660844250363|\n", + "|2017| 18907| 53.4| 17.2| 70.6| 267.80453257790373|\n", + "|2018| 18020| 54.4| 17.3| 71.7| 251.32496513249652|\n", + "|2019| 17808| 55.5| 17.4| 72.9| 244.2798353909465|\n", + "+----+-------------------------------+---------------------------+----------------------+------------------+--------------------------+\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+----+--------------+---------------------------+----------------------+-----------+---------------+-------+------------------+-----------+\n", + "|Year|Cars and Taxis|Light Commercial,Vehicles 1|Heavy Goods Vehicles 2|Motorcycles|Buses & Coaches|Total 3|All motor vehicles|Pedal Cycle|\n", + "+----+--------------+---------------------------+----------------------+-----------+---------------+-------+------------------+-----------+\n", + "|2005| 244| 38.4| 18| 3.3| 3.2| 6.5| 306.9| 2.7|\n", + "|2006| 246.9| 39.9| 18| 3.2| 3.3| 6.5| 311.4| 2.8|\n", + "|2007| 247.3| 41.9| 18.2| 3.4| 3.4| 6.8| 314.1| 2.6|\n", + "|2008| 245.4| 41.6| 17.8| 3.1| 3.1| 6.3| 311| 2.8|\n", + "|2009| 244.8| 40.7| 16.3| 3.2| 3.1| 6.3| 308.1| 3|\n", + "|2010| 241.9| 41.4| 16.4| 2.9| 3.2| 6.1| 305.8| 3|\n", + "|2011| 244.3| 42| 16| 2.9| 3| 5.9| 308.2| 3.1|\n", + "|2012| 245.5| 42.2| 15.6| 2.9| 2.8| 5.7| 309| 3.1|\n", + "|2013| 246.6| 43.8| 15.8| 2.8| 2.9| 5.7| 311.9| 3.1|\n", + "|2014| 253.5| 46.6| 16.2| 2.9| 2.9| 5.8| 322.2| 3.5|\n", + "|2015| 258.1| 48.9| 16.8| 2.9| 2.8| 5.7| 329.6| 3.2|\n", + "|2016| 263.9| 51.7| 17| 3| 2.6| 5.6| 338.2| 3.2|\n", + "|2017| 269| 53.4| 17.2| 3| 2.6| 5.5| 345.2| 3.3|\n", + "|2018| 272.3| 54.4| 17.3| 3| 2.5| 5.5| 349.5| 3.3|\n", + "|2019| 278.2| 55.5| 17.4| 3| 2.4| 5.4| 356.5| 3.5|\n", + "+----+--------------+---------------------------+----------------------+-----------+---------------+-------+------------------+-----------+\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+----+------------------------------+\n", + "|Year|Total accidents of Pedal Cycle|\n", + "+----+------------------------------+\n", + "|2007| 16607|\n", + "|2018| 18125|\n", + "|2015| 19440|\n", + "|2006| 16611|\n", + "|2013| 20049|\n", + "|2014| 21979|\n", + "|2019| 17437|\n", + "|2012| 19708|\n", + "|2009| 17599|\n", + "|2016| 19047|\n", + "|2005| 17039|\n", + "|2010| 17811|\n", + "|2011| 19883|\n", + "|2008| 16797|\n", + "|2017| 18954|\n", + "+----+------------------------------+\n", + "\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#resultBusperbill.show()\n", + "resultGoodsperbill=resultGoodsperbilll.withColumn('Accidents per billion mile', resultGoodsperbilll[1]/resultGoodsperbilll[4])\n", + "resultGoodsperbill.show()\n", + "resultGoodsperbillp = resultGoodsperbill.toPandas()\n", + "resultGoodsperbillp.plot.bar(x=\"Year\", y=\"Accidents per billion mile\")\n", + "\n", + "\n", + "Billionvehiclemiles20052017_df.show()\n", + "#Billionvehiclemiles20052017PedalCycle_df.show()\n", + "cycleAccidentovertheyeards_df.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+----+------------------------------+-----------+\n", + "|Year|Total accidents of Pedal Cycle|Pedal Cycle|\n", + "+----+------------------------------+-----------+\n", + "|2005| 17039| 2.7|\n", + "|2006| 16611| 2.8|\n", + "|2007| 16607| 2.6|\n", + "|2008| 16797| 2.8|\n", + "|2009| 17599| 3|\n", + "|2010| 17811| 3|\n", + "|2011| 19883| 3.1|\n", + "|2012| 19708| 3.1|\n", + "|2013| 20049| 3.1|\n", + "|2014| 21979| 3.5|\n", + "|2015| 19440| 3.2|\n", + "|2016| 19047| 3.2|\n", + "|2017| 18954| 3.3|\n", + "|2018| 18125| 3.3|\n", + "|2019| 17437| 3.5|\n", + "+----+------------------------------+-----------+\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+----+------------------------------+-----------+--------------------------+\n", + "|Year|Total accidents of Pedal Cycle|Pedal Cycle|Accidents per billion mile|\n", + "+----+------------------------------+-----------+--------------------------+\n", + "|2005| 17039| 2.7| 6310.74074074074|\n", + "|2006| 16611| 2.8| 5932.5|\n", + "|2007| 16607| 2.6| 6387.307692307692|\n", + "|2008| 16797| 2.8| 5998.928571428572|\n", + "|2009| 17599| 3| 5866.333333333333|\n", + "|2010| 17811| 3| 5937.0|\n", + "|2011| 19883| 3.1| 6413.870967741936|\n", + "|2012| 19708| 3.1| 6357.419354838709|\n", + "|2013| 20049| 3.1| 6467.419354838709|\n", + "|2014| 21979| 3.5| 6279.714285714285|\n", + "|2015| 19440| 3.2| 6075.0|\n", + "|2016| 19047| 3.2| 5952.1875|\n", + "|2017| 18954| 3.3| 5743.636363636364|\n", + "|2018| 18125| 3.3| 5492.424242424243|\n", + "|2019| 17437| 3.5| 4982.0|\n", + "+----+------------------------------+-----------+--------------------------+\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "data": { + "text/plain": [ + "<AxesSubplot:xlabel='Year'>" + ] + }, + "execution_count": 84, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#MotorcycleAccidentovertheyeards_df\n", + "Billionvehiclemiles20052017PedalCycle_df=Billionvehiclemiles20052017_df.select(col(\"Year\"),col(\"Pedal Cycle\")).sort(\"Year\")\n", + "resultBCperbill=cycleAccidentovertheyeards_df.join(Billionvehiclemiles20052017PedalCycle_df, on=['Year'], how='left_outer').sort('Year')\n", + "resultBCperbill.show()\n", + "resultBCperbill=resultBCperbill.withColumn('Accidents per billion mile', resultBCperbill[1]/resultBCperbill[2])\n", + "resultBCperbill.show()\n", + "resultBCperbillmm = resultBCperbill.toPandas()\n", + "resultBCperbillmm.plot.bar(x=\"Year\", y=\"Accidents per billion mile\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAABssAAAHlCAYAAACzn8KCAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8rg+JYAAAACXBIWXMAAAsTAAALEwEAmpwYAABVnUlEQVR4nO3debhdVX0//vcnAwEjMgjytcwOFUQBMRWHqggOSK3YVqlDKyCWrxMWKL8aW7/lxopFSxFrnWgFwTLUCaXWCQekFlSCIqIoUgsKRWQWAokB1u+Pu4OXmOEk2eeOr9fznGfvs/Y+a38267m5Obyz1q7WWgAAAAAAAGAmmjXRBQAAAAAAAMBEEZYBAAAAAAAwYwnLAAAAAAAAmLGEZQAAAAAAAMxYwjIAAAAAAABmLGEZAAAAAAAAM9aciS5gvGy11VZtp512mugyAAAAAAAAGGeXXHLJTa21rVd1bMaEZTvttFMWL1480WUAAAAAAAAwzqrqmtUdswwjAAAAAAAAM5awDAAAAAAAgBlLWAYAAAAAAMCMNWOeWQYAAAAAADAsy5cvz7XXXpulS5dOdCkz2sYbb5ztttsuc+fOHfgzwjIAAAAAAIANdO2112bTTTfNTjvtlKqa6HJmpNZabr755lx77bXZeeedB/6cZRgBAAAAAAA20NKlS/PQhz5UUDaBqioPfehD13l237iHZVV1VFV9v6our6qzqmrjqtq5qr5ZVVdV1b9V1UbdufO691d1x3ca08+bu/YfVdXzxvs+AAAAAAAAxhKUTbz1GYNxDcuqatskb0yyoLX2uCSzk7w0yTuSvKu19qgktyY5rPvIYUlu7drf1Z2Xqnps97ndkuyf5H1VNXs87wUAAAAAAGAyueGGG/Lyl788j3jEI/LEJz4xT3nKU3LOOedscL/77LNPFi9e3EOFk9NEPLNsTpJNqmp5kgcluT7Jvkle3h0/LclIkvcnObDbT5KPJ/mnGo0ED0xydmttWZL/qaqrkjwpyUXjdA8AAAAAAACrddLxx+f2Zct662+zefNy5MKFqz3eWsuLXvSiHHzwwTnzzDOTJNdcc03OPffc3mqYrsY1LGutXVdVJyT5aZK7k3wxySVJbmut3dOddm2Sbbv9bZP8rPvsPVV1e5KHdu3fGNP12M8AAAAAAABMqNuXLcuxIyO99bdoLX195StfyUYbbZTXvOY197ftuOOOOeKII7J06dK89rWvzeLFizNnzpyceOKJedaznrXa9rvvvjuHHnpovvvd72aXXXbJ3XffnSS59957c9hhh2Xx4sWpqrzqVa/KUUcd1ds9TpRxDcuqaouMzgrbOcltST6W0WUUh3W9w5McniQ77LDDsC4DAAAAAAAwob7//e9nr732WuWx9773vamqfO9738sPf/jDPPe5z82VV1652vb3v//9edCDHpQrrrgil1122f39Xnrppbnuuuty+eWXJ0luu+228bq9oRrXZ5YleXaS/2mt3dhaW57kk0melmTzqloR3G2X5Lpu/7ok2ydJd3yzJDePbV/FZ+7XWju5tbagtbZg6623Hsb9AAAAAAAATDqvf/3rs8cee+R3fud38vWvfz1/8id/kiTZZZddsuOOO+bKK69cbfsFF1xwf/vuu++e3XffPUnyiEc8Ij/5yU9yxBFH5POf/3we8pCHTMzN9Wy8w7KfJnlyVT2oe/bYfkl+kOSrSV7cnXNwkk93++d279Md/0prrXXtL62qeVW1c5JHJ/nWON0DAAAAAADApLLbbrvl29/+9v3v3/ve9+bLX/5ybrzxxt6uscUWW+S73/1u9tlnn3zgAx/Iq1/96t76nkjjGpa11r6Z5ONJvp3ke931T07ypiRHV9VVGX0m2Ye6j3woyUO79qOTLOz6+X6Sj2Y0aPt8kte31u4dx1sBAAAAAACYNPbdd98sXbo073//++9vu+uuu5IkT3/603PGGWckSa688sr89Kc/zWMe85jVtj/jGc/ImWeemSS5/PLLc9lllyVJbrrpptx33335oz/6o7ztbW97QDg3lY3rM8uSpLV2bJJjV2r+SZInreLcpUlespp+jktyXO8FAgAAAAAATDFVlU996lM56qij8s53vjNbb7115s+fn3e84x058MAD89rXvjaPf/zjM2fOnHz4wx/OvHnz8rrXvW6V7a997Wtz6KGHZtddd82uu+6aJz7xiUmS6667Loceemjuu+++JMnf/d3fTeQt96ZGVzWc/hYsWNAWL1480WUAAAAAAADT0BVXXJFdd931/vcnHX98bl+2rLf+N5s3L0cuXNhbf9PZymORJFV1SWttwarOH/eZZQAAAAAAANOdYGvqGNdnlgEAAAAAAMBkIiwDAAAAAABgxrIMI+Pm+BOOz7Il/a3POm/+vCw8xjRWAAAAAABg/QnLGDfLlizLSEZ6629kSX99AQAAAAAAM5NlGAEAAAAAAJixhGUAAAAAAADTQFXlT/7kT+5/f88992TrrbfOC17wgjV+7vzzz8+FF1447PJWaZ999snixYsn5NorWIYRAAAAAACgZ8efcHyWLVnWW3/z5s/LwmMWrvGc+fPn5/LLL8/dd9+dTTbZJOedd1623XbbtfZ9/vnn58EPfnCe+tSnDlzPPffckzlzpkfMND3uAgAAAAAAYBJZtmRZRjLSW38jSwbr64ADDsh//Md/5MUvfnHOOuusvOxlL8t//ud/JkluueWWvOpVr8pPfvKTPOhBD8rJJ5+chzzkIfnABz6Q2bNn51//9V/znve8J9tvv31e9apX5aabbsrWW2+dU089NTvssEMOOeSQbLzxxvnOd76Tpz3taXnd616X17zmNbnxxhsze/bsfOxjH8uiRYvyh3/4h3nRi16UJHnFK16Rgw46KC94wQvypje9KZ///Ocza9as/Nmf/VmOOOKIB9T+xS9+Mccee2yWLVuWRz7ykTn11FPz4Ac/uLf/hqtjGUYAAAAAAIBp4qUvfWnOPvvsLF26NJdddln23nvv+48de+yxecITnpDLLrssb3/72/PKV74yO+20U17zmtfkqKOOyqWXXpqnP/3pOeKII3LwwQfnsssuyyte8Yq88Y1vvL+Pa6+9NhdeeGFOPPHEvOIVr8jrX//6fPe7382FF16Yhz/84TnssMPy4Q9/OEly++2358ILL8zv/d7v5eSTT87VV1+dSy+99P5+x7rpppvytre9LV/60pfy7W9/OwsWLMiJJ544Lv/NzCwDAAAAAACYJnbfffdcffXVOeuss3LAAQc84NjXv/71fOITn0iS7Lvvvrn55pvzy1/+8jf6uOiii/LJT34ySfKnf/qn+cu//Mv7j73kJS/J7Nmzc8cdd+S6667LH/zBHyRJNt544yTJM5/5zLzuda/LjTfemE984hP5oz/6o8yZMydf+tKX8prXvOb+pRu33HLLB1zzG9/4Rn7wgx/kaU97WpLkV7/6VZ7ylKf08Z9krYRlAAAAAAAA08gLX/jCHHPMMTn//PNz880399r3/Pnz13rOK1/5yvzrv/5rzj777Jx66qkD9dtay3Oe85ycddZZG1riOrMMIwAAAAAAwDTyqle9Kscee2we//jHP6D96U9/es4444wkyfnnn5+tttoqD3nIQ7LpppvmjjvuuP+8pz71qTn77LOTJGeccUae/vSn/8Y1Nt1002y33Xb51Kc+lSRZtmxZ7rrrriTJIYcckpNOOilJ8tjHPjZJ8pznPCcf/OAHc8899yQZfX7aWE9+8pPzX//1X7nqqquSJEuWLMmVV165If8ZBiYsAwAAAAAAmEa22267BzxnbIWRkZFccskl2X333bNw4cKcdtppSZLf//3fzznnnJM999wz//mf/5n3vOc9OfXUU7P77rvnIx/5SN797nev8jof+chH8o//+I/Zfffd89SnPjU///nPkyTbbLNNdt111xx66KH3n/vqV786O+ywQ3bffffsscceOfPMMx/Q19Zbb50Pf/jDednLXpbdd989T3nKU/LDH/6wr/8ka1SttXG50ERbsGBBW7x48USXMaMtWrQoIxnprb+RjOTYY4/trT8AAAAAAFhfV1xxRXbdddf73x9/wvFZtmRZb/3Pmz8vC49Z2Ft/w3TXXXfl8Y9/fL797W9ns802G/frrzwWSVJVl7TWFqzqfM8sgxlsJv9hDQAAAAAwTDP1/5V+6UtfymGHHZajjjpqQoKy9SEsgxls2ZJl/c72W9JfXwAAAAAATD3Pfvazc80110x0GevEM8sAAAAAAACYsYRlAAAAAAAAzFjCMgAAAAAAAGYsYRkAAAAAAAAzlrAMAAAAAABgGpg9e3b23HPP7LHHHtlrr71y4YUXTnRJU8KciS4AAAAAAABgujn++JOybNntvfU3b95mWbjwyDWes8kmm+TSSy9NknzhC1/Im9/85nzta1/rrYbpSlgGAAAAAADQs2XLbs/IyLG99Tcysmidzv/lL3+ZLbbYIkly/vnn54QTTshnPvOZJMkb3vCGLFiwIIccckgWLlyYc889N3PmzMlzn/vcnHDCCb3VPFUIywAAAIBJ5/gTjs+yJct662/e/HlZeMzC3voDAJiM7r777uy5555ZunRprr/++nzlK19Z4/k333xzzjnnnPzwhz9MVeW2224bn0InGWEZAAAAMOksW7IsIxnprb+RJf31BQAwWY1dhvGiiy7KK1/5ylx++eWrPX+zzTbLxhtvnMMOOywveMEL8oIXvGCcKp1cZk10AQAAAAAAAPTrKU95Sm666abceOONmTNnTu677777jy1dujRJMmfOnHzrW9/Ki1/84nzmM5/J/vvvP1HlTigzywAAAAAAAKaZH/7wh7n33nvz0Ic+NDvuuGN+8IMfZNmyZbn77rvz5S9/Ob/7u7+bO++8M3fddVcOOOCAPO1pT8sjHvGIiS57QgjLAAAAAAAApoEVzyxLktZaTjvttMyePTvbb799DjrooDzucY/LzjvvnCc84QlJkjvuuCMHHnhgli5dmtZaTjzxxAmsfuIIywAAAAAAAHo2b95mGRlZ1Gt/a3Pvvfeu9tg73/nOvPOd7/yN9m9961sbVNd0ICwDAAAAYK2OP+H4LFuyrNc+582fl4XHLOy1TwCYLBYuPHKiS2BAwjIAAAAA1mrZkmUZyUivfY4s6bc/AID1MWuiCwAAAAAAAICJYmYZAABMU5bLAgAAgLUTlgEAwDRluSwAAABYO2EZAKxF3zMzzMoAAAAAgMlDWAYAa9H3zAyzMgAAAAAYhp///Oc58sgjc/HFF2fzzTfPNttsk5NOOim//du/PdGlTWrCMgAAAAAAgJ6deOLxueOO/lYr2nTTeTn66NWvVtRayx/8wR/k4IMPztlnn50k+e53v5sbbrhhrWFZay2ttcyaNau3eqcSYRnAJGPJPwAAAACY+u64Y1me+cyR3vr72tfW3NdXv/rVzJ07N695zWvub9tjjz1y5513Zr/99sutt96a5cuX521ve1sOPPDAXH311Xne856XvffeO5dcckk++9nPZscdd+yt3qlEWAYwyVjyDwAAAABYV5dffnme+MQn/kb7xhtvnHPOOScPechDctNNN+XJT35yXvjCFyZJfvzjH+e0007Lk5/85PEud1IRlgEAAAAAAExTrbX81V/9VS644ILMmjUr1113XW644YYkyY477jjjg7JEWAYAAAAAADDl7bbbbvn4xz/+G+1nnHFGbrzxxlxyySWZO3dudtpppyxdujRJMn/+/PEuc1KamU9qAwAAAAAAmEb23XffLFu2LCeffPL9bZdddlmuueaaPOxhD8vcuXPz1a9+Nddcc80EVjk5jWtYVlWPqapLx7x+WVVHVtWWVXVeVf24227RnV9V9Y9VdVVVXVZVe43p6+Du/B9X1cHjeR8AAAAAAACTSVXlnHPOyZe+9KU88pGPzG677ZY3v/nNOeCAA7J48eI8/vGPz+mnn55ddtllokuddMZ1GcbW2o+S7JkkVTU7yXVJzkmyMMmXW2vHV9XC7v2bkjw/yaO7195J3p9k76raMsmxSRYkaUkuqapzW2u3juf9AAAAAAAArMqmm87L17420mt/a/Nbv/Vb+ehHP/ob7RdddNEqz7/88ss3uK7pYCKfWbZfkv9urV1TVQcm2adrPy3J+RkNyw5McnprrSX5RlVtXlUP7849r7V2S5JU1XlJ9k9y1rjeAQAAAFPe8Sccn2VLlvXa57z587LwmIW99gkAwNRy9NH+PjhVTGRY9tL8OtzaprV2fbf/8yTbdPvbJvnZmM9c27Wtrh0AAADWybIlyzKSkV77HFnSb38AAMDwjOszy1aoqo2SvDDJx1Y+1s0iaz1d5/CqWlxVi2+88cY+ugQAAAAAAGAamaiZZc9P8u3W2g3d+xuq6uGtteu7ZRZ/0bVfl2T7MZ/brmu7Lr9etnFF+/krX6S1dnKSk5NkwYIFvQRwAAAAG6LvJf8s9wcAALBh1iksq6rHZDSk2rK19ncbcN2X5YHPFzs3ycFJju+2nx7T/oaqOjvJ3klu7wK1LyR5e1Vt0Z333CRv3oB6AAAAxkXfS/5Z7g8AAGDDDLwMY1X9Y5LvJ3lfkr/t2r5dVfdW1R+vQz/zkzwnySfHNB+f5DlV9eMkz+7eJ8lnk/wkyVVJ/jnJ65KktXZLV8PF3eutXRsAAAAAAAAMbKCwrKpel+QN3fnVvZLk3d3+iwe9YGttSWvtoa2128e03dxa26+19ujW2rNXBF9t1Otba49srT2+tbZ4zGdOaa09qnudOuj1AQAAAAAApqPZs2dnzz33zOMe97i85CUvyV133TXwZ/fZZ58sXrx4vc7ZZ5998pjHPCZ77rlndt1115x88snrXPuaHHLIIfn4xz/ea59jDboM42uStCQnJvmLMe1f7LZP6LMoAAAAAACAqewdJ5yYpUvu6K2/jedvmjcdc/Qaz9lkk01y6aWXJkle8YpX5AMf+ECOPnrNn+nLGWeckQULFuSWW27JIx/5yBxyyCHZaKONxuXaG2rQsOzR3fbYPDAsu7nb/lZvFQEAAAAAAzn+hOOzbMmy3vqbN39eFh6zsLf+AGaypUvuyKl3L+itv0Oz5llfK3v605+eyy67LEuWLMkRRxyRyy+/PMuXL8/IyEgOPPDA3H333Tn00EPz3e9+N7vsskvuvvvu+z/72te+NhdffHHuvvvuvPjFL86iRYsGvu6dd96Z+fPnZ/bs2TnllFNy2WWX5aSTTkqS/PM//3N+8IMf5F3veldOP/30nHDCCamq7L777nnf+96X3XffPVdeeWXmzp2bX/7yl9ljjz1y5ZVXPqD/Sy65JEcffXTuvPPObLXVVvnwhz+chz/84ev032Zlg4Zly5JslOTBK7WvGOWlG1QFAAAAALDOli1ZlpGM9NbfyJL++gJg4txzzz353Oc+l/333z/HHXdc9t1335xyyim57bbb8qQnPSnPfvaz88EPfjAPetCDcsUVV+Syyy7LXnvtdf/njzvuuGy55Za59957s99+++Wyyy7L7rvvvsZrvuIVr8i8efPy4x//OCeddFJmz56dgw46KMcdd1z+/u//PnPnzs2pp56aD37wg/n+97+ft73tbbnwwguz1VZb5ZZbbsmmm26affbZJ//xH/+RF73oRTn77LPzh3/4h5k7d+7911i+fHmOOOKIfPrTn87WW2+df/u3f8tf//Vf55RTTtmg/16DhmWXJNknyXtXNFTVEUn+v4wuz7hucSYAAAAAAAC9uvvuu7PnnnsmGZ1Zdthhh+WpT31qzj333JxwwglJkqVLl+anP/1pLrjggrzxjW9Mkuy+++4PCMM++tGP5uSTT84999yT66+/Pj/4wQ/WGpatWIbxxhtvzFOf+tTsv//+2XHHHbPvvvvmM5/5THbdddcsX748j3/84/Oe97wnL3nJS7LVVlslSbbccsskyatf/eq8853vzIte9KKceuqp+ed//ucHXONHP/pRLr/88jznOc9Jktx7770bPKssGTwse2eSZyX5w4yGY0lyUpLq3p+wwZUAAAAAAACw3sY+s2yF1lo+8YlP5DGPecxAffzP//xPTjjhhFx88cXZYostcsghh2Tp0sEXGNx6662z11575Zvf/GZ23HHHvPrVr87b3/727LLLLjn00EPX+NmnPe1pufrqq3P++efn3nvvzeMe97jfuJfddtstF1100cD1DGLWICe11r6Q5M+S3JHRgGzF644kr22tfbHXqgAAAAAAANhgz3ve8/Ke97wnrY3OhfrOd76TJHnGM56RM888M0ly+eWX57LLLkuS/PKXv8z8+fOz2Wab5YYbbsjnPve5dbreXXfdle985zt55CMfmSTZe++987Of/SxnnnlmXvaylyVJ9t1333zsYx/LzTffnCS55ZZb7v/8K1/5yrz85S9fZbD2mMc8JjfeeOP9Ydny5cvz/e9/f53qW5VBZ5altfahqjo7yVOTbJ3kpiQXttbu3OAqAAAAAAAA6N3/+3//L0ceeWR233333Hfffdl5553zmc98Jq997Wtz6KGHZtddd82uu+6aJz7xiUmSPfbYI094whOyyy67ZPvtt8/Tnva0ga7zile8IptsskmWLVuWQw455P7+kuSggw7KpZdemi222CJJsttuu+Wv//qv88xnPjOzZ8/OE57whHz4wx++v5+3vOUt9wdrY2200Ub5+Mc/nje+8Y25/fbbc8899+TII4/MbrvttkH/jQYOy5KktbYkyXkbdEUAAAAAAIBpbuP5m+bQLO61v7W5887fnN+0ySab5IMf/OAq288+++xV9rMiuFrZ+eefv07tK3z961/PUUcd9YC2gw8+OAcffPAqz33xi1+czTfffJX17LnnnrngggvWeL11tdqwrKq+sg79tNbafj3UAwAwkONPOD7Llizrrb958+dl4TELe+sPAAAAmNnedMzRE13ChLvtttvypCc9KXvssUf222/tMdIRRxyRz33uc/nsZz87DtX92ppmlu2TpA3QRw14HgBAb5YtWZaRjPTW38iS/vqaqQSYAAAAwFibb755rrzyyoHPf8973jPEalZvTWHZTyMEAwBgQAJMAAAAYCpabVjWWttpHOsAAAAAAACY0lprqaqJLmNGa23d54HNGkIdAAAAAAAAM8rGG2+cm2++eb3CGvrRWsvNN9+cjTfeeJ0+t9qZZVV1ymi/7bBuf20FvGqdrgwAAAAAADBNbLfddrn22mtz4403TnQpM9rGG2+c7bbbbp0+s6Znlh2S5L4kh3X7a4tChWUAAAAAAMCMNHfu3Oy8884TXQbrYU1hWZLUavYBAAAAAABgylttWNZam7WqfQAAAAAAAJguhGAAAAAAAADMWGtbhvF+VbVFkucn2T7JvJWPt9be2mNdAAAAAAAAMHQDhWVV9Zwkn0gyfw2nCcsAAAAAAACYUgadWfYPSR68huOth1oAAAAAAABgXA0alj0io4HYXyT5jyS/GlpFAAAAAAAAME4GDcsuTLJfkjNba78YYj0AAAAAAFPW8Sccn2VLlvXW37z587LwmIW99QfAbxo0LPuzJOcl+WxV/VOSnya5Z+wJrbULeq4NAAAAGIJ3nHBili65o9c+N56/ad50zNG99gkwFS1bsiwjGemtv5El/fUFwKoNGpbNyejSi09I8qFVHG/r0BcAAAAwgZYuuSOn3r2g1z4PzeJe+wMAgPEyaMB1apJdk9QQawEAAAAAAIBxNWhYtuKfm52d5GtJlg6nHAAAgPVnaTkAAADW1aBh2feT7JXkNa21Xw6xHgBmGP9TE4A+WVoOAACAdTVoWPZ/k3wxyQlV9XdJrmmt3Te8sgCYKfxPTQAAAABgIg0all3cbQ/rXql6wOPLWmtt0L4AAACYQfqeSW4WOQAA0KdBA65a+ykAAADwm/qeSW4WOQAA0KdBw7LThloFTFP+BS0AAAAAAExuA4VlrbVDh10ITEf+BS0AAAAAAExunjMGAAAAMAlYnQQAYGIIywAAAAAmAauTAABMjFkTXQAAAAAAAABMFGEZAAAAAAAAM5awDAAAAAAAgBlLWAYAAAAAAMCMNWfQE6vquUlenGT7JPNWOtxaa/v1WRgAAAAAAAAM20BhWVUdk+QdqzucpPVWEQAAAAAAAIyTQWeWvSGjoViS/CLJ0gjIAAAAAAAAmOIGDcu2ymg4tn9r7bwh1gMAAAAAAADjZtaA53252142rEIAAAAAAABgvA06s+wfkjwjySer6q1JfpzknrEntNZ+OkhHVbV5kn9J8riMzlZ7VZIfJfm3JDsluTrJQa21W6uqkrw7yQFJ7kpySGvt210/Byd5S9ft21prpw14LwAAAACwVu844cQsXXJHb/1tPH/TvOmYo3vrDwDox6Bh2Ve77ZOTfHYVx9s69PXuJJ9vrb24qjZK8qAkf5Xky62146tqYZKFSd6U5PlJHt299k7y/iR7V9WWSY5NsqC79iVVdW5r7dYBawAAAACANVq65I6ceveC3vo7NIt76wtYN8efcHyWLVnWW3/z5s/LwmMW9tYfMLEGDbiqj4tV1WYZnaF2SJK01n6V5FdVdWCSfbrTTktyfkbDsgOTnN5aa0m+UVWbV9XDu3PPa63d0vV7XpL9k5zVR50AAAAAAEwfy5Ysy0hGeutvZEl/fQETb9CwbFFP19s5yY1JTq2qPZJckuTPk2zTWru+O+fnSbbp9rdN8rMxn7+2a1tdOwAAAAAAAAxsoLCstdZXWDYnyV5JjmitfbOq3p3RJRfHXqtVVevjYlV1eJLDk2SHHXboo0sAmBE8mwEAAACAmWLQmWVJkqp6RpLnJtk6yU1JvtBau2Adurg2ybWttW927z+e0bDshqp6eGvt+m6ZxV90x69Lsv2Yz2/XtV2XXy/buKL9/JUv1lo7OcnJSbJgwYJeAjgAmAk8mwEAAACAmWKgsKyqZiU5I8lBKx1aWFUfS/Ly1tp9a+untfbzqvpZVT2mtfajJPsl+UH3OjjJ8d32091Hzk3yhqo6O8neSW7vArUvJHl7VW3RnffcJG8e5F4AAAAAgKnLShgA9G3QmWVHJfnj1Rx7SZKLk/zDgH0dkeSMqtooyU+SHJpkVpKPVtVhSa7Jr0O5zyY5IMlVSe7qzk1r7Zaq+tvuukny1tbaLQNeH5jB/IUagD75vQIAMP6shAFA3wYNyw5O0pJ8NclbM7qc4nZJ/l9GZ4cdkgHDstbapUlW9dtsv1Wc25K8fjX9nJLklEGuCbCCv1AD0Ce/VwAAAGDqGzQse3S3fWlr7aZu/ydV9fIkNyR5ZO+VAQAAAAAAwJDNGvC8X