From a15d22651ac3ed4263bc71021624fdc75c778ac2 Mon Sep 17 00:00:00 2001 From: "Orakzai, Asfandyar (PG/T - Computer Science)" <ao00965@surrey.ac.uk> Date: Tue, 10 Jan 2023 15:24:25 +0000 Subject: [PATCH] Upload New File --- from1997.ipynb | 15589 +++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 15589 insertions(+) create mode 100644 from1997.ipynb diff --git a/from1997.ipynb b/from1997.ipynb new file mode 100644 index 0000000..0cbadb9 --- /dev/null +++ b/from1997.ipynb @@ -0,0 +1,15589 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "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", + "# If we want Spark to run locally with 'k' worker threads, we can specify as \"local[k]\".\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": 2, + "metadata": {}, + "outputs": [], + "source": [ + "Accident_Information_df = spark.read.format('csv')\\\n", + " .option('header',True).option('escape','\"')\\\n", + " .load('/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", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DataFrame[accident_index: string, accident_year: string, accident_reference: string, location_easting_osgr: string, location_northing_osgr: string, longitude: string, latitude: string, police_force: string, accident_severity: string, number_of_vehicles: string, number_of_casualties: string, date: string, day_of_week: string, time: string, local_authority_district: string, local_authority_ons_district: string, local_authority_highway: string, first_road_class: string, first_road_number: string, road_type: string, speed_limit: string, junction_detail: string, junction_control: string, second_road_class: string, second_road_number: string, pedestrian_crossing_human_control: string, pedestrian_crossing_physical_facilities: string, light_conditions: string, weather_conditions: string, road_surface_conditions: string, special_conditions_at_site: string, carriageway_hazards: string, urban_or_rural_area: string, did_police_officer_attend_scene_of_accident: string, trunk_road_flag: string, lsoa_of_accident_location: string]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "A2018" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+--------------+-------------+------------------+---------------------+----------------------+---------+---------+------------+-----------------+------------------+--------------------+----------+-----------+-----+------------------------+----------------------------+-----------------------+----------------+-----------------+---------+-----------+---------------+----------------+-----------------+------------------+---------------------------------+---------------------------------------+----------------+------------------+-----------------------+--------------------------+-------------------+-------------------+-------------------------------------------+---------------+-------------------------+\n", + "|accident_index|accident_year|accident_reference|location_easting_osgr|location_northing_osgr|longitude| latitude|police_force|accident_severity|number_of_vehicles|number_of_casualties| date|day_of_week| time|local_authority_district|local_authority_ons_district|local_authority_highway|first_road_class|first_road_number|road_type|speed_limit|junction_detail|junction_control|second_road_class|second_road_number|pedestrian_crossing_human_control|pedestrian_crossing_physical_facilities|light_conditions|weather_conditions|road_surface_conditions|special_conditions_at_site|carriageway_hazards|urban_or_rural_area|did_police_officer_attend_scene_of_accident|trunk_road_flag|lsoa_of_accident_location|\n", + "+--------------+-------------+------------------+---------------------+----------------------+---------+---------+------------+-----------------+------------------+--------------------+----------+-----------+-----+------------------------+----------------------------+-----------------------+----------------+-----------------+---------+-----------+---------------+----------------+-----------------+------------------+---------------------------------+---------------------------------------+----------------+------------------+-----------------------+--------------------------+-------------------+-------------------+-------------------------------------------+---------------+-------------------------+\n", + "| 200501BS00001| 2005| 01BS00001| 525680| 178240| -0.19117|51.489096| 1| 2| 1| 1|04/01/2005| 3|17:42| 12| E09000020| E09000020| 3| 3218| 6| 30| 0| -1| -1| -1| 0| 1| 1| 2| 2| 0| 0| 1| 1| 2| E01002849|\n", + "| 200501BS00002| 2005| 01BS00002| 524170| 181650|-0.211708|51.520075| 1| 3| 1| 1|05/01/2005| 4|17:36| 12| E09000020| E09000020| 4| 450| 3| 30| 6| 2| 5| 0| 0| 5| 4| 1| 1| 0| 0| 1| 1| 2| E01002909|\n", + "| 200501BS00003| 2005| 01BS00003| 524520| 182240|-0.206458|51.525301| 1| 3| 2| 1|06/01/2005| 5|00:15| 12| E09000020| E09000020| 5| 0| 6| 30| 0| -1| -1| -1| 0| 0| 4| 1| 1| 0| 0| 1| 1| 2| E01002857|\n", + "| 200501BS00004| 2005| 01BS00004| 526900| 177530|-0.173862|51.482442| 1| 3| 1| 1|07/01/2005| 6|10:35| 12| E09000020| E09000020| 3| 3220| 6| 30| 0| -1| -1| -1| 0| 0| 1| 1| 1| 0| 0| 1| 1| 2| E01002840|\n", + "| 200501BS00005| 2005| 01BS00005| 528060| 179040|-0.156618|51.495752| 1| 3| 1| 1|10/01/2005| 2|21:13| 12| E09000020| E09000020| 6| 0| 6| 30| 0| -1| -1| -1| 0| 0| 7| 1| 2| 0| 0| 1| 1| 2| E01002863|\n", + "| 200501BS00006| 2005| 01BS00006| 524770| 181160|-0.203238| 51.51554| 1| 3| 2| 1|11/01/2005| 3|12:40| 12| E09000020| E09000020| 6| 0| 6| 30| 0| -1| -1| -1| 0| 0| 1| 2| 2| 6| 0| 1| 1| 2| E01002832|\n", + "| 200501BS00007| 2005| 01BS00007| 524220| 180830|-0.211277|51.512695| 1| 3| 2| 1|13/01/2005| 5|20:40| 12| E09000020| E09000020| 5| 0| 6| 30| 3| 4| 6| 0| 0| 0| 4| 1| 1| 0| 0| 1| 1| 2| E01002875|\n", + "| 200501BS00009| 2005| 01BS00009| 525890| 179710|-0.187623| 51.50226| 1| 3| 1| 2|14/01/2005| 6|17:35| 12| E09000020| E09000020| 3| 315| 3| 30| 0| -1| -1| -1| 0| 0| 1| 1| 1| 0| 0| 1| 1| 2| E01002889|\n", + "| 200501BS00010| 2005| 01BS00010| 527350| 177650|-0.167342| 51.48342| 1| 3| 2| 2|15/01/2005| 7|22:43| 12| E09000020| E09000020| 3| 3212| 6| 30| 6| 2| 4| 304| 0| 5| 4| 1| 1| 0| 0| 1| 1| 2| E01002900|\n", + "| 200501BS00011| 2005| 01BS00011| 524550| 180810|-0.206531|51.512443| 1| 3| 2| 5|15/01/2005| 7|16:00| 12| E09000020| E09000020| 4| 450| 6| 30| 3| 4| 5| 0| 0| 8| 1| 1| 1| 0| 0| 1| 1| 2| E01002875|\n", + "| 200501BS00012| 2005| 01BS00012| 526240| 178900|-0.182872|51.494902| 1| 3| 1| 1|16/01/2005| 1|00:42| 12| E09000020| E09000020| 3| 4| 6| 30| 6| 2| 4| 325| 0| 5| 4| 1| 1| 0| 0| 1| 1| 2| E01002835|\n", + "| 200501BS00014| 2005| 01BS00014| 526170| 177690|-0.184312|51.484044| 1| 3| 2| 1|25/01/2005| 3|20:48| 12| E09000020| E09000020| 3| 3220| 6| 30| 6| 2| 3| 308| 0| 5| 4| 1| 2| 0| 0| 1| 1| 2| E01002912|\n", + "| 200501BS00015| 2005| 01BS00015| 525590| 178520|-0.192366|51.491632| 1| 3| 1| 1|11/01/2005| 3|12:55| 12| E09000020| E09000020| 6| 0| 2| 30| 3| 4| 3| 3220| 0| 1| 1| 2| 2| 0| 0| 1| 1| 2| E01002849|\n", + "| 200501BS00016| 2005| 01BS00016| 527990| 178690|-0.157753|51.492622| 1| 3| 2| 1|18/01/2005| 3|05:01| 12| E09000020| E09000020| 3| 3217| 2| 30| 3| 4| 3| 3216| 0| 0| 4| 2| 2| 0| 0| 1| 1| 2| E01002902|\n", + "| 200501BS00017| 2005| 01BS00017| 526700| 178970|-0.176224|51.495429| 1| 3| 1| 2|18/01/2005| 3|11:15| 12| E09000020| E09000020| 3| 4| 3| 30| 0| -1| -1| -1| 0| 0| 1| 1| 1| 0| 0| 1| 1| 2| E01002821|\n", + "| 200501BS00018| 2005| 01BS00018| 526460| 177460| -0.18022|51.481912| 1| 3| 1| 1|18/01/2005| 3|10:50| 12| E09000020| E09000020| 3| 3217| 6| 30| 3| 4| 6| 0| 0| 1| 1| 1| 1| 0| 0| 1| 1| 2| E01002840|\n", + "| 200501BS00019| 2005| 01BS00019| 524680| 179450|-0.205139|51.500191| 1| 2| 2| 1|20/01/2005| 5|00:15| 12| E09000020| E09000020| 6| 0| 6| 30| 3| 4| 6| 0| 0| 0| 4| 1| 1| 0| 0| 1| 1| 2| E01002864|\n", + "| 200501BS00020| 2005| 01BS00020| 527000| 179020|-0.171887|51.495811| 1| 3| 2| 1|21/01/2005| 6|09:15| 12| E09000020| E09000020| 3| 3218| 6| 30| 3| 4| 3| 4| 0| 0| 1| 1| 1| 0| 0| 1| 1| 2| E01002821|\n", + "| 200501BS00021| 2005| 01BS00021| 527810| 178010| -0.16059|51.486552| 1| 3| 2| 1|21/01/2005| 6|21:16| 12| E09000020| E09000020| 4| 302| 6| 30| 0| -1| -1| -1| 0| 0| 4| 1| 1| 0| 0| 1| 1| 2| E01002901|\n", + "| 200501BS00022| 2005| 01BS00022| 526790| 178980|-0.174925|51.495498| 1| 2| 1| 1|08/01/2005| 7|03:00| 12| E09000020| E09000020| 3| 4| 6| 30| 3| 4| 6| 0| 0| 0| 4| 1| 1| 0| 0| 1| 1| 2| E01002821|\n", + "+--------------+-------------+------------------+---------------------+----------------------+---------+---------+------------+-----------------+------------------+--------------------+----------+-----------+-----+------------------------+----------------------------+-----------------------+----------------+-----------------+---------+-----------+---------------+----------------+-----------------+------------------+---------------------------------+---------------------------------------+----------------+------------------+-----------------------+--------------------------+-------------------+-------------------+-------------------------------------------+---------------+-------------------------+\n", + "only showing top 20 rows\n", + "\n" + ] + } + ], + "source": [ + "\n", + "A2005=A2018.filter(A2018.accident_year>2004)\n", + "A20052020=A2005.filter(A2005.accident_year<2020)\n", + "A20052020.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "A2018=A20052020" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DataFrame[accident_index: string, accident_year: string, accident_reference: string, location_easting_osgr: string, location_northing_osgr: string, longitude: string, latitude: string, police_force: string, accident_severity: string, number_of_vehicles: string, number_of_casualties: string, date: string, day_of_week: string, time: string, local_authority_district: string, local_authority_ons_district: string, local_authority_highway: string, first_road_class: string, first_road_number: string, road_type: string, speed_limit: string, junction_detail: string, junction_control: string, second_road_class: string, second_road_number: string, pedestrian_crossing_human_control: string, pedestrian_crossing_physical_facilities: string, light_conditions: string, weather_conditions: string, road_surface_conditions: string, special_conditions_at_site: string, carriageway_hazards: string, urban_or_rural_area: string, did_police_officer_attend_scene_of_accident: string, trunk_road_flag: string, lsoa_of_accident_location: string]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "A2018" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "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", + "\n", + "A2018=A2018.withColumn(\n", + " \"first_road_class\",\n", + " when(\n", + " col(\"first_road_class\") == 1,\n", + " \"M\"\n", + " ).otherwise(col(\"first_road_class\")),\n", + ")\n", + "A2018=A2018.withColumn(\n", + " \"first_road_class\",\n", + " when(\n", + " col(\"first_road_class\") == 2,\n", + " \"A\"\n", + " ).otherwise(col(\"first_road_class\")),\n", + ")\n", + "A2018=A2018.withColumn(\n", + " \"first_road_class\",\n", + " when(\n", + " col(\"first_road_class\") == 3,\n", + " \"A\"\n", + " ).otherwise(col(\"first_road_class\")),\n", + ")\n", + "A2018=A2018.withColumn(\n", + " \"first_road_class\",\n", + " when(\n", + " col(\"first_road_class\") == 4,\n", + " \"B\"\n", + " ).otherwise(col(\"first_road_class\")),\n", + ")\n", + "A2018=A2018.withColumn(\n", + " \"first_road_class\",\n", + " when(\n", + " col(\"first_road_class\") == 5,\n", + " \"C\"\n", + " ).otherwise(col(\"first_road_class\")),\n", + ")\n", + "A2018=A2018.withColumn(\n", + " \"first_road_class\",\n", + " when(\n", + " col(\"first_road_class\") == 6,\n", + " \"U\"\n", + " ).otherwise(col(\"first_road_class\")),\n", + ")\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+--------------+-------------+------------------+---------------------+----------------------+---------+---------+------------+-----------------+------------------+--------------------+----------+-----------+-----+------------------------+----------------------------+-----------------------+----------------+-----------------+---------+-----------+---------------+----------------+-----------------+------------------+---------------------------------+---------------------------------------+----------------+------------------+-----------------------+--------------------------+-------------------+-------------------+-------------------------------------------+---------------+-------------------------+\n", + "|accident_index|accident_year|accident_reference|location_easting_osgr|location_northing_osgr|longitude| latitude|police_force|accident_severity|number_of_vehicles|number_of_casualties| date|day_of_week| time|local_authority_district|local_authority_ons_district|local_authority_highway|first_road_class|first_road_number|road_type|speed_limit|junction_detail|junction_control|second_road_class|second_road_number|pedestrian_crossing_human_control|pedestrian_crossing_physical_facilities|light_conditions|weather_conditions|road_surface_conditions|special_conditions_at_site|carriageway_hazards|urban_or_rural_area|did_police_officer_attend_scene_of_accident|trunk_road_flag|lsoa_of_accident_location|\n", + "+--------------+-------------+------------------+---------------------+----------------------+---------+---------+------------+-----------------+------------------+--------------------+----------+-----------+-----+------------------------+----------------------------+-----------------------+----------------+-----------------+---------+-----------+---------------+----------------+-----------------+------------------+---------------------------------+---------------------------------------+----------------+------------------+-----------------------+--------------------------+-------------------+-------------------+-------------------------------------------+---------------+-------------------------+\n", + "| 200501BS00001| 2005| 01BS00001| 525680| 178240| -0.19117|51.489096| 1| 2| 1| 1|04/01/2005| 3|17:42| 12| E09000020| E09000020| A| 3218| 6| 30| 0| -1| -1| -1| 0| 1| 1| 2| 2| 0| 0| 1| 1| 2| E01002849|\n", + "| 200501BS00002| 2005| 01BS00002| 524170| 181650|-0.211708|51.520075| 1| 3| 1| 1|05/01/2005| 4|17:36| 12| E09000020| E09000020| B| 450| 3| 30| 6| 2| 5| 0| 0| 5| 4| 1| 1| 0| 0| 1| 1| 2| E01002909|\n", + "| 200501BS00003| 2005| 01BS00003| 524520| 182240|-0.206458|51.525301| 1| 3| 2| 1|06/01/2005| 5|00:15| 12| E09000020| E09000020| C| 0| 6| 30| 0| -1| -1| -1| 0| 0| 4| 1| 1| 0| 0| 1| 1| 2| E01002857|\n", + "| 200501BS00004| 2005| 01BS00004| 526900| 177530|-0.173862|51.482442| 1| 3| 1| 1|07/01/2005| 6|10:35| 12| E09000020| E09000020| A| 3220| 6| 30| 0| -1| -1| -1| 0| 0| 1| 1| 1| 0| 0| 1| 1| 2| E01002840|\n", + "| 200501BS00005| 2005| 01BS00005| 528060| 179040|-0.156618|51.495752| 1| 3| 1| 1|10/01/2005| 2|21:13| 12| E09000020| E09000020| U| 0| 6| 30| 0| -1| -1| -1| 0| 0| 7| 1| 2| 0| 0| 1| 1| 2| E01002863|\n", + "| 200501BS00006| 2005| 01BS00006| 524770| 181160|-0.203238| 51.51554| 1| 3| 2| 1|11/01/2005| 3|12:40| 12| E09000020| E09000020| U| 0| 6| 30| 0| -1| -1| -1| 0| 0| 1| 2| 2| 6| 0| 1| 1| 2| E01002832|\n", + "| 200501BS00007| 2005| 01BS00007| 524220| 180830|-0.211277|51.512695| 1| 3| 2| 1|13/01/2005| 5|20:40| 12| E09000020| E09000020| C| 0| 6| 30| 3| 4| 6| 0| 0| 0| 4| 1| 1| 0| 0| 1| 1| 2| E01002875|\n", + "| 200501BS00009| 2005| 01BS00009| 525890| 179710|-0.187623| 51.50226| 1| 3| 1| 2|14/01/2005| 6|17:35| 12| E09000020| E09000020| A| 315| 3| 30| 0| -1| -1| -1| 0| 0| 1| 1| 1| 0| 0| 1| 1| 2| E01002889|\n", + "| 200501BS00010| 2005| 01BS00010| 527350| 177650|-0.167342| 51.48342| 1| 3| 2| 2|15/01/2005| 7|22:43| 12| E09000020| E09000020| A| 3212| 6| 30| 6| 2| 4| 304| 0| 5| 4| 1| 1| 0| 0| 1| 1| 2| E01002900|\n", + "| 200501BS00011| 2005| 01BS00011| 524550| 180810|-0.206531|51.512443| 1| 3| 2| 5|15/01/2005| 7|16:00| 12| E09000020| E09000020| B| 450| 6| 30| 3| 4| 5| 0| 0| 8| 1| 1| 1| 0| 0| 1| 1| 2| E01002875|\n", + "| 200501BS00012| 2005| 01BS00012| 526240| 178900|-0.182872|51.494902| 1| 3| 1| 1|16/01/2005| 1|00:42| 12| E09000020| E09000020| A| 4| 6| 30| 6| 2| 4| 325| 0| 5| 4| 1| 1| 0| 0| 1| 1| 2| E01002835|\n", + "| 200501BS00014| 2005| 01BS00014| 526170| 177690|-0.184312|51.484044| 1| 3| 2| 1|25/01/2005| 3|20:48| 12| E09000020| E09000020| A| 3220| 6| 30| 6| 2| 3| 308| 0| 5| 4| 1| 2| 0| 0| 1| 1| 2| E01002912|\n", + "| 200501BS00015| 2005| 01BS00015| 525590| 178520|-0.192366|51.491632| 1| 3| 1| 1|11/01/2005| 3|12:55| 12| E09000020| E09000020| U| 0| 2| 30| 3| 4| 3| 3220| 0| 1| 1| 2| 2| 0| 0| 1| 1| 2| E01002849|\n", + "| 200501BS00016| 2005| 01BS00016| 527990| 178690|-0.157753|51.492622| 1| 3| 2| 1|18/01/2005| 3|05:01| 12| E09000020| E09000020| A| 3217| 2| 30| 3| 4| 3| 3216| 0| 0| 4| 2| 2| 0| 0| 1| 1| 2| E01002902|\n", + "| 200501BS00017| 2005| 01BS00017| 526700| 178970|-0.176224|51.495429| 1| 3| 1| 2|18/01/2005| 3|11:15| 12| E09000020| E09000020| A| 4| 3| 30| 0| -1| -1| -1| 0| 0| 1| 1| 1| 0| 0| 1| 1| 2| E01002821|\n", + "| 200501BS00018| 2005| 01BS00018| 526460| 177460| -0.18022|51.481912| 1| 3| 1| 1|18/01/2005| 3|10:50| 12| E09000020| E09000020| A| 3217| 6| 30| 3| 4| 6| 0| 0| 1| 1| 1| 1| 0| 0| 1| 1| 2| E01002840|\n", + "| 200501BS00019| 2005| 01BS00019| 524680| 179450|-0.205139|51.500191| 1| 2| 2| 1|20/01/2005| 5|00:15| 12| E09000020| E09000020| U| 0| 6| 30| 3| 4| 6| 0| 0| 0| 4| 1| 1| 0| 0| 1| 1| 2| E01002864|\n", + "| 200501BS00020| 2005| 01BS00020| 527000| 179020|-0.171887|51.495811| 1| 3| 2| 1|21/01/2005| 6|09:15| 12| E09000020| E09000020| A| 3218| 6| 30| 3| 4| 3| 4| 0| 0| 1| 1| 1| 0| 0| 1| 1| 2| E01002821|\n", + "| 200501BS00021| 2005| 01BS00021| 527810| 178010| -0.16059|51.486552| 1| 3| 2| 1|21/01/2005| 6|21:16| 12| E09000020| E09000020| B| 302| 6| 30| 0| -1| -1| -1| 0| 0| 4| 1| 1| 0| 0| 1| 1| 2| E01002901|\n", + "| 200501BS00022| 2005| 01BS00022| 526790| 178980|-0.174925|51.495498| 1| 2| 1| 1|08/01/2005| 7|03:00| 12| E09000020| E09000020| A| 4| 6| 30| 3| 4| 6| 0| 0| 0| 4| 1| 1| 0| 0| 1| 1| 2| E01002821|\n", + "+--------------+-------------+------------------+---------------------+----------------------+---------+---------+------------+-----------------+------------------+--------------------+----------+-----------+-----+------------------------+----------------------------+-----------------------+----------------+-----------------+---------+-----------+---------------+----------------+-----------------+------------------+---------------------------------+---------------------------------------+----------------+------------------+-----------------------+--------------------------+-------------------+-------------------+-------------------------------------------+---------------+-------------------------+\n", + "only showing top 20 rows\n", + "\n" + ] + } + ], + "source": [ + "A2018.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+----------------+-------------+---------------+\n", + "|first_road_class|accident_year|Total accidents|\n", + "+----------------+-------------+---------------+\n", + "| U| 2005| 60026|\n", + "| C| 2005| 16500|\n", + "| A| 2005| 89020|\n", + "| M| 2005| 8198|\n", + "| B| 2005| 24991|\n", + "| A| 2006| 84509|\n", + "| U| 2006| 56291|\n", + "| B| 2006| 23826|\n", + "| M| 2006| 7920|\n", + "| C| 2006| 16615|\n", + "| B| 2007| 23292|\n", + "| U| 2007| 53284|\n", + "| C| 2007| 16247|\n", + "| A| 2007| 81804|\n", + "| M| 2007| 7488|\n", + "| A| 2008| 77266|\n", + "| U| 2008| 49140|\n", + "| C| 2008| 15600|\n", + "| M| 2008| 6822|\n", + "| B| 2008| 21763|\n", + "+----------------+-------------+---------------+\n", + "only showing top 20 rows\n", + "\n" + ] + } + ], + "source": [ + "A2018t_df = A2018.groupby(\"first_road_class\",'accident_year').agg(F.count(A2018.accident_index).alias('Total accidents'))\n", + "A2018t_df=A2018t_df.sort(\"accident_year\")\n", + "A2018t_df.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+---------+----+---------------+\n", + "|road_name|year|Total accidents|\n", + "+---------+----+---------------+\n", + "| M|2005| 8198|\n", + "| A|2005| 89020|\n", + "| U|2005| 60026|\n", + "| B|2005| 24991|\n", + "| C|2005| 16500|\n", + "| C|2006| 16615|\n", + "| U|2006| 56291|\n", + "| B|2006| 23826|\n", + "| A|2006| 84509|\n", + "| M|2006| 7920|\n", + "| B|2007| 23292|\n", + "| A|2007| 81804|\n", + "| C|2007| 16247|\n", + "| M|2007| 7488|\n", + "| U|2007| 53284|\n", + "| A|2008| 77266|\n", + "| U|2008| 49140|\n", + "| B|2008| 21763|\n", + "| C|2008| 15600|\n", + "| M|2008| 6822|\n", + "+---------+----+---------------+\n", + "only showing top 20 rows\n", + "\n" + ] + } + ], + "source": [ + "A2018t_dftt = A2018t_df.withColumnRenamed(\"first_road_class\", \"road_name\")\\\n", + " .withColumnRenamed(\"accident_year\", \"year\")\n", + "A2018t_dftt.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>road_name</th>\n", + " <th>year</th>\n", + " <th>Total accidents</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>U</td>\n", + " <td>2005</td>\n", + " <td>60026</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>A</td>\n", + " <td>2005</td>\n", + " <td>89020</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>B</td>\n", + " <td>2005</td>\n", + " <td>24991</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>C</td>\n", + " <td>2005</td>\n", + " <td>16500</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>M</td>\n", + " <td>2005</td>\n", + " <td>8198</td>\n", + " </tr>\n", + " <tr>\n", + " <th>...</th>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " </tr>\n", + " <tr>\n", + " <th>70</th>\n", + " <td>A</td>\n", + " <td>2019</td>\n", + " <td>52662</td>\n", + " </tr>\n", + " <tr>\n", + " <th>71</th>\n", + " <td>M</td>\n", + " <td>2019</td>\n", + " <td>3810</td>\n", + " </tr>\n", + " <tr>\n", + " <th>72</th>\n", + " <td>C</td>\n", + " <td>2019</td>\n", + " <td>6067</td>\n", + " </tr>\n", + " <tr>\n", + " <th>73</th>\n", + " <td>U</td>\n", + " <td>2019</td>\n", + " <td>40459</td>\n", + " </tr>\n", + " <tr>\n", + " <th>74</th>\n", + " <td>B</td>\n", + " <td>2019</td>\n", + " <td>14538</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "<p>75 rows × 3 columns</p>\n", + "</div>" + ], + "text/plain": [ + " road_name year Total accidents\n", + "0 U 2005 60026\n", + "1 A 2005 89020\n", + "2 B 2005 24991\n", + "3 C 2005 16500\n", + "4 M 2005 8198\n", + ".. ... ... ...\n", + "70 A 2019 52662\n", + "71 M 2019 3810\n", + "72 C 2019 6067\n", + "73 U 2019 40459\n", + "74 B 2019 14538\n", + "\n", + "[75 rows x 3 columns]" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "A2018t_dftt_df=A2018t_dftt.toPandas()\n", + "A2018t_dftt_df" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+--------------+-------------------+----+-------------------+----+---------+-----------+---------------+------------------+--------------------+--------------------+---------+-------------+---------+------------------------+----------------------+-------+--------+------------+------------+--------------+-------------------+------------+--------------------------+--------------+-----------------+----+-----------------+-----------------+-------------------------+----------------------------+-----------------------+-----------------------+--------+------------------+\n", + "|Count_point_id|Direction_of_travel|year|Count_date |hour|Region_id|Region_name|Region_ons_code|Local_authority_id|Local_authority_name|Local_authority_code|Road_name|Road_category|Road_type|Start_junction_road_name|End_junction_road_name|Easting|Northing|Latitude |Longitude |Link_length_km|Link_length_miles |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", + "|55 |W |2005|2005-10-17 00:00:00|14 |1 |South West |E12000009 |1 |Isles of Scilly |E06000053 |A3110 |PA |Major |A3111 |A3112 |91800 |10890 |49.918585039|-6.295093656|4.00 |2.49000000000000000|0 |0 |7 |0 |0 |1 |0 |0 |0 |0 |0 |1 |8 |\n", + "|55 |E |2005|2005-10-17 00:00:00|18 |1 |South West |E12000009 |1 |Isles of Scilly |E06000053 |A3110 |PA |Major |A3111 |A3112 |91800 |10890 |49.918585039|-6.295093656|4.00 |2.49000000000000000|0 |0 |2 |0 |1 |0 |0 |0 |0 |0 |0 |0 |3 |\n", + "|55 |W |2005|2005-10-17 00:00:00|13 |1 |South West |E12000009 |1 |Isles of Scilly |E06000053 |A3110 |PA |Major |A3111 |A3112 |91800 |10890 |49.918585039|-6.295093656|4.00 |2.49000000000000000|0 |1 |9 |0 |1 |2 |0 |0 |0 |0 |0 |2 |13 |\n", + "|55 |E |2005|2005-10-17 00:00:00|13 |1 |South West |E12000009 |1 |Isles of Scilly |E06000053 |A3110 |PA |Major |A3111 |A3112 |91800 |10890 |49.918585039|-6.295093656|4.00 |2.49000000000000000|7 |0 |10 |0 |3 |1 |0 |0 |0 |0 |0 |1 |14 |\n", + "|55 |E |2005|2005-10-17 00:00:00|17 |1 |South West |E12000009 |1 |Isles of Scilly |E06000053 |A3110 |PA |Major |A3111 |A3112 |91800 |10890 |49.918585039|-6.295093656|4.00 |2.49000000000000000|3 |2 |5 |1 |3 |0 |0 |0 |0 |0 |0 |0 |11 |\n", + "|55 |W |2005|2005-10-17 00:00:00|9 |1 |South West |E12000009 |1 |Isles of Scilly |E06000053 |A3110 |PA |Major |A3111 |A3112 |91800 |10890 |49.918585039|-6.295093656|4.00 |2.49000000000000000|1 |0 |5 |1 |3 |1 |1 |0 |0 |0 |0 |2 |11 |\n", + "|55 |W |2005|2005-10-17 00:00:00|12 |1 |South West |E12000009 |1 |Isles of Scilly |E06000053 |A3110 |PA |Major |A3111 |A3112 |91800 |10890 |49.918585039|-6.295093656|4.00 |2.49000000000000000|5 |2 |8 |0 |1 |2 |0 |0 |0 |0 |0 |2 |13 |\n", + "|55 |E |2005|2005-10-17 00:00:00|9 |1 |South West |E12000009 |1 |Isles of Scilly |E06000053 |A3110 |PA |Major |A3111 |A3112 |91800 |10890 |49.918585039|-6.295093656|4.00 |2.49000000000000000|3 |0 |7 |1 |4 |0 |1 |0 |0 |0 |0 |1 |13 |\n", + "|55 |E |2005|2005-10-17 00:00:00|12 |1 |South West |E12000009 |1 |Isles of Scilly |E06000053 |A3110 |PA |Major |A3111 |A3112 |91800 |10890 |49.918585039|-6.295093656|4.00 |2.49000000000000000|5 |0 |11 |1 |1 |0 |0 |0 |0 |0 |0 |0 |13 |\n", + "|55 |E |2005|2005-10-17 00:00:00|15 |1 |South West |E12000009 |1 |Isles of Scilly |E06000053 |A3110 |PA |Major |A3111 |A3112 |91800 |10890 |49.918585039|-6.295093656|4.00 |2.49000000000000000|1 |0 |6 |1 |2 |1 |0 |0 |0 |0 |0 |1 |10 |\n", + "|55 |E |2005|2005-10-17 00:00:00|16 |1 |South West |E12000009 |1 |Isles of Scilly |E06000053 |A3110 |PA |Major |A3111 |A3112 |91800 |10890 |49.918585039|-6.295093656|4.00 |2.49000000000000000|0 |1 |7 |1 |6 |2 |0 |0 |0 |0 |0 |2 |17 |\n", + "|55 |W |2005|2005-10-17 00:00:00|7 |1 |South West |E12000009 |1 |Isles of Scilly |E06000053 |A3110 |PA |Major |A3111 |A3112 |91800 |10890 |49.918585039|-6.295093656|4.00 |2.49000000000000000|1 |0 |2 |0 |2 |0 |0 |0 |0 |0 |0 |0 |4 |\n", + "|55 |W |2005|2005-10-17 00:00:00|8 |1 |South West |E12000009 |1 |Isles of Scilly |E06000053 |A3110 |PA |Major |A3111 |A3112 |91800 |10890 |49.918585039|-6.295093656|4.00 |2.49000000000000000|0 |0 |5 |0 |0 |0 |0 |0 |0 |0 |0 |0 |5 |\n", + "|55 |W |2005|2005-10-17 00:00:00|10 |1 |South West |E12000009 |1 |Isles of Scilly |E06000053 |A3110 |PA |Major |A3111 |A3112 |91800 |10890 |49.918585039|-6.295093656|4.00 |2.49000000000000000|0 |0 |5 |0 |8 |0 |0 |0 |0 |0 |0 |0 |13 |\n", + "|55 |W |2005|2005-10-17 00:00:00|11 |1 |South West |E12000009 |1 |Isles of Scilly |E06000053 |A3110 |PA |Major |A3111 |A3112 |91800 |10890 |49.918585039|-6.295093656|4.00 |2.49000000000000000|2 |0 |10 |0 |2 |0 |0 |0 |0 |0 |0 |0 |12 |\n", + "|55 |E |2005|2005-10-17 00:00:00|7 |1 |South West |E12000009 |1 |Isles of Scilly |E06000053 |A3110 |PA |Major |A3111 |A3112 |91800 |10890 |49.918585039|-6.295093656|4.00 |2.49000000000000000|0 |0 |4 |0 |3 |0 |0 |0 |0 |0 |0 |0 |7 |\n", + "|55 |E |2005|2005-10-17 00:00:00|8 |1 |South West |E12000009 |1 |Isles of Scilly |E06000053 |A3110 |PA |Major |A3111 |A3112 |91800 |10890 |49.918585039|-6.295093656|4.00 |2.49000000000000000|2 |3 |9 |0 |3 |1 |0 |0 |0 |0 |0 |1 |16 |\n", + "|55 |E |2005|2005-10-17 00:00:00|10 |1 |South West |E12000009 |1 |Isles of Scilly |E06000053 |A3110 |PA |Major |A3111 |A3112 |91800 |10890 |49.918585039|-6.295093656|4.00 |2.49000000000000000|0 |3 |2 |2 |0 |0 |0 |0 |0 |0 |0 |0 |7 |\n", + "|55 |E |2005|2005-10-17 00:00:00|11 |1 |South West |E12000009 |1 |Isles of Scilly |E06000053 |A3110 |PA |Major |A3111 |A3112 |91800 |10890 |49.918585039|-6.295093656|4.00 |2.49000000000000000|2 |0 |5 |0 |3 |1 |0 |0 |0 |0 |0 |1 |9 |\n", + "|55 |E |2005|2005-10-17 00:00:00|14 |1 |South West |E12000009 |1 |Isles of Scilly |E06000053 |A3110 |PA |Major |A3111 |A3112 |91800 |10890 |49.918585039|-6.295093656|4.00 |2.49000000000000000|2 |2 |11 |1 |2 |1 |1 |0 |0 |0 |0 |2 |18 |\n", + "+--------------+-------------------+----+-------------------+----+---------+-----------+---------------+------------------+--------------------+--------------------+---------+-------------+---------+------------------------+----------------------+-------+--------+------------+------------+--------------+-------------------+------------+--------------------------+--------------+-----------------+----+-----------------+-----------------+-------------------------+----------------------------+-----------------------+-----------------------+--------+------------------+\n", + "only showing top 20 rows\n", + "\n" + ] + } + ], + "source": [ + "Trafficvolume = spark.read.format('csv')\\\n", + " .option('header',True).option('escape','\"')\\\n", + " .load('/Users/Asfandyar/Downloads/dft_traffic_counts_raw_counts-2.csv')\n", + "# changing the type of column(\"Year'\") to interger type\n", + "Trafficvolume = Trafficvolume.withColumn('year',F.col('year').cast(IntegerType()))\n", + "Trafficvolume=Trafficvolume.filter(Trafficvolume.year>2004)\n", + "Trafficvolume=Trafficvolume.filter(Trafficvolume.year<2020)\n", + "Trafficvolume.sort(\"year\").show(truncate=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+--------------+-------------------+----+-------------------+----+---------+-----------+---------------+------------------+--------------------+--------------------+---------+-------------+---------+------------------------+----------------------+-------+--------+------------+------------+--------------+-----------------+------------+--------------------------+--------------+-----------------+----+-----------------+-----------------+-------------------------+----------------------------+-----------------------+-----------------------+--------+------------------+\n", + "|Count_point_id|Direction_of_travel|year| Count_date|hour|Region_id|Region_name|Region_ons_code|Local_authority_id|Local_authority_name|Local_authority_code|Road_name|Road_category|Road_type|Start_junction_road_name|End_junction_road_name|Easting|Northing| Latitude| Longitude|Link_length_km|Link_length_miles|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", + "| 910006| N|2005|2005-05-12 00:00:00| 7| 1| South West| E12000009| 71| Devon| E10000008| B3215| MB| Minor| null| null| 259800| 95826|50.745066995|-3.988533100| null| null| 1| 1| 43| 1| 14| 3| 5| 2| 0| 0| 4| 14| 73|\n", + "| 910006| N|2005|2005-05-12 00:00:00| 8| 1| South West| E12000009| 71| Devon| E10000008| B3215| MB| Minor| null| null| 259800| 95826|50.745066995|-3.988533100| null| null| 0| 2| 77| 3| 19| 2| 4| 1| 1| 0| 3| 11| 112|\n", + "| 910006| N|2005|2005-05-12 00:00:00| 9| 1| South West| E12000009| 71| Devon| E10000008| B3215| MB| Minor| null| null| 259800| 95826|50.745066995|-3.988533100| null| null| 1| 0| 48| 1| 11| 2| 2| 2| 0| 0| 0| 6| 66|\n", + "| 910006| N|2005|2005-05-12 00:00:00| 10| 1| South West| E12000009| 71| Devon| E10000008| B3215| MB| Minor| null| null| 259800| 95826|50.745066995|-3.988533100| null| null| 0| 0| 66| 1| 12| 2| 1| 2| 0| 0| 2| 7| 86|\n", + "+--------------+-------------------+----+-------------------+----+---------+-----------+---------------+------------------+--------------------+--------------------+---------+-------------+---------+------------------------+----------------------+-------+--------+------------+------------+--------------+-----------------+------------+--------------------------+--------------+-----------------+----+-----------------+-----------------+-------------------------+----------------------------+-----------------------+-----------------------+--------+------------------+\n", + "only showing top 4 rows\n", + "\n" + ] + } + ], + "source": [ + "Trafficvolume.filter(col(\"road_name\").contains(\"B\")).show(4)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+---+--------------+----+---------+------------------+---------+-------------+---------+------------------------+----------------------+-------+--------+-----------+-----------+--------------+-----------------+--------+----+-----------------+--------------------------+------------+--------------------------+--------------+-----------------+----+-----------------+-----------------+-------------------------+----------------------------+-----------------------+-----------------------+--------+------------------+\n", + "| id|count_point_id|year|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|estimation_method|estimation_method_detailed|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| 27294|2019| 5| 85| A560| PA| Major| LA Boundary| M56| 380000| 389400|53.40104100|-2.30226770| 2.80| 1.74| 40| 0| Estimated| Estimated using p...| 214| 91| 22425| 391|2768| 158| 54| 24| 7| 2| 8| 253| 25927|\n", + "| 2| 1153|2019| 3| 30| A905| PA| Major| M9| M9 slip| 292310| 680000|56.00081400|-3.72835390| 1.10| 0.68| 30| 0| Counted| Manual count| 2| 23| 10119| 35|1787| 192| 99| 72| 25| 391| 216| 996| 12959|\n", + "+---+--------------+----+---------+------------------+---------+-------------+---------+------------------------+----------------------+-------+--------+-----------+-----------+--------------+-----------------+--------+----+-----------------+--------------------------+------------+--------------------------+--------------+-----------------+----+-----------------+-----------------+-------------------------+----------------------------+-----------------------+-----------------------+--------+------------------+\n", + "only showing top 2 rows\n", + "\n" + ] + } + ], + "source": [ + "Trafficvolume.filter(col(\"road_name\").contains(\"A\")).show(2)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+---------+----+------------------+\n", + "|road_name|year|all_motor_vehicles|\n", + "+---------+----+------------------+\n", + "| A3110|2005| 8|\n", + "| A3110|2005| 3|\n", + "| A3110|2005| 13|\n", + "| A3110|2005| 14|\n", + "| A3110|2005| 11|\n", + "| A3110|2005| 11|\n", + "| A3110|2005| 13|\n", + "| A3110|2005| 13|\n", + "| A3110|2005| 13|\n", + "| A3110|2005| 10|\n", + "| A3110|2005| 17|\n", + "| A3110|2005| 4|\n", + "| A3110|2005| 5|\n", + "| A3110|2005| 13|\n", + "| A3110|2005| 12|\n", + "| A3110|2005| 7|\n", + "| A3110|2005| 16|\n", + "| A3110|2005| 7|\n", + "| A3110|2005| 9|\n", + "| A3110|2005| 18|\n", + "+---------+----+------------------+\n", + "only showing top 20 rows\n", + "\n" + ] + } + ], + "source": [ + "TrafficvolumeGrouped=Trafficvolume.select(col(\"road_name\"),col(\"year\"),col(\"all_motor_vehicles\")).sort(\"year\")\n", + "\n", + "TrafficvolumeGrouped.