3XbbVZqX/F+eT/lAAAAAAAAwPgZdGbZd5I8Pcnnquqfk/wso8sw/llGl2f8znDKAwAg6f/ZWInnYwEAADBxjj/h+Cxbsqy3/ubNn5eFxyzsrT9mlkHDsn9I8owk2yYZGdNeGQ3LTuy3LAAAxur72ViJ52MBAAAwcZYtWZaRB8QNG2ZkSX99MfMMtAxja+3fk7w+yZ0ZDchWvO5M8sbW2rlDqxAAAAAAAACGZNCZZWmtvb+qPpLkqUm2SnJTkotaa/2uBwQAAAAAAADjZOCwLElaa3cm+eKQagEAAAAAAIBxtdqwrKq+kqS11vbr9tektdb267c0AAAAAAAAGK41zSzbJ0lbxf7Kag3HAAAAAACYxt5xwolZuqS/p/VsPH/TvOmYo3vrD2Bt1hSW/TTJfWP2BWIAAAAAADzA0iV35NS7F/TW36FZ3FtfAINYbVjWWttpVfsAAAAAAAAwXcya6AIAAAAAAABgoqx2ZllVnbIuHbXWXrXh5QAAAAAAAMD4WdMzyw7Juj2nTFgGAAAAAADAlLKmsCxJalyqAAAAAAAAgAmw2rCsteZ5ZgAAAAAAAExrAjEAAAAAAABmrNXOLKuqv1mXjlprb93wcgAAAAAAAGD8rOmZZSNJ2jr0JSwDAAAAAABgSllTWJYkNWA/6xKqAQAAAAAAwKSwprBs53GrAgAAAAAAACbAasOy1to141kIAAAAAAAAjLfVhmVV9cokaa2dvmJ/TVprp/dZGAAAAAAAsO7eccKJWbrkjl773Hj+pnnTMUf32idMFmtahvHDSe5Lcnq3v6bnkrXuPAAAAAAAYAItXXJHTr17Qa99HprFvfYHk8mawrIkqdXsAwAAAAAAwJS3prDsWavZBwAAAAAAgGlhtWFZa+1rq9oHAAAAAACA6WJtyzDer6o2TfLGJE9Lsm2S65J8Pcl7Wmv9PikQAAAAAAAAxsFAYVlVPS7Jl5JsPab5cUmel+SIqnpOa+3yIdQHAAAAAAAAQzNrwPPel+RhSSrJ8iQ3dNtKsk2SfxpKdQAAAAAAADBEg4ZlC5K0JO9Msnlr7beSbJbk77vjTxpCbQAAAAAAADBUg4Zl13fbt7XWliZJa21Zkrd17df1XRgAAAAAAAAM26Bh2XEZXXLxOSu1P7vbvr23igAAAAAAAGCczFndgao6ZaWmnyf5WFX9V5KfJdkuye9mdNbZ7yY5dVhFAgAAAAAAwDCsNixLckhGn1O2st9d6f3/6c49rJ+SAAAAAAAAYHysKSxLRpdeBAAAAAAAgGlptWFZa23Q55kBAAAAAADAlCQQAwAAAAAAYMYSlgEAAAAAADBjCcsAAAAAAACYsYRlAAAAAAAAzFjCMgAAAAAAAGasOWs7oarmJfm/3dtPtNauG25JAAAAAAAAMD7WGpa11pZV1TuTzE1y2vBLAgAAAAAAgPEx6DKM3+m2m23oBavq6qr6XlVdWlWLu7Ytq+q8qvpxt92ia6+q+sequqqqLquqvcb0c3B3/o+r6uANrQsAAAAAAICZZ9Cw7A1Jbkvykaraq6o22sDrPqu1tmdrbUH3fmGSL7fWHp3ky937JHl+kkd3r8OTvD8ZDdeSHJtk7yRPSnLsioANAAAAAAAABjVoWPatJJsn+d0kFye5u6ruHfO6ZwPrODC/XuLxtCQvGtN+ehv1jSSbV9XDkzwvyXmttVtaa7cmOS/J/htYAwAAAAAAADPMoGFZDfAaVEvyxaq6pKoO79q2aa1d3+3/PMk23f62SX425rPXdm2rawcAAAAAAICBzRnwvNPWfsrAfre1dl1VPSzJeVX1w7EHW2utqlofF+rCuMOTZIcdduijSwAAAAAAAKaRgcKy1tqhfV2wtXZdt/1FVZ2T0WeO3VBVD2+tXd8ts/iL7vTrkmw/5uPbdW3XJdlnpfbzV3Gtk5OcnCQLFizoJYADAAAAAABYV+844cQsXXJHb/1tPH/TvOmYo3vrbyYbdGZZkqSqNkvy5CRbttbOWteLVdX8JLNaa3d0+89N8tYk5yY5OMnx3fbT3UfOTfKGqjo7yd5Jbu8CtS8keXtVbdGd99wkb17XegAAAAAAAMbD0iV35NS7F/TW36FZ3FtfM93AYVlVHZ3RYGuTjD537KyqOi/JI5O8rrX2+QG62SbJOVW14tpnttY+X1UXJ/loVR2W5JokB3XnfzbJAUmuSnJXkkOTpLV2S1X9bZKLu/Pe2lq7ZdB7AQAAAAAAgGTAsKyqDkpywioOfSrJe5K8LMlaw7LW2k+S7LGK9puT7LeK9pbk9avp65Qkp6ztmgAAAAAAALA6swY87y/SzSZbqf0z3fbJvVUEAAAAAAAA42TQsOzx3fZ1K7X/b7fdtp9yAAAAAAAAYPwMGpbdu5r2XdZyHAAAAAAAACatQcOyy7vt365oqKrfT3JGRpdnvKznugAAAAAAAGDoBg3L3pOkkrw+o+FYknwqyW7d/vv6LQsAAAAAAACGb6CwrLV2ZpK3Jbkvo6HZildLcnxr7ayhVQgAAAAAAABDMmfQE1trf1NVpyR5TpKtk9yU5LzW2v8MqzgAAAAAAAAYpoHDsiRprV1dVZ9Osm2S61prvxhOWQAAAAAAADB8gz6zLFX10qr6cZLrkyxOcn1VXVlVfzy06gAAAAAAAGCIBgrLquq1Sc5I8og88Jllj0pyZlW9ZmgVAgAAAAAAwJAMOrPsLzMajt2V5CNJju+2S7r2Nw2lOgAAAAAAABiiQZ9Ztk2SluT5rbWvr2isqn9J8rUkDxtCbQAAAAAAADBUg84su7jbfm+l9hXvL+qnHAAAAAAAABg/g4Zlr09yS5L3VtXOVTWnqnZK8p4k1yd53ZDqAwAAAAAAgKEZdBnG73bbl3WvlV1RVSv2W2tt0H4BAAAAAABgwgwaatXaTwEAAAAAAICpZdCw7LShVgEAAAAAAAATYKCwrLV26LALAQAAAAAAgPE2a6ILAAAAAAAAgIkiLAMAAAAAAGDGEpYBAAAAAAAwYwnLAAAAAAAAmLGEZQAAAAAAAMxYA4VlVfXnVbX1sIsBAAAAAACA8TTozLJ3Jbm2qv69qg6qqnnDLAoAAAAAAADGw7oswzg3yQFJzkry86o6uaqePpyyAAAAAAAAYPgGDct+O8mbk1ySpJJsluTVSc6vqp9U1aKqesSQagQAAAAAAIChGCgsa61d1Vp7R2vtSUl2THJUku9kNDjbMclbkvyoqs6oqocMrVoAAAAAAADo0Zz1+MwjkuyV5DFJWkYDsySZneSlGV2u8aBeqgMAAAAAAIAhGigsq6rtkhzSvXZe0Zzkrow+w+z9SX4ryTlJntd3kQAAAAAAADAMg84suzqj4diKWWRXJPlAktNba7d3bd+uqqszOvMMAAAAAAAAJr1Bw7JZSZZndObY+1trX1vNef9fks36KAwAAAAAAACGbdCw7C1J/qW19os1ndRa+9QGVwQAAAAAAADjZNaA5z0no88m+w1V9daqWtRfSQAAAAAAADA+Bp1Z9swkbTXH3tIdO7aXigAAAAAAAGCcDDqzbJWqare+CgEAAAAAAIDxttqwrKqOrap7q+redLPKVrwf035Zd+z68SkXAAAAAAAA+rO2ZRir27aV3q/so/2UAwAAAAAAAONnTWHZpUlO6/YPzmhgdtqY4y3JrUkujrAMAAAAAACAKWi1YVlr7dNJPp0kVfWs0aZ26HgVBgAAAAAAAMO2tmUYkySttZ2GXAcAAAAAAACMu9WGZVV1SkZnkx3W7a9Ja60d1m9pAAAAAAAAMFxrmll2SJL7khzW7be19CUsAwAAAAAAYEpZ2zKMtZp9AAAAAAAAmPJmre5Aa21Wa232mP01vtblolU1u6q+U1Wf6d7vXFXfrKqrqurfqmqjrn1e9/6q7vhOY/p4c9f+o6p63nrdPQAAAAAAADPaOoVcPfrzJFeMef+OJO9qrT0qya359ZKOhyW5tWt/V3dequqxSV6aZLck+yd5X1XNHqfaAQAAAAAAmCZWuwxjVf3NunTUWnvrIOdV1XZJfi/JcUmOrqpKsm+Sl3ennJZkJMn7kxzY7SfJx5P8U3f+gUnObq0tS/I/VXVVkicluWhdagYAAAAAAGBmW9Mzy0aStHXoa6CwLMlJSf4yyabd+4cmua21dk/3/tok23b72yb5WZK01u6pqtu787dN8o0xfY79zP2q6vAkhyfJDjvsMOh9AAAAAAAAMEOsbRnGGvA1kKp6QZJftNYuWa9q11Fr7eTW2oLW2oKtt956PC4JAAAAAADAFLKmmWU7D+F6T0vywqo6IMnGSR6S5N1JNq+qOd3ssu2SXNedf12S7ZNcW1VzkmyW5OYx7SuM/QwAAAAAAAAMZLVhWWvtmr4v1lp7c5I3J0lV7ZPkmNbaK6rqY0lenOTsJAcn+XT3kXO79xd1x7/SWmtVdW6SM6vqxCS/leTRSb7Vd70AAAAAAABMb2uaWfYAVbVRkgOS/HZGZ4U9QGtt0GeWrcqbkpxdVW9L8p0kH+raP5TkI1V1VZJbkry0u9b3q+qjSX6Q5J4kr2+t3bsB1wcAAAAAAGAGGigsq6qdknwlyY5rOG2dwrLW2vlJzu/2f5LkSas4Z2mSl6zm88clOW5drgkAAAAAAABjDTqz7LgkO63heNvwUgAAAAAAAGB8zRrwvGdlNBB7dfe+JXl8kk8muSqrmBUGAAAAAAAAk92gYdlW3fasMW0/SPJ/kzwqo88cAwAAAAAAgCll0LDszm57z5j9vfLrEG3/PosCAAAAAACA8TBoWPa/3XabJFd0+xckuaTbv73PogAAAAAAAGA8DBqWLU5SSZ6S5P3d/iZJHtQdP7n/0gAAAAAAAGC45gx43mEZfT7Z8tbafVV1R5I/SrJRkv9orZ06rAIBAAAAAABgWAYKy1pr9ya5d8z7TyT5xLCKAgAAAAAAgPEwUFhWVa9c2zmttdM3vBwAAAAAAAAYP4Muw/jhJG0Nx1sSYRkAAAAAAABTyqBhWZLU0KoAAAAAAACACTBoWPasld7PTrJTkiOSPDrJoT3WBAAAAAAAAONioLCstfa1VbVX1SeT/DzJC5N8rMe6AAAAAAAAYOhmbeDnN+62v7+hhQAAAAAAAMB4G2hmWVV9ZRXNGyd5XJKNktzcZ1EAAAAAAAAwHgZ9Ztk+Sdoq2qvbfqSXagAAAAAAAGAcDRqW/TS/GZYtS3Jtko8n+ec+iwIAAAAAAIDxMFBY1lrbach1AAAAAAAAwLibtb4frKrfqqrdq2rQ2WkAAAAAAAAwqQwUllXVYVV1blX9Wff+bzK6NON3kvy4qh41xBoBAAAAAABgKAadFfaKJM9McnJVbZnkLfl10LZDkr9J8sr+y4PVW758dhYtWtRbf/PmbZaFC4/srT8AAAAAAGDyGzQs27XbXpLkyd3nPpvkv5Icl2Sf3iuDtZg7996MjBzbW38jI/0FbwAAAAAAwNQw6DPLtui2v0iyW5KW5F+T/EPX/rCe6wIAAAAAAIChGzQsu7Xb/n6S53X7P07y4G7/zj6LAgAAAAAAgPEwaFh2SZJK8okkz0pye5JLkzyqO35134UBAAAAAADAsA0alv1NklsyGpjdl+SvWmv3JvmD7vh/DqE2AAAAAAAAGKo5g5zUWvt2Ve2QZJck17XWbugOvSvJB5LcPKT6AAAAAAAAYGgGCsuSpLV2V5Jvr9T2i94rAgAAAAAAgHEycFhWVfsneU2S306y8UqHW2vtkX0WBgAAAAAAAMM2UFhWVX+U5KMr3o451Lr3ree6AAAAAAAAYOhmDXjeURkNxa7t3rckl3T71yb5Ws91AQAAAAAAwNANGpY9PqMB2XNWNLTWfifJK5NsleTv+i8NAAAAAAAAhmvQsGzFM8quSnJvklTVvCSf6I6d0H9pAAAAAAAAMFwDPbMsyS1JHpZkfpKbk2yd5G+SLOmOP6r/0gAAAAAAAGC4Bg3LrspoWLZjkguTvCjJwu5YS/KD3isDAAAAAACAIRs0LDslybUZfT7ZXyf5nSTbdsduTXJk75UBAAAAAADAkA0UlrXWTk1y6or3VbVbkicn2SjJha21W4ZTHgAAAAAAAAzPasOyqqokmyZJa+2XY491779YVQ9ZcW5rrQ2zUAAAAAAAAOjbrDUcOzyjSyz+xxrO+Xx3zp/1WRQAAAAAAACMhzWFZX/cbY9fwzl/l6SSvLS3igAAAAAAAGCcrCks26XbXrCGc85f6VwAAAAAAACYMtYUlm3ZbZev4Zx7VjoXAAAAAAAApow1hWU3dtvnreGc5650LgAAAAAAAEwZc9Zw7OsZfW7ZB6rqrtbaeWMPVtWzk3wgSevOBWa45ctnZ9GiRb32OW/eZlm48Mhe+wQAAAAAgBXWFJa9J8lBSR6W5PNV9cMkP+qOPSajzymrjIZl/zjIxapq44w+A21ed+2Pt9aOraqdk5yd5KFJLknyp621X1XVvCSnJ3likpuT/HFr7equrzcnOSzJvUne2Fr7wqA3DQzH3Ln3ZmTk2F77HBnpN3wDAAAAAICxVrsMY2vtwiQjGQ3EKqPh2IHda0VQliTHttYuGvB6y5Ls21rbI8meSfavqicneUeSd7XWHpXk1oyGYOm2t3bt7+rOS1U9NslLk+yWZP8k76uq2QPWAAAAAAAAAEnW/MyytNb+NslLklyWX4dmK16XJfmj1trbBr1YG3Vn93Zu92pJ9k3y8a79tCQv6vYP7N6nO75fVVXXfnZrbVlr7X+SXJXkSYPWAQAAAAAAAMmal2FMkrTWPpHkE1X1f5LskNFw66ettRvW54LdDLBLkjwqyXuT/HeS21pr93SnXJtk225/2yQ/6+q4p6puz+hSjdsm+caYbsd+Zuy1Dk9yeJLssMMO61MuAPTO8/0AAAAAYPJYa1i2Qmvt50l+vqEXbK3dm2TPqto8yTkZXdJxKFprJyc5OUkWLFjQhnUdAFgXnu8HAAAAAJPHGpdhHKbW2m1JvprkKUk2r6oVwd12Sa7r9q9Lsn2SdMc3S3Lz2PZVfAYAAAAAAAAGMq5hWVVt3c0oS1VtkuQ5Sa7IaGj24u60g5N8uts/t3uf7vhXWmuta39pVc2rqp2TPDrJt8blJgAAAAAAAJg2Bl6GsScPT3Ja99yyWUk+2lr7TFX9IMnZVfW2JN9J8qHu/A8l+UhVXZXkliQvTZLW2ver6qNJfpDkniSv75Z3BAAAAAAAgIGNa1jWWrssyRNW0f6TJE9aRfvSJC9ZTV/HJTmu7xoBAAAAAACYOVa7DGNV/VNVPa+qNhrPggAAAAAAAGC8rOmZZYcn+WySm6vqU1V1WFU9fJzqAgAAAAAAgKFb0zKMWyXZP8kLuu0Lk7Sq+k6Sf0/yH621xcMvEQAAAAAAAIZjtTPLWmu/bK19tLX2yiTbJHlaknckmZvk2CTfrKr/rap/rqoDq+pB41MyAAAAAAAA9GNNM8vu11prSS7qXn9VVdsn+f2Mzjp7RZLDkixNIjADmGSWL5+dRYsW9dbfvHmbZeHCI3vrDwAAAABgIg0Ulq2stfazJO9L8r6q2iTJc5L8Xp+FAdCPuXPvzcjIsb31NzLSX/AGk4lgGQAAAGBmWq+wrKpmJzkoydZJvtxaOzfJuX0WBgAwngTLAAAAADPTap9ZNlZVnVhVv6yqFf/X51NJ/jXJu5JcUlX7Dqk+AAAAAAAAGJqBwrIkz0gyP8kXq2rnjC65WN1royQLh1MeAAAAAAAADM+gYdlO3faKJAu6/ZOSPLPb372/kgAAAAAAAGB8DBqWze+2dybZLUlL8rUkF3Xtm/dbFgAAAAAAAAzfoGHZDd327Un+uNu/IsnW3f4tfRYFAAAAAAAA42HOgOedl+SwJEdl9DllP2mtXVlVz+2O/3AYxQEAAJPL8uWzs2jRot76mzdvsyxceGRv/QEAAMC6GjQs+6sk2yV5WpJrkhzetT+je//v/ZcGAABMNnPn3puRkWN7629kpL/gDQAAANbHQGFZa+3GJM9fRftbkryl76IAAAAAAABgPAz0zLKq+p+q+u/VHDu9qk7rtywAAAAAAAAYvkGXYdwxSVvNsT/pjh3cS0UAAAAAAAAwTlYbllXVQ5JsvlLb9klqTNPjuu19vVcGAMCMtnz57Cxa1O/zrObN2ywLFx7Za58AAADA1LammWVHJfmbbn/FrLKrV3FeS3JdjzUBAEDmzr03IyPH9trnyEi/4RsAAAAw9a1tGcYVs8jaSu9X9v5+ygEAAGBd9D0L0wxMAABgpllTWHb+mP1jMxqYjf0G1pLcmuTi1to3+i8NAACAtel7FqYZmAAAwEyz2rCstfa1JF9Lkqp61mhT860JAAAAAACAaWNtyzAmSVpr+wy5DgAAAAAAABh3A4VlVTUryauTvDjJ9knmrXRKa609sufaAAAAAAAAYKgGCsuS/F2SY7r9WsXx1k85AAAAAAAAMH4GDcv+NKMh2dIkl3VbARkAAAAAAABT2qBh2YMyGo7t3Vr73hDrAQAAAAAAgHEza8Dzzu22dw2rEAAAAAAAABhvg84s+1SS30/yuar6hyQ/TnLP2BNaaxf0WxoAAAAAAAAM16Bh2cczugzjQ5K8bxXH2zr0BQAAAAAAAJPCugRcNbQqAAAAAAAAYAIMGpYdOtQqAAAAAIZo+fLZWbRoUW/9zZu3WRYuPLK3/gAAmDgDhWWttdOGXQgAAADAsMyde29GRo7trb+Rkf6CNwAAJtY6PWesqp6b5FlJtmitvaaqdugO/W9r7Z7eqwMAAAAAAIAhGigsq6o5ST6R5AVjml+T5PQkT0/yZ0lO6b06AAAAmIIs+QcAAFPHoDPLFib5/THvW7f9pyTPSPKHEZYBAABAEkv+AQDAVDJrwPP+NKMB2VErtZ/fbR/XV0EAAAAAAAAwXgYNy3bqth9Yqf2ObrtNL9UAAAAAAADAOBo0LFsRij1spfZnrXQcAAAAAAAApoxBw7ILu+2ZKxqq6h+S/FtGl2f8r57rAgAAAAAAgKEbNCw7Lsk9SZ6a0XAsSY5MsmnX/ne9VwYAAAAAAABDNmeQk1pr36yqFyZ5b5JHjDn0P0ne0Fr71jCKAwAAAADGz/Lls7No0aLe+ps3b7MsXHhkb/0BwDAMFJYlSWvtC0keVVWPTrJ1kptaa1cOrTIAAAAApjXBzOQzd+69GRk5trf+Rkb6G9+Zqu+fk8TPCsDKBg7LVmit/TjJj9fnYlW1fZLTk2yT0eUcT26tvbuqtszo8892SnJ1koNaa7dWVSV5d5IDktyV5JDW2re7vg5O8pau67e11k5bn5oAAAAAmBiCGVi7vn9OEj8rACtbbVhWVT9Zh35aa+2RA5x3T5K/aK19u6o2TXJJVZ2X5JAkX26tHV9VC5MsTPKmJM9P8ujutXeS9yfZuwvXjk2yIKOh2yVVdW5r7dZ1qBkAAAAAAIAZbk0zy3ZaRVtLUqt43wa5WGvt+iTXd/t3VNUVSbZNcmCSfbrTTktyfkbDsgOTnN5aa0m+UVWbV9XDu3PPa63dkiRd4LZ/krMGqQMAAAAAAACSNYdlF+SBIdhuSbZK8tMk/5vkt5LskOSWJJet64WraqckT0jyzSTbdEFakvw8o8s0JqNB2s/GfOzarm117QAAAAAAADCw1YZlrbV9VuxX1YFJPp7kta21D45pf22S9yR537pctKoenOQTSY5srf1y9NFk91+3VdVAM9UGuM7hSQ5Pkh122KGPLgEAAAAAAJhG1jSzbKy3J5mV5IyV2v81yXuTvDWjYdpaVdXcjAZlZ7TWPtk131BVD2+tXd8ts/iLrv26JNuP+fh2Xdt1+fWyjSvaz1/5Wq21k5OcnCQLFizoJYADAAAAAGBmW758dhYtWtRbf/PmbZaFC4/srT9g3Qwalj2i2x5dVW9vrd1TVXOSHN217zxIJzU6hexDSa5orZ045tC5SQ5Ocny3/fSY9jdU1dlJ9k5yexeofSHJ26tqi+685yZ584D3AgAAAAAA623u3HszMnJsb/2NjPQXvAHrbtCw7LtJfifJsUn+sqpuyujzyzbJ6HPNLh2wn6cl+dMk36uqFZ/5q4yGZB+tqsOSXJPkoO7YZ5MckOSqJHclOTRJWmu3VNXfJrm4O++trbVbBqwBAAAAAAAAkgwelr0+yZeSbJbkQUnGPgDstu74WrXWvp6kVnN4v1Wc31bXd2vtlCSnDHJdAAAAAAAAWJWBwrLW2iVV9ciMLrv4lCQPT3J9kv9KcpJZXQAAAAAAAExFg84sSxeIvWWItQAAAAAAAMC4Wm1YVlXPSJLW2gUr9tektXZBn4UBAAAAAADAsK1pZtn5Se7rzjk/SVvDuW0tfQEAAAAAAMCks7aAq1azDwAAAAAAAFPemsKyQ1ezDwAAAAAAMGksXz47ixYt6rXPefM2y8KFR/baJ5PTasOy1tppq9oHAAAAAACYTObOvTcjI8f22ufISL/hG5PXQM8Zq6oFSR6b5L9ba/81pv13kzwiyQ9aa4uHUyIAAAAAAAAMx6wBzzsxyalJtlip/SFJPpzkH3qsCQAAAAAAAMbFoGHZ47rt11Zq/89u+/h+ygEAAAAAAIDxM2hYtkm3XXlm2RYrHQcAAAAAAIApY9Cw7Jpu++6q2ixJquohSd7dtV/dc10AAAAAAAAwdIOGZeckqSQvTPKLqvpZkhu79y3JJ4dTHgAAAAAAAAzPoGHZcUkuz2hgNjfJtt22knwvyduHUh0AAAAAAAAM0ZxBTmqt3VlVT01yVJL9k2yd0Zlln03y7tbakuGVCAAAAAAAAMMxUFiWjAZmSf62ewEAAAAAAMCUN1BYVlXPT/I7Sb7dWvvMmPbfT/KEJBe31j43nBIBAAAAAABgOAadWbYoyROT7LdS+21JRpJcnERYBgAAAAAAwJQya8DzHtNtv7VS+yXddpd+ygEAAAAAAIDxM2hYNrfbbr9S+w7dduBnnwEAAAAAAMBkMWhY9uNu+6Gq2rWqZlfVY5P880rHAQAAAAAAYMoYNCw7M0kleUqSy5P8Ksn3kjw1SUtyxlCqAwAAAAAAgCEaNCw7McmXMxqYrfz6cpJ3DaU6AAAAAAAAGKKBnjXWWlteVc9L8vIkz0+ydZIbk3w2yVmttfuGVyIAAAAAAAAMx0BhWZJ0gdi/dq/7VdX8qjqwtXZm38UBAAAAAADAMA26DOMDVNWDquqPq+qTSX6R5PR+ywIAAAAAAIDhG3hmWVVtnOQFSf44o0sxbrLiUJLWf2kAAAAAAAAwXGsMy6pqXpIDMhqQ/V6SB6041G1bku8mOXtYBQIAAAAAAMCwrDYsq6ozMjqT7MErmsYcvirJo5KktfaEoVUHAAAAAAAAQ7SmmWUvy+jMsUqyNMlXkvx799oyyWVDrw4AAAAAAACGaJBnlrWMBmRnJ/lCa+2uqtpiuGUBAAAAAADA8K0pLLtnzPEXd69lVfXVjD6nDAAAAAAAAKa0WWs49rAkhyX5QpJ7M7oc48ZJ9k/yphUnVdUbquphwywSAAAAAAAAhmG1YVlr7bbW2qmttecn+T9JDk/ypST3ZTQ4W+HdSX421CoBAAAAAABgCNY0s+x+rbVbWmv/0lp7bpKHJ3ltkq/m18HZIM8+AwAAAAAAgElloLBsrNbaTa21D7bW9kuybZIjkvxn75UBAAAAAADAkK1zWDZWa+0XrbX3ttb26akeAAAAAAAAGDcbFJYBAAAAAADAVCYsAwAAAAAAYMYSlgEAAAAAADBjzZnoAmCyqFqeRYsWTXQZAAAAAADAOBKWQae1uXnmM0d67fNrX+u3PwAAAAAAoF+WYQQAAAAAAGDGEpYBAAAAAAAwY43rMoxVdUqSFyT5RWvtcV3blkn+LclOSa5OclBr7daqqiTvTnJAkruSHNJa+3b3mYOTvKXr9m2ttdPG8z6A8eNZcgAAAAAADNN4P7Psw0n+KcnpY9oWJvlya+34qlrYvX9TkucneXT32jvJ+5Ps3YVrxyZZkKQluaSqzm2t3TpudwGMm76fJec5cgAAAAAAjDWuYVlr7YKq2mml5gOT7NPtn5bk/IyGZQcmOb211pJ8o6o2r6qHd+ee11q7JUmq6rwk+yc5a9j1A2C2HwAAAAAwvYz3zLJV2aa1dn23//Mk23T72yb52Zjzru3aVtcOwDgw2w8AAAAAmE5mTXQBY3WzyFpf/VXV4VW1uKoW33jjjX11CwAAAAAAwDQxGcKyG7rlFdNtf9G1X5dk+zHnbde1ra79N7TWTm6tLWitLdh66617LxwAAAAAAICpbTIsw3hukoOTHN9tPz2m/Q1VdXaSvZPc3lq7vqq+kOTtVbVFd95zk7x5nGsGgEnFs+QAAAAAYP2Ma1hWVWcl2SfJVlV1bZJjMxqSfbSqDktyTZKDutM/m+SAJFcluSvJoUnSWrulqv42ycXdeW9trd0ybjcBAJOQZ8kBAAAAwPoZ17Cstfay1RzabxXntiSvX00/pyQ5pcfSAAB6ZbYfAAAAwNQwGZZhBACYdsz2AwAAAJgaZk10AQAAAAAAADBRzCwDAGDGsDwmAAAAsDJhGQAAM4blMQEAAICVCcsAAIAJY7YfAAAAE01YBgAATBiz/SYfASYAADDTCMsAAAC4X98BZiLEBAAAJjdhGQAAAMA6MgsTAGD6EJYBAADAJCeYmXzMwgQAmD6EZQAAADDJeb4fDEawDACsD2EZAAAAANOCYBkAWB/CMgAAAABgKMz2A2AqEJYBAAAAAEPh+X4ATAWzJroAAAAAAAAAmChmlgEAAAAAzCCWxwR4IGEZAAAAAMAM0vfymJbGBKY6YRkAAAAAAEwgs/1gYgnLAAAAAABgApntBxNLWAYAAAAAALASM/5mDmEZAAAAAADASsz4mzlmTXQBAAAAAAAAMFHMLGOVTjr++Ny+bNlElwEAAAAAADBUwjJW6fZly3LsyEivfS7quT8AAAAAAIANJSyDKcYDJQEAAAAAoD/CMphi+pzxZ7YfAAAAAAAz3ayJLgAAAAAAAAAmipllABvI0pgAAAAAAFOXsAxgA/W5NGZieUwAAAAAgPEkLANgWjLjDwAAAAAYhLAMgGmpzxl/Zvv1Q4A5ORkXAAAAYKYTlgEA48KSpZOTYHnyEWBOTsYFBuNnBQCAqUhYBgAAk4gAc3IyLpOPUGZy8rMy+fhZAQBYO2EZAAAAU45QBgbjZ2XyEWACwOQjLAMAAACAcWJ5cgCYfIRlAAAAAMCMZsYfwMwmLAMAAAAAZjRLlk4+AszJybgwXQnLAAAAAACYVCxZOjkJlpmuZk10AQAAAAAAADBRhGUAAAAAAADMWMIyAAAAAAAAZixhGQAAAAAAADOWsAwAAAAAAIAZS1gGAAAAAADAjCUsAwAAAAAAYMaa0mFZVe1fVT+qqquqauFE1wMAAAAAAMDUMmXDsqqaneS9SZ6f5LFJXlZVj53YqgAAAAAAAJhKpmxYluRJSa5qrf2ktfarJGcnOXCCawIAAAAAAGAKmcph2bZJfjbm/bVdGwAAAAAAAAykWmsTXcN6qaoXJ9m/tfbq7v2fJtm7tfaGMeccnuTw7u1jkvxo3AtlfWyV5KaJLoIHMCaTk3GZfIzJ5GRcJh9jMjkZl8nHmEw+xmRyMi6TjzGZnIzL5GNMJifjMvkYE/qyY2tt61UdmDPelfTouiTbj3m/Xdd2v9bayUlOHs+i2HBVtbi1tmCi6+DXjMnkZFwmH2MyORmXyceYTE7GZfIxJpOPMZmcjMvkY0wmJ+My+RiTycm4TD7GhPEwlZdhvDjJo6tq56raKMlLk5w7wTUBAAAAAAAwhUzZmWWttXuq6g1JvpBkdpJTWmvfn+CyAAAAAAAAmEKmbFiWJK21zyb57ETXQe8snTn5GJPJybhMPsZkcjIuk48xmZyMy+RjTCYfYzI5GZfJx5hMTsZl8jEmk5NxmXyMCUNXrbWJrgEAAAAAAAAmxFR+ZhkAAAAAAABsEGEZQ1dV21fVV6vqB1X1/ar68659y6o6r6p+3G236Nqrqv6xqq6qqsuqaq8xfd1bVZd2r3Mn6p6mup7HZIeq+mJVXdH1t9ME3daU19e4VNWzxvycXFpVS6vqRRN4a1NWzz8r7+z6uKI7pybqvqa6nsflHVV1eff644m6p6luPcZkl6q6qKqWVdUxK/W1f1X9qBuvhRNxP9NFz+NySlX9oqoun4h7mS76GpPV9cP66XFcNq6qb1XVd7t+Fk3UPU11ff751R2fXVXfqarPjPe9TBc9/065uqq+V6PfVRZPxP1MFz2Py+ZV9fGq+mGNfmd5ykTc01TX4++Ux9QDv9f/sqqOnKDbmvJ6/lk5quvj8qo6q6o2noh7mup6HpM/78bj+35O2BDCMsbDPUn+orX22CRPTvL6qnpskoVJvtxae3SSL3fvk+T5SR7dvQ5P8v4xfd3dWtuze71w3O5g+ulzTE5P8vettV2TPCnJL8bnFqalXsaltfbVFT8nSfZNcleSL47njUwjvYxJVT01ydOS7J7kcUl+J8kzx/E+ppu+xuX3kuyVZM8keyc5pqoeMo73MZ2s65jckuSNSU4Y20lVzU7y3oyO2WOTvKzrh/XTy7h0Ppxk/6FXPP31NSar64f109e4LEuyb2ttj4z+btm/qp48DvVPR33++ZUkf57kiuGWPO31PSbP6r6zLBhy3dNdn+Py7iSfb63tkmSP+JlZX72MSWvtR2O+1z8xo9/rzxmfW5iW+vq+sm3XvqC19rgks5O8dHxuYdrpa0wel+TPMvr/JPdI8oKqetT43ALTjbCMoWutXd9a+3a3f0dG/8K1bZIDk5zWnXZakhd1+wcmOb2N+kaSzavq4eNb9fTW15h0v8TmtNbO6/q6s7V21zjeyrQypJ+VFyf5nHFZPz2OSUuycZKNksxLMjfJDeN1H9NNj+Py2CQXtNbuaa0tSXJZhAHrZV3HpLX2i9baxUmWr9TVk5Jc1Vr7SWvtV0nO7vpgPfQ4LmmtXZDRL6hsgL7GZA39sB56HJfWWruzezu3e3lI+Hro88+vqtouye8l+ZfhVz599Tkm9KevcamqzZI8I8mHuvN+1Vq7bRxuYdoZ0s/Kfkn+u7V2zbDqnu56Hpc5STapqjlJHpTkf4db/fTU45jsmuSbrbW7Wmv3JPlakj8c/h0wHQnLGFc1ukTfE5J8M8k2rbXru0M/T7JNt79tkp+N+di1+fWX/42ranFVfaMsK9eLDRyT305yW1V9skaXNfn7blYAG6iHn5UVXprkrOFVOnNsyJi01i5K8tUk13evL7TW/EvNHmzgz8p3M/qv/h9UVVsleVaS7cej7ulswDFZnUH+XGM9bOC4MAR9jclK/bCBNnRcanS5v0szutrCea0147KBevhZOSnJXya5bxj1zUQ9jElL8sWquqSqDh9OlTPPBo7LzkluTHJq993+X6pq/tCKnSF6/PuX7/U92pBxaa1dl9GZTT/N6Hf721trVvLZQBv4s3J5kqdX1UOr6kFJDojv9awnYRnjpqoenOQTSY5srf1y7LHWWstg/+pyxza6TMPLk5xUVY/sv9KZo4cxmZPk6UmOyeiyco9Ickj/lc4sPf2spJs58/gkX+i9yBlmQ8ekWwJg1yTbZfR//O9bVU8fUrkzxoaOS/el5rNJLszol8+Lktw7nGpnhr7+/KJfxmXy6fF3/Wr7Yd31MS6ttXvb6JJZ2yV5Urc0EOuph7+DvSDJL1prlwyvypmlpz+/fre1tldGl11+fVU9o/9KZ5aevtvvleT9rbUnJFmSXy99xnro8Xf9RklemORjvRc5A/Xwe2WLjM582jnJbyWZX1V/MqRyZ4QevtdfkeQdGX38yOeTXBrf61lPwjLGRVXNzegffGe01j7ZNd+wYsm4brviWVfX5YH/AmC7rm3Fv+BIa+0nSc7P6L86YD30NCbXJrm0jS6XdU+ST2X0L9isp75+VjoHJTmntWbpkw3Q05j8QZJvtNGlSu9M8rkkHpi9AXr8vXJcG30WwHOSVJIrx6P+6Wgdx2R11vbnGuuop3GhR32NyWr6YT31/bPSLV/21Vjed731NCZPS/LCqro6o0v77ltV/zqkkqe9vn5Oxnyv/0VGn8H0pOFUPDP0NC7XJrl2zGzYj8d3+/XW8++U5yf5dmvNMv4bqKdxeXaS/2mt3dj9v5ZPJnnqsGqe7nr8vfKh1toTW2vPSHJrfK9nPQnLGLqqqoyue31Fa+3EMYfOTXJwt39wkk+PaX9ljXpyRqc0X19VW1TVvK7PrTL6xecH43IT00xfY5Lk4ow++2fr7rx9Y0zWW4/jssLLYqmGDdLjmPw0yTOrak73l8FnxgOz11uPv1dmV9VDuz53T7J7Rv81GutoPcZkdS5O8uiq2rn7V7Qv7fpgPfQ4LvSkrzFZQz+shx7HZeuq2rzb3yTJc5L8sPeCZ4C+xqS19ubW2nattZ0y+jvlK601MwDWQ48/J/OratMV+0mem9EltFgPPf6s/DzJz6rqMV3TfvHdfr0M4e9fvtf3oMdx+WmSJ9foUv6V0Z8V3+3XQ58/K1X1sG67Q0afV3Zmv9UyU9TobEYYnqr63ST/meR7+fU68X+V0XVoP5pkhyTXJDmotXZL94flP2X0X2HeleTQ1triqnpqkg92fcxKclJr7UPjejPTRF9j0vX1nCT/kNEZGZckOby19qtxvJ1po+dx2SnJfyXZvrXm+Qzrqcc/v2YneV9GH5rdkny+tXb0uN7MNNLjuGyc5Nvd53+Z5DWttUvH7UamkfUYk/+TZHGSh3Tn35nksa21X1bVARl9vszsJKe01o4bz3uZTnoel7OS7JNkqyQ3JDnW38PWXV9jktFw/zf6aa19dpxuZVrpcVx2yuiD6Gdn9PvKR1trbx2/O5k++vzza0yf+yQ5prX2gnG6jWmlx5+TrTI6mywZXfrvTL/r11/Pv+v3TPIvSTZK8pOM/p351nG8nWmh5zGZn9Fw5hGttdvH906ml57HZVGSP05yT5LvJHl1a23ZeN7PdNDzmPxnkocmWZ7k6Nbal8f1Zpg2hGUAAAAAAADMWJZhBAAAAAAAYMYSlgEAAAAAADBjCcsAAAAAAACYsYRlAAAAAAAAzFjCMgAAAAAAAGYsYRkAAMAkVlUbVdWPqqpV1X1V9eSVjv9xd6xV1Vcmqk4AAICpqlprE10DAAAAa1BVz03yhe7txUn2bq21qnpQkh8l2S7J8iR7ttZ+MEFlAgAATElmlgEAAExyrbUvJvlE9/Z3kryq2/+rjAZlSfLu8QjKupluvksCAADThi84AAAAU8NRSZZ0+39XVXslOaZ7f12SRVW1eVX9Q1X9d1X9qqpurKqzq+rRYzvqzrm0qm6uquXdeZ+uqj1XOm/F8o4frqq/qKqfJlma5CHDvVUAAIDxYxlGAACAKaKqFib5u+7tkiTzu/2XJvlskouS7LaKj96a0aUbf9z18/Mk26zivNuT7Npau747r435/BZjztuitXbb+t8JAADA5GFmGQAAwNRxYkafUZb8Oij7cmvt35IcmdGg7FdJ9k+ycZLHJ/lFRoOuvx3TzxuSPKrrY5Mkz+vaN0vyslVcd4uMLvm4WZJd8+sZbgAAAFPenIkuAAAAgMG01n5VVa9P8qWuaXlGg68keX633SjJ51fx8WeN2V+W5JQke2R0ScUac+y3V/HZ77fWVsxo++V6lA4AADBpCcsAAACmkNbal6vuz7b+t7X2w25/67V8dMskqaq9k3wyq/8+uPEq2i5f1zoBAACmCsswAgAATA83ddtbksxurdXYV0ZnnCXJi/LroOz3u/ZN19L30r6LBQAAmCyEZQAAANPDiqUXt0xyYlVtVVWbVNXeVfWeJH/ZHd9ozGfuyOhMsrePY50AAACTirAMAABgejgpyYolGf88yY1J7kryjYw+12yT7th/jPnM+Rl9BtkB41IhAADAJCQsAwAAmAZaa7cneUqSf0jy30l+ldElGS9JclyS07vzvpLktUmuTnJ3kq8ked74VwwAADA5VGttomsAAAAAAACACWFmGQAAAAAAADOWsAwAAAAAAIAZS1gGAAAAAADAjCUsAwAAAAAAYMYSlgEAAAAAADBjCcsAAAAAAACYsYRlAAAAAAAAzFjCMgAAAAAAAGYsYRkAAAAAAAAz1v8PpRXsMilqLBEAAAAASUVORK5CYII=", + "text/plain": [ + "<Figure size 2160x576 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + " \n", + "# set width of bar\n", + "barWidth = 0.2\n", + "fig = plt.subplots(figsize =(30, 8))\n", + " \n", + "# set height of bar\n", + "#resultGoodsperbillp.plot.bar(x=\"Year\", y=\"Accidents per billion mile\")\n", + "IT = resultGoodsperbillp[\"Accidents per billion mile\"]\n", + "ECE = resultMCperbillp[\"Accidents per billion mile\"]\n", + "CSE = resultBusperbillp[\"Accidents per billion mile\"]\n", + "CAR = resultcarperbillp[\"Accidents per billion mile\"]\n", + "by = resultBCperbillmm[\"Accidents per billion mile\"]\n", + "# Set position of bar on X axis\n", + "br1 = np.arange(len(IT))\n", + "br2 = [x + barWidth for x in br1]\n", + "br3 = [x + barWidth for x in br2]\n", + "br4 = [x + barWidth for x in br3]\n", + "br5 = [x + barWidth for x in br4]\n", + "#resultcarperbillp\n", + " \n", + "# Make the plot\n", + "plt.bar(br1, IT, color ='r', width = barWidth,\n", + " edgecolor ='grey', label ='Goods')\n", + "plt.bar(br2, ECE, color ='g', width = barWidth,\n", + " edgecolor ='grey', label ='Motorcycle')\n", + "plt.bar(br3, CSE, color ='b', width = barWidth,\n", + " edgecolor ='grey', label ='Bus')\n", + "plt.bar(br4, CAR, color ='y', width = barWidth,\n", + " edgecolor ='grey', label ='Car')\n", + "plt.bar(br5, by, width = barWidth,\n", + " edgecolor ='grey', label ='Pedal Bycyle')\n", + " \n", + " \n", + "# Adding Xticks\n", + "plt.xlabel('Year', fontweight ='bold', fontsize = 15)\n", + "plt.ylabel('Accidents/ Casuality per billion mile', fontweight ='bold', fontsize = 15)\n", + "plt.xticks([r + barWidth for r in range(len(IT))],\n", + " resultBusperbillp[\"Year\"])\n", + " \n", + "plt.legend()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+---+--------------+-------------------+----+----------+----+---------+------------------+---------+-------------+---------+------------------------+----------------------+-------+--------+-----------+-----------+--------------+-----------------+--------+----+------------+--------------------------+--------------+-----------------+----+-----------------+-----------------+-------------------------+----------------------------+-----------------------+-----------------------+--------+------------------+\n", + "| id|count_point_id|direction_of_travel|year|count_date|hour|region_id|local_authority_id|road_name|road_category|road_type|start_junction_road_name|end_junction_road_name|easting|northing| latitude| longitude|link_length_km|link_length_miles|sequence|ramp|pedal_cycles|two_wheeled_motor_vehicles|cars_and_taxis|buses_and_coaches|lgvs|hgvs_2_rigid_axle|hgvs_3_rigid_axle|hgvs_4_or_more_rigid_axle|hgvs_3_or_4_articulated_axle|hgvs_5_articulated_axle|hgvs_6_articulated_axle|all_hgvs|all_motor_vehicles|\n", + "+---+--------------+-------------------+----+----------+----+---------+------------------+---------+-------------+---------+------------------------+----------------------+-------+--------+-----------+-----------+--------------+-----------------+--------+----+------------+--------------------------+--------------+-----------------+----+-----------------+-----------------+-------------------------+----------------------------+-----------------------+-----------------------+--------+------------------+\n", + "| 1| 931537| S|2003|2003-05-14| 15| 8| 79| C| MCU| Minor| null| null| 459680| 401640|53.50807068|-1.10160464| null| null| null|null| 5| 3| 469| 6| 34| 1| 0| 0| 0| 0| 0| 1| 513|\n", + "| 2| 931537| S|2003|2003-05-14| 16| 8| 79| C| MCU| Minor| null| null| 459680| 401640|53.50807068|-1.10160464| null| null| null|null| 9| 4| 803| 5| 68| 0| 1| 0| 0| 0| 0| 1| 881|\n", + "| 3| 931537| S|2003|2003-05-14| 17| 8| 79| C| MCU| Minor| null| null| 459680| 401640|53.50807068|-1.10160464| null| null| null|null| 2| 9| 1000| 6| 81| 0| 0| 0| 0| 0| 0| 0| 1096|\n", + "| 4| 931537| S|2003|2003-05-14| 18| 8| 79| C| MCU| Minor| null| null| 459680| 401640|53.50807068|-1.10160464| null| null| null|null| 1| 5| 719| 1| 54| 1| 0| 0| 0| 0| 0| 1| 780|\n", + "| 5| 931537| S|2003|2003-05-14| 7| 8| 79| C| MCU| Minor| null| null| 459680| 401640|53.50807068|-1.10160464| null| null| null|null| 2| 2| 163| 1| 24| 1| 0| 0| 0| 0| 0| 1| 191|\n", + "| 6| 931537| S|2003|2003-05-14| 8| 8| 79| C| MCU| Minor| null| null| 459680| 401640|53.50807068|-1.10160464| null| null| null|null| 0| 0| 227| 1| 37| 9| 0| 0| 0| 0| 0| 9| 274|\n", + "| 7| 931537| S|2003|2003-05-14| 9| 8| 79| C| MCU| Minor| null| null| 459680| 401640|53.50807068|-1.10160464| null| null| null|null| 0| 0| 175| 4| 25| 2| 1| 0| 0| 0| 0| 3| 207|\n", + "| 8| 931537| S|2003|2003-05-14| 10| 8| 79| C| MCU| Minor| null| null| 459680| 401640|53.50807068|-1.10160464| null| null| null|null| 4| 0| 194| 5| 33| 3| 1| 0| 0| 0| 0| 4| 236|\n", + "| 9| 931537| S|2003|2003-05-14| 11| 8| 79| C| MCU| Minor| null| null| 459680| 401640|53.50807068|-1.10160464| null| null| null|null| 1| 2| 287| 5| 34| 5| 0| 0| 0| 0| 0| 5| 333|\n", + "| 10| 931537| S|2003|2003-05-14| 12| 8| 79| C| MCU| Minor| null| null| 459680| 401640|53.50807068|-1.10160464| null| null| null|null| 1| 2| 423| 5| 32| 6| 1| 1| 0| 0| 0| 8| 470|\n", + "| 11| 931537| N|2003|2003-05-14| 13| 8| 79| C| MCU| Minor| null| null| 459680| 401640|53.50807068|-1.10160464| null| null| null|null| 2| 2| 360| 3| 27| 3| 1| 0| 0| 0| 0| 4| 396|\n", + "| 12| 931537| N|2003|2003-05-14| 14| 8| 79| C| MCU| Minor| null| null| 459680| 401640|53.50807068|-1.10160464| null| null| null|null| 0| 1| 149| 3| 22| 0| 0| 0| 1| 0| 1| 2| 177|\n", + "| 13| 931537| N|2003|2003-05-14| 15| 8| 79| C| MCU| Minor| null| null| 459680| 401640|53.50807068|-1.10160464| null| null| null|null| 2| 0| 289| 5| 17| 2| 0| 0| 0| 0| 0| 2| 313|\n", + "| 14| 931537| N|2003|2003-05-14| 16| 8| 79| C| MCU| Minor| null| null| 459680| 401640|53.50807068|-1.10160464| null| null| null|null| 1| 0| 435| 5| 35| 1| 1| 0| 0| 0| 0| 2| 477|\n", + "| 15| 931537| N|2003|2003-05-14| 17| 8| 79| C| MCU| Minor| null| null| 459680| 401640|53.50807068|-1.10160464| null| null| null|null| 1| 1| 325| 5| 25| 0| 0| 0| 0| 0| 0| 0| 356|\n", + "| 16| 931537| N|2003|2003-05-14| 18| 8| 79| C| MCU| Minor| null| null| 459680| 401640|53.50807068|-1.10160464| null| null| null|null| 0| 2| 271| 2| 13| 0| 0| 0| 0| 0| 0| 0| 288|\n", + "| 17| 931538| N|2003|2003-07-01| 7| 8| 79| U| MCU| Minor| null| null| 460990| 406860|53.55483386|-1.08083995| null| null| null|null| 6| 2| 277| 13| 54| 4| 2| 1| 0| 5| 2| 14| 360|\n", + "| 18| 931538| N|2003|2003-07-01| 8| 8| 79| U| MCU| Minor| null| null| 460990| 406860|53.55483386|-1.08083995| null| null| null|null| 0| 3| 378| 13| 104| 18| 1| 1| 5| 7| 3| 35| 533|\n", + "| 19| 931538| N|2003|2003-07-01| 9| 8| 79| U| MCU| Minor| null| null| 460990| 406860|53.55483386|-1.08083995| null| null| null|null| 2| 2| 349| 10| 121| 17| 1| 2| 0| 1| 2| 23| 505|\n", + "| 20| 931538| N|2003|2003-07-01| 10| 8| 79| U| MCU| Minor| null| null| 460990| 406860|53.55483386|-1.08083995| null| null| null|null| 2| 0| 434| 7| 124| 29| 3| 5| 2| 1| 1| 41| 606|\n", + "+---+--------------+-------------------+----+----------+----+---------+------------------+---------+-------------+---------+------------------------+----------------------+-------+--------+-----------+-----------+--------------+-----------------+--------+----+------------+--------------------------+--------------+-----------------+----+-----------------+-----------------+-------------------------+----------------------------+-----------------------+-----------------------+--------+------------------+\n", + "only showing top 20 rows\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+---------+-------------+\n", + "|road_name|Total Traffic|\n", + "+---------+-------------+\n", + "| A23| 10608|\n", + "| A6010| 1488|\n", + "| A736| 840|\n", + "| A1009| 408|\n", + "| A644| 1248|\n", + "| A287| 1608|\n", + "| A762| 144|\n", + "| A2208| 480|\n", + "| A1421| 96|\n", + "| A197| 816|\n", + "| A3044| 624|\n", + "| A6089| 192|\n", + "| A5137| 168|\n", + "| B5231| 576|\n", + "| A1192| 24|\n", + "| B4283| 24|\n", + "| B5157| 48|\n", + "| B1099| 48|\n", + "| B3191| 24|\n", + "| B5129| 960|\n", + "| A659| 1152|\n", + "| A428| 4776|\n", + "| A194| 1320|\n", + "| A2211| 216|\n", + "| B6165| 312|\n", + "| B3048| 720|\n", + "| B1112| 792|\n", + "| B7018| 432|\n", + "| A5460| 840|\n", + "| A1039| 192|\n", + "| A826| 48|\n", + "| B251| 264|\n", + "| B5132| 456|\n", + "| B6104| 360|\n", + "| B111| 24|\n", + "| B1080| 24|\n", + "| B5117| 24|\n", + "| B5224| 24|\n", + "| A229| 3168|\n", + "| A4047| 288|\n", + "| A3066| 432|\n", + "| A501| 3708|\n", + "| A4304| 684|\n", + "| A193| 2292|\n", + "| B4287| 192|\n", + "| A3021| 360|\n", + "| B4495| 432|\n", + "| A4050| 528|\n", + "| B5211| 672|\n", + "| A589| 756|\n", + "| B5422| 432|\n", + "| A2050| 600|\n", + "| A4175| 696|\n", + "| B3021| 240|\n", + "| A4102| 360|\n", + "| A4252| 144|\n", + "| B661| 240|\n", + "| B501| 48|\n", + "| B104| 24|\n", + "| B3033| 48|\n", + "| A174| 2508|\n", + "| A47| 9660|\n", + "| B3058| 552|\n", + "| M54| 1344|\n", + "| A76| 960|\n", + "| A255| 456|\n", + "| A1085| 720|\n", + "| B3266| 264|\n", + "| A3126| 96|\n", + "| A5145| 888|\n", + "| A4233| 588|\n", + "| B383| 240|\n", + "| B3130| 384|\n", + "| B5218| 288|\n", + "| A5630| 144|\n", + "| B582| 672|\n", + "| B9088| 312|\n", + "| B4144| 96|\n", + "| B5140| 24|\n", + "| B3053| 24|\n", + "| A65| 3552|\n", + "| A3054| 960|\n", + "| B6160| 1032|\n", + "| A486| 348|\n", + "| A615| 648|\n", + "| A749| 600|\n", + "| B8034| 384|\n", + "| A433| 504|\n", + "| A5061| 312|\n", + "| A1003| 420|\n", + "| A816| 216|\n", + "| A5048| 204|\n", + "| B2157| 264|\n", + "| B402| 24|\n", + "| B777| 24|\n", + "| B2054| 24|\n", + "| A444| 2664|\n", + "| A9| 5004|\n", + "| A1155| 552|\n", + "| B6175| 504|\n", + "| B465| 480|\n", + "| A532| 312|\n", + "| A516| 600|\n", + "| A6033| 360|\n", + "| A1207| 72|\n", + "| B6097| 264|\n", + "| A935| 192|\n", + "| B2028| 1416|\n", + "| B4028| 264|\n", + "| B3133| 576|\n", + "| B6282| 408|\n", + "| B4030| 312|\n", + "| B6464| 24|\n", + "| B3164| 24|\n", + "| A424| 336|\n", + "| A46| 13176|\n", + "| A4189| 744|\n", + "| A432| 1344|\n", + "| B1008| 264|\n", + "| A692| 960|\n", + "| A4034| 600|\n", + "| A172| 1008|\n", + "| A1130| 888|\n", + "| A5149| 480|\n", + "| A5151| 288|\n", + "| B6050| 288|\n", + "| A4060| 336|\n", + "| B6107| 216|\n", + "| A8014| 96|\n", + "| A8011| 288|\n", + "| B385| 264|\n", + "| B6268| 240|\n", + "| B1134| 264|\n", + "| B451| 240|\n", + "| B4085| 48|\n", + "| B714| 48|\n", + "| B4428| 24|\n", + "| B4047| 24|\n", + "| B119| 24|\n", + "| A1237| 1032|\n", + "| B4623| 216|\n", + "| A1243| 432|\n", + "| B1028| 240|\n", + "| A5046| 264|\n", + "| A5117| 1008|\n", + "| A1062| 72|\n", + "| A251| 408|\n", + "| A1082| 72|\n", + "| A957| 168|\n", + "| A1029| 384|\n", + "| A920| 300|\n", + "| B4488| 48|\n", + "| B1065| 24|\n", + "| B208| 24|\n", + "| B1451| 24|\n", + "| A447| 576|\n", + "| B1016| 192|\n", + "| A4161| 1248|\n", + "| A2990| 768|\n", + "| A4139| 864|\n", + "| B1018| 288|\n", + "| B1007| 600|\n", + "| A4025| 312|\n", + "| A3005| 504|\n", + "| A5047| 768|\n", + "| A4208| 72|\n", + "| A4600| 816|\n", + "| B1121| 240|\n", + "| A328| 144|\n", + "| A4243| 96|\n", + "| B4194| 264|\n", + "| B4214| 456|\n", + "| B4061| 552|\n", + "| B4088| 216|\n", + "| B222| 504|\n", + "| B3168| 624|\n", + "| B9122| 24|\n", + "| B6068| 96|\n", + "| B9022| 24|\n", + "| B1353| 24|\n", + "| B3417| 72|\n", + "| B487| 24|\n", + "| B6447| 24|\n", + "| A92| 2724|\n", + "| B5066| 288|\n", + "| A6079| 216|\n", + "| B3233| 264|\n", + "| B4025| 240|\n", + "| A5060| 72|\n", + "| B6541| 288|\n", + "| A351| 528|\n", + "| A5284| 192|\n", + "| B2192| 288|\n", + "| A6129| 288|\n", + "| B3066| 288|\n", + "| B6123| 624|\n", + "| B4436| 264|\n", + "| B9176| 312|\n", + "| B778| 48|\n", + "| B2220| 24|\n", + "| B175| 384|\n", + "| B1037| 480|\n", + "| A848| 96|\n", + "| B5321| 192|\n", + "| B730| 432|\n", + "| B3323| 960|\n", + "| A176| 1176|\n", + "| B2144| 216|\n", + "| B4360| 264|\n", + "| B6427| 264|\n", + "| B1297| 288|\n", + "| A5184| 48|\n", + "| B3079| 312|\n", + "| B1434| 72|\n", + "| B377| 72|\n", + "| B1319| 24|\n", + "| A62| 5112|\n", + "| A272| 4404|\n", + "| A955| 312|\n", + "| A430| 744|\n", + "| B4620| 576|\n", + "| B4180| 168|\n", + "| A1092| 216|\n", + "| B6170| 480|\n", + "| B9080| 384|\n", + "| A5230| 360|\n", + "| B6358| 444|\n", + "| A720| 1104|\n", + "| B3124| 240|\n", + "| A3259| 168|\n", + "| B4073| 288|\n", + "| B6263| 240|\n", + "| B4363| 24|\n", + "| B6385| 48|\n", + "| B1265| 24|\n", + "| B742| 24|\n", + "| B4482| 24|\n", + "| B224| 24|\n", + "| B6178| 24|\n", + "| B4378| 24|\n", + "| A1205| 192|\n", + "| A5192| 288|\n", + "| B1248| 504|\n", + "| B5082| 192|\n", + "| B573| 312|\n", + "| B4027| 1104|\n", + "| B1377| 468|\n", + "| B195| 240|\n", + "| B3055| 888|\n", + "| B2202| 240|\n", + "| B2036| 1464|\n", + "| B3081| 744|\n", + "| A1148| 96|\n", + "| B1191| 384|\n", + "| A5250| 252|\n", + "| A2036| 144|\n", + "| A929| 216|\n", + "| B6106| 576|\n", + "| B6034| 360|\n", + "| B6323| 24|\n", + "| B3206| 24|\n", + "| B2179| 72|\n", + "| B1083| 24|\n", + "| A335| 1200|\n", + "| A1086| 576|\n", + "| A496| 720|\n", + "| A187| 768|\n", + "| A499| 600|\n", + "| A498| 240|\n", + "| A308| 4956|\n", + "| A156| 408|\n", + "| A491| 1860|\n", + "| B5166| 600|\n", + "| A896| 84|\n", + "| A609| 1308|\n", + "| A332| 1512|\n", + "| A3082| 264|\n", + "| A663| 792|\n", + "| A284| 312|\n", + "| A1146| 72|\n", + "| B1195| 336|\n", + "| B4256| 264|\n", + "| B6214| 288|\n", + "| B1133| 264|\n", + "| A1245| 240|\n", + "| B769| 72|\n", + "| B2166| 48|\n", + "| B4553| 72|\n", + "| B6445| 24|\n", + "| B6240| 24|\n", + "| A4085| 216|\n", + "| B6238| 192|\n", + "| B6141| 240|\n", + "| A305| 804|\n", + "| B5269| 600|\n", + "| A916| 168|\n", + "| A480| 216|\n", + "| A5076| 480|\n", + "| A6464| 96|\n", + "| A4115| 48|\n", + "| B6027| 264|\n", + "| B1000| 264|\n", + "| B5194| 192|\n", + "| B5101| 288|\n", + "| B6087| 48|\n", + "| B6306| 24|\n", + "| B4477| 24|\n", + "| A35| 7512|\n", + "| A2041| 336|\n", + "| A410| 696|\n", + "| A1081| 2040|\n", + "| B5156| 192|\n", + "| B158| 864|\n", + "| A673| 1056|\n", + "| A204| 204|\n", + "| B252| 60|\n", + "| B5097| 288|\n", + "| B1106| 624|\n", + "| A4106| 312|\n", + "| A5159| 72|\n", + "| B1160| 24|\n", + "| B6199| 264|\n", + "| A420| 3900|\n", + "| A6| 21144|\n", + "| A1065| 1152|\n", + "| A5300| 456|\n", + "| A198| 576|\n", + "| A1054| 144|\n", + "| A2500| 96|\n", + "| B5444| 528|\n", + "| B2114| 288|\n", + "| B4166| 504|\n", + "| B2079| 48|\n", + "| B165| 96|\n", + "| B641| 24|\n", + "| B6359| 24|\n", + "| A1214| 1032|\n", + "| A6131| 240|\n", + "| B4425| 216|\n", + "| B4499| 240|\n", + "| B1227| 216|\n", + "| B161| 216|\n", + "| B1508| 264|\n", + "| B1081| 72|\n", + "| B5229| 24|\n", + "| B400| 12|\n", + "| A5042| 12|\n", + "| A3009| 12|\n", + "| B1125| 24|\n", + "| B1381| 24|\n", + "| A6053| 552|\n", + "| A4157| 408|\n", + "| A275| 456|\n", + "| B4116| 1152|\n", + "| A4043| 216|\n", + "| A1055| 1224|\n", + "| B6186| 252|\n", + "| B6317| 1272|\n", + "| A4130| 2016|\n", + "| B5291| 228|\n", + "| A316| 2016|\n", + "| B2146| 264|\n", + "| A3123| 96|\n", + "| B1352| 168|\n", + "| B3143| 168|\n", + "| B5189| 48|\n", + "| B8019| 24|\n", + "| A4971| 24|\n", + "| B672| 24|\n", + "| A60| 6024|\n", + "| M4| 