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+---------+-----------+----+------------------+\n", + "|road_name|road_number|year|all_motor_vehicles|\n", + "+---------+-----------+----+------------------+\n", + "| A| A3110|2005| 8|\n", + "| A| A3110|2005| 3|\n", + "| A| A3110|2005| 13|\n", + "| A| A3110|2005| 14|\n", + "| A| A3110|2005| 11|\n", + "| A| A3110|2005| 11|\n", + "| A| A3110|2005| 13|\n", + "| A| A3110|2005| 13|\n", + "| A| A3110|2005| 13|\n", + "| A| A3110|2005| 10|\n", + "| A| A3110|2005| 17|\n", + "| A| A3110|2005| 4|\n", + "| A| A3110|2005| 5|\n", + "| A| A3110|2005| 13|\n", + "| A| A3110|2005| 12|\n", + "| A| A3110|2005| 7|\n", + "| A| A3110|2005| 16|\n", + "| A| A3110|2005| 7|\n", + "| A| A3110|2005| 9|\n", + "| A| A3110|2005| 18|\n", + "+---------+-----------+----+------------------+\n", + "only showing top 20 rows\n", + "\n" + ] + } + ], + "source": [ + "import pyspark.sql.functions as f\n", + "\n", + "TrafficvolumeGroupedupdated=TrafficvolumeGrouped.select(\n", + " f.regexp_extract(\"road_name\", pattern=\"^[A-Za-z]+(?=)\", idx=0).alias('road_name'),\n", + " f.regexp_replace(\"road_name\", \"^[A-Za-z]+_\", \"\").alias(\"road_number\"),\n", + " \"year\",\n", + " \"all_motor_vehicles\"\n", + "\n", + ")\n", + "TrafficvolumeGroupedupdated.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+---------+-----------+----+------------------+\n", + "|road_name|road_number|year|all_motor_vehicles|\n", + "+---------+-----------+----+------------------+\n", + "| A| A166|2005| 10341|\n", + "| M| M1|2005| 133632|\n", + "| M| M56|2005| 90854|\n", + "| A| A5|2005| 24688|\n", + "| M| M56|2005| 90854|\n", + "| A| A4020|2005| 32080|\n", + "| A| A478|2005| 3208|\n", + "| A| A143|2005| 8991|\n", + "| A| A5|2005| 24688|\n", + "| A| A27|2005| 35179|\n", + "| A| A5|2005| 22904|\n", + "| A| A1|2005| 48493|\n", + "| M| M180|2005| 35871|\n", + "| A| A541|2005| 4965|\n", + "| A| A14|2005| 32212|\n", + "| A| A500|2005| 45882|\n", + "| A| A2030|2005| 21274|\n", + "| A| A201|2005| 41123|\n", + "| M| M6|2005| 116669|\n", + "| A| A5|2005| 22904|\n", + "+---------+-----------+----+------------------+\n", + "only showing top 20 rows\n", + "\n" + ] + } + ], + "source": [ + "import pyspark.sql.functions as f\n", + "\n", + "TrafficvolumeGroupedupdated=TrafficvolumeGrouped.select(\n", + " f.regexp_extract(\"road_name\", pattern=\"^[A-Za-z]+(?=)\", idx=0).alias('road_name'),\n", + " f.regexp_replace(\"road_name\", \"^[A-Za-z]+_\", \"\").alias(\"road_number\"),\n", + " \"year\",\n", + " \"all_motor_vehicles\"\n", + "\n", + ")\n", + "TrafficvolumeGroupedupdated.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+---------+----+------------------+\n", + "|road_name|year|all_motor_vehicles|\n", + "+---------+----+------------------+\n", + "| A|2005| 8|\n", + "| A|2005| 3|\n", + "| A|2005| 13|\n", + "| A|2005| 14|\n", + "| A|2005| 11|\n", + "| A|2005| 11|\n", + "| A|2005| 13|\n", + "| A|2005| 13|\n", + "| A|2005| 13|\n", + "| A|2005| 10|\n", + "| A|2005| 17|\n", + "| A|2005| 4|\n", + "| A|2005| 5|\n", + "| A|2005| 13|\n", + "| A|2005| 12|\n", + "| A|2005| 7|\n", + "| A|2005| 16|\n", + "| A|2005| 7|\n", + "| A|2005| 9|\n", + "| A|2005| 18|\n", + "+---------+----+------------------+\n", + "only showing top 20 rows\n", + "\n" + ] + } + ], + "source": [ + "TrafficvolumeGroupedupdated=TrafficvolumeGroupedupdated.select(col(\"road_name\"),col(\"year\"),col(\"all_motor_vehicles\")).sort(\"year\")\n", + "\n", + "TrafficvolumeGroupedupdated.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+---------+----+------------------+\n", + "|road_name|year|all_motor_vehicles|\n", + "+---------+----+------------------+\n", + "| A|2005| 7.8689351E7|\n", + "| M|2005| 2.4352716E7|\n", + "| U|2005| 7369477.0|\n", + "| C|2005| 3816208.0|\n", + "| B|2005| 4709562.0|\n", + "| M|2006| 3.0686368E7|\n", + "| A|2006| 7.7760371E7|\n", + "| U|2006| 8209734.0|\n", + "| C|2006| 4186058.0|\n", + "| B|2006| 5203139.0|\n", + "| A|2007| 8.0678016E7|\n", + "| M|2007| 2.7693584E7|\n", + "| U|2007| 7824099.0|\n", + "| B|2007| 5008270.0|\n", + "| C|2007| 3995513.0|\n", + "| M|2008| 2.8008346E7|\n", + "| A|2008| 7.6143383E7|\n", + "| U|2008| 1.0834031E7|\n", + "| B|2008| 1.2802995E7|\n", + "| C|2008| 7579313.0|\n", + "+---------+----+------------------+\n", + "only showing top 20 rows\n", + "\n" + ] + } + ], + "source": [ + "TrafficvolumeGroupedupdated_U = TrafficvolumeGroupedupdated.groupby('road_name','year').agg(F.sum(TrafficvolumeGroupedupdated['all_motor_vehicles']).alias('all_motor_vehicles'))\n", + "TrafficvolumeGroupedupdated_U.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "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>road_name</th>\n", + " <th>year</th>\n", + " <th>all_motor_vehicles</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>A</td>\n", + " <td>2005</td>\n", + " <td>78689351.0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>M</td>\n", + " <td>2005</td>\n", + " <td>24352716.0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>U</td>\n", + " <td>2005</td>\n", + " <td>7369477.0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>C</td>\n", + " <td>2005</td>\n", + " <td>3816208.0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>B</td>\n", + " <td>2005</td>\n", + " <td>4709562.0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>...</th>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " </tr>\n", + " <tr>\n", + " <th>70</th>\n", + " <td>A</td>\n", + " <td>2019</td>\n", + " <td>64275975.0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>71</th>\n", + " <td>M</td>\n", + " <td>2019</td>\n", + " <td>25629481.0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>72</th>\n", + " <td>C</td>\n", + " <td>2019</td>\n", + " <td>6641590.0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>73</th>\n", + " <td>U</td>\n", + " <td>2019</td>\n", + " <td>7504917.0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>74</th>\n", + " <td>B</td>\n", + " <td>2019</td>\n", + " <td>10353469.0</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "<p>75 rows × 3 columns</p>\n", + "</div>" + ], + "text/plain": [ + " road_name year all_motor_vehicles\n", + "0 A 2005 78689351.0\n", + "1 M 2005 24352716.0\n", + "2 U 2005 7369477.0\n", + "3 C 2005 3816208.0\n", + "4 B 2005 4709562.0\n", + ".. ... ... ...\n", + "70 A 2019 64275975.0\n", + "71 M 2019 25629481.0\n", + "72 C 2019 6641590.0\n", + "73 U 2019 7504917.0\n", + "74 B 2019 10353469.0\n", + "\n", + "[75 rows x 3 columns]" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "TrafficvolumeGroupedupdated_df=TrafficvolumeGroupedupdated_U.toPandas()\n", + "TrafficvolumeGroupedupdated_df" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "ename": "FileNotFoundError", + "evalue": "[Errno 2] No such file or directory: '/Users/Asfandyar/Desktop/disertation/diseration_final/Road lengths (miles).csv'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m<ipython-input-20-1f973df72743>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpandas\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m'/Users/Asfandyar/Desktop/disertation/diseration_final/Road lengths (miles).csv'\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 3\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m\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/io/parsers.py\u001b[0m in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision)\u001b[0m\n\u001b[1;32m 686\u001b[0m )\n\u001b[1;32m 687\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 688\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\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 689\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 690\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/io/parsers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m 452\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 453\u001b[0m \u001b[0;31m# Create the parser.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 454\u001b[0;31m \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfp_or_buf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\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 455\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 456\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\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/io/parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m 946\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"has_index_names\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"has_index_names\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 947\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 948\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\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 949\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 950\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mclose\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/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[0;34m(self, engine)\u001b[0m\n\u001b[1;32m 1178\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"c\"\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 1179\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"c\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1180\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mCParserWrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\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 1181\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 1182\u001b[0m \u001b[0;32mif\u001b[0m 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pd.read_csv ('/Users/Asfandyar/Desktop/disertation/diseration_final/Road lengths (miles).csv')\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "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>road_name</th>\n", + " <th>link_length_km</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>2005</td>\n", + " <td>M</td>\n", + " <td>2,186</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>2006</td>\n", + " <td>M</td>\n", + " <td>2,209</td>\n", + " </tr>\n", 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columns</p>\n", + "</div>" + ], + "text/plain": [ + " road_name year all_motor_vehicles link_length_km\n", + "0 A 2005 304830080.0 29,035\n", + "1 M 2005 69459850.0 2,186\n", + "2 U 2005 9582941.0 138,679\n", + "3 C 2005 4808652.0 52,480\n", + "4 B 2005 6014961.0 18,758\n", + ".. ... ... ... ...\n", + "70 A 2019 313820374.0 29,489\n", + "71 M 2019 82658967.0 2,320\n", + "72 B 2019 14788382.0 18,842\n", + "73 U 2019 15609375.0 143,965\n", + "74 C 2019 10864438.0 53,371\n", + "\n", + "[75 rows x 4 columns]" + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "result22=pd.merge(TrafficvolumeGroupedupdated_df, roadlenghth, on=['year','road_name'])\n", + "result22" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "numpy.float64" + ] + }, + "execution_count": 74, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "result22[\"link_length_km\"]=result22[\"link_length_km\"].str.replace(',','')\n", + "result22[\"link_length_km\"] = result22[\"link_length_km\"].astype(float)\n", + "type(result22[\"all_motor_vehicles\"][0])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "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>road_name</th>\n", + " <th>year</th>\n", + " <th>all_motor_vehicles</th>\n", + " <th>link_length_km</th>\n", + " <th>Trafficvolume</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " 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+ "text/plain": [ + "numpy.int64" + ] + }, + "execution_count": 100, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "A2018t_dftt_df['year'] = A2018t_dftt_df['year'].astype(int)\n", + "type(A2018t_dftt_df['year'][0])" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'result22' 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<ipython-input-21-ddab6a4f0577>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mA2018t_dftt_df\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mresult23\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmerge\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mA2018t_dftt_df\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult22\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'road_name'\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 3\u001b[0m \u001b[0mresult23\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Accident Probability\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresult23\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[0mresult23\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Trafficvolume\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m 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\u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0ma\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mresult23\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msort_values\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'road_name'\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[0ma\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m15\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;31mNameError\u001b[0m: name 'result23' is not defined" + ] + } + ], + "source": [ + "a=result23.sort_values(['year','road_name'])\n", + "a[15:]" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'result22' 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<ipython-input-23-632ff6e558ce>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mA2018t_dftt_df\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mresult23\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmerge\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mA2018t_dftt_df\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult22\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'road_name'\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 3\u001b[0m \u001b[0mresult23\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Accident Probability\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresult23\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[0mresult23\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Trafficvolume\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mresult23\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mresult23\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Total accidents'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Trafficvolume'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\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 5\u001b[0m \u001b[0mresult23\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mNameError\u001b[0m: name 'result22' is not defined" + ] + } + ], + "source": [ + "A2018t_dftt_df\n", + "result23=pd.merge(A2018t_dftt_df, result22, on=['year','road_name'])\n", + "result23[\"Accident Probability\"] = result23[\"Total accidents\"] / result23[\"Trafficvolume\"]\n", + "result23=result23.drop(['Total accidents', 'Trafficvolume'], axis=1)\n", + "result23" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+---------+----+--------------------+\n", + "|road_name|year|Accident Probability|\n", + "+---------+----+--------------------+\n", + "| U|2005|4.516789885144348E-8|\n", + "| A|2005|1.005791450121627...|\n", + "| B|2005|2.214951837226388...|\n", + "| C|2005|6.538328991261544E-8|\n", + "| M|2005|5.399131625350205E-8|\n", + "| A|2006|9.413521408368926E-9|\n", + "| B|2006|2.096725946868243...|\n", + "| C|2006|6.546777719038307E-8|\n", + "| M|2006|5.038509017626139E-8|\n", + "| U|2006|4.102376616046979E-8|\n", + "| M|2007| 4.71074087264444E-8|\n", + "| C|2007|6.514438225144233E-8|\n", + "| B|2007|2.079900630221721E-7|\n", + "| U|2007|4.007207149830060...|\n", + "| A|2007|9.179304317035398E-9|\n", + "| B|2008|7.913422366704336E-8|\n", + "| C|2008|3.357093021292479E-8|\n", + "| A|2008|8.824428961761015E-9|\n", + "| U|2008|2.707572198570955...|\n", + "| M|2008|4.330104607551410...|\n", + "+---------+----+--------------------+\n", + "only showing top 20 rows\n", + "\n" + ] + } + ], + "source": [ + "#park.conf.set(\"spark.sql.execution.arrow.enabled\",\"true\")\n", + "Accidenteeachyearwrtroad=spark.createDataFrame(result23) \n", + "Accidenteeachyearwrtroad.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "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>road_name</th>\n", + " <th>year</th>\n", + " <th>Accident Probability</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>A</td>\n", + " <td>2005</td>\n", + " <td>1.005791e-08</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>A</td>\n", + " <td>2006</td>\n", + " <td>9.413521e-09</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>A</td>\n", + " <td>2007</td>\n", + " <td>9.179304e-09</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>A</td>\n", + " <td>2008</td>\n", + " <td>8.824429e-09</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>A</td>\n", + " <td>2009</td>\n", + " <td>8.503109e-09</td>\n", + " </tr>\n", + " <tr>\n", + " <th>5</th>\n", + " <td>A</td>\n", + " <td>2010</td>\n", + " <td>8.157576e-09</td>\n", + " </tr>\n", + " <tr>\n", + " <th>6</th>\n", + " <td>A</td>\n", + " <td>2011</td>\n", + " <td>8.111517e-09</td>\n", + " </tr>\n", + " <tr>\n", + " <th>7</th>\n", + " <td>A</td>\n", + " <td>2012</td>\n", + " <td>7.868686e-09</td>\n", + " </tr>\n", + " <tr>\n", + " <th>8</th>\n", + " <td>A</td>\n", + " <td>2013</td>\n", + " <td>7.556207e-09</td>\n", + " </tr>\n", + " <tr>\n", + " <th>9</th>\n", + " <td>A</td>\n", + " <td>2014</td>\n", + " <td>7.803071e-09</td>\n", + " </tr>\n", + " <tr>\n", + " <th>10</th>\n", + " <td>A</td>\n", + " <td>2015</td>\n", + " <td>7.292539e-09</td>\n", + " </tr>\n", + " <tr>\n", + " <th>11</th>\n", + " <td>A</td>\n", + " <td>2016</td>\n", + " <td>6.842843e-09</td>\n", + " </tr>\n", + " <tr>\n", + " <th>12</th>\n", + " <td>A</td>\n", + " <td>2017</td>\n", + " <td>6.235877e-09</td>\n", + " </tr>\n", + " <tr>\n", + " <th>13</th>\n", + " <td>A</td>\n", + " <td>2018</td>\n", + " <td>5.876891e-09</td>\n", + " </tr>\n", + " <tr>\n", + " <th>14</th>\n", + " <td>A</td>\n", + " <td>2019</td>\n", + " <td>5.690575e-09</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " road_name year Accident Probability\n", + "0 A 2005 1.005791e-08\n", + "1 A 2006 9.413521e-09\n", + "2 A 2007 9.179304e-09\n", + "3 A 2008 8.824429e-09\n", + "4 A 2009 8.503109e-09\n", + "5 A 2010 8.157576e-09\n", + "6 A 2011 8.111517e-09\n", + "7 A 2012 7.868686e-09\n", + "8 A 2013 7.556207e-09\n", + "9 A 2014 7.803071e-09\n", + "10 A 2015 7.292539e-09\n", + "11 A 2016 6.842843e-09\n", + "12 A 2017 6.235877e-09\n", + "13 A 2018 5.876891e-09\n", + "14 A 2019 5.690575e-09" + ] + }, + "execution_count": 295, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "A=Accidenteeachyearwrtroad.filter(Accidenteeachyearwrtroad.road_name.contains(\"A\")).toPandas()\n", + "B=Accidenteeachyearwrtroad.filter(Accidenteeachyearwrtroad.road_name.contains(\"B\")).toPandas()\n", + "C=Accidenteeachyearwrtroad.filter(Accidenteeachyearwrtroad.road_name.contains(\"C\")).toPandas()\n", + "M=Accidenteeachyearwrtroad.filter(Accidenteeachyearwrtroad.road_name.contains(\"M\")).toPandas()\n", + "U=Accidenteeachyearwrtroad.filter(Accidenteeachyearwrtroad.road_name.contains(\"U\")).toPandas()\n", + "A" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "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[\"Accident Probability\"]\n", + "ECE = B[\"Accident Probability\"]\n", + "CAC = C[\"Accident Probability\"]\n", + "CSE = M[\"Accident Probability\"]\n", + "CAR = U[\"Accident Probability\"]\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 probability', 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()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "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>road_name</th>\n", + " <th>year</th>\n", + " <th>Accident Probability</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>U</td>\n", + " <td>2005</td>\n", + " <td>4.516790e-08</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>A</td>\n", + " <td>2005</td>\n", + " <td>1.005791e-08</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>B</td>\n", + " <td>2005</td>\n", + " <td>2.214952e-07</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>C</td>\n", + " <td>2005</td>\n", + " <td>6.538329e-08</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>M</td>\n", + " <td>2005</td>\n", + " <td>5.399132e-08</td>\n", + " </tr>\n", + " <tr>\n", + " <th>...</th>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " </tr>\n", + " <tr>\n", + " <th>70</th>\n", + " <td>A</td>\n", + " <td>2019</td>\n", + " <td>5.690575e-09</td>\n", + " </tr>\n", + " <tr>\n", + " <th>71</th>\n", + " <td>M</td>\n", + " <td>2019</td>\n", + " <td>1.986767e-08</td>\n", + " </tr>\n", + " <tr>\n", + " <th>72</th>\n", + " <td>C</td>\n", + " <td>2019</td>\n", + " <td>1.046312e-08</td>\n", + " </tr>\n", + " <tr>\n", + " <th>73</th>\n", + " <td>U</td>\n", + " <td>2019</td>\n", + " <td>1.800415e-08</td>\n", + " </tr>\n", + " <tr>\n", + " <th>74</th>\n", + " <td>B</td>\n", + " <td>2019</td>\n", + " <td>5.217434e-08</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "<p>75 rows × 3 columns</p>\n", + "</div>" + ], + "text/plain": [ + " road_name year Accident Probability\n", + "0 U 2005 4.516790e-08\n", + "1 A 2005 1.005791e-08\n", + "2 B 2005 2.214952e-07\n", + "3 C 2005 6.538329e-08\n", + "4 M 2005 5.399132e-08\n", + ".. ... ... ...\n", + "70 A 2019 5.690575e-09\n", + "71 M 2019 1.986767e-08\n", + "72 C 2019 1.046312e-08\n", + "73 U 2019 1.800415e-08\n", + "74 B 2019 5.217434e-08\n", + "\n", + "[75 rows x 3 columns]" + ] + }, + "execution_count": 292, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "result23" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "numpy.float64" + ] + }, + "execution_count": 120, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "type(result23['Accident Probability'][0])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "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>road_name</th>\n", + " <th>A</th>\n", + " <th>B</th>\n", + " <th>C</th>\n", + " <th>M</th>\n", + " <th>U</th>\n", + " </tr>\n", + " <tr>\n", + " <th>year</th>\n", + " <th></th>\n", + " <th></th>\n", + " <th></th>\n", + " <th></th>\n", + " <th></th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>2005</th>\n", + " <td>1.005791e-08</td>\n", + " <td>2.214952e-07</td>\n", + " <td>6.538329e-08</td>\n", + " <td>5.399132e-08</td>\n", + " <td>4.516790e-08</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2006</th>\n", + " <td>9.413521e-09</td>\n", + " <td>2.096726e-07</td>\n", + " <td>6.546778e-08</td>\n", + " <td>5.038509e-08</td>\n", + " <td>4.102377e-08</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2007</th>\n", + " <td>9.179304e-09</td>\n", + " <td>2.079901e-07</td>\n", + " <td>6.514438e-08</td>\n", + " <td>4.710741e-08</td>\n", + " <td>4.007207e-08</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2008</th>\n", + " <td>8.824429e-09</td>\n", + " <td>7.913422e-08</td>\n", + " <td>3.357093e-08</td>\n", + " <td>4.330105e-08</td>\n", + " <td>2.707572e-08</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2009</th>\n", + " <td>8.503109e-09</td>\n", + " <td>8.951662e-08</td>\n", + " <td>3.621914e-08</td>\n", + " <td>3.888188e-08</td>\n", + " <td>2.610466e-08</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2010</th>\n", + " <td>8.157576e-09</td>\n", + " <td>1.418152e-07</td>\n", + " <td>7.625352e-08</td>\n", + " <td>3.876555e-08</td>\n", + " <td>1.014358e-07</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2011</th>\n", + " <td>8.111517e-09</td>\n", + " <td>1.373427e-07</td>\n", + " <td>7.448675e-08</td>\n", + " <td>3.385673e-08</td>\n", + " <td>9.322585e-08</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2012</th>\n", + " <td>7.868686e-09</td>\n", + " <td>1.327289e-07</td>\n", + " <td>7.053123e-08</td>\n", + " <td>3.139057e-08</td>\n", + " <td>9.103661e-08</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2013</th>\n", + " <td>7.556207e-09</td>\n", + " <td>1.255869e-07</td>\n", + " <td>6.371915e-08</td>\n", + " <td>2.952941e-08</td>\n", + " <td>8.912023e-08</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2014</th>\n", + " <td>7.803071e-09</td>\n", + " <td>1.274604e-07</td>\n", + " <td>6.743923e-08</td>\n", + " <td>3.021198e-08</td>\n", + " <td>9.374086e-08</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2015</th>\n", + " <td>7.292539e-09</td>\n", + " <td>1.167907e-07</td>\n", + " <td>5.701929e-08</td>\n", + " <td>2.901572e-08</td>\n", + " <td>9.380434e-08</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2016</th>\n", + " <td>6.842843e-09</td>\n", + " <td>1.135032e-07</td>\n", + " <td>4.834328e-08</td>\n", + " <td>2.768551e-08</td>\n", + " <td>9.777092e-08</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2017</th>\n", + " <td>6.235877e-09</td>\n", + " <td>1.023019e-07</td>\n", + " <td>4.155090e-08</td>\n", + " <td>2.384943e-08</td>\n", + " <td>1.031197e-07</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2018</th>\n", + " <td>5.876891e-09</td>\n", + " <td>7.506489e-08</td>\n", + " <td>1.970560e-08</td>\n", + " <td>2.239598e-08</td>\n", + " <td>3.039856e-08</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2019</th>\n", + " <td>5.690575e-09</td>\n", + " <td>5.217434e-08</td>\n", + " <td>1.046312e-08</td>\n", + " <td>1.986767e-08</td>\n", + " <td>1.800415e-08</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + "road_name A B C M \\\n", + "year \n", + "2005 1.005791e-08 2.214952e-07 6.538329e-08 5.399132e-08 \n", + "2006 9.413521e-09 2.096726e-07 6.546778e-08 5.038509e-08 \n", + "2007 9.179304e-09 2.079901e-07 6.514438e-08 4.710741e-08 \n", + "2008 8.824429e-09 7.913422e-08 3.357093e-08 4.330105e-08 \n", + "2009 8.503109e-09 8.951662e-08 3.621914e-08 3.888188e-08 \n", + "2010 8.157576e-09 1.418152e-07 7.625352e-08 3.876555e-08 \n", + "2011 8.111517e-09 1.373427e-07 7.448675e-08 3.385673e-08 \n", + "2012 7.868686e-09 1.327289e-07 7.053123e-08 3.139057e-08 \n", + "2013 7.556207e-09 1.255869e-07 6.371915e-08 2.952941e-08 \n", + "2014 7.803071e-09 1.274604e-07 6.743923e-08 3.021198e-08 \n", + "2015 7.292539e-09 1.167907e-07 5.701929e-08 2.901572e-08 \n", + "2016 6.842843e-09 1.135032e-07 4.834328e-08 2.768551e-08 \n", + "2017 6.235877e-09 1.023019e-07 4.155090e-08 2.384943e-08 \n", + "2018 5.876891e-09 7.506489e-08 1.970560e-08 2.239598e-08 \n", + "2019 5.690575e-09 5.217434e-08 1.046312e-08 1.986767e-08 \n", + "\n", + "road_name U \n", + "year \n", + "2005 4.516790e-08 \n", + "2006 4.102377e-08 \n", + "2007 4.007207e-08 \n", + "2008 2.707572e-08 \n", + "2009 2.610466e-08 \n", + "2010 1.014358e-07 \n", + "2011 9.322585e-08 \n", + "2012 9.103661e-08 \n", + "2013 8.912023e-08 \n", + "2014 9.374086e-08 \n", + "2015 9.380434e-08 \n", + "2016 9.777092e-08 \n", + "2017 1.031197e-07 \n", + "2018 3.039856e-08 \n", + "2019 1.800415e-08 " + ] + }, + "execution_count": 297, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "result23_opiv=result23.pivot_table('Accident Probability', ['year'], 'road_name')\n", + "result23_opiv" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[2005,\n", + " 2006,\n", + " 2007,\n", + " 2008,\n", + " 2009,\n", + " 2010,\n", + " 2011,\n", + " 2012,\n", + " 2013,\n", + " 2014,\n", + " 2015,\n", + " 2016,\n", + " 2017,\n", + " 2018,\n", + " 2019]" + ] + }, + "execution_count": 291, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "YEARList = result23['year'].unique().tolist()\n", + "YEARList\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'road_name' 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<ipython-input-281-1a46aeecf56f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mresult23_opiv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mroad_name\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mNameError\u001b[0m: name 'road_name' is not defined" + ] + } + ], + "source": [ + "result23_opiv.loc[]" + ] + }, + { + "cell_type": "code", + "execution_count": 309, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 2000x1600 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt \n", + "\n", + "from matplotlib.pyplot import figure\n", + "plt.figure(figsize=(20, 16),dpi=100)\n", + "plt.rcParams.update({'font.size': 22})\n", + "YEARList = result23['year'].unique().tolist()\n", + "M = result23_opiv ['M'].tolist()\n", + "A = result23_opiv ['A'].tolist()\n", + "B = result23_opiv ['B'].tolist()\n", + "C = result23_opiv ['C'].tolist()\n", + "U = result23_opiv ['U'].tolist()\n", + "\n", + "\n", + "plt.plot(YEARList, M, label = 'M', marker='o', linewidth=3)\n", + "plt.plot(YEARList, A, label = 'A', marker='o', linewidth=3)\n", + "plt.plot(YEARList, B, label = 'B', marker='o', linewidth=3)\n", + "plt.plot(YEARList, C, label = 'C', marker='o', linewidth=3)\n", + "plt.plot(YEARList, U, label = 'U', marker='o', linewidth=3)\n", + "\n", + "\n", + "plt.xlabel('YEAR Number')\n", + "plt.ylabel('Accident Probability')\n", + "plt.legend(loc='upper left')\n", + "plt.xticks(YEARList)\n", + "#plt.yticks([1000, 2000, 4000, 6000, 8000, 10000, 12000, 15000, 18000])\n", + "plt.title('Accident Probability over road ')\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Over the years" + ] + }, + { + "cell_type": "code", + "execution_count": 159, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+----------------+---------------+\n", + "|first_road_class|Total accidents|\n", + "+----------------+---------------+\n", + "| B| 286824|\n", + "| M| 86106|\n", + "| U| 687752|\n", + "| C| 188025|\n", + "| A| 1038720|\n", + "+----------------+---------------+\n", + "\n" + ] + } + ], + "source": [ + "A2018t_df_notyear = A2018.groupby(\"first_road_class\").agg(F.count(A2018.accident_index).alias('Total accidents'))\n", + "A2018t_df_notyear.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 160, + "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>road_name</th>\n", + " <th>Total accidents</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>B</td>\n", + " <td>286824</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>M</td>\n", + " <td>86106</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>U</td>\n", + " <td>687752</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>C</td>\n", + " 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4.567078846E9|\n", + "+---------+------------------+\n", + "\n" + ] + } + ], + "source": [ + "\n", + "\n", + "TrafficvolumeGrouped_notyear=TrafficvolumeGroupedupdated.select(col(\"road_name\"),col(\"all_motor_vehicles\"))\n", + "TrafficvolumeGrouped_notyear = TrafficvolumeGrouped_notyear.groupby('road_name').agg(F.sum(TrafficvolumeGroupedupdated['all_motor_vehicles']).alias('all_motor_vehicles'))\n", + "\n", + "TrafficvolumeGrouped_notyear.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 164, + "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", + 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"TrafficvolumeGrouped_notyear_df" + ] + }, + { + "cell_type": "code", + "execution_count": 177, + "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>road_name</th>\n", + " <th>link_length_km</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>A</td>\n", + " <td>29,489</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>B</td>\n", + " <td>18,842</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>C</td>\n", + " <td>53,371</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " 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{}, + "output_type": "execute_result" + } + ], + "source": [ + "road_length_traffic" + ] + }, + { + "cell_type": "code", + "execution_count": 187, + "metadata": {}, + "outputs": [ + { + "ename": "KeyError", + "evalue": "'link_length_km'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/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 2897\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-> 2898\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 2899\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;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32mpandas/_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: 'link_length_km'", + "\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<ipython-input-187-e6620f4bbac7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m#road_length_traffic[\"link_length_km\"]=road_length_traffic[\"link_length_km\"].str.replace(',','')\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;31m#road_length_traffic[\"link_length_km\"] = road_length_traffic[\"link_length_km\"].astype(float)\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[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult22\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"link_length_km\"\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[0;32m/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 2904\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 2905\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-> 2906\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 2907\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 2908\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/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/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 2898\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 2899\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-> 2900\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 2901\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2902\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mtolerance\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\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;31mKeyError\u001b[0m: 'link_length_km'" + ] + } + ], + "source": [ + "#road_length_traffic[\"link_length_km\"]=road_length_traffic[\"link_length_km\"].str.replace(',','')\n", + "#road_length_traffic[\"link_length_km\"] = road_length_traffic[\"link_length_km\"].astype(float)" + ] + }, + { + "cell_type": "code", + "execution_count": 188, + "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>road_name</th>\n", + " <th>all_motor_vehicles</th>\n", + " <th>link_length_km</th>\n", + " <th>Trafficvolume</th>\n", + " </tr>\n", + " </thead>\n", + " 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"code", + "execution_count": 190, + "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>road_name</th>\n", + " <th>Total accidents</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>B</td>\n", + " <td>286824</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>M</td>\n", + " <td>86106</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>U</td>\n", + " <td>687752</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>C</td>\n", + " <td>188025</td>\n", + " </tr>\n", + " <tr>\n", + " 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Probability</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>B</td>\n", + " <td>1.159381e-07</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>M</td>\n", + " <td>3.295608e-08</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>U</td>\n", + " <td>4.586325e-08</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>C</td>\n", + " <td>4.534713e-08</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>A</td>\n", + " <td>7.712585e-09</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " road_name Accident Probability\n", + "0 B 1.159381e-07\n", + "1 M 3.295608e-08\n", + "2 U 4.586325e-08\n", + "3 C 4.534713e-08\n", + "4 A 7.712585e-09" + ] + }, + "execution_count": 310, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "result24=pd.merge(A2018t_df_notyear_df, road_length_traffic, on=['road_name'])\n", + "result24[\"Accident Probability\"] = result24[\"Total accidents\"] / result24[\"Trafficvolume\"]\n", + "result24=result24.drop(['Total accidents', 'Trafficvolume'], axis=1)\n", + "result24" + ] + }, + { + "cell_type": "code", + "execution_count": 311, + "metadata": {}, + "outputs": [], + "source": [ + "result24=result24.sort_values('road_name')" + ] + }, + { + "cell_type": "code", + "execution_count": 312, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 1440x720 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax=result24.plot.bar('road_name','Accident Probability', rot=0,title=\"Accidents probabilty over road type \",figsize=(20, 10),color=\"Orange\")" + ] + }, + { + "cell_type": "code", + "execution_count": 216, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+--------------+-------------+------------------+---------------------+----------------------+---------+---------+------------+-----------------+------------------+--------------------+----------+-----------+-----+------------------------+----------------------------+-----------------------+----------------+-----------------+------------------+-----------+---------------+----------------+-----------------+------------------+---------------------------------+---------------------------------------+----------------+------------------+-----------------------+--------------------------+-------------------+-------------------+-------------------------------------------+---------------+-------------------------+\n", + "|accident_index|accident_year|accident_reference|location_easting_osgr|location_northing_osgr|longitude| latitude|police_force|Accident_Severity|number_of_vehicles|number_of_casualties| date|day_of_week| time|local_authority_district|local_authority_ons_district|local_authority_highway|first_road_class|first_road_number| Road_Type|speed_limit|junction_detail|junction_control|second_road_class|second_road_number|pedestrian_crossing_human_control|pedestrian_crossing_physical_facilities|light_conditions|weather_conditions|road_surface_conditions|special_conditions_at_site|carriageway_hazards|urban_or_rural_area|did_police_officer_attend_scene_of_accident|trunk_road_flag|lsoa_of_accident_location|\n", + "+--------------+-------------+------------------+---------------------+----------------------+---------+---------+------------+-----------------+------------------+--------------------+----------+-----------+-----+------------------------+----------------------------+-----------------------+----------------+-----------------+------------------+-----------+---------------+----------------+-----------------+------------------+---------------------------------+---------------------------------------+----------------+------------------+-----------------------+--------------------------+-------------------+-------------------+-------------------------------------------+---------------+-------------------------+\n", + "| 200501BS00001| 2005| 01BS00001| 525680| 178240| -0.19117|51.489096| 1| Serious| 1| 1|04/01/2005| 3|17:42| 12| E09000020| E09000020| A| 3218|Single carriageway| 30| 0| -1| -1| -1| 0| 1| 1| 2| 2| 0| 0| 1| 1| 2| E01002849|\n", + "| 200501BS00002| 2005| 01BS00002| 524170| 181650|-0.211708|51.520075| 1| Slight| 1| 1|05/01/2005| 4|17:36| 12| E09000020| E09000020| B| 450| Dual carriageway| 30| 6| 2| 5| 0| 0| 5| 4| 1| 1| 0| 0| 1| 1| 2| E01002909|\n", + "+--------------+-------------+------------------+---------------------+----------------------+---------+---------+------------+-----------------+------------------+--------------------+----------+-----------+-----+------------------------+----------------------------+-----------------------+----------------+-----------------+------------------+-----------+---------------+----------------+-----------------+------------------+---------------------------------+---------------------------------------+----------------+------------------+-----------------------+--------------------------+-------------------+-------------------+-------------------------------------------+---------------+-------------------------+\n", + "only showing top 2 rows\n", + "\n" + ] + } + ], + "source": [ + "A2018=A2018.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", + "A2018.show(2)" + ] + }, + { + "cell_type": "code", + "execution_count": 217, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+------------------+----------------+---------------+\n", + "| Road_Type|first_road_class|Total accidents|\n", + "+------------------+----------------+---------------+\n", + "| Unknown| A| 5797|\n", + "| One way street| B| 3750|\n", + "| Unknown| U| 9791|\n", + "|Single carriageway| C| 169205|\n", + "| Roundabout| B| 16432|\n", + "| Roundabout| C| 8134|\n", + "| Slip road| C| 540|\n", + "| One way street| A| 18225|\n", + "| Unknown| M| 506|\n", + "| One way street| C| 3877|\n", + "| Dual carriageway| M| 76215|\n", + "|Single carriageway| A| 672829|\n", + "|Single carriageway| M| 361|\n", + "| Unknown| C| 1360|\n", + "| Slip road| U| 2937|\n", + "| Dual carriageway| A| 229185|\n", + "|Single carriageway| U| 606426|\n", + "| One way street| M| 164|\n", + "| Slip road| M| 6758|\n", + "| Dual carriageway| B| 11574|\n", + "+------------------+----------------+---------------+\n", + "only showing top 20 rows\n", + "\n" + ] + } + ], + "source": [ + "dangeorusroadtype = A2018.groupby('Road_Type','first_road_class').agg(F.count(A2018.accident_index).alias('Total accidents'))\n", + "dangeorusroadtype.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 218, + "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>Road_Type</th>\n", + " <th>first_road_class</th>\n", + " <th>Total accidents</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>Unknown</td>\n", + " <td>A</td>\n", + " <td>5797</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>One way street</td>\n", + " <td>B</td>\n", + " <td>3750</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>Unknown</td>\n", + " <td>U</td>\n", + " <td>9791</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>Single carriageway</td>\n", + " <td>C</td>\n", + " <td>169205</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>Roundabout</td>\n", + " <td>B</td>\n", + " <td>16432</td>\n", + " </tr>\n", + " <tr>\n", + " <th>5</th>\n", + " <td>Roundabout</td>\n", + " <td>C</td>\n", + " <td>8134</td>\n", + " </tr>\n", + " <tr>\n", + " <th>6</th>\n", + " <td>Slip road</td>\n", + " <td>C</td>\n", + " <td>540</td>\n", + " </tr>\n", + " <tr>\n", + " <th>7</th>\n", + " <td>One way street</td>\n", + " <td>A</td>\n", + " <td>18225</td>\n", + " </tr>\n", + " <tr>\n", + " <th>8</th>\n", + " <td>Unknown</td>\n", + " <td>M</td>\n", + " <td>506</td>\n", + " </tr>\n", + " <tr>\n", + " <th>9</th>\n", + " <td>One way street</td>\n", + " <td>C</td>\n", + " <td>3877</td>\n", + " </tr>\n", + " <tr>\n", + " <th>10</th>\n", + " <td>Dual carriageway</td>\n", + " <td>M</td>\n", + " <td>76215</td>\n", + " </tr>\n", + " <tr>\n", + " <th>11</th>\n", + " <td>Single carriageway</td>\n", + " <td>A</td>\n", + " <td>672829</td>\n", + " </tr>\n", + " <tr>\n", + " <th>12</th>\n", + " <td>Single carriageway</td>\n", + " <td>M</td>\n", + " <td>361</td>\n", + " </tr>\n", + " <tr>\n", + " <th>13</th>\n", + " <td>Unknown</td>\n", + " <td>C</td>\n", + " <td>1360</td>\n", + " </tr>\n", + " <tr>\n", + " <th>14</th>\n", + " <td>Slip road</td>\n", + " <td>U</td>\n", + " <td>2937</td>\n", + " </tr>\n", + " <tr>\n", + " <th>15</th>\n", + " <td>Dual carriageway</td>\n", + " <td>A</td>\n", + " <td>229185</td>\n", + " </tr>\n", + " <tr>\n", + " <th>16</th>\n", + " <td>Single carriageway</td>\n", + " <td>U</td>\n", + " <td>606426</td>\n", + " </tr>\n", + " <tr>\n", + " <th>17</th>\n", + " <td>One way street</td>\n", + " <td>M</td>\n", + " <td>164</td>\n", + " </tr>\n", + " <tr>\n", + " <th>18</th>\n", + " <td>Slip road</td>\n", + " <td>M</td>\n", + " <td>6758</td>\n", + " </tr>\n", + " <tr>\n", + " <th>19</th>\n", + " <td>Dual carriageway</td>\n", + " <td>B</td>\n", + " <td>11574</td>\n", + " </tr>\n", + " <tr>\n", + " <th>20</th>\n", + " <td>Dual carriageway</td>\n", + " <td>C</td>\n", + " <td>4909</td>\n", + " </tr>\n", + " <tr>\n", + " <th>21</th>\n", + " <td>Roundabout</td>\n", + " <td>A</td>\n", + " <td>98800</td>\n", + " </tr>\n", + " <tr>\n", + " <th>22</th>\n", + " <td>Single carriageway</td>\n", + " <td>B</td>\n", + " <td>252704</td>\n", + " </tr>\n", + " <tr>\n", + " <th>23</th>\n", + " <td>One way street</td>\n", + " <td>U</td>\n", + " <td>23272</td>\n", + " </tr>\n", + " <tr>\n", + " <th>24</th>\n", + " <td>Roundabout</td>\n", + " <td>U</td>\n", + " <td>25739</td>\n", + " </tr>\n", + " <tr>\n", + " <th>25</th>\n", + " <td>Roundabout</td>\n", + " <td>M</td>\n", + " <td>2102</td>\n", + " </tr>\n", + " <tr>\n", + " <th>26</th>\n", + " <td>Dual carriageway</td>\n", + " <td>U</td>\n", + " <td>19587</td>\n", + " </tr>\n", + " <tr>\n", + " <th>27</th>\n", + " <td>Slip road</td>\n", + " <td>A</td>\n", + " <td>13883</td>\n", + " </tr>\n", + " <tr>\n", + " <th>28</th>\n", + " <td>Slip road</td>\n", + " <td>B</td>\n", + " <td>887</td>\n", + " </tr>\n", + " <tr>\n", + " <th>29</th>\n", + " <td>Unknown</td>\n", + " <td>B</td>\n", + " <td>1477</td>\n", + " </tr>\n", + " <tr>\n", + " <th>30</th>\n", + " <td>Data missing or out of range</td>\n", + " <td>A</td>\n", + " <td>1</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " Road_Type first_road_class Total accidents\n", + "0 Unknown A 5797\n", + "1 One way street B 3750\n", + "2 Unknown U 9791\n", + "3 Single carriageway C 169205\n", + "4 Roundabout B 16432\n", + "5 Roundabout C 8134\n", + "6 Slip road C 540\n", + "7 One way street A 18225\n", + "8 Unknown M 506\n", + "9 One way street C 3877\n", + "10 Dual carriageway M 76215\n", + "11 Single carriageway A 672829\n", + "12 Single carriageway M 361\n", + "13 Unknown C 1360\n", + "14 Slip road U 2937\n", + "15 Dual carriageway A 229185\n", + "16 Single carriageway U 606426\n", + "17 One way street M 164\n", + "18 Slip road M 6758\n", + "19 Dual carriageway B 11574\n", + "20 Dual carriageway C 4909\n", + "21 Roundabout A 98800\n", + "22 Single carriageway B 252704\n", + "23 One way street U 23272\n", + "24 Roundabout U 25739\n", + "25 Roundabout M 2102\n", + "26 Dual carriageway U 19587\n", + "27 Slip road A 13883\n", + "28 Slip road B 887\n", + "29 Unknown B 1477\n", + "30 Data missing or out of range A 1" + ] + }, + "execution_count": 218, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dangeorusroadtype_df=dangeorusroadtype.toPandas()\n", + "dangeorusroadtype_df" + ] + }, + { + "cell_type": "code", + "execution_count": 222, + "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>Road_Type</th>\n", + " <th>road_name</th>\n", + " <th>Total accidents</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>Unknown</td>\n", + " <td>A</td>\n", + " <td>5797</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>One way street</td>\n", + " <td>B</td>\n", + " <td>3750</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>Unknown</td>\n", + " <td>U</td>\n", + " <td>9791</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>Single carriageway</td>\n", + " <td>C</td>\n", + " <td>169205</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>Roundabout</td>\n", + " <td>B</td>\n", + " <td>16432</td>\n", + " </tr>\n", + " <tr>\n", + " <th>5</th>\n", + " <td>Roundabout</td>\n", + " <td>C</td>\n", + " <td>8134</td>\n", + " </tr>\n", + " <tr>\n", + " <th>6</th>\n", + " <td>Slip road</td>\n", + " <td>C</td>\n", + " <td>540</td>\n", + " </tr>\n", + " <tr>\n", + " <th>7</th>\n", + " <td>One way street</td>\n", + " <td>A</td>\n", + " <td>18225</td>\n", + " </tr>\n", + " <tr>\n", + " <th>8</th>\n", + " <td>Unknown</td>\n", + " <td>M</td>\n", + " <td>506</td>\n", + " </tr>\n", + " <tr>\n", + " <th>9</th>\n", + " <td>One way street</td>\n", + " <td>C</td>\n", + " <td>3877</td>\n", + " </tr>\n", + " <tr>\n", + " <th>10</th>\n", + " <td>Dual carriageway</td>\n", + " <td>M</td>\n", + " <td>76215</td>\n", + " </tr>\n", + " <tr>\n", + " <th>11</th>\n", + " <td>Single carriageway</td>\n", + " <td>A</td>\n", + " <td>672829</td>\n", + " </tr>\n", + " <tr>\n", + " <th>12</th>\n", + " <td>Single carriageway</td>\n", + " <td>M</td>\n", + " <td>361</td>\n", + " </tr>\n", + " <tr>\n", + " <th>13</th>\n", + " <td>Unknown</td>\n", + " <td>C</td>\n", + " <td>1360</td>\n", + " </tr>\n", + " <tr>\n", + " <th>14</th>\n", + " <td>Slip road</td>\n", + " <td>U</td>\n", + " <td>2937</td>\n", + " </tr>\n", + " <tr>\n", + " <th>15</th>\n", + " <td>Dual carriageway</td>\n", + " <td>A</td>\n", + " <td>229185</td>\n", + " </tr>\n", + " <tr>\n", + " <th>16</th>\n", + " <td>Single carriageway</td>\n", + " <td>U</td>\n", + " <td>606426</td>\n", + " </tr>\n", + " <tr>\n", + " <th>17</th>\n", + " <td>One way street</td>\n", + " <td>M</td>\n", + " <td>164</td>\n", + " </tr>\n", + " <tr>\n", + " <th>18</th>\n", + " <td>Slip road</td>\n", + " <td>M</td>\n", + " <td>6758</td>\n", + " </tr>\n", + " <tr>\n", + " <th>19</th>\n", + " <td>Dual carriageway</td>\n", + " <td>B</td>\n", + " <td>11574</td>\n", + " </tr>\n", + " <tr>\n", + " <th>20</th>\n", + " <td>Dual carriageway</td>\n", + " <td>C</td>\n", + " <td>4909</td>\n", + " </tr>\n", + " <tr>\n", + " <th>21</th>\n", + " <td>Roundabout</td>\n", + " <td>A</td>\n", + " <td>98800</td>\n", + " </tr>\n", + " <tr>\n", + " <th>22</th>\n", + " <td>Single carriageway</td>\n", + " <td>B</td>\n", + " <td>252704</td>\n", + " </tr>\n", + " <tr>\n", + " <th>23</th>\n", + " <td>One way street</td>\n", + " <td>U</td>\n", + " <td>23272</td>\n", + " </tr>\n", + " <tr>\n", + " <th>24</th>\n", + " <td>Roundabout</td>\n", + " <td>U</td>\n", + " <td>25739</td>\n", + " </tr>\n", + " <tr>\n", + " <th>25</th>\n", + " <td>Roundabout</td>\n", + " <td>M</td>\n", + " <td>2102</td>\n", + " </tr>\n", + " <tr>\n", + " <th>26</th>\n", + " <td>Dual carriageway</td>\n", + " <td>U</td>\n", + " <td>19587</td>\n", + " </tr>\n", + " <tr>\n", + " <th>27</th>\n", + " <td>Slip road</td>\n", + " <td>A</td>\n", + " <td>13883</td>\n", + " </tr>\n", + " <tr>\n", + " <th>28</th>\n", + " <td>Slip road</td>\n", + " <td>B</td>\n", + " <td>887</td>\n", + " </tr>\n", + " <tr>\n", + " <th>29</th>\n", + " <td>Unknown</td>\n", + " <td>B</td>\n", + " <td>1477</td>\n", + " </tr>\n", + " <tr>\n", + " <th>30</th>\n", + " <td>Data missing or out of range</td>\n", + " <td>A</td>\n", + " <td>1</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " Road_Type road_name Total accidents\n", + "0 Unknown A 5797\n", + "1 One way street B 3750\n", + "2 Unknown U 9791\n", + "3 Single carriageway C 169205\n", + "4 Roundabout B 16432\n", + "5 Roundabout C 8134\n", + "6 Slip road C 540\n", + "7 One way street A 18225\n", + "8 Unknown M 506\n", + "9 One way street C 3877\n", + "10 Dual carriageway M 76215\n", + "11 Single carriageway A 672829\n", + "12 Single carriageway M 361\n", + "13 Unknown C 1360\n", + "14 Slip road U 2937\n", + "15 Dual carriageway A 229185\n", + "16 Single carriageway U 606426\n", + "17 One way street M 164\n", + "18 Slip road M 6758\n", + "19 Dual carriageway B 11574\n", + "20 Dual carriageway C 4909\n", + "21 Roundabout A 98800\n", + "22 Single carriageway B 252704\n", + "23 One way street U 23272\n", + "24 Roundabout U 25739\n", + "25 Roundabout M 2102\n", + "26 Dual carriageway U 19587\n", + "27 Slip road A 13883\n", + "28 Slip road B 887\n", + "29 Unknown B 1477\n", + "30 Data missing or out of range A 1" + ] + }, + "execution_count": 222, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "dangeorusroadtype_df=dangeorusroadtype_df.rename(columns={\"first_road_class\": \"road_name\"})\n", + "dangeorusroadtype_df" + ] + }, + { + "cell_type": "code", + "execution_count": 314, + "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>Road_Type</th>\n", + " <th>road_name</th>\n", + " <th>Total accidents</th>\n", + " <th>Trafficvolume</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>Unknown</td>\n", + " <td>A</td>\n", + " <td>5797</td>\n", + " <td>1.346786e+14</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>One way street</td>\n", + " <td>A</td>\n", + " <td>18225</td>\n", + " <td>1.346786e+14</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>Single carriageway</td>\n", + " <td>A</td>\n", + " <td>672829</td>\n", + " <td>1.346786e+14</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>Dual carriageway</td>\n", + " <td>A</td>\n", + " <td>229185</td>\n", + " <td>1.346786e+14</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>Roundabout</td>\n", + " <td>A</td>\n", + " <td>98800</td>\n", + " <td>1.346786e+14</td>\n", + " </tr>\n", + " <tr>\n", + " <th>5</th>\n", + " <td>Slip road</td>\n", + " <td>A</td>\n", + " <td>13883</td>\n", + " <td>1.346786e+14</td>\n", + " </tr>\n", + " <tr>\n", + " <th>6</th>\n", + " <td>Data missing or out of range</td>\n", + " <td>A</td>\n", + " <td>1</td>\n", + " <td>1.346786e+14</td>\n", + " </tr>\n", + " <tr>\n", + " <th>7</th>\n", + " <td>One way street</td>\n", + " <td>B</td>\n", + " <td>3750</td>\n", + " <td>2.473941e+12</td>\n", + " </tr>\n", + " <tr>\n", + " <th>8</th>\n", + " <td>Roundabout</td>\n", + " <td>B</td>\n", + " <td>16432</td>\n", + " <td>2.473941e+12</td>\n", + " </tr>\n", + " <tr>\n", + " <th>9</th>\n", + " <td>Dual carriageway</td>\n", + " <td>B</td>\n", + " <td>11574</td>\n", + " <td>2.473941e+12</td>\n", + " </tr>\n", + " <tr>\n", + " <th>10</th>\n", + " <td>Single carriageway</td>\n", + " <td>B</td>\n", + " <td>252704</td>\n", + " <td>2.473941e+12</td>\n", + " </tr>\n", + " <tr>\n", + " <th>11</th>\n", + " <td>Slip road</td>\n", + " <td>B</td>\n", + " <td>887</td>\n", + " <td>2.473941e+12</td>\n", + " </tr>\n", + " <tr>\n", + " <th>12</th>\n", + " <td>Unknown</td>\n", + " <td>B</td>\n", + " <td>1477</td>\n", + " <td>2.473941e+12</td>\n", + " </tr>\n", + " <tr>\n", + " <th>13</th>\n", + " <td>Unknown</td>\n", + " <td>U</td>\n", + " <td>9791</td>\n", + " <td>1.499571e+13</td>\n", + " </tr>\n", + " <tr>\n", + " <th>14</th>\n", + " <td>Slip road</td>\n", + " <td>U</td>\n", + " <td>2937</td>\n", + " <td>1.499571e+13</td>\n", + " </tr>\n", + " <tr>\n", + " <th>15</th>\n", + " <td>Single carriageway</td>\n", + " <td>U</td>\n", + " <td>606426</td>\n", + " <td>1.499571e+13</td>\n", + " </tr>\n", + " <tr>\n", + " <th>16</th>\n", + " <td>One way street</td>\n", + " <td>U</td>\n", + " <td>23272</td>\n", + " <td>1.499571e+13</td>\n", + " </tr>\n", + " <tr>\n", + " <th>17</th>\n", + " <td>Roundabout</td>\n", + " <td>U</td>\n", + " <td>25739</td>\n", + " <td>1.499571e+13</td>\n", + " </tr>\n", + " <tr>\n", + " <th>18</th>\n", + " <td>Dual carriageway</td>\n", + " <td>U</td>\n", + " <td>19587</td>\n", + " <td>1.499571e+13</td>\n", + " </tr>\n", + " <tr>\n", + " <th>19</th>\n", + " <td>Single carriageway</td>\n", + " <td>C</td>\n", + " <td>169205</td>\n", + " <td>4.146348e+12</td>\n", + " </tr>\n", + " <tr>\n", + " <th>20</th>\n", + " <td>Roundabout</td>\n", + " <td>C</td>\n", + " <td>8134</td>\n", + " <td>4.146348e+12</td>\n", + " </tr>\n", + " <tr>\n", + " <th>21</th>\n", + " <td>Slip road</td>\n", + " <td>C</td>\n", + " <td>540</td>\n", + " <td>4.146348e+12</td>\n", + " </tr>\n", + " <tr>\n", + " <th>22</th>\n", + " <td>One way street</td>\n", + " <td>C</td>\n", + " <td>3877</td>\n", + " <td>4.146348e+12</td>\n", + " </tr>\n", + " <tr>\n", + " <th>23</th>\n", + " <td>Unknown</td>\n", + " <td>C</td>\n", + " <td>1360</td>\n", + " <td>4.146348e+12</td>\n", + " </tr>\n", + " <tr>\n", + " <th>24</th>\n", + " <td>Dual carriageway</td>\n", + " <td>C</td>\n", + " <td>4909</td>\n", + " <td>4.146348e+12</td>\n", + " </tr>\n", + " <tr>\n", + " <th>25</th>\n", + " <td>Unknown</td>\n", + " <td>M</td>\n", + " <td>506</td>\n", + " <td>2.612750e+12</td>\n", + " </tr>\n", + " <tr>\n", + " <th>26</th>\n", + " <td>Dual carriageway</td>\n", + " <td>M</td>\n", + " <td>76215</td>\n", + " <td>2.612750e+12</td>\n", + " </tr>\n", + " <tr>\n", + " <th>27</th>\n", + " <td>Single carriageway</td>\n", + " <td>M</td>\n", + " <td>361</td>\n", + " <td>2.612750e+12</td>\n", + " </tr>\n", + " <tr>\n", + " <th>28</th>\n", + " <td>One way street</td>\n", + " <td>M</td>\n", + " <td>164</td>\n", + " <td>2.612750e+12</td>\n", + " </tr>\n", + " <tr>\n", + " <th>29</th>\n", + " <td>Slip road</td>\n", + " <td>M</td>\n", + " <td>6758</td>\n", + " <td>2.612750e+12</td>\n", + " </tr>\n", + " <tr>\n", + " <th>30</th>\n", + " <td>Roundabout</td>\n", + " <td>M</td>\n", + " <td>2102</td>\n", + " <td>2.612750e+12</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " Road_Type road_name Total accidents Trafficvolume\n", + "0 Unknown A 5797 1.346786e+14\n", + "1 One way street A 18225 1.346786e+14\n", + "2 Single carriageway A 672829 1.346786e+14\n", + "3 Dual carriageway A 229185 1.346786e+14\n", + "4 Roundabout A 98800 1.346786e+14\n", + "5 Slip road A 13883 1.346786e+14\n", + "6 Data missing or out of range A 1 1.346786e+14\n", + "7 One way street B 3750 2.473941e+12\n", + "8 Roundabout B 16432 2.473941e+12\n", + "9 Dual carriageway B 11574 2.473941e+12\n", + "10 Single carriageway B 252704 2.473941e+12\n", + "11 Slip road B 887 2.473941e+12\n", + "12 Unknown B 1477 2.473941e+12\n", + "13 Unknown U 9791 1.499571e+13\n", + "14 Slip road U 2937 1.499571e+13\n", + "15 Single carriageway U 606426 1.499571e+13\n", + "16 One way street U 23272 1.499571e+13\n", + "17 Roundabout U 25739 1.499571e+13\n", + "18 Dual carriageway U 19587 1.499571e+13\n", + "19 Single carriageway C 169205 4.146348e+12\n", + "20 Roundabout C 8134 4.146348e+12\n", + "21 Slip road C 540 4.146348e+12\n", + "22 One way street C 3877 4.146348e+12\n", + "23 Unknown C 1360 4.146348e+12\n", + "24 Dual carriageway C 4909 4.146348e+12\n", + "25 Unknown M 506 2.612750e+12\n", + "26 Dual carriageway M 76215 2.612750e+12\n", + "27 Single carriageway M 361 2.612750e+12\n", + "28 One way street M 164 2.612750e+12\n", + "29 Slip road M 6758 2.612750e+12\n", + "30 Roundabout M 2102 2.612750e+12" + ] + }, + "execution_count": 314, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "result30=pd.merge(dangeorusroadtype_df, road_length_traffic, on=['road_name'])\n", + "result30" + ] + }, + { + "cell_type": "code", + "execution_count": 315, + "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>Road_Type</th>\n", + " <th>road_name</th>\n", + " <th>Accident Probability</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>Unknown</td>\n", + " <td>A</td>\n", + " <td>4.304322e-11</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>One way street</td>\n", + " <td>A</td>\n", + " <td>1.353222e-10</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>Single carriageway</td>\n", + " <td>A</td>\n", + " <td>4.995813e-09</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>Dual carriageway</td>\n", + " <td>A</td>\n", + " <td>1.701718e-09</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>Roundabout</td>\n", + " <td>A</td>\n", + " <td>7.335984e-10</td>\n", + " </tr>\n", + " <tr>\n", + " <th>5</th>\n", + " <td>Slip road</td>\n", + " <td>A</td>\n", + " <td>1.030825e-10</td>\n", + " </tr>\n", + " <tr>\n", + " <th>6</th>\n", + " <td>Data missing or out of range</td>\n", + " <td>A</td>\n", + " <td>7.425085e-15</td>\n", + " </tr>\n", + " <tr>\n", + " <th>7</th>\n", + " <td>One way street</td>\n", + " <td>B</td>\n", + " <td>1.515800e-09</td>\n", + " </tr>\n", + " <tr>\n", + " <th>8</th>\n", + " <td>Roundabout</td>\n", + " <td>B</td>\n", + " <td>6.642033e-09</td>\n", + " </tr>\n", + " <tr>\n", + " <th>9</th>\n", + " <td>Dual carriageway</td>\n", + " <td>B</td>\n", + " <td>4.678365e-09</td>\n", + " </tr>\n", + " <tr>\n", + " <th>10</th>\n", + " <td>Single carriageway</td>\n", + " <td>B</td>\n", + " <td>1.021463e-07</td>\n", + " </tr>\n", + " <tr>\n", + " <th>11</th>\n", + " <td>Slip road</td>\n", + " <td>B</td>\n", + " <td>3.585372e-10</td>\n", + " </tr>\n", + " <tr>\n", + " <th>12</th>\n", + " <td>Unknown</td>\n", + " <td>B</td>\n", + " <td>5.970231e-10</td>\n", + " </tr>\n", + " <tr>\n", + " <th>13</th>\n", + " <td>Unknown</td>\n", + " <td>U</td>\n", + " <td>6.529201e-10</td>\n", + " </tr>\n", + " <tr>\n", + " <th>14</th>\n", + " <td>Slip road</td>\n", + " <td>U</td>\n", + " <td>1.958560e-10</td>\n", + " </tr>\n", + " <tr>\n", + " <th>15</th>\n", + " <td>Single carriageway</td>\n", + " <td>U</td>\n", + " <td>4.043997e-08</td>\n", + " </tr>\n", + " <tr>\n", + " <th>16</th>\n", + " <td>One way street</td>\n", + " <td>U</td>\n", + " <td>1.551911e-09</td>\n", + " </tr>\n", + " <tr>\n", + " <th>17</th>\n", + " <td>Roundabout</td>\n", + " <td>U</td>\n", + " <td>1.716424e-09</td>\n", + " </tr>\n", + " <tr>\n", + " <th>18</th>\n", + " <td>Dual carriageway</td>\n", + " <td>U</td>\n", + " <td>1.306174e-09</td>\n", + " </tr>\n", + " <tr>\n", + " <th>19</th>\n", + " <td>Single carriageway</td>\n", + " <td>C</td>\n", + " <td>4.080820e-08</td>\n", + " </tr>\n", + " <tr>\n", + " <th>20</th>\n", + " <td>Roundabout</td>\n", + " <td>C</td>\n", + " <td>1.961726e-09</td>\n", + " </tr>\n", + " <tr>\n", + " <th>21</th>\n", + " <td>Slip road</td>\n", + " <td>C</td>\n", + " <td>1.302351e-10</td>\n", + " </tr>\n", + " <tr>\n", + " <th>22</th>\n", + " <td>One way street</td>\n", + " <td>C</td>\n", + " <td>9.350397e-10</td>\n", + " </tr>\n", + " <tr>\n", + " <th>23</th>\n", + " <td>Unknown</td>\n", + " <td>C</td>\n", + " <td>3.279995e-10</td>\n", + " </tr>\n", + " <tr>\n", + " <th>24</th>\n", + " <td>Dual carriageway</td>\n", + " <td>C</td>\n", + " <td>1.183933e-09</td>\n", + " </tr>\n", + " <tr>\n", + " <th>25</th>\n", + " <td>Unknown</td>\n", + " <td>M</td>\n", + " <td>1.936657e-10</td>\n", + " </tr>\n", + " <tr>\n", + " <th>26</th>\n", + " <td>Dual carriageway</td>\n", + " <td>M</td>\n", + " <td>2.917041e-08</td>\n", + " </tr>\n", + " <tr>\n", + " <th>27</th>\n", + " <td>Single carriageway</td>\n", + " <td>M</td>\n", + " <td>1.381686e-10</td>\n", + " </tr>\n", + " <tr>\n", + " <th>28</th>\n", + " <td>One way street</td>\n", + " <td>M</td>\n", + " <td>6.276911e-11</td>\n", + " </tr>\n", + " <tr>\n", + " <th>29</th>\n", + " <td>Slip road</td>\n", + " <td>M</td>\n", + " <td>2.586547e-09</td>\n", + " </tr>\n", + " <tr>\n", + " <th>30</th>\n", + " <td>Roundabout</td>\n", + " <td>M</td>\n", + " <td>8.045163e-10</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " Road_Type road_name Accident Probability\n", + "0 Unknown A 4.304322e-11\n", + "1 One way street A 1.353222e-10\n", + "2 Single carriageway A 4.995813e-09\n", + "3 Dual carriageway A 1.701718e-09\n", + "4 Roundabout A 7.335984e-10\n", + "5 Slip road A 1.030825e-10\n", + "6 Data missing or out of range A 7.425085e-15\n", + "7 One way street B 1.515800e-09\n", + "8 Roundabout B 6.642033e-09\n", + "9 Dual carriageway B 4.678365e-09\n", + "10 Single carriageway B 1.021463e-07\n", + "11 Slip road B 3.585372e-10\n", + "12 Unknown B 5.970231e-10\n", + "13 Unknown U 6.529201e-10\n", + "14 Slip road U 1.958560e-10\n", + "15 Single carriageway U 4.043997e-08\n", + "16 One way street U 1.551911e-09\n", + "17 Roundabout U 1.716424e-09\n", + "18 Dual carriageway U 1.306174e-09\n", + "19 Single carriageway C 4.080820e-08\n", + "20 Roundabout C 1.961726e-09\n", + "21 Slip road C 1.302351e-10\n", + "22 One way street C 9.350397e-10\n", + "23 Unknown C 3.279995e-10\n", + "24 Dual carriageway C 1.183933e-09\n", + "25 Unknown M 1.936657e-10\n", + "26 Dual carriageway M 2.917041e-08\n", + "27 Single carriageway M 1.381686e-10\n", + "28 One way street M 6.276911e-11\n", + "29 Slip road M 2.586547e-09\n", + "30 Roundabout M 8.045163e-10" + ] + }, + "execution_count": 315, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "result30[\"Accident Probability\"] = result30[\"Total accidents\"] / result30[\"Trafficvolume\"]\n", + "result30=result30.drop(['Total accidents', 'Trafficvolume'], axis=1)\n", + "result30" + ] + }, + { + "cell_type": "code", + "execution_count": 225, + "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>Road_Type</th>\n", + " <th>Accident Probability</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>Unknown</td>\n", + " <td>7.807318e+17</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>One way street</td>\n", + " <td>2.454517e+18</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>Single carriageway</td>\n", + " <td>9.061566e+19</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>Dual carriageway</td>\n", + " <td>3.086631e+19</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>Roundabout</td>\n", + " <td>1.330624e+19</td>\n", + " </tr>\n", + " <tr>\n", + " <th>5</th>\n", + " <td>Slip road</td>\n", + " <td>1.869743e+18</td>\n", + " </tr>\n", + " <tr>\n", + " <th>6</th>\n", + " <td>Data missing or out of range</td>\n", + " <td>1.346786e+14</td>\n", + " </tr>\n", + " <tr>\n", + " <th>7</th>\n", + " <td>One way street</td>\n", + " <td>9.277280e+15</td>\n", + " </tr>\n", + " <tr>\n", + " <th>8</th>\n", + " <td>Roundabout</td>\n", + " <td>4.065180e+16</td>\n", + " </tr>\n", + " <tr>\n", + " <th>9</th>\n", + " <td>Dual carriageway</td>\n", + " <td>2.863340e+16</td>\n", + " </tr>\n", + " <tr>\n", + " <th>10</th>\n", + " <td>Single carriageway</td>\n", + " <td>6.251749e+17</td>\n", + " </tr>\n", + " <tr>\n", + " <th>11</th>\n", + " <td>Slip road</td>\n", + " <td>2.194386e+15</td>\n", + " </tr>\n", + " <tr>\n", + " <th>12</th>\n", + " <td>Unknown</td>\n", + " <td>3.654011e+15</td>\n", + " </tr>\n", + " <tr>\n", + " <th>13</th>\n", + " <td>Unknown</td>\n", + " <td>1.468230e+17</td>\n", + " </tr>\n", + " <tr>\n", + " <th>14</th>\n", + " <td>Slip road</td>\n", + " <td>4.404240e+16</td>\n", + " </tr>\n", + " <tr>\n", + " <th>15</th>\n", + " <td>Single carriageway</td>\n", + " <td>9.093788e+18</td>\n", + " </tr>\n", + " <tr>\n", + " <th>16</th>\n", + " <td>One way street</td>\n", + " <td>3.489801e+17</td>\n", + " </tr>\n", + " <tr>\n", + " <th>17</th>\n", + " <td>Roundabout</td>\n", + " <td>3.859746e+17</td>\n", + " </tr>\n", + " <tr>\n", + " <th>18</th>\n", + " <td>Dual carriageway</td>\n", + " <td>2.937210e+17</td>\n", + " </tr>\n", + " <tr>\n", + " <th>19</th>\n", + " <td>Single carriageway</td>\n", + " <td>7.015828e+17</td>\n", + " </tr>\n", + " <tr>\n", + " <th>20</th>\n", + " <td>Roundabout</td>\n", + " <td>3.372640e+16</td>\n", + " </tr>\n", + " <tr>\n", + " <th>21</th>\n", + " <td>Slip road</td>\n", + " <td>2.239028e+15</td>\n", + " </tr>\n", + " <tr>\n", + " <th>22</th>\n", + " <td>One way street</td>\n", + " <td>1.607539e+16</td>\n", + " </tr>\n", + " <tr>\n", + " <th>23</th>\n", + " <td>Unknown</td>\n", + " <td>5.639033e+15</td>\n", + " </tr>\n", + " <tr>\n", + " <th>24</th>\n", + " <td>Dual carriageway</td>\n", + " <td>2.035442e+16</td>\n", + " </tr>\n", + " <tr>\n", + " <th>25</th>\n", + " <td>Unknown</td>\n", + " <td>1.322052e+15</td>\n", + " </tr>\n", + " <tr>\n", + " <th>26</th>\n", + " <td>Dual carriageway</td>\n", + " <td>1.991307e+17</td>\n", + " </tr>\n", + " <tr>\n", + " <th>27</th>\n", + " <td>Single carriageway</td>\n", + " <td>9.432028e+14</td>\n", + " </tr>\n", + " <tr>\n", + " <th>28</th>\n", + " <td>One way street</td>\n", + " <td>4.284910e+14</td>\n", + " </tr>\n", + " <tr>\n", + " <th>29</th>\n", + " <td>Slip road</td>\n", + " <td>1.765697e+16</td>\n", + " </tr>\n", + " <tr>\n", + " <th>30</th>\n", + " <td>Roundabout</td>\n", + " <td>5.492001e+15</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " Road_Type Accident Probability\n", + "0 Unknown 7.807318e+17\n", + "1 One way street 2.454517e+18\n", + "2 Single carriageway 9.061566e+19\n", + "3 Dual carriageway 3.086631e+19\n", + "4 Roundabout 1.330624e+19\n", + "5 Slip road 1.869743e+18\n", + "6 Data missing or out of range 1.346786e+14\n", + "7 One way street 9.277280e+15\n", + "8 Roundabout 4.065180e+16\n", + "9 Dual carriageway 2.863340e+16\n", + "10 Single carriageway 6.251749e+17\n", + "11 Slip road 2.194386e+15\n", + "12 Unknown 3.654011e+15\n", + "13 Unknown 1.468230e+17\n", + "14 Slip road 4.404240e+16\n", + "15 Single carriageway 9.093788e+18\n", + "16 One way street 3.489801e+17\n", + "17 Roundabout 3.859746e+17\n", + "18 Dual carriageway 2.937210e+17\n", + "19 Single carriageway 7.015828e+17\n", + "20 Roundabout 3.372640e+16\n", + "21 Slip road 2.239028e+15\n", + "22 One way street 1.607539e+16\n", + "23 Unknown 5.639033e+15\n", + "24 Dual carriageway 2.035442e+16\n", + "25 Unknown 1.322052e+15\n", + "26 Dual carriageway 1.991307e+17\n", + "27 Single carriageway 9.432028e+14\n", + "28 One way street 4.284910e+14\n", + "29 Slip road 1.765697e+16\n", + "30 Roundabout 5.492001e+15" + ] + }, + "execution_count": 225, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "result30=result30.drop(['road_name'], axis=1)\n", + "result30" + ] + }, + { + "cell_type": "code", + "execution_count": 316, + "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>Road_Type</th>\n", + " <th>Accident Probability</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>Unknown</td>\n", + " <td>1.814652e-09</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>One way street</td>\n", + " <td>4.200841e-09</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>Single carriageway</td>\n", + " <td>1.885285e-07</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>Dual carriageway</td>\n", + " <td>3.804060e-08</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>Roundabout</td>\n", + " <td>1.185830e-08</td>\n", + " </tr>\n", + " <tr>\n", + " <th>5</th>\n", + " <td>Slip road</td>\n", + " <td>3.374257e-09</td>\n", + " </tr>\n", + " <tr>\n", + " <th>6</th>\n", + " <td>Data missing or out of range</td>\n", + " <td>7.425085e-15</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " Road_Type Accident Probability\n", + "0 Unknown 1.814652e-09\n", + "1 One way street 4.200841e-09\n", + "2 Single carriageway 1.885285e-07\n", + "3 Dual carriageway 3.804060e-08\n", + "4 Roundabout 1.185830e-08\n", + "5 Slip road 3.374257e-09\n", + "6 Data missing or out of range 7.425085e-15" + ] + }, + "execution_count": 316, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "result30_df = result30.groupby('Road_Type', sort=False)[\"Accident Probability\"].sum().reset_index(name ='Accident Probability')\n", + "result30_df" + ] + }, + { + "cell_type": "code", + "execution_count": 317, + "metadata": {}, + "outputs": [], + "source": [ + "result30_df=result30_df.drop(labels=[6],axis=0)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 318, + "metadata": {}, + "outputs": [], + "source": [ + "result30_df=result30_df.sort_values('Road_Type')" + ] + }, + { + "cell_type": "code", + "execution_count": 319, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 1440x720 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax=result30_df.plot.bar('Road_Type','Accident Probability', rot=90,title=\"Accidents probabilty over road type \",figsize=(20, 10),color=\"Orange\")" + ] + }, + { + "cell_type": "code", + "execution_count": 320, + "metadata": {}, + "outputs": [], + "source": [ + "A2018=A2018.