17652|\n", + "| M27| 4896|\n", + "| B6260| 324|\n", + "| A2199| 240|\n", + "| B9131| 408|\n", + "| B6052| 168|\n", + "| B4562| 816|\n", + "| A6070| 168|\n", + "| A2102| 72|\n", + "| B4280| 216|\n", + "| A3023| 384|\n", + "| A1114| 864|\n", + "| B4255| 24|\n", + "| B6245| 48|\n", + "| B6418| 48|\n", + "| B3351| 48|\n", + "| A4811| 24|\n", + "| B321| 24|\n", + "| B1218| 24|\n", + "| A483| 6228|\n", + "| A1077| 1056|\n", + "| B2034| 240|\n", + "| A422| 3216|\n", + "| A3124| 312|\n", + "| A4129| 240|\n", + "| B4291| 432|\n", + "| A4055| 696|\n", + "| B5110| 168|\n", + "| A1027| 624|\n", + "| A2042| 312|\n", + "| A2209| 144|\n", + "| B4541| 264|\n", + "| B6271| 264|\n", + "| B4296| 264|\n", + "| B6168| 24|\n", + "| A5758| 24|\n", + "| B367| 48|\n", + "| B6270| 600|\n", + "| B1299| 216|\n", + "| A119| 504|\n", + "| A311| 288|\n", + "| A1041| 456|\n", + "| A575| 828|\n", + "| A2270| 432|\n", + "| A6014| 144|\n", + "| A770| 216|\n", + "| A4104| 408|\n", + "| A4219| 72|\n", + "| A6058| 120|\n", + "| B6267| 264|\n", + "| B349| 288|\n", + "| B3070| 264|\n", + "| B3289| 288|\n", + "| B7079| 24|\n", + "| B5431| 24|\n", + "| A68| 3204|\n", + "| A406| 8712|\n", + "| A4042| 1752|\n", + "| B5394| 168|\n", + "| A1034| 216|\n", + "| A232| 2928|\n", + "| B3440| 216|\n", + "| A5268| 504|\n", + "| A6011| 552|\n", + "| B3091| 240|\n", + "| B4128| 480|\n", + "| A6104| 648|\n", + "| B4577| 168|\n", + "| A743| 48|\n", + "| B3148| 216|\n", + "| B5240| 264|\n", + "| B1101| 48|\n", + "| B489| 72|\n", + "| A6175| 216|\n", + "| A570| 2400|\n", + "| A1068| 936|\n", + "| B4393| 216|\n", + "| A166| 672|\n", + "| B4211| 576|\n", + "| A1028| 96|\n", + "| A2014| 300|\n", + "| B5322| 228|\n", + "| M66| 936|\n", + "| B380| 240|\n", + "| A804| 216|\n", + "| A5020| 264|\n", + "| B4353| 240|\n", + "| B6250| 288|\n", + "| B5192| 264|\n", + "| A6183| 24|\n", + "| B4454| 96|\n", + "| A61| 12852|\n", + "| A19| 12108|\n", + "| A4090| 408|\n", + "| A1158| 324|\n", + "| A553| 888|\n", + "| A646| 1152|\n", + "| A645| 1056|\n", + "| A535| 288|\n", + "| B6275| 456|\n", + "| A713| 552|\n", + "| A5194| 72|\n", + "| B3300| 312|\n", + "| B1341| 240|\n", + "| B3359| 24|\n", + "| B3350| 24|\n", + "| B1306| 24|\n", + "| B4506| 24|\n", + "| B4555| 48|\n", + "| B472| 24|\n", + "| M45| 240|\n", + "| A37| 3144|\n", + "| A1(M)| 3672|\n", + "| B3215| 480|\n", + "| A6136| 456|\n", + "| A2025| 96|\n", + "| B2099| 240|\n", + "| A6141| 168|\n", + "| B1061| 288|\n", + "| A888| 60|\n", + "| A3033| 96|\n", + "| B6019| 288|\n", + "| B245| 336|\n", + "| B3272| 528|\n", + "| A4207| 120|\n", + "| B1223| 24|\n", + "| A4193| 24|\n", + "| B7007| 24|\n", + "| B9150| 24|\n", + "| A5509| 24|\n", + "| B3061| 24|\n", + "| B4386| 768|\n", + "| B6200| 192|\n", + "| A461| 2832|\n", + "| A273| 792|\n", + "| B2128| 888|\n", + "| A3094| 240|\n", + "| A1302| 360|\n", + "| B6303| 288|\n", + "| A672| 360|\n", + "| B4417| 264|\n", + "| A218| 372|\n", + "| A741| 240|\n", + "| A4231| 144|\n", + "| A6060| 72|\n", + "| B3110| 288|\n", + "| B2450| 240|\n", + "| B4135| 24|\n", + "| A221| 792|\n", + "| A16| 3672|\n", + "| A21| 4416|\n", + "| A5025| 840|\n", + "| A2212| 288|\n", + "| A6009| 528|\n", + "| A131| 1536|\n", + "| A467| 1200|\n", + "| A4127| 768|\n", + "| B4019| 432|\n", + "| A4160| 576|\n", + "| B1118| 240|\n", + "| B4455| 1344|\n", + "| B3152| 240|\n", + "| B4556| 240|\n", + "| B1151| 72|\n", + "| B992| 408|\n", + "| A3040| 144|\n", + "| A2039| 24|\n", + "| B900| 216|\n", + "| B3180| 216|\n", + "| A1(T)| 144|\n", + "| B6395| 24|\n", + "| B6461| 24|\n", + "| B4357| 24|\n", + "| B6433| 24|\n", + "| M53| 2808|\n", + "| A67| 1008|\n", + "| A343| 1080|\n", + "| M40| 6600|\n", + "| A103| 600|\n", + "| A118| 1908|\n", + "| A380| 2556|\n", + "| A264| 2976|\n", + "| A3211| 2184|\n", + "| A687| 72|\n", + "| B6392| 336|\n", + "| A4312| 240|\n", + "| A319| 408|\n", + "| A5106| 120|\n", + "| A5098| 312|\n", + "| B6443| 504|\n", + "| B1331| 264|\n", + "| A894| 156|\n", + "| A867| 72|\n", + "| B4052| 240|\n", + "| B1074| 312|\n", + "| B324| 264|\n", + "| B4319| 264|\n", + "| B4237| 144|\n", + "| B4550| 96|\n", + "| B4169| 24|\n", + "| B6411| 48|\n", + "| B1383| 48|\n", + "| A244| 1596|\n", + "| A1094| 360|\n", + "| A631| 3216|\n", + "| A661| 864|\n", + "| B3001| 984|\n", + "| A963| 204|\n", + "| A379| 3504|\n", + "| A5114| 72|\n", + "| B5246| 528|\n", + "| A5012| 264|\n", + "| A4400| 756|\n", + "| A5181| 144|\n", + "| A866| 72|\n", + "| A1044| 240|\n", + "| B3163| 528|\n", + "| B5112| 264|\n", + "| B4596| 264|\n", + "| B4081| 552|\n", + "| A810| 96|\n", + "| B2182| 288|\n", + "| B6130| 24|\n", + "| B9133| 24|\n", + "| B5320| 48|\n", + "| B5036| 24|\n", + "| A500| 3240|\n", + "| B4176| 1488|\n", + "| B4306| 432|\n", + "| A616| 2136|\n", + "| A565| 3036|\n", + "| A600| 1368|\n", + "| A711| 360|\n", + "| A730| 168|\n", + "| A2045| 168|\n", + "| B186| 720|\n", + "| A847| 120|\n", + "| B5213| 360|\n", + "| B275| 72|\n", + "| B4326| 12|\n", + "| B4219| 24|\n", + "| B1322| 72|\n", + "| B3387| 48|\n", + "| A320| 1992|\n", + "| A735| 456|\n", + "| A17| 2376|\n", + "| A628| 1992|\n", + "| A6052| 216|\n", + "| B5088| 240|\n", + "| A4079| 48|\n", + "| B783| 264|\n", + "| A58M| 312|\n", + "| A6539| 144|\n", + "| B232| 72|\n", + "| B6349| 24|\n", + "| B5065| 24|\n", + "| B1355| 24|\n", + "| A2| 12012|\n", + "| A6101| 264|\n", + "| A143| 2976|\n", + "| A638| 4716|\n", + "| A8| 4092|\n", + "| A4006| 336|\n", + "| A400| 1428|\n", + "| A5102| 768|\n", + "| B802| 432|\n", + "| B6389| 240|\n", + "| A607| 2676|\n", + "| B2123| 240|\n", + "| A1290| 312|\n", + "| A5190| 792|\n", + "| A3027| 264|\n", + "| A5063| 600|\n", + "| A8006| 48|\n", + "| B482| 432|\n", + "| B5017| 792|\n", + "| B6354| 288|\n", + "| A2101| 120|\n", + "| A2206| 72|\n", + "| B5013| 504|\n", + "| B1167| 288|\n", + "| B6036| 264|\n", + "| B4501| 24|\n", + "| B3330| 24|\n", + "| B5128| 24|\n", + "| A59| 8676|\n", + "| A196| 408|\n", + "| A43| 3840|\n", + "| A835| 432|\n", + "| A1166| 312|\n", + "| A658| 1680|\n", + "| A5189| 288|\n", + "| M275| 312|\n", + "| A6022| 144|\n", + "| A293| 264|\n", + "| A948| 228|\n", + "| B1283| 264|\n", + "| A813| 72|\n", + "| B2004| 48|\n", + "| B2199| 24|\n", + "| B3158| 24|\n", + "| B1213| 24|\n", + "| B3142| 48|\n", + "| A6105| 408|\n", + "| A18| 4032|\n", + "| A228| 1956|\n", + "| B3007| 480|\n", + "| A621| 864|\n", + "| B7017| 408|\n", + "| A4100| 408|\n", + "| A3051| 264|\n", + "| A6076| 336|\n", + "| B9113| 456|\n", + "| B3016| 528|\n", + "| A1341| 24|\n", + "| B2103| 24|\n", + "| A66| 6096|\n", + "| A324| 480|\n", + "| A530| 1368|\n", + "| B1043| 504|\n", + "| A1203| 1128|\n", + "| A5082| 312|\n", + "| A73| 1368|\n", + "| A4148| 840|\n", + "| A6050| 48|\n", + "| A1301| 288|\n", + "| A195| 624|\n", + "| B2165| 288|\n", + "| A126| 960|\n", + "| B1356| 288|\n", + "| B3390| 288|\n", + "| B4343| 168|\n", + "| A722| 72|\n", + "| A6188| 72|\n", + "| B6305| 48|\n", + "| B3099| 24|\n", + "| A595| 2664|\n", + "| B4236| 408|\n", + "| B3230| 384|\n", + "| A314| 312|\n", + "| B4193| 240|\n", + "| A394| 600|\n", + "| B6023| 504|\n", + "| B4286| 144|\n", + "| B317| 240|\n", + "| A161| 648|\n", + "| B221| 240|\n", + "| B671| 552|\n", + "| B3247| 312|\n", + "| A6026| 120|\n", + "| B7029| 432|\n", + "| A4099| 96|\n", + "| A5040| 156|\n", + "| A5045| 72|\n", + "| B259| 240|\n", + "| B8033| 48|\n", + "| B4444| 48|\n", + "| B912| 48|\n", + "| B290| 48|\n", + "| B4338| 120|\n", + "| B6221| 24|\n", + "| B4579| 24|\n", + "| B5062| 792|\n", + "| A32| 1800|\n", + "| B173| 168|\n", + "| A4311| 216|\n", + "| A1321| 216|\n", + "| B6295| 240|\n", + "| A819| 84|\n", + "| A5088| 288|\n", + "| B5282| 264|\n", + "| B4557| 252|\n", + "| A6063| 72|\n", + "| B471| 24|\n", + "| B183| 48|\n", + "| A484| 1992|\n", + "| A947| 588|\n", + "| B526| 1344|\n", + "| B5377| 264|\n", + "| B857| 432|\n", + "| A822| 252|\n", + "| A540| 1416|\n", + "| A5267| 240|\n", + "| B9057| 420|\n", + "| B1242| 360|\n", + "| B1064| 288|\n", + "| B2132| 288|\n", + "| B1111| 264|\n", + "| B1264| 48|\n", + "| B6028| 48|\n", + "| A903| 24|\n", + "| B3345| 24|\n", + "| A3400| 1776|\n", + "| B2130| 576|\n", + "| A134| 3000|\n", + "| B4337| 264|\n", + "| A3062| 144|\n", + "| A106| 1020|\n", + "| B6233| 240|\n", + "| A601| 1452|\n", + "| A1019| 96|\n", + "| A185| 384|\n", + "| A409| 408|\n", + "| B269| 288|\n", + "| B3078| 1200|\n", + "| B4145| 48|\n", + "| B6346| 24|\n", + "| B6266| 60|\n", + "| A165| 2760|\n", + "| A4114| 552|\n", + "| A407| 408|\n", + "| A6003| 1608|\n", + "| A22| 4188|\n", + "| B4278| 144|\n", + "| A1152| 120|\n", + "| A285| 384|\n", + "| B5425| 168|\n", + "| B827| 384|\n", + "| A372| 624|\n", + "| A5038| 1488|\n", + "| A5008| 312|\n", + "| B4397| 312|\n", + "| B4161| 216|\n", + "| A1179| 96|\n", + "| A4185| 120|\n", + "| A5091| 72|\n", + "| B4187| 240|\n", + "| A5052| 72|\n", + "| B168| 24|\n", + "| A4051| 792|\n", + "| B6157| 288|\n", + "| B829| 480|\n", + "| B4096| 240|\n", + "| A1303| 972|\n", + "| A4107| 360|\n", + "| A699| 132|\n", + "| B6051| 216|\n", + "| A625| 672|\n", + "| A4126| 192|\n", + "| A2038| 240|\n", + "| A945| 72|\n", + "| A6001| 384|\n", + "| B5010| 528|\n", + "| B6230| 480|\n", + "| A5141| 360|\n", + "| B469| 264|\n", + "| B6530| 24|\n", + "| A2690| 48|\n", + "| B2124| 24|\n", + "| B6313| 144|\n", + "| B1255| 168|\n", + "| B9071| 528|\n", + "| B4204| 576|\n", + "| A1110| 72|\n", + "| A295| 276|\n", + "| B2050| 528|\n", + "| B5021| 48|\n", + "| B4580| 48|\n", + "| B3374| 24|\n", + "| B5421| 12|\n", + "| A2700| 24|\n", + "| B6465| 216|\n", + "| B1051| 552|\n", + "| A248| 240|\n", + "| A4440| 1344|\n", + "| A761| 648|\n", + "| B530| 552|\n", + "| A4172| 72|\n", + "| B664| 552|\n", + "| B9170| 528|\n", + "| A728| 336|\n", + "| B6480| 792|\n", + "| A883| 96|\n", + "| B506| 192|\n", + "| B191| 264|\n", + "| B3075| 264|\n", + "| B6412| 264|\n", + "| B3040| 264|\n", + "| A412| 2088|\n", + "| B3084| 600|\n", + "| B7052| 24|\n", + "| B1441| 24|\n", + "| A36| 4440|\n", + "| B4009| 1224|\n", + "| A694| 600|\n", + "| A506| 792|\n", + "| A690| 2388|\n", + "| B5085| 432|\n", + "| B1452| 240|\n", + "| A1022| 288|\n", + "| B1414| 552|\n", + "| A966| 120|\n", + "| B3303| 768|\n", + "| A9011| 48|\n", + "| A38M| 288|\n", + "| B467| 360|\n", + "| B4524| 264|\n", + "| A3000| 48|\n", + "| B669| 24|\n", + "| A313| 312|\n", + "| B3046| 528|\n", + "| B743| 816|\n", + "| A915| 672|\n", + "| A537| 1416|\n", + "| A212| 1944|\n", + "| A183| 1728|\n", + "| A4136| 552|\n", + "| A642| 1656|\n", + "| B214| 240|\n", + "| B1115| 1248|\n", + "| A952| 156|\n", + "| B7020| 792|\n", + "| B4192| 768|\n", + "| A4143| 120|\n", + "| A978| 240|\n", + "| A5116| 240|\n", + "| B4086| 864|\n", + "| B4515| 240|\n", + "| B1094| 264|\n", + "| A751| 48|\n", + "| B5390| 48|\n", + "| B4518| 24|\n", + "| B7045| 48|\n", + "| B5477| 24|\n", + "| B4151| 384|\n", + "| A146| 1488|\n", + "| B6113| 216|\n", + "| B4333| 216|\n", + "| A4094| 600|\n", + "| A707| 144|\n", + "| A718| 216|\n", + "| B6376| 96|\n", + "| B740| 24|\n", + "| M62| 10248|\n", + "| B741| 792|\n", + "| B676| 480|\n", + "| B2118| 216|\n", + "| A340| 1056|\n", + "| A1304| 528|\n", + "| B2150| 600|\n", + "| B507| 240|\n", + "| A639| 1704|\n", + "| B172| 168|\n", + "| B1052| 312|\n", + "| B4640| 264|\n", + "| B6481| 168|\n", + "| A1172| 72|\n", + "| A6187| 552|\n", + "| B1082| 240|\n", + "| A4133| 504|\n", + "| A930| 288|\n", + "| A1309| 528|\n", + "| B1325| 240|\n", + "| A5480| 216|\n", + "| A4242| 72|\n", + "| B4155| 264|\n", + "| B1260| 288|\n", + "| B2205| 312|\n", + "| B3187| 96|\n", + "| B5209| 48|\n", + "| A291| 240|\n", + "| A3100| 1224|\n", + "| M6| 17688|\n", + "| A6072| 408|\n", + "| B4380| 408|\n", + "| B1002| 336|\n", + "| A240| 1464|\n", + "| A4089| 288|\n", + "| B679| 216|\n", + "| B4080| 312|\n", + "| A6045| 216|\n", + "| A4535| 120|\n", + "| B952| 432|\n", + "| B4285| 408|\n", + "| B1039| 288|\n", + "| B323| 240|\n", + "| B1174| 24|\n", + "| B1440| 24|\n", + "| A880| 24|\n", + "| A371| 1392|\n", + "| A127| 4872|\n", + "| A33| 3744|\n", + "| B6918| 192|\n", + "| A109| 576|\n", + "| B160| 504|\n", + "| A3093| 216|\n", + "| A611| 1248|\n", + "| B3260| 240|\n", + "| A4221| 48|\n", + "| A4205| 48|\n", + "| B6273| 576|\n", + "| B| 444|\n", + "| A1048| 72|\n", + "| A41| 16908|\n", + "| A1232| 360|\n", + "| A5054| 288|\n", + "| A547| 1488|\n", + "| B3082| 672|\n", + "| A347| 960|\n", + "| A5053| 216|\n", + "| A1096| 288|\n", + "| B478| 504|\n", + "| A5101| 72|\n", + "| A139| 192|\n", + "| A3011| 168|\n", + "| A6186| 168|\n", + "| B6119| 264|\n", + "| B3011| 264|\n", + "| B7086| 48|\n", + "| A388| 1800|\n", + "| A57| 11004|\n", + "| A875| 96|\n", + "| A4086| 420|\n", + "| A2029| 276|\n", + "| A54| 2040|\n", + "| A153| 1248|\n", + "| A3052| 1248|\n", + "| A3079| 144|\n", + "| A4095| 960|\n", + "| B4059| 288|\n", + "| A2032| 288|\n", + "| A1095| 72|\n", + "| B413| 264|\n", + "| A179| 576|\n", + "| A1263| 120|\n", + "| B668| 216|\n", + "| A820| 72|\n", + "| B656| 528|\n", + "| B5381| 264|\n", + "| B6528| 312|\n", + "| B682| 528|\n", + "| B1269| 288|\n", + "| B519| 312|\n", + "| B6025| 264|\n", + "| B670| 264|\n", + "| B4233| 48|\n", + "| A6004| 480|\n", + "| A322| 3456|\n", + "| A739| 624|\n", + "| A5058| 1704|\n", + "| A495| 792|\n", + "| B2029| 264|\n", + "| A323| 1320|\n", + "| A4109| 384|\n", + "| B2163| 528|\n", + "| B4507| 288|\n", + "| A9012| 48|\n", + "| A2011| 480|\n", + "| A910| 168|\n", + "| A4171| 72|\n", + "| B365| 336|\n", + "| B5159| 288|\n", + "| B6017| 216|\n", + "| A2300| 180|\n", + "| B5057| 192|\n", + "| B4389| 24|\n", + "| B1411| 48|\n", + "| B4504| 408|\n", + "| A206| 2088|\n", + "| B6067| 264|\n", + "| A940| 108|\n", + "| A1231| 2280|\n", + "| A4178| 240|\n", + "| B1057| 480|\n", + "| A2000| 72|\n", + "| A270| 1320|\n", + "| M602| 552|\n", + "| A1098| 264|\n", + "| A926| 408|\n", + "| B3107| 240|\n", + "| A4137| 96|\n", + "| A6095| 216|\n", + "| B4084| 552|\n", + "| B1224| 312|\n", + "| B5371| 48|\n", + "| B4371| 72|\n", + "| B1447| 24|\n", + "| B509| 24|\n", + "| A437| 864|\n", + "| A321| 1320|\n", + "| A38| 30600|\n", + "| A3098| 384|\n", + "| B3012| 216|\n", + "| A257| 408|\n", + "| A384| 168|\n", + "| A6185| 264|\n", + "| B893| 324|\n", + "| B3008| 48|\n", + "| A148| 2172|\n", + "| A52| 8784|\n", + "| A6120| 1800|\n", + "| B959| 312|\n", + "| M18| 2292|\n", + "| A300| 312|\n", + "| A1168| 240|\n", + "| A4180| 696|\n", + "| A6048| 216|\n", + "| B5187| 264|\n", + "| B6310| 48|\n", + "| B3269| 504|\n", + "| A518| 1968|\n", + "| M42| 4944|\n", + "| B1029| 840|\n", + "| A5134| 408|\n", + "| A306| 528|\n", + "| A539| 600|\n", + "| A6181| 648|\n", + "| A1017| 504|\n", + "| A821| 168|\n", + "| B5119| 288|\n", + "| B3297| 240|\n", + "| B3087| 264|\n", + "| B1516| 312|\n", + "| B1315| 288|\n", + "| A401| 780|\n", + "| B5169| 264|\n", + "| B4057| 96|\n", + "| B6378| 48|\n", + "| B4070| 480|\n", + "| A29| 1944|\n", + "| B1464| 216|\n", + "| A3047| 1080|\n", + "| B5493| 432|\n", + "| B304| 264|\n", + "| B3178| 576|\n", + "| A1133| 384|\n", + "| A262| 504|\n", + "| B5139| 240|\n", + "| A138| 408|\n", + "| B6026| 528|\n", + "| B1256| 528|\n", + "| B1354| 288|\n", + "| B4638| 264|\n", + "| B1332| 288|\n", + "| B5433| 48|\n", + "| B1211| 72|\n", + "| B3391| 24|\n", + "| B6352| 72|\n", + "| B2127| 72|\n", + "| B4630| 24|\n", + "| B1173| 24|\n", + "| B6391| 24|\n", + "| A479| 360|\n", + "| B3293| 216|\n", + "| A975| 120|\n", + "| A691| 744|\n", + "| A242| 96|\n", + "| A4501| 96|\n", + "| B4082| 360|\n", + "| B4003| 264|\n", + "| A8003| 72|\n", + "| B1079| 24|\n", + "| A449| 6720|\n", + "| A572| 1656|\n", + "| B4020| 168|\n", + "| A5205| 312|\n", + "| A4113| 264|\n", + "| B1206| 480|\n", + "| B3254| 1464|\n", + "| B916| 384|\n", + "| B4017| 312|\n", + "| B6219| 240|\n", + "| B6419| 72|\n", + "| B143| 36|\n", + "| A514| 1104|\n", + "| B6163| 240|\n", + "| A4118| 456|\n", + "| A653| 1524|\n", + "| B1102| 312|\n", + "| B765| 384|\n", + "| A3202| 276|\n", + "| A924| 216|\n", + "| A3122| 312|\n", + "| A383| 408|\n", + "| A6016| 72|\n", + "| A1078| 324|\n", + "| M49| 168|\n", + "| A6142| 72|\n", + "| B4282| 264|\n", + "| B2131| 264|\n", + "| B6243| 336|\n", + "| B3385| 216|\n", + "| B6167| 552|\n", + "| B485| 24|\n", + "| A504| 1272|\n", + "| B243| 552|\n", + "| B1145| 1992|\n", + "| B1022| 1032|\n", + "| A517| 216|\n", + "| A513| 864|\n", + "| A278| 288|\n", + "| B5074| 504|\n", + "| B4391| 264|\n", + "| B3153| 48|\n", + "| B7083| 48|\n", + "| B6252| 24|\n", + "| A686| 552|\n", + "| A635| 4224|\n", + "| B361| 504|\n", + "| B789| 504|\n", + "| A415| 1224|\n", + "| A271| 576|\n", + "| A1107| 156|\n", + "| A809| 336|\n", + "| B1032| 444|\n", + "| A2008| 48|\n", + "| B1062| 24|\n", + "| B8087| 24|\n", + "| B282| 24|\n", + "| A361| 7800|\n", + "| B5378| 360|\n", + "| A4018| 864|\n", + "| A4226| 360|\n", + "| B1013| 816|\n", + "| A2001| 96|\n", + "| B6002| 240|\n", + "| B4060| 528|\n", + "| B3432| 288|\n", + "| B2030| 72|\n", + "| B5078| 24|\n", + "| B6274| 168|\n", + "| A3072| 936|\n", + "| A865| 360|\n", + "| A6002| 552|\n", + "| A5152| 672|\n", + "| A490| 552|\n", + "| A1080| 216|\n", + "| B2135| 480|\n", + "| A1300| 312|\n", + "| A3074| 240|\n", + "| A5049| 408|\n", + "| B6399| 420|\n", + "| A873| 72|\n", + "| A960| 120|\n", + "| B1312| 240|\n", + "| B3184| 48|\n", + "| B1135| 96|\n", + "| B5173| 24|\n", + "| A3280| 24|\n", + "| A53| 3108|\n", + "| A11| 4620|\n", + "| A408| 384|\n", + "| A393| 348|\n", + "| A6037| 792|\n", + "| A5019| 144|\n", + "| A5001| 192|\n", + "| B4514| 240|\n", + "| B510| 312|\n", + "| B3430| 288|\n", + "| A4098| 48|\n", + "| B1291| 24|\n", + "| B7015| 24|\n", + "| B817| 48|\n", + "| B6004| 24|\n", + "| B4329| 504|\n", + "| A162| 840|\n", + "| A533| 3120|\n", + "| A469| 1872|\n", + "| B6413| 540|\n", + "| A1046| 480|\n", + "| M74| 2232|\n", + "| A557| 1536|\n", + "| B3137| 192|\n", + "| B4055| 792|\n", + "| A977| 408|\n", + "| B1035| 240|\n", + "| B6280| 312|\n", + "| A1M| 48|\n", + "| B3090| 120|\n", + "| B2212| 24|\n", + "| B5069| 120|\n", + "| B326| 48|\n", + "| B4331| 48|\n", + "| B5408| 24|\n", + "| B803| 744|\n", + "| A27| 13200|\n", + "| B255| 216|\n", + "| A488| 792|\n", + "| A655| 624|\n", + "| B109| 288|\n", + "| A1037| 144|\n", + "| A1099| 168|\n", + "| A885| 120|\n", + "| A710| 168|\n", + "| A1251| 120|\n", + "| B1026| 1056|\n", + "| B3196| 24|\n", + "| B862| 24|\n", + "| B1292| 24|\n", + "| B3049| 144|\n", + "| B2178| 24|\n", + "| A404| 6192|\n", + "| A5204| 348|\n", + "| M57| 1392|\n", + "| A719| 396|\n", + "| A111| 792|\n", + "| B1181| 240|\n", + "| B4146| 552|\n", + "| A1140| 72|\n", + "| A136| 48|\n", + "| B6137| 312|\n", + "| B6242| 264|\n", + "| B3333| 264|\n", + "| B651| 312|\n", + "| B5127| 48|\n", + "| B905| 24|\n", + "| B4208| 48|\n", + "| B3252| 24|\n", + "| B3114| 24|\n", + "| U| 1308888|\n", + "| A1006| 240|\n", + "| A141| 1392|\n", + "| A99| 288|\n", + "| B880| 480|\n", + "| A669| 888|\n", + "| A318| 240|\n", + "| A4165| 288|\n", + "| B6112| 312|\n", + "| B4598| 288|\n", + "| B6057| 264|\n", + "| B581| 48|\n", + "| A868| 24|\n", + "| B6294| 24|\n", + "| A82| 2940|\n", + "| A1023| 456|\n", + "| A515| 1560|\n", + "| A4540| 2784|\n", + "| A89| 1368|\n", + "| A579| 1224|\n", + "| A640| 816|\n", + "| A5147| 480|\n", + "| A1090| 216|\n", + "| A1308| 168|\n", + "| B4375| 264|\n", + "| B142| 312|\n", + "| B3120| 96|\n", + "| B4257| 24|\n", + "| A560| 2436|\n", + "| A832| 576|\n", + "| A292| 564|\n", + "| B5217| 192|\n", + "| A678| 792|\n", + "| A373| 144|\n", + "| B4112| 456|\n", + "| A8015| 48|\n", + "| A823| 672|\n", + "| A5272| 600|\n", + "| A1434| 336|\n", + "| A779| 192|\n", + "| A3110| 96|\n", + "| B4595| 288|\n", + "| A986| 48|\n", + "| B6225| 72|\n", + "| B6397| 456|\n", + "| A4421| 360|\n", + "| B6403| 720|\n", + "| A466| 1104|\n", + "| A1031| 480|\n", + "| A1064| 288|\n", + "| A698| 672|\n", + "| B3135| 504|\n", + "| A827| 276|\n", + "| A5128| 432|\n", + "| B729| 384|\n", + "| B4005| 360|\n", + "| A4138| 456|\n", + "| A849| 156|\n", + "| B4569| 504|\n", + "| A1184| 504|\n", + "| B3071| 168|\n", + "| B6226| 240|\n", + "| A1156| 1044|\n", + "| A4020| 2364|\n", + "| A369| 672|\n", + "| A4230| 192|\n", + "| A5124| 96|\n", + "| A705| 192|\n", + "| B1456| 312|\n", + "| B1339| 264|\n", + "| B3339| 48|\n", + "| B6012| 48|\n", + "| B1461| 24|\n", + "| M11| 3504|\n", + "| A130| 2496|\n", + "| B4540| 288|\n", + "| A6109| 576|\n", + "| B4035| 1344|\n", + "| A472| 1608|\n", + "| A4119| 1992|\n", + "| A4031| 780|\n", + "| A223| 1008|\n", + "| B5023| 672|\n", + "| A4301| 144|\n", + "| B781| 504|\n", + "| B511| 192|\n", + "| A4111| 168|\n", + "| B6139| 504|\n", + "| B5135| 552|\n", + "| B1021| 576|\n", + "| B4173| 24|\n", + "| B5409| 24|\n", + "| A366| 408|\n", + "| A107| 1032|\n", + "| B1098| 408|\n", + "| A70| 1152|\n", + "| A464| 384|\n", + "| A403| 408|\n", + "| A709| 288|\n", + "| A831| 156|\n", + "| A4128| 336|\n", + "| A133| 2340|\n", + "| A921| 672|\n", + "| A637| 552|\n", + "| A5120| 624|\n", + "| A160| 216|\n", + "| A6098| 144|\n", + "| A5033| 144|\n", + "| B6524| 240|\n", + "| B6234| 264|\n", + "| A747| 96|\n", + "| B3116| 48|\n", + "| B1075| 24|\n", + "| B319| 24|\n", + "| A367| 1164|\n", + "| B5420| 240|\n", + "| B6301| 192|\n", + "| B997| 384|\n", + "| A3216| 504|\n", + "| A1122| 528|\n", + "| B655| 720|\n", + "| A980| 156|\n", + "| A181| 576|\n", + "| A448| 1440|\n", + "| B9077| 408|\n", + "| B3261| 240|\n", + "| A759| 360|\n", + "| A5067| 408|\n", + "| B5047| 168|\n", + "| B4053| 120|\n", + "| B4413| 528|\n", + "| B3401| 240|\n", + "| B121| 12|\n", + "| A386| 3504|\n", + "| A4065| 144|\n", + "| A2034| 312|\n", + "| B5243| 216|\n", + "| A5007| 408|\n", + "| A551| 1140|\n", + "| A6008| 1008|\n", + "| A3025| 456|\n", + "| A5227| 96|\n", + "| B1147| 624|\n", + "| B1050| 312|\n", + "| B4520| 192|\n", + "| A5115| 72|\n", + "| B521| 240|\n", + "| B6053| 312|\n", + "| B3034| 504|\n", + "| B4058| 660|\n", + "| A5068| 96|\n", + "| B758| 72|\n", + "| B7000| 72|\n", + "| B531| 24|\n", + "| A1067| 1248|\n", + "| A438| 2160|\n", + "| A102| 1152|\n", + "| A137| 1176|\n", + "| A4048| 672|\n", + "| A502| 432|\n", + "| A1045| 72|\n", + "| A7066| 72|\n", + "| A746| 144|\n", + "| A2213| 288|\n", + "| B1502| 384|\n", + "| B9070| 24|\n", + "| B5392| 24|\n", + "| A1101| 1548|\n", + "| A474| 1128|\n", + "| B2102| 672|\n", + "| A4067| 1968|\n", + "| A214| 1476|\n", + "| B771| 432|\n", + "| A5006| 252|\n", + "| A5140| 288|\n", + "| A817| 72|\n", + "| B4175| 288|\n", + "| B9002| 24|\n", + "| B1210| 48|\n", + "| B6041| 48|\n", + "| B6048| 24|\n", + "| B3268| 24|\n", + "| B9074| 24|\n", + "| A5022| 48|\n", + "| B2215| 24|\n", + "| A360| 888|\n", + "| B5368| 480|\n", + "| A1242| 420|\n", + "| B477| 216|\n", + "| B4092| 480|\n", + "| A455| 96|\n", + "| B1090| 528|\n", + "| B832| 432|\n", + "| B6152| 264|\n", + "| B4443| 216|\n", + "| B4312| 24|\n", + "| B6164| 24|\n", + "| B6468| 72|\n", + "| B5203| 24|\n", + "| B3212| 480|\n", + "| A282| 1080|\n", + "| A619| 1680|\n", + "| A3020| 984|\n", + "| A1089| 480|\n", + "| B6325| 168|\n", + "| A6114| 240|\n", + "| A937| 180|\n", + "| B3284| 216|\n", + "| A356| 384|\n", + "| B5292| 204|\n", + "| B6449| 264|\n", + "| B2115| 240|\n", + "| B6254| 384|\n", + "| B680| 72|\n", + "| B591| 24|\n", + "| B3111| 72|\n", + "| B3207| 168|\n", + "| B4350| 24|\n", + "| B2067| 528|\n", + "| B1393| 456|\n", + "| B1110| 168|\n", + "+---------+-------------+\n", + "only showing top 1500 rows\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+---------+-------------+--------------------+\n", + "|road_name|Total Traffic|road_name_new_column|\n", + "+---------+-------------+--------------------+\n", + "| A23| 10608| A|\n", + "| A6010| 1488| A|\n", + "| A736| 840| A|\n", + "| A1009| 408| A|\n", + "| A644| 1248| A|\n", + "| A287| 1608| A|\n", + "| A762| 144| A|\n", + "| A2208| 480| A|\n", + "| A1421| 96| A|\n", + "| A197| 816| A|\n", + "| A3044| 624| A|\n", + "| A6089| 192| A|\n", + "| A5137| 168| A|\n", + "| B5231| 576| B|\n", + "| A1192| 24| A|\n", + "| B4283| 24| B|\n", + "| B5157| 48| B|\n", + "| B1099| 48| B|\n", + "| B3191| 24| B|\n", + "| B5129| 960| B|\n", + "| A659| 1152| A|\n", + "| A428| 4776| A|\n", + "| A194| 1320| A|\n", + "| A2211| 216| A|\n", + "| B6165| 312| B|\n", + "| B3048| 720| B|\n", + "| B1112| 792| B|\n", + "| B7018| 432| B|\n", + "| A5460| 840| A|\n", + "| A1039| 192| A|\n", + "| A826| 48| A|\n", + "| B251| 264| B|\n", + "| B5132| 456| B|\n", + "| B6104| 360| B|\n", + "| B111| 24| B|\n", + "| B1080| 24| B|\n", + "| B5117| 24| B|\n", + "| B5224| 24| B|\n", + "| A229| 3168| A|\n", + "| A4047| 288| A|\n", + "| A3066| 432| A|\n", + "| A501| 3708| A|\n", + "| A4304| 684| A|\n", + "| A193| 2292| A|\n", + "| B4287| 192| B|\n", + "| A3021| 360| A|\n", + "| B4495| 432| B|\n", + "| A4050| 528| A|\n", + "| B5211| 672| B|\n", + "| A589| 756| A|\n", + "| B5422| 432| B|\n", + "| A2050| 600| A|\n", + "| A4175| 696| A|\n", + "| B3021| 240| B|\n", + "| A4102| 360| A|\n", + "| A4252| 144| A|\n", + "| B661| 240| B|\n", + "| B501| 48| B|\n", + "| B104| 24| B|\n", + "| B3033| 48| B|\n", + "| A174| 2508| A|\n", + "| A47| 9660| A|\n", + "| B3058| 552| B|\n", + "| M54| 1344| M|\n", + "| A76| 960| A|\n", + "| A255| 456| A|\n", + "| A1085| 720| A|\n", + "| B3266| 264| B|\n", + "| A3126| 96| A|\n", + "| A5145| 888| A|\n", + "| A4233| 588| A|\n", + "| B383| 240| B|\n", + "| B3130| 384| B|\n", + "| B5218| 288| B|\n", + "| A5630| 144| A|\n", + "| B582| 672| B|\n", + "| B9088| 312| B|\n", + "| B4144| 96| B|\n", + "| B5140| 24| B|\n", + "| B3053| 24| B|\n", + "| A65| 3552| A|\n", + "| A3054| 960| A|\n", + "| B6160| 1032| B|\n", + "| A486| 348| A|\n", + "| A615| 648| A|\n", + "| A749| 600| A|\n", + "| B8034| 384| B|\n", + "| A433| 504| A|\n", + "| A5061| 312| A|\n", + "| A1003| 420| A|\n", + "| A816| 216| A|\n", + "| A5048| 204| A|\n", + "| B2157| 264| B|\n", + "| B402| 24| B|\n", + "| B777| 24| B|\n", + "| B2054| 24| B|\n", + "| A444| 2664| A|\n", + "| A9| 5004| A|\n", + "| A1155| 552| A|\n", + "| B6175| 504| B|\n", + "| B465| 480| B|\n", + "| A532| 312| A|\n", + "| A516| 600| A|\n", + "| A6033| 360| A|\n", + "| A1207| 72| A|\n", + "| B6097| 264| B|\n", + "| A935| 192| A|\n", + "| B2028| 1416| B|\n", + "| B4028| 264| B|\n", + "| B3133| 576| B|\n", + "| B6282| 408| B|\n", + "| B4030| 312| B|\n", + "| B6464| 24| B|\n", + "| B3164| 24| B|\n", + "| A424| 336| A|\n", + "| A46| 13176| A|\n", + "| A4189| 744| A|\n", + "| A432| 1344| A|\n", + "| B1008| 264| B|\n", + "| A692| 960| A|\n", + "| A4034| 600| A|\n", + "| A172| 1008| A|\n", + "| A1130| 888| A|\n", + "| A5149| 480| A|\n", + "| A5151| 288| A|\n", + "| B6050| 288| B|\n", + "| A4060| 336| A|\n", + "| B6107| 216| B|\n", + "| A8014| 96| A|\n", + "| A8011| 288| A|\n", + "| B385| 264| B|\n", + "| B6268| 240| B|\n", + "| B1134| 264| B|\n", + "| B451| 240| B|\n", + "| B4085| 48| B|\n", + "| B714| 48| B|\n", + "| B4428| 24| B|\n", + "| B4047| 24| B|\n", + "| B119| 24| B|\n", + "| A1237| 1032| A|\n", + "| B4623| 216| B|\n", + "| A1243| 432| A|\n", + "| B1028| 240| B|\n", + "| A5046| 264| A|\n", + "| A5117| 1008| A|\n", + "| A1062| 72| A|\n", + "| A251| 408| A|\n", + "| A1082| 72| A|\n", + "| A957| 168| A|\n", + "| A1029| 384| A|\n", + "+---------+-------------+--------------------+\n", + "only showing top 150 rows\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+--------------------+-------------+\n", + "|road_name_new_column|Total Traffic|\n", + "+--------------------+-------------+\n", + "| B| 539088|\n", + "| M| 178572|\n", + "| U| 1308888|\n", + "| C| 606864|\n", + "| A| 1858428|\n", + "+--------------------+-------------+\n", + "\n" + ] + } + ], + "source": [ + "Traffic_Information_df = spark.read.format('csv')\\\n", + " .option('header',True).option('escape','\"')\\\n", + " .load('/Users/Asfandyar/Downloads/archive/dft_traffic_counts_raw_counts.csv')\n", + "Traffic_Information_df.show()\n", + "\n", + "Roadname = Traffic_Information_df.groupby('road_name').agg(F.count(Traffic_Information_df.id).alias('Total Traffic'))\n", + "Roadname.show(1500)\n", + "from pyspark.sql.functions import concat, col, lit\n", + "Roadnamemodified=Roadname.withColumn('road_name_new_column', concat(Roadname.road_name.substr(1, 1),\n", + " Roadname.road_name.substr(8, 1)))\n", + "Roadnamemodified.show(150)\n", + "Roadnamemodifiedtotal = Roadnamemodified.groupby('road_name_new_column').agg(F.sum(Roadnamemodified['Total Traffic']).alias('Total Traffic'))\n", + "Roadnamemodifiedtotal.show()\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+--------------+-------+\n", + "|1st_Road_Class|Total A|\n", + "+--------------+-------+\n", + "| B| 286824|\n", + "| M| 86106|\n", + "| U| 687752|\n", + "| C| 188025|\n", + "| A|1038720|\n", + "+--------------+-------+\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+--------------+-------+--------------------+\n", + "|1st_Road_Class|Total A|road_name_new_column|\n", + "+--------------+-------+--------------------+\n", + "| B| 286824| B|\n", + "| M| 86106| M|\n", + "| U| 687752| U|\n", + "| C| 188025| C|\n", + "| A|1038720| A|\n", + "+--------------+-------+--------------------+\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+----+-----------------+\n", + "|year|Total Car Traffic|\n", + "+----+-----------------+\n", + "|2005| 9.2766517E7|\n", + "|2006| 9.7602126E7|\n", + "|2007| 9.6845918E7|\n", + "|2008| 1.05637815E8|\n", + "|2009| 1.17599098E8|\n", + "|2010| 1.02291226E8|\n", + "|2011| 8.4719961E7|\n", + "|2012| 8.9234522E7|\n", + "|2013| 8.6574897E7|\n", + "|2014| 7.4808552E7|\n", + "|2015| 7.4801088E7|\n", + "|2016| 5.9471537E7|\n", + "|2017| 8.4906251E7|\n", + "|2018| 6.3794973E7|\n", + "|2019| 8.7997293E7|\n", + "+----+-----------------+\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [] + } + ], + "source": [ + "from pyspark.sql.functions import concat, col, lit\n", + "RoadnameAA=Accident_Information_df.withColumn('1st_Road_Class', concat(Accident_Information_df['1st_Road_Class'].substr(1, 1),Accident_Information_df['1st_Road_Class'].substr(13, 1)))\n", + "#RoadnameAA.show(1500)\n", + "RoadnameAA = RoadnameAA.groupby('1st_Road_Class').agg(F.count(RoadnameAA.Accident_Index).alias('Total A'))\n", + "RoadnameAA = RoadnameAA.withColumn('Total A',F.col('Total A').cast(IntegerType()))\n", + "\n", + "\n", + "RoadnameAA.show(1500)\n", + "from pyspark.sql.functions import concat, col, lit\n", + "RoadnameAA=RoadnameAA.withColumn('road_name_new_column', concat(RoadnameAA['1st_Road_Class'].substr(1, 1),RoadnameAA['1st_Road_Class'].substr(13, 1)))\n", + "RoadnameAA.show(1500)\n", + "TrafficCar=Traffic_Information_df.filter(Traffic_Information_df.year<2020)\n", + "TrafficCar=TrafficCar.filter(TrafficCar.year>2004)\n", + "TrafficCar = TrafficCar.groupby('year').agg(F.sum('cars_and_taxis').alias('Total Car Traffic')).sort('Year')\n", + "TrafficCar.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+----+----------------------------+\n", + "|year|Total motor_vehicles Traffic|\n", + "+----+----------------------------+\n", + "|2005| 1115317.0|\n", + "|2006| 1168934.0|\n", + "|2007| 1138034.0|\n", + "|2008| 1315257.0|\n", + "|2009| 1507060.0|\n", + "|2010| 1273367.0|\n", + "|2011| 950644.0|\n", + "|2012| 958121.0|\n", + "|2013| 967069.0|\n", + "|2014| 784029.0|\n", + "|2015| 856466.0|\n", + "|2016| 666533.0|\n", + "|2017| 853334.0|\n", + "|2018| 771576.0|\n", + "|2019| 804999.0|\n", + "+----+----------------------------+\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [] + } + ], + "source": [ + "TrafficMotorbike=Traffic_Information_df.filter(Traffic_Information_df.year<2020)\n", + "TrafficMotorbike=TrafficMotorbike.filter(TrafficMotorbike.year>2004)\n", + "TrafficMotorbike = TrafficMotorbike.groupby('year').agg(F.sum('two_wheeled_motor_vehicles').alias('Total motor_vehicles Traffic')).sort('Year')\n", + "TrafficMotorbike.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+----+-------------------------------+\n", + "|year|Total buses_and_coaches Traffic|\n", + "+----+-------------------------------+\n", + "|2005| 1362999.0|\n", + "|2006| 1508627.0|\n", + "|2007| 1384637.0|\n", + "|2008| 1571692.0|\n", + "|2009| 1691613.0|\n", + "|2010| 1296666.0|\n", + "|2011| 1033484.0|\n", + "|2012| 1072480.0|\n", + "|2013| 1069725.0|\n", + "|2014| 737729.0|\n", + "|2015| 755523.0|\n", + "|2016| 737774.0|\n", + "|2017| 897009.0|\n", + "|2018| 916189.0|\n", + "|2019| 849027.0|\n", + "+----+-------------------------------+\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [] + } + ], + "source": [ + "TrafficBus=Traffic_Information_df.filter(Traffic_Information_df.year<2020)\n", + "TrafficBus=TrafficBus.filter(TrafficBus.year>2004)\n", + "TrafficBus = TrafficBus.groupby('year').agg(F.sum('buses_and_coaches').alias('Total buses_and_coaches Traffic')).sort('Year')\n", + "TrafficBus.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+----+-------------------+\n", + "|year|Total Goods Traffic|\n", + "+----+-------------------+\n", + "|2005| 8607455.0|\n", + "|2006| 9318097.0|\n", + "|2007| 9018842.0|\n", + "|2008| 8991471.0|\n", + "|2009| 8687451.0|\n", + "|2010| 8615195.0|\n", + "|2011| 7251054.0|\n", + "|2012| 7666973.0|\n", + "|2013| 7129170.0|\n", + "|2014| 6949892.0|\n", + "|2015| 7157127.0|\n", + "|2016| 4232998.0|\n", + "|2017| 6439283.0|\n", + "|2018| 3744201.0|\n", + "|2019| 7066061.0|\n", + "+----+-------------------+\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [] + } + ], + "source": [ + "TrafficGoods=Traffic_Information_df.filter(Traffic_Information_df.year<2020)\n", + "TrafficGoods=TrafficGoods.filter(TrafficGoods.year>2004)\n", + "TrafficGoods = TrafficGoods.groupby('year').agg(F.sum('all_hgvs').alias('Total Goods Traffic')).sort('Year')\n", + "TrafficGoods.