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": 322, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+---------------------------------+----------------+---------------+\n", + "|Junction_Detail |first_road_class|Total accidents|\n", + "+---------------------------------+----------------+---------------+\n", + "|Data missing or out of range |M |1 |\n", + "|99 |M |1 |\n", + "|Data missing or out of range |B |2 |\n", + "|Data missing or out of range |C |2 |\n", + "|Mini-roundabout |M |6 |\n", + "|Data missing or out of range |A |8 |\n", + "|Data missing or out of range |U |11 |\n", + "|Private drive or entrance |M |12 |\n", + "|Crossroads |M |70 |\n", + "|99 |C |75 |\n", + "|99 |B |168 |\n", + "|More than 4 arms (not roundabout)|M |172 |\n", + "|T or staggered junction |M |361 |\n", + "|Slip road |C |509 |\n", + "|Other junction |M |522 |\n", + "|Slip road |B |1148 |\n", + "|99 |U |1311 |\n", + "|99 |A |1324 |\n", + "|More than 4 arms (not roundabout)|C |1706 |\n", + "|Slip road |U |2493 |\n", + "+---------------------------------+----------------+---------------+\n", + "only showing top 20 rows\n", + "\n" + ] + } + ], + "source": [ + "Junction_Detail_df= A2018.groupby('Junction_Detail','first_road_class').agg(F.count(A2018.accident_index).alias('Total accidents'))\n", + "Junction_Detail_df.sort(\"Total accidents\").show(truncate=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 323, + "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>Junction_Detail</th>\n", + " <th>road_name</th>\n", + " <th>Accident Probability</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>99</td>\n", + " <td>A</td>\n", + " <td>9.830813e-12</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>Other junction</td>\n", + " <td>A</td>\n", + " <td>2.369567e-10</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>T or staggered junction</td>\n", + " <td>A</td>\n", + " <td>2.267057e-09</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>Crossroads</td>\n", + " <td>A</td>\n", + " <td>7.870219e-10</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>More than 4 arms (not roundabout)</td>\n", + " <td>A</td>\n", + " <td>1.300801e-10</td>\n", + " </tr>\n", + " <tr>\n", + " <th>5</th>\n", + " <td>Mini-roundabout</td>\n", + " <td>A</td>\n", + " <td>4.775072e-11</td>\n", + " </tr>\n", + " <tr>\n", + " <th>6</th>\n", + " <td>Not at junction or within 20 metres</td>\n", + " <td>A</td>\n", + " <td>2.833286e-09</td>\n", + " </tr>\n", + " <tr>\n", + " <th>7</th>\n", + " <td>Roundabout</td>\n", + " <td>A</td>\n", + " <td>9.934616e-10</td>\n", + " </tr>\n", + " <tr>\n", + " <th>8</th>\n", + " <td>Private drive or entrance</td>\n", + " <td>A</td>\n", + " <td>2.514505e-10</td>\n", + " </tr>\n", + " <tr>\n", + " <th>9</th>\n", + " <td>Slip road</td>\n", + " <td>A</td>\n", + " <td>1.556298e-10</td>\n", + " </tr>\n", + " <tr>\n", + " <th>10</th>\n", + " <td>Data missing or out of range</td>\n", + " <td>A</td>\n", + " <td>5.940068e-14</td>\n", + " </tr>\n", + " <tr>\n", + " <th>11</th>\n", + " <td>T or staggered junction</td>\n", + " <td>B</td>\n", + " <td>3.862137e-08</td>\n", + " </tr>\n", + " <tr>\n", + " <th>12</th>\n", + " <td>Roundabout</td>\n", + " <td>B</td>\n", + " <td>7.941579e-09</td>\n", + " </tr>\n", + " <tr>\n", + " <th>13</th>\n", + " <td>Mini-roundabout</td>\n", + " <td>B</td>\n", + " <td>2.066743e-09</td>\n", + " </tr>\n", + " <tr>\n", + " <th>14</th>\n", + " <td>Data missing or out of range</td>\n", + " <td>B</td>\n", + " <td>8.084266e-13</td>\n", + " </tr>\n", + " <tr>\n", + " <th>15</th>\n", + " <td>Crossroads</td>\n", + " <td>B</td>\n", + " <td>1.103583e-08</td>\n", + " </tr>\n", + " <tr>\n", + " <th>16</th>\n", + " <td>Not at junction or within 20 metres</td>\n", + " <td>B</td>\n", + " <td>4.608921e-08</td>\n", + " </tr>\n", + " <tr>\n", + " <th>17</th>\n", + " <td>More than 4 arms (not roundabout)</td>\n", + " <td>B</td>\n", + " <td>1.269230e-09</td>\n", + " </tr>\n", + " <tr>\n", + " <th>18</th>\n", + " <td>99</td>\n", + " <td>B</td>\n", + " <td>6.790784e-11</td>\n", + " </tr>\n", + " <tr>\n", + " <th>19</th>\n", + " <td>Private drive or entrance</td>\n", + " <td>B</td>\n", + " <td>4.817818e-09</td>\n", + " </tr>\n", + " <tr>\n", + " <th>20</th>\n", + " <td>Slip road</td>\n", + " <td>B</td>\n", + " <td>4.640369e-10</td>\n", + " </tr>\n", + " <tr>\n", + " <th>21</th>\n", + " <td>Other junction</td>\n", + " <td>B</td>\n", + " <td>3.563545e-09</td>\n", + " </tr>\n", + " <tr>\n", + " <th>22</th>\n", + " <td>Crossroads</td>\n", + " <td>M</td>\n", + " <td>2.679169e-11</td>\n", + " </tr>\n", + " <tr>\n", + " <th>23</th>\n", + " <td>Data missing or out of range</td>\n", + " <td>M</td>\n", + " <td>3.827385e-13</td>\n", + " </tr>\n", + " <tr>\n", + " <th>24</th>\n", + " <td>Private drive or entrance</td>\n", + " <td>M</td>\n", + " <td>4.592862e-12</td>\n", + " </tr>\n", + " <tr>\n", + " <th>25</th>\n", + " <td>More than 4 arms (not roundabout)</td>\n", + " <td>M</td>\n", + " <td>6.583102e-11</td>\n", + " </tr>\n", + " <tr>\n", + " <th>26</th>\n", + " <td>Slip road</td>\n", + " <td>M</td>\n", + " <td>3.173285e-09</td>\n", + " </tr>\n", + " <tr>\n", + " <th>27</th>\n", + " <td>Other junction</td>\n", + " <td>M</td>\n", + " <td>1.997895e-10</td>\n", + " </tr>\n", + " <tr>\n", + " <th>28</th>\n", + " <td>Mini-roundabout</td>\n", + " <td>M</td>\n", + " <td>2.296431e-12</td>\n", + " </tr>\n", + " <tr>\n", + " <th>29</th>\n", + " <td>Roundabout</td>\n", + " <td>M</td>\n", + " <td>1.747201e-09</td>\n", + " </tr>\n", + " <tr>\n", + " <th>30</th>\n", + " <td>T or staggered junction</td>\n", + " <td>M</td>\n", + " <td>1.381686e-10</td>\n", + " </tr>\n", + " <tr>\n", + " <th>31</th>\n", + " <td>99</td>\n", + " <td>M</td>\n", + " <td>3.827385e-13</td>\n", + " </tr>\n", + " <tr>\n", + " <th>32</th>\n", + " <td>Not at junction or within 20 metres</td>\n", + " <td>M</td>\n", + " <td>2.759736e-08</td>\n", + " </tr>\n", + " <tr>\n", + " <th>33</th>\n", + " <td>Roundabout</td>\n", + " <td>C</td>\n", + " <td>2.113908e-09</td>\n", + " </tr>\n", + " <tr>\n", + " <th>34</th>\n", + " <td>Slip road</td>\n", + " <td>C</td>\n", + " <td>1.227586e-10</td>\n", + " </tr>\n", + " <tr>\n", + " <th>35</th>\n", + " <td>99</td>\n", + " <td>C</td>\n", + " <td>1.808821e-11</td>\n", + " </tr>\n", + " <tr>\n", + " <th>36</th>\n", + " <td>Other junction</td>\n", + " <td>C</td>\n", + " <td>9.191221e-10</td>\n", + " </tr>\n", + " <tr>\n", + " <th>37</th>\n", + " <td>Mini-roundabout</td>\n", + " <td>C</td>\n", + " <td>9.683220e-10</td>\n", + " </tr>\n", + " <tr>\n", + " <th>38</th>\n", + " <td>T or staggered junction</td>\n", + " <td>C</td>\n", + " <td>1.593475e-08</td>\n", + " </tr>\n", + " <tr>\n", + " <th>39</th>\n", + " <td>Private drive or entrance</td>\n", + " <td>C</td>\n", + " <td>1.843550e-09</td>\n", + " </tr>\n", + " <tr>\n", + " <th>40</th>\n", + " <td>More than 4 arms (not roundabout)</td>\n", + " <td>C</td>\n", + " <td>4.114464e-10</td>\n", + " </tr>\n", + " <tr>\n", + " <th>41</th>\n", + " <td>Not at junction or within 20 metres</td>\n", + " <td>C</td>\n", + " <td>1.789165e-08</td>\n", + " </tr>\n", + " <tr>\n", + " <th>42</th>\n", + " <td>Crossroads</td>\n", + " <td>C</td>\n", + " <td>5.123062e-09</td>\n", + " </tr>\n", + " <tr>\n", + " <th>43</th>\n", + " <td>Data missing or out of range</td>\n", + " <td>C</td>\n", + " <td>4.823522e-13</td>\n", + " </tr>\n", + " <tr>\n", + " <th>44</th>\n", + " <td>99</td>\n", + " <td>U</td>\n", + " <td>8.742501e-11</td>\n", + " </tr>\n", + " <tr>\n", + " <th>45</th>\n", + " <td>Not at junction or within 20 metres</td>\n", + " <td>U</td>\n", + " <td>1.909860e-08</td>\n", + " </tr>\n", + " <tr>\n", + " <th>46</th>\n", + " <td>Slip road</td>\n", + " <td>U</td>\n", + " <td>1.662476e-10</td>\n", + " </tr>\n", + " <tr>\n", + " <th>47</th>\n", + " <td>Private drive or entrance</td>\n", + " <td>U</td>\n", + " <td>1.656074e-09</td>\n", + " </tr>\n", + " <tr>\n", + " <th>48</th>\n", + " <td>More than 4 arms (not roundabout)</td>\n", + " <td>U</td>\n", + " <td>4.054493e-10</td>\n", + " </tr>\n", + " <tr>\n", + " <th>49</th>\n", + " <td>T or staggered junction</td>\n", + " <td>U</td>\n", + " <td>1.584880e-08</td>\n", + " </tr>\n", + " <tr>\n", + " <th>50</th>\n", + " <td>Other junction</td>\n", + " <td>U</td>\n", + " <td>1.663543e-09</td>\n", + " </tr>\n", + " <tr>\n", + " <th>51</th>\n", + " <td>Roundabout</td>\n", + " <td>U</td>\n", + " <td>1.973631e-09</td>\n", + " </tr>\n", + " <tr>\n", + " <th>52</th>\n", + " <td>Mini-roundabout</td>\n", + " <td>U</td>\n", + " <td>6.737260e-10</td>\n", + " </tr>\n", + " <tr>\n", + " <th>53</th>\n", + " <td>Data missing or out of range</td>\n", + " <td>U</td>\n", + " <td>7.335432e-13</td>\n", + " </tr>\n", + " <tr>\n", + " <th>54</th>\n", + " <td>Crossroads</td>\n", + " <td>U</td>\n", + " <td>4.289027e-09</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " Junction_Detail road_name Accident Probability\n", + "0 99 A 9.830813e-12\n", + "1 Other junction A 2.369567e-10\n", + "2 T or staggered junction A 2.267057e-09\n", + "3 Crossroads A 7.870219e-10\n", + "4 More than 4 arms (not roundabout) A 1.300801e-10\n", + "5 Mini-roundabout A 4.775072e-11\n", + "6 Not at junction or within 20 metres A 2.833286e-09\n", + "7 Roundabout A 9.934616e-10\n", + "8 Private drive or entrance A 2.514505e-10\n", + "9 Slip road A 1.556298e-10\n", + "10 Data missing or out of range A 5.940068e-14\n", + "11 T or staggered junction B 3.862137e-08\n", + "12 Roundabout B 7.941579e-09\n", + "13 Mini-roundabout B 2.066743e-09\n", + "14 Data missing or out of range B 8.084266e-13\n", + "15 Crossroads B 1.103583e-08\n", + "16 Not at junction or within 20 metres B 4.608921e-08\n", + "17 More than 4 arms (not roundabout) B 1.269230e-09\n", + "18 99 B 6.790784e-11\n", + "19 Private drive or entrance B 4.817818e-09\n", + "20 Slip road B 4.640369e-10\n", + "21 Other junction B 3.563545e-09\n", + "22 Crossroads M 2.679169e-11\n", + "23 Data missing or out of range M 3.827385e-13\n", + "24 Private drive or entrance M 4.592862e-12\n", + "25 More than 4 arms (not roundabout) M 6.583102e-11\n", + "26 Slip road M 3.173285e-09\n", + "27 Other junction M 1.997895e-10\n", + "28 Mini-roundabout M 2.296431e-12\n", + "29 Roundabout M 1.747201e-09\n", + "30 T or staggered junction M 1.381686e-10\n", + "31 99 M 3.827385e-13\n", + "32 Not at junction or within 20 metres M 2.759736e-08\n", + "33 Roundabout C 2.113908e-09\n", + "34 Slip road C 1.227586e-10\n", + "35 99 C 1.808821e-11\n", + "36 Other junction C 9.191221e-10\n", + "37 Mini-roundabout C 9.683220e-10\n", + "38 T or staggered junction C 1.593475e-08\n", + "39 Private drive or entrance C 1.843550e-09\n", + "40 More than 4 arms (not roundabout) C 4.114464e-10\n", + "41 Not at junction or within 20 metres C 1.789165e-08\n", + "42 Crossroads C 5.123062e-09\n", + "43 Data missing or out of range C 4.823522e-13\n", + "44 99 U 8.742501e-11\n", + "45 Not at junction or within 20 metres U 1.909860e-08\n", + "46 Slip road U 1.662476e-10\n", + "47 Private drive or entrance U 1.656074e-09\n", + "48 More than 4 arms (not roundabout) U 4.054493e-10\n", + "49 T or staggered junction U 1.584880e-08\n", + "50 Other junction U 1.663543e-09\n", + "51 Roundabout U 1.973631e-09\n", + "52 Mini-roundabout U 6.737260e-10\n", + "53 Data missing or out of range U 7.335432e-13\n", + "54 Crossroads U 4.289027e-09" + ] + }, + "execution_count": 323, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Junction_Detail_df=Junction_Detail_df.toPandas()\n", + "\n", + "Junction_Detail_df=Junction_Detail_df.rename(columns={\"first_road_class\": \"road_name\"})\n", + "\n", + "result35=pd.merge(Junction_Detail_df, road_length_traffic, on=['road_name'])\n", + "result35[\"Accident Probability\"] = result35[\"Total accidents\"] / result35[\"Trafficvolume\"]\n", + "result35=result35.drop(['Total accidents', 'Trafficvolume'], axis=1)\n", + "result35" + ] + }, + { + "cell_type": "code", + "execution_count": 326, + "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>Junction_Detail</th>\n", + " <th>Accident Probability</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>99</td>\n", + " <td>1.836346e-10</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>Other junction</td>\n", + " <td>6.582955e-09</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>T or staggered junction</td>\n", + " <td>7.281014e-08</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>Crossroads</td>\n", + " <td>2.126173e-08</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>More than 4 arms (not roundabout)</td>\n", + " <td>2.282037e-09</td>\n", + " </tr>\n", + " <tr>\n", + " <th>5</th>\n", + " <td>Mini-roundabout</td>\n", + " <td>3.758838e-09</td>\n", + " </tr>\n", + " <tr>\n", + " <th>6</th>\n", + " <td>Not at junction or within 20 metres</td>\n", + " <td>1.135101e-07</td>\n", + " </tr>\n", + " <tr>\n", + " <th>7</th>\n", + " <td>Roundabout</td>\n", + " <td>1.476978e-08</td>\n", + " </tr>\n", + " <tr>\n", + " <th>8</th>\n", + " <td>Private drive or entrance</td>\n", + " <td>8.573486e-09</td>\n", + " </tr>\n", + " <tr>\n", + " <th>9</th>\n", + " <td>Slip road</td>\n", + " <td>4.081958e-09</td>\n", + " </tr>\n", + " <tr>\n", + " <th>10</th>\n", + " <td>Data missing or out of range</td>\n", + " <td>2.466461e-12</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " Junction_Detail Accident Probability\n", + "0 99 1.836346e-10\n", + "1 Other junction 6.582955e-09\n", + "2 T or staggered junction 7.281014e-08\n", + "3 Crossroads 2.126173e-08\n", + "4 More than 4 arms (not roundabout) 2.282037e-09\n", + "5 Mini-roundabout 3.758838e-09\n", + "6 Not at junction or within 20 metres 1.135101e-07\n", + "7 Roundabout 1.476978e-08\n", + "8 Private drive or entrance 8.573486e-09\n", + "9 Slip road 4.081958e-09\n", + "10 Data missing or out of range 2.466461e-12" + ] + }, + "execution_count": 326, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#result35=result35.drop(['road_name'], axis=1)\n", + "result35_df = result35.groupby('Junction_Detail', sort=False)[\"Accident Probability\"].sum().reset_index(name ='Accident Probability')\n", + "result35_df" + ] + }, + { + "cell_type": "code", + "execution_count": 333, + "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>Junction_Detail</th>\n", + " <th>Accident Probability</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>99</td>\n", + " <td>1.836346e-10</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>Crossroads</td>\n", + " <td>2.126173e-08</td>\n", + " </tr>\n", + " <tr>\n", + " <th>5</th>\n", + " <td>Mini-roundabout</td>\n", + " <td>3.758838e-09</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>More than 4 arms (not roundabout)</td>\n", + " <td>2.282037e-09</td>\n", + " </tr>\n", + " <tr>\n", + " <th>6</th>\n", + " <td>Not at junction or within 20 metres</td>\n", + " <td>1.135101e-07</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>Other junction</td>\n", + " <td>6.582955e-09</td>\n", + " </tr>\n", + " <tr>\n", + " <th>8</th>\n", + " <td>Private drive or entrance</td>\n", + " <td>8.573486e-09</td>\n", + " </tr>\n", + " <tr>\n", + " <th>7</th>\n", + " <td>Roundabout</td>\n", + " <td>1.476978e-08</td>\n", + " </tr>\n", + " <tr>\n", + " <th>9</th>\n", + " <td>Slip road</td>\n", + " <td>4.081958e-09</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>T or staggered junction</td>\n", + " <td>7.281014e-08</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " Junction_Detail Accident Probability\n", + "0 99 1.836346e-10\n", + "3 Crossroads 2.126173e-08\n", + "5 Mini-roundabout 3.758838e-09\n", + "4 More than 4 arms (not roundabout) 2.282037e-09\n", + "6 Not at junction or within 20 metres 1.135101e-07\n", + "1 Other junction 6.582955e-09\n", + "8 Private drive or entrance 8.573486e-09\n", + "7 Roundabout 1.476978e-08\n", + "9 Slip road 4.081958e-09\n", + "2 T or staggered junction 7.281014e-08" + ] + }, + "execution_count": 333, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#result35_df=result35_df.drop(labels=[10],axis=0)\n", + "result35_df=result35_df.sort_values('Junction_Detail')\n", + "result35_df" + ] + }, + { + "cell_type": "code", + "execution_count": 334, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 1440x720 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax=result35_df.plot.bar('Junction_Detail','Accident Probability', rot=90,title=\"Accidents probabilty over Junction Type \",figsize=(20, 10),color=\"Orange\")" + ] + }, + { + "cell_type": "code", + "execution_count": 339, + "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", + "|3784148|6963 |E |2005|2005-04-26|13 |1 |138 |A354 |PA |Major |A350 Blandford Bypass (roundabout)|B3081 Handley Cross Roundabout|391860 |110170 |50.89095683|-2.11710057|14.90 |9.26 |100 |null|0 |1 |168 |0 |31 |8 |2 |3 |2 |5 |2 |22 |222 |\n", + "|3784140|6963 |W |2005|2005-04-26|17 |1 |138 |A354 |PA |Major |A350 Blandford Bypass (roundabout)|B3081 Handley Cross Roundabout|391860 |110170 |50.89095683|-2.11710057|14.90 |9.26 |100 |null|1 |2 |298 |2 |66 |4 |1 |2 |2 |1 |0 |10 |378 |\n", + "|3784147|6963 |W |2005|2005-04-26|12 |1 |138 |A354 |PA |Major |A350 Blandford Bypass (roundabout)|B3081 Handley Cross Roundabout|391860 |110170 |50.89095683|-2.11710057|14.90 |9.26 |100 |null|0 |1 |118 |2 |36 |5 |2 |4 |1 |2 |2 |16 |173 |\n", + "|3784135|6963 |E |2005|2005-04-26|12 |1 |138 |A354 |PA |Major |A350 Blandford Bypass (roundabout)|B3081 Handley Cross Roundabout|391860 |110170 |50.89095683|-2.11710057|14.90 |9.26 |100 |null|0 |2 |157 |1 |34 |14 |4 |3 |4 |0 |0 |25 |219 |\n", + "|3784139|6963 |W |2005|2005-04-26|16 |1 |138 |A354 |PA |Major |A350 Blandford Bypass (roundabout)|B3081 Handley Cross Roundabout|391860 |110170 |50.89095683|-2.11710057|14.90 |9.26 |100 |null|0 |2 |240 |3 |57 |4 |3 |1 |2 |3 |0 |13 |315 |\n", + "|3784143|6963 |W |2005|2005-04-26|8 |1 |138 |A354 |PA |Major |A350 Blandford Bypass (roundabout)|B3081 Handley Cross Roundabout|391860 |110170 |50.89095683|-2.11710057|14.90 |9.26 |100 |null|0 |1 |256 |1 |54 |10 |0 |2 |1 |4 |0 |17 |329 |\n", + "|3784146|6963 |W |2005|2005-04-26|11 |1 |138 |A354 |PA |Major |A350 Blandford Bypass (roundabout)|B3081 Handley Cross Roundabout|391860 |110170 |50.89095683|-2.11710057|14.90 |9.26 |100 |null|0 |1 |120 |2 |31 |9 |3 |2 |0 |2 |1 |17 |171 |\n", + "|3784131|6963 |E |2005|2005-04-26|8 |1 |138 |A354 |PA |Major |A350 Blandford Bypass (roundabout)|B3081 Handley Cross Roundabout|391860 |110170 |50.89095683|-2.11710057|14.90 |9.26 |100 |null|0 |0 |274 |0 |47 |14 |2 |0 |2 |2 |4 |24 |345 |\n", + "|3784134|6963 |E |2005|2005-04-26|11 |1 |138 |A354 |PA |Major |A350 Blandford Bypass (roundabout)|B3081 Handley Cross Roundabout|391860 |110170 |50.89095683|-2.11710057|14.90 |9.26 |100 |null|0 |1 |140 |1 |34 |13 |3 |1 |1 |4 |2 |24 |200 |\n", + "|3784137|6963 |W |2005|2005-04-26|14 |1 |138 |A354 |PA |Major |A350 Blandford Bypass (roundabout)|B3081 Handley Cross Roundabout|391860 |110170 |50.89095683|-2.11710057|14.90 |9.26 |100 |null|1 |1 |136 |1 |29 |7 |3 |0 |3 |3 |1 |17 |184 |\n", + "|3784138|6963 |W |2005|2005-04-26|15 |1 |138 |A354 |PA |Major |A350 Blandford Bypass (roundabout)|B3081 Handley Cross Roundabout|391860 |110170 |50.89095683|-2.11710057|14.90 |9.26 |100 |null|0 |1 |178 |9 |29 |6 |1 |3 |0 |2 |0 |12 |229 |\n", + "|3784141|6963 |W |2005|2005-04-26|18 |1 |138 |A354 |PA |Major |A350 Blandford Bypass (roundabout)|B3081 Handley Cross Roundabout|391860 |110170 |50.89095683|-2.11710057|14.90 |9.26 |100 |null|0 |5 |240 |2 |31 |2 |1 |1 |0 |2 |0 |6 |284 |\n", + "|3784142|6963 |W |2005|2005-04-26|7 |1 |138 |A354 |PA |Major |A350 Blandford Bypass (roundabout)|B3081 Handley Cross Roundabout|391860 |110170 |50.89095683|-2.11710057|14.90 |9.26 |100 |null|0 |2 |192 |0 |69 |8 |0 |0 |3 |1 |0 |12 |275 |\n", + "|3784144|6963 |W |2005|2005-04-26|9 |1 |138 |A354 |PA |Major |A350 Blandford Bypass (roundabout)|B3081 Handley Cross Roundabout|391860 |110170 |50.89095683|-2.11710057|14.90 |9.26 |100 |null|0 |0 |167 |2 |43 |7 |2 |2 |0 |0 |5 |16 |228 |\n", + "|3784145|6963 |W |2005|2005-04-26|10 |1 |138 |A354 |PA |Major |A350 Blandford Bypass (roundabout)|B3081 Handley Cross Roundabout|391860 |110170 |50.89095683|-2.11710057|14.90 |9.26 |100 |null|0 |1 |143 |0 |39 |6 |4 |3 |1 |5 |3 |22 |205 |\n", + "|3784130|6963 |E |2005|2005-04-26|7 |1 |138 |A354 |PA |Major |A350 Blandford Bypass (roundabout)|B3081 Handley Cross Roundabout|391860 |110170 |50.89095683|-2.11710057|14.90 |9.26 |100 |null|0 |2 |293 |4 |64 |7 |1 |2 |0 |1 |3 |14 |377 |\n", + "|3784150|6963 |E |2005|2005-04-26|15 |1 |138 |A354 |PA |Major |A350 Blandford Bypass (roundabout)|B3081 Handley Cross Roundabout|391860 |110170 |50.89095683|-2.11710057|14.90 |9.26 |100 |null|2 |1 |178 |1 |48 |4 |2 |2 |0 |1 |3 |12 |240 |\n", + "|3784132|6963 |E |2005|2005-04-26|9 |1 |138 |A354 |PA |Major |A350 Blandford Bypass (roundabout)|B3081 Handley Cross Roundabout|391860 |110170 |50.89095683|-2.11710057|14.90 |9.26 |100 |null|0 |1 |161 |4 |37 |9 |3 |3 |1 |2 |5 |23 |226 |\n", + "|3784133|6963 |E |2005|2005-04-26|10 |1 |138 |A354 |PA |Major |A350 Blandford Bypass (roundabout)|B3081 Handley Cross Roundabout|391860 |110170 |50.89095683|-2.11710057|14.90 |9.26 |100 |null|0 |1 |132 |2 |40 |6 |2 |3 |4 |2 |2 |19 |194 |\n", + "|3784136|6963 |W |2005|2005-04-26|13 |1 |138 |A354 |PA |Major |A350 Blandford Bypass (roundabout)|B3081 Handley Cross Roundabout|391860 |110170 |50.89095683|-2.11710057|14.90 |9.26 |100 |null|0 |3 |159 |1 |36 |8 |2 |1 |0 |4 |1 |16 |215 |\n", + "+-------+--------------+-------------------+----+----------+----+---------+------------------+---------+-------------+---------+----------------------------------+------------------------------+-------+--------+-----------+-----------+--------------+-----------------+--------+----+------------+--------------------------+--------------+-----------------+----+-----------------+-----------------+-------------------------+----------------------------+-----------------------+-----------------------+--------+------------------+\n", + "only showing top 20 rows\n", + "\n" + ] + } + ], + "source": [ + "road_traf = spark.read.format('csv')\\\n", + " .option('header',True).option('escape','\"')\\\n", + " .load('/Users/Asfandyar/Downloads/dft_traffic_counts_raw_counts.csv')\n", + "# changing the type of column(\"Year'\") to interger type\n", + "road_traf = road_traf.withColumn('year',F.col('year').cast(IntegerType()))\n", + "road_traf=road_traf.filter(road_traf.year>2004)\n", + "road_traf=road_traf.filter(road_traf.year<2020)\n", + "road_traf.sort(\"year\").show(truncate=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 344, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+--------------+-------------+------------------+---------------------+----------------------+---------+---------+------------+-----------------+------------------+--------------------+----------+-----------+-----+------------------------+----------------------------+-----------------------+----------------+-----------------+------------------+-----------+--------------------+----------------+-----------------+------------------+---------------------------------+---------------------------------------+----------------+------------------+-----------------------+--------------------------+-------------------+-------------------+-------------------------------------------+---------------+-------------------------+\n", + "|accident_index|accident_year|accident_reference|location_easting_osgr|location_northing_osgr|longitude| latitude|police_force|Accident_Severity|number_of_vehicles|number_of_casualties| date|day_of_week| time|local_authority_district|local_authority_ons_district|local_authority_highway|first_road_class|first_road_number| Road_Type|speed_limit| Junction_Detail|junction_control|second_road_class|second_road_number|pedestrian_crossing_human_control|pedestrian_crossing_physical_facilities|light_conditions|weather_conditions|road_surface_conditions|special_conditions_at_site|carriageway_hazards|urban_or_rural_area|did_police_officer_attend_scene_of_accident|trunk_road_flag|lsoa_of_accident_location|\n", + "+--------------+-------------+------------------+---------------------+----------------------+---------+---------+------------+-----------------+------------------+--------------------+----------+-----------+-----+------------------------+----------------------------+-----------------------+----------------+-----------------+------------------+-----------+--------------------+----------------+-----------------+------------------+---------------------------------+---------------------------------------+----------------+------------------+-----------------------+--------------------------+-------------------+-------------------+-------------------------------------------+---------------+-------------------------+\n", + "| 200501BS00001| 2005| 01BS00001| 525680| 178240| -0.19117|51.489096| 1| Serious| 1| 1|04/01/2005| 3|17:42| 12| E09000020| E09000020| A| 3218|Single carriageway| 30|Not at junction o...| -1| -1| -1| 0| 1| 1| 2| 2| 0| 0| 1| 1| 2| E01002849|\n", + "+--------------+-------------+------------------+---------------------+----------------------+---------+---------+------------+-----------------+------------------+--------------------+----------+-----------+-----+------------------------+----------------------------+-----------------------+----------------+-----------------+------------------+-----------+--------------------+----------------+-----------------+------------------+---------------------------------+---------------------------------------+----------------+------------------+-----------------------+--------------------------+-------------------+-------------------+-------------------------------------------+---------------+-------------------------+\n", + "only showing top 1 row\n", + "\n" + ] + } + ], + "source": [ + "A2018.show(1)" + ] + }, + { + "cell_type": "code", + "execution_count": 346, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Row(hour=0, Total accidents=34976)" + ] + }, + "execution_count": 346, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from pyspark.sql.functions import *\n", + "#Timestamp String to DateType\n", + "Accident_Information20052019_dfff=A2018.withColumn(\"timestamp\",to_timestamp(\"time\"))\n", + "Accident_Information20052019_dfff\n", + "TimeAccident_dfhour = Accident_Information20052019_dfff.withColumn('hour',hour(Accident_Information20052019_dfff.timestamp))\n", + "#Time of week accidents\n", + "TimeAccident_df = TimeAccident_dfhour.groupby('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=TimeAccident_df.sort(\"hour\")\n", + "TimeAccident_df.head()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 352, + "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>hour</th>\n", + " <th>Total accidents</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>0</td>\n", + " <td>34976</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>1</td>\n", + " <td>25340</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>2</td>\n", + " <td>20055</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>3</td>\n", + " <td>16310</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>4</td>\n", + " <td>12989</td>\n", + " </tr>\n", + " <tr>\n", + " <th>5</th>\n", + " <td>5</td>\n", + " <td>19228</td>\n", + " </tr>\n", + " 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+ " <th>17</th>\n", + " <td>17</td>\n", + " <td>202694</td>\n", + " </tr>\n", + " <tr>\n", + " <th>18</th>\n", + " <td>18</td>\n", + " <td>159655</td>\n", + " </tr>\n", + " <tr>\n", + " <th>19</th>\n", + " <td>19</td>\n", + " <td>118915</td>\n", + " </tr>\n", + " <tr>\n", + " <th>20</th>\n", + " <td>20</td>\n", + " <td>87214</td>\n", + " </tr>\n", + " <tr>\n", + " <th>21</th>\n", + " <td>21</td>\n", + " <td>69608</td>\n", + " </tr>\n", + " <tr>\n", + " <th>22</th>\n", + " <td>22</td>\n", + " <td>60838</td>\n", + " </tr>\n", + " <tr>\n", + " <th>23</th>\n", + " <td>23</td>\n", + " <td>48123</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " hour Total accidents\n", + "0 0 34976\n", + "1 1 25340\n", + "2 2 20055\n", + "3 3 16310\n", + "4 4 12989\n", + "5 5 19228\n", + "6 6 41476\n", + "7 7 97895\n", + "8 8 166145\n", + "9 9 112595\n", + "10 10 103173\n", + "11 11 117755\n", + "12 12 133996\n", + "13 13 137581\n", + "14 14 138775\n", + "15 15 176700\n", + "16 16 185391\n", + "17 17 202694\n", + "18 18 159655\n", + "19 19 118915\n", + "20 20 87214\n", + "21 21 69608\n", + "22 22 60838\n", + "23 23 48123" + ] + }, + "execution_count": 352, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "TimeAccident_df_df=TimeAccident_df.toPandas()\n", + "TimeAccident_df_df" + ] + }, + { + "cell_type": "code", + "execution_count": 353, + "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>Time of day</th>\n", + " <th>Monday</th>\n", + " <th>Tuesday</th>\n", + " <th>Wednesday</th>\n", + " <th>Thursday</th>\n", + " <th>Friday</th>\n", + " <th>Saturday</th>\n", + " <th>Sunday</th>\n", + " <th>hour</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>00:00-01:00</td>\n", + " <td>10.3</td>\n", + " <td>10.8</td>\n", + " <td>11.4</td>\n", + " <td>11.7</td>\n", + " <td>12.6</td>\n", + " <td>17.3</td>\n", + " <td>18.7</td>\n", + " <td>0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>01:00-02:00</td>\n", + " <td>6.6</td>\n", + " <td>7.2</td>\n", + " <td>7.7</td>\n", + " <td>7.7</td>\n", + " <td>8.4</td>\n", + " <td>11.2</td>\n", + " <td>11.7</td>\n", + " <td>1</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>02:00-03:00</td>\n", + " <td>5.5</td>\n", + " <td>6.3</td>\n", + " <td>6.5</td>\n", + " <td>6.6</td>\n", + " <td>6.9</td>\n", + " <td>8.4</td>\n", + " <td>8.0</td>\n", + " <td>2</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>03:00-04:00</td>\n", + " 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<tr>\n", + " <th>15</th>\n", + " <td>15:00-16:00</td>\n", + " <td>191.7</td>\n", + " <td>194.7</td>\n", + " <td>198.0</td>\n", + " <td>199.9</td>\n", + " <td>216.5</td>\n", + " <td>161.3</td>\n", + " <td>152.0</td>\n", + " <td>15</td>\n", + " </tr>\n", + " <tr>\n", + " <th>16</th>\n", + " <td>16:00-17:00</td>\n", + " <td>206.9</td>\n", + " <td>213.1</td>\n", + " <td>214.0</td>\n", + " <td>214.8</td>\n", + " <td>217.5</td>\n", + " <td>154.4</td>\n", + " <td>142.9</td>\n", + " <td>16</td>\n", + " </tr>\n", + " <tr>\n", + " <th>17</th>\n", + " <td>17:00-18:00</td>\n", + " <td>198.9</td>\n", + " <td>206.0</td>\n", + " <td>207.5</td>\n", + " <td>207.5</td>\n", + " <td>202.1</td>\n", + " <td>142.3</td>\n", + " <td>123.6</td>\n", + " <td>17</td>\n", + " </tr>\n", + " <tr>\n", + " <th>18</th>\n", + " <td>18:00-19:00</td>\n", + " <td>143.1</td>\n", + " <td>150.6</td>\n", + " <td>152.9</td>\n", + " <td>155.5</td>\n", + " <td>158.5</td>\n", + " <td>118.0</td>\n", + " <td>105.3</td>\n", + " <td>18</td>\n", + " </tr>\n", + " <tr>\n", + " <th>19</th>\n", + " <td>19:00-20:00</td>\n", + " <td>97.7</td>\n", + " <td>102.8</td>\n", + " <td>105.7</td>\n", + " <td>109.5</td>\n", + " <td>114.9</td>\n", + " <td>90.6</td>\n", + " <td>85.5</td>\n", + " <td>19</td>\n", + " </tr>\n", + " <tr>\n", + " <th>20</th>\n", + " <td>20:00-21:00</td>\n", + " <td>68.2</td>\n", + " <td>71.4</td>\n", + " <td>73.8</td>\n", + " <td>76.7</td>\n", + " <td>80.1</td>\n", + " <td>66.3</td>\n", + " <td>65.1</td>\n", + " <td>20</td>\n", + " </tr>\n", + " <tr>\n", + " <th>21</th>\n", + " <td>21:00-22:00</td>\n", + " <td>48.4</td>\n", + " <td>51.1</td>\n", + " <td>52.9</td>\n", + " <td>54.7</td>\n", + " <td>57.3</td>\n", + " <td>50.1</td>\n", + " <td>45.7</td>\n", + " <td>21</td>\n", + " </tr>\n", + " <tr>\n", + " <th>22</th>\n", + " <td>22:00-23:00</td>\n", + " <td>32.3</td>\n", + " <td>35.2</td>\n", + " <td>36.9</td>\n", + " <td>37.9</td>\n", + " <td>42.6</td>\n", + " <td>40.0</td>\n", + " <td>30.0</td>\n", + " <td>22</td>\n", + " </tr>\n", + " <tr>\n", + " <th>23</th>\n", + " <td>23:00-00:00</td>\n", + " <td>17.9</td>\n", + " <td>19.2</td>\n", + " <td>20.4</td>\n", + " <td>21.5</td>\n", + " <td>27.6</td>\n", + " <td>28.3</td>\n", + " <td>17.5</td>\n", + " <td>23</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " Time of day Monday Tuesday Wednesday Thursday Friday Saturday \\\n", + "0 00:00-01:00 10.3 10.8 11.4 11.7 12.6 17.3 \n", + "1 01:00-02:00 6.6 7.2 7.7 7.7 8.4 11.2 \n", + "2 02:00-03:00 5.5 6.3 6.5 6.6 6.9 8.4 \n", + "3 03:00-04:00 7.1 7.3 7.5 7.6 7.8 7.8 \n", + "4 04:00-05:00 14.1 13.1 13.1 13.1 13.0 9.5 \n", + "5 05:00-06:00 41.1 39.4 39.0 38.6 36.8 19.5 \n", + "6 06:00-07:00 95.3 96.4 95.0 93.6 87.4 35.6 \n", + "7 07:00-08:00 170.1 175.0 172.9 170.5 159.1 60.2 \n", + "8 08:00-09:00 185.9 191.8 191.1 190.4 180.9 92.0 \n", + "9 09:00-10:00 149.3 152.2 152.3 153.3 153.4 127.0 \n", + "10 10:00-11:00 148.6 147.0 148.2 150.4 159.2 159.4 \n", + "11 11:00-12:00 157.1 154.0 156.2 158.6 172.7 178.9 \n", + "12 12:00-13:00 162.2 159.6 162.6 165.3 183.6 184.4 \n", + "13 13:00-14:00 163.6 162.2 165.5 168.1 188.1 178.7 \n", + "14 14:00-15:00 174.0 174.0 178.2 180.0 199.2 170.8 \n", + "15 15:00-16:00 191.7 194.7 198.0 199.9 216.5 161.3 \n", + "16 16:00-17:00 206.9 213.1 214.0 214.8 217.5 154.4 \n", + "17 17:00-18:00 198.9 206.0 207.5 207.5 202.1 142.3 \n", + "18 18:00-19:00 143.1 150.6 152.9 155.5 158.5 118.0 \n", + "19 19:00-20:00 97.7 102.8 105.7 109.5 114.9 90.6 \n", + "20 20:00-21:00 68.2 71.4 73.8 76.7 80.1 66.3 \n", + "21 21:00-22:00 48.4 51.1 52.9 54.7 57.3 50.1 \n", + "22 22:00-23:00 32.3 35.2 36.9 37.9 42.6 40.0 \n", + "23 23:00-00:00 17.9 19.2 20.4 21.5 27.6 28.3 \n", + "\n", + " Sunday hour \n", + "0 18.7 0 \n", + "1 11.7 1 \n", + "2 8.0 2 \n", + "3 6.7 3 \n", + "4 7.1 4 \n", + "5 12.8 5 \n", + "6 22.5 6 \n", + "7 34.6 7 \n", + "8 49.5 8 \n", + "9 85.3 9 \n", + "10 128.0 10 \n", + "11 154.4 11 \n", + "12 166.2 12 \n", + "13 164.0 13 \n", + "14 158.4 14 \n", + "15 152.0 15 \n", + "16 142.9 16 \n", + "17 123.6 17 \n", + "18 105.3 18 \n", + "19 85.5 19 \n", + "20 65.1 20 \n", + "21 45.7 21 \n", + "22 30.0 22 \n", + "23 17.5 23 " + ] + }, + "execution_count": 353, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "df = pd.read_csv ('/Users/Asfandyar/Desktop/disertation/diseration_final/weekdist.csv')\n", + "df['hour'] = df.index\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 381, + "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>hour</th>\n", + " <th>Total accidents</th>\n", + " <th>Time of day</th>\n", + " <th>Monday_Normalized</th>\n", + " <th>Tuesday_Normalized</th>\n", + " <th>Wednesday_Normalized</th>\n", + " <th>Thursday_Normalized</th>\n", + " <th>Friday_Normalized</th>\n", + " <th>Saturday_Normalized</th>\n", + " <th>Sunday_Normalized</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>0</td>\n", + " <td>34976</td>\n", + " <td>00:00-01:00</td>\n", + " <td>3395.728155</td>\n", + " <td>3238.518519</td>\n", + " <td>3068.070175</td>\n", + " <td>2989.401709</td>\n", + " <td>2775.873016</td>\n", + " <td>2021.734104</td>\n", + " <td>1870.374332</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>1</td>\n", + " <td>25340</td>\n", + " <td>01:00-02:00</td>\n", + " <td>3839.393939</td>\n", + " <td>3519.444444</td>\n", + " <td>3290.909091</td>\n", + " <td>3290.909091</td>\n", + " <td>3016.666667</td>\n", + " <td>2262.500000</td>\n", + " <td>2165.811966</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>2</td>\n", + " <td>20055</td>\n", + " <td>02:00-03:00</td>\n", + " <td>3646.363636</td>\n", + " <td>3183.333333</td>\n", + " <td>3085.384615</td>\n", + " <td>3038.636364</td>\n", + " <td>2906.521739</td>\n", + " <td>2387.500000</td>\n", + " <td>2506.875000</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>3</td>\n", + " <td>16310</td>\n", + " <td>03:00-04:00</td>\n", + " <td>2297.183099</td>\n", + " <td>2234.246575</td>\n", + " <td>2174.666667</td>\n", + " <td>2146.052632</td>\n", + " <td>2091.025641</td>\n", + " <td>2091.025641</td>\n", + " <td>2434.328358</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>4</td>\n", + " <td>12989</td>\n", + " <td>04:00-05:00</td>\n", + " <td>921.205674</td>\n", + " <td>991.526718</td>\n", + " <td>991.526718</td>\n", + " <td>991.526718</td>\n", + " <td>999.153846</td>\n", + " <td>1367.263158</td>\n", + " <td>1829.436620</td>\n", + " </tr>\n", + " <tr>\n", + " <th>5</th>\n", + " <td>5</td>\n", + " <td>19228</td>\n", + " <td>05:00-06:00</td>\n", + " <td>467.834550</td>\n", + " <td>488.020305</td>\n", + " <td>493.025641</td>\n", + " <td>498.134715</td>\n", + " <td>522.500000</td>\n", + " <td>986.051282</td>\n", + " <td>1502.187500</td>\n", + " </tr>\n", + " <tr>\n", + " <th>6</th>\n", + " <td>6</td>\n", + " <td>41476</td>\n", + " <td>06:00-07:00</td>\n", + " <td>435.215110</td>\n", + " <td>430.248963</td>\n", + " <td>436.589474</td>\n", + " <td>443.119658</td>\n", + " <td>474.553776</td>\n", + " <td>1165.056180</td>\n", + " <td>1843.377778</td>\n", + " </tr>\n", + " <tr>\n", + " <th>7</th>\n", + " <td>7</td>\n", + " <td>97895</td>\n", + " <td>07:00-08:00</td>\n", + " <td>575.514403</td>\n", + " <td>559.400000</td>\n", + " <td>566.194332</td>\n", + " <td>574.164223</td>\n", + " <td>615.304840</td>\n", + " <td>1626.162791</td>\n", + " <td>2829.335260</td>\n", + " </tr>\n", + " <tr>\n", + " <th>8</th>\n", + " <td>8</td>\n", + " <td>166145</td>\n", + " <td>08:00-09:00</td>\n", + " <td>893.733190</td>\n", + " <td>866.240876</td>\n", + " <td>869.413919</td>\n", + " <td>872.610294</td>\n", + " <td>918.435600</td>\n", + " <td>1805.923913</td>\n", + " <td>3356.464646</td>\n", + " </tr>\n", + " <tr>\n", + " <th>9</th>\n", + " <td>9</td>\n", + " <td>112595</td>\n", + " <td>09:00-10:00</td>\n", + " <td>754.152713</td>\n", + " <td>739.783180</td>\n", + " <td>739.297439</td>\n", + " <td>734.474886</td>\n", + " <td>733.996089</td>\n", + " <td>886.574803</td>\n", + " <td>1319.988277</td>\n", + " </tr>\n", + " <tr>\n", + " <th>10</th>\n", + " <td>10</td>\n", + " <td>103173</td>\n", + " <td>10:00-11:00</td>\n", + " <td>694.300135</td>\n", + " <td>701.857143</td>\n", + " <td>696.174089</td>\n", + " <td>685.990691</td>\n", + " <td>648.071608</td>\n", + " <td>647.258469</td>\n", + " <td>806.039062</td>\n", + " </tr>\n", + " <tr>\n", + " <th>11</th>\n", + " <td>11</td>\n", + " <td>117755</td>\n", + " <td>11:00-12:00</td>\n", + " <td>749.554424</td>\n", + " <td>764.642857</td>\n", + " <td>753.873239</td>\n", + " <td>742.465322</td>\n", + " <td>681.847134</td>\n", + " <td>658.216881</td>\n", + " <td>762.661917</td>\n", + " </tr>\n", + " <tr>\n", + " <th>12</th>\n", + " <td>12</td>\n", + " <td>133996</td>\n", + " <td>12:00-13:00</td>\n", + " <td>826.115906</td>\n", + " <td>839.573935</td>\n", + " <td>824.083641</td>\n", + " <td>810.623109</td>\n", + " <td>729.825708</td>\n", + " <td>726.659436</td>\n", + " <td>806.233454</td>\n", + " </tr>\n", + " <tr>\n", + " <th>13</th>\n", + " <td>13</td>\n", + " <td>137581</td>\n", + " <td>13:00-14:00</td>\n", + " <td>840.959658</td>\n", + " <td>848.218249</td>\n", + " <td>831.305136</td>\n", + " <td>818.447353</td>\n", + " <td>731.424774</td>\n", + " <td>769.899273</td>\n", + " <td>838.908537</td>\n", + " </tr>\n", + " <tr>\n", + " <th>14</th>\n", + " <td>14</td>\n", + " <td>138775</td>\n", + " <td>14:00-15:00</td>\n", + " <td>797.557471</td>\n", + " <td>797.557471</td>\n", + " <td>778.759820</td>\n", + " <td>770.972222</td>\n", + " <td>696.661647</td>\n", + " <td>812.500000</td>\n", + " <td>876.104798</td>\n", + " </tr>\n", + " <tr>\n", + " <th>15</th>\n", + " <td>15</td>\n", + " <td>176700</td>\n", + " <td>15:00-16:00</td>\n", + " <td>921.752739</td>\n", + " <td>907.550077</td>\n", + " <td>892.424242</td>\n", + " <td>883.941971</td>\n", + " <td>816.166282</td>\n", + " <td>1095.474272</td>\n", + " <td>1162.500000</td>\n", + " </tr>\n", + " <tr>\n", + " <th>16</th>\n", + " <td>16</td>\n", + " <td>185391</td>\n", + " <td>16:00-17:00</td>\n", + " <td>896.041566</td>\n", + " <td>869.971844</td>\n", + " <td>866.313084</td>\n", + " <td>863.086592</td>\n", + " <td>852.372414</td>\n", + " <td>1200.718912</td>\n", + " <td>1297.347796</td>\n", + " </tr>\n", + " <tr>\n", + " <th>17</th>\n", + " <td>17</td>\n", + " <td>202694</td>\n", + " <td>17:00-18:00</td>\n", + " <td>1019.074912</td>\n", + " <td>983.951456</td>\n", + " <td>976.838554</td>\n", + " <td>976.838554</td>\n", + " <td>1002.939139</td>\n", + " <td>1424.413212</td>\n", + " <td>1639.919094</td>\n", + " </tr>\n", + " <tr>\n", + " <th>18</th>\n", + " <td>18</td>\n", + " <td>159655</td>\n", + " <td>18:00-19:00</td>\n", + " <td>1115.688330</td>\n", + " <td>1060.126162</td>\n", + " <td>1044.179202</td>\n", + " <td>1026.720257</td>\n", + " <td>1007.287066</td>\n", + " <td>1353.008475</td>\n", + " <td>1516.191833</td>\n", + " </tr>\n", + " <tr>\n", + " <th>19</th>\n", + " <td>19</td>\n", + " <td>118915</td>\n", + " <td>19:00-20:00</td>\n", + " <td>1217.144319</td>\n", + " <td>1156.760700</td>\n", + " <td>1125.023652</td>\n", + " <td>1085.981735</td>\n", + " <td>1034.943429</td>\n", + " <td>1312.527594</td>\n", + " <td>1390.818713</td>\n", + " </tr>\n", + " <tr>\n", + " <th>20</th>\n", + " <td>20</td>\n", + " <td>87214</td>\n", + " <td>20:00-21:00</td>\n", + " <td>1278.797654</td>\n", + " <td>1221.484594</td>\n", + " <td>1181.761518</td>\n", + " <td>1137.079531</td>\n", + " <td>1088.813983</td>\n", + " <td>1315.444947</td>\n", + " <td>1339.692780</td>\n", + " </tr>\n", + " <tr>\n", + " <th>21</th>\n", + " <td>21</td>\n", + " <td>69608</td>\n", + " <td>21:00-22:00</td>\n", + " <td>1438.181818</td>\n", + " <td>1362.191781</td>\n", + " <td>1315.841210</td>\n", + " <td>1272.541133</td>\n", + " <td>1214.799302</td>\n", + " <td>1389.381238</td>\n", + " <td>1523.150985</td>\n", + " </tr>\n", + " <tr>\n", + " <th>22</th>\n", + " <td>22</td>\n", + " <td>60838</td>\n", + " <td>22:00-23:00</td>\n", + " <td>1883.529412</td>\n", + " <td>1728.352273</td>\n", + " <td>1648.726287</td>\n", + " <td>1605.224274</td>\n", + " <td>1428.122066</td>\n", + " <td>1520.950000</td>\n", + " <td>2027.933333</td>\n", + " </tr>\n", + " <tr>\n", + " <th>23</th>\n", + " <td>23</td>\n", + " <td>48123</td>\n", + " <td>23:00-00:00</td>\n", + " <td>2688.435754</td>\n", + " <td>2506.406250</td>\n", + " <td>2358.970588</td>\n", + " <td>2238.279070</td>\n", + " <td>1743.586957</td>\n", + " <td>1700.459364</td>\n", + " <td>2749.885714</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " hour Total accidents Time of day Monday_Normalized Tuesday_Normalized \\\n", + "0 0 34976 00:00-01:00 3395.728155 3238.518519 \n", + "1 1 25340 01:00-02:00 3839.393939 3519.444444 \n", + "2 2 20055 02:00-03:00 3646.363636 3183.333333 \n", + "3 3 16310 03:00-04:00 2297.183099 2234.246575 \n", + "4 4 12989 04:00-05:00 921.205674 991.526718 \n", + "5 5 19228 05:00-06:00 467.834550 488.020305 \n", + "6 6 41476 06:00-07:00 435.215110 430.248963 \n", + "7 7 97895 07:00-08:00 575.514403 559.400000 \n", + "8 8 166145 08:00-09:00 893.733190 866.240876 \n", + "9 9 112595 09:00-10:00 754.152713 739.783180 \n", + "10 10 103173 10:00-11:00 694.300135 701.857143 \n", + "11 11 117755 11:00-12:00 749.554424 764.642857 \n", + "12 12 133996 12:00-13:00 826.115906 839.573935 \n", + "13 13 137581 13:00-14:00 840.959658 848.218249 \n", + "14 14 138775 14:00-15:00 797.557471 797.557471 \n", + "15 15 176700 15:00-16:00 921.752739 907.550077 \n", + "16 16 185391 16:00-17:00 896.041566 869.971844 \n", + "17 17 202694 17:00-18:00 1019.074912 983.951456 \n", + "18 18 159655 18:00-19:00 1115.688330 1060.126162 \n", + "19 19 118915 19:00-20:00 1217.144319 1156.760700 \n", + "20 20 87214 20:00-21:00 1278.797654 1221.484594 \n", + "21 21 69608 21:00-22:00 1438.181818 1362.191781 \n", + "22 22 60838 22:00-23:00 1883.529412 1728.352273 \n", + "23 23 48123 23:00-00:00 2688.435754 2506.406250 \n", + "\n", + " Wednesday_Normalized Thursday_Normalized Friday_Normalized \\\n", + "0 3068.070175 2989.401709 2775.873016 \n", + "1 3290.909091 3290.909091 3016.666667 \n", + "2 3085.384615 3038.636364 2906.521739 \n", + "3 2174.666667 2146.052632 2091.025641 \n", + "4 991.526718 991.526718 999.153846 \n", + "5 493.025641 498.134715 522.500000 \n", + "6 436.589474 443.119658 474.553776 \n", + "7 566.194332 574.164223 615.304840 \n", + "8 869.413919 872.610294 918.435600 \n", + "9 739.297439 734.474886 733.996089 \n", + "10 696.174089 685.990691 648.071608 \n", + "11 753.873239 742.465322 681.847134 \n", + "12 824.083641 810.623109 729.825708 \n", + "13 831.305136 818.447353 731.424774 \n", + "14 778.759820 770.972222 696.661647 \n", + "15 892.424242 883.941971 816.166282 \n", + "16 866.313084 863.086592 852.372414 \n", + "17 976.838554 976.838554 1002.939139 \n", + "18 1044.179202 1026.720257 1007.287066 \n", + "19 1125.023652 1085.981735 1034.943429 \n", + "20 1181.761518 1137.079531 1088.813983 \n", + "21 1315.841210 1272.541133 1214.799302 \n", + "22 1648.726287 1605.224274 1428.122066 \n", + "23 2358.970588 2238.279070 1743.586957 \n", + "\n", + " Saturday_Normalized Sunday_Normalized \n", + "0 2021.734104 1870.374332 \n", + "1 2262.500000 2165.811966 \n", + "2 2387.500000 2506.875000 \n", + "3 2091.025641 2434.328358 \n", + "4 1367.263158 1829.436620 \n", + "5 986.051282 1502.187500 \n", + "6 1165.056180 1843.377778 \n", + "7 1626.162791 2829.335260 \n", + "8 1805.923913 3356.464646 \n", + "9 886.574803 1319.988277 \n", + "10 647.258469 806.039062 \n", + "11 658.216881 762.661917 \n", + "12 726.659436 806.233454 \n", + "13 769.899273 838.908537 \n", + "14 812.500000 876.104798 \n", + "15 1095.474272 1162.500000 \n", + "16 1200.718912 1297.347796 \n", + "17 1424.413212 1639.919094 \n", + "18 1353.008475 1516.191833 \n", + "19 1312.527594 1390.818713 \n", + "20 1315.444947 1339.692780 \n", + "21 1389.381238 1523.150985 \n", + "22 1520.950000 2027.933333 \n", + "23 1700.459364 2749.885714 " + ] + }, + "execution_count": 381, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "result60=pd.merge(TimeAccident_df_df, df, on=['hour'])\n", + "result60[\"Monday_Normalized\"] = result60[\"Total accidents\"] / result60[\"Monday\"]\n", + "result60[\"Tuesday_Normalized\"] = result60[\"Total accidents\"] / result60[\"Tuesday\"]\n", + "result60[\"Wednesday_Normalized\"] = result60[\"Total accidents\"] / result60[\"Wednesday\"]\n", + "result60[\"Thursday_Normalized\"] = result60[\"Total accidents\"] / result60[\"Thursday\"]\n", + "result60[\"Friday_Normalized\"] = result60[\"Total accidents\"] / result60[\"Friday\"]\n", + "result60[\"Saturday_Normalized\"] = result60[\"Total accidents\"] / result60[\"Saturday\"]\n", + "result60[\"Sunday_Normalized\"] = result60[\"Total accidents\"] / result60[\"Sunday\"]\n", + "result60 = result60.drop(['Monday','Tuesday','Wednesday','Thursday','Friday','Saturday','Sunday'], axis=1)\n", + "result60" + ] + }, + { + "cell_type": "code", + "execution_count": 453, + "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>hour</th>\n", + " <th>Total accidents</th>\n", + " <th>Time of day</th>\n", + " <th>Monday_Normalized</th>\n", + " <th>Tuesday_Normalized</th>\n", + " <th>Wednesday_Normalized</th>\n", + " <th>Thursday_Normalized</th>\n", + " <th>Friday_Normalized</th>\n", + " <th>Saturday_Normalized</th>\n", + " <th>Sunday_Normalized</th>\n", + " <th>Total accidents_Normalized</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>0</td>\n", + " <td>34976</td>\n", + " <td>00:00-01:00</td>\n", + " <td>3395.728155</td>\n", + " <td>3238.518519</td>\n", + " <td>3068.070175</td>\n", + " <td>2989.401709</td>\n", + " <td>2775.873016</td>\n", + " <td>2021.734104</td>\n", + " <td>1870.374332</td>\n", + " <td>38719.400020</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>1</td>\n", + " <td>25340</td>\n", + " <td>01:00-02:00</td>\n", + " <td>3839.393939</td>\n", + " <td>3519.444444</td>\n", + " <td>3290.909091</td>\n", + " <td>3290.909091</td>\n", + " <td>3016.666667</td>\n", + " <td>2262.500000</td>\n", + " <td>2165.811966</td>\n", + " <td>42771.270396</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>2</td>\n", + " <td>20055</td>\n", + " <td>02:00-03:00</td>\n", + " <td>3646.363636</td>\n", + " <td>3183.333333</td>\n", + " <td>3085.384615</td>\n", + " <td>3038.636364</td>\n", + " <td>2906.521739</td>\n", + " <td>2387.500000</td>\n", + " <td>2506.875000</td>\n", + " <td>41509.229376</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>3</td>\n", + " <td>16310</td>\n", + " <td>03:00-04:00</td>\n", + " <td>2297.183099</td>\n", + " <td>2234.246575</td>\n", + " <td>2174.666667</td>\n", + " <td>2146.052632</td>\n", + " <td>2091.025641</td>\n", + " <td>2091.025641</td>\n", + " <td>2434.328358</td>\n", + " <td>30937.057225</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>4</td>\n", + " <td>12989</td>\n", + " <td>04:00-05:00</td>\n", + " <td>921.205674</td>\n", + " <td>991.526718</td>\n", + " <td>991.526718</td>\n", + " <td>991.526718</td>\n", + " <td>999.153846</td>\n", + " <td>1367.263158</td>\n", + " <td>1829.436620</td>\n", + " <td>16183.278900</td>\n", + " </tr>\n", + " <tr>\n", + " <th>5</th>\n", + " <td>5</td>\n", + " <td>19228</td>\n", + " <td>05:00-06:00</td>\n", + " <td>467.834550</td>\n", + " <td>488.020305</td>\n", + " <td>493.025641</td>\n", + " <td>498.134715</td>\n", + " <td>522.500000</td>\n", + " <td>986.051282</td>\n", + " <td>1502.187500</td>\n", + " <td>9915.507985</td>\n", + " </tr>\n", + " <tr>\n", + " <th>6</th>\n", + " <td>6</td>\n", + " <td>41476</td>\n", + " <td>06:00-07:00</td>\n", + " <td>435.215110</td>\n", + " <td>430.248963</td>\n", + " <td>436.589474</td>\n", + " <td>443.119658</td>\n", + " <td>474.553776</td>\n", + " <td>1165.056180</td>\n", + " <td>1843.377778</td>\n", + " <td>10456.321876</td>\n", + " </tr>\n", + " <tr>\n", + " <th>7</th>\n", + " <td>7</td>\n", + " <td>97895</td>\n", + " <td>07:00-08:00</td>\n", + " <td>575.514403</td>\n", + " <td>559.400000</td>\n", + " <td>566.194332</td>\n", + " <td>574.164223</td>\n", + " <td>615.304840</td>\n", + " <td>1626.162791</td>\n", + " <td>2829.335260</td>\n", + " <td>14692.151697</td>\n", + " </tr>\n", + " <tr>\n", + " <th>8</th>\n", + " <td>8</td>\n", + " <td>166145</td>\n", + " <td>08:00-09:00</td>\n", + " <td>893.733190</td>\n", + " <td>866.240876</td>\n", + " <td>869.413919</td>\n", + " <td>872.610294</td>\n", + " <td>918.435600</td>\n", + " <td>1805.923913</td>\n", + " <td>3356.464646</td>\n", + " <td>19165.644877</td>\n", + " </tr>\n", + " <tr>\n", + " <th>9</th>\n", + " <td>9</td>\n", + " <td>112595</td>\n", + " <td>09:00-10:00</td>\n", + " <td>754.152713</td>\n", + " <td>739.783180</td>\n", + " <td>739.297439</td>\n", + " <td>734.474886</td>\n", + " <td>733.996089</td>\n", + " <td>886.574803</td>\n", + " <td>1319.988277</td>\n", + " <td>11816.534773</td>\n", + " </tr>\n", + " <tr>\n", + " <th>10</th>\n", + " <td>10</td>\n", + " <td>103173</td>\n", + " <td>10:00-11:00</td>\n", + " <td>694.300135</td>\n", + " <td>701.857143</td>\n", + " <td>696.174089</td>\n", + " <td>685.990691</td>\n", + " <td>648.071608</td>\n", + " <td>647.258469</td>\n", + " <td>806.039062</td>\n", + " <td>9759.382396</td>\n", + " </tr>\n", + " <tr>\n", + " <th>11</th>\n", + " <td>11</td>\n", + " <td>117755</td>\n", + " <td>11:00-12:00</td>\n", + " <td>749.554424</td>\n", + " <td>764.642857</td>\n", + " <td>753.873239</td>\n", + " <td>742.465322</td>\n", + " <td>681.847134</td>\n", + " <td>658.216881</td>\n", + " <td>762.661917</td>\n", + " <td>10226.523548</td>\n", + " </tr>\n", + " <tr>\n", + " <th>12</th>\n", + " <td>12</td>\n", + " <td>133996</td>\n", + " <td>12:00-13:00</td>\n", + " <td>826.115906</td>\n", + " <td>839.573935</td>\n", + " <td>824.083641</td>\n", + " <td>810.623109</td>\n", + " <td>729.825708</td>\n", + " <td>726.659436</td>\n", + " <td>806.233454</td>\n", + " <td>11126.230378</td>\n", + " </tr>\n", + " <tr>\n", + " <th>13</th>\n", + " <td>13</td>\n", + " <td>137581</td>\n", + " <td>13:00-14:00</td>\n", + " <td>840.959658</td>\n", + " <td>848.218249</td>\n", + " <td>831.305136</td>\n", + " <td>818.447353</td>\n", + " <td>731.424774</td>\n", + " <td>769.899273</td>\n", + " <td>838.908537</td>\n", + " <td>11358.325957</td>\n", + " </tr>\n", + " <tr>\n", + " <th>14</th>\n", + " <td>14</td>\n", + " <td>138775</td>\n", + " <td>14:00-15:00</td>\n", + " <td>797.557471</td>\n", + " <td>797.557471</td>\n", + " <td>778.759820</td>\n", + " <td>770.972222</td>\n", + " <td>696.661647</td>\n", + " <td>812.500000</td>\n", + " <td>876.104798</td>\n", + " <td>11060.226859</td>\n", + " </tr>\n", + " <tr>\n", + " <th>15</th>\n", + " <td>15</td>\n", + " <td>176700</td>\n", + " <td>15:00-16:00</td>\n", + " <td>921.752739</td>\n", + " <td>907.550077</td>\n", + " <td>892.424242</td>\n", + " <td>883.941971</td>\n", + " <td>816.166282</td>\n", + " <td>1095.474272</td>\n", + " <td>1162.500000</td>\n", + " <td>13359.619165</td>\n", + " </tr>\n", + " <tr>\n", + " <th>16</th>\n", + " <td>16</td>\n", + " <td>185391</td>\n", + " <td>16:00-17:00</td>\n", + " <td>896.041566</td>\n", + " <td>869.971844</td>\n", + " <td>866.313084</td>\n", + " <td>863.086592</td>\n", + " <td>852.372414</td>\n", + " <td>1200.718912</td>\n", + " <td>1297.347796</td>\n", + " <td>13691.704416</td>\n", + " </tr>\n", + " <tr>\n", + " <th>17</th>\n", + " <td>17</td>\n", + " <td>202694</td>\n", + " <td>17:00-18:00</td>\n", + " <td>1019.074912</td>\n", + " <td>983.951456</td>\n", + " <td>976.838554</td>\n", + " <td>976.838554</td>\n", + " <td>1002.939139</td>\n", + " <td>1424.413212</td>\n", + " <td>1639.919094</td>\n", + " <td>16047.949842</td>\n", + " </tr>\n", + " <tr>\n", + " <th>18</th>\n", + " <td>18</td>\n", + " <td>159655</td>\n", + " <td>18:00-19:00</td>\n", + " <td>1115.688330</td>\n", + " <td>1060.126162</td>\n", + " <td>1044.179202</td>\n", + " <td>1026.720257</td>\n", + " <td>1007.287066</td>\n", + " <td>1353.008475</td>\n", + " <td>1516.191833</td>\n", + " <td>16246.402650</td>\n", + " </tr>\n", + " <tr>\n", + " <th>19</th>\n", + " <td>19</td>\n", + " <td>118915</td>\n", + " <td>19:00-20:00</td>\n", + " <td>1217.144319</td>\n", + " <td>1156.760700</td>\n", + " <td>1125.023652</td>\n", + " <td>1085.981735</td>\n", + " <td>1034.943429</td>\n", + " <td>1312.527594</td>\n", + " <td>1390.818713</td>\n", + " <td>16646.400286</td>\n", + " </tr>\n", + " <tr>\n", + " <th>20</th>\n", + " <td>20</td>\n", + " <td>87214</td>\n", + " <td>20:00-21:00</td>\n", + " <td>1278.797654</td>\n", + " <td>1221.484594</td>\n", + " <td>1181.761518</td>\n", + " <td>1137.079531</td>\n", + " <td>1088.813983</td>\n", + " <td>1315.444947</td>\n", + " <td>1339.692780</td>\n", + " <td>17126.150012</td>\n", + " </tr>\n", + " <tr>\n", + " <th>21</th>\n", + " <td>21</td>\n", + " <td>69608</td>\n", + " <td>21:00-22:00</td>\n", + " <td>1438.181818</td>\n", + " <td>1362.191781</td>\n", + " <td>1315.841210</td>\n", + " <td>1272.541133</td>\n", + " <td>1214.799302</td>\n", + " <td>1389.381238</td>\n", + " <td>1523.150985</td>\n", + " <td>19032.174933</td>\n", + " </tr>\n", + " <tr>\n", + " <th>22</th>\n", + " <td>22</td>\n", + " <td>60838</td>\n", + " <td>22:00-23:00</td>\n", + " <td>1883.529412</td>\n", + " <td>1728.352273</td>\n", + " <td>1648.726287</td>\n", + " <td>1605.224274</td>\n", + " <td>1428.122066</td>\n", + " <td>1520.950000</td>\n", + " <td>2027.933333</td>\n", + " <td>23685.675290</td>\n", + " </tr>\n", + " <tr>\n", + " <th>23</th>\n", + " <td>23</td>\n", + " <td>48123</td>\n", + " <td>23:00-00:00</td>\n", + " <td>2688.435754</td>\n", + " <td>2506.406250</td>\n", + " <td>2358.970588</td>\n", + " <td>2238.279070</td>\n", + " <td>1743.586957</td>\n", + " <td>1700.459364</td>\n", + " <td>2749.885714</td>\n", + " <td>31972.047394</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " hour Total accidents Time of day Monday_Normalized Tuesday_Normalized \\\n", + "0 0 34976 00:00-01:00 3395.728155 3238.518519 \n", + "1 1 25340 01:00-02:00 3839.393939 3519.444444 \n", + "2 2 20055 02:00-03:00 3646.363636 3183.333333 \n", + "3 3 16310 03:00-04:00 2297.183099 2234.246575 \n", + "4 4 12989 04:00-05:00 921.205674 991.526718 \n", + "5 5 19228 05:00-06:00 467.834550 488.020305 \n", + "6 6 41476 06:00-07:00 435.215110 430.248963 \n", + "7 7 97895 07:00-08:00 575.514403 559.400000 \n", + "8 8 166145 08:00-09:00 893.733190 866.240876 \n", + "9 9 112595 09:00-10:00 754.152713 739.783180 \n", + "10 10 103173 10:00-11:00 694.300135 701.857143 \n", + "11 11 117755 11:00-12:00 749.554424 764.642857 \n", + "12 12 133996 12:00-13:00 826.115906 839.573935 \n", + "13 13 137581 13:00-14:00 840.959658 848.218249 \n", + "14 14 138775 14:00-15:00 797.557471 797.557471 \n", + "15 15 176700 15:00-16:00 921.752739 907.550077 \n", + "16 16 185391 16:00-17:00 896.041566 869.971844 \n", + "17 17 202694 17:00-18:00 1019.074912 983.951456 \n", + "18 18 159655 18:00-19:00 1115.688330 1060.126162 \n", + "19 19 118915 19:00-20:00 1217.144319 1156.760700 \n", + "20 20 87214 20:00-21:00 1278.797654 1221.484594 \n", + "21 21 69608 21:00-22:00 1438.181818 1362.191781 \n", + "22 22 60838 22:00-23:00 1883.529412 1728.352273 \n", + "23 23 48123 23:00-00:00 2688.435754 2506.406250 \n", + "\n", + " Wednesday_Normalized Thursday_Normalized Friday_Normalized \\\n", + "0 3068.070175 2989.401709 2775.873016 \n", + "1 3290.909091 3290.909091 3016.666667 \n", + "2 3085.384615 3038.636364 2906.521739 \n", + "3 2174.666667 2146.052632 2091.025641 \n", + "4 991.526718 991.526718 999.153846 \n", + "5 493.025641 498.134715 522.500000 \n", + "6 436.589474 443.119658 474.553776 \n", + "7 566.194332 574.164223 615.304840 \n", + "8 869.413919 872.610294 918.435600 \n", + "9 739.297439 734.474886 733.996089 \n", + "10 696.174089 685.990691 648.071608 \n", + "11 753.873239 742.465322 681.847134 \n", + "12 824.083641 810.623109 729.825708 \n", + "13 831.305136 818.447353 731.424774 \n", + "14 778.759820 770.972222 696.661647 \n", + "15 892.424242 883.941971 816.166282 \n", + "16 866.313084 863.086592 852.372414 \n", + "17 976.838554 976.838554 1002.939139 \n", + "18 1044.179202 1026.720257 1007.287066 \n", + "19 1125.023652 1085.981735 1034.943429 \n", + "20 1181.761518 1137.079531 1088.813983 \n", + "21 1315.841210 1272.541133 1214.799302 \n", + "22 1648.726287 1605.224274 1428.122066 \n", + "23 2358.970588 2238.279070 1743.586957 \n", + "\n", + " Saturday_Normalized Sunday_Normalized Total accidents_Normalized \n", + "0 2021.734104 1870.374332 38719.400020 \n", + "1 2262.500000 2165.811966 42771.270396 \n", + "2 2387.500000 2506.875000 41509.229376 \n", + "3 2091.025641 2434.328358 30937.057225 \n", + "4 1367.263158 1829.436620 16183.278900 \n", + "5 986.051282 1502.187500 9915.507985 \n", + "6 1165.056180 1843.377778 10456.321876 \n", + "7 1626.162791 2829.335260 14692.151697 \n", + "8 1805.923913 3356.464646 19165.644877 \n", + "9 886.574803 1319.988277 11816.534773 \n", + "10 647.258469 806.039062 9759.382396 \n", + "11 658.216881 762.661917 10226.523548 \n", + "12 726.659436 806.233454 11126.230378 \n", + "13 769.899273 838.908537 11358.325957 \n", + "14 812.500000 876.104798 11060.226859 \n", + "15 1095.474272 1162.500000 13359.619165 \n", + "16 1200.718912 1297.347796 13691.704416 \n", + "17 1424.413212 1639.919094 16047.949842 \n", + "18 1353.008475 1516.191833 16246.402650 \n", + "19 1312.527594 1390.818713 16646.400286 \n", + "20 1315.444947 1339.692780 17126.150012 \n", + "21 1389.381238 1523.150985 19032.174933 \n", + "22 1520.950000 2027.933333 23685.675290 \n", + "23 1700.459364 2749.885714 31972.047394 " + ] + }, + "execution_count": 453, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "result61=result60\n", + "column_list = list(result61)\n", + "column_list.remove(\"Time of day\")\n", + "column_list.remove(\"hour\")\n", + "column_list.remove(\"Total accidents\")\n", + "\n", + "result61[\"Total accidents_Normalized\"] = result61[column_list].sum(axis=1)\n", + "result61" + ] + }, + { + "cell_type": "code", + "execution_count": 383, + "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>hour</th>\n", + " <th>Total accidents_Normalized</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>0</td>\n", + " <td>19359.700010</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>1</td>\n", + " <td>21385.635198</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>2</td>\n", + " <td>20754.614688</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>3</td>\n", + " <td>15468.528612</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>4</td>\n", + " <td>8091.639450</td>\n", + " </tr>\n", + " <tr>\n", + " <th>5</th>\n", + " <td>5</td>\n", + " <td>4957.753993</td>\n", + " </tr>\n", + " <tr>\n", + " <th>6</th>\n", + " <td>6</td>\n", + " <td>5228.160938</td>\n", + " </tr>\n", + " <tr>\n", + " <th>7</th>\n", + " <td>7</td>\n", + " <td>7346.075849</td>\n", + " </tr>\n", + " <tr>\n", + " <th>8</th>\n", + " <td>8</td>\n", + " <td>9582.822439</td>\n", + " </tr>\n", + " <tr>\n", + " <th>9</th>\n", + " <td>9</td>\n", + " <td>5908.267386</td>\n", + " </tr>\n", + " <tr>\n", + " <th>10</th>\n", + " <td>10</td>\n", + " <td>4879.691198</td>\n", + " </tr>\n", + " <tr>\n", + " <th>11</th>\n", + " <td>11</td>\n", + " <td>5113.261774</td>\n", + " </tr>\n", + " <tr>\n", + " <th>12</th>\n", + " <td>12</td>\n", + " <td>5563.115189</td>\n", + " </tr>\n", + " <tr>\n", + " <th>13</th>\n", + " <td>13</td>\n", + " <td>5679.162979</td>\n", + " </tr>\n", + " <tr>\n", + " <th>14</th>\n", + " <td>14</td>\n", + " <td>5530.113430</td>\n", + " </tr>\n", + " <tr>\n", + " <th>15</th>\n", + " <td>15</td>\n", + " <td>6679.809582</td>\n", + " </tr>\n", + " <tr>\n", + " <th>16</th>\n", + " <td>16</td>\n", + " <td>6845.852208</td>\n", + " </tr>\n", + " <tr>\n", + " <th>17</th>\n", + " <td>17</td>\n", + " <td>8023.974921</td>\n", + " </tr>\n", + " <tr>\n", + " <th>18</th>\n", + " <td>18</td>\n", + " <td>8123.201325</td>\n", + " </tr>\n", + " <tr>\n", + " <th>19</th>\n", + " <td>19</td>\n", + " <td>8323.200143</td>\n", + " </tr>\n", + " <tr>\n", + " <th>20</th>\n", + " <td>20</td>\n", + " <td>8563.075006</td>\n", + " </tr>\n", + " <tr>\n", + " <th>21</th>\n", + " <td>21</td>\n", + " <td>9516.087466</td>\n", + " </tr>\n", + " <tr>\n", + " <th>22</th>\n", + " <td>22</td>\n", + " <td>11842.837645</td>\n", + " </tr>\n", + " <tr>\n", + " <th>23</th>\n", + " <td>23</td>\n", + " <td>15986.023697</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " hour Total accidents_Normalized\n", + "0 0 19359.700010\n", + "1 1 21385.635198\n", + "2 2 20754.614688\n", + "3 3 15468.528612\n", + "4 4 8091.639450\n", + "5 5 4957.753993\n", + "6 6 5228.160938\n", + "7 7 7346.075849\n", + "8 8 9582.822439\n", + "9 9 5908.267386\n", + "10 10 4879.691198\n", + "11 11 5113.261774\n", + "12 12 5563.115189\n", + "13 13 5679.162979\n", + "14 14 5530.113430\n", + "15 15 6679.809582\n", + "16 16 6845.852208\n", + "17 17 8023.974921\n", + "18 18 8123.201325\n", + "19 19 8323.200143\n", + "20 20 8563.075006\n", + "21 21 9516.087466\n", + "22 22 11842.837645\n", + "23 23 15986.023697" + ] + }, + "execution_count": 383, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "result61_U = result61.drop(['Total accidents',\t'Time of day',\t'Monday_Normalized',\t'Tuesday_Normalized',\t'Wednesday_Normalized',\t'Thursday_Normalized',\t'Friday_Normalized',\t'Saturday_Normalized',\t'Sunday_Normalized'], axis=1)\n", + "result61_U" + ] + }, + { + "cell_type": "code", + "execution_count": 386, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 1440x720 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax=result61_U.plot.bar('hour','Total accidents_Normalized', rot=90,title=\"Accidents Normalized by distribution \",figsize=(20, 10),color=\"Orange\")" + ] + }, + { + "cell_type": "code", + "execution_count": 390, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+----+--------------------------+\n", + "|hour|Total accidents_Normalized|\n", + "+----+--------------------------+\n", + "| 0| 19359.70001016869|\n", + "| 1| 21385.635198135198|\n", + "| 2| 20754.614687848385|\n", + "| 3| 15468.528612439864|\n", + "| 4| 8091.639450197514|\n", + "| 5| 4957.753992549703|\n", + "| 6| 5228.16093793462|\n", + "| 7| 7346.0758486866125|\n", + "| 8| 9582.822438618019|\n", + "| 9| 5908.267386272002|\n", + "| 10| 4879.691197804757|\n", + "| 11| 5113.261773872439|\n", + "| 12| 5563.115189199872|\n", + "| 13| 5679.162978660302|\n", + "| 14| 5530.113429743588|\n", + "| 15| 6679.8095824043885|\n", + "| 16| 6845.852207840923|\n", + "| 17| 8023.974921176663|\n", + "| 18| 8123.201324866291|\n", + "| 19| 8323.20014307673|\n", + "+----+--------------------------+\n", + "only showing top 20 rows\n", + "\n" + ] + } + ], + "source": [ + "timeinthree=spark.createDataFrame(result61_U) \n", + "timeinthree.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 391, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+--------------------+--------------------------+\n", + "| hour|Total accidents_Normalized|\n", + "+--------------------+--------------------------+\n", + "| Off peak| 19359.70001016869|\n", + "| Off peak| 21385.635198135198|\n", + "| Off peak| 20754.614687848385|\n", + "| Off peak| 15468.528612439864|\n", + "| Off peak| 8091.639450197514|\n", + "| Off peak| 4957.753992549703|\n", + "| Off peak| 5228.16093793462|\n", + "|07:00-10:00 Rush ...| 7346.0758486866125|\n", + "|07:00-10:00 Rush ...| 9582.822438618019|\n", + "|07:00-10:00 Rush ...| 5908.267386272002|\n", + "|07:00-10:00 Rush ...| 4879.691197804757|\n", + "| Off peak| 5113.261773872439|\n", + "| Off peak| 5563.115189199872|\n", + "| Off peak| 5679.162978660302|\n", + "| Off peak| 5530.113429743588|\n", + "| Off peak| 6679.8095824043885|\n", + "|16:00-19:00 Rush ...| 6845.852207840923|\n", + "|16:00-19:00 Rush ...| 8023.974921176663|\n", + "|16:00-19:00 Rush ...| 8123.201324866291|\n", + "|16:00-19:00 Rush ...| 8323.20014307673|\n", + "+--------------------+--------------------------+\n", + "only showing top 20 rows\n", + "\n" + ] + } + ], + "source": [ + "timeinthree=timeinthree.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\")))\n", + "timeinthree.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 392, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+--------------------+--------------------------+\n", + "| hour|Total accidents_Normalized|\n", + "+--------------------+--------------------------+\n", + "| Off peak| 169719.51965787067|\n", + "|07:00-10:00 Rush ...| 27716.856871381395|\n", + "|16:00-19:00 Rush ...| 31316.22859696061|\n", + "+--------------------+--------------------------+\n", + "\n" + ] + } + ], + "source": [ + "timeinthree = timeinthree.groupby('hour').agg(F.sum(timeinthree['Total accidents_Normalized']).alias('Total accidents_Normalized'))\n", + "timeinthree.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 394, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "timeinthree=timeinthree.toPandas()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 396, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "timeinthree=timeinthree.sort_values('hour')" + ] + }, + { + "cell_type": "code", + "execution_count": 397, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 1440x720 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax=timeinthree.plot.bar('hour','Total accidents_Normalized', rot=90,title=\"Accidents Normalized by distribution \",figsize=(20, 10),color=\"Orange\")" + ] + }, + { + "cell_type": "code", + "execution_count": 398, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DataFrame[accident_index: string, accident_year: string, accident_reference: string, location_easting_osgr: string, location_northing_osgr: string, longitude: string, latitude: string, police_force: string, Accident_Severity: string, number_of_vehicles: string, number_of_casualties: string, date: string, day_of_week: string, time: string, local_authority_district: string, local_authority_ons_district: string, local_authority_highway: string, first_road_class: string, first_road_number: string, Road_Type: string, speed_limit: string, Junction_Detail: string, junction_control: string, second_road_class: string, second_road_number: string, pedestrian_crossing_human_control: string, pedestrian_crossing_physical_facilities: string, light_conditions: string, weather_conditions: string, road_surface_conditions: string, special_conditions_at_site: string, carriageway_hazards: string, urban_or_rural_area: string, did_police_officer_attend_scene_of_accident: string, trunk_road_flag: string, lsoa_of_accident_location: string]" + ] + }, + "execution_count": 398, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "A2018" + ] + }, + { + "cell_type": "code", + "execution_count": 451, + "metadata": {}, + "outputs": [], + "source": [ + "A2018=A2018.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": 454, + "metadata": {}, + "outputs": [ + { + "ename": "Py4JJavaError", + "evalue": "An error occurred while calling o1446.showString.\n: org.apache.spark.SparkException: Job aborted due to stage failure: Task 1 in stage 225.0 failed 1 times, most recent failure: Lost task 1.0 in stage 225.0 (TID 5877) (10.77.207.174 executor driver): java.io.IOException: No space left on device\n\tat java.io.FileOutputStream.writeBytes(Native Method)\n\tat java.io.FileOutputStream.write(FileOutputStream.java:326)\n\tat java.io.BufferedOutputStream.flushBuffer(BufferedOutputStream.java:82)\n\tat java.io.BufferedOutputStream.flush(BufferedOutputStream.java:140)\n\tat java.io.DataOutputStream.flush(DataOutputStream.java:123)\n\tat java.io.FilterOutputStream.close(FilterOutputStream.java:158)\n\tat org.apache.spark.shuffle.IndexShuffleBlockResolver.$anonfun$writeIndexFileAndCommit$3(IndexShuffleBlockResolver.scala:305)\n\tat org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1448)\n\tat org.apache.spark.shuffle.IndexShuffleBlockResolver.writeIndexFileAndCommit(IndexShuffleBlockResolver.scala:305)\n\tat org.apache.spark.shuffle.sort.io.LocalDiskShuffleMapOutputWriter.commitAllPartitions(LocalDiskShuffleMapOutputWriter.java:118)\n\tat org.apache.spark.shuffle.sort.BypassMergeSortShuffleWriter.write(BypassMergeSortShuffleWriter.java:133)\n\tat org.apache.spark.shuffle.ShuffleWriteProcessor.write(ShuffleWriteProcessor.scala:59)\n\tat org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:99)\n\tat org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:52)\n\tat org.apache.spark.scheduler.Task.run(Task.scala:131)\n\tat org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:497)\n\tat org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1439)\n\tat org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:500)\n\tat java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)\n\tat java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)\n\tat java.lang.Thread.run(Thread.java:748)\n\nDriver stacktrace:\n\tat org.apache.spark.scheduler.DAGScheduler.failJobAndIndependentStages(DAGScheduler.scala:2258)\n\tat org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2(DAGScheduler.scala:2207)\n\tat org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2$adapted(DAGScheduler.scala:2206)\n\tat scala.collection.mutable.ResizableArray.foreach(ResizableArray.scala:62)\n\tat scala.collection.mutable.ResizableArray.foreach$(ResizableArray.scala:55)\n\tat scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:49)\n\tat org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:2206)\n\tat org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1(DAGScheduler.scala:1079)\n\tat org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1$adapted(DAGScheduler.scala:1079)\n\tat scala.Option.foreach(Option.scala:407)\n\tat org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:1079)\n\tat org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2445)\n\tat org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2387)\n\tat org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2376)\n\tat org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49)\n\tat org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:868)\n\tat org.apache.spark.SparkContext.runJob(SparkContext.scala:2196)\n\tat org.apache.spark.SparkContext.runJob(SparkContext.scala:2217)\n\tat org.apache.spark.SparkContext.runJob(SparkContext.scala:2236)\n\tat org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:472)\n\tat org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:425)\n\tat org.apache.spark.sql.execution.CollectLimitExec.executeCollect(limit.scala:47)\n\tat org.apache.spark.sql.Dataset.collectFromPlan(Dataset.scala:3696)\n\tat org.apache.spark.sql.Dataset.$anonfun$head$1(Dataset.scala:2722)\n\tat org.apache.spark.sql.Dataset.$anonfun$withAction$1(Dataset.scala:3687)\n\tat org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$5(SQLExecution.scala:103)\n\tat org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:163)\n\tat org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$1(SQLExecution.scala:90)\n\tat org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:775)\n\tat org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:64)\n\tat org.apache.spark.sql.Dataset.withAction(Dataset.scala:3685)\n\tat org.apache.spark.sql.Dataset.head(Dataset.scala:2722)\n\tat org.apache.spark.sql.Dataset.take(Dataset.scala:2929)\n\tat org.apache.spark.sql.Dataset.getRows(Dataset.scala:301)\n\tat org.apache.spark.sql.Dataset.showString(Dataset.scala:338)\n\tat sun.reflect.GeneratedMethodAccessor531.invoke(Unknown Source)\n\tat sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\n\tat java.lang.reflect.Method.invoke(Method.java:498)\n\tat py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)\n\tat py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)\n\tat py4j.Gateway.invoke(Gateway.java:282)\n\tat py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)\n\tat py4j.commands.CallCommand.execute(CallCommand.java:79)\n\tat py4j.GatewayConnection.run(GatewayConnection.java:238)\n\tat java.lang.Thread.run(Thread.java:748)\nCaused by: java.io.IOException: No space left on device\n\tat java.io.FileOutputStream.writeBytes(Native Method)\n\tat java.io.FileOutputStream.write(FileOutputStream.java:326)\n\tat java.io.BufferedOutputStream.flushBuffer(BufferedOutputStream.java:82)\n\tat java.io.BufferedOutputStream.flush(BufferedOutputStream.java:140)\n\tat java.io.DataOutputStream.flush(DataOutputStream.java:123)\n\tat java.io.FilterOutputStream.close(FilterOutputStream.java:158)\n\tat org.apache.spark.shuffle.IndexShuffleBlockResolver.$anonfun$writeIndexFileAndCommit$3(IndexShuffleBlockResolver.scala:305)\n\tat org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1448)\n\tat org.apache.spark.shuffle.IndexShuffleBlockResolver.writeIndexFileAndCommit(IndexShuffleBlockResolver.scala:305)\n\tat org.apache.spark.shuffle.sort.io.LocalDiskShuffleMapOutputWriter.commitAllPartitions(LocalDiskShuffleMapOutputWriter.java:118)\n\tat org.apache.spark.shuffle.sort.BypassMergeSortShuffleWriter.write(BypassMergeSortShuffleWriter.java:133)\n\tat org.apache.spark.shuffle.ShuffleWriteProcessor.write(ShuffleWriteProcessor.scala:59)\n\tat org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:99)\n\tat org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:52)\n\tat org.apache.spark.scheduler.Task.run(Task.scala:131)\n\tat org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:497)\n\tat org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1439)\n\tat org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:500)\n\tat java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)\n\tat java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)\n\t... 1 more\n", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mPy4JJavaError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m<ipython-input-454-98784b03ee8d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mDayAccidentwrtseverity_df\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mA2018\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Day_of_Week'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0magg\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcount\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mA2018\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maccident_index\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0malias\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[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;31m#DayAccident_df.sort(\"Year\").show(truncate=False)\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[0mDayAccidentwrtseverity_df\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[0m", + "\u001b[0;32m/usr/local/Cellar/apache-spark/3.1.2/libexec/python/pyspark/sql/dataframe.py\u001b[0m in \u001b[0;36mshow\u001b[0;34m(self, n, truncate, vertical)\u001b[0m\n\u001b[1;32m 482\u001b[0m \"\"\"\n\u001b[1;32m 483\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtruncate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbool\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mtruncate\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 484\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshowString\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m20\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvertical\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 485\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 486\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshowString\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtruncate\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvertical\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/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/py4j/java_gateway.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m 1303\u001b[0m \u001b[0manswer\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[0msend_command\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcommand\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1304\u001b[0m return_value = get_return_value(\n\u001b[0;32m-> 1305\u001b[0;31m answer, self.gateway_client, self.target_id, self.name)\n\u001b[0m\u001b[1;32m 1306\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1307\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mtemp_arg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtemp_args\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/Cellar/apache-spark/3.1.2/libexec/python/pyspark/sql/utils.py\u001b[0m in \u001b[0;36mdeco\u001b[0;34m(*a, **kw)\u001b[0m\n\u001b[1;32m 109\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mdeco\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\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 110\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--> 111\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\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 112\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mpy4j\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprotocol\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPy4JJavaError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 113\u001b[0m \u001b[0mconverted\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconvert_exception\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjava_exception\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\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/py4j/protocol.py\u001b[0m in \u001b[0;36mget_return_value\u001b[0;34m(answer, gateway_client, target_id, name)\u001b[0m\n\u001b[1;32m 326\u001b[0m raise Py4JJavaError(\n\u001b[1;32m 327\u001b[0m \u001b[0;34m\"An error occurred while calling {0}{1}{2}.\\n\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 328\u001b[0;31m format(target_id, \".\", name), value)\n\u001b[0m\u001b[1;32m 329\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 330\u001b[0m raise Py4JError(\n", + "\u001b[0;31mPy4JJavaError\u001b[0m: An error occurred while calling o1446.showString.\n: org.apache.spark.SparkException: Job aborted due to stage failure: Task 1 in stage 225.0 failed 1 times, most recent failure: Lost task 1.0 in stage 225.0 (TID 5877) (10.77.207.174 executor driver): java.io.IOException: No space left on device\n\tat java.io.FileOutputStream.writeBytes(Native Method)\n\tat java.io.FileOutputStream.write(FileOutputStream.java:326)\n\tat java.io.BufferedOutputStream.flushBuffer(BufferedOutputStream.java:82)\n\tat java.io.BufferedOutputStream.flush(BufferedOutputStream.java:140)\n\tat java.io.DataOutputStream.flush(DataOutputStream.java:123)\n\tat java.io.FilterOutputStream.close(FilterOutputStream.java:158)\n\tat org.apache.spark.shuffle.IndexShuffleBlockResolver.$anonfun$writeIndexFileAndCommit$3(IndexShuffleBlockResolver.scala:305)\n\tat org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1448)\n\tat org.apache.spark.shuffle.IndexShuffleBlockResolver.writeIndexFileAndCommit(IndexShuffleBlockResolver.scala:305)\n\tat org.apache.spark.shuffle.sort.io.LocalDiskShuffleMapOutputWriter.commitAllPartitions(LocalDiskShuffleMapOutputWriter.java:118)\n\tat org.apache.spark.shuffle.sort.BypassMergeSortShuffleWriter.write(BypassMergeSortShuffleWriter.java:133)\n\tat org.apache.spark.shuffle.ShuffleWriteProcessor.write(ShuffleWriteProcessor.scala:59)\n\tat org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:99)\n\tat org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:52)\n\tat org.apache.spark.scheduler.Task.run(Task.scala:131)\n\tat org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:497)\n\tat org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1439)\n\tat org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:500)\n\tat java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)\n\tat java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)\n\tat java.lang.Thread.run(Thread.java:748)\n\nDriver stacktrace:\n\tat org.apache.spark.scheduler.DAGScheduler.failJobAndIndependentStages(DAGScheduler.scala:2258)\n\tat org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2(DAGScheduler.scala:2207)\n\tat org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2$adapted(DAGScheduler.scala:2206)\n\tat scala.collection.mutable.ResizableArray.foreach(ResizableArray.scala:62)\n\tat scala.collection.mutable.ResizableArray.foreach$(ResizableArray.scala:55)\n\tat scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:49)\n\tat org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:2206)\n\tat org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1(DAGScheduler.scala:1079)\n\tat org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1$adapted(DAGScheduler.scala:1079)\n\tat scala.Option.foreach(Option.scala:407)\n\tat org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:1079)\n\tat org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2445)\n\tat org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2387)\n\tat org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2376)\n\tat org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49)\n\tat org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:868)\n\tat org.apache.spark.SparkContext.runJob(SparkContext.scala:2196)\n\tat org.apache.spark.SparkContext.runJob(SparkContext.scala:2217)\n\tat org.apache.spark.SparkContext.runJob(SparkContext.scala:2236)\n\tat org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:472)\n\tat org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:425)\n\tat org.apache.spark.sql.execution.CollectLimitExec.executeCollect(limit.scala:47)\n\tat org.apache.spark.sql.Dataset.collectFromPlan(Dataset.scala:3696)\n\tat org.apache.spark.sql.Dataset.$anonfun$head$1(Dataset.scala:2722)\n\tat org.apache.spark.sql.Dataset.$anonfun$withAction$1(Dataset.scala:3687)\n\tat org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$5(SQLExecution.scala:103)\n\tat org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:163)\n\tat org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$1(SQLExecution.scala:90)\n\tat org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:775)\n\tat org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:64)\n\tat org.apache.spark.sql.Dataset.withAction(Dataset.scala:3685)\n\tat org.apache.spark.sql.Dataset.head(Dataset.scala:2722)\n\tat org.apache.spark.sql.Dataset.take(Dataset.scala:2929)\n\tat org.apache.spark.sql.Dataset.getRows(Dataset.scala:301)\n\tat org.apache.spark.sql.Dataset.showString(Dataset.scala:338)\n\tat sun.reflect.GeneratedMethodAccessor531.invoke(Unknown Source)\n\tat sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\n\tat java.lang.reflect.Method.invoke(Method.java:498)\n\tat py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)\n\tat py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)\n\tat py4j.Gateway.invoke(Gateway.java:282)\n\tat py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)\n\tat py4j.commands.CallCommand.execute(CallCommand.java:79)\n\tat py4j.GatewayConnection.run(GatewayConnection.java:238)\n\tat java.lang.Thread.run(Thread.java:748)\nCaused by: java.io.IOException: No space left on device\n\tat java.io.FileOutputStream.writeBytes(Native Method)\n\tat java.io.FileOutputStream.write(FileOutputStream.java:326)\n\tat java.io.BufferedOutputStream.flushBuffer(BufferedOutputStream.java:82)\n\tat java.io.BufferedOutputStream.flush(BufferedOutputStream.java:140)\n\tat java.io.DataOutputStream.flush(DataOutputStream.java:123)\n\tat java.io.FilterOutputStream.close(FilterOutputStream.java:158)\n\tat org.apache.spark.shuffle.IndexShuffleBlockResolver.$anonfun$writeIndexFileAndCommit$3(IndexShuffleBlockResolver.scala:305)\n\tat org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1448)\n\tat org.apache.spark.shuffle.IndexShuffleBlockResolver.writeIndexFileAndCommit(IndexShuffleBlockResolver.scala:305)\n\tat org.apache.spark.shuffle.sort.io.LocalDiskShuffleMapOutputWriter.commitAllPartitions(LocalDiskShuffleMapOutputWriter.java:118)\n\tat org.apache.spark.shuffle.sort.BypassMergeSortShuffleWriter.write(BypassMergeSortShuffleWriter.java:133)\n\tat org.apache.spark.shuffle.ShuffleWriteProcessor.write(ShuffleWriteProcessor.scala:59)\n\tat org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:99)\n\tat org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:52)\n\tat org.apache.spark.scheduler.Task.run(Task.scala:131)\n\tat org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:497)\n\tat org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1439)\n\tat org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:500)\n\tat java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)\n\tat java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)\n\t... 1 more\n" + ] + } + ], + "source": [ + "DayAccidentwrtseverity_df = A2018.groupby('Day_of_Week').agg(F.count(A2018.accident_index).alias('Total accidents'))\n", + "#DayAccident_df.sort(\"Year\").show(truncate=False)\n", + "DayAccidentwrtseverity_df.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 496, + "metadata": {}, + "outputs": [], + "source": [ + "result62_U = result61.drop(['Time of day','hour','Total accidents_Normalized','Total accidents'], axis=1)\n", + "column_list = list(result61)" + ] + }, + { + "cell_type": "code", + "execution_count": 497, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/ipykernel_launcher.py:1: FutureWarning: The pandas.np module is deprecated and will be removed from pandas in a future version. Import numpy directly instead\n", + " \"\"\"Entry point for launching an IPython kernel.\n" + ] + } + ], + "source": [ + "sums = result62_U.select_dtypes(pd.np.number).sum().rename('total')\n", + "\n", + "# append sums to the data frame\n", + "result62_U=result62_U.append(sums)" + ] + }, + { + "cell_type": "code", + "execution_count": 498, + "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>Monday_Normalized</th>\n", + " <th>Tuesday_Normalized</th>\n", + " <th>Wednesday_Normalized</th>\n", + " <th>Thursday_Normalized</th>\n", + " <th>Friday_Normalized</th>\n", + " <th>Saturday_Normalized</th>\n", + " <th>Sunday_Normalized</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>3395.728155</td>\n", + " <td>3238.518519</td>\n", + " <td>3068.070175</td>\n", + " <td>2989.401709</td>\n", + " <td>2775.873016</td>\n", + " <td>2021.734104</td>\n", + " <td>1870.374332</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>3839.393939</td>\n", + " <td>3519.444444</td>\n", + " <td>3290.909091</td>\n", + " <td>3290.909091</td>\n", + " <td>3016.666667</td>\n", + " <td>2262.500000</td>\n", + " <td>2165.811966</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>3646.363636</td>\n", + " <td>3183.333333</td>\n", + " <td>3085.384615</td>\n", + " <td>3038.636364</td>\n", + " <td>2906.521739</td>\n", + " <td>2387.500000</td>\n", + " <td>2506.875000</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>2297.183099</td>\n", + " <td>2234.246575</td>\n", + " <td>2174.666667</td>\n", + " <td>2146.052632</td>\n", + " <td>2091.025641</td>\n", + " <td>2091.025641</td>\n", + " <td>2434.328358</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>921.205674</td>\n", + " <td>991.526718</td>\n", + " <td>991.526718</td>\n", + " <td>991.526718</td>\n", + " <td>999.153846</td>\n", + " <td>1367.263158</td>\n", + " <td>1829.436620</td>\n", + " </tr>\n", + " <tr>\n", + " <th>5</th>\n", + " <td>467.834550</td>\n", + " <td>488.020305</td>\n", + " <td>493.025641</td>\n", + " <td>498.134715</td>\n", + " <td>522.500000</td>\n", + " <td>986.051282</td>\n", + " <td>1502.187500</td>\n", + " </tr>\n", + " <tr>\n", + " <th>6</th>\n", + " <td>435.215110</td>\n", + " <td>430.248963</td>\n", + " <td>436.589474</td>\n", + " <td>443.119658</td>\n", + " <td>474.553776</td>\n", + " <td>1165.056180</td>\n", + " <td>1843.377778</td>\n", + " </tr>\n", + " <tr>\n", + " <th>7</th>\n", + " <td>575.514403</td>\n", + " <td>559.400000</td>\n", + " 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"output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "dayofweek = pd.read_csv ('/Users/Asfandyar/Desktop/disertation/diseration_final/dayofweek.csv')\n", + "dayofweek\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 417, + "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>Day_of_Week</th>\n", + " <th>Total accidents</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>Wednesday</td>\n", + " <td>344752</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>Tuesday</td>\n", + " <td>341840</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>Friday</td>\n", + " <td>374260</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>Thursday</td>\n", + " <td>344869</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>Saturday</td>\n", + " <td>304778</td>\n", + " </tr>\n", + " <tr>\n", + " <th>5</th>\n", + " <td>Monday</td>\n", + " <td>324865</td>\n", + " </tr>\n", + " <tr>\n", + " <th>6</th>\n", + " <td>Sunday</td>\n", + " <td>252063</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " Day_of_Week Total accidents\n", + "0 Wednesday 344752\n", + "1 Tuesday 341840\n", + "2 Friday 374260\n", + "3 Thursday 344869\n", + "4 Saturday 304778\n", + "5 Monday 324865\n", + "6 Sunday 252063" + ] + }, + "execution_count": 417, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "DayAccidentwrtseverity_df_df=DayAccidentwrtseverity_df.toPandas()\n", + "DayAccidentwrtseverity_df_df" + ] + }, + { + "cell_type": "code", + "execution_count": 501, + "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>Day_of_Week</th>\n", + " <th>Total accidents</th>\n", + " <th>total</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>Wednesday</td>\n", + " <td>344752</td>\n", + " <td>31009.35234</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>Tuesday</td>\n", + " <td>341840</td>\n", + " <td>31999.40771</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>Friday</td>\n", + " <td>374260</td>\n", + " <td>28730.89272</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>Thursday</td>\n", + " <td>344869</td>\n", + " <td>30497.22211</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>Saturday</td>\n", + " <td>304778</td>\n", + " <td>32526.70394</td>\n", + " </tr>\n", + " <tr>\n", + " <th>5</th>\n", + " <td>Monday</td>\n", + " <td>324865</td>\n", + " <td>33593.45857</td>\n", + " </tr>\n", + " <tr>\n", + " <th>6</th>\n", + " <td>Sunday</td>\n", + " <td>252063</td>\n", + " <td>40395.56775</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " Day_of_Week Total accidents total\n", + "0 Wednesday 344752 31009.35234\n", + "1 Tuesday 341840 31999.40771\n", + "2 Friday 374260 28730.89272\n", + "3 Thursday 344869 30497.22211\n", + "4 Saturday 304778 32526.70394\n", + "5 Monday 324865 33593.45857\n", + "6 Sunday 252063 40395.56775" + ] + }, + "execution_count": 501, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "result70=pd.merge(DayAccidentwrtseverity_df_df, dayofweek, on=['Day_of_Week'])\n", + "result70" + ] + }, + { + "cell_type": "code", + "execution_count": 502, + "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>Day_of_Week</th>\n", + " <th>Total accidents</th>\n", + " <th>total</th>\n", + " <th>Total accidents_Normalized</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>Wednesday</td>\n", + " <td>344752</td>\n", + " <td>31009.35234</td>\n", + " <td>11.117678</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>Tuesday</td>\n", + " <td>341840</td>\n", + " <td>31999.40771</td>\n", + " <td>10.682698</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>Friday</td>\n", + " <td>374260</td>\n", + " <td>28730.89272</td>\n", + " <td>13.026396</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>Thursday</td>\n", + " <td>344869</td>\n", + " <td>30497.22211</td>\n", + " <td>11.308210</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>Saturday</td>\n", + " <td>304778</td>\n", + " <td>32526.70394</td>\n", + " <td>9.370086</td>\n", + " </tr>\n", + " <tr>\n", + " <th>5</th>\n", + " <td>Monday</td>\n", + " <td>324865</td>\n", + " <td>33593.45857</td>\n", + " <td>9.670484</td>\n", + " </tr>\n", + " <tr>\n", + " <th>6</th>\n", + " <td>Sunday</td>\n", + " <td>252063</td>\n", + " <td>40395.56775</td>\n", + " <td>6.239868</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " Day_of_Week Total accidents total Total accidents_Normalized\n", + "0 Wednesday 344752 31009.35234 11.117678\n", + "1 Tuesday 341840 31999.40771 10.682698\n", + "2 Friday 374260 28730.89272 13.026396\n", + "3 Thursday 344869 30497.22211 11.308210\n", + "4 Saturday 304778 32526.70394 9.370086\n", + "5 Monday 324865 33593.45857 9.670484\n", + "6 Sunday 252063 40395.56775 6.239868" + ] + }, + "execution_count": 502, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "result70[\"Total accidents_Normalized\"] = result70[\"Total accidents\"] / result70[\"total\"]\n", + "result70" + ] + }, + { + "cell_type": "code", + "execution_count": 507, + "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>Day_of_Week</th>\n", + " <th>Total accidents_Normalized</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>Friday</td>\n", + " <td>13.026396</td>\n", + " </tr>\n", + " <tr>\n", + " <th>5</th>\n", + " <td>Monday</td>\n", + " <td>9.670484</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>Saturday</td>\n", + " <td>9.370086</td>\n", + " </tr>\n", + " <tr>\n", + " <th>6</th>\n", + " <td>Sunday</td>\n", + " <td>6.239868</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>Thursday</td>\n", + " <td>11.308210</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>Tuesday</td>\n", + " <td>10.682698</td>\n", + " </tr>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>Wednesday</td>\n", + " <td>11.117678</td>\n", + " </tr>\n", + " </tbody>\n", + 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Monday| 9.670483892662203|\n", + "| Sunday| 6.239867738955098|\n", + "+-----------+--------------------------+\n", + "\n" + ] + } + ], + "source": [ + "result70_U_spark=spark.createDataFrame(result70_U) \n", + "result70_U_spark.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 509, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+-----------+--------------------------+\n", + "|Day_of_Week|Total accidents_Normalized|\n", + "+-----------+--------------------------+\n", + "| Week Day| 11.117678183665026|\n", + "| Week Day| 10.68269772671989|\n", + "| Week Day| 13.026396487132892|\n", + "| Week Day| 11.308210261121385|\n", + "| Weekend| 9.370085593738768|\n", + "| Week Day| 9.670483892662203|\n", + "| Weekend| 6.239867738955098|\n", + "+-----------+--------------------------+\n", + "\n" + ] + } + ], + "source": [ + "result70_U_spark=result70_U_spark.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", + ")\n", + "result70_U_spark.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 510, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+-----------+--------------------------+\n", + "|Day_of_Week|Total accidents_Normalized|\n", + "+-----------+--------------------------+\n", + "| Week Day| 55.80546655130139|\n", + "| Weekend| 15.609953332693866|\n", + "+-----------+--------------------------+\n", + "\n" + ] + } + ], + "source": [ + "result70_U_spark = result70_U_spark.groupby('Day_of_Week').agg(F.sum(result70_U_spark['Total accidents_Normalized']).alias('Total accidents_Normalized'))\n", + "result70_U_spark.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 512, + "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>Day_of_Week</th>\n", + " <th>Total accidents_Normalized</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>Week Day</td>\n", + " <td>55.805467</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>Weekend</td>\n", + " <td>15.609953</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " Day_of_Week Total accidents_Normalized\n", + "0 Week Day 55.805467\n", + "1 Weekend 15.609953" + ] + }, + "execution_count": 512, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "result70_U_spark_df=result70_U_spark.toPandas()\n", + "result70_U_spark_df" + ] + }, + { + "cell_type": "code", + "execution_count": 514, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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6cdiem+SDSS6sql9U1dMXZd+jmOh7aUEW5rEY7709XSb7M2oinytT2YMM4B5NaASwdHhBuvF4ki48On+M2/MGbWanP3bIRL48j2Z4UDSZPyCGf9lfdcxW468f2sdfW2s1wduOi135YmitHd9ae0K6H5e7pxvH59fp/s/dNck5VbXpJB92qLfRwzJvfJixDH++J/q8TGaYOBVeNZj+IckTWmufaa1d0Fq7trV2c2vtlszr2TMlqmr9JN9K95j9Mckeg3FXRjP8vfHMhXhtXzbKPhb1vbWwFvm1Mjj9c+jUs5cOxqxJuh6Ts9LdlzFPQxrHZHzGLFBr7ZetteekuwrlzumuxjZ0xbRHJfnORMcUW0IW5rEY/nxNaLy6TFHoOsyy+BkFcI8lNAJYOizKZbFHbjN0asejF3I/w08JWdhtx3PZsH8/ZKxGg9O2xup5MXS6xjpVNWWnFE2FQWDxzdbaga21LdINAjwn3Q/P103ysb6b5MeD2bcO+1E+msuG/XvkqUIjDZ1+c9l4jZYCjxpMT2it3TZGm0dM1cEHA9N/M8nG6a4E96zBWD5juSzdayFZ9PGVLhtMx3xvDUzWpcQXdJyh9WNdUv0z6UKJ9ZMM9czZZzD96ngD94/jxswbwH7KH4fW2j9aa6e21t45CKcfkeTadL1U/mvcjZesiT5Xf2+t3TRs+fAB6Ee72MKQDRapqom7bNi/l5XPKIB7LKERwDSrqs2TbDOYPWJBvQ2SvHHQ9lFVNfyH8NBVYLYfXHVsQlprv868MR8WJbway4WZN1bReH+Ff84464bu06xMbLyeqTI0FtJ4Ycy4WmtfyrxBjSfrh/xwbx1MH5zxryQ0/HnZc6xGg1OtNhvM9q58t5RZcTAd7/kZ2TNvUgwGRj423SlMdyd5QWvtovG2aa3dmOSng9nnL+Khh56THavq3mPUtnaSHRZx/yPtOrgi4mjHeWjmjT/2o9HatNYuT/KDwew+VbV9ks0H84tyatrQmF5Dxxuvvi2STPrA9IPn+X8HsyPf08PHT1vkz41F9JCqGjU4GjxGQ6dCnz1i9fXpTsFL5j2foxnvdLzJuN/L4mcUwD2W0Ahg+g0Pao6bQPsvZ14vheHbfjTdF/5Z6a7eNebAnqP8uPrgYLpjVb1mnO1mTfTqQYMBjb8wmH3u4EfiyP3dJ8mh4+zjkswbFPyIBY0HVFXrDcaHmmzXDaZj/oW9qjYcXO1qrPX3Grb9dWO1W1SttR9k3phXbxmn3d3pQo4keU5V/cvINlW1fOa9Jm7P6FfzW5r8cTB96ogrjCVJqurFSZ42Rcd+e+adNvqGQa+viXj/YLp9Vb1+vIZVNbuqNhux+NjBdKWMcqn5gfdmXqC2uDZKNyh0r7YkRw5m70ryxXH2MTQg9m5Jhu7z71pri/OD/9gJ1HfUouy4qlapqo0X0Gyop9jI9/Tw+anumTOao8YY3+xNSTYc/Hu+q9UNLhTw88HsS0bbfvBeesLI5cP8PfNOc1uk+72MfkYB3GMJjQCm0SCAGeoVcmFr7YIFbdNauyrJmYPZFw2ditRauzLzfjRtn+4S9S+tqk2q6t5VtVlVvbCqvplk/xG7PSrzej58sKo+P7i89HpVtU5VbV1Vb0p3BaKFOU3s7en+ej0ryYlV9e9VtXFVrTsYA+SsdKdB3DDOPvZP8tck6wzu09uqaquqWntQ2xZV9ZKq+nK6wXan4nLqPxtMH1BVrx48LssNbkP/lz4l3WDXn6yq5wwe7zUH93eXdL2m1hm0/d+RB5gkQ72NFvQY/HeSv6Q7reb4qnpTVT1g8JjunOSHSZ40aPtfrbVrp6bcSfOVwfTBSU6oqicMXhsPq6p3p/sBOllXEZurqp6dbnybpPvR+qmqWnWc29zAtbX2tSRfGsy+v6r+r6qeWVUbDN6vm1TV0wf1/yEjTmlsrZ2XeQHNv1XV56pqy6paq6oeU1VfTHdlsj9mclyW5B1VddTgcV2rqrZNd/n2oZ4n7x5cDXEs/5cuTFkh83offmbs5hPy9cz7PHxHVR1ZVQ8dVt930r03L1uEfa+b5A9V9c2q2reqHjF4Xd1n8Br7TJJdBm1HvqcvTXeqYpIcXFUPqqqVhj43FqGWhXFZuufkxKradvBYPLSqjkzyjkGbM5N8Y5Rth3p9bZXky1X1yMHn2MOr6ogkn828y9z3DK4iOfRee01VPaqqVl6E+72sfUYB3HO11tzc3NzcpumW7tSRNri9eSG2+3/DtnvaiHVvSvLPYetHu71ulH2une6S0uNt15Lce8R2Q9scO0at26f78TTavm5P8qx0P3JaksPG2MdD0g0mvaDaWpJHjdj2sMHyyxbwmI55P9IFW78f43jHDtrsM8H6Dl/E18rQ9qM+RsPa/WDE8cZ6TB+R5KoF1Pq+JDXKtjsOa7PpAuoZ97md6HOU7hLzQ8fcccS62el6pI11Py5OdwroqNsvqM6x6hu2fKK3TUdsv0K6HoIT2fbIUepaJV3vsrG2+cJEHtsJvu5emfE/H76cZPYE9veBYdv8M8mGi1LXiH3eJ+N/PrwzXXDYkpw2yvb7DLUd5zU33u3kJKuMst93jbXNGI/xPgvxfPTaDnt+jk1y+Dj1XpTkPmPsf1a6oG2sbb+a5F9Hux/D9vHKcbbfdLR6p+szKov5/nBzc3ObCTc9jQCm1/DTyxam98lXM2/siPnGIWqtvSfd4KAfTnJJklvTXcr50nQ/Bv41o/x1v7V2XZKd0l1O+vh04xzdma5nwK+SfCzdlYNuHLnteFp36snDk3wi3Q+AOwf7/lKSbVpr35rAPi5Jdwn1vdNdoWqotjuSXJnkpCSHJNm8tfbLhalvgvfhtiRPTPcD/9LMP2DskK+kGyvkqCTnDOq6I8ltSX6b7ofcE1prh0x2fSOMeWracK3r1fbQdKcHnpvueb0zXd3/m2T71tobW2ttqgqdLK07neXZSQ5MckG6x/3mJL9M1/tq6yRXT1uBY2it3dlae1W6Xh0fSxdu3ZwuTLk+3fPyoXSn1r1xlO1vTfee/I90V1f8R7rn8cdJ/rW1Nt7YVgvrznQ9dg5M8ot0Vy67Jd24OHu31p4/eB4W5FPD/v391tqfFrew1to16Z7jw9I9hren+9w6Jd1V7MY8BXYBrkiyXbpBrn+YLji+Nd1jcVWSE9KNSfXUNvpA3v+Z5N/TfR4MBedLxOBzZs90j8F16R6Ti9Pdl8cOHrPRtpuT7qqPB6b73L8t3Wvq7CT7ttaemwVcwr619vEkL0oXCv09806nXtj7sMx8RgHck5XPWQAAloTqBv7/7WD2ea21r05nPQDA+PQ0AgBgSdlnML0uyTensQ4AYAKERgAATLnqruj4isHssa21O8drDwBMP6ERAABToqpmDa6ctXG6K3Otl27MqQ9Ma2EAwIQIjQAAmCpvTTdo/xXpBo1Okne01q6avpIAgIlabroLAABgmXdnuisPfjjJMdNcCwAwQfeYq6ets846bdNNN53uMgAAAACWGT/72c/+1lpbd7R195ieRptuumnOO++86S4DAAAAYJlRVZePtc6YRgAAAAD0CI0AAAAA6BEaAQAAANAjNAIAAACgR2gEAAAAQI/QCAAAAIAeoREAAAAAPUIjAAAAAHqERgAAAAD0LDfdBQAAAEvWHXfckeuvvz4333xz7r777ukuB4BJMHv27Ky22mpZa621suKKK07KPoVGAAAwg9xxxx254oorsuaaa2bTTTfN8ssvn6qa7rIAWAyttdx111256aabcsUVV+R+97vfpARHTk8DAIAZ5Prrr8+aa66ZddZZJyussILACGAZUFVZYYUVss4662TNNdfM9ddfPyn7FRoBAMAMcvPNN2f11Vef7jIAmCKrr756br755knZl9AIAABmkLvvvjvLL7/8dJcBwBRZfvnlJ228OqERAADMME5JA1h2TeZnvNAIAAAAgB6hEQAAAAA9QiMAAAAAeoRGAADA/I6re9ZtElTVQt/22WefSTn24jr22GOXqnoWx6Lel9NOOy1VlR133HFK6ron2Geffea+Nt/4xjeO2e7f//3fU1U57LDDllxxS4nLLrssVZVNN920t27TTTdNVeWyyy5b4nUtyNBze+yxxy7xYy+3xI8IAACwlNl77717y66++uqcdNJJWWWVVbLXXnv11m+//fYLdYzDDjssb3vb2/Jf//VfM/IH+0ywzz775LOf/Ww+85nPTGuI9+EPfzive93rstFGG01bDSwbhEYAAMCMN9pf8E877bScdNJJWWeddablL/wz0R577JFtttkma6yxxnSXco+18sor5x//+Ef+67/+K5/61Kemu5x7jB/+8Ie56667suGGG053KUsVp6cBAACwVFhjjTXykIc8JPe9732nu5R7rFe+8pVZaaWV8tnPfjaXXHLJdJdzj/HABz4wD3nIQ7L88stPdylLFaERAADAIrjooovyspe9LBtvvHFWXHHFrLPOOnnmM5+Z7373u722VZW3ve1tSZK3ve1t842NNPxUtZ/85Cc58MADs/XWW+c+97lPVlhhhWywwQbZa6+9cs4550xq/V//+tfz8pe/PFtssUXufe97Z6WVVspmm22WV7/61bnyyivH3K61lq985St5xjOekfXWWy8rrLBCNtxwwzz5yU/Ohz70oVG3Oemkk/Kc5zwnG2ywQVZYYYWsv/762W677fLud787t91229x2CxrT6Pjjj892222XVVddNWuuuWae8pSn5PTTT1/gfb3yyitzwAEH5MEPfnDuda97ZfXVV892222XY489Nq21Xvsdd9wxVZXTTjstP/vZz7Lbbrtl7bXXzkorrZRHPepRvR48Q2PlfPazn02S7LvvvvM9x8N7qv30pz/Nc5/73Gy44YZZfvnls8Yaa2SzzTbLi170opxyyikLvC8LsuGGG+Y1r3lN7r777rz5zW9e6O1PPPHEPOMZz8g666yTFVZYIRtvvHH23nvvXHzxxaO2Hz4W0PHHH5+ddtopa665Zqoqv/jFL+YbR2jOnDk58sgjs8UWW+Re97pXNtpoo7z+9a/PP/7xjyTJ3//+97zuda/LpptumhVXXDGbb755jjzyyFGPe/nll+fwww/PTjvtNPc9uNZaa2WnnXbKcccdt9D3e7QxjQ477LAFjm022vhI1113Xf7zP/8zj3jEI7LqqqtmlVVWyWMe85gcddRRueuuu0Y9/q233ppDDz00D3zgA7Piiitm4403zv7775/rrrtuoe/LZHJ6GsumSRoQEWA+L+p/qQRgZjrhhBPyvOc9L3fccUe22GKLPPGJT8xVV12Vk046Kd/97nfzn//5n/nv//7vue333nvv/OIXv8gvf/nLPOpRj8qjH/3oueuG//vQQw/Naaedli222CKPe9zjsuKKK+Y3v/lNvv71r+f444/P//7v/+a5z33upNyH5z//+VlppZXysIc9LP/yL/+SO+64I7/4xS/ykY98JF/5ylfyox/9KA960IPm2+bOO+/Mc5/73JxwwgmZPXt2ttlmm9zvfvfLNddckwsvvDCnnHJKXvOa18xt31rL/vvvn49+9KNJkq233jo77LBDrr/++lx88cU5+OCD8/znP3/UH94jvec978lBBx2UJNl2222zySab5IILLsjOO+883zFHOvXUU7PHHnvkxhtvzGabbZanP/3pueWWW3LOOedk3333zSmnnJLPfe5zo277ve99L0ceeWQe/OAH56lPfWquuOKKnH322XnFK16RG264IW94wxuSJKuuumr23nvvnHXWWfn973+f7bbbLpttttnc/Qz9++STT84uu+ySu+66K1tuuWW222673HXXXbnqqqvyta99Lauvvnp23nnnBT4WC3LwwQfn4x//eP7v//4vP/nJT/L4xz9+QtsdcsghOeKIIzJr1qxsv/322XDDDfOrX/0qn/vc5/KVr3wlX/va17LLLruMuu373//+HH300Xnc4x6XZzzjGbnyyisza9b8/VRe9KIX5dvf/nZ23HHHbLbZZjnjjDNy1FFH5eKLL84Xv/jFbLPNNrn55puz/fbb5+9//3tOP/30vOENb8jtt9/eC8A+//nP5y1vecvcHkLbbbddrrrqqpx55pk57bTTcs455+SDH/zgoj2AA49+9KNHHe8sydz38+zZs+dbfsEFF+TpT396/vznP2ejjTbKjjvumDlz5uQnP/lJXv/61+fEE0/Md77znaywwgpzt7n11luz00475dxzz83qq6+eZzzjGZk9e3a+9KUv5fvf/3622GKLxbofi0NoBAAAsBCuvvrqvPSlL80dd9yR97///Xn9618/d91pp52WXXbZJe94xzuy/fbb52lPe1qSrgfNYYcdll/+8pfZfffdxxwI+41vfGO++MUv5j73uc98y7/1rW9lzz33zKte9arssssuWXnllRf7fhx33HHZdddd59vXP//5z7ztbW/LO97xjhxwwAG9XlNvetObcsIJJ+RBD3pQvvnNb+YhD3nI3HV33313TjzxxPna/8///E8++tGP5j73uU+OP/74bLPNNnPXtdZy6qmnZs0111xgreeff37e/OY3Z7nllss3vvGNPOtZz5q77r3vfW/e9KY3jbrdX/7yl+y555655ZZbcuyxx+ZlL3tZqro/MF955ZXZbbfd8vnPfz4777zzqL2b3v3ud+dTn/pUXv7yl89d9oUvfCEvfelL8/a3vz377bdfVl555bnjXu2zzz75/e9/n1e84hWj7u/www/PXXfdleOOOy4vfOEL51t33XXXTdqVu9Zaa60cdNBBefOb35xDDjlkQj2YvvOd7+SII47IKqusku985zt50pOeNHfd0GP84he/OL/97W+z3nrr9bb/6Ec/mm9/+9u9UGnoPl1++eVZaaWV8tvf/jYbbLBBku452HLLLfO9730vO+ywQx71qEfl85//fFZaaaUkXa+nXXfdNUcccURe97rXzfdafdrTnpY99tijF6j87ne/m9vr7cUvfvGEA7PR7L777tl99917y3/9619n2223zaxZs/L+979/7vLbbrstz372s/PnP/85hx9+eN74xjdmueW62OX666/P85///PzgBz/Iu971rvk+A9761rfm3HPPzSMe8Yj84Ac/mPv43nDDDdlll11ywgknLPJ9WFxOTwMAAFgIn/jEJ3LTTTdlu+22my8wSrrTmoZ6vbzvfe9b6H0//elP7wVGSfKsZz0rz33uc3P99dfn1FNPXbTCR3je857XC5+WW265/Pd//3c22GCDfP/738/NN988d91f//rXHHPMMZk1a1a+8Y1vzBcYJcns2bOz2267zZ3/5z//mXe+851JutBseGCUdKfs7bzzzhMa9Proo4/O3XffnRe/+MXzBUZJcuCBB2arrbYadbsPfOAD+fvf/543vOEN2XvvvecGRkmy8cYb5xOf+ESSjHla3Z577jlfYJQkL3nJS/LQhz40N910U84777wF1j7cNddckyR5xjOe0Vu39tprj3k/FsUBBxyQ+973vjn11FNz0kknLbD9UPhxwAEHzBcYJd1jvM022+TGG2+c+5iNtO+++47ZC2nIBz/4wbmBUdI9By95yUuSdKHSMcccMzcwSpJddtklj3zkI3PzzTf3HuvHPvaxo/bA2XzzzfOWt7wlSfK1r31t3HoWxdVXX51nPvOZufHGG3PUUUfNFyode+yx+eMf/5jnPe95Ofjgg+cGRkkX5H32s5/N8ssvnw9/+MNzT4u87bbb8vGPfzxJ9/gMD+Tufe9755hjjpnvdbuk6WkEAACwEIbG0BnrtJWXv/zlefe7352zzjord999d+/0lQX529/+lm9/+9u58MILc8MNN+Sf//xnkuTCCy9Mkvz2t79d4I/zifrtb3+b733ve7n00ktzyy23ZM6cOUm6wGfOnDm59NJLs+WWWyZJTjnllNx5553ZbrvtJnS6zHnnnZe//e1v2WijjfL0pz99seocesyHAoaRXvKSl+RnP/tZb/l3vvOdJBnzlL6tttoqq666an7xi1/k9ttvny+wSJJdd9111O0e8pCH5OKLL86f//znCd+HJHnc4x6XX//613nRi16UQw89NNtss81Cvz4mauWVV85b3/rW7LfffjnkkEPy1Kc+dczw4Z///Gd+9KMfJcmY40ntu+++Oeecc3Laaafl0EMP7a1/znOeM249yy+/fJ785Cf3lg+durf11ltnnXXW6a3ffPPN86tf/WrUx/r222/PSSedlHPPPTfXXntt7rjjjiRdD7Oke31PpltvvTW77rprLr/88hxwwAF57WtfO9/6Bb3eNthgg2y++eb59a9/nd/97nd50IMelJ/97Ge55ZZbsuGGG2bHHXfsbfPIRz4yj3zkI/PLX/5yUu/LRAmNAAAAFsKf/vSnJMn973//UddvuummmTVrVm6//fZcd911o57KM5aPfexj8w0MPJqbbrpp4QoexT//+c/sv//++eQnPznqQNCjHevyyy9Pkl4Po7EMtX/wgx+8GJV2rrrqqiTjP+aj+cMf/pCk65WyINddd13vcuv3u9/9Rm27+uqrJ+lCi4Vx+OGH55e//GW++93v5rvf/W5WXnnlbL311tl5553z0pe+NA94wAMWan8L8opXvCLvf//7c/755+fLX/5yXvCCF4za7rrrrssdd9yRWbNmZZNNNhm1zVBtQ6//kcbabsj6668/akC26qqrJkk22mijUbcbWj/ysf7xj3+c5z3veXNfG6OZjPfKkLvvvjsvfOEL87Of/Sy77777qAN0D73eJjLu2LXXXpsHPehBC3xtJ93rW2gEAABwDzLZp4yce+652W+//bLccsvlve99b571rGdlo402ysorr5yqypvf/OYcfvjh44Y8E/U///M/+cQnPpENNtggRx55ZLbddtust956WXHFFZN0A03/+Mc/nu9YC3t/p/OUmiF33313knmDfo9n6L4PN3Ig58W1/vrr57zzzstpp52Wk08+OT/60Y/yk5/8JGeccUbe8Y535GMf+1jvdLjFsdxyy+Ud73hHXvCCF+Qtb3lL9tprrwVus6jP273uda9x1y/osVyYx/of//hH9thjj1xzzTX513/91+y3337ZbLPNstpqq2XWrFn5/ve/n6c97WmT8l4Z8rrXvS7f+ta38vjHPz7HHXfcqPUOvd522WWXUXtNDbf22mtPWm1TSWgEAACwEDbccMNccskl+cMf/jDq6TaXXXZZ5syZk5VWWilrrbXWhPf79a9/Pa21vPa1r80b3/jG3vpLL710seoe7qtf/WqSrmfTaKdgjXasoV43v/nNbyZ0jIVtP54NN9wwf/jDH3LZZZflgQ98YG/9WANIb7zxxrn00kvzlre8ZVqvQDXcrFmzsvPOO8+9Stqtt96ao48+OgcffHBe/epXZ6+99prbk2kyPO95z8t73vOe/PznPx9zPKK11147K664Yu64445cdtll2XzzzXtthnrRjOyNNR3OOOOMXHPNNdlqq63yyU9+srd+Mt8rSXLkkUfm6KOPzgMe8ICccMIJYwZkG2+8cX7zm99kv/32m/AppEOP53iDoE/WAOmLwkDYAAAAC2GHHXZIkjEv0/6Zz3wmSbL99tvPNxDu0CW2h8YoGun6669P0v3wHOnaa6/NySefvOhFL8SxTj755Fx77bW95TvvvHOWX375nH322bn44osXeIytttoq66yzTq666qoJDcQ8nqHH/Itf/OKo68daPjTg9FBINtUW9ByPZpVVVslBBx2UjTbaKLfffvukhGzDVVUOP/zwJMnb3/72UU99XG655bLddtslGft1feyxxybJqOPuLGnjvX6T7sqAk+Ub3/hGDjzwwKy11lr5zne+M+7ppovyettqq62yyiqr5KqrrsoZZ5zRW3/hhRfmV7/61cIXPkmERgAAAAvh3/7t37LaaqvlrLPOygc/+MH51p1xxhlzr8T1hje8Yb51Qz0KxgpchsYK+tznPpdbbrll7vKbb745L3/5y3PDDTdM1l2Ye6xjjjlm7uDXSfL73/8+r3rVq0bdZr311surXvWqzJkzJ3vuuWdvkOG777473/rWt+bOL7/88jnkkEOSdIMo//SnP52vfWstp556am688cYF1vvqV786s2bNyuc///m5gw0POeqoo8a8itmBBx6Y1VdfPe9617vy4Q9/eNQw56KLLso3vvGNBdYwEQt6jt/3vvflyiuv7C0/77zz8pe//CWzZs0aMwhZHE996lOz00475eqrr86Xv/zlUdsMXQnwAx/4wNxBsYcceeSR+fGPf5w11lgjr3jFKya9voU19Po95ZRTcskll8xdPmfOnLz97W/v1b+ozjnnnLzkJS/J8ssvn+OPP36B43O98pWvzMYbb5zPfvazOeyww0YN6P74xz/mC1/4wtz5lVdeee5jesABB8wX2N54443Zf//9J/U0u4Xl9DQAAICFsP766+fzn/98nv/85+eAAw7IJz/5yTz84Q/Pn//855x55pmZM2dO/vM//7N3xbCnPe1pWXnllfONb3wjT3rSk/LABz5w7mXqd9ttt+y77775wAc+kJ///Od5wAMekO233z6ttZxxxhlZYYUV8vKXvzyf/vSnJ+U+HHLIIfne976Xj33sYzn11FOz5ZZb5vrrr8/pp5+eJzzhCVl//fVz9tln97Z773vfm9///vf5zne+ky222CJPeMITstFGG+Wvf/1rLrjggvz1r3+d7wfuf/zHf+Tiiy/OJz/5yWyzzTbZeuuts9lmm+X666/Pr3/961x55ZX54x//mDXWWGPcerfaaqu84x3vyJvf/Obsuuuu2XbbbbPJJpvkggsuyEUXXZTXvva1vQAv6XqiHH/88dlrr73y7//+73nnO9+ZLbbYIuutt15uuOGGXHDBBbnyyivz/Oc/f4FX/5qIZz/72Xn729+eD3zgA7nwwguz0UYbpary8pe/PNtuu23e8Y535MADD8xDH/rQPPShD82KK66YK6+8MmeffXbmzJmTgw8+OOuvv/5i1zGaI444Io9//OPHHGR9l112yUEHHZR3v/vdedKTnpQnPvGJ2WCDDXLBBRfkwgsvzEorrZQvfOELuc997jMl9S2MxzzmMdl1113z7W9/O49+9KOz0047ZY011si5556bK664Im9605vynve8Z7GPc+ihh+a2227LZpttlk996lP51Kc+1Wuzzjrr5H3ve1+SbtDuE088Mbvuumve9ra35UMf+lAe+chHZoMNNsjNN9+ciy++OJdeemke//jHz3clwHe84x0588wz8/Of/zybbbZZdt5558yePTunnnpq7n3ve2e33XbLCSecsNj3Z1EIjQAAgPm9aPr+qn1P8exnPzvnnXde3v3ud+eUU07J1772tay22mp56lOfmte85jV55jOf2dtm/fXXz7e//e28/e1vz/nnn5+zzjorrbVstNFG2W233bLmmmvmvPPOy1ve8pacfPLJOfHEE7PeeuvlOc95Tt7+9rfnYx/72KTV/4QnPCHnnntuDj300Jx33nn55je/mfvf//459NBDc9BBB+VpT3vaqNutuOKK+da3vpXjjjsun/nMZ3L++efnnHPOyXrrrZdHPvKR2WOPPeZrX1X5xCc+kWc/+9n56Ec/mp/+9Kf5xS9+kbXXXjubbbZZXvOa10w4JDnkkEPy4Ac/OO973/ty/vnn54ILLsjWW2+dk08+ObNmzRo1NEqSnXbaKRdddFE+9KEP5cQTT8w555yTu+66K+uvv34e8IAHZP/995/Q1a4m4tGPfnS+/OUv533ve1/OPvvsuT3Gtt9++2y77bb58Ic/nJNPPjnnnXdeTj311Nx22225733vm2c961nZf//989SnPnVS6hjN4x73uOy55575+te/PmabI444Ittvv32OPvronHvuuTn77LOz3nrr5aUvfWkOPvjgPOxhD5uy+hbW17/+9Rx11FH5/Oc/n9NOOy2rrrpqnvCEJ+S4447LbbfdNimh0dDA1pdeeumY4yRtsskmc0OjJHnEIx6RX/3qV/nIRz6Sb37zm/n5z3+es88+O+uuu2423njjvPCFL+wNSL7qqqvm9NNPzzvf+c586Utfyne+852su+662WuvvfLOd75z1DHOlpSazm5OC2PrrbduY3U5hJ7jpv9KDcAyyI8oYBlw8cUX56EPfeh0lwHAFFqYz/qq+llrbevR1hnTCAAAAIAeoREAAAAAPcY0AgAAgKXIG9/4xvztb3+bUNvdd989u++++9QWxIwlNAIAAIClyNe+9rVcfvnlE2q76aabCo2YMkIjAAAAWIpcdtll010CJDGmEQAAAACjEBoBAAAA0CM0AgAAAKBHaAQAADNMa226SwBgikzmZ7zQCAAAZpDZs2fnrrvumu4yAJgid911V2bPnj0p+xIaAQDADLLaaqvlpptumu4yAJgiN910U1ZbbbVJ2ZfQCAAAZpC11lorf//73/O3v/0td955p1PVAJYBrbXceeed+dvf/pa///3vWWuttSZlv8tNyl4AAIB7hBVXXDH3u9/9cv311+eyyy7L3XffPd0lATAJZs+endVWWy33u9/9suKKK07KPoVGAAAww6y44oq5733vm/ve977TXQoASzGnpwEAAADQIzQCAAAAoEdoBAAAAECP0AgAAACAHqERAAAAAD1CIwAAAAB6hEYAAAAA9AiNAAAAAOgRGgEAAADQIzQCAAAAoEdoBAAAAECP0AgAAACAHqERAAAAAD1CIwAAAAB6hEYAAAAA9AiNAAAAAOgRGgEAAADQIzQCAAAAoEdoBAAAAECP0AgAAACAHqERAAAAAD0TDo2q6tiqauPcLhlju1lV9eqqOq+qbqmqG6vqzKp64eTdDQAAAAAm03KLsM2Pklw6yvK/jFxQVbOTfCPJbkluSvL9JCsmeXKS46pqm9baAYtQAwAAAABTaFFCo0+21o6dYNvXpQuMfp1k59baNUlSVZsnOTPJa6vqlNbaNxehDgAAAACmyJSNaTToZfSmwex+Q4FRkrTWfpfkoMHsoVNVAwAAAACLZioHwn5CkvWSXNVaO2OU9V9NcleSx1bVhlNYBwAAAAALaVFOT9upqh6ZZNUk1yQ5K8nJrbU5I9ptOZieO9pOWmv/qKqLkjx6cPvTItQCAAAAwBRYlNDoZaMs+3VVvaC1dsGwZfcfTC8fZ19XpAuM7j9OGwAAAACWsIU5Pe0XSV6b5GHpehltkGTXJL8cLPvBiNPMVh1Mbx1nn7cMpquNtrKqXllV51XVeddee+1ClAoAAADA4phwaNRa+0Br7UOttYtba7e21v7SWjsxyeOSnJNu/KJDJrO41trHW2tbt9a2XnfddSdz1wAAAACMY7EHwm6t3Znk8MHsM4etGupFtMo4mw/1Rrp5cesAAAAAYPJM1tXTLhlMh5+edtlgusk42208oi0AAAAAS4HJCo3WHkxvGbbs54PpY0fboKpWTvLwwez5k1QHAAAAAJNgskKj5w2m5w5b9uMk1ybZqKqeNMo2z02yfJJzW2t/mqQ6AAAAAJgEEwqNqurRVbVrVc0esXy5qnpDuquqJclRQ+taa3cnec9g9piqWm/YdpsnOWIw+85FLR4AAACAqbHcBNttmuT/klxfVT9P8td0p6Q9IskGSeYkeVNr7aQR2x2V5ElJnpXkd1X1w3S9i/4lyUpJPtRa++bi3gkAAAAAJtdEQ6NfJvmfJI9L8rAkT0zSklyV5DNJPtxa+9nIjVprd1fV7kn2T7JvkqcluTvJz5J8pLV23OLeAQAAAAAm34RCo9baH5O8blEO0Fqbk+TowQ0AAACAe4DJGggbAAAAgGWI0AgAAACAHqERAAAAAD1CIwAAAAB6hEYAAAAA9AiNAAAAAOgRGgEAAADQIzQCAAAAoEdoBAAAAECP0AgAAACAHqERAAAAAD1CIwAAAAB6hEYAAAAA9AiNAAAAAOgRGgEAAADQIzQCAAAAoEdoBAAAAECP0AgAAACAHqERAAAAAD1CIwAAAAB6hEYAAAAA9AiNAAAAAOgRGgEAAADQIzQCAAAAoEdoBAAAAECP0AgAAACAHqERAAAAAD1CIwAAAAB6hEYAAAAA9AiNAAAAAOgRGgEAAADQIzQCAAAAoEdoBAAAAECP0AgAAACAHqERAAAAAD1CIwAAAAB6hEYAAAAA9AiNAAAAAOgRGgEAAADQIzQCAAAAoEdoBAAAAECP0AgAAACAHqERAAAAAD1CIwAAAAB6hEYAAAAA9AiNAAAAAOgRGgEAAADQIzQCAAAAoEdoBAAAAECP0AgAAACAHqERAAAAAD1CIwAAAAB6hEYAAAAA9AiNAAAAAOgRGgEAAADQIzQCAAAAoEdoBAAAAECP0AgAAACAHqERAAAAAD1CIwAAAAB6hEYAAAAA9AiNAAAAAOgRGgEAAADQIzQCAAAAoEdoBAAAAECP0AgAAACAHqERAAAAAD1CIwAAAAB6hEYAAAAA9AiNAAAAAOgRGgEAAADQIzQCAAAAoEdoBAAAAECP0AgAAACAHqERAAAAAD1CIwAAAAB6Fjk0qqp3VVUb3N44TrsXVdWZVXVjVd1SVedV1aurSmAFAAAAsJRapOCmqh6b5E1J2gLafTjJF5NsneTMJCcneVCSo5N8TXAEAAAAsHRa6NCmqlZM8tkk1yT55jjt9kyyf5KrkzyytbZra22PJJsnuTjJHklesyhFAwAAADC1FqWnz9uTPDTJq5LcOE67QwbTg1prvxta2Fq7Jsl+g9mD9TYCAAAAWPosVGBTVY9P8oYkx7XWvjVOu42SbJXkziRfHbm+tXZ6kj8lWT/JNgtTAwAAAABTb8KhUVWtlO60tOuTHLCA5lsOphe11m4bo825I9oCAAAAsJRYbiHavjPJg5O8oLX2twW0vf9gevk4ba4Y0RYAAACApcSEehpV1bZJXpfk+NbalyewyaqD6a3jtLllMF1tnOO+sqrOq6rzrr322omUCgAAAMAkWGBoVFX3SnJskpvSXQ1tiWmtfby1tnVrbet11113SR4aAAAAYEabyOlp70qyeZKXt9b+MsH9DvUiWmWcNkO9kW6e4D4BAAAAWEImEhrtkWROkr2rau8R6x4ymO5XVbsmubS19ooklw2WbzLOfjceTC8bpw0AAAAA02CiA2HPSrLDOOsfMLjdezB//mC6RVXda4wrqD12RFsAAAAAlhILHNOotbZpa61GuyX57KDZgYNljx5sc2WSnydZIclzR+6zqnZIslGSq5P8eJLuCwAAAACTZEJXT1tEhw+m766qzYYWVtV6ST4ymD2itTZnCmsAAAAAYBFM9PS0hdZa+1pVHZNkvyQXVNUPktyV5MlJVk9yfJKjp+r4AAAAACy6KQuNkqS1tn9VnZXk1enGRJqd5JIkn05yjF5GAAAAAEunxQqNWmv7JNlnAW2OS3Lc4hwHAAAAgCVrKsc0AgAAAOAeSmgEAAAAQI/QCAAAAIAeoREAAAAAPUIjAAAAAHqERgAAAAD0CI0AAAAA6BEaAQAAANAjNAIAAACgR2gEAAAAQI/QCAAAAIAeoREAAAAAPUIjAAAAAHqERgAAAAD0CI0AAAAA6BEaAQAAANAjNAIAAACgR2gEAAAAQI/QCAAAAIAeoREAAAAAPUIjAAAAAHqERgAAAAD0CI0AAAAA6BEaAQAAANAjNAIAAACgR2gEAAAAQI/QCAAAAIAeoREAAAAAPUIjAAAAAHqERgAAAAD0CI0AAAAA6BEaAQAAANAjNAIAAACgR2gEAAAAQI/QCAAAAIAeoREAAAAAPUIjAAAAAHqERgAAAAD0CI0AAAAA6BEaAQAAANAjNAIAAACgR2gEAAAAQI/QCAAAAIAeoREAAAAAPUIjAAAAAHqERgAAAAD0CI0AAAAA6BEaAQAAANAjNAIAAACgR2gEAAAAQI/QCAAAAIAeoREAAAAAPUIjAAAAAHqERgAAAAD0CI0AAAAA6BEaAQAAANAjNAIAAACgR2gEAAAAQI/QCAAAAIAeoREAAAAAPUIjAAAAAHqERgAAAAD0CI0AAAAA6BEaAQAAANAjNAIAAACgR2gEAAAAQI/QCAAAAIAeoREAAAAAPUIjAAAAAHqERgAAAAD0CI0AAAAA6BEaAQAAANAjNAIAAACgR2gEAAAAQI/QCAAAAICeCYdGVfWaqvpKVV1cVddV1V1VdW1V/aCqXlJVNcZ2s6rq1VV1XlXdUlU3VtWZVfXCybsbAAAAAEym5Rai7UFJ1ktyYZKzk9yaZJMkOyd5cpK9quo5rbU5QxtU1ewk30iyW5Kbknw/yYqD9sdV1TattQMm444AAAAAMHkWJjR6QZLzW2u3Dl9YVVsk+WGSZyfZO8lnhq1+XbrA6NdJdm6tXTPYZvMkZyZ5bVWd0lr75iLfAwAAAAAm3YRPT2utnTUyMBosvyjJhwezTxlaPuhl9KbB7H5DgdFgm9+l67mUJIcubNEAAAAATK3JGgj7n4PpHcOWPSHd6WxXtdbOGGWbrya5K8ljq2rDSaoDAAAAgEmw2KFRVd0/yasGsycMW7XlYHruaNu11v6R5KLB7KMXtw4AAAAAJs/CjGmUJKmqfZPskGT5JBsl2TZd+PSu1tr/DWt6/8H08nF2d0W6wOj+47QBAAAAYAlb6NAoyXbpBrwe8s8kb0ly5Ih2qw6mvXGQhrllMF1tEeoAAAAAYIos9OlprbVXtNYqycpJtkjygSSHJTmnqjaYzOKq6pVVdV5VnXfttddO5q4BAAAAGMcij2nUWruttfbr1tqBSQ5J8qgkRw9rMtSLaJVxdjPUG+nmMY7x8dba1q21rdddd91FLRUAAACAhTRZV087djB9VlUtP/j3ZYPpJuNst/GItgAAAAAsBSYrNPp7urGNlkuy1mDZzwfTx462QVWtnOThg9nzJ6kOAAAAACbBZIVGT0oXGN2Q5G+DZT9Ocm2SjarqSaNs89x0V2A7t7X2p0mqAwAAAIBJMKHQqKq2r6pdq6p3tbWq2i7Jpwazn2qt3Z0kg+l7BsuPqar1hm2zeZIjBrPvXNTiAQAAAJgavRBoDJsl+UySG6rq50muTrJakgcmedigzYlJ3jJiu6PS9UJ6VpLfVdUP0/Uu+pckKyX5UGvtm4t1DwAAAACYdBMNjU5P8t9Jnphk8yTbJql04dHXk3yhtXb8yI1aa3dX1e5J9k+yb5KnJbk7yc+SfKS1dtxi1g8AAADAFJhQaNRa+2OSty7KAVprc5IcPbgBAAAAcA8wWQNhAwAAALAMERoBAAAA0CM0AgAAAKBHaAQAAABAj9AIAAAAgB6hEQAAAAA9QiMAAAAAeoRGAAAAAPQIjQAAAADoERoBAAAA0CM0AgAAAKBHaAQAAABAj9AIAAAAgB6hEQAAAAA9QiMAAAAAeoRGAAAAAPQIjQAAAADoERoBAAAA0CM0AgAAAKBHaAQAAABAj9AIAAAAgB6hEQAAAAA9QiMAAAAAeoRGAAAAAPQIjQAAAADoERoBAAAA0CM0AgAAAKBHaAQAAABAj9AIAAAAgB6hEQAAAAA9QiMAAAAAeoRGAAAAAPQIjQAAAADoERoBAAAA0CM0AgAAAKBHaAQAAABAj9AIAAAAgB6hEQAAAAA9QiMAAAAAeoRGAAAAAPQIjQAAAADoERoBAAAA0CM0AgAAAKBHaAQAAABAj9AIAAAAgB6hEQAAAAA9QiMAAAAAeoRGAAAAAPQIjQAAAADoERoBAAAA0CM0AgAAAKBHaAQAAABAj9AIAAAAgB6hEQAAAAA9QiMAAAAAeoRGAAAAAPQIjQAAAADoERoBAAAA0CM0AgAAAKBHaAQAAABAj9AIAAAAgB6hEQAAAAA9QiMAAAAAeoRGAAAAAPQIjQAAAADoERoBAAAA0CM0AgAAAKBHaAQAAABAj9AIAAAAgB6hEQAAAAA9QiMAAAAAeoRGAAAAAPQIjQAAAADoERoBAAAA0CM0AgAAAKBHaAQAAABAz4RCo6pavqqeXFXvr6rzquqmqrqzqv5UVV+rqh0XsP2LqurMqrqxqm4Z7OPVVSW0AgAAAFgKLTfBdjskOXnw76uTnJHk1iQPS7Jnkj2r6r9ba28duWFVfTjJ/kluT/LDJHcleXKSo5M8uar2aq3NWax7AQAAAMCkmmhPnzlJvp7kSa21+7bWdm2tPb+19ogkL0hyd5K3VNVOwzeqqj3TBUZXJ3nkYLs9kmye5OIkeyR5zSTdFwAAAAAmyYRCo9baKa21vVprZ46y7stJjh3MvmTE6kMG04Naa78bts01SfYbzB7sNDUAAACApctkhTXnD6YbDS2oqo2SbJXkziRfHblBa+30JH9Ksn6SbSapDgAAAAAmwWSFRpsPpn8ZtmzLwfSi1tptY2x37oi2AAAAACwFFjs0qqr1k+wzmP36sFX3H0wvH2fzK0a0BQAAAGApsFihUVUtl+QLSdZI8sPW2reGrV51ML11nF3cMpiutjh1AAAAADC5Fren0UeTPDnJlekPgr3YquqVVXVeVZ137bXXTvbuAQAAABjDIodGVfU/Sf41ydVJntxau3pEk6FeRKuMs5uh3kg3j7aytfbx1trWrbWt11133UUtFQAAAICFtEihUVW9P8lrk1ybLjD63SjNLhtMNxlnVxuPaAsAAADAUmChQ6Oqek+S1ye5Lsm/tNZ+PUbT8wfTLarqXmO0eeyItgAAAAAsBRYqNKqqI5IcmOTvSZ7SWvvVWG1ba1cm+XmSFZI8d5R97ZBko3Snt/14YeoAAAAAYGpNODSqqnckOSjJDekCo4n0Djp8MH13VW02bF/rJfnIYPaI1tqcidYBAAAAwNRbbiKNqmq3JIcOZi9N8pqqGq3pJa21I4ZmWmtfq6pjkuyX5IKq+kGSu9JdcW31JMcnOXqRqwcAAABgSkwoNEqy1rB/bz24jeb0JEcMX9Ba27+qzkry6iQ7JJmd5JIkn05yjF5GAAAAAEufCYVGrbVjkxy7qAdprR2X5LhF3R4AAACAJWuhr54GAAAAwLJPaAQAAABAj9AIAAAAgB6hEQAAAAA9QiMAAAAAeoRGAAAAAPQIjQAAAADoERoBAAAA0CM0AgAAAKBHaAQAAABAj9AIAAAAgB6hEQAAAAA9QiMAAAAAeoRGAAAAAPQIjQAAAADoERoBAAAA0CM0AgAAAKBHaAQAAABAj9AIAAAAgB6hEQAAAAA9QiMAAAAAeoRGAAAAAPQIjQAAAADoERoBAAAA0CM0AgAAAKBHaAQAAABAj9AIAAAAgB6hEQAAAAA9QiMAAAAAeoRGAAAAAPQIjQAAAADoWW66CwAAgBntuJruCoBl0YvadFfAMkBPIwAAAAB6hEYAAAAA9AiNAAAAAOgRGgEAAADQIzQCAAAAoEdoBAAAAECP0AgAAACAHqERAAAAAD1CIwAAAAB6hEYAAAAA9AiNAAAAAOgRGgEAAADQIzQCAAAAoEdoBAAAAECP0AgAAACAHqERAAAAAD1CIwAAAAB6hEYAAAAA9AiNAAAAAOgRGgEAAADQIzQCAAAAoEdoBAAAAECP0AgAAACAHqERAAAAAD1CIwAAAAB6hEYAAAAA9AiNAAAAAOgRGgEAAADQIzQCAAAAoEdoBAAAAECP0AgAAACAHqERAAAAAD1CIwAAAAB6hEYAAAAA9AiNAAAAAOgRGgEAAADQIzQCAAAAoEdoBAAAAECP0AgAAACAHqERAAAAAD1CIwAAAAB6hEYAAAAA9AiNAAAAAOgRGgEAAADQM+HQqKoeXFUHVNUXquqSqppTVa2q9prAti+qqjOr6saquqWqzquqV1eV0AoAAABgKbTcQrTdL8kBC3uAqvpwkv2T3J7kh0nuSvLkJEcneXJV7dVam7Ow+wUAAABg6ixMT58Lk7w3yfOTbJbk9AVtUFV7pguMrk7yyNbarq21PZJsnuTiJHskec3CFg0AAADA1JpwT6PW2ieHz1fVRDY7ZDA9qLX2u2H7uqaq9ktyWpKDq+pDehsBAAAALD2mbEyhqtooyVZJ7kzy1ZHrW2unJ/lTkvWTbDNVdQAAAACw8KZyIOotB9OLWmu3jdHm3BFtAQAAAFgKTGVodP/B9PJx2lwxoi0AAAAAS4GpDI1WHUxvHafNLYPpaqOtrKpXVtV5VXXetddeO6nFAQAAADC2qQyNFltr7eOtta1ba1uvu+66010OAAAAwIwxlaHRUC+iVcZpM9Qb6eYprAMAAACAhTSVodFlg+km47TZeERbAAAAAJYCUxkanT+YblFV9xqjzWNHtAUAAABgKTBloVFr7cokP0+yQpLnjlxfVTsk2SjJ1Ul+PFV1AAAAALDwpnog7MMH03dX1WZDC6tqvSQfGcwe0VqbM8V1AAAAALAQlptow6p6TOYFPUnysMH0XVX1xqGFrbVthv37a1V1TJL9klxQVT9IcleSJydZPcnxSY5e5OoBAAAAmBITDo3ShTyPH2X55uNt1Frbv6rOSvLqJDskmZ3kkiSfTnKMXkYAAAAAS58Jh0attdOS1KIcpLV2XJLjFmVbAAAAAJa8qR7TCAAAAIB7IKERAAAAAD1CIwAAAAB6hEYAAAAA9AiNAAAAAOgRGgEAAADQIzQCAAAAoEdoBAAAAECP0AgAAACAHqERAAAAAD1CIwAAAAB6hEYAAAAA9AiNAAAAAOgRGgEAAADQIzQCAAAAoEdoBAAAAECP0AgAAACAHqERAAAAAD1CIwAAAAB6hEYAAAAA9AiNAAAAAOgRGgEAAADQIzQCAAAAoEdoBAAAAECP0AgAAACAHqERAAAAAD1CIwAAAAB6hEYAAAAA9AiNAAAAAOgRGgEAAADQIzQCAAAAoEdoBAAAAECP0AgAAACAHqERAAAAAD1CIwAAAAB6hEYAAAAA9AiNAAAAAOgRGgEAAADQIzQCAAAAoEdoBAAAAECP0AgAAACAHqERAAAAAD1CIwAAAAB6hEYAAAAA9AiNAAAAAOgRGgEAAADQIzQCAAAAoEdoBAAAAECP0AgAAACAHqERAAAAAD1CIwAAAAB6hEYAAAAA9AiNAAAAAOgRGgEAAADQIzQCAAAAoEdoBAAAAECP0AgAAACAHqERAAAAAD1CIwAAAAB6hEYAAAAA9AiNAAAAAOgRGgEAAADQIzQCAAAAoEdoBAAAAECP0AgAAACAHqERAAAAAD1CIwAAAAB6hEYAAAAA9AiNAAAAAOgRGgEAAADQIzQCAAAAoEdoBAAAAECP0AgAAACAHqERAAAAAD1CIwAAAAB6hEYAAAAA9Cyx0KiqXlRVZ1bVjVV1S1WdV1WvrirBFQAAAMBSZokENlX14SRfTLJ1kjOTnJzkQUmOTvI1wREAAADA0mXKw5qq2jPJ/kmuTvLI1tqurbU9kmye5OIkeyR5zVTXAQAAAMDELYkePocMpge11n43tLC1dk2S/QazB+ttBAAAALD0mNKgpqo2SrJVkjuTfHXk+tba6Un+lGT9JNtMZS0AAAAATNxU9+7ZcjC9qLV22xhtzh3RFgAAAIBpNtWh0f0H08vHaXPFiLYAAAAATLPlpnj/qw6mt47T5pbBdLWRK6rqlUleOdSuqn4zibUBJMk6Sf423UVwD/Himu4KAJjZfG9h4nxvYeI2GWvFVIdGi6W19vEkH5/uOoBlV1Wd11rberrrAABYEN9bgCVtqk9PG+pFtMo4bYZ6I908xbUAAAAAMEFTHRpdNpiO2dUpycYj2gIAAAAwzaY6NDp/MN2iqu41RpvHjmgLsCQ5BRYAuKfwvQVYoqq1NrUHqPpZksck2bu19rkR63ZIclqSq5Ns2FqbM6XFAAAAADAhU93TKEkOH0zfXVWbDS2sqvWSfGQwe4TACAAAAGDpMeU9jZKkqj6SZL8ktyf5QZK7kjw5yepJjk+yV2vt7ikvBAAAAIAJWSKhUZJU1YuSvDrJI5LMTnJJkk8nOUYvIwAAAIClyxILjQCmS1Wdk+ToJF9prd053fUAAADcEwiNgGVeVc1J0pJcl+RTST7aWrt8eqsCAABYui2JgbABptuzk5ycZO0kByW5tKq+WVVPm96yAAAAll56GgEzRlU9MMn+SfZOsla63ke/T3JMks+01m6YvuoAgJmqqhbnokCttbbcpBUDMIzQCJhxqmqlJC9MFyBtlS48uj3J/yb5SGvt59NYHgAwwwxOpV9krTVnkABTwocLMOO01m5vrX2mtfbYJI9PclySeyXZN8m5VXV2Ve01rUUCADNGa23WyFuSo5L8I8mRSbZMsubgtmWS9ye5NcmRAiNgKulpBMxYVXWfJP+W5JVJNhosvinJ6ul6H52T5DmttWump0IAYCaqqn9N8tEkO7fWzhyjzfZJTk2yf2vtE0uyPmDmEBoBM05VPSndqWm7J1k+yT+TfD3JB5P8NMmzkrwlyWOSfKm19qLpqRQAmImq6mdJbmyt7byAdqckuXdr7TFLpjJgpjFgGjAjVNWqSV6WZL8kD0tSSa5J8rEkH22tXT2s+Ter6ttJfpnkqUu6VgBgxntwkm9OoN1fkjxuimsBZjChEbDMq6qPJHlxklXThUU/Tder6KuttbtG26a1dndV/TTdldYAAJakO9KNXbQgWw7aAkwJg6YBM8GrkqyY5ItJHt9a26a1dtxYgdEwZyT53JRXBwAwvzOSPLiq/ruqauTK6rw9yUMGbQGmhDGNgGVeVf1nko+31v463bUAACxIVT08yU+SrJTk90m+lOSPg9WbJnlBks2S3J5km9baBdNQJjADCI0AAACWMlW1Q7pe0huku6rrfKvTjWf0ktbaqUu6NmDmEBoBAAAshapqpSR7JdkhyUaDxX9KcnqSr7XWbpuu2oCZQWgEzBhVtUGSZyd5UJLV0/2VbqTWWvvXJVoYAADAUkhoBMwIVfW6JEckWX744sG0DZtvrbXZS7A0AACApdJy010AwFSrqqclOTLJTUnel2THJE9I8v/SDSK5Z5L7J/mfJL+YliIBAEZRVbOTrJ1uUOxRtdauWHIVATOJnkbAMq+qTkzy9HRXFzm3qj6T5GVDPYqqaoUkR6e7EslWrbXfTV+1AABJVT0+yduTPDHJiuM0ba01nQGAKSE0ApZ5VfXXJH9srT1+MD9faDRYtny6S9me1lp7yfRUCgCQVNV2SX6QeWHR39P1mB5Va+3+S6IuYOaRSAMzwRpJ/jBs/s4kqapVWmu3Jklr7a6q+lGSnaahPgCA4d6WLjD6RJK3tNb+Os31ADPUrOkuAGAJ+Fu6q6UNuX4w3XREu5WSrLkkCgIAGMfjklzcWvt/AiNgOgmNgJngsiSbDJv/Rborpb1gaEFVrZdugOzLl2BdAACjqSS/mu4iAIRGwEzwwyQPrar7DeZPTDc2wJur6stV9f4kP02yapLjp6dEAIC5Lkiy/nQXAWAgbGCZV1UPTfL6JJ9rrZ05WPbsJMcludewpucnedLQOEcAANOhqp6f5ItJtm6t/WKaywFmMKERMGNV1YZJdk2yVpJLkpzQWrt7eqsCAEiq6m1J9k/y1iQnttaumOaSgBlIaAQAALAUqaqF+SNWa625KjYwJXy4AAAALF1qitoCLBQ9jYBlWlXNSrJ7kqck2SLJ2knmJLk+3SCTJ6c7Lc2HIQAAwDBCI2CZVVVbpxvs+oFDi0Y0GfoA/E2SFxloEgAAYB6hEbBMqqqtkpyWZJUktyf5XpLzkvwtyawk6yTZKsnTkqyU5JYkT2yt/XI66gUAAFjaCI2AZU5VVbpTzx6W5GtJ9mutXTdG23WSHJNkzyS/aq09eknVCQAwnqraLMn/S/KEJOsm+WZr7U2DdY9P8qgkX2mt3TBtRQLLNANhA8uip6QLjE5urT1vvIattb9V1fOSfD/JzlX1L621HyyJIgEAxlJV/5rkw0lWGCxq6XpKD1k53R++7krymSVbHTBTzJruAgCmwLPTfbE6eCKNB4NgH5RuzKPdp64sAIAFq6rtknws3Sn2ByZ5fPpjM56e5MYkuy3Z6oCZRE8jYFm0VZI/tdbOn+gGrbWfV9VVSR4zdWUBAEzIm9L9AewZrbUfJ0l39v08rbU5VXV+kocu+fKAmUJPI2BZtEm6MY0W1q+S3H+SawEAWFhPSPLTocBoHFcnue8SqAeYoYRGwLJo9STXL8J2Nwy2BQCYTmskuWoC7VaNs0eAKSQ0ApZF90o3KOTCuivJSpNcCwDAwvprJtb7+cFJ/jTFtQAzmNAIAABg6fKjJI+pqq3HalBVT0nyoCSnLamigJlHV0ZgWfXoqnrrwm4zFYUAACyko5I8N8k3quoVSX4wfGVVPSnJp5P8M8mHlnx5wExR3ZWmAZYdVTUn3RVHFnrTJK21NnuSSwIAWChV9YYk7033neamdOMu3pjudPp10n1veX1r7QPTVSOw7NPTCFgWfXa6CwAAWByttfdX1a+THJbksYPF9x5ML0jyltbaCdNQGjCD6GkEAACwFKuqtdMNjD07yZWttT8PWzertTZn2ooDlmkGwgYAAFiKVNVLh8+31q5rrZ3XWvvJiMCoknxuiRcIzBhCIwAAgKXLJ6pq54m0S/LCqS4GmLmERgAAAEuXfyT5elU9fKwGVfXBJC9PN74RwJQQGgEAACxd9kiyUpLvVNUGI1dW1buT/HuSS5I8ZQnXBswgQiMAAIClSGvt9CT7JtkwyXerarWhdVX1X0kOTPKHJE9urV07PVUCM4HQCAAAYCnTWvtSkkOSPCLJN6pquao6KMl/JbkyXWD0l+msEVj2VWttumsAAABgFFX14ST7JTk/yaOT/DXJDq21305nXcDMIDQCGKaqZrXW5kx3HQAASffdJMk3kuyW5G9JdmqtXTS9VQEzhdAIWOZV1Utba5+fQLtK8vnW2kuWQFkAAEmSqnrrApqskuQ/knw7yS9GrGuttf+eiroAhEbAMq+qbk/yzNbaKQto98kk+7bWZi+ZygAAkqqak6QlqTGajLZuaFnz3QWYKstNdwEAS8A/kny9qp7YWrtwtAZV9cEkL0/yqyVaGQBA8rbpLgBgNHoaAcu8qtohyfeSXJtkm9ban0esf3e6S9dekm5gSZeuBQAAZrxZ010AwFRrrZ2eZN8kGyb5blWtNrSuqv4rXWD0h3SXrhUYAQAARE8jYAapqjclOSLJD5M8I8kbkhye5MokT2qtXT6N5QEA9FTVGkkem2TdJJe31s6e5pKAGURPI2DGaK29J8kxSZ6c5CdJ3pXkmiRPERgBAEuTqlqjqj6d5K9JTkryhSSvGLb+FVX156raZrpqBJZ9QiNgpnlNkhOSbJnkuiT/0lr77fSWBAAwT1WtkuS0JPsk+XuS76Z/9bRvJ7lPkt2XYGnADOPqacAyp6reuoAmv0nyzyRnJdmzqvYctq611v57yooDAFiwNyZ5VLreRa9qrf2jquYMb9Bau7qqfp1k5+koEJgZjGkELHMGX6pa+n+RGzLauqFlrbU2ewrLAwAYV1VdmOTeSR7YWrtjsGxOkmNbay8f1u7r6a4Mu+G0FAos8/Q0ApZFb5vuAgAAFsMDkpw0FBiN4/Ykay+BeoAZSmgELHNaa0IjAOCe7K4kK02g3cZJbpniWoAZzEDYAAAAS5ffJNmyqlYcq0FVrZlu3KMLllhVwIwjNAJmlMHla/+lql5YVdtOdz0AAKP4WpL1krx7nDbvSrJqkq8skYqAGUloBMwIg7Do00n+muSkdFcjecWw9a+oqj9X1TbTVSMAwMDRSS5O8pqqOquqXj9YvmlV7VdVpyR5ZbpeRp+ariKBZZ+rpwHLvKpaJclZ6bpw/zXJeUmemWFXIKmq9ZP8Kcl7W2sHT1etAABJUlUbJvlqkm0y7CqvQ6uT/CzJ7q21P01PhcBMYCBsYCZ4Y7rA6AtJXtVa+8fgsrVztdaurqpfJ9l5OgoEABhuEAZtW1VPT/fHrgckmZ3kyiTfTXJ80wMAmGJCI2AmeG6SPyf5twVcuva36f6aBwCwVGitfS/J96a7DmBmMqYRMBM8IMm5CwiMkuT2JGsvgXoAAACWekIjYCa4K8lKE2i3cZJbprgWAIAJqarNquq9g8Gwf1NV7xm27vFV9cqquvc0lggs45yeBswEv0myZVWtOFZvo6paM924Rz9fopUBAIyiqv41yYeTrDBY1JKsM6zJykmOSffHsc8s2eqAmUJPI2Am+FqS9ZK8e5w270qyapKvLJGKAADGUFXbJflYulPnD0zy+HRXTBvu9CQ3JtltyVYHzCR6GgEzwdFJ9k7ymqraOsk3Bss3rar90g2UvUOSC5J8anpKBACY603pehY9o7X24ySpmj8zaq3Nqarzkzx0yZcHzBRCI2CZ11r7R1U9NclXk2yb5AmDVTsMbpXkZ0l2b63dOT1VAgDM9YQkPx0KjMZxdZKtl0A9wAwlNAJmhNban5JsW1VPT/LMdFdUm53kyiTfTXJ8a61NY4kAAEPWSHLVBNqtGr/pgCnkAwaYUVpr30vyvemuAwBgHH9Ncv8JtHtwkj9NcS3ADGYgbAAAgKXLj5I8ZjAW46iq6ilJHpTktCVVFDDzCI2AGaOqNquq91bVWVX1m6p6z7B1j6+qV1bVvaexRACAJDkq3ZiL36iqp1bVfL/bqupJST6d5J9JPjQN9QEzhNPTgBmhqv41yYeTrDBY1JKsM6zJykmOSXJXks8s2eoAAOZprf2kqt6U5L3pxl68Kd13l92rapd032EqyetbaxdMX6XAsk5PI2CZV1XbJflYktuTHJjk8em+aA13epIbk+y2ZKsDAOhrrb0/yS5Jzks3MHYluXeSdZNcmO6qrx+YrvqAmUFPI2AmeFO6v849Y+jStVXzZ0attTlVdX6Shy758gCAmayq/pTk1HTjE53WWrs0SVpr303y3apaO93A2LOTXNla+/N01QrMLEIjYCZ4QpKfDgVG47g6yZgDTgIATJH7Jnnh4Jaq+nO6AOnUdCHSH5JcN23VATOW0AiYCdZIctUE2q0an4sAwJL3rCQ7JtkhyZZJNkzy4iQvSpKquirzh0iXTUeRwMxTrbXprgFgSlXVlUn+0lp73LBlc5Ic21p7+bBllySZ1Vp70DSUCQCQqlotyfaZFyI9JvP+qDX04+2KzB8iXbFkqwRmCn9RB2aCHyXZq6q2bq2dN1qDqnpKkgcl+eQSrQwAYJjW2s3prpj23SSpqlXThUg7JNkpXYi0SZKXDW4tftcBU8TV04CZ4Kh0Vxz5RlU9tarm++yrqicl+XSSfyb50DTUBwAwqtbaLa2177XWDmmtbZNknSRvTXJDuu83I68ICzBpnJ4GzAhV9YYk703317ibkqye5MYkd6X78lVJXu/StQDA0qS6S75ule50tR3T9TpaLfPCootba1tMS3HAMk9oBMwYVfWMJIcleeyIVRckeUtr7YQlXhQAwDCDHtFDIdEOmT8kaum+t5yR5PQkZ7TWrp2eSoGZQGgEzDhVtXaS+yeZneTK1tqfp7kkAGAGq6rHZl5Pou0yLyS6O8n5mRcSndlau2FaigRmJKERsMypqj9lcDWRdFcUuXR6KwIAGNvgqq4t3Wnz52VeSPSj1tot01kbMLMJjYBlzrAvXkP+nPkvS/uH6agLAGA0w767XJTke5nXq+imaS0MmPGERsAyp6p2ybxxALZMdxpaMi9Iuirzh0iXLdkKAQDmqaqD031v2S7Jqum+s8xJ8qvMP37R9dNWJDAjCY2AZVpVrZZuAMkd030Ze0yS5Qarhz4Ar8j8IdIVS7ZKAICkqmanGwR7hyQ7Jdk23RVf2+D268wLkU5vrf11mkoFZgihETCjVNWq6UKkoS9jQyHS0Idha60tN8bmAABLzOBKao/J/INkrzFY3ZL8Nl149KrpqA9Y9gmNgBmtqlZP8tok/5FkzXSh0ezxtwIAWPIGIdKjkzwr3fcX312AKeWv6cCMUlWVrtv3joPb9pl3WdskuWRaCgMAGENVrZLkiZn3/eUxmTdmI8CUERoBy7TBX+SGQqIdMn9I1JJckPkHmLx2eioFAOgMTqd/YrrvLjtm/pBo6A9dl6f7/nLqkq4PmDmERsAyp6oem/nP/R8Kie5Ocn7mhURnttZumJYiAQAGhoVEOw5uw6/+OhQSXZF5IZGrvwJLhDGNgGVOVc1J14voriTnZV5I9KPW2i3TWRsAwEhVdWf6IdGVmT8k+uN01AbMbHoaAcuy3yY5O8mPkvxYYAQALKWWS3JV5g+J/jC9JQHoaQQsg6rq4HRjAGyXZNV0vY7mJPlV5h+/6PppKxIAYKCqHtha+/101wEwktAIWGZV1ex0g2DvkGSnJNsmWT1diNSS/DrzQqTTW2t/naZSAQAAljpCI2DGGFxJ7TGZf5DsNQarW7rT2U5vrb1qOuoDAABYmgiNgBlrECI9Osmzkrw2yZpJWmtt9njbAQAAzAQGwgZmnKpaJfNf1vYxmXfFEgAAACI0AmaAqlo1XUi0Q/oh0dBlbS/PvCuWAAAAzHhCI2CZMywk2nFw2zL9kOiKzH9Z28uWaJEAAABLOWMaAcucqroz/ZDoyswfEv1xOmoDAAC4p9DTCFgWLZfkqswfEv1heksCAAC4ZxEaAcuizVtrv5/uIgAAAO7JnJ4GAAAAQM+s6S4AAAAAgKWP0AgAAACAHqERALBIquqyqmrDbnOq6uaqurKqflBVh1fVI6e7ziWhOgdX1UVVdfvg8bhhIfdx1mC7V4+xfv1hj/Ux49Tx10Gbpy3CXVlkVbXp4LiXLcnjAgBTx0DYAMDiOinJ1YN/r5xk3SRbJ3lykoOr6ltJXtlau3qM7ZcFr05yeJIbk5yY5OYk/1jIfZyWZLskOyb58Cjrdxzj38Ntke7x/2eSsxby+AAA8xEaAQCL64jW2mnDF1TVrCTPSnLkYHp6VW3bWrtuGupbEp47NG2tnbyI+zgtyaFJdhhj/dDy85NsWVX3aa1dM0abc1trty5iHQAASZyeBgBMgdbanNbaN9P1OLo0yYOSvH96q5pSGw+mv1uMffwoyZ1J1q2qLUZZv2OSvyX56LD50dokXQAFALBYhEYAwJRprf09yesGsy+pqvWH1lXVv1TVh6vql1V1XVXdUVWXV9Vnq+qhI/dVVT8cjJnzgrGOV1XvH7R5z6LWXFWrVNWhg7puHdx+UVVvrqqVR7Q9rapakvsPFv1x2LhD+yzMcVtrtyX56WB2xxHHuU+ShyQ5I8npo7UZeNJgeuqI7deuqndU1QVVdcvgPv28qv6jqpYfq6aqelpVnVBV11TVnVX1l6r636p6xMLct6pao6pOGTwux1fVvRZmewBgegiNAICp9p0k1yeZnWSnYcs/muRf042/c8ag3Z1JXpbkvKrafsR+PjSY7j/aQQZBxL5J5iQZdaDoBamqdZL8OMk70vUeOmlw2yTJO5OcXVVrDdvke0k+m2ToVLCvD+Y/m66H1cI6bTDdccTyodPOTm+t/SbJNSPbVNXDkqyX5K50vZaGlj8iya/Snfp278ExTh/cpyOTfLeqVhhZSFX9T7r794wkv09yfJK/JHlBkp9W1TMncoeqauN04yvtlOQjSZ4zCMgAgKWc0AgAmFKttZbk54PZ4addvTHJ+q21rVpre7TW9kh3Gtur0g2o/fGqqmHtv5Xk8iRPrKqHj3KoFyZZM8l3W2t/XMRyP5LkEUnOTPKA1tpzWmvPSfKAJGcneVSGDVLdWjuitbZPutPGkuSNrbV9BrdFGYj6tMH0SSOWzw2NBtMzkjxk0ANpZJufttb+kcwN0r6ZZIMkhyS5f2tt19baM5NsnuQH6QYsf/Pwg1XVq5K8NslFSR7RWtu2tfa81tpjkuyRblzML1bVmuPdmap6VJJz0j3vB7XWXt1am7OAxwAAWEoIjQCAJWEoVFl7aEFr7fjW2g3DG7XOx9IFNA9N8rBh6+5OF+oko/c2Glr2kVHWLVBVbZJkr3Q9lf5teG2D0+z+bbDueYPeM1Ph7HS9rdYb9BwasmOSG5JcMJgfCo92GNEmmX88o33SnTr3lUHA9c+hFa2165Psna5n0quHArqqmp3krYNmz2utXTK8wNba8Uk+lq7X0kvGuiNV9dR04ds6SV7UWlvkUwYBgOkhNAIAloSh7xzz9TKpqo2q6v9V1VFV9amqOraqjk0yNPbRg0bs55NJbks3PtJqw/bz+CRbJflDulOqFsUTk1SScwangM2ntfbrJD8Z3JeRPYEmxeC0rZ8MZndMkqpaN114duawXjpnDG8zMBQgDR/PaOgUsq+Ocbw/pxu8e510PY+S5NFJ7pvkosF9Hs1QaPWE0VZW1b5JTkxyd5Kntta+NMZ+AICl2HLTXQAAMCOsM5heP7Sgqt6W7rSo8b6PrD58prV2fVV9Mckrkrw0/Z5HxyzG6U8bDqbjndr2h3RByYbjtFlcp6ULsHZMd/9GnpqWJBcmuS7zgqWHJLlPul5KZw9r94DB9Kvzn+k3qnWT/HbYNlsMBvle0DYjbZTk00lakqe31n4yShsA4B5AaAQATKnBaU9bDmYvGCzbM90pUDcneX2SU5L8ZWiA5Ko6Lt0YRaMlHR9KFxrtl+QjVbV2kucluT1dWLG4FhSUTLXTkrwl88KiHQfTuaFRa61V1VlJnl1V6w1r85MRg0zPHkxPzLxTBMdy3Yht/pRuzKPxXDLKsr8m+WWSpyf5QFU9Y+RpiADAPYPQCACYarukG6D6rswbb+e5g+mbW2ufHGWbzcbaWWvtV1V1RpInVdWTkmyTZKUkxw7G6VlUfxpMHzBOm6F1fxqnzeI6O8kdmTeu0Q7pwrXzR7Q7Pcmz0wVGQwHTaSPaXJnkwel6YJ04weNfOZj+ZTDI98K6c1DXl5PsnuSUqnpKa+26cbcCAJY6xjQCAKbM4OpaRw1mP9da++vg30OXrb9ylG0emnk9k8byocH039NdbS0ZdlWzRXRmul5G21TVyLGUhup6fLpxmc4YuX6ytNZuz7xxjfZKd+WxHw0GAh9u+LhGo41nlCTfHUyfm4n7abpeR1tW1Zjh3Xhaa3cOjvm/6Z7L00Zc6Q0AuAcQGgEAk66qZlXVbknOTddr6JIkBw5rMnRa079V1QrDtlsvyWez4N7Qx6cLnJ6b7upg57bWzlucmltrlyf5errvRx+rqjWG1XXvdFcMm5XuSmS9sGuSnTaYvi7dKXqnj9LmF0luSvKCdANX35HkxyPafDzd47R3VR1WVSuP3ElV3b+q5l4FrbV2V5L/Tnea2vFV9bhRtlmhqnYbjKU0qsGV2l6S7pTBhyc5o6o2Gqs9ALD0qdam+7R9AOCeqKouS7JJkpOSXD1YvFK6wZEfk+6S7EkX8Py/Yb2MUlUPSPLzJGukCzV+kuRe6XrMXJnkN+lObdq3tXbsGMc/JMm7BrP7tNY+Own3aZ10vXUenm7Q7tMGq3ZKd4rdL5PsPPI0uGGPxf1ba5dNQh07pRvnaci2rbWRgVCq6jtJnjGYPb21tuMobR6R5NtJ7pfuPv0qyZ+TrJbkoelCvZ+01rYZsd2RSf5jMPurJL9Pd+rZhul6D62S5Bmtte8N2m+abhDxy1trmw7bTyX5YLpeYX9M8uTW2niDjQMASwk9jQCAxfW0JHsneVm68YselORnSQ5P8ojW2h7DA6Mkaa39IV3w8KV0PWmelS7A+Hi6q5PdOIHjnjyYXpdu/JzF1lr72+D4b0k3btEzBrcrkxyaZLvFHDdpon6crudQkvwjyVi9qIb3QDpttAattQuSPDLdlep+ly7Q22sw/Vu6XkWvHGW716cL8b6ULjDbJd3g1uukC6FenO6UvnG1zmuSvCddr7AzRjv9DwBY+uhpBADcI1XVUelO33pPa+2gaS4HAGCZIzQCAO5xqmrjJBcnWT7JZktgjCEAgBlnQYNMAgAsNarqiCQbJXlKujF13iswAgCYGnoaAQD3GIMBp++X5C9JPp/kLYOrfY3W9n3pxt+ZiONba8dPRo3Djn9wkjGvLjbCWa21T07m8QEAFpeeRgDAPcbwq3JNwF7prmg2EZelu8rbZHp6uoGkJ0poBAAsVfQ0AgAAAKBn1nQXAAAAAMDSR2j0/9uxAwEAAAAAQf7WA6xQGAEAAAAw0ggAAACAkUYAAAAAjDQCAAAAYKQRAAAAABOCOcIbYc4k1gAAAABJRU5ErkJggg==", + "text/plain": [ + "<Figure size 1440x720 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax=result70_U_spark_df.plot.bar('Day_of_Week','Total accidents_Normalized', rot=90,title=\"Accidents Normalized by distribution \",figsize=(20, 10),color=\"Orange\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "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": null, + "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": null, + "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": null, + "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": null, + "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": null, + "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": null, + "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": null, + "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": null, + "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 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+ "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": null, + "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": null, + "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": null, + "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": 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, month: string]" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "TimeAccident_dfmonth_weath" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "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": null, + "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": null, + "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": 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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", + "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": 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, month: int]" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "TimeAccident_dfmonth" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "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": null, + "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": null, + "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": null, + "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": null, + "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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", 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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", + " 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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": null, + "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": null, + "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", + " 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" <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": null, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "data": { + "image/png": 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oqusa2ZZ7zO5QWmVHgvkJcL8xJg64H3jFlTeznm5MK/teMsakGGNSoqO9/y8apQB2HSnngXe28vX+Yj7cerj9E84CH2w5zFeZxfxi6gj6hATZHY5TpCZEAnh0NZkdCWYOsNjafgdHuwpAHhDX7LhYq6yt8tgWygEKrCo0rK+FToxfKY9VUV3HXW9tIqyHPwm9ezJvXbbdIdmurKqO3y/dybi4cG6ZHG93OE4T0TOAEf1CPLqh344Ecxi40Nq+BNhnbS8BbrN6k6UBZVY11zLgchGJsBr3LweWWfvKRSTN6j12G/BBs2s19Tab06xcqW7LGMND724nu6SK52eNZ845A9maW8aOvDK7Q7PV05/uprSqjqeuH4OPj/eNeWlLWmIU6YdKqWvwzHYYV3dTng98AwwXkVwRuR1Hr6/nRGQr8BSOHmMAHwMHgEzgZeAuAGNMCfA7YIP1esIqwzrmX9Y5+4FPrPKngSkisg+4zHqvVLf22teHWLr9CA9eMZzUxCiunxBLkL8Pb53FTzEbs0qYvz6b//7eIJIGhLZ/gpdJTYjkRF0D23I9848Il07Xb4yZ1cquiS0ca4C7W7nOq8CrLZSnA6NbKC8GLj2jYJXyYpuzS3nq411cNrIPd5zvWNMkrIc/144dwJIteTx89Uh6BZ5dq3PUNTTy68U7GBAWxH2XDbM7HJeY3KwdZuJAzxvupyP5lfJypcdrufutTfQNDeK5m8adUg00OzWe47UNvL85r40rdE+vfHmQPQUVPD5tND27aXKN6hXIsL69PLYdRhOMUl6ssdFw/9tbOFpZywu3TCAs2P+U/ePiwknqH8q8ddk4KgnODjklVfzlP3u5PKkvU5JaHKXQbaQmRJF+qMQj22E0wSjlxV5YncnqPUU8cm0SY2PDv7NfRJidGs/OI+Vs9dB6emczxvDokgx8RXjsulF2h+NyaYlRVNU2eGRnDk0wSnmpr/cf5f9W7OW65AHcmtp699vp42PoGeDLW2uz3BidfT7dkc+q3YXcP2UYA8J72B2Oy33bDuN51WSaYJTyQoXl1dw7fwsJvXvyhxvGtDnlfK9AP64bF8OH2w5TdqLOjVG6X0V1HY99mEFS/1B+eO4gu8Nxi+iQQIb06eWRC5BpglHKy9Q3NPLT+Zs5XlPPi7dO7FAD9i2p8VTXNfLeptx2j/Vmzy3fS2FFDU/dMAY/37Pn11tqQiTph0qp97B2mLPnX0CpbuK5FXtZf7CEJ68fzbC+HVvPZHRMGMmxYcxb330b+7fnlvHGN4f4QdpAxsWF2x2OW6UlRlFZU0/G4XK7QzmFJhilvMjKXQW8uHo/sybHc8OEM1vqd3ZqPHsLKtmYVeqi6OzT0Gj49Xvb6d0rkAeuGG53OG6Xmuhoh/G0ajJNMEp5iZySKn7+9lZGDQjl0WuTzvj8a5MHEBLo1y1H9r/xzSG255Xx22uTCA3yb/+EbqZPSBCJvXt6XEO/JhilvEBNfQN3z9tEozG8cMuETi2WFRzgx/UTYli6/Qilx2tdEKU98suqeW75Xi4cFs3VY/rbHY5tUhOj2HCwhIZGz6kC1QSjlBd4cukutuWW8cyNyQyM6tnp68xOjae2vpF3u1Fj/+MfZlDX0Mjvpo1uszddd5eWGElFTT07PagdRhOMUh5uydbDvPFNFj86P4Gpo/t16Voj+oUycWBEt2nsX7W7gE925HPvpUOJjwq2OxxbpSZEAZ7VDqMJRikPlllYyUPvbiNlYAS/mDrCKdecPTmeA0XHPa6+/kxV1dbzyPsZDO3Tix9ZE3yezfqFBTEoKtijFiDTBKOUh6qqreeutzYS5O/L87PH4++kcR1Xj+1PWA9/3lrn3SP7/7pyH3nHTvDUDWMI8NNfZeDorrzeg9ph9F9FKQ9kjOE37+9gX2Elf505jv5hzpvyJMjfl+9PiGVZRj5HK2ucdl132p1fzitfHOTmlDgmDYq0OxyPkZoYSXl1PbvzPaMdRhOMUh5o4YYcFm/K495LhnL+0GinX392ahx1DYZ30r2vsb+x0fDwezsI7eHPQ1c6p9qwu2hqh/GU6s92E4yI3CQiIdb2b0RksYhMcH1oSp2dduSV8dslGZw/tDf3XjrUJfcY0ieE1IRI5q/PptFDqlM66t1NuWzMKuXXV40komeA3eF4lAHhPYiPDGadh7TDdOQJ5hFjTIWInIdj+eFXgBddG5ZSZ6fy6jrunreJyOAA/nLzOHxduIb87NR4skuq+DLzqMvu4Wzl1XX88dPdTIgP5/sTYuwOxyOlJkSy/lCJR/zh0JEE02B9vRp4yRizFNA/G5RyMmMMD76zldzSE/xt9niiegW69H5TR/cjsmcA87xoZP/c/+yj+HgtT5zlY17akpYYxbGqOvYUVNgdSocSTJ6I/BO4GfhYRAI7cp6IvCoihSKyo1nZQhHZYr0OicgWq3yQiJxotu8fzc6ZKCLbRSRTROaK9VMlIpEiskJE9llfI6xysY7LFJFtWp2nvMUrXx5kWUYBD00dQYobGq4D/Xy5aWIsK3YVUFBe7fL7dVVmYQWvfX2ImZPiGR0TZnc4HqtpXjJP6K7ckQQzA1gGXGGMOQZEAg924LzXgKnNC4wxNxtjxhljxgHvAoub7d7ftM8Yc2ez8heBHwFDrVfTNR8CVhpjhgIrrfcAVzY79g60Ok95gY1ZJTz9yW4uT+rL/5yf4Lb7zpocT0Oj4e0NOW67Z2cYY3j8w50EB/jywOXD7A7Ho8VGBBMb0YN1HtDQ35EE809jzGJjzD4AY8wR4AftnWSMWQO0+Amtp5AZwPy2riEi/YFQY8xa4xh2/AYw3do9DXjd2n79tPI3jMNaINy6jlIeqbiyhrvf2syA8B48c1OyW6t+BvXuyXlDerNgQ47HjJ1oyfKdBXyx7yg/nzLM5VWH3UFqQhTrDhbb3g7TkQRzyqLWIuILTOzifc8HCpqSliVBRDaLyOcicr5VFgM070eZa5UB9LWSHUA+0LfZOTmtnHMKEblDRNJFJL2oqKgLH0epzmloNNy3cAslVbW8cMsEwnq4fybg2anx5B07wed7C91+746ormvgdx/tZHjfEG5NG2h3OF4hLTGS0qo69hVW2hpHqwlGRH4lIhXAWBEpt14VQCHwQRfvO4tTn16OAPHGmPHAz4F5IhLa0YtZTzdnnKqNMS8ZY1KMMSnR0c4fa6BUe/62KpMv9h3lsWtH2dauMCWpL9EhgR7b2P/SmgPklp7g0euSzqpVKrsiLbFpPIy97TCt/msZY/5gjAkBnjHGhFqvEGNMlDHmV529oYj4ATcAC5vdq8YYU2xtbwT2A8OAPKD5qkqxVhlAQVPVl/W16c+vPCCulXOU8hhf7jvKX1bu5frxMcyaHNf+CS7i7+vDjJRYVu0u5PCxE7bF0ZK8Yyd4YXUmV4/pz7mDe9sdjteIjehBTHgP2ye+bPfPAWPMr0QkRkTOFZELml5duOdlwG5jzMmqLxGJtqreEJFEHA30B6wqsHIRSbPabW7j26enJcAca3vOaeW3Wb3J0oCyZlVpSnmE/LJqfrZgM0P79OLJ6+3vcjtzUjwGWOBhjf1PLd0FwK+vHmlzJN5FREhNiGTdgRJbZ83uSHfjp4GvgN/g6D32IPBAB86bD3wDDBeRXBG53do1k+827l8AbLO6LS8C7jTGNHUQuAv4F5CJ48nmE6v8aWCKiOzDkbSetso/Bg5Yx79sna+Ux6hraOSn8zZxoq6BF26ZQHCAn90hERcZzIXDolm4IZv6hka7wwHg6/1HWbr9CHddNISYcOfNxXa2SE2MpPh4LZk2tsN05Cf7emC4MeaMZsUzxsxqpfyHLZS9i6PbckvHpwOjWygvBi5todwAd59JrEq50zPL9pCeVcpfZ45jSJ8Qu8M5afbkeO7490ZW7i7kilFdW3emq+obGnl8yU7iIntwxwU6FX9nnGyHOVjC0L72/Jx1pMXsAHD2LXKtlAssz8jnpTUHuDUtnmnjPGuqk0tG9KFfaJBHNPa/uTaLPQUV/ObqpE4tD60gPjKYfqFBtjb0d+QJpgrYIiIrgZNPMcaYe10WlVLdUHZxFf/7zlbGxITxyDVJdofzHX6+Ptw8KY65q/aRU1JFXKQ9K0QWV9bwfyv2cv7Q3lye1Lf9E1SLRIS0xEi+zCzGGGNLO19HEswS66WUaoUxhuO1DRyrquVYVZ3jdaJp2/H1sz2FCPDCLRMI9PPMv8pnTo7j+VX7mL8+22kraJ6pZ5fvoaq2gUevTbK984O3S02M4v0th9lfdJwhfXq5/f7tJhhjzOsi0gPHOJU9bohJKdsYY6iqbeDYiTpKj9dSdsKRLEqrmrZrKbUSSNmJU7frGlrvrdPD35fokEDmzhpv25NBR/QP68ElI/rydnou9102zO0rRW7PLWPBhhxu/16CR7VPeaumdph1B4s9M8GIyLXAszhmUE4QkXHAE8aY61wcm1Iu9e7GXD7NyP/2qeNEHWVVddS20Yuqh78v4cH+hPXwJyI4gKF9ehEe7E94cADhPfxP2w4gItif0B7+XtWOcEtqPP/ZVcCKnQVcPdZ9sywZY3h0yQ6iegZy72WuWQfnbDMoKpg+IYGsPVDCLanunwWhI1VkjwGTgdUAxpgt1lgVpbxWXUMjT3y0kwA/HwZH92RwdC8ievoT1iOA8GB/IoKbbwecTCrelCg664Jh0cSE92De+iy3Jpj3NuexKfsYz9w4ltAg7VfkDI52mCjWHrCnHaYjCabOGFN2WmCe0VFeqU5af7CEshN1/OPWiUwdbW+XXE/j6yPMmhzHs8v3cvDocRJ693T5PStr6vnDJ7sZFxfO9yfEtn+C6rDUxEiWbD3MoeIqt/xbNteRCtYMEZkN+IrIUBF5HvjaxXEp5VLLMvIJ8vfhwmE6B11LZqTE4ecjzF/vni7Lz6/aR1FFDY9fNwofF67ieTZKTbBvXrKOJJh7cMyoXINjBH45cJ8LY1LKpRobDcszCrhgaDQ9Arp/lVdn9AkNYkpSX95Jz6GmvqH9E7pgf1Elr355kBkpsSTHhbv0XmejwdE96d0rkHWemGCMMVXGmIeNMZOsmYcfNsZ4/vJ3SrViW14Z+eXVto9W93SzU+Mprarj0x35LruHMYYnPtxJkJ8vD15hT7fo7k5ESE2MZK0N85K12gYjIh/SxhT42otMeatlGfn4+giXjuxjdyge7XuDezMwKpi31mW7bNaBlbsK+XxvEY9ck0R0iC4k5ippiVEs3XaE7JIqBka5rx2mrSeYZ4HngIPACRwTR74MVOKYdFIpr7QsI5+0xEjCgwPsDsWj+fgIsybHs/5gCZmFFU6/fnVdA098tJMhfXpx2zm6kJgrpSVEAu5vh2lrPZjPjTGfA98zxtxsjPnQes3GsSKlUl4ns7CCA0XHtXqsg26cGIu/r/CWC+Yne+XLg2SXVPHYtaPw14XEXGpIn15E9Qxg3YEWV7F3mY78q/ZsPu5FRBIA9/Z1U8pJlmUUAHB5kiaYjujdK5Cpo/vz7sZcquuc19h/pOwEf1uVydRR/ThvqC4k5mrftsMUu7UdpiMJ5n5gtYisFpHPgc/QXmTKSy3LyCc5Lpx+YUF2h+I1Zk+Op7y6no+2OW/dvj98vJtGY3hYFxJzm7TEKA6XVZNb6r5VSzvSi+xTHCtM/gy4F8faMMtcHZhSznb42Am25ZZxxSidofdMpCVGkhjdk3nrspxyvXUHilmy9TA/vnCwR8/L1t00jYf5xo3tMK0mGBG5xPp6A3A1MNh6XW2VKeVVlmc4uttq+8uZERFmT45nU/Yxdh0p79K16hsaeXRJBjHhPfjJhYOdFKHqiKF9ehER7O/Wdpi2nmAutL5e28LrGhfHpZTTLcsoYEifXgyOdv+sst7uxomxBPj5dHkxsvkbctidX8HDV4/UQa5u5uMjpCZEse6g+55gWh0HY4x51Pr6X26LRikXKT1ey/pDJdx5oc7T2hnhwQFcM6Y/723O46ErR9AzsCPTGJ6q9Hgtzy3fw7mDo7hS53+zRWpiJJ9m5JNbWkVshOurJ9ttgxGRp0QkvNn7CBH5fQfOe1VECkVkR7OyhSKyxXodEpEtzfb9SkQyRWSPiFzRrHyqVZYpIg81K08QkXVW+UIRCbDKA633mdb+QR34Pqhu7j+7CmhoNFo91gWzU+OprKnnw62HO3X+cyv2UFFdz6PXjtKFxGxycn0YN1WTdaQX2ZXGmGNNb4wxpcBVHTjvNWBq8wJrPM04Y8w44F1gMYCIJAEzccx5NhV4QUR8RcQX+DtwJZAEzLKOBfgj8GdjzBCgFLjdKr8dKLXK/2wdp85yyzIKGBAWxJiYMLtD8VoTB0YwvG8I8zoxAWbG4TLmrcvmB2kDGd5PFxKzy/C+IYQH+7ttwGVHEoyviJycw8Fa3bLdOR2MMWuAFtOkOP58mYFj8kyAacACY0yNMeYgkIljDZrJQKYx5oAxphZYAEyzzr8EWGSd/zowvdm1Xre2FwGXiv65dFarqq3ni31FXD6qn/7l3AUiwuzUeLbllrE9t6zD5xljeHzJTsKDA7h/yjAXRqja4+MjTB4UybqDnvME8xawUkRuF5HbgRV8+wu8s84HCowx+6z3MUBOs/25Vllr5VHAMWNM/Wnlp1zL2l9mHf8dInKHiKSLSHpRUVEXP5LyVJ/vKaKmvpHLtXtyl00fH0OQvw/z1ne8y/KSrYdZf6iEX1wxnLAeupCY3VITo8guqeLwMdePh+nIOJg/Ar8HRlqv3xlj/tTF+87i26cX2xhjXrJmiE6JjtZ1Qbqr5TsLiAj2Z/KgSLtD8XphPfy5LnkAH2w5TEV1XbvHH6+p56mPdzEmJoybUuLcEKFqT1qi4/+BO3qTdaSRPwFYbYx5wBjzALCmKw3nIuIH3AAsbFacBzT/6Yu1ylorLwbCrWs1Lz/lWtb+MOt4dRaqa2hk5a4CLh3ZFz+d78opZqcOpKq2gfe3tN/Y//fPMikor+Gx60bhqwuJeYQR/UIJDfJj7X7XV5N15H/cO5y6RHKDVdZZlwG7jTG5zcqWADOtHmAJOGYOWA9sAIZaPcYCcHQEWGIck+l8BtxonT8H+KDZteZY2zcCq4y7F0FQHmPtgWLKq+u195gTJceGMWpAKPPWZbc5r9Who8f51xcHuWFCDBMHRrgxQtUWXx9hckKkZzzBAH5WAzsA1na785yLyHzgG2C4iORa7TfgSBKnVI8ZYzKAt4GdwKfA3caYBqsN5afAMmAX8LZ1LMAvgZ+LSCaONpZXrPJXgCir/OfAQ6iz1rKMfIIDfDlfJ1R0mqbG/l1Hytmcc6zV43730U4C/Hx4aKouJOZp0hKjOFRcRX6Za9eO7MhoqSIRuc4YswRARKYBR9s7yRgzq5XyH7ZS/iTwZAvlHwMft1B+AEcvs9PLq4Gb2otPdX9NSyNfOCyaIH8dNe5M08bF8NTSXcxbl82E+O8+nXy2u5CVuwv59VUj6BOqE4t6mqZ5ydYdLHbZYnLQsSeYO4Ffi0i2iOTgeHL4scsiUspJtuQeo7CiRqvHXKBXoB/Txsfw0bbDlFWd2thfW9/IEx/tJDG6Jz88N8GmCFVbkgaEEhLox1oXD7jsSC+y/caYNBwDHUcaY841xmS6NCqlnGBZRj5+PsLFI3RpZFeYPTme6rpGFm/OPaX81a8OcvDocX57TRIBftqxwhP5+giTEiJZ5+IBlx2aUEhErsYxyj6oaaCaMeYJF8alVJcY46geO2dwlI69cJHRMWEkx4Uzb102Pzx3ECJCQXk1z6/cx2Uj+3LRcE3sniwtMZJVuwspLK92WTVmR7op/wO4GbgHEBztG7qAtvJo+worOXhUl0Z2tVsmx7OvsJINh0oBePqT3dQ1Gh65RhcS83RN7TBrXTiqvyPPr+caY27DMb/X48A5gM73oDzash2OtV+mJOnofVe6Jrk/IYF+zFuXRfqhEt7bnMcd5ycyMEpXVfd0owaE0ivQz6XVZB2pImuaT6BKRAbgGLTY32URKeUEy3bmMz4+nL7ag8mlggP8uGFCDPM35LDrSAX9QoO462JdSMwb+Pn6kDIowqUTX3bkCeYja7r+Z4BNwCFgnssiUqqLckur2JFXrtVjbjI7dSC19Y3sKajg11ePJDjgzNeKUfZITYhif9FxiipqXHL9dn8SjDG/szbfFZGPgCBjTMenUlXKzZZnFAC6NLK7DO8XwnlDeiMC147Vyg1v0nxesmvGDnD69c/oTw1jTA3gmlSnlJMsy8hnWN9eJPTWdgB3ef2/J2OM0eUQvMzomDCmjupHRHC7k7N0ij7Lqm6luLKGDYdKuPviIXaHclZxTGSpycXb+Pv68I8fTHTZ9XUUlOpWVu4qpNFo9ZhSnqDVJxgRmdDWicaYTc4PR6muWZaRT0x4D0YNCLU7FKXOem1VkT3Xxj6DY8lipTxGZU09X2Qe5ZbUeG0LUMoDtJpgjDEXuzMQpbrq8z1F1NY3avWYUh6io3ORjcYx2eXJUWvGmDdcFZRSnbEsI5/IngFM0qWRlfII7SYYEXkUuAhHgvkYuBL4EtAEozxGbX0jn+0u5Mox/XRpXqU8REd6kd0IXArkG2P+C0jGsc69Uh7j6/1HqajRpZGV8iQdSTAnjDGNQL2IhAKFQJxrw1LqzCzLKKBngC/fG6JLIyvlKTrSBpNuzUX2MrARqAS+cWVQSp2JhkbDip0FXDS8jy6NrJQH6ciKlncZY44ZY/4BTAHmWFVlbRKRV0WkUER2nFZ+j4jsFpEMEfmTVTZIRE6IyBbr9Y9mx08Uke0ikikic8XqfyoikSKyQkT2WV8jrHKxjssUkW3tjedR3m9LTilHK2u4fJROza+UJ+nIgmMrm7aNMYeMMdual7XhNWDqade6GJgGJBtjRgHPNtu93xgzznrd2az8ReBHwFDr1XTNh4CVxpihwErrPTg6ITQde4d1vksdPnai/YOUyyzLKMDfV5dGVsrTtJpgRCRIRCKB3iISYT0xRIrIICCmvQsbY9YApy+V9hPgaWvSTIwxhW1dQ0T6A6HGmLXGGIOj59p0a/c04HVr+/XTyt8wDmuBcOs6LvH8yn1c/OxqyqvrXHUL1QZjDMsy8jl3cG9Cg3RpZKU8SVtPMD/G0eYyAsc6MBut1wfA3zp5v2HA+SKyTkQ+F5FJzfYliMhmq/x8qywGyG12TC7fJre+xpgj1nY+0LfZOTmtnHMKEblDRNJFJL2oqKhTH+jC4dHU1Dfy4dbDnTpfdc2eggqyiqu095hSHqjVBGOM+asxJgF4wBiT0OyVbIzpbILxAyKBNOBB4G2rTeUIEG+MGQ/8HJhn9VjrEOvpxpxpMMaYl4wxKcaYlOjo6DM9HYAxMWGM6BfC2xty2j9YOd2yHQWI6NLISnmijnRT/qeI3Csii6zXT0Wks3URucBiq/pqPdAI9DbG1BhjigGMMRuB/TiedvKA2Gbnx1plAAVNVV/W16bqtjxO7Ubd/BynExFuSolja24Zu/PLXXUb1YplGflMjI8gOiTQ7lCUUqfpSIJ5AZhofW3a7mzD+fvAxQAiMgwIAI6KSLSI+FrliTga6A9YVWDlIpJmPenchqOKDmAJMMfannNa+W1Wb7I0oKxZVZpLTB83AH9f4Z303PYP9jInahvsDqFVOSVV7DyiSyMr5anaauRvGiMzyRgzxxizynr9FzCptfOanT8fx3iZ4SKSKyK3A68CiVbX5QU4ujwb4AJgm4hsARYBdxpjmjoI3AX8C8jE8WTziVX+NDBFRPYBl1nvwTGdzQHr+Jet810qqlcgl43sy3ub86itb3T17dzm0NHjTPz9Cl5YnWl3KC1alpEP6NovSnmqtgZargcmAA0iMtgYsx9OPmG0+2etMWZWK7tubeHYd4F3W7lOOjC6hfJiHFPYnF5ugLvbi8/ZZqTE8cmOfFbtLmDq6O6xLvmba7Ooqm3gueV7mTwokhQPm0RyeUYBI/qFEB8VbHcoSqkWtFVF1jRj4APAZyKyWkRWA6uA/3V1YN7m/KG96RsayNvdpJqsuq6BdzbmcvHwaGIjevCzBVsoq/KcrthHK2vYkFWiTy9KebC2Eky0iPwcGAf8E0diWYWj2mm860PzLn6+Pnx/Qiyr9xRSUF5tdzhd9uHWw5SdqOOOCwYzd+Z4CsqreWjxNhwPiPb7z84CjC6NrJRHayvB+AK9gBAcVWlivfysMnWaGSlxNBp4d5P3P8W8uS6bIX16kZYYSXJcOA9eMZxPduQzf71ndMdelpFPXGQPRvbXH0WlPFVbbTBHjDFPuC2SbmBQ755MTojknfRcfnLhYK9dtnd7bhlbc47x6LVJJz/Dj85P5MvMozz+YQYpgyIY1te+X+wV1XV8lVnMbecM9NrvsVJng460wagzMCMljoNHj5OeVWp3KJ325tosevj7csOEb4cg+fgIz81IJiTIj3vmbaa6zr7uy6v3FFHb0MgVo7V6TClP1laC+U4PLdW+q8b0o2eAr9eO7C87UccHW/OYNm4AYT1OHU/bJySI52aMY09BBU8u3WVThI7qsaieAUyIj7AtBqVU+9qaKub0iSpVBwQH+HFt8gCWbj9CZU293eGcscWbcqmua+TWtIEt7r9wWDR3XJDIv9dm8emOfDdHBzX1DazeU8SUpL66NLJSHq4jI/nVGbopJY6q2gaWbvOuCTCNMby1LpvkuHBGx7S+KvYDlw9nbGwYv3x3m9uXKvg6s5hKXRpZKa+gCcYFJsSHMzi6p9eNiVl7oITMwkpuTY1v87gAPx/mzhxPfUMj9y3YQn2D+2YvWJaRT69AP84dEuW2eyqlOkcTjAuICDNS4tiYVUpmYaXd4XTYm2uzCOvhz7XJA9o9dlDvnvz++tGsP1TC3z5zz1Qy3y6NHE2gny6NrJSn0wTjItdPiMHXR3hno3c09heWV7MsI5+bJsZ2eF3768fHcsOEGOau3Me6A8UujhA2ZpVSfLxWq8eU8hKaYFykT0gQFw/vw7sb86hzYxVSZy3ckEN9o+GWVhr3W/PEtNHERwZz38ItHKuqdVF0Dssy8gnw9eGi4Z1bu0cp5V6aYFxoRkosRytr+HxP51bLdJf6hkbmr8/mvCG9Sejd84zO7RXox/OzJnC0soZfLHLdVDJNSyN/b0gUIbo0slJeQROMC108og+9ewXwdrpnV5Ot2l3I4bJqbk1ru3G/NWNiw/jl1BEs31nAm+uynRydw84j5eSWntDqMaW8iCYYF/L39eGGCbGs2l1IUUWN3eG06s112fQNdaxp01n//b0ELhwWze8+2umSlT2XZxTgI3CZLo2slNfQBONiM1JiqW80vL/ZZas2d0lW8XHW7C1i1uR4/Hw7/+PQNJVMaJA/98zb7PSVMJdl5JMyMJLevXRpZKW8hSYYFxvSJ4QJ8eG8nZ7jMVPdNzdvXTa+PsLMSZ2rHmuud69A/nxzMvsKK3nio51OiM4hu7iK3fkVXD5Kn16U8iaaYNxgRkoc+wor2ZJzzO5QTlFd18Db6TlMGdmXfmFBTrnm+UOjufPCwcxfn83H24845Zq6NLJS3sllCUZEXhWRQhHZcVr5PSKyW0QyRORPzcp/JSKZIrJHRK5oVj7VKssUkYealSeIyDqrfKGIBFjlgdb7TGv/IFd9xo66emx/evj7etzI/o+3H6G0qo4fnHNmXZPb87+XDyM5LpyH3t1GbmlVl6+3LCOfpP6hxEXq0shKeRNXPsG8BkxtXiAiFwPTgGRjzCjgWas8CZgJjLLOeUFEfEXEF/g7cCWQBMyyjgX4I/BnY8wQoBS43Sq/HSi1yv9sHWerkCB/rhrTnw+3HnZ620RXvLk2i8TePTl3sHOnXfH39eH5meNpNHR5Kpmiiho2Zpfq04tSXshlCcYYswY4fUbmnwBPG2NqrGMKrfJpwAJjTI0x5iCQCUy2XpnGmAPGmFpgATBNHKtMXQIsss5/HZje7FqvW9uLgEvFA1almpESS2VNvdOqjbpq5+FyNmUfY3ZqvEsW7YqPCubJ60eTnlXK3JX7On2dFU1LI4/W9helvI2722CGAedbVVefi8gkqzwGaD5YJNcqa608CjhmjKk/rfyUa1n7y6zjbTU5IZJBUcEeMybmzXVZBPr5cOPE2PYP7qRp42K4aWIsz3+WyTf7OzeVzLKMfAZGBTPcxhU0lVKd4+4E4wdEAmnAg8Dbdj5diMgdIpIuIulFRa4dbS8i3JQSx7qDJRw6etyl92pPRXUd72/O49rkAYQHB7j0Xo9dN4qEqJ7ct3AzJcfPbCqZ8uo6vt5/lCtG9dOlkZXyQu5OMLnAYuOwHmgEegN5QFyz42KtstbKi4FwEfE7rZzm51j7w6zjv8MY85IxJsUYkxId7fr5rW6YEIOPwKKN9jb2v7c5j6raBn5whvOOdUbPQD/mzhpP6fE6frFo6xl11f5sdyF1DYYrtHuyUl7J3QnmfeBiABEZBgQAR4ElwEyrB1gCMBRYD2wAhlo9xgJwdARYYhy/pT4DbrSuOwf4wNpeYr3H2r/KeMgAlP5hPbhgWDSLNubS0GhPSMYY3lybxZiYMJLjwt1yz9ExYTx05Qj+s6uQN77J6vB5yzMKiA4JZHycLo2slDdyZTfl+cA3wHARyRWR24FXgUSr6/ICYI71NJMBvA3sBD4F7jbGNFhtKD8FlgG7gLetYwF+CfxcRDJxtLG8YpW/AkRZ5T8HTnZt9gQzUuLIL6/mi332TIC54VApewsqOz3vWGf91/cGccmIPjz58S52Hm5/KpnqugZW7ylkSlJffHRpZKW8kl/7h3SOMWZWK7tubeX4J4EnWyj/GPi4hfIDOHqZnV5eDdx0RsG60aUj+xAR7M876blcNLyP2+//5tosQoL8OrSomDOJCM/cOJYr//oF98zfxIf3nEdwQOs/fl9lHuV4bYN2T1bKi+lIfjcL9PNl+vgYlu/MP+NG764qqqjhkx1HuHFibJu/3F0lqlcgf7l5HAeOHufxJW1PJbMsI5+QID/OSbS9A6BSqpM0wdjg5klx1DUYPtji3gkw307Poa7BcEuq6xv3W3PukN7cddFgFqbn8OHWwy0eU9/QyH92FXLJiD4E+OmPqFLeSv/32mBEv1DGxoaxcIP7JsBsaDTMW5fNOYlRDOnTyy33bM19lw1jfHw4v168nZyS704lk55VSokujayU19MEY5ObUuLYnV9BRgcavJ3h872F5B07wa1u6JrcHn9fH+bOHA/AvQs2f2dJ6WUZ+QT4+XDhMF0aWSlvpgnGJtclDyDQz8dtI/v//U0W0SGBHjPlfVxkME/dMIbN2cf484q9J8uNMSzPKOCCob3pGej+diKllPNogrFJWA9/po7ux/ub86iuc+0EmDklVazeW8SsSXH4d2FRMWe7NnkAN6fE8eLn+/kq8ygAGYfLyTt2gsu1ekwpr+c5v23OQjNS4iivrj+53omrzFufjQAzJ7t37EtHPHpdEom9e3L/wi0UV9awLCPfsTRyF5ZvVkp5Bk0wNjonMYqY8B6848J1YmrqG3h7Qw6XjuzLgPAeLrtPZwUH+PH8rAkcO1HHA+9sZVlGPpMGRRLZ07VzpCmlXE8TjI18fISbUmL5av/RFntTOcOnO/IpPl7rEY37rUkaEMrDV43ksz1F7C2o1N5jSnUTmmBs1jRd/rubXPMU8+baLAZGBXP+kN4uub6z3HbOQC4b2RcRPKYjglKqazTB2Cw2IpjvDe7NO+m5NDp5Aszd+eVsOFTKLanxHj+fl4gwd9Y4Fv/kXGIjdGlkpboDTTAe4KaUWPKOneCbA51blKs1b63NJsDPh5smxrV/sAcIDvBjfLzOnKxUd6EJxgNcMaofoUF+Th0TU1lTz3ub87hmTH8itMFcKWUDTTAeIMjfl2njYvhkRz5lVXVOueb7m/OorKnn1nM8t3FfKdW9aYLxEDdPiqO2vpEl21qeAPJMNC0qltQ/lPFuWlRMKaVOpwnGQ4waEMrI/qG844Rqsk3ZpezOr+DWtIG6lr1SyjaaYDyEiDAjJZZtuWXsOtK1CTDfXJtNr0A/po1z76JiSinVnCYYDzJ9XAwBvj5dGtlfcryWpduOcMOEGJ0sUillK00wHiSiZwBTkvry3uZcauo7NwHm2+k51DY0evTIfaXU2cFlCUZEXhWRQhHZ0azsMRHJE5Et1usqq3yQiJxoVv6PZudMFJHtIpIpInPFalQQkUgRWSEi+6yvEVa5WMdlisg2EZngqs/oCjelxFJaVcfKXYVnfG6jtajY5IRIhvUNcUF0SinVca58gnkNmNpC+Z+NMeOs18fNyvc3K7+zWfmLwI+Aodar6ZoPASuNMUOBldZ7gCubHXuHdb7XOH9oNP1Cgzo1JmbNviKyS6r06UUp5RFclmCMMWuAkq5cQ0T6A6HGmLXGsbbwG8B0a/c04HVr+/XTyt8wDmuBcOs6XsHXR7hxYixr9hZxpOzEGZ375tpsevcKYKpOFqmU8gB2tMH81Kq6erWpWsuSICKbReRzETnfKosBmrd451plAH2NMUes7Xygb7Nzclo5xyvcODGWRgOLN+V1+Jy8YydYtbuAmyfFEeCnTWtKKfu5+zfRi8BgYBxwBHjOKj8CxBtjxgM/B+aJSGhHL2o93ZzxTJEicoeIpItIelFR0Zme7jKDevckNSGSt9NzcHy09s1fl40BZnngomJKqbOTWxOMMabAGNNgjGkEXgYmW+U1xphia3sjsB8YBuQBsc0uEWuVARQ0VX1ZX5taxfOAuFbOOT2el4wxKcaYlOjoaGd8RKeZkRJHVnEV6w+2X8tYW9/Igg05XDK8j85ErJTyGG5NMKe1hVwP7LDKo0XE19pOxNFAf8CqAisXkTSr99htwAfW+UuAOdb2nNPKb7N6k6UBZc2q0rzGlWP60SvQj7c7MCZm+c58jlbWaOO+UsqjuLKb8nzgG2C4iOSKyO3An6wux9uAi4H7rcMvALaJyBZgEXCnMabpT/e7gH8BmTiebD6xyp8GpojIPuAy6z3Ax8AB6/iXrfO9TnCAH9cm9+fj7UeoqG57Asw312YRG9GDC4Z51lOYUurs5rKh3saYWS0Uv9LKse8C77ayLx0Y3UJ5MXBpC+UGuPuMgvVQM1LimL8+h6XbjjCzlbaVfQUVrD1Qwi+njsDXwxcVU0qdXbS7kQcbFxfO0D692hwT89a6bAJ8fZiREtvqMUopZQdNMB7MMQFmHJuyj5FZWPGd/VW19by7MZcrx/QjqlegDREqpVTrNMF4uOnjY/DzkRYb+5dsOUxFTb027iulPJImGA8XHRLIJSP6sHhTLnUNjSfLjTH8e20WI/qFkDJQ17FXSnkeTTBeYEZKHEcra/ls97cTYG7JOUbG4XJu0UXFlFIeShOMF7hoeDTRIYGnVJO9uTabngG+XD/eq2bBUUqdRTTBeAE/Xx9umBDDZ3sKKayo5lhVLR9tO8z08TH00kXFlFIeShOMl7hpYhwNjYb3NuWxaGMuNfW6qJhSyrPpn79eYkifXkwcGMHb6Tk0NBpSBkYwsn+H5wNVSim30ycYLzIjJZb9Rcc5VKyLiimlPJ8mGC9y9dgB9PD3JbJnAFeO0UXFlFKeTavIvEivQD9+N300wQG+BPr52h2OUkq1SROMl7lxos45ppTyDlpFppRSyiU0wSillHIJTTBKKaVcQhOMUkopl9AEo5RSyiU0wSillHIJTTBKKaVcQhOMUkoplxBjjN0xeAQRKQKy7I6jg3oDR+0OwkW682eD7v359LN5r658voHGmOiWdmiC8UIikm6MSbE7Dlfozp8Nuvfn08/mvVz1+bSKTCmllEtoglFKKeUSmmC800t2B+BC3fmzQff+fPrZvJdLPp+2wSillHIJfYJRSinlEppglFJKuYQmGC8iInEi8pmI7BSRDBH5md0xOZuI+IrIZhH5yO5YnElEwkVkkYjsFpFdInKO3TE5i4jcb/087hCR+SISZHdMXSEir4pIoYjsaFYWKSIrRGSf9TXCzhg7q5XP9oz1c7lNRN4TkXBn3U8TjHepB/7XGJMEpAF3i0iSzTE528+AXXYH4QJ/BT41xowAkukmn1FEYoB7gRRjzGjAF5hpb1Rd9how9bSyh4CVxpihwErrvTd6je9+thXAaGPMWGAv8Ctn3UwTjBcxxhwxxmyytitw/JKKsTcq5xGRWOBq4F92x+JMIhIGXAC8AmCMqTXGHLM1KOfyA3qIiB8QDBy2OZ4uMcasAUpOK54GvG5tvw5Md2dMztLSZzPGLDfG1Ftv1wJOW5ddE4yXEpFBwHhgnc2hONNfgF8AjTbH4WwJQBHw/6zqv3+JSE+7g3IGY0we8CyQDRwByowxy+2NyiX6GmOOWNv5QF87g3Gh/wY+cdbFNMF4IRHpBbwL3GeMKbc7HmcQkWuAQmPMRrtjcQE/YALwojFmPHAc761iOYXVFjENRxIdAPQUkVvtjcq1jGNsR7cb3yEiD+Oohn/LWdfUBONlRMQfR3J5yxiz2O54nOh7wHUicghYAFwiIm/aG5LT5AK5xpimp81FOBJOd3AZcNAYU2SMqQMWA+faHJMrFIhIfwDra6HN8TiViPwQuAa4xThxcKQmGC8iIoKjHn+XMeb/7I7HmYwxvzLGxBpjBuFoJF5ljOkWfwkbY/KBHBEZbhVdCuy0MSRnygbSRCTY+vm8lG7SgeE0S4A51vYc4AMbY3EqEZmKo2r6OmNMlTOvrQnGu3wP+AGOv+63WK+r7A5Kdcg9wFsisg0YBzxlbzjOYT2VLQI2Adtx/E7x6mlVRGQ+8A0wXERyReR24Glgiojsw/HU9rSdMXZWK5/tb0AIsML6nfIPp91Pp4pRSinlCvoEo5RSyiU0wSillHIJTTBKKaVcQhOMUkopl9AEo5RSyiU0wSjlJiJimg8eFRE/ESnq7MzR1gzNdzV7f1F3m4VaeTdNMEq5z3FgtIj0sN5PAfK6cL1w4K72DlLKLppglHKvj3HMGA0wC5jftMNac+R9a12OtSIy1ip/zFrHY7WIHBCRe61TngYGW4PjnrHKejVbd+Yta3S9UrbQBKOUey0AZlqLco3l1NmwHwc2W+ty/Bp4o9m+EcAVwGTgUWtOuoeA/caYccaYB63jxgP3AUlAIo7ZH5SyhSYYpdzIGLMNGITj6eXj03afB/zbOm4VECUioda+pcaYGmPMURwTLbY2Xfx6Y0yuMaYR2GLdSylb+NkdgFJnoSU41lC5CIjq4Dk1zbYbaP3/bkePU8rl9AlGKfd7FXjcGLP9tPIvgFvA0SMMONrOej8VOCYpVMoj6V83SrmZMSYXmNvCrseAV60Zl6v4dnr41q5TLCJficgOHKsQLnV2rEp1hc6mrJRSyiW0ikwppZRLaIJRSinlEppglFJKuYQmGKWUUi6hCUYppZRLaIJRSinlEppglFJKucT/B2eI2xK8NU99AAAAAElFTkSuQmCC", + "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": null, + "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": null, + "metadata": {}, + "outputs": [ + { + "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": [ + "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": null, + "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": null, + "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": null, + "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": 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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", + "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": null, + "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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R8QZwVhHrDQCmS5oHzAIejIhfARcDF0paCPQFftyqqM3MbIsUO6pnlSRFREB2Epci7vCNiHnAfo2Uvwgc2JpAzcys7RSb/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": [ + "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": null, + "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": null, + "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": null, + "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": null, + "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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k1Kfpn5ZU39ryzMysOtqzpX9qRIyMiJZ75V4FzI+IocD89BjgDGBo+pkC3AzZmwQwFTgBGAVMbXmjMDOzzlHO7p0JwMw0PBOYWNB+W2QeAXpLGgCcDsyLiOaI2ATMA8aXsXwzM2unUot+AL+StFjSlNTWPyLWpeEXgP5peCCwpmDeptTWWvtOJE2R1CipccOGDSV2z8zMStGtxOlOjoi1kt4BzJP0ZOHIiAhJUYkORcR0YDpAXV1dRTLNzCxT0pZ+RKxNv18Efkq2T3592m1D+v1imnwtcGjB7INSW2vtZmbWSdos+pLeJumglmFgHPAHYC7QcgZOPXB3Gp4LfCKdxTMa2Jx2A90HjJPUJx3AHZfazMysk5Sye6c/8FNJLdP/OCJ+KWkRcIekycBzwHlp+nuBM4FVwBvARQAR0SzpWmBRmu5LEdFcsTUxM7M2tVn0I+JZYESR9o3A2CLtAVzaStYMYEb7u2lmZpXgb+SameWIi76ZWY646JuZ5YiLvplZjrjom5nliIu+mVmOuOibmeWIi76ZWY646JuZ5YiLvplZjrjom5nliIu+mVmOuOibmeWIi76ZWY646JuZ5YiLvplZjrjom5nlSMlFX1JXSY9L+nl6PETSo5JWSbpd0n6pff/0eFUaP7gg4wup/SlJp1d8bczMbI/as6V/BbCy4PH1wA0R8S5gEzA5tU8GNqX2G9J0SBoGTAKOAsYDN0nqWl73zcysPUoq+pIGAWcB30uPBXwAmJ0mmQlMTMMT0mPS+LFp+gnArIj4S0T8iezG6aMqsA5mZlaiUrf0/wv4PLA9Pe4LvBwRW9PjJmBgGh4IrAFI4zen6Xe0F5nHzMw6QZtFX9LZwIsRsbgT+oOkKZIaJTVu2LChMxZpZpYbpWzpnwScI2k1MItst843gN6SuqVpBgFr0/Ba4FCANL4XsLGwvcg8O0TE9Iioi4i6fv36tXuFzMysdW0W/Yj4QkQMiojBZAdiF0TEBcCvgQ+nyeqBu9Pw3PSYNH5BRERqn5TO7hkCDAUWVmxNzMysTd3anqRVVwKzJH0ZeBy4JbXfAvxA0iqgmeyNgohYIekO4AlgK3BpRGwrY/lmZtZO7Sr6EfEA8EAafpYiZ99ExJvAR1qZ/zrguvZ20szMKsPfyDUzyxEXfTOzHHHRNzPLERd9M7MccdE3M8sRF30zsxxx0TczyxEXfTOzHHHRNzPLERd9M7MccdE3M8sRF30zsxxx0TczyxEXfTOzHHHRNzPLERd9M7MccdE3M8uRNou+pB6SFkpaKmmFpGtS+xBJj0paJel2Sful9v3T41Vp/OCCrC+k9qcknV61tTIzs6JK2dL/C/CBiBgBjATGSxoNXA/cEBHvAjYBk9P0k4FNqf2GNB2ShpHdL/coYDxwk6SuFVwXMzNrQ5tFPzKvpYfd008AHwBmp/aZwMQ0PCE9Jo0fK0mpfVZE/CUi/gSsosg9ds3MrHpK2qcvqaukJcCLwDzgGeDliNiaJmkCBqbhgcAagDR+M9C3sL3IPGZm1glKKvoRsS0iRgKDyLbO312tDkmaIqlRUuOGDRuqtRgzs1xq19k7EfEy8GvgvUBvSd3SqEHA2jS8FjgUII3vBWwsbC8yT+EypkdEXUTU9evXrz3dMzOzNpRy9k4/Sb3TcE/gNGAlWfH/cJqsHrg7Dc9Nj0njF0REpPZJ6eyeIcBQYGGF1sPMzErQre1JGADMTGfadAHuiIifS3oCmCXpy8DjwC1p+luAH0haBTSTnbFDRKyQdAfwBLAVuDQitlV2dczMbE/aLPoRsQw4tkj7sxQ5+yYi3gQ+0krWdcB17e+mmZlVgr+Ra2aWIy76ZmY54qJvZpYjLvpmZjniom9mliMu+mZmOeKib2aWIy76ZmY54qJvZpYjLvpmZjniom9mliMu+mZmOeKib2aWIy76ZmY54qJvZpYjLvpmZjniom9mliMu+mZmOVLKjdEPlfRrSU9IWiHpitR+sKR5kp5Ov/ukdkm6UdIqScskHVeQVZ+mf1pSfWvLNDOz6ihlS38r8JmIGAaMBi6VNAy4CpgfEUOB+ekxwBnA0PQzBbgZsjcJYCpwAtm9dae2vFGYmVnnaLPoR8S6iHgsDb8KrAQGAhOAmWmymcDENDwBuC0yjwC9JQ0ATgfmRURzRGwC5gHjK7kyZma2Z+3apy9pMHAs8CjQPyLWpVEvAP3T8EBgTcFsTamttXYzM+skJRd9SQcCPwH+LSJeKRwXEQFEJTokaYqkRkmNGzZsqESkmZklJRV9Sd3JCv6PIuKu1Lw+7bYh/X4xta8FDi2YfVBqa619JxExPSLqIqKuX79+7VkXMzNrQyln7wi4BVgZEf9ZMGou0HIGTj1wd0H7J9JZPKOBzWk30H3AOEl90gHccanNzMw6SbcSpjkJ+DiwXNKS1PYfwDTgDkmTgeeA89K4e4EzgVXAG8BFABHRLOlaYFGa7ksR0VyJlTAzs9K0WfQj4iFArYweW2T6AC5tJWsGMKM9HTQzs8rxN3LNzHLERd/MLEdc9M3McsRF38wsR1z0zcxyxEXfzCxHXPTNzHLERd/MLEdc9M3McsRF38wsR1z0zcxyxEXfzCxHXPTNzHLERd/MLEdc9M3McsRF38wsR1z0zcxypJR75M6Q9KKkPxS0HSxpnqSn0+8+qV2SbpS0StIySccVzFOfpn9aUn2xZZmZWXWVsqV/KzB+l7argPkRMRSYnx4DnAEMTT9TgJshe5MApgInAKOAqS1vFGZm1nnaLPoR8SCw6w3MJwAz0/BMYGJB+22ReQToLWkAcDowLyKaI2ITMI/d30jMzKzKOrpPv39ErEvDLwD90/BAYE3BdE2prbV2MzPrRGUfyI2IAKICfQFA0hRJjZIaN2zYUKlYMzOj40V/fdptQ/r9YmpfCxxaMN2g1NZa+24iYnpE1EVEXb9+/TrYPTMzK6ajRX8u0HIGTj1wd0H7J9JZPKOBzWk30H3AOEl90gHccanNzMw6Ube2JpDUAIwBDpHURHYWzjTgDkmTgeeA89Lk9wJnAquAN4CLACKiWdK1wKI03ZciYteDw2ZmVmVtFv2IOL+VUWOLTBvApa3kzABmtKt3ZmZWUf5GrplZjrjom5nliIu+mVmOuOibmeWIi76ZWY646JuZ5YiLvplZjrjom5nliIu+mVmOuOibmeWIi76ZWY646JuZ5YiLvplZjrjom5nliIu+mVmOuOibmeWIi76ZWY646JuZ5UinF31J4yU9JWmVpKs6e/lmZnnWqUVfUlfg28AZwDDgfEnDOrMPZmZ51tlb+qOAVRHxbET8FZgFTOjkPpiZ5ZYiovMWJn0YGB8RF6fHHwdOiIjLCqaZAkxJD48Eniox/hDgpQp2tzOyay23mtm1llvN7FrLrWZ2reVWM7s9uYdHRL9iI7pVrj+VERHTgentnU9SY0TUVaFLVcuutdxqZtdabjWzay23mtm1llvN7ErldvbunbXAoQWPB6U2MzPrBJ1d9BcBQyUNkbQfMAmY28l9MDPLrU7dvRMRWyVdBtwHdAVmRMSKCsW3e5fQPpBda7nVzK613Gpm11puNbNrLbea2RXJ7dQDuWZmtnf5G7lmZjniom9mliMu+mZmOeKib2aWI/vcl7M6QtIQ4FjgiYh4ssysw4AXI+JNSQIuBI4DngC+GxFbO5h7DvCriHiznP61kn0KsD4inpJ0EvBeYGVE3FOB7AOB8WTfr9gG/JFsPbaXmftusktwDExNa4G5EbGynNw9LO+iiPh+GfO/m6yvj0bEawXt4yPil2X2bRQQEbEoXYtqPPBkRNxbTm6R5dwWEZ+ocObJZJdX+UNE/KrMrBPI/m9fkdQTuIq/vfa+EhGbO5h7OfDTiFhTTv+K5Lacdv7niLhf0v8CTgRWAtMj4q0y8/8n8CF2fu39OCJeKSu3Fs/ekTQnIiam4QnAfwEPkD3hX42IW8vI/gMwKiLekHQ98E5gDvABgIj4ZAdztwCvA78AGoD7ImJbR/tZkPtfZC+6bmSnwo5Ny3g/8HhEfK6M7POAzwLLgFOBh8k+HR4DXBARyzuYeyVwPtm1l5pS8yCyF9CsiJjW0T7vYZnPR8RhHZz3cuBSshfzSOCKiLg7jXssIo4ro19TyS5A2A2YB5wA/Bo4jex/5LoO5u76/ReR/Q0XAETEOR3MXRgRo9LwJWTPy0+BccDPyvnbSVoBjEindk8H3gBmk/1Pj4iID3UwdzPZa+8ZstfenRGxoaP9LMj9Ednf7QDgZeBA4K7UX0VEfRnZlwNnAw8CZwKPp2V8EPjXiHigwx2PiJr7IStmLcMPA0PS8CHA0jKznygYXgx0KXjc4ez0R+sDXALMB9YD3wHeX2Z/V5C9oA8ANgEHpPbuZFtf5WQvK8g7hKwIAQwHHi4j949A9yLt+wFPl9nfYj/Lgb+UkbscODANDwYayQr/Tv+LZWR3TX+/V4C3p/aewLIych8DfgiMIdsAGAOsS8Md/p/b5bW3COiXht8GLC/zuVhZ2P9dxi0pp89kGyvjgFuADcAvgXrgoHL+39Lvbun13DU9Vjl/u8L/izR8APBAGj6s3P+5Wt2nX/jxpFtE/AkgIl4CytrtAKyR9IE0vJp02QhJfcvMjYjYFBHfjYixwAiyj63TJJXzsTMi+29oWe+W52Y75R+zEbAlDb8OvCMtcBnw9jJytwP/UKR9AOX9/foDnwD+ucjPxjJyu0TapRMRq8kK6BmS/pPsOSrH1ojYFhFvAM9E+ugeEVso77moI9to+SKwObItwy0R8ZuI+E0ZuV0k9UmvB0XaYo6I14EO7fos8AdJF6XhpZLqACQdAZSzqyQiYntE/CoiJpP9791Ethvt2TJyu6RdPAeRFeZeqX1/so2ucrXsft+f7FMEEfF8udm1uk9/hKRXyF5w+0saEBHr0h+ga5nZFwO3Sboa2AwskbQE6A38exm5OxWHiHgBuBG4UdLhZeTeI+m3QA/ge8Adkh4h26J7sIxcgHuBX0p6kOwFcieApIMpr9j9GzBf0tNAyxveYcC7gMtam6kEPyfbIl+y6whJD5SRu17SyJbciHhN0tnADLJdXeX4q6QDUtE/vqVRUi/KKPqRHXO5QdKd6fd6KvN670X2ZiIgCl57B1L+G+DFwDck/V+yq0n+Pm0QrUnjOmrX195bZJd/mSvpgDJybwGeJKs5XwTulPQsMJps12U5vgcskvQo8D7gegBJ/YDmcoJrcp9+ayT1Bt4TEb+vQNZ7gCPIXihNwKIo4+ClpDFRzn64PWe/l2xr5hFJ7yTb7/c8MLucPqfsM8lueLM0Iualti5ku2f+UkZuF7JjEYUHchdFBY5zVJqkQWRb5C8UGXdSRPyujOz9iz2Pkg4BBkQHj5sUyTsLOCki/qMSeUXyDwD6t3zqLjPr7cAQ0msvItaXmXdERPyx3H61kv0PABHx51R//gl4PiIWViD7KOA9ZLtpyzpBZafcWi76kvpTUDTK/efojOxay612dpFlHRgFZ8fkNbea2bWWW83sWsutRHZNFn1JxwI3k33UbLk08yCyo9v/OyIeLyN7JNkB1mLZ/xoRj+Uht9rZe1hmh8+y+XvKrWZ2reVWM7vWciuRXav79L8PfCoiHi1slDQauJXsIGlH3bqH7O+XkV1ruVXLltTasRGRDljlIbea2bWWW83sWsutdnatnr3ztl0LEUBEPEJ26ti+mF1rudXM/grZ6asH7fJzIOX9T9ZabjWzay23mtm1llvV7Frd0v+FpHuA2/jb2R+Hkp2uV9a3I6uYXWu51cx+DJgTEYt3HSGpnLM0ai23mtm1llvN7FrLrWp2Te7TB5B0BsW/xl/2V9erlV1rudXKlnQk0BxFvhUpqX9HDxTXWm41s2stt5rZtZZb9exaLfpmZtZ+NblPX1IvSdMkrZTULGljGp6WzpXd57JrLbeT+vxknnNrsc9+LqqfW+3smiz6wB1k15k5NSIOjoi+ZBeTejmN2xezay23mtktuWN2yd2Us9xa7LOfi+rnVjc7yrhwz976AZ7qyLi9mV1rubXY51rLrcU++7mo7ecionYvuPacpM8r+6YokB3cUHbJ3nKvmV2t7FrLrWa2c6ufXWu51cyutdyqZtdq0f8o0Bf4jaRNkprJrqd/MHDePppda7nVzHZu9bNrLbea2bWWW93scj4m7M0f4N1kFzc6cJf28ftqdq3l1mKfay23Fvvs56LGn4tyO7Y3foDLgafI7mi1GphQMO6xfTG71nJrsc+1lluLffZzUdvPRUTtFv1q38mo4tm1lluLfa613Frss5+L2n4uIqJmL8Ow052MJI0BZiu7GUm5N3KoVnat5VYz27nVz6613Gpm11puVbNr9UDuemWX/QWyOxmR3UT4EMq/k1G1smstt5rZzq1+dq3lVjO71nKrm13Ox4S99UN2Tff/0cq4k/bF7FrLrcU+11puLfbZz0VtPxcR4WvvmJnlSa3u3jEzsw5w0TczyxEXfbMCyjyk7D4CLW0fkVTuDWnM9gnep2+2C0lHA3cCx5LdXe5xsm9BPtOBrG4RsbXCXTTrMBd9syIk/T/gdbJ7Ab8OHA4cDXQHro6IuyUNBn7A3+4XfFlEPJzOqb6W7DK4746IIzq392atc9E3K0LS28juU/pX4OfAioj4obIbWCwk+xQQwPaIeFPSUKAhIupS0b8HODoi/rQ3+m/Wmlr9Rq5ZVUXE65JuB14ju6rhP0v6bBrdAzgM+DPwrfQlmm1A4Rb9Qhd82xe56Ju1bnv6EXBuRDxVOFLS1cB6YATZSRFvFox+vZP6aNYuPnvHrG33AZ+WJABJx6b2XsC6iNgOfBzoupf6Z1YyF32ztl1LdgB3maQV6THATUC9pKVk1z731r3t83wg18wsR7ylb2aWIy76ZmY54qJvZpYjLvpmZjniom9mliMu+mZmOeKib2aWIy76ZmY58v8Baxkho43t3BMAAAAASUVORK5CYII=", + "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": null, + "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": 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|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": null, + "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": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "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": null, + "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", + 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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| 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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", + 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"| 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| 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"+---------+-------------+--------------------+\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": null, + "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": null, + "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": null, + "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": null, + "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": null, + "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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re3ejb1QQb31zzC77U0rZT7v70EUkBpgMLGrBtg+IyFYR2aqXXTvei2sO4O9t4Wdp9rsq0npytBdZx4rZe+Ks3farlGo/e5wUfQ74lTGm2dugG2MWG2NSjTGpkZGRdji0asqxU6V8kJXPtOG9CA9q+V1lWmJKSgy+Fi/e1la6Ui7FHoGeCrwtIoeB24AXRWSSHfar2mHR2gNYRHhgZB+777tbF18mDuzBsm25lFfV2H3/Sqm2aXegG2MSjDHxxph44F3gIWPM8vbuV7XdiTPlvLs1l9tSY+kR0rrpN1tq6vA4zpZX8+mO4w7Zv1Kq9VoybPEtYBOQJCK5InKfiMwUkZmOL0+1xeJ1B6kxhlmjHHdz56v7hBMfHqjdLkq5kGYHJhtjprV0Z8aYGe2qRrVbUUkFb35zhFuGRBMXFuiw44gIU4f3Yt6KveQUlNA3Kshhx1JKtYxeKephXttwiIrqWh5Kb/rmz/Zy69BYvL1Ep9VVykVooHuQM2VVvLHxCDcM7NkhLebIYD/GXdGd97blUlGtJ0eVcjYNdA/yxsbDnKuo5qHRjus7v9i04b04XVrF57u+77BjKqUap4HuIc5XVPPahkOM7R/FgOiQDjvuiL4RxHYL0Gl1lXIBGuge4s2vj3K6tIqHxzi+77w+Ly/hztQ4Nh4o4kjR+Q49tlKqIQ10D1BeVcPirw5yzWXhDO3V8Tdxvj01DouX8PYWHcKolDNpoHuAf289RuG5Ch7p4NZ5nR4h/oxOiuLfW3Opqml2BgillINooLu5qppaXlp7kKG9Qrm6T7jT6pg2PI6TJRWs2qMnR5VyFg10N7c8M4+84jIeHdMPEXFaHaMSI+nR1V+n1VXKiTTQ3VhNreHFNQcYEN2V9CTnzl7pbfHijmFxrNtfSO7pUqfWolRnpYHuxj7dcZxDJ8/z8Oi+Tm2d17kjNRaAd7bmOrkSpTqn9t1kUjnF8sw8/rJyL/nF5Xh7CRWVrnGVZmy3QEb2i+SdLcf4xZi+eFu0vaBUR9JXnJtZnpnHU8t2kF9cDkB1reHXy3eyPDPPyZVZTRsex4mz5azdp3ekUqqjaaC7mfkrsym76KYSZVU1zF+Z7aSKGhp7eXcigvz05KhSTqCB7mbyi8tatbyj+Vi8uD01lozsAk6cKXd2OUp1KhrobqZnaON3IIoODejgSpo2dVgcNbWGf2/VVrpSHUkD3c2Mv6L7D5YF+FiYMyHJCdU0rnd4F9L6hrN06zFqa42zy1Gq09BAdyPVNbV8tf8k3YP9iA71R4CY0ACenpLMpJQYZ5fXwNRhvcg9Xcb6nJPOLkWpTkOHLbqR9zPzOFB4npfuHsrEgT2dXc4ljR/QnW6BPry95SgjE5170ZNSnYW20N1ERXUNz325n+SYECYM6OHscprl523h1qGxfL7rewrPVTi7HKU6BQ10N7F0yzHyist4ckKSS1wV2hJTh/eiutbw3ja9clSpjqCB7gbKKmtYsDqH4QlhjOwX4exyWqxvVBB9Irowf2U2CXM/IW3eape5AEopT6SB7gb+vukwhecqmONGrXOwXtV67HQpNbUGA+QVl/HUsh0a6ko5iAa6iztbXsVLaw+QnhTJsPgwZ5fTKvNXZlNV03DYoitd1aqUp9FAd3GvfHWI4tIqnhzvOuPMW8rVr2pVytNooLuwU+crefWrg9yQ3IOBMSHOLqfVmrp61ZWualXKk2igu7BFa3Ioq6rhiXGJzi6lTeZMSCLAx9Jgmatd1aqUJ9ELi1zUiTPlvLHpCJNTYukbFezsctqk7urV+SuzySsuw9tLXPKqVqU8hbbQXdSC1fupNYbHruvn7FLaZVJKDBvmjuF3P76C6lrDlb27ObskpTyWBroLOlpUytItx5g6rBdxYYHOLscu0pOiAFiTXeDkSpTyXBroLui5L/fhbREeHdPX2aXYTUJEF3qHB5KRrXcyUspRNNBdzL7vz/F+Vh7Tr44nqmvjc5+7q9FJUWw8cJLyKte4B6pSnkYD3cU8+/k+uvh6M3PUZc4uxe5GJUVSXlXLN4dOObsUpTySBroL2Z5bzGe7TnD/tQl06+Lr7HLs7uo+4fh5e5Gh/ehKOYQGugt55vN9dAv04b4RCc4uxSH8fSxcfVk4a7UfXSmH0EB3EV8fLGLdvkJmpV9GsL+Ps8txmPTESA6ePM+RovPOLkUpj6OB7gKMMTzzeTbdu/px79Xxzi7Hof5v+KK20pWyNw10F7B2XyFbDp/mkTH98L/oUnlPEx/RhYSILjoeXSkHaDbQReQ1ESkQkZ1NrP+JiGwXkR0islFEBtu/TM9V1zqPCwvgztQ4Z5fTIUYlRrLpYJEOX1TKzlrSQl8CTLzE+kPAKGNMMvCfwGI71NVpfLbzBDvzzvLY2ER8vTvHP0zptuGLmw8WObsUpTxKswlijFkHNDlw2Biz0Rhz2vbtZiDWTrV5vJpaa+u8b1RQp5qw6qo+4fj7eGk/ulJ2Zu8m4X3AiqZWisgDIrJVRLYWFuqL+f3MPA4UnufJ8YlYvNzn1nLt5e9j4eo+4azdp38DStmT3QJdREZjDfRfNbWNMWaxMSbVGJMaGRlpr0O7pcrqWp77ch/JMSFMGNDD2eV0uPSkKA6dPM/hkzp8USl7sUugi8gg4BXgFmOMdoy2wNItR8k9XcaTbnbjZ3sZrbMvKmV37Q50EekFLAPuMcbsa39Jnq+ssoYFq3MYHh/GyH4Rzi7HKXqFB9InogtrtNtFKbtp9o5FIvIWkA5EiEgu8DvAB8AY8xLwWyAceNHW0qw2xqQ6qmBP8MamwxScq+CFu4Z2ytZ5nVFJkbz59VHKq2o8fvy9Uh2h2UA3xkxrZv39wP12q8gJlmfmMX9lNvnFZUSHBjBnQpLDRp2cK69i0doDjEqMZHhCmEOO4S5GJ0Xx+obDbDpYdKELRinVdp1j4PMlLM/M46llO8grLsMAecVlPLVsB8sz8xxyvFe+OkRxaRVPjtcbJQ9PCCPAx6KTdSllJ50+0OevzKbsoisWy6pq+MvKvXY/1qnzlby6/hDXD+xBcmyI3ffvbupmX9TpdJWyj04d6GdKq8grLmt0XX5xOb/9YCebDxZRU2vscryX1h6gtLKaJ8Yl2mV/nmB0UiRHiko5pMMXlWq3ZvvQPVFNreGtb47y18+zm9zG38eLd7Ye441NR4gM9uOGgT24IbknqfFhbboI6Puz5fx942EmpcTQr3twe8r3KNbZF3exJruAhAjPnAdeqY7S6QJ988Eifv/hLvaeOMePEsK4NjGChasPNOh2CfCx8PSUZMZd0Z3Vewv4ZPtx3t5yjL9vOkJUsB/XD+zBjYOiSe3dDa8WhvuC1fupNYbHr9PWeX1xYYH0iexCRnYhP03TQFeqPTpNoOeeLuXpT/fyyY7jxIQGsPCuodyQ3AMRITY0sMlRLj8eHM2PB0dzvqKaVXsL+PSicL8huSc3DurJlb2aDvejRaW8/c0xpg6PIy4ssCN/bLcwOimKf2w+QlllDQG+OnxRqbby+EAvrazmpTUHeHndQUTgiXGJPDCyT4Nxz5NSYpodptjFz5ubB0dz8+BoSiqqbS33fN765ihLNh6me1c/rh/YMNzrhkPW9dP3iwpy6M/qrtKTInl1/SE2HyxidH8dvqhUW3lsoBtj+Gj7cZ7+dA/Hz5Rz8+Bo5l7fn+jQgHbvO+iicF+153s+2X6cN23h3qOrP4ndg9h86BSV1bUXHjdvRTYhAb6dambFlqgbvpiRXaCBrlQ7eGSg78w7wx8+2sWWw6cZEN2V56elMCzeMRfxBPl5c8uQGG4ZEsO58ipW7y3g4+3H+WL39z/YtqyqhvkrszXQL+LnbSGtbzhrsgsxxnTqq2eVag+PCvSTJRU8szKbpVuPERboy7wpydyeGtdhU9MG+/tcCPeEuZ/Q2GDH/CaGSXZ2o5Ki+HJPAYdOnqdPpHZNKdUWHhHoldW1vLHpMH/7cj9lVTXcl5bAL67rR1d/H6fVFB0a0OgYd3t0+Xii9ETrdMoZ2YUa6Eq1kVsFemNzroQE+vCfH+/mYOF50pMi+Y+bruAyFwiEOROSeGrZjh8Mh5wzQS/5b0xcWCB9o4JYk13AfSN0+KJSbeE2gV4350pdQOYVl/HEO1nUGugT0YXXZwxzqRNqdf3kHTXplydIT4zkDR2+qFSbuU2gNzbnSq2Brv7efPbYSJe8wXJLhkOq/5OeFMUr6w+x6eBJxvTv7uxylHI7rpeCTWjqZOK58mqXDHPVesMSuhHoayFjr86+qFRbuE0SNnUyUU8yeg4/bwvXXBbBmn0FGGOfCdGU6kzcJtDnTEgi4KK72uhJRs+TnhTJsVNlHNTZF5VqNbcJ9EkpMTw9JZmY0AAEiAkN4OkpydpH7WHSk2zDF/fqHOlKtZbbnBQFPcnYGcR2C6RfVBBr9xVy/7V9nF2OUm7FbVroqvNIT4rk64OnKK2sdnYpSrkVDXTlctKToqisqWVjTpGzS1HKrWigK5eTGt+NLr4W1uzTfnSlWkMDXbkcP28L1/SNuDD7olKqZTTQlUtKT4ok93QZBwpLnF2KUm5DA125JOvNo2FNtl41qlRLaaArlxQTGkBi9yANdKVaQQNduaz0pCi+OXSK8xU6fFGpltBAVy4rPSnSOnzxgA5fVKolNNCVy0rtHWYdvpitwxeVagkNdOWyfL29SNPhi0q1mAa6cmmj+0eRV1xGToEOX1SqORroyqXVzb6oo12Uap4GunJpPUMCSOoeTIb2oyvVLA105fLS+0ey5fApSnT4olKXpIGuXF56YhRVNYaNOSedXYpSLk0DXbm81PhuBPl5k6H96Epdkga6cnk+Fi9G9I1gbbbePFqpS9FAV24hPSmS/DPl7Nfhi0o1qdlAF5HXRKRARHY2sV5E5HkRyRGR7SIy1P5lqs5ulN48WqlmtaSFvgSYeIn11wP9bB8PAIvaX5ZSDfUMCaB/j2Adj67UJTQb6MaYdcCpS2xyC/CGsdoMhIpIT3sVqFSd9KQoth45xbnyKmeXopRLskcfegxwrN73ubZlPyAiD4jIVhHZWlioLS3VOulJkVTVGDbozaOValSHnhQ1xiw2xqQaY1IjIyM78tDKA1zZuxvBft6s1ZtHK9UoewR6HhBX7/tY2zKl7MrH4sWIfjr7olJNsUegfwjcaxvtchVwxhhz3A77VeoH0pMiOX6mnOzvzzm7FKVcjndzG4jIW0A6ECEiucDvAB8AY8xLwKfADUAOUAr81FHFKlX/5tH9e3R1cjVKuZZmA90YM62Z9QZ42G4VKXUJ3bv6c3nPrqzJLmDmqMucXU6LLM/MY/7KbPKLy4gODWDOhCQmpTQ6bkCpdtErRZXbSU+KZOvh024xfHF5Zh5PLdtBXnEZBsgrLuOpZTtYnqmnmZT9aaArtzM6KYrqWsMGB8y+uDwzj7R5q0mY+wlp81a3OXhLKqo5WFjCf32ym7KqmgbryqpqmL8y2x7lKtVAs10uSrmaob1CCfb3Zk12IRMH2u8atrrWdF0A17WmASalxFBbazhdWknBuQrrx9lyCksqKDhbQeG5CgrOlds+V1BaWXOpQ5FfXGa3upWqo4Gu3I63xYs+EYG8s/UYS7cca1e/dE2tobi0klPnK5tsTc959zvmrdjLyZIKqmt/OFwy2M+byK5+RAb5kRwbSlSwn/Wjqx//9fEeis5X/uAx0aEBra5VqeZooCu3szwzj93Hz1GXrfVb0hMH9uB0aSVFJdaQrv9RdL6SU+crOH2+iqLzFZw6X0lxWRXNDWmvqjGM6BdRL6j9iaz7OtifAF9Lk48VpEGrH8DXIsyZkNTu50Gpi2mgK7czf2U2VTUNU7isqobHl2bx2NLGH+MlENbFl26BvoR18SWpRzBhXXwJs30fFuTHHz7c1WhrOiY0gGduH9ymWuv+a6gb5WLxEsK6+HLz4Og27U+pS9FAV26nqf5nA8yZkHQhuMODfC+EdkiAD15ecsn91taaH7SmA3ws7W5NT0qJuRDsH2TlMfvtLFbsPMGNg3QOO2VfGujK7USHBpDXSKjHhAbw8Oi+bd7vxa1pR4wZv2lQNAtW5/C3Vfu4fmCPZt9klGoNDXTlduZMSHJISxoatqYdweIlzB7bj0ffyuSTHcf5sXa9KDvScejK7UxKieHpKcnEhAYgWFvmT09JdpurL29M7kli9yCe+3IfNY2MmlGqrbSFrtySo1vSjuTlJTx2XSIP/WsbH2/P55Yh7vlzKNejLXSlnGDigB707xHM377cT3VNrbPLUR5CA10pJ7C20vtx8OR5Pvwu39nlKA+hga6Uk4y/ogdX9OzK86u0la7sQwNdKSepa6UfLipleZa20lX7aaAr5UTjrujOwJiuLFi9nyptpat20kBXyolEhMfGJnKkqJT3t+kc6ap9NNCVcrKxl0cxKDaEBRnaSlfto4GulJOJCI9fl8ixU2W8922us8tRbkwDXSkXkJ4UyZC4UBaszqGyWlvpqm000JVyASLWES95xWX8+9tjzi5HuSkNdKVcxKjESIb2CmXh6hwqqi99CzulGqOBrpSLEBEeH5dI/ply3tmqfemq9TTQlXIhI/pGkNq7GwtX51Bepa101Toa6Eq5EBHhiXGJnDhbztIt2peuWkcDXSkXc/Vl4QxPCOPFNc5ppS/PzCNt3moS5n5C2rzVLM/UC57chQa6Ui6mblz692crePProx167OWZeTy1bAd5xWUYIK+4jKeW7XDZUNc3n4Y00JVyQVdfFs5VfcJYtPZAh7bS56/MbnBrP4Cyqhrmr8zusBpayt3efDqCBrpSLurx6xIpPFfBPzcf6bBj5jdy8+1LLXcmd3rz6Sga6Eq5qB/1CSetbzgvrT1AaWW1w4+XU3AOi5c0ui46NMDhx28td3rz6Sga6Eq5sMevS+RkSaXDW+mf7jjOLS9swN/HC1/vhrEQ4GNhzoQkhx6/LZp6kwkP8u3gSlyHBrpSLiw1Poxr+0Xw8tqDDmmlV9fU8vSne3joX9tI7BHMF0+M4i+3DiKmXljef22CS96Q+8nxichF/1AIcPp8JZ/tPOGUmpxNA10pF/f4uESKzlfyxib7ttJPllRwz6vf8PK6g9xzVW/efuAqeoYEMCklhg1zx7DnjxOJCPIl61ixXY9rL2FBfhgDoQE+CBATGsB/ThpAcmwos/71LW9sOuzsEjuct7MLUEpd2tBe3RiVGMnLaw9w91W9CfJr/8s28+hpHvrXNk6dr+Svtw/m1itjf7BNgK+F+6/tw7wVe8k6VsyQuNB2H9dejDH87ct9RIf4s2bO6AbdRLcOjePRtzL57Qe7yCsu41cT+uPVxLkBT6MtdKXcwOPjEjldWsXfNx5u136MMfxz8xHueHkT3hZh2UPXNBrmde6+qjehgT68sHp/u45rb+tzTrLtaDGzRvf9YZ+/r4WX77mSu6/qxctrD/L4O1mdZrIzDXSl3MCQuFDG9I/if786yLnyqjbto7yqhif/vZ3fLN9JWt8IPnpkBAOiQy75mCA/b36WlsCXewrYlX+mTce1N2vrfD89Q/y5I7XxNyOLl/CftwzklxOT+CArnxmvbeFMWdueN3eiga6Um3jsun4Ul1axZMPhVj/22KlSbl20kfe25TJ7bD9emz6M0MCWjQaZfk08wX7evLA6p9XHdYRNB4rYeuQ0s9Ivw8/b0uR2IsJD6X35nzsHs/XIKe54aRPHz3j2kEYNdKXcxKDYUK673NpKP9uKVvqa7AJuWrCeY6dKeW1GKo+PS2xVn3JIgA/Tr4lnxc4T7Pv+XFtKt6vnVu2ne1c/7kiNa9H2k1NiWfLT4eQVlzF54Ub2njjr4Aqb5uipCloU6CIyUUSyRSRHROY2sr6XiGSISKaIbBeRG+xapVIKgMeuS+RseTWvrz/c7La1tYbnV+3np0u20DPEn48eHcGY/t3bdNyfjUgg0NfCwgznttI3Hyzim0OnmDnqMvx9mm6dXyytbwTvPHg1BsPtizax8cBJB1bZuI6YqqDZQBcRC7AQuB64ApgmIldctNlvgHeMMSnAVOBFu1WolLpgYEwI46/ozivrD16yT/hMWRU/f2Mrz36xj0lDYnj/oTR6h3dp83HDuvhy91W9+ei7fA6dPN/m/bTX377cT2SwH9OG92r1Y6+I7sqyh9LoEeLP9Ne+4YOsjp3zpSOmKmhJC304kGOMOWiMqQTeBm65aBsDdLV9HQLk261CpVQDj12XyLnyal5df6jR9XuOn+XmF9azdl8hf7h5AM/eMZgA35a3Zpty/7UJ+Fi8eNFJrfRvDp1i08GiVrfO64sJDeDdmdcwtFc3Zr+dxUtrD2CMsXOlDdXUGtbvP0leB0xV0JJAjwHqz7Sfa1tW3++Bu0UkF/gUeNQu1SmlfuCK6K5MHNCD19cf4kxpw1b68sw8Jr+4gbLKGpY+eBXTr4lHLr6cso2igv2ZNrwX72fmcexUqV322RrPr9pPRJAfd7WhdV5fSKAPb9w3nJsG9WTeir38/sNd1NTaP9T3HD/Lf3+6h2vmreLuV7+mqd+CPefJsdeFRdOAJcaYv4rI1cA/RGSgMaa2/kYi8gDwAECvXu37pSjVmT02rh+f7TrBiD+vpqSimp4h/lwWFcRX+08yPD6MF36SQlSwv92P++CoPrz59VFeWnuAP01Otvv+m/LtkVOszznJ/7vhcrv8t+HnbeH5qSlEhwaweN1BTpwt529TU9rc8q9z4kw5H2Tl8X5mHntPnMPbS0hPiuS3N8VSWlnNbz/Y1aDbxd7z5LQk0POA+qeTY23L6rsPmAhgjNkkIv5ABFBQfyNjzGJgMUBqaqpj/89RyoPtPX4Oi8C5Cuv8Lvlnysk/U86oxAhemT4MH4tjBrD1DAngttRY/r01l0fH9KNHiP3fNBrz3Jf7Ce/iy0+usl9D0MtL+PUNl9MzxJ8/frybu/53M69OH0a3Lq2b3KukoprPdp7g/cxcNh4owhjrdQN/vGUANyb3JDzI78K2PhYv5q/MJr+4jOjQAOZMSLLrPDktCfQtQD8RScAa5FOBuy7a5igwFlgiIpcD/kCh3apUSjUwf2U2NY00iXIKzjsszOvMGnUZS7cc4+V1B/jdjwc49FgA246e5qv9J5l7fX8Cfe0/W8lP0xLo0dWf2UuzuHXRRv7+s+HEhQVe8jHVNbV8lXOS97fl8fnuE5RX1dIrLJBHx/RjckoMCRGNn4CelBLj0InOmn12jDHVIvIIsBKwAK8ZY3aJyB+BrcaYD4H/D/hfEXkc6wnSGcbRZxqU6sScORd4XFggk1NiePProzyU3pfIYL/mH9QOz6/aT1gXX+65qrfDjnF9ck8igv24/+9bmfziBqZfHc/bW441aEnfMiSaHXlneD8zj4++y+dkSSUhAT7cOjSWKUNjGNqrm93OV7SVOCt3U1NTzdatW51ybKXcXdq81Y2OmogJDWDD3DEOP/7BwhKue3YtP7+2D0/dcLnDjpN1rJhJCzfwy4lJPJTe12HHqZNTUMJtizZSfNGQUG8vIayLDwXnKvG1eDH28igmpcQwOinqB3PJOJqIfGuMSW1snc62qJQbmjMhiaeW7XDoCbZL6RMZxE2DovnH5iPMHHVZq/udW+r5VfsJDfTh3qvjHbL/i/WNCrKeGL0o0KtrDcWl1Tw9JZkbBvYkJNCnQ+ppLb30Xyk3NCklhqenJBMTGnBhLvCnpyR36I0oHhnTl9LKGl7b0Ph4+PbakXuG1XsLuH9Egl2mDG6p78+WN7q8qqaWacN7uWyYg7bQlXJbjj7B1pzE7sFMHNCDJRsOc/+1fQgJsG/Q/W3Vfrr6ezP9mni77rc50aEBjXZnueJ9VS+mLXSlVJs9MqYv5yqqeaOd87RfbGfeGb7c8z33jehDsH/HtojnTEgi4KLx6K56X9WLaaArpdpsYEwIY/tH8eqGQ5yvsN89Txes3k+wvzcz0uLtts+WcoXurLbSLhelVLs8MqYvk1/cyD83H+HBUZe1e397jp9l5a7vmT22n927cVrK2d1ZbaUtdKVUu6T06sa1/SL4368OUlbZ/lu9Pb9qP8G2OyWp1tFAV0q12yOj+3KypJK3txxt136yT5xjxc4TzEiLd+nRJK5KA10p1W4/6hPO8IQwXl57sF03ZH5+9X66+Fq4b4S2zttCA10pZRe/GNOPE2fLeffb3DY9fv/35/h0x3GmXxPf4vudqoY00JVSdpHWN5whcaEsWnOAqpra5h9wkQWrcwjwsXD/tX0cUF3noIGulLILEeEXY/uSe7qM91t5n8ycghI+2p7PvVfHE+agaQQ6Aw10pZTdjE6KYkB0V17MyGnVXYBeWL0ff28LP79W+87bQwNdKWU3IsKjY/pyuKiUj7e37NbCBwtL+PC7fO65uneDm0Go1tNAV0rZ1fgrepDYPYgXVudQ24JW+gsZOfh6e/Fz7TtvNw10pZRdeXkJD4/uy/6CElbuOnHJbQ+fPM8HWfn85Ee9HX6jjM5AA10pZXc3DYomIaILC1bncKmb6CzMyMHbS3hwlLbO7UEDXSlldxYv4aH0y9h9/Cyr9xY0us3RolKWZeZx1496ERXcMTeb9nQa6Eoph5iUEkNstwCeb6KVvjAjB4uXMNMOE3opKw10pZRD+Fi8eCi9L98dK2Z9zskG646dKuW9bblMGxZH967aOrcXDXSllMPcemUMPUP8WbAqp8HyF9ccwEuEmenaOrcnDXSllMP4eVt4cGQfvjl8is0HiwDIKy7j3W+PccewWHqGuP5t3dyJBrpSyqGmDu9FRJAfL6y2ttJfzLB+npXe15lleSQNdKWUQ/n7WHhgZALrc04y9I+f86+vj+Jj8WLLoVPOLs3jaKArpRwuxHaj51OlVQCUVtbw1LIdLG/lJF7q0jTQlVIO9/zqnB8sK6uqYf7KbCdU47k00JVSDpdfXNaq5aptNNCVUg4XHdr4aJamlqu20UBXSjncnAlJBPhYGiwL8LEwZ0KSkyryTN7OLkAp5fkmpcQAMH9lNvnFZUSHBjBnQtKF5co+NNCVUh1iUkqMBriDaZeLUkp5CA10pZTyEBroSinlITTQlVLKQ2igK6WUh5BL3e/PoQcWKQSOtPHhEcDJZrdyHe5UrzvVCu5VrzvVCu5VrzvVCu2rt7cxJrKxFU4L9PYQka3GmFRn19FS7lSvO9UK7lWvO9UK7lWvO9UKjqtXu1yUUspDaKArpZSHcNdAX+zsAlrJnep1p1rBvep1p1rBvep1p1rBQfW6ZR+6UkqpH3LXFrpSSqmLaKArpZSHcJlAF5E4EckQkd0isktEZtuWh4nIFyKy3/a5m225iMjzIpIjIttFZGi9fdWISJbt40NXrVVERterM0tEykVkkqvWa1v3ZxHZafu40wVq7S8im0SkQkSevGhfr4lIgYjstHed9qxVRPxF5BsR+c62nz+4cr22dYdFZIft73arq9YqIkkXvcbOishjrlqvbd1s2+trV6trNca4xAfQExhq+zoY2AdcAfwFmGtbPhf4s+3rG4AVgABXAV/X21eJu9Rab59hwCkg0FXrBW4EvsA67XIXYAvQ1cm1RgHDgD8BT160r5HAUGCni/wdNFqr7XkOsn3tA3wNXOWq9drWHQYiHPG82rvWevu0ACewXpjjkvUCA4GdQKDtdfYl0LeldbhMC90Yc9wYs8329TlgDxAD3AL83bbZ34FJtq9vAd4wVpuBUBHp6ca13gasMMaUunC9VwDrjDHVxpjzwHZgojNrNcYUGGO2AFWN7Gsd1jdJh7BXrbbnucT2rY/tw+6jFez53Dqag2odCxwwxrT1CvWOqPdyrA2oUmNMNbAWmNLSOlwm0OsTkXggBWtLpbsx5rht1Qmgu+3rGOBYvYfl2pYB+IvIVhHZ7IguDDvXWmcq8JbjKrVqZ73fARNFJFBEIoDRQJyTa3UJ7a1VRCwikgUUAF8YY752UKl1x4unfc+tAT4XkW9F5AHHVGllx78DV3qNNWUncK2IhItIINb/llv8GnO5OxaJSBDwHvCYMeasiFxYZ4wxItKSlktvY0yeiPQBVovIDmPMARetFVvrNxlYae8aLzpOu+o1xnwuIsOAjUAhsAmoccVaO5I9ajXG1ABDRCQUeF9EBhpjHNX3b4/ndoTtNRYFfCEie23/EblirYiIL3Az8JS9a7zoOO19je0RkT8DnwPngSxa8RpzqRa6iPhgfTL+ZYxZZlv8fV33hO1zgW15Hg3fuWJtyzDG1H0+CKzB+m7pkrXa3AG8b4xx2L+2dnxu/2SMGWKMGYe173efk2t1KnvXaowpBjKwc1dWHXvVW+81VgC8Dwx31Vptrge2GWO+t3eddez43L5qjLnSGDMSOE0rXmMuE+hifSt7FdhjjHm23qoPgem2r6cDH9Rbfq9YXQWcMcYcF5FuIuJn22cEkAbsdsVa6z1uGg78V9COz61FRMJt+xwEDMLaknBmrU5jr1pFJNLWMkdEAoBxwF4XrreLiATXfQ2Mx9pV4HK11uNqr7FL7SvK9rkX1v7zN1tciHHQWerWfgAjsPbLbcf6b0YW1v6jcGAVsB/rGd8w838jAxYCB4AdQKpt+TW277+zfb7PVWu1rYvH2vr1coPn1h/rm+NuYDMwxAVq7YG1j/8sUGz7uqtt3VvAcawnnnLt/bdgr1qxvjFm2vazE/iti/wdNFVvH6yvr++AXcD/c9Vabeu6AEVAiAu9xi5V71dYX2PfAWNbU4de+q+UUh7CZbpclFJKtY8GulJKeQgNdKWU8hAa6Eop5SE00JVSykNooCullIfQQFeqHUTE4uwalKqjga46DRH5o9SbX1pE/iTWuafniMgWsc79/od665fbJp/aVX8CKhEpEZG/ish3wNUd+1Mo1TQNdNWZvAbcCyAiXlhn3zsB9MM6F8kQ4EoRGWnb/mfGmCuBVOAXddMeYL3y8GtjzGBjzPoOrF+pS3K52RaVchRjzGERKRKRFKzTmGZivcnAeNvXAEFYA34d1hCfbFseZ1tehHX2u/c6snalWkIDXXU2rwAzsM6l8RrWmx48bYx5uf5GIpIOXAdcbYwpFZE1WOeyASg31ululXIp2uWiOpv3sU5NOwzr/PMrgZ/Z5rFGRGJss92FAKdtYd4f6634lHJp2kJXnYoxplJEMoBiWyv7cxG5HNhkuxlBCXA38BkwU0T2ANlYZ5dUyqXpbIuqU7GdDN0G3G6M2e/sepSyJ+1yUZ2GiFwB5ACrNMyVJ9IWulJKeQhtoSullIfQQFdKKQ+hga6UUh5CA10ppTyEBrpSSnmI/x9z/0XRmMFlNwAAAABJRU5ErkJggg==", + "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": null, + "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": null, + "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": null, + "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": null, + "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": null, + "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": null, + "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 +} -- GitLab