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "TrafficCar_df = TrafficCar.toPandas()\n", + "TrafficMotorbike_df=TrafficMotorbike.toPandas()\n", + "TrafficBus_df=TrafficBus.toPandas()\n", + "TrafficGoods_df=TrafficGoods.toPandas()\n", + "#display(plt.show())\n", + "TrafficCar_df.plot(x='year', y='Total Car Traffic',marker='o')\n", + "TrafficMotorbike_df.plot(x='year', y='Total motor_vehicles Traffic',marker='o')\n", + "TrafficBus_df.plot(x='year', y='Total buses_and_coaches Traffic',marker='o')\n", + "TrafficGoods_df.plot(x='year', y='Total Goods Traffic',marker='o')\n", + "\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+---+--------------+-------------------+----+----------+----+---------+------------------+---------+-------------+---------+------------------------+----------------------+-------+--------+-----------+-----------+--------------+-----------------+--------+----+------------+--------------------------+--------------+-----------------+----+-----------------+-----------------+-------------------------+----------------------------+-----------------------+-----------------------+--------+------------------+--------------------+\n", + "| id|count_point_id|direction_of_travel|year|count_date|hour|region_id|local_authority_id|road_name|road_category|road_type|start_junction_road_name|end_junction_road_name|easting|northing| latitude| longitude|link_length_km|link_length_miles|sequence|ramp|pedal_cycles|two_wheeled_motor_vehicles|cars_and_taxis|buses_and_coaches|lgvs|hgvs_2_rigid_axle|hgvs_3_rigid_axle|hgvs_4_or_more_rigid_axle|hgvs_3_or_4_articulated_axle|hgvs_5_articulated_axle|hgvs_6_articulated_axle|all_hgvs|all_motor_vehicles|road_name_new_column|\n", + "+---+--------------+-------------------+----+----------+----+---------+------------------+---------+-------------+---------+------------------------+----------------------+-------+--------+-----------+-----------+--------------+-----------------+--------+----+------------+--------------------------+--------------+-----------------+----+-----------------+-----------------+-------------------------+----------------------------+-----------------------+-----------------------+--------+------------------+--------------------+\n", + "| 1| 931537| S|2003|2003-05-14| 15| 8| 79| C| MCU| Minor| null| null| 459680| 401640|53.50807068|-1.10160464| null| null| null|null| 5| 3| 469| 6| 34| 1| 0| 0| 0| 0| 0| 1| 513| C|\n", + "| 2| 931537| S|2003|2003-05-14| 16| 8| 79| C| MCU| Minor| null| null| 459680| 401640|53.50807068|-1.10160464| null| null| null|null| 9| 4| 803| 5| 68| 0| 1| 0| 0| 0| 0| 1| 881| C|\n", + "| 3| 931537| S|2003|2003-05-14| 17| 8| 79| C| MCU| Minor| null| null| 459680| 401640|53.50807068|-1.10160464| null| null| null|null| 2| 9| 1000| 6| 81| 0| 0| 0| 0| 0| 0| 0| 1096| C|\n", + "| 4| 931537| S|2003|2003-05-14| 18| 8| 79| C| MCU| Minor| null| null| 459680| 401640|53.50807068|-1.10160464| null| null| null|null| 1| 5| 719| 1| 54| 1| 0| 0| 0| 0| 0| 1| 780| C|\n", + "| 5| 931537| S|2003|2003-05-14| 7| 8| 79| C| MCU| Minor| null| null| 459680| 401640|53.50807068|-1.10160464| null| null| null|null| 2| 2| 163| 1| 24| 1| 0| 0| 0| 0| 0| 1| 191| C|\n", + "| 6| 931537| S|2003|2003-05-14| 8| 8| 79| C| MCU| Minor| null| null| 459680| 401640|53.50807068|-1.10160464| null| null| null|null| 0| 0| 227| 1| 37| 9| 0| 0| 0| 0| 0| 9| 274| C|\n", + "| 7| 931537| S|2003|2003-05-14| 9| 8| 79| C| MCU| Minor| null| null| 459680| 401640|53.50807068|-1.10160464| null| null| null|null| 0| 0| 175| 4| 25| 2| 1| 0| 0| 0| 0| 3| 207| C|\n", + "| 8| 931537| S|2003|2003-05-14| 10| 8| 79| C| MCU| Minor| null| null| 459680| 401640|53.50807068|-1.10160464| null| null| null|null| 4| 0| 194| 5| 33| 3| 1| 0| 0| 0| 0| 4| 236| C|\n", + "| 9| 931537| S|2003|2003-05-14| 11| 8| 79| C| MCU| Minor| null| null| 459680| 401640|53.50807068|-1.10160464| null| null| null|null| 1| 2| 287| 5| 34| 5| 0| 0| 0| 0| 0| 5| 333| C|\n", + "| 10| 931537| S|2003|2003-05-14| 12| 8| 79| C| MCU| Minor| null| null| 459680| 401640|53.50807068|-1.10160464| null| null| null|null| 1| 2| 423| 5| 32| 6| 1| 1| 0| 0| 0| 8| 470| C|\n", + "| 11| 931537| N|2003|2003-05-14| 13| 8| 79| C| MCU| Minor| null| null| 459680| 401640|53.50807068|-1.10160464| null| null| null|null| 2| 2| 360| 3| 27| 3| 1| 0| 0| 0| 0| 4| 396| C|\n", + "| 12| 931537| N|2003|2003-05-14| 14| 8| 79| C| MCU| Minor| null| null| 459680| 401640|53.50807068|-1.10160464| null| null| null|null| 0| 1| 149| 3| 22| 0| 0| 0| 1| 0| 1| 2| 177| C|\n", + "| 13| 931537| N|2003|2003-05-14| 15| 8| 79| C| MCU| Minor| null| null| 459680| 401640|53.50807068|-1.10160464| null| null| null|null| 2| 0| 289| 5| 17| 2| 0| 0| 0| 0| 0| 2| 313| C|\n", + "| 14| 931537| N|2003|2003-05-14| 16| 8| 79| C| MCU| Minor| null| null| 459680| 401640|53.50807068|-1.10160464| null| null| null|null| 1| 0| 435| 5| 35| 1| 1| 0| 0| 0| 0| 2| 477| C|\n", + "| 15| 931537| N|2003|2003-05-14| 17| 8| 79| C| MCU| Minor| null| null| 459680| 401640|53.50807068|-1.10160464| null| null| null|null| 1| 1| 325| 5| 25| 0| 0| 0| 0| 0| 0| 0| 356| C|\n", + "| 16| 931537| N|2003|2003-05-14| 18| 8| 79| C| MCU| Minor| null| null| 459680| 401640|53.50807068|-1.10160464| null| null| null|null| 0| 2| 271| 2| 13| 0| 0| 0| 0| 0| 0| 0| 288| C|\n", + "| 17| 931538| N|2003|2003-07-01| 7| 8| 79| U| MCU| Minor| null| null| 460990| 406860|53.55483386|-1.08083995| null| null| null|null| 6| 2| 277| 13| 54| 4| 2| 1| 0| 5| 2| 14| 360| U|\n", + "| 18| 931538| N|2003|2003-07-01| 8| 8| 79| U| MCU| Minor| null| null| 460990| 406860|53.55483386|-1.08083995| null| null| null|null| 0| 3| 378| 13| 104| 18| 1| 1| 5| 7| 3| 35| 533| U|\n", + "| 19| 931538| N|2003|2003-07-01| 9| 8| 79| U| MCU| Minor| null| null| 460990| 406860|53.55483386|-1.08083995| null| null| null|null| 2| 2| 349| 10| 121| 17| 1| 2| 0| 1| 2| 23| 505| U|\n", + "| 20| 931538| N|2003|2003-07-01| 10| 8| 79| U| MCU| Minor| null| null| 460990| 406860|53.55483386|-1.08083995| null| null| null|null| 2| 0| 434| 7| 124| 29| 3| 5| 2| 1| 1| 41| 606| U|\n", + "| 21| 931538| N|2003|2003-07-01| 11| 8| 79| U| MCU| Minor| null| null| 460990| 406860|53.55483386|-1.08083995| null| null| null|null| 0| 2| 472| 11| 132| 15| 0| 2| 2| 4| 0| 23| 640| U|\n", + "| 22| 931538| N|2003|2003-07-01| 12| 8| 79| U| MCU| Minor| null| null| 460990| 406860|53.55483386|-1.08083995| null| null| null|null| 2| 3| 445| 8| 93| 17| 2| 1| 1| 3| 1| 25| 574| U|\n", + "| 23| 931538| S|2003|2003-07-01| 13| 8| 79| U| MCU| Minor| null| null| 460990| 406860|53.55483386|-1.08083995| null| null| null|null| 0| 1| 598| 5| 131| 20| 2| 0| 4| 4| 4| 34| 769| U|\n", + "| 24| 931538| S|2003|2003-07-01| 14| 8| 79| U| MCU| Minor| null| null| 460990| 406860|53.55483386|-1.08083995| null| null| null|null| 3| 2| 523| 10| 106| 18| 4| 1| 0| 5| 3| 31| 672| U|\n", + "| 25| 931538| S|2003|2003-07-01| 15| 8| 79| U| MCU| Minor| null| null| 460990| 406860|53.55483386|-1.08083995| null| null| null|null| 3| 1| 528| 8| 121| 19| 2| 2| 1| 0| 1| 25| 683| U|\n", + "| 26| 931538| S|2003|2003-07-01| 16| 8| 79| U| MCU| Minor| null| null| 460990| 406860|53.55483386|-1.08083995| null| null| null|null| 9| 4| 545| 8| 93| 12| 2| 0| 1| 1| 0| 16| 666| U|\n", + "| 27| 931538| S|2003|2003-07-01| 17| 8| 79| U| MCU| Minor| null| null| 460990| 406860|53.55483386|-1.08083995| null| null| null|null| 3| 4| 543| 5| 62| 4| 1| 0| 0| 3| 0| 8| 622| U|\n", + "| 28| 931538| S|2003|2003-07-01| 18| 8| 79| U| MCU| Minor| null| null| 460990| 406860|53.55483386|-1.08083995| null| null| null|null| 6| 6| 388| 4| 38| 3| 1| 0| 1| 0| 1| 6| 442| U|\n", + "| 29| 931538| S|2003|2003-07-01| 7| 8| 79| U| MCU| Minor| null| null| 460990| 406860|53.55483386|-1.08083995| null| null| null|null| 6| 6| 604| 3| 92| 3| 1| 2| 3| 3| 3| 15| 720| U|\n", + "| 30| 931538| S|2003|2003-07-01| 8| 8| 79| U| MCU| Minor| null| null| 460990| 406860|53.55483386|-1.08083995| null| null| null|null| 0| 2| 832| 9| 102| 15| 1| 3| 2| 2| 3| 26| 971| U|\n", + "| 31| 931538| S|2003|2003-07-01| 9| 8| 79| U| MCU| Minor| null| null| 460990| 406860|53.55483386|-1.08083995| null| null| null|null| 0| 0| 679| 10| 167| 21| 5| 0| 0| 2| 2| 30| 886| U|\n", + "| 32| 931538| S|2003|2003-07-01| 10| 8| 79| U| MCU| Minor| null| null| 460990| 406860|53.55483386|-1.08083995| null| null| null|null| 1| 0| 488| 8| 130| 23| 2| 2| 1| 2| 3| 33| 659| U|\n", + "| 33| 931538| S|2003|2003-07-01| 11| 8| 79| U| MCU| Minor| null| null| 460990| 406860|53.55483386|-1.08083995| null| null| null|null| 3| 1| 518| 7| 119| 17| 2| 2| 5| 1| 0| 27| 672| U|\n", + "| 34| 931538| S|2003|2003-07-01| 12| 8| 79| U| MCU| Minor| null| null| 460990| 406860|53.55483386|-1.08083995| null| null| null|null| 0| 2| 484| 7| 95| 17| 1| 1| 1| 2| 0| 22| 610| U|\n", + "| 35| 931538| N|2003|2003-07-01| 13| 8| 79| U| MCU| Minor| null| null| 460990| 406860|53.55483386|-1.08083995| null| null| null|null| 2| 3| 501| 4| 119| 12| 1| 0| 4| 2| 2| 21| 648| U|\n", + "| 36| 931538| N|2003|2003-07-01| 14| 8| 79| U| MCU| Minor| null| null| 460990| 406860|53.55483386|-1.08083995| null| null| null|null| 2| 5| 574| 10| 115| 19| 2| 4| 1| 3| 2| 31| 735| U|\n", + "| 37| 931538| N|2003|2003-07-01| 15| 8| 79| U| MCU| Minor| null| null| 460990| 406860|53.55483386|-1.08083995| null| null| null|null| 3| 1| 779| 7| 141| 22| 2| 4| 5| 5| 0| 38| 966| U|\n", + "| 38| 931538| N|2003|2003-07-01| 16| 8| 79| U| MCU| Minor| null| null| 460990| 406860|53.55483386|-1.08083995| null| null| null|null| 12| 4| 996| 8| 138| 12| 1| 0| 3| 4| 0| 20| 1166| U|\n", + "| 39| 931538| N|2003|2003-07-01| 17| 8| 79| U| MCU| Minor| null| null| 460990| 406860|53.55483386|-1.08083995| null| null| null|null| 7| 7| 1079| 4| 117| 3| 1| 4| 2| 0| 0| 10| 1217| U|\n", + "| 40| 931538| N|2003|2003-07-01| 18| 8| 79| U| MCU| Minor| null| null| 460990| 406860|53.55483386|-1.08083995| null| null| null|null| 3| 5| 820| 4| 61| 1| 0| 1| 0| 2| 0| 4| 894| U|\n", + "| 41| 931569| N|2003|2003-09-24| 7| 9| 83| C| MCU| Minor| null| null| 488530| 194180|51.63946821|-0.72208763| null| null| null|null| 0| 5| 362| 6| 59| 7| 0| 0| 0| 0| 0| 7| 439| C|\n", + "| 42| 931569| N|2003|2003-09-24| 8| 9| 83| C| MCU| Minor| null| null| 488530| 194180|51.63946821|-0.72208763| null| null| null|null| 0| 0| 598| 10| 59| 11| 1| 0| 0| 0| 0| 12| 679| C|\n", + "| 43| 931569| N|2003|2003-09-24| 9| 9| 83| C| MCU| Minor| null| null| 488530| 194180|51.63946821|-0.72208763| null| null| null|null| 1| 3| 287| 7| 31| 6| 1| 1| 1| 1| 1| 11| 339| C|\n", + "| 44| 931569| N|2003|2003-09-24| 10| 9| 83| C| MCU| Minor| null| null| 488530| 194180|51.63946821|-0.72208763| null| null| null|null| 1| 1| 210| 6| 33| 7| 0| 0| 1| 0| 0| 8| 258| C|\n", + "| 45| 931569| N|2003|2003-09-24| 11| 9| 83| C| MCU| Minor| null| null| 488530| 194180|51.63946821|-0.72208763| null| null| null|null| 0| 1| 280| 7| 40| 6| 0| 0| 0| 0| 0| 6| 334| C|\n", + "| 46| 931569| N|2003|2003-09-24| 12| 9| 83| C| MCU| Minor| null| null| 488530| 194180|51.63946821|-0.72208763| null| null| null|null| 0| 5| 271| 6| 41| 9| 1| 0| 0| 0| 0| 10| 333| C|\n", + "| 47| 931569| S|2003|2003-09-24| 13| 9| 83| C| MCU| Minor| null| null| 488530| 194180|51.63946821|-0.72208763| null| null| null|null| 1| 2| 226| 6| 31| 7| 0| 1| 0| 0| 0| 8| 273| C|\n", + "| 48| 931569| S|2003|2003-09-24| 14| 9| 83| C| MCU| Minor| null| null| 488530| 194180|51.63946821|-0.72208763| null| null| null|null| 0| 3| 211| 9| 40| 5| 0| 1| 0| 0| 0| 6| 269| C|\n", + "| 49| 931569| S|2003|2003-09-24| 15| 9| 83| C| MCU| Minor| null| null| 488530| 194180|51.63946821|-0.72208763| null| null| null|null| 0| 3| 381| 6| 30| 6| 0| 1| 0| 0| 1| 8| 428| C|\n", + "| 50| 931569| S|2003|2003-09-24| 16| 9| 83| C| MCU| Minor| null| null| 488530| 194180|51.63946821|-0.72208763| null| null| null|null| 1| 6| 379| 11| 66| 7| 0| 0| 0| 0| 0| 7| 469| C|\n", + "| 51| 931569| S|2003|2003-09-24| 17| 9| 83| C| MCU| Minor| null| null| 488530| 194180|51.63946821|-0.72208763| null| null| null|null| 1| 5| 382| 5| 53| 6| 0| 1| 0| 0| 0| 7| 452| C|\n", + "| 52| 931569| S|2003|2003-09-24| 18| 9| 83| C| MCU| Minor| null| null| 488530| 194180|51.63946821|-0.72208763| null| null| null|null| 2| 6| 432| 8| 30| 4| 0| 0| 0| 0| 0| 4| 480| C|\n", + "| 53| 931569| S|2003|2003-09-24| 7| 9| 83| C| MCU| Minor| null| null| 488530| 194180|51.63946821|-0.72208763| null| null| null|null| 2| 2| 245| 5| 46| 9| 0| 0| 0| 0| 0| 9| 307| C|\n", + "| 54| 931569| S|2003|2003-09-24| 8| 9| 83| C| MCU| Minor| null| null| 488530| 194180|51.63946821|-0.72208763| null| null| null|null| 0| 4| 431| 8| 36| 2| 2| 0| 0| 0| 0| 4| 483| C|\n", + "| 55| 931569| S|2003|2003-09-24| 9| 9| 83| C| MCU| Minor| null| null| 488530| 194180|51.63946821|-0.72208763| null| null| null|null| 0| 1| 342| 8| 44| 7| 0| 0| 0| 0| 0| 7| 402| C|\n", + "| 56| 931569| S|2003|2003-09-24| 10| 9| 83| C| MCU| Minor| null| null| 488530| 194180|51.63946821|-0.72208763| null| null| null|null| 2| 1| 216| 7| 50| 8| 0| 0| 0| 0| 0| 8| 282| C|\n", + "| 57| 931569| S|2003|2003-09-24| 11| 9| 83| C| MCU| Minor| null| null| 488530| 194180|51.63946821|-0.72208763| null| null| null|null| 1| 2| 249| 6| 38| 5| 0| 0| 1| 0| 0| 6| 301| C|\n", + "| 58| 931569| S|2003|2003-09-24| 12| 9| 83| C| MCU| Minor| null| null| 488530| 194180|51.63946821|-0.72208763| null| null| null|null| 0| 1| 277| 10| 54| 15| 0| 0| 0| 0| 0| 15| 357| C|\n", + "| 59| 931569| N|2003|2003-09-24| 13| 9| 83| C| MCU| Minor| null| null| 488530| 194180|51.63946821|-0.72208763| null| null| null|null| 0| 1| 293| 8| 45| 6| 1| 1| 0| 0| 0| 8| 355| C|\n", + "| 60| 931569| N|2003|2003-09-24| 14| 9| 83| C| MCU| Minor| null| null| 488530| 194180|51.63946821|-0.72208763| null| null| null|null| 0| 1| 318| 6| 41| 1| 1| 0| 0| 0| 0| 2| 368| C|\n", + "| 61| 931569| N|2003|2003-09-24| 15| 9| 83| C| MCU| Minor| null| null| 488530| 194180|51.63946821|-0.72208763| null| null| null|null| 3| 3| 334| 9| 44| 8| 1| 0| 0| 0| 0| 9| 399| C|\n", + "| 62| 931569| N|2003|2003-09-24| 16| 9| 83| C| MCU| Minor| null| null| 488530| 194180|51.63946821|-0.72208763| null| null| null|null| 0| 2| 392| 8| 64| 9| 2| 0| 0| 0| 0| 11| 477| C|\n", + "| 63| 931569| N|2003|2003-09-24| 17| 9| 83| C| MCU| Minor| null| null| 488530| 194180|51.63946821|-0.72208763| null| null| null|null| 1| 5| 514| 9| 60| 5| 0| 0| 0| 0| 0| 5| 593| C|\n", + "| 64| 931569| N|2003|2003-09-24| 18| 9| 83| C| MCU| Minor| null| null| 488530| 194180|51.63946821|-0.72208763| null| null| null|null| 1| 8| 410| 4| 29| 1| 0| 0| 0| 0| 0| 1| 452| C|\n", + "| 65| 931570| E|2003|2003-04-04| 7| 9| 83| C| MCU| Minor| null| null| 495900| 197280|51.66612606|-0.61476998| null| null| null|null| 1| 2| 317| 5| 34| 5| 1| 0| 0| 0| 0| 6| 364| C|\n", + "| 66| 931570| E|2003|2003-04-04| 8| 9| 83| C| MCU| Minor| null| null| 495900| 197280|51.66612606|-0.61476998| null| null| null|null| 2| 3| 484| 6| 28| 8| 2| 0| 0| 0| 0| 10| 531| C|\n", + "| 67| 931570| E|2003|2003-04-04| 9| 9| 83| C| MCU| Minor| null| null| 495900| 197280|51.66612606|-0.61476998| null| null| null|null| 0| 1| 433| 5| 30| 8| 0| 0| 1| 0| 0| 9| 478| C|\n", + "| 68| 931570| E|2003|2003-04-04| 10| 9| 83| C| MCU| Minor| null| null| 495900| 197280|51.66612606|-0.61476998| null| null| null|null| 0| 0| 345| 6| 36| 7| 0| 0| 0| 1| 0| 8| 395| C|\n", + "| 69| 931570| E|2003|2003-04-04| 11| 9| 83| C| MCU| Minor| null| null| 495900| 197280|51.66612606|-0.61476998| null| null| null|null| 4| 0| 376| 5| 40| 7| 0| 0| 0| 1| 0| 8| 429| C|\n", + "| 70| 931570| E|2003|2003-04-04| 12| 9| 83| C| MCU| Minor| null| null| 495900| 197280|51.66612606|-0.61476998| null| null| null|null| 2| 3| 431| 5| 40| 8| 1| 0| 2| 0| 0| 11| 490| C|\n", + "| 71| 931570| W|2003|2003-04-04| 13| 9| 83| C| MCU| Minor| null| null| 495900| 197280|51.66612606|-0.61476998| null| null| null|null| 1| 5| 460| 3| 44| 5| 1| 0| 1| 0| 0| 7| 519| C|\n", + "| 72| 931570| W|2003|2003-04-04| 14| 9| 83| C| MCU| Minor| null| null| 495900| 197280|51.66612606|-0.61476998| null| null| null|null| 0| 3| 501| 5| 44| 6| 0| 0| 0| 0| 0| 6| 559| C|\n", + "| 73| 931570| W|2003|2003-04-04| 15| 9| 83| C| MCU| Minor| null| null| 495900| 197280|51.66612606|-0.61476998| null| null| null|null| 1| 5| 470| 5| 39| 4| 0| 0| 0| 0| 1| 5| 524| C|\n", + "| 74| 931570| W|2003|2003-04-04| 16| 9| 83| C| MCU| Minor| null| null| 495900| 197280|51.66612606|-0.61476998| null| null| null|null| 3| 4| 474| 2| 37| 5| 0| 0| 0| 0| 0| 5| 522| C|\n", + "| 75| 931570| W|2003|2003-04-04| 17| 9| 83| C| MCU| Minor| null| null| 495900| 197280|51.66612606|-0.61476998| null| null| null|null| 4| 6| 634| 5| 27| 1| 0| 0| 0| 0| 0| 1| 673| C|\n", + "| 76| 931570| W|2003|2003-04-04| 18| 9| 83| C| MCU| Minor| null| null| 495900| 197280|51.66612606|-0.61476998| null| null| null|null| 2| 2| 512| 2| 15| 1| 0| 0| 0| 0| 0| 1| 532| C|\n", + "| 77| 931570| W|2003|2003-04-04| 7| 9| 83| C| MCU| Minor| null| null| 495900| 197280|51.66612606|-0.61476998| null| null| null|null| 0| 1| 233| 4| 26| 7| 0| 0| 0| 0| 0| 7| 271| C|\n", + "| 78| 931570| W|2003|2003-04-04| 8| 9| 83| C| MCU| Minor| null| null| 495900| 197280|51.66612606|-0.61476998| null| null| null|null| 3| 5| 630| 1| 40| 11| 0| 0| 0| 0| 0| 11| 687| C|\n", + "| 79| 931570| W|2003|2003-04-04| 9| 9| 83| C| MCU| Minor| null| null| 495900| 197280|51.66612606|-0.61476998| null| null| null|null| 1| 1| 420| 4| 40| 12| 1| 0| 1| 0| 0| 14| 479| C|\n", + "| 80| 931570| W|2003|2003-04-04| 10| 9| 83| C| MCU| Minor| null| null| 495900| 197280|51.66612606|-0.61476998| null| null| null|null| 2| 2| 409| 3| 38| 5| 1| 0| 0| 2| 0| 8| 460| C|\n", + "| 81| 931570| W|2003|2003-04-04| 11| 9| 83| C| MCU| Minor| null| null| 495900| 197280|51.66612606|-0.61476998| null| null| null|null| 1| 1| 452| 2| 47| 14| 0| 0| 0| 0| 0| 14| 516| C|\n", + "| 82| 931570| W|2003|2003-04-04| 12| 9| 83| C| MCU| Minor| null| null| 495900| 197280|51.66612606|-0.61476998| null| null| null|null| 0| 0| 467| 3| 48| 9| 0| 0| 0| 0| 0| 9| 527| C|\n", + "| 83| 931570| E|2003|2003-04-04| 13| 9| 83| C| MCU| Minor| null| null| 495900| 197280|51.66612606|-0.61476998| null| null| null|null| 1| 3| 379| 5| 34| 5| 1| 0| 0| 0| 0| 6| 427| C|\n", + "| 84| 931570| E|2003|2003-04-04| 14| 9| 83| C| MCU| Minor| null| null| 495900| 197280|51.66612606|-0.61476998| null| null| null|null| 0| 3| 451| 4| 25| 6| 0| 0| 1| 0| 0| 7| 490| C|\n", + "| 85| 931570| E|2003|2003-04-04| 15| 9| 83| C| MCU| Minor| null| null| 495900| 197280|51.66612606|-0.61476998| null| null| null|null| 1| 3| 473| 4| 35| 2| 0| 0| 0| 0| 1| 3| 518| C|\n", + "| 86| 931570| E|2003|2003-04-04| 16| 9| 83| C| MCU| Minor| null| null| 495900| 197280|51.66612606|-0.61476998| null| null| null|null| 1| 3| 500| 4| 35| 5| 0| 0| 0| 0| 0| 5| 547| C|\n", + "| 87| 931570| E|2003|2003-04-04| 17| 9| 83| C| MCU| Minor| null| null| 495900| 197280|51.66612606|-0.61476998| null| null| null|null| 2| 5| 527| 4| 28| 2| 0| 0| 0| 0| 0| 2| 566| C|\n", + "| 88| 931570| E|2003|2003-04-04| 18| 9| 83| C| MCU| Minor| null| null| 495900| 197280|51.66612606|-0.61476998| null| null| null|null| 1| 1| 338| 2| 16| 1| 0| 0| 0| 0| 0| 1| 358| C|\n", + "| 89| 931619| J|2003|2003-04-10| 13| 1| 139| C| MCU| Minor| null| null| 181587| 56465|50.36745400|-5.07261284| null| null| null|null| 0| 0| 58| 0| 12| 3| 0| 0| 0| 0| 1| 4| 74| C|\n", + "| 90| 931619| J|2003|2003-04-10| 14| 1| 139| C| MCU| Minor| null| null| 181587| 56465|50.36745400|-5.07261284| null| null| null|null| 1| 1| 56| 2| 9| 2| 0| 0| 0| 0| 1| 3| 71| C|\n", + "| 91| 931619| J|2003|2003-04-10| 15| 1| 139| C| MCU| Minor| null| null| 181587| 56465|50.36745400|-5.07261284| null| null| null|null| 0| 0| 70| 3| 14| 3| 0| 0| 1| 0| 0| 4| 91| C|\n", + "| 92| 931619| J|2003|2003-04-10| 16| 1| 139| C| MCU| Minor| null| null| 181587| 56465|50.36745400|-5.07261284| null| null| null|null| 0| 4| 101| 0| 18| 6| 0| 0| 0| 0| 0| 6| 129| C|\n", + "| 93| 931619| J|2003|2003-04-10| 17| 1| 139| C| MCU| Minor| null| null| 181587| 56465|50.36745400|-5.07261284| null| null| null|null| 0| 1| 101| 4| 12| 1| 0| 0| 0| 0| 0| 1| 119| C|\n", + "| 94| 931619| J|2003|2003-04-10| 18| 1| 139| C| MCU| Minor| null| null| 181587| 56465|50.36745400|-5.07261284| null| null| null|null| 0| 1| 75| 1| 7| 2| 0| 0| 0| 0| 0| 2| 86| C|\n", + "| 95| 931619| J|2003|2003-04-10| 7| 1| 139| C| MCU| Minor| null| null| 181587| 56465|50.36745400|-5.07261284| null| null| null|null| 0| 0| 46| 0| 14| 2| 0| 0| 1| 0| 0| 3| 63| C|\n", + "| 96| 931619| J|2003|2003-04-10| 8| 1| 139| C| MCU| Minor| null| null| 181587| 56465|50.36745400|-5.07261284| null| null| null|null| 0| 2| 77| 3| 15| 3| 1| 0| 2| 0| 0| 6| 103| C|\n", + "| 97| 931619| J|2003|2003-04-10| 9| 1| 139| C| MCU| Minor| null| null| 181587| 56465|50.36745400|-5.07261284| null| null| null|null| 0| 1| 58| 2| 18| 4| 1| 0| 0| 0| 0| 5| 84| C|\n", + "| 98| 931619| J|2003|2003-04-10| 10| 1| 139| C| MCU| Minor| null| null| 181587| 56465|50.36745400|-5.07261284| null| null| null|null| 1| 1| 85| 2| 9| 3| 0| 0| 0| 0| 0| 3| 100| C|\n", + "| 99| 931619| J|2003|2003-04-10| 11| 1| 139| C| MCU| Minor| null| null| 181587| 56465|50.36745400|-5.07261284| null| null| null|null| 0| 0| 78| 2| 13| 8| 0| 1| 0| 0| 0| 9| 102| C|\n", + "|100| 931619| J|2003|2003-04-10| 12| 1| 139| C| MCU| Minor| null| null| 181587| 56465|50.36745400|-5.07261284| null| null| null|null| 0| 0| 75| 2| 13| 0| 0| 0| 2| 0| 0| 2| 92| C|\n", + "|101| 931620| N|2003|2003-03-21| 7| 1| 139| C| MCU| Minor| null| null| 171470| 47260|50.28095813|-5.20906620| null| null| null|null| 0| 0| 13| 0| 4| 1| 0| 0| 0| 0| 0| 1| 18| C|\n", + "|102| 931620| N|2003|2003-03-21| 8| 1| 139| C| MCU| Minor| null| null| 171470| 47260|50.28095813|-5.20906620| null| null| null|null| 0| 0| 45| 1| 9| 0| 0| 0| 0| 0| 1| 1| 56| C|\n", + "|103| 931620| N|2003|2003-03-21| 9| 1| 139| C| MCU| Minor| null| null| 171470| 47260|50.28095813|-5.20906620| null| null| null|null| 0| 0| 20| 2| 4| 1| 1| 0| 0| 0| 0| 2| 28| C|\n", + "|104| 931620| N|2003|2003-03-21| 10| 1| 139| C| MCU| Minor| null| null| 171470| 47260|50.28095813|-5.20906620| null| null| null|null| 0| 0| 15| 2| 4| 0| 0| 0| 0| 0| 0| 0| 21| C|\n", + "|105| 931620| N|2003|2003-03-21| 11| 1| 139| C| MCU| Minor| null| null| 171470| 47260|50.28095813|-5.20906620| null| null| null|null| 1| 0| 28| 2| 3| 1| 1| 0| 0| 0| 0| 2| 35| C|\n", + "|106| 931620| N|2003|2003-03-21| 12| 1| 139| C| MCU| Minor| null| null| 171470| 47260|50.28095813|-5.20906620| null| null| null|null| 0| 0| 16| 3| 8| 1| 0| 0| 0| 0| 0| 1| 28| C|\n", + "|107| 931620| S|2003|2003-03-21| 13| 1| 139| C| MCU| Minor| null| null| 171470| 47260|50.28095813|-5.20906620| null| null| null|null| 0| 0| 25| 2| 3| 1| 0| 0| 0| 0| 0| 1| 31| C|\n", + "|108| 931620| S|2003|2003-03-21| 14| 1| 139| C| MCU| Minor| null| null| 171470| 47260|50.28095813|-5.20906620| null| null| null|null| 0| 0| 24| 1| 3| 1| 0| 0| 0| 0| 0| 1| 29| C|\n", + "|109| 931620| S|2003|2003-03-21| 15| 1| 139| C| MCU| Minor| null| null| 171470| 47260|50.28095813|-5.20906620| null| null| null|null| 0| 1| 39| 2| 2| 0| 0| 0| 0| 0| 0| 0| 44| C|\n", + "|110| 931620| S|2003|2003-03-21| 16| 1| 139| C| MCU| Minor| null| null| 171470| 47260|50.28095813|-5.20906620| null| null| null|null| 0| 1| 35| 2| 6| 1| 0| 0| 0| 0| 0| 1| 45| C|\n", + "|111| 931620| S|2003|2003-03-21| 17| 1| 139| C| MCU| Minor| null| null| 171470| 47260|50.28095813|-5.20906620| null| null| null|null| 1| 0| 30| 2| 1| 0| 0| 0| 0| 0| 0| 0| 33| C|\n", + "|112| 931620| S|2003|2003-03-21| 18| 1| 139| C| MCU| Minor| null| null| 171470| 47260|50.28095813|-5.20906620| null| null| null|null| 0| 3| 37| 1| 4| 0| 0| 0| 0| 0| 0| 0| 45| C|\n", + "|113| 931620| S|2003|2003-03-21| 7| 1| 139| C| MCU| Minor| null| null| 171470| 47260|50.28095813|-5.20906620| null| null| null|null| 0| 1| 25| 2| 5| 1| 0| 0| 0| 0| 0| 1| 34| C|\n", + "|114| 931620| S|2003|2003-03-21| 8| 1| 139| C| MCU| Minor| null| null| 171470| 47260|50.28095813|-5.20906620| null| null| null|null| 0| 0| 34| 2| 4| 0| 0| 0| 0| 0| 0| 0| 40| C|\n", + "|115| 931620| S|2003|2003-03-21| 9| 1| 139| C| MCU| Minor| null| null| 171470| 47260|50.28095813|-5.20906620| null| null| null|null| 0| 0| 25| 3| 3| 1| 0| 0| 0| 0| 0| 1| 32| C|\n", + "|116| 931620| S|2003|2003-03-21| 10| 1| 139| C| MCU| Minor| null| null| 171470| 47260|50.28095813|-5.20906620| null| null| null|null| 1| 0| 17| 1| 3| 0| 0| 0| 0| 0| 0| 0| 21| C|\n", + "|117| 931620| S|2003|2003-03-21| 11| 1| 139| C| MCU| Minor| null| null| 171470| 47260|50.28095813|-5.20906620| null| null| null|null| 0| 0| 26| 2| 2| 2| 0| 0| 0| 0| 0| 2| 32| C|\n", + "|118| 931620| S|2003|2003-03-21| 12| 1| 139| C| MCU| Minor| null| null| 171470| 47260|50.28095813|-5.20906620| null| null| null|null| 1| 0| 13| 1| 7| 0| 0| 0| 0| 0| 0| 0| 21| C|\n", + "|119| 931620| N|2003|2003-03-21| 13| 1| 139| C| MCU| Minor| null| null| 171470| 47260|50.28095813|-5.20906620| null| null| null|null| 1| 0| 21| 1| 4| 0| 0| 0| 0| 0| 0| 0| 26| C|\n", + "|120| 931620| N|2003|2003-03-21| 14| 1| 139| C| MCU| Minor| null| null| 171470| 47260|50.28095813|-5.20906620| null| null| null|null| 0| 1| 32| 3| 2| 1| 0| 0| 0| 0| 0| 1| 39| C|\n", + "|121| 931620| N|2003|2003-03-21| 15| 1| 139| C| MCU| Minor| null| null| 171470| 47260|50.28095813|-5.20906620| null| null| null|null| 0| 0| 31| 1| 2| 1| 0| 0| 0| 0| 0| 1| 35| C|\n", + "|122| 931620| N|2003|2003-03-21| 16| 1| 139| C| MCU| Minor| null| null| 171470| 47260|50.28095813|-5.20906620| null| null| null|null| 0| 0| 30| 2| 0| 0| 0| 0| 0| 0| 0| 0| 32| C|\n", + "|123| 931620| N|2003|2003-03-21| 17| 1| 139| C| MCU| Minor| null| null| 171470| 47260|50.28095813|-5.20906620| null| null| null|null| 0| 1| 36| 3| 3| 1| 1| 0| 0| 0| 0| 2| 45| C|\n", + "|124| 931620| N|2003|2003-03-21| 18| 1| 139| C| MCU| Minor| null| null| 171470| 47260|50.28095813|-5.20906620| null| null| null|null| 4| 0| 29| 3| 1| 0| 0| 0| 0| 0| 0| 0| 33| C|\n", + "|125| 931702| E|2003|2003-05-06| 7| 4| 14| U| MCU| Minor| null| null| 297400| 268460|52.30464301|-3.50618282| null| null| null|null| 0| 1| 2| 0| 0| 0| 0| 0| 0| 0| 0| 0| 3| U|\n", + "|126| 931702| E|2003|2003-05-06| 8| 4| 14| U| MCU| Minor| null| null| 297400| 268460|52.30464301|-3.50618282| null| null| null|null| 0| 0| 19| 0| 1| 0| 0| 0| 0| 0| 0| 0| 20| U|\n", + "|127| 931702| E|2003|2003-05-06| 9| 4| 14| U| MCU| Minor| null| null| 297400| 268460|52.30464301|-3.50618282| null| null| null|null| 0| 0| 6| 1| 1| 0| 0| 0| 0| 0| 0| 0| 8| U|\n", + "|128| 931702| E|2003|2003-05-06| 10| 4| 14| U| MCU| Minor| null| null| 297400| 268460|52.30464301|-3.50618282| null| null| null|null| 0| 0| 5| 0| 1| 0| 0| 0| 0| 0| 0| 0| 6| U|\n", + "|129| 931702| E|2003|2003-05-06| 11| 4| 14| U| MCU| Minor| null| null| 297400| 268460|52.30464301|-3.50618282| null| null| null|null| 0| 0| 10| 0| 1| 0| 1| 0| 0| 0| 0| 1| 12| U|\n", + "|130| 931702| E|2003|2003-05-06| 12| 4| 14| U| MCU| Minor| null| null| 297400| 268460|52.30464301|-3.50618282| null| null| null|null| 0| 0| 9| 0| 0| 0| 1| 0| 0| 0| 0| 1| 10| U|\n", + "|131| 931702| W|2003|2003-05-06| 13| 4| 14| U| MCU| Minor| null| null| 297400| 268460|52.30464301|-3.50618282| null| null| null|null| 0| 0| 6| 1| 0| 1| 0| 0| 0| 0| 0| 1| 8| U|\n", + "|132| 931702| W|2003|2003-05-06| 14| 4| 14| U| MCU| Minor| null| null| 297400| 268460|52.30464301|-3.50618282| null| null| null|null| 0| 0| 10| 0| 3| 0| 0| 0| 0| 0| 0| 0| 13| U|\n", + "|133| 931702| W|2003|2003-05-06| 15| 4| 14| U| MCU| Minor| null| null| 297400| 268460|52.30464301|-3.50618282| null| null| null|null| 0| 0| 23| 0| 3| 0| 0| 0| 0| 0| 0| 0| 26| U|\n", + "|134| 931702| W|2003|2003-05-06| 16| 4| 14| U| MCU| Minor| null| null| 297400| 268460|52.30464301|-3.50618282| null| null| null|null| 0| 0| 10| 0| 3| 0| 0| 0| 0| 0| 0| 0| 13| U|\n", + "|135| 931702| W|2003|2003-05-06| 17| 4| 14| U| MCU| Minor| null| null| 297400| 268460|52.30464301|-3.50618282| null| null| null|null| 0| 0| 10| 0| 4| 0| 0| 0| 0| 0| 0| 0| 14| U|\n", + "|136| 931702| W|2003|2003-05-06| 18| 4| 14| U| MCU| Minor| null| null| 297400| 268460|52.30464301|-3.50618282| null| null| null|null| 0| 0| 13| 0| 2| 0| 0| 0| 0| 0| 0| 0| 15| U|\n", + "|137| 931702| W|2003|2003-05-06| 7| 4| 14| U| MCU| Minor| null| null| 297400| 268460|52.30464301|-3.50618282| null| null| null|null| 0| 0| 13| 0| 0| 0| 0| 0| 0| 0| 0| 0| 13| U|\n", + "|138| 931702| W|2003|2003-05-06| 8| 4| 14| U| MCU| Minor| null| null| 297400| 268460|52.30464301|-3.50618282| null| null| null|null| 0| 0| 16| 0| 3| 0| 0| 0| 0| 0| 0| 0| 19| U|\n", + "|139| 931702| W|2003|2003-05-06| 9| 4| 14| U| MCU| Minor| null| null| 297400| 268460|52.30464301|-3.50618282| null| null| null|null| 0| 0| 12| 0| 0| 0| 0| 0| 0| 0| 0| 0| 12| U|\n", + "|140| 931702| W|2003|2003-05-06| 10| 4| 14| U| MCU| Minor| null| null| 297400| 268460|52.30464301|-3.50618282| null| null| null|null| 0| 0| 14| 0| 0| 0| 0| 0| 0| 0| 0| 0| 14| U|\n", + "|141| 931702| W|2003|2003-05-06| 11| 4| 14| U| MCU| Minor| null| null| 297400| 268460|52.30464301|-3.50618282| null| null| null|null| 0| 0| 9| 1| 0| 0| 0| 0| 0| 0| 0| 0| 10| U|\n", + "|142| 931702| W|2003|2003-05-06| 12| 4| 14| U| MCU| Minor| null| null| 297400| 268460|52.30464301|-3.50618282| null| null| null|null| 0| 0| 8| 0| 2| 0| 0| 0| 0| 0| 0| 0| 10| U|\n", + "|143| 931702| E|2003|2003-05-06| 13| 4| 14| U| MCU| Minor| null| null| 297400| 268460|52.30464301|-3.50618282| null| null| null|null| 0| 0| 4| 0| 0| 0| 0| 0| 0| 0| 0| 0| 4| U|\n", + "|144| 931702| E|2003|2003-05-06| 14| 4| 14| U| MCU| Minor| null| null| 297400| 268460|52.30464301|-3.50618282| null| null| null|null| 0| 0| 6| 1| 0| 1| 0| 0| 0| 0| 0| 1| 8| U|\n", + "|145| 931702| E|2003|2003-05-06| 15| 4| 14| U| MCU| Minor| null| null| 297400| 268460|52.30464301|-3.50618282| null| null| null|null| 0| 2| 20| 1| 2| 0| 0| 0| 0| 0| 0| 0| 25| U|\n", + "|146| 931702| E|2003|2003-05-06| 16| 4| 14| U| MCU| Minor| null| null| 297400| 268460|52.30464301|-3.50618282| null| null| null|null| 0| 0| 13| 1| 2| 0| 0| 0| 0| 0| 0| 0| 16| U|\n", + "|147| 931702| E|2003|2003-05-06| 17| 4| 14| U| MCU| Minor| null| null| 297400| 268460|52.30464301|-3.50618282| null| null| null|null| 0| 0| 14| 1| 0| 0| 0| 0| 0| 0| 0| 0| 15| U|\n", + "|148| 931702| E|2003|2003-05-06| 18| 4| 14| U| MCU| Minor| null| null| 297400| 268460|52.30464301|-3.50618282| null| null| null|null| 0| 0| 9| 0| 3| 0| 0| 0| 0| 0| 0| 0| 12| U|\n", + "|149| 931852| E|2003|2003-06-04| 7| 3| 39| U| MCU| Minor| null| null| 247200| 643060|55.65695978|-4.43027005| null| null| null|null| 0| 0| 11| 0| 5| 4| 0| 0| 0| 0| 0| 4| 20| U|\n", + "|150| 931852| E|2003|2003-06-04| 8| 3| 39| U| MCU| Minor| null| null| 247200| 643060|55.65695978|-4.43027005| null| null| null|null| 0| 0| 19| 2| 4| 1| 0| 0| 0| 0| 0| 1| 26| U|\n", + "+---+--------------+-------------------+----+----------+----+---------+------------------+---------+-------------+---------+------------------------+----------------------+-------+--------+-----------+-----------+--------------+-----------------+--------+----+------------+--------------------------+--------------+-----------------+----+-----------------+-----------------+-------------------------+----------------------------+-----------------------+-----------------------+--------+------------------+--------------------+\n", + "only showing top 150 rows\n", + "\n" + ] + } + ], + "source": [ + "#Trafficeeachyear = Traffic_Information_df.groupby('road_name').agg(F.count(Traffic_Information_df.id).alias('Total Traffic'))\n", + "#Trafficeeachyear.show(1500)\n", + "from pyspark.sql.functions import concat, col, lit\n", + "Trafficeeachyear=Traffic_Information_df.withColumn('road_name_new_column', concat(Traffic_Information_df.road_name.substr(1, 1),Traffic_Information_df.road_name.substr(8, 1)))\n", + "Trafficeeachyear=Trafficeeachyear.filter(Trafficeeachyear.year<2020)\n", + "#Trafficeeachyear=Trafficeeachyear.filter(Trafficeeachyear.year>2004)\n", + "Trafficeeachyear.show(150)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+--------------------+----+-------------+\n", + "|road_name_new_column|year|Total_Traffic|\n", + "+--------------------+----+-------------+\n", + "| C|2000| 2110295.0|\n", + "| U|2000| 2341215.0|\n", + "| B|2000| 2957168.0|\n", + "| M|2000| 3.1670389E7|\n", + "| A|2000| 9.0800748E7|\n", + "| B|2001| 3280136.0|\n", + "| U|2001| 2339426.0|\n", + "| A|2001| 9.9314421E7|\n", + "| M|2001| 3.1703444E7|\n", + "| C|2001| 2263570.0|\n", + "| M|2002| 2.9488912E7|\n", + "| C|2002| 2366815.0|\n", + "| A|2002| 9.5485006E7|\n", + "| U|2002| 2415006.0|\n", + "| B|2002| 3722732.0|\n", + "| M|2003| 2.6506861E7|\n", + "| A|2003| 8.7778804E7|\n", + "| B|2003| 5249863.0|\n", + "| U|2003| 8329385.0|\n", + "| C|2003| 4100266.0|\n", + "+--------------------+----+-------------+\n", + "only showing top 20 rows\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>road_name_new_column</th>\n", + " <th>year</th>\n", + " <th>Total_Traffic</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>U</td>\n", + " <td>2000</td>\n", + " <td>2341215.0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>U</td>\n", + " <td>2001</td>\n", + " <td>2339426.0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>U</td>\n", + " <td>2002</td>\n", + " <td>2415006.0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>U</td>\n", + " <td>2003</td>\n", + " <td>8329385.0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>U</td>\n", + " <td>2004</td>\n", + " <td>8312359.0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>5</th>\n", + " <td>U</td>\n", + " <td>2005</td>\n", + " <td>7369477.0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>6</th>\n", + " <td>U</td>\n", + " <td>2006</td>\n", + " <td>8209734.0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>7</th>\n", + " <td>U</td>\n", + " <td>2007</td>\n", + " <td>7824099.0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>8</th>\n", + " <td>U</td>\n", + " <td>2008</td>\n", + " <td>10962590.0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>9</th>\n", + " <td>U</td>\n", + " <td>2009</td>\n", + " <td>10734396.0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>10</th>\n", + " <td>U</td>\n", + " <td>2010</td>\n", + " <td>2739797.0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>11</th>\n", + " <td>U</td>\n", + " <td>2011</td>\n", + " <td>2691316.0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>12</th>\n", + " <td>U</td>\n", + " <td>2012</td>\n", + " <td>2673956.0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>13</th>\n", + " <td>U</td>\n", + " <td>2013</td>\n", + " <td>2638774.0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>14</th>\n", + " <td>U</td>\n", + " <td>2014</td>\n", + " <td>2590397.0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>15</th>\n", + " <td>U</td>\n", + " <td>2015</td>\n", + " <td>2572862.0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>16</th>\n", + " <td>U</td>\n", + " <td>2016</td>\n", + " <td>2444829.0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>17</th>\n", + " <td>U</td>\n", + " <td>2017</td>\n", + " <td>2552781.0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>18</th>\n", + " <td>U</td>\n", + " <td>2018</td>\n", + " <td>8770359.0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>19</th>\n", + " <td>U</td>\n", + " <td>2019</td>\n", + " <td>7501575.0</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " road_name_new_column year Total_Traffic\n", + "0 U 2000 2341215.0\n", + "1 U 2001 2339426.0\n", + "2 U 2002 2415006.0\n", + "3 U 2003 8329385.0\n", + "4 U 2004 8312359.0\n", + "5 U 2005 7369477.0\n", + "6 U 2006 8209734.0\n", + "7 U 2007 7824099.0\n", + "8 U 2008 10962590.0\n", + "9 U 2009 10734396.0\n", + "10 U 2010 2739797.0\n", + "11 U 2011 2691316.0\n", + "12 U 2012 2673956.0\n", + "13 U 2013 2638774.0\n", + "14 U 2014 2590397.0\n", + "15 U 2015 2572862.0\n", + "16 U 2016 2444829.0\n", + "17 U 2017 2552781.0\n", + "18 U 2018 8770359.0\n", + "19 U 2019 7501575.0" + ] + }, + "execution_count": 93, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Trafficeeachyear_df = Trafficeeachyear.groupby('road_name_new_column','year').agg(F.sum(Trafficeeachyear.all_motor_vehicles).alias('Total_Traffic')).sort('year')\n", + "Trafficeeachyear_df.show()\n", + "#Trafficeeachyearrr_df = Trafficeeachyear.groupby('road_name_new_column','year').\n", + "A=Trafficeeachyear_df.filter(Trafficeeachyear_df.road_name_new_column.contains(\"A\")).toPandas()\n", + "B=Trafficeeachyear_df.filter(Trafficeeachyear_df.road_name_new_column.contains(\"B\")).toPandas()\n", + "C=Trafficeeachyear_df.filter(Trafficeeachyear_df.road_name_new_column.contains(\"C\")).toPandas()\n", + "M=Trafficeeachyear_df.filter(Trafficeeachyear_df.road_name_new_column.contains(\"M\")).toPandas()\n", + "U=Trafficeeachyear_df.filter(Trafficeeachyear_df.road_name_new_column.contains(\"U\")).toPandas()\n", + "U\n", + "#BusAccident_df=Vehicle_Information_df.filter(Vehicle_Information_df.Vehicle_Type.contains(\"bus\")|Vehicle_Information_df.Vehicle_Type.contains(\"Bus\"))\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+--------------------+----+--------------+\n", + "|road_name_new_column|Year|Total_Accident|\n", + "+--------------------+----+--------------+\n", + "| M|2005| 8198|\n", + "| C|2005| 16500|\n", + "| B|2005| 24991|\n", + "| U|2005| 60026|\n", + "| A|2005| 89020|\n", + "| B|2006| 23826|\n", + "| C|2006| 16615|\n", + "| M|2006| 7920|\n", + "| U|2006| 56291|\n", + "| A|2006| 84509|\n", + "| B|2007| 23292|\n", + "| M|2007| 7488|\n", + "| U|2007| 53284|\n", + "| C|2007| 16247|\n", + "| A|2007| 81804|\n", + "| A|2008| 77266|\n", + "| U|2008| 49140|\n", + "| M|2008| 6822|\n", + "| C|2008| 15600|\n", + "| B|2008| 21763|\n", + "| U|2009| 46458|\n", + "| A|2009| 74620|\n", + "| B|2009| 20933|\n", + "| M|2009| 6172|\n", + "| C|2009| 15371|\n", + "| C|2010| 13947|\n", + "| U|2010| 43938|\n", + "| M|2010| 6066|\n", + "| B|2010| 19755|\n", + "| A|2010| 70708|\n", + "| M|2011| 5379|\n", + "| U|2011| 42216|\n", + "| C|2011| 14037|\n", + "| B|2011| 19513|\n", + "| A|2011| 70329|\n", + "| M|2012| 5212|\n", + "| B|2012| 18795|\n", + "| A|2012| 67569|\n", + "| U|2012| 40770|\n", + "| C|2012| 13225|\n", + "| A|2013| 64837|\n", + "| U|2013| 38990|\n", + "| B|2013| 17830|\n", + "| M|2013| 4983|\n", + "| C|2013| 12020|\n", + "| A|2014| 68212|\n", + "| B|2014| 18573|\n", + "| M|2014| 5246|\n", + "| C|2014| 12969|\n", + "| U|2014| 41322|\n", + "| M|2015| 5148|\n", + "| C|2015| 11069|\n", + "| U|2015| 41940|\n", + "| A|2015| 64682|\n", + "| B|2015| 17217|\n", + "| B|2016| 16627|\n", + "| A|2016| 61853|\n", + "| U|2016| 43762|\n", + "| C|2016| 9372|\n", + "| M|2016| 5007|\n", + "| C|2017| 7981|\n", + "| M|2017| 4430|\n", + "| B|2017| 14961|\n", + "| A|2017| 56809|\n", + "| U|2017| 45801|\n", + "| M|2018| 4225|\n", + "| B|2018| 14210|\n", + "| A|2018| 53840|\n", + "| C|2018| 7005|\n", + "| U|2018| 43355|\n", + "| C|2019| 6067|\n", + "| U|2019| 40459|\n", + "| M|2019| 3810|\n", + "| A|2019| 52662|\n", + "| B|2019| 14538|\n", + "+--------------------+----+--------------+\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [] + } + ], + "source": [ + "from pyspark.sql.functions import concat, col, lit\n", + "Accidenteeachyearwrtroad=Accident_Information20052019_df.withColumn('road_name_new_column', concat(Accident_Information20052019_df['1st_Road_Class'].substr(1, 1),Accident_Information20052019_df['1st_Road_Class'].substr(13, 1)))\n", + "#Trafficeeachyear=Trafficeeachyear.filter(Trafficeeachyear.year<2017)\n", + "#Trafficeeachyear=Trafficeeachyear.filter(Trafficeeachyear.year>2004)\n", + "#Accidenteeachyearwrtroad.show(150)\n", + "Accidenteeachyearwrtroad = Accidenteeachyearwrtroad.groupby('road_name_new_column','Year').agg(F.count(Accidenteeachyearwrtroad.Accident_Index).alias('Total_Accident')).sort('Year')\n", + "Accidenteeachyearwrtroad.show(150)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [] + } + ], + "source": [ + "A=Accidenteeachyearwrtroad.filter(Accidenteeachyearwrtroad.road_name_new_column.contains(\"A\")).toPandas()\n", + "B=Accidenteeachyearwrtroad.filter(Accidenteeachyearwrtroad.road_name_new_column.contains(\"B\")).toPandas()\n", + "C=Accidenteeachyearwrtroad.filter(Accidenteeachyearwrtroad.road_name_new_column.contains(\"C\")).toPandas()\n", + "M=Accidenteeachyearwrtroad.filter(Accidenteeachyearwrtroad.road_name_new_column.contains(\"M\")).toPandas()\n", + "U=Accidenteeachyearwrtroad.filter(Accidenteeachyearwrtroad.road_name_new_column.contains(\"U\")).toPandas()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "<Figure size 1080x576 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + " \n", + "# set width of bar\n", + "barWidth = 0.2\n", + "fig = plt.subplots(figsize =(15, 8))\n", + " \n", + "# set height of bar\n", + "#resultGoodsperbillp.plot.bar(x=\"Year\", y=\"Accidents per billion mile\")\n", + "IT = A[\"Total_Accident\"]\n", + "ECE = B[\"Total_Accident\"]\n", + "CAC = C[\"Total_Accident\"]\n", + "CSE = M[\"Total_Accident\"]\n", + "CAR = U[\"Total_Accident\"]\n", + "\n", + "# Set position of bar on X axis\n", + "br1 = np.arange(len(IT))\n", + "br2 = [x + barWidth for x in br1]\n", + "br3 = [x + barWidth for x in br2]\n", + "br4 = [x + barWidth for x in br3]\n", + "br5 = [x + barWidth for x in br4]\n", + " \n", + "# Make the plot\n", + "plt.bar(br1, IT, color ='r', width = barWidth,\n", + " edgecolor ='grey', label ='Road A')\n", + "plt.bar(br2, ECE, color ='g', width = barWidth,\n", + " edgecolor ='grey', label ='Road B')\n", + "plt.bar(br3, CAC, color ='b', width = barWidth,\n", + " edgecolor ='grey', label ='Road C')\n", + "plt.bar(br4, CAR, color ='y', width = barWidth,\n", + " edgecolor ='grey', label ='Road U')\n", + "plt.bar(br5, CSE, width = barWidth,\n", + " edgecolor ='grey', label ='Road M')\n", + " \n", + " \n", + "# Adding Xticks\n", + "plt.xlabel('Year', fontweight ='bold', fontsize = 15)\n", + "plt.ylabel('Accidents', fontweight ='bold', fontsize = 15)\n", + "plt.xticks([r + barWidth for r in range(len(IT))],\n", + " A[\"Year\"])\n", + " \n", + "plt.legend()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "ename": "KeyError", + "evalue": "'Total_Traffic'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/usr/local/lib/python3.9/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m 3360\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3361\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3362\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.9/site-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.9/site-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", + "\u001b[0;31mKeyError\u001b[0m: 'Total_Traffic'", + "\nThe above exception was the direct cause of the following exception:\n", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/var/folders/v0/jqv1xcw13pn37fh0ppsl8b_w0000gp/T/ipykernel_10114/1013960879.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0;31m# set height of bar\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0;31m#resultGoodsperbillp.plot.bar(x=\"Year\", y=\"Accidents per billion mile\")\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m \u001b[0mIT\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mA\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Total_Traffic\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 11\u001b[0m \u001b[0mECE\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mB\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Total_Traffic\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0mCSE\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mM\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Total_Traffic\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.9/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 3453\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3454\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3455\u001b[0;31m \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3456\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3457\u001b[0m \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.9/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m 3361\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3362\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3363\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3364\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3365\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mis_scalar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0misna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhasnans\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mKeyError\u001b[0m: 'Total_Traffic'" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "<Figure size 1080x720 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + " \n", + "# set width of bar\n", + "barWidth = 0.2\n", + "fig = plt.subplots(figsize =(15, 10))\n", + " \n", + "# set height of bar\n", + "#resultGoodsperbillp.plot.bar(x=\"Year\", y=\"Accidents per billion mile\")\n", + "IT = A[\"Total_Traffic\"]\n", + "ECE = B[\"Total_Traffic\"]\n", + "CSE = M[\"Total_Traffic\"]\n", + "CAR = U[\"Total_Traffic\"]\n", + "CAC = C[\"Total_Traffic\"]\n", + "# Set position of bar on X axis\n", + "br1 = np.arange(len(IT))\n", + "br2 = [x + barWidth for x in br1]\n", + "br3 = [x + barWidth for x in br2]\n", + "br4 = [x + barWidth for x in br3]\n", + "br5 = [x + barWidth for x in br4]\n", + " \n", + "# Make the plot\n", + "plt.bar(br1, IT, color ='r', width = barWidth,\n", + " edgecolor ='grey', label ='Road A')\n", + "plt.bar(br2, ECE, color ='g', width = barWidth,\n", + " edgecolor ='grey', label ='Road B')\n", + "plt.bar(br3, CSE, color ='b', width = barWidth,\n", + " edgecolor ='grey', label ='Road M')\n", + "plt.bar(br4, CAR, color ='y', width = barWidth,\n", + " edgecolor ='grey', label ='Road U')\n", + "plt.bar(br5, CAC, width = barWidth,\n", + " edgecolor ='grey', label ='Road C')\n", + " \n", + " \n", + "# Adding Xticks\n", + "plt.xlabel('Year', fontweight ='bold', fontsize = 15)\n", + "plt.ylabel('Traffic', fontweight ='bold', fontsize = 15)\n", + "plt.xticks([r + barWidth for r in range(len(IT))],\n", + " A[\"year\"])\n", + " \n", + "plt.legend()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+----+------------------------------+-----------+\n", + "|Year|Total accidents of Pedal Cycle|Pedal Cycle|\n", + "+----+------------------------------+-----------+\n", + "|2005| 17039| 2.7|\n", + "|2006| 16611| 2.8|\n", + "|2007| 16607| 2.6|\n", + "|2008| 16797| 2.8|\n", + "|2009| 17599| 3|\n", + "|2010| 17811| 3|\n", + "|2011| 19883| 3.1|\n", + "|2012| 19708| 3.1|\n", + "|2013| 20049| 3.1|\n", + "|2014| 21979| 3.5|\n", + "|2015| 19440| 3.2|\n", + "|2016| 19047| 3.2|\n", + "|2017| 18954| 3.3|\n", + "|2018| 18125| 3.3|\n", + "|2019| 17437| 3.5|\n", + "+----+------------------------------+-----------+\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+----+------------------------------+-----------+--------------------------+\n", + "|Year|Total accidents of Pedal Cycle|Pedal Cycle|Accidents per billion mile|\n", + "+----+------------------------------+-----------+--------------------------+\n", + "|2005| 17039| 2.7| 6310.74074074074|\n", + "|2006| 16611| 2.8| 5932.5|\n", + "|2007| 16607| 2.6| 6387.307692307692|\n", + "|2008| 16797| 2.8| 5998.928571428572|\n", + "|2009| 17599| 3| 5866.333333333333|\n", + "|2010| 17811| 3| 5937.0|\n", + "|2011| 19883| 3.1| 6413.870967741936|\n", + "|2012| 19708| 3.1| 6357.419354838709|\n", + "|2013| 20049| 3.1| 6467.419354838709|\n", + "|2014| 21979| 3.5| 6279.714285714285|\n", + "|2015| 19440| 3.2| 6075.0|\n", + "|2016| 19047| 3.2| 5952.1875|\n", + "|2017| 18954| 3.3| 5743.636363636364|\n", + "|2018| 18125| 3.3| 5492.424242424243|\n", + "|2019| 17437| 3.5| 4982.0|\n", + "+----+------------------------------+-----------+--------------------------+\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "ename": "NameError", + "evalue": "name 'AT' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/var/folders/v0/jqv1xcw13pn37fh0ppsl8b_w0000gp/T/ipykernel_532/4288679720.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0mForA\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mAA\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mAT\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mon\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Year'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhow\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'left_outer'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msort\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Year'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 12\u001b[0m \u001b[0mForA\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mForA\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwithColumn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Accidents per Year Traffic probabilty'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mForA\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mForA\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mNameError\u001b[0m: name 'AT' is not defined" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#MotorcycleAccidentovertheyeards_df\n", + "Billionvehiclemiles20052017PedalCycle_df=Billionvehiclemiles20052017_df.select(col(\"Year\"),col(\"Pedal Cycle\")).sort(\"Year\")\n", + "resultBCperbill=cycleAccidentovertheyeards_df.join(Billionvehiclemiles20052017PedalCycle_df, on=['Year'], how='left_outer').sort('Year')\n", + "resultBCperbill.show()\n", + "resultBCperbill=resultBCperbill.withColumn('Accidents per billion mile', resultBCperbill[1]/resultBCperbill[2])\n", + "resultBCperbill.show()\n", + "resultBCperbillmm = resultBCperbill.toPandas()\n", + "resultBCperbillmm.plot.bar(x=\"Year\", y=\"Accidents per billion mile\")\n", + "\n", + "\n", + "ForA=AA.join(AT, on=['Year'], how='left_outer').sort('Year')\n", + "ForA=ForA.withColumn('Accidents per Year Traffic probabilty', ForA[2]/ForA[4])\n", + "\n", + "ForA.show()\n", + "ForA = ForA.toPandas()\n", + "ForA.plot.bar(x=\"Year\", y=\"Accidents per Year Traffic probabilty\")\n", + "\n", + "AA=Accidenteeachyearwrtroad.filter(Accidenteeachyearwrtroad.road_name_new_column.contains(\"A\"))\n", + "AT=Trafficeeachyear_df.filter(Trafficeeachyear_df.road_name_new_column.contains(\"A\"))\n", + "AA.show()\n", + "AT.show()\n", + "\n" + ] + } + ], + "metadata": { + "interpreter": { + "hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49" + }, + "kernelspec": { + "display_name": "Python 3.9.7 64-bit", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +}