diff --git a/Disertationmainfile3.ipynb b/Disertationmainfile3.ipynb
index 4b34508d004cba385c00e956deedcba505be4e50..182aaec63d3dfe73e8c4929ec404901d369b7cd7 100644
--- a/Disertationmainfile3.ipynb
+++ b/Disertationmainfile3.ipynb
@@ -92,6 +92,7 @@
}
],
"source": [
+ "#A2018 = Accident_Information_df\n",
"\n",
"\n",
"A2005=A2018.filter(A2018.accident_year>2004)\n",
@@ -4626,57 +4627,45 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "# Over the years"
+ "# Normalized after time"
]
},
{
"cell_type": "code",
- "execution_count": 111,
+ "execution_count": 5,
"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"
- ]
+ "data": {
+ "text/plain": [
+ "Row(hour=0, Total accidents=34976)"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
}
],
"source": [
- "TrafficvolumeGroupedupdated.show()"
+ "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": 116,
+ "execution_count": 6,
"metadata": {},
"outputs": [
{
@@ -4700,105 +4689,3534 @@
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
- " <th>road_name</th>\n",
+ " <th>hour</th>\n",
+ " <th>Total accidents</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
- " <td>B</td>\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",
+ " <tr>\n",
+ " <th>6</th>\n",
+ " <td>6</td>\n",
+ " <td>41476</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>7</th>\n",
+ " <td>7</td>\n",
+ " <td>97895</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>8</th>\n",
+ " <td>8</td>\n",
+ " <td>166145</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>9</th>\n",
+ " <td>9</td>\n",
+ " <td>112595</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>10</th>\n",
+ " <td>10</td>\n",
+ " <td>103173</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>11</th>\n",
+ " <td>11</td>\n",
+ " <td>117755</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>12</th>\n",
+ " <td>12</td>\n",
+ " <td>133996</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>13</th>\n",
+ " <td>13</td>\n",
+ " <td>137581</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>14</th>\n",
+ " <td>14</td>\n",
+ " <td>138775</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>15</th>\n",
+ " <td>15</td>\n",
+ " <td>176700</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>16</th>\n",
+ " <td>16</td>\n",
+ " <td>185391</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <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": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "TimeAccident_df_df=TimeAccident_df.toPandas()\n",
+ "TimeAccident_df_df"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "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>103000000</td>\n",
+ " <td>108000000</td>\n",
+ " <td>114000000</td>\n",
+ " <td>117000000</td>\n",
+ " <td>126000000</td>\n",
+ " <td>173000000</td>\n",
+ " <td>187000000</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>01:00-02:00</td>\n",
+ " <td>66000000</td>\n",
+ " <td>72000000</td>\n",
+ " <td>77000000</td>\n",
+ " <td>77000000</td>\n",
+ " <td>84000000</td>\n",
+ " <td>112000000</td>\n",
+ " <td>117000000</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>02:00-03:00</td>\n",
+ " <td>55000000</td>\n",
+ " <td>63000000</td>\n",
+ " <td>65000000</td>\n",
+ " <td>66000000</td>\n",
+ " <td>69000000</td>\n",
+ " <td>84000000</td>\n",
+ " <td>80000000</td>\n",
+ " <td>2</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>03:00-04:00</td>\n",
+ " <td>71000000</td>\n",
+ " <td>73000000</td>\n",
+ " <td>75000000</td>\n",
+ " <td>76000000</td>\n",
+ " <td>78000000</td>\n",
+ " <td>78000000</td>\n",
+ " <td>67000000</td>\n",
+ " <td>3</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>04:00-05:00</td>\n",
+ " <td>141000000</td>\n",
+ " <td>131000000</td>\n",
+ " <td>131000000</td>\n",
+ " <td>131000000</td>\n",
+ " <td>130000000</td>\n",
+ " <td>95000000</td>\n",
+ " <td>71000000</td>\n",
+ " <td>4</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>5</th>\n",
+ " <td>05:00-06:00</td>\n",
+ " <td>411000000</td>\n",
+ " <td>394000000</td>\n",
+ " <td>390000000</td>\n",
+ " <td>386000000</td>\n",
+ " <td>368000000</td>\n",
+ " <td>195000000</td>\n",
+ " <td>128000000</td>\n",
+ " <td>5</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>6</th>\n",
+ " <td>06:00-07:00</td>\n",
+ " <td>953000000</td>\n",
+ " <td>964000000</td>\n",
+ " <td>950000000</td>\n",
+ " <td>936000000</td>\n",
+ " <td>874000000</td>\n",
+ " <td>356000000</td>\n",
+ " <td>225000000</td>\n",
+ " <td>6</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>7</th>\n",
+ " <td>07:00-08:00</td>\n",
+ " <td>1701000000</td>\n",
+ " <td>1750000000</td>\n",
+ " <td>1729000000</td>\n",
+ " <td>1705000000</td>\n",
+ " <td>1591000000</td>\n",
+ " <td>602000000</td>\n",
+ " <td>346000000</td>\n",
+ " <td>7</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>8</th>\n",
+ " <td>08:00-09:00</td>\n",
+ " <td>1859000000</td>\n",
+ " <td>1918000000</td>\n",
+ " <td>1911000000</td>\n",
+ " <td>1904000000</td>\n",
+ " <td>1809000000</td>\n",
+ " <td>920000000</td>\n",
+ " <td>495000000</td>\n",
+ " <td>8</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>9</th>\n",
+ " <td>09:00-10:00</td>\n",
+ " <td>1493000000</td>\n",
+ " <td>1522000000</td>\n",
+ " <td>1523000000</td>\n",
+ " <td>1533000000</td>\n",
+ " <td>1534000000</td>\n",
+ " <td>1270000000</td>\n",
+ " <td>853000000</td>\n",
+ " <td>9</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>10</th>\n",
+ " <td>10:00-11:00</td>\n",
+ " <td>1486000000</td>\n",
+ " <td>1470000000</td>\n",
+ " <td>1482000000</td>\n",
+ " <td>1504000000</td>\n",
+ " <td>1592000000</td>\n",
+ " <td>1594000000</td>\n",
+ " <td>1280000000</td>\n",
+ " <td>10</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>11</th>\n",
+ " <td>11:00-12:00</td>\n",
+ " <td>1571000000</td>\n",
+ " <td>1540000000</td>\n",
+ " <td>1562000000</td>\n",
+ " <td>1586000000</td>\n",
+ " <td>1727000000</td>\n",
+ " <td>1789000000</td>\n",
+ " <td>1544000000</td>\n",
+ " <td>11</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>12</th>\n",
+ " <td>12:00-13:00</td>\n",
+ " <td>1622000000</td>\n",
+ " <td>1596000000</td>\n",
+ " <td>1626000000</td>\n",
+ " <td>1653000000</td>\n",
+ " <td>1836000000</td>\n",
+ " <td>1844000000</td>\n",
+ " <td>1662000000</td>\n",
+ " <td>12</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>13</th>\n",
+ " <td>13:00-14:00</td>\n",
+ " <td>1636000000</td>\n",
+ " <td>1622000000</td>\n",
+ " <td>1655000000</td>\n",
+ " <td>1681000000</td>\n",
+ " <td>1881000000</td>\n",
+ " <td>1787000000</td>\n",
+ " <td>1640000000</td>\n",
+ " <td>13</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>14</th>\n",
+ " <td>14:00-15:00</td>\n",
+ " <td>1740000000</td>\n",
+ " <td>1740000000</td>\n",
+ " <td>1782000000</td>\n",
+ " <td>1800000000</td>\n",
+ " <td>1992000000</td>\n",
+ " <td>1708000000</td>\n",
+ " <td>1584000000</td>\n",
+ " <td>14</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>15</th>\n",
+ " <td>15:00-16:00</td>\n",
+ " <td>1917000000</td>\n",
+ " <td>1947000000</td>\n",
+ " <td>1980000000</td>\n",
+ " <td>1999000000</td>\n",
+ " <td>2165000000</td>\n",
+ " <td>1613000000</td>\n",
+ " <td>1520000000</td>\n",
+ " <td>15</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>16</th>\n",
+ " <td>16:00-17:00</td>\n",
+ " <td>2069000000</td>\n",
+ " <td>2131000000</td>\n",
+ " <td>2140000000</td>\n",
+ " <td>2148000000</td>\n",
+ " <td>2175000000</td>\n",
+ " <td>1544000000</td>\n",
+ " <td>1429000000</td>\n",
+ " <td>16</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>17</th>\n",
+ " <td>17:00-18:00</td>\n",
+ " <td>1989000000</td>\n",
+ " <td>2060000000</td>\n",
+ " <td>2075000000</td>\n",
+ " <td>2075000000</td>\n",
+ " <td>2021000000</td>\n",
+ " <td>1423000000</td>\n",
+ " <td>1236000000</td>\n",
+ " <td>17</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>18</th>\n",
+ " <td>18:00-19:00</td>\n",
+ " <td>1431000000</td>\n",
+ " <td>1506000000</td>\n",
+ " <td>1529000000</td>\n",
+ " <td>1555000000</td>\n",
+ " <td>1585000000</td>\n",
+ " <td>1180000000</td>\n",
+ " <td>1053000000</td>\n",
+ " <td>18</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>19</th>\n",
+ " <td>19:00-20:00</td>\n",
+ " <td>977000000</td>\n",
+ " <td>1028000000</td>\n",
+ " <td>1057000000</td>\n",
+ " <td>1095000000</td>\n",
+ " <td>1149000000</td>\n",
+ " <td>906000000</td>\n",
+ " <td>855000000</td>\n",
+ " <td>19</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>20</th>\n",
+ " <td>20:00-21:00</td>\n",
+ " <td>682000000</td>\n",
+ " <td>714000000</td>\n",
+ " <td>738000000</td>\n",
+ " <td>767000000</td>\n",
+ " <td>801000000</td>\n",
+ " <td>663000000</td>\n",
+ " <td>651000000</td>\n",
+ " <td>20</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>21</th>\n",
+ " <td>21:00-22:00</td>\n",
+ " <td>484000000</td>\n",
+ " <td>511000000</td>\n",
+ " <td>529000000</td>\n",
+ " <td>547000000</td>\n",
+ " <td>573000000</td>\n",
+ " <td>501000000</td>\n",
+ " <td>457000000</td>\n",
+ " <td>21</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>22</th>\n",
+ " <td>22:00-23:00</td>\n",
+ " <td>323000000</td>\n",
+ " <td>352000000</td>\n",
+ " <td>369000000</td>\n",
+ " <td>379000000</td>\n",
+ " <td>426000000</td>\n",
+ " <td>400000000</td>\n",
+ " <td>300000000</td>\n",
+ " <td>22</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>23</th>\n",
+ " <td>23:00-00:00</td>\n",
+ " <td>179000000</td>\n",
+ " <td>192000000</td>\n",
+ " <td>204000000</td>\n",
+ " <td>215000000</td>\n",
+ " <td>276000000</td>\n",
+ " <td>283000000</td>\n",
+ " <td>175000000</td>\n",
+ " <td>23</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " Time of day Monday Tuesday Wednesday Thursday Friday \\\n",
+ "0 00:00-01:00 103000000 108000000 114000000 117000000 126000000 \n",
+ "1 01:00-02:00 66000000 72000000 77000000 77000000 84000000 \n",
+ "2 02:00-03:00 55000000 63000000 65000000 66000000 69000000 \n",
+ "3 03:00-04:00 71000000 73000000 75000000 76000000 78000000 \n",
+ "4 04:00-05:00 141000000 131000000 131000000 131000000 130000000 \n",
+ "5 05:00-06:00 411000000 394000000 390000000 386000000 368000000 \n",
+ "6 06:00-07:00 953000000 964000000 950000000 936000000 874000000 \n",
+ "7 07:00-08:00 1701000000 1750000000 1729000000 1705000000 1591000000 \n",
+ "8 08:00-09:00 1859000000 1918000000 1911000000 1904000000 1809000000 \n",
+ "9 09:00-10:00 1493000000 1522000000 1523000000 1533000000 1534000000 \n",
+ "10 10:00-11:00 1486000000 1470000000 1482000000 1504000000 1592000000 \n",
+ "11 11:00-12:00 1571000000 1540000000 1562000000 1586000000 1727000000 \n",
+ "12 12:00-13:00 1622000000 1596000000 1626000000 1653000000 1836000000 \n",
+ "13 13:00-14:00 1636000000 1622000000 1655000000 1681000000 1881000000 \n",
+ "14 14:00-15:00 1740000000 1740000000 1782000000 1800000000 1992000000 \n",
+ "15 15:00-16:00 1917000000 1947000000 1980000000 1999000000 2165000000 \n",
+ "16 16:00-17:00 2069000000 2131000000 2140000000 2148000000 2175000000 \n",
+ "17 17:00-18:00 1989000000 2060000000 2075000000 2075000000 2021000000 \n",
+ "18 18:00-19:00 1431000000 1506000000 1529000000 1555000000 1585000000 \n",
+ "19 19:00-20:00 977000000 1028000000 1057000000 1095000000 1149000000 \n",
+ "20 20:00-21:00 682000000 714000000 738000000 767000000 801000000 \n",
+ "21 21:00-22:00 484000000 511000000 529000000 547000000 573000000 \n",
+ "22 22:00-23:00 323000000 352000000 369000000 379000000 426000000 \n",
+ "23 23:00-00:00 179000000 192000000 204000000 215000000 276000000 \n",
+ "\n",
+ " Saturday Sunday hour \n",
+ "0 173000000 187000000 0 \n",
+ "1 112000000 117000000 1 \n",
+ "2 84000000 80000000 2 \n",
+ "3 78000000 67000000 3 \n",
+ "4 95000000 71000000 4 \n",
+ "5 195000000 128000000 5 \n",
+ "6 356000000 225000000 6 \n",
+ "7 602000000 346000000 7 \n",
+ "8 920000000 495000000 8 \n",
+ "9 1270000000 853000000 9 \n",
+ "10 1594000000 1280000000 10 \n",
+ "11 1789000000 1544000000 11 \n",
+ "12 1844000000 1662000000 12 \n",
+ "13 1787000000 1640000000 13 \n",
+ "14 1708000000 1584000000 14 \n",
+ "15 1613000000 1520000000 15 \n",
+ "16 1544000000 1429000000 16 \n",
+ "17 1423000000 1236000000 17 \n",
+ "18 1180000000 1053000000 18 \n",
+ "19 906000000 855000000 19 \n",
+ "20 663000000 651000000 20 \n",
+ "21 501000000 457000000 21 \n",
+ "22 400000000 300000000 22 \n",
+ "23 283000000 175000000 23 "
+ ]
+ },
+ "execution_count": 7,
+ "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": 8,
+ "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>0.000340</td>\n",
+ " <td>0.000324</td>\n",
+ " <td>0.000307</td>\n",
+ " <td>0.000299</td>\n",
+ " <td>0.000278</td>\n",
+ " <td>0.000202</td>\n",
+ " <td>0.000187</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>0.000384</td>\n",
+ " <td>0.000352</td>\n",
+ " <td>0.000329</td>\n",
+ " <td>0.000329</td>\n",
+ " <td>0.000302</td>\n",
+ " <td>0.000226</td>\n",
+ " <td>0.000217</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>0.000365</td>\n",
+ " <td>0.000318</td>\n",
+ " <td>0.000309</td>\n",
+ " <td>0.000304</td>\n",
+ " <td>0.000291</td>\n",
+ " <td>0.000239</td>\n",
+ " <td>0.000251</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>0.000230</td>\n",
+ " <td>0.000223</td>\n",
+ " <td>0.000217</td>\n",
+ " <td>0.000215</td>\n",
+ " <td>0.000209</td>\n",
+ " <td>0.000209</td>\n",
+ " <td>0.000243</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>0.000092</td>\n",
+ " <td>0.000099</td>\n",
+ " <td>0.000099</td>\n",
+ " <td>0.000099</td>\n",
+ " <td>0.000100</td>\n",
+ " <td>0.000137</td>\n",
+ " <td>0.000183</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>0.000047</td>\n",
+ " <td>0.000049</td>\n",
+ " <td>0.000049</td>\n",
+ " <td>0.000050</td>\n",
+ " <td>0.000052</td>\n",
+ " <td>0.000099</td>\n",
+ " <td>0.000150</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>0.000044</td>\n",
+ " <td>0.000043</td>\n",
+ " <td>0.000044</td>\n",
+ " <td>0.000044</td>\n",
+ " <td>0.000047</td>\n",
+ " <td>0.000117</td>\n",
+ " <td>0.000184</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>0.000058</td>\n",
+ " <td>0.000056</td>\n",
+ " <td>0.000057</td>\n",
+ " <td>0.000057</td>\n",
+ " <td>0.000062</td>\n",
+ " <td>0.000163</td>\n",
+ " <td>0.000283</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>0.000089</td>\n",
+ " <td>0.000087</td>\n",
+ " <td>0.000087</td>\n",
+ " <td>0.000087</td>\n",
+ " <td>0.000092</td>\n",
+ " <td>0.000181</td>\n",
+ " <td>0.000336</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>0.000075</td>\n",
+ " <td>0.000074</td>\n",
+ " <td>0.000074</td>\n",
+ " <td>0.000073</td>\n",
+ " <td>0.000073</td>\n",
+ " <td>0.000089</td>\n",
+ " <td>0.000132</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>0.000069</td>\n",
+ " <td>0.000070</td>\n",
+ " <td>0.000070</td>\n",
+ " <td>0.000069</td>\n",
+ " <td>0.000065</td>\n",
+ " <td>0.000065</td>\n",
+ " <td>0.000081</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>0.000075</td>\n",
+ " <td>0.000076</td>\n",
+ " <td>0.000075</td>\n",
+ " <td>0.000074</td>\n",
+ " <td>0.000068</td>\n",
+ " <td>0.000066</td>\n",
+ " <td>0.000076</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>0.000083</td>\n",
+ " <td>0.000084</td>\n",
+ " <td>0.000082</td>\n",
+ " <td>0.000081</td>\n",
+ " <td>0.000073</td>\n",
+ " <td>0.000073</td>\n",
+ " <td>0.000081</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>0.000084</td>\n",
+ " <td>0.000085</td>\n",
+ " <td>0.000083</td>\n",
+ " <td>0.000082</td>\n",
+ " <td>0.000073</td>\n",
+ " <td>0.000077</td>\n",
+ " <td>0.000084</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>0.000080</td>\n",
+ " <td>0.000080</td>\n",
+ " <td>0.000078</td>\n",
+ " <td>0.000077</td>\n",
+ " <td>0.000070</td>\n",
+ " <td>0.000081</td>\n",
+ " <td>0.000088</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>0.000092</td>\n",
+ " <td>0.000091</td>\n",
+ " <td>0.000089</td>\n",
+ " <td>0.000088</td>\n",
+ " <td>0.000082</td>\n",
+ " <td>0.000110</td>\n",
+ " <td>0.000116</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>0.000090</td>\n",
+ " <td>0.000087</td>\n",
+ " <td>0.000087</td>\n",
+ " <td>0.000086</td>\n",
+ " <td>0.000085</td>\n",
+ " <td>0.000120</td>\n",
+ " <td>0.000130</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>0.000102</td>\n",
+ " <td>0.000098</td>\n",
+ " <td>0.000098</td>\n",
+ " <td>0.000098</td>\n",
+ " <td>0.000100</td>\n",
+ " <td>0.000142</td>\n",
+ " <td>0.000164</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>0.000112</td>\n",
+ " <td>0.000106</td>\n",
+ " <td>0.000104</td>\n",
+ " <td>0.000103</td>\n",
+ " <td>0.000101</td>\n",
+ " <td>0.000135</td>\n",
+ " <td>0.000152</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>0.000122</td>\n",
+ " <td>0.000116</td>\n",
+ " <td>0.000113</td>\n",
+ " <td>0.000109</td>\n",
+ " <td>0.000103</td>\n",
+ " <td>0.000131</td>\n",
+ " <td>0.000139</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>0.000128</td>\n",
+ " <td>0.000122</td>\n",
+ " <td>0.000118</td>\n",
+ " <td>0.000114</td>\n",
+ " <td>0.000109</td>\n",
+ " <td>0.000132</td>\n",
+ " <td>0.000134</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>0.000144</td>\n",
+ " <td>0.000136</td>\n",
+ " <td>0.000132</td>\n",
+ " <td>0.000127</td>\n",
+ " <td>0.000121</td>\n",
+ " <td>0.000139</td>\n",
+ " <td>0.000152</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>0.000188</td>\n",
+ " <td>0.000173</td>\n",
+ " <td>0.000165</td>\n",
+ " <td>0.000161</td>\n",
+ " <td>0.000143</td>\n",
+ " <td>0.000152</td>\n",
+ " <td>0.000203</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>0.000269</td>\n",
+ " <td>0.000251</td>\n",
+ " <td>0.000236</td>\n",
+ " <td>0.000224</td>\n",
+ " <td>0.000174</td>\n",
+ " <td>0.000170</td>\n",
+ " <td>0.000275</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 0.000340 0.000324 \n",
+ "1 1 25340 01:00-02:00 0.000384 0.000352 \n",
+ "2 2 20055 02:00-03:00 0.000365 0.000318 \n",
+ "3 3 16310 03:00-04:00 0.000230 0.000223 \n",
+ "4 4 12989 04:00-05:00 0.000092 0.000099 \n",
+ "5 5 19228 05:00-06:00 0.000047 0.000049 \n",
+ "6 6 41476 06:00-07:00 0.000044 0.000043 \n",
+ "7 7 97895 07:00-08:00 0.000058 0.000056 \n",
+ "8 8 166145 08:00-09:00 0.000089 0.000087 \n",
+ "9 9 112595 09:00-10:00 0.000075 0.000074 \n",
+ "10 10 103173 10:00-11:00 0.000069 0.000070 \n",
+ "11 11 117755 11:00-12:00 0.000075 0.000076 \n",
+ "12 12 133996 12:00-13:00 0.000083 0.000084 \n",
+ "13 13 137581 13:00-14:00 0.000084 0.000085 \n",
+ "14 14 138775 14:00-15:00 0.000080 0.000080 \n",
+ "15 15 176700 15:00-16:00 0.000092 0.000091 \n",
+ "16 16 185391 16:00-17:00 0.000090 0.000087 \n",
+ "17 17 202694 17:00-18:00 0.000102 0.000098 \n",
+ "18 18 159655 18:00-19:00 0.000112 0.000106 \n",
+ "19 19 118915 19:00-20:00 0.000122 0.000116 \n",
+ "20 20 87214 20:00-21:00 0.000128 0.000122 \n",
+ "21 21 69608 21:00-22:00 0.000144 0.000136 \n",
+ "22 22 60838 22:00-23:00 0.000188 0.000173 \n",
+ "23 23 48123 23:00-00:00 0.000269 0.000251 \n",
+ "\n",
+ " Wednesday_Normalized Thursday_Normalized Friday_Normalized \\\n",
+ "0 0.000307 0.000299 0.000278 \n",
+ "1 0.000329 0.000329 0.000302 \n",
+ "2 0.000309 0.000304 0.000291 \n",
+ "3 0.000217 0.000215 0.000209 \n",
+ "4 0.000099 0.000099 0.000100 \n",
+ "5 0.000049 0.000050 0.000052 \n",
+ "6 0.000044 0.000044 0.000047 \n",
+ "7 0.000057 0.000057 0.000062 \n",
+ "8 0.000087 0.000087 0.000092 \n",
+ "9 0.000074 0.000073 0.000073 \n",
+ "10 0.000070 0.000069 0.000065 \n",
+ "11 0.000075 0.000074 0.000068 \n",
+ "12 0.000082 0.000081 0.000073 \n",
+ "13 0.000083 0.000082 0.000073 \n",
+ "14 0.000078 0.000077 0.000070 \n",
+ "15 0.000089 0.000088 0.000082 \n",
+ "16 0.000087 0.000086 0.000085 \n",
+ "17 0.000098 0.000098 0.000100 \n",
+ "18 0.000104 0.000103 0.000101 \n",
+ "19 0.000113 0.000109 0.000103 \n",
+ "20 0.000118 0.000114 0.000109 \n",
+ "21 0.000132 0.000127 0.000121 \n",
+ "22 0.000165 0.000161 0.000143 \n",
+ "23 0.000236 0.000224 0.000174 \n",
+ "\n",
+ " Saturday_Normalized Sunday_Normalized \n",
+ "0 0.000202 0.000187 \n",
+ "1 0.000226 0.000217 \n",
+ "2 0.000239 0.000251 \n",
+ "3 0.000209 0.000243 \n",
+ "4 0.000137 0.000183 \n",
+ "5 0.000099 0.000150 \n",
+ "6 0.000117 0.000184 \n",
+ "7 0.000163 0.000283 \n",
+ "8 0.000181 0.000336 \n",
+ "9 0.000089 0.000132 \n",
+ "10 0.000065 0.000081 \n",
+ "11 0.000066 0.000076 \n",
+ "12 0.000073 0.000081 \n",
+ "13 0.000077 0.000084 \n",
+ "14 0.000081 0.000088 \n",
+ "15 0.000110 0.000116 \n",
+ "16 0.000120 0.000130 \n",
+ "17 0.000142 0.000164 \n",
+ "18 0.000135 0.000152 \n",
+ "19 0.000131 0.000139 \n",
+ "20 0.000132 0.000134 \n",
+ "21 0.000139 0.000152 \n",
+ "22 0.000152 0.000203 \n",
+ "23 0.000170 0.000275 "
+ ]
+ },
+ "execution_count": 8,
+ "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": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>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>0.000340</td>\n",
+ " <td>0.000324</td>\n",
+ " <td>0.000307</td>\n",
+ " <td>0.000299</td>\n",
+ " <td>0.000278</td>\n",
+ " <td>0.000202</td>\n",
+ " <td>0.000187</td>\n",
+ " <td>0.001936</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>0.000384</td>\n",
+ " <td>0.000352</td>\n",
+ " <td>0.000329</td>\n",
+ " <td>0.000329</td>\n",
+ " <td>0.000302</td>\n",
+ " <td>0.000226</td>\n",
+ " <td>0.000217</td>\n",
+ " <td>0.002139</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>0.000365</td>\n",
+ " <td>0.000318</td>\n",
+ " <td>0.000309</td>\n",
+ " <td>0.000304</td>\n",
+ " <td>0.000291</td>\n",
+ " <td>0.000239</td>\n",
+ " <td>0.000251</td>\n",
+ " <td>0.002075</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>0.000230</td>\n",
+ " <td>0.000223</td>\n",
+ " <td>0.000217</td>\n",
+ " <td>0.000215</td>\n",
+ " <td>0.000209</td>\n",
+ " <td>0.000209</td>\n",
+ " <td>0.000243</td>\n",
+ " <td>0.001547</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>0.000092</td>\n",
+ " <td>0.000099</td>\n",
+ " <td>0.000099</td>\n",
+ " <td>0.000099</td>\n",
+ " <td>0.000100</td>\n",
+ " <td>0.000137</td>\n",
+ " <td>0.000183</td>\n",
+ " <td>0.000809</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>0.000047</td>\n",
+ " <td>0.000049</td>\n",
+ " <td>0.000049</td>\n",
+ " <td>0.000050</td>\n",
+ " <td>0.000052</td>\n",
+ " <td>0.000099</td>\n",
+ " <td>0.000150</td>\n",
+ " <td>0.000496</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>0.000044</td>\n",
+ " <td>0.000043</td>\n",
+ " <td>0.000044</td>\n",
+ " <td>0.000044</td>\n",
+ " <td>0.000047</td>\n",
+ " <td>0.000117</td>\n",
+ " <td>0.000184</td>\n",
+ " <td>0.000523</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>0.000058</td>\n",
+ " <td>0.000056</td>\n",
+ " <td>0.000057</td>\n",
+ " <td>0.000057</td>\n",
+ " <td>0.000062</td>\n",
+ " <td>0.000163</td>\n",
+ " <td>0.000283</td>\n",
+ " <td>0.000735</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>0.000089</td>\n",
+ " <td>0.000087</td>\n",
+ " <td>0.000087</td>\n",
+ " <td>0.000087</td>\n",
+ " <td>0.000092</td>\n",
+ " <td>0.000181</td>\n",
+ " <td>0.000336</td>\n",
+ " <td>0.000958</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>0.000075</td>\n",
+ " <td>0.000074</td>\n",
+ " <td>0.000074</td>\n",
+ " <td>0.000073</td>\n",
+ " <td>0.000073</td>\n",
+ " <td>0.000089</td>\n",
+ " <td>0.000132</td>\n",
+ " <td>0.000591</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>0.000069</td>\n",
+ " <td>0.000070</td>\n",
+ " <td>0.000070</td>\n",
+ " <td>0.000069</td>\n",
+ " <td>0.000065</td>\n",
+ " <td>0.000065</td>\n",
+ " <td>0.000081</td>\n",
+ " <td>0.000488</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>0.000075</td>\n",
+ " <td>0.000076</td>\n",
+ " <td>0.000075</td>\n",
+ " <td>0.000074</td>\n",
+ " <td>0.000068</td>\n",
+ " <td>0.000066</td>\n",
+ " <td>0.000076</td>\n",
+ " <td>0.000511</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>0.000083</td>\n",
+ " <td>0.000084</td>\n",
+ " <td>0.000082</td>\n",
+ " <td>0.000081</td>\n",
+ " <td>0.000073</td>\n",
+ " <td>0.000073</td>\n",
+ " <td>0.000081</td>\n",
+ " <td>0.000556</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>0.000084</td>\n",
+ " <td>0.000085</td>\n",
+ " <td>0.000083</td>\n",
+ " <td>0.000082</td>\n",
+ " <td>0.000073</td>\n",
+ " <td>0.000077</td>\n",
+ " <td>0.000084</td>\n",
+ " <td>0.000568</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>0.000080</td>\n",
+ " <td>0.000080</td>\n",
+ " <td>0.000078</td>\n",
+ " <td>0.000077</td>\n",
+ " <td>0.000070</td>\n",
+ " <td>0.000081</td>\n",
+ " <td>0.000088</td>\n",
+ " <td>0.000553</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>0.000092</td>\n",
+ " <td>0.000091</td>\n",
+ " <td>0.000089</td>\n",
+ " <td>0.000088</td>\n",
+ " <td>0.000082</td>\n",
+ " <td>0.000110</td>\n",
+ " <td>0.000116</td>\n",
+ " <td>0.000668</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>0.000090</td>\n",
+ " <td>0.000087</td>\n",
+ " <td>0.000087</td>\n",
+ " <td>0.000086</td>\n",
+ " <td>0.000085</td>\n",
+ " <td>0.000120</td>\n",
+ " <td>0.000130</td>\n",
+ " <td>0.000685</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>0.000102</td>\n",
+ " <td>0.000098</td>\n",
+ " <td>0.000098</td>\n",
+ " <td>0.000098</td>\n",
+ " <td>0.000100</td>\n",
+ " <td>0.000142</td>\n",
+ " <td>0.000164</td>\n",
+ " <td>0.000802</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>0.000112</td>\n",
+ " <td>0.000106</td>\n",
+ " <td>0.000104</td>\n",
+ " <td>0.000103</td>\n",
+ " <td>0.000101</td>\n",
+ " <td>0.000135</td>\n",
+ " <td>0.000152</td>\n",
+ " <td>0.000812</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>0.000122</td>\n",
+ " <td>0.000116</td>\n",
+ " <td>0.000113</td>\n",
+ " <td>0.000109</td>\n",
+ " <td>0.000103</td>\n",
+ " <td>0.000131</td>\n",
+ " <td>0.000139</td>\n",
+ " <td>0.000832</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>0.000128</td>\n",
+ " <td>0.000122</td>\n",
+ " <td>0.000118</td>\n",
+ " <td>0.000114</td>\n",
+ " <td>0.000109</td>\n",
+ " <td>0.000132</td>\n",
+ " <td>0.000134</td>\n",
+ " <td>0.000856</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>0.000144</td>\n",
+ " <td>0.000136</td>\n",
+ " <td>0.000132</td>\n",
+ " <td>0.000127</td>\n",
+ " <td>0.000121</td>\n",
+ " <td>0.000139</td>\n",
+ " <td>0.000152</td>\n",
+ " <td>0.000952</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>0.000188</td>\n",
+ " <td>0.000173</td>\n",
+ " <td>0.000165</td>\n",
+ " <td>0.000161</td>\n",
+ " <td>0.000143</td>\n",
+ " <td>0.000152</td>\n",
+ " <td>0.000203</td>\n",
+ " <td>0.001184</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>0.000269</td>\n",
+ " <td>0.000251</td>\n",
+ " <td>0.000236</td>\n",
+ " <td>0.000224</td>\n",
+ " <td>0.000174</td>\n",
+ " <td>0.000170</td>\n",
+ " <td>0.000275</td>\n",
+ " <td>0.001599</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 0.000340 0.000324 \n",
+ "1 1 25340 01:00-02:00 0.000384 0.000352 \n",
+ "2 2 20055 02:00-03:00 0.000365 0.000318 \n",
+ "3 3 16310 03:00-04:00 0.000230 0.000223 \n",
+ "4 4 12989 04:00-05:00 0.000092 0.000099 \n",
+ "5 5 19228 05:00-06:00 0.000047 0.000049 \n",
+ "6 6 41476 06:00-07:00 0.000044 0.000043 \n",
+ "7 7 97895 07:00-08:00 0.000058 0.000056 \n",
+ "8 8 166145 08:00-09:00 0.000089 0.000087 \n",
+ "9 9 112595 09:00-10:00 0.000075 0.000074 \n",
+ "10 10 103173 10:00-11:00 0.000069 0.000070 \n",
+ "11 11 117755 11:00-12:00 0.000075 0.000076 \n",
+ "12 12 133996 12:00-13:00 0.000083 0.000084 \n",
+ "13 13 137581 13:00-14:00 0.000084 0.000085 \n",
+ "14 14 138775 14:00-15:00 0.000080 0.000080 \n",
+ "15 15 176700 15:00-16:00 0.000092 0.000091 \n",
+ "16 16 185391 16:00-17:00 0.000090 0.000087 \n",
+ "17 17 202694 17:00-18:00 0.000102 0.000098 \n",
+ "18 18 159655 18:00-19:00 0.000112 0.000106 \n",
+ "19 19 118915 19:00-20:00 0.000122 0.000116 \n",
+ "20 20 87214 20:00-21:00 0.000128 0.000122 \n",
+ "21 21 69608 21:00-22:00 0.000144 0.000136 \n",
+ "22 22 60838 22:00-23:00 0.000188 0.000173 \n",
+ "23 23 48123 23:00-00:00 0.000269 0.000251 \n",
+ "\n",
+ " Wednesday_Normalized Thursday_Normalized Friday_Normalized \\\n",
+ "0 0.000307 0.000299 0.000278 \n",
+ "1 0.000329 0.000329 0.000302 \n",
+ "2 0.000309 0.000304 0.000291 \n",
+ "3 0.000217 0.000215 0.000209 \n",
+ "4 0.000099 0.000099 0.000100 \n",
+ "5 0.000049 0.000050 0.000052 \n",
+ "6 0.000044 0.000044 0.000047 \n",
+ "7 0.000057 0.000057 0.000062 \n",
+ "8 0.000087 0.000087 0.000092 \n",
+ "9 0.000074 0.000073 0.000073 \n",
+ "10 0.000070 0.000069 0.000065 \n",
+ "11 0.000075 0.000074 0.000068 \n",
+ "12 0.000082 0.000081 0.000073 \n",
+ "13 0.000083 0.000082 0.000073 \n",
+ "14 0.000078 0.000077 0.000070 \n",
+ "15 0.000089 0.000088 0.000082 \n",
+ "16 0.000087 0.000086 0.000085 \n",
+ "17 0.000098 0.000098 0.000100 \n",
+ "18 0.000104 0.000103 0.000101 \n",
+ "19 0.000113 0.000109 0.000103 \n",
+ "20 0.000118 0.000114 0.000109 \n",
+ "21 0.000132 0.000127 0.000121 \n",
+ "22 0.000165 0.000161 0.000143 \n",
+ "23 0.000236 0.000224 0.000174 \n",
+ "\n",
+ " Saturday_Normalized Sunday_Normalized Total accidents_Normalized \n",
+ "0 0.000202 0.000187 0.001936 \n",
+ "1 0.000226 0.000217 0.002139 \n",
+ "2 0.000239 0.000251 0.002075 \n",
+ "3 0.000209 0.000243 0.001547 \n",
+ "4 0.000137 0.000183 0.000809 \n",
+ "5 0.000099 0.000150 0.000496 \n",
+ "6 0.000117 0.000184 0.000523 \n",
+ "7 0.000163 0.000283 0.000735 \n",
+ "8 0.000181 0.000336 0.000958 \n",
+ "9 0.000089 0.000132 0.000591 \n",
+ "10 0.000065 0.000081 0.000488 \n",
+ "11 0.000066 0.000076 0.000511 \n",
+ "12 0.000073 0.000081 0.000556 \n",
+ "13 0.000077 0.000084 0.000568 \n",
+ "14 0.000081 0.000088 0.000553 \n",
+ "15 0.000110 0.000116 0.000668 \n",
+ "16 0.000120 0.000130 0.000685 \n",
+ "17 0.000142 0.000164 0.000802 \n",
+ "18 0.000135 0.000152 0.000812 \n",
+ "19 0.000131 0.000139 0.000832 \n",
+ "20 0.000132 0.000134 0.000856 \n",
+ "21 0.000139 0.000152 0.000952 \n",
+ "22 0.000152 0.000203 0.001184 \n",
+ "23 0.000170 0.000275 0.001599 "
+ ]
+ },
+ "execution_count": 9,
+ "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": 10,
+ "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>0.001936</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>1</td>\n",
+ " <td>0.002139</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>2</td>\n",
+ " <td>0.002075</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>3</td>\n",
+ " <td>0.001547</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>4</td>\n",
+ " <td>0.000809</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>5</th>\n",
+ " <td>5</td>\n",
+ " <td>0.000496</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>6</th>\n",
+ " <td>6</td>\n",
+ " <td>0.000523</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>7</th>\n",
+ " <td>7</td>\n",
+ " <td>0.000735</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>8</th>\n",
+ " <td>8</td>\n",
+ " <td>0.000958</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>9</th>\n",
+ " <td>9</td>\n",
+ " <td>0.000591</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>10</th>\n",
+ " <td>10</td>\n",
+ " <td>0.000488</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>11</th>\n",
+ " <td>11</td>\n",
+ " <td>0.000511</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>12</th>\n",
+ " <td>12</td>\n",
+ " <td>0.000556</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>13</th>\n",
+ " <td>13</td>\n",
+ " <td>0.000568</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>14</th>\n",
+ " <td>14</td>\n",
+ " <td>0.000553</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>15</th>\n",
+ " <td>15</td>\n",
+ " <td>0.000668</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>16</th>\n",
+ " <td>16</td>\n",
+ " <td>0.000685</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>17</th>\n",
+ " <td>17</td>\n",
+ " <td>0.000802</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>18</th>\n",
+ " <td>18</td>\n",
+ " <td>0.000812</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>19</th>\n",
+ " <td>19</td>\n",
+ " <td>0.000832</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>20</th>\n",
+ " <td>20</td>\n",
+ " <td>0.000856</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>21</th>\n",
+ " <td>21</td>\n",
+ " <td>0.000952</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>22</th>\n",
+ " <td>22</td>\n",
+ " <td>0.001184</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>23</th>\n",
+ " <td>23</td>\n",
+ " <td>0.001599</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " hour Total accidents_Normalized\n",
+ "0 0 0.001936\n",
+ "1 1 0.002139\n",
+ "2 2 0.002075\n",
+ "3 3 0.001547\n",
+ "4 4 0.000809\n",
+ "5 5 0.000496\n",
+ "6 6 0.000523\n",
+ "7 7 0.000735\n",
+ "8 8 0.000958\n",
+ "9 9 0.000591\n",
+ "10 10 0.000488\n",
+ "11 11 0.000511\n",
+ "12 12 0.000556\n",
+ "13 13 0.000568\n",
+ "14 14 0.000553\n",
+ "15 15 0.000668\n",
+ "16 16 0.000685\n",
+ "17 17 0.000802\n",
+ "18 18 0.000812\n",
+ "19 19 0.000832\n",
+ "20 20 0.000856\n",
+ "21 21 0.000952\n",
+ "22 22 0.001184\n",
+ "23 23 0.001599"
+ ]
+ },
+ "execution_count": 10,
+ "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": 11,
+ "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": "markdown",
+ "metadata": {},
+ "source": [
+ "# Over the years"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 111,
+ "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.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 116,
+ "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",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>B</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>M</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>U</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>C</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>A</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " road_name\n",
+ "0 B\n",
+ "1 M\n",
+ "2 U\n",
+ "3 C\n",
+ "4 A"
+ ]
+ },
+ "execution_count": 116,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "road_length_traffic"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 102,
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "KeyError",
+ "evalue": "'Trafficvolume'",
+ "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: 'Trafficvolume'",
+ "\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-102-20660c43864d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 14\u001b[0m \u001b[0mresult24\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_df_notyear_df\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mroad_length_traffic\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mon\u001b[0m\u001b[0;34m=\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[0;32m---> 15\u001b[0;31m \u001b[0mresult24\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[0mresult24\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[0mresult24\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[0m\u001b[1;32m 16\u001b[0m \u001b[0mresult24\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mresult24\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 17\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mresult24\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'road_name'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'Accident Probability'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrot\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"Accidents probabilty over road type \"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"Orange\"\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/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: 'Trafficvolume'"
+ ]
+ }
+ ],
+ "source": [
+ "A2018t_df_notyear = A2018.groupby(\"first_road_class\").agg(F.count(A2018.accident_index).alias('Total accidents'))\n",
+ "A2018t_df_notyear = A2018t_df_notyear.withColumnRenamed(\"first_road_class\", \"road_name\")\n",
+ "A2018t_df_notyear_df=A2018t_df_notyear.toPandas()\n",
+ "\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_df=TrafficvolumeGrouped_notyear.toPandas()\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 118,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 120,
+ "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",
+ " <th>Trafficvolume</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>B</td>\n",
+ " <td>286824</td>\n",
+ " <td>2.057755e+12</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>M</td>\n",
+ " <td>86106</td>\n",
+ " <td>8.846338e+11</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>U</td>\n",
+ " <td>687752</td>\n",
+ " <td>1.175274e+13</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>C</td>\n",
+ " <td>188025</td>\n",
+ " <td>3.374912e+12</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>A</td>\n",
+ " <td>1038720</td>\n",
+ " <td>3.126184e+13</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " road_name Total accidents Trafficvolume\n",
+ "0 B 286824 2.057755e+12\n",
+ "1 M 86106 8.846338e+11\n",
+ "2 U 687752 1.175274e+13\n",
+ "3 C 188025 3.374912e+12\n",
+ "4 A 1038720 3.126184e+13"
+ ]
+ },
+ "execution_count": 120,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "road_length_total = pd.read_csv ('/Users/Asfandyar/Desktop/disertation/diseration_final/road_length.csv')\n",
+ "road_length_traffic=pd.merge(TrafficvolumeGrouped_notyear_df, road_length_total, on=['road_name'])\n",
+ "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)\n",
+ "road_length_traffic[\"Trafficvolume\"] = road_length_traffic[\"all_motor_vehicles\"] * road_length_traffic[\"link_length_km\"]\n",
+ "road_length_traffic=road_length_traffic.drop(['all_motor_vehicles', 'link_length_km'], axis=1)\n",
+ "result24=pd.merge(A2018t_df_notyear_df, road_length_traffic, on=['road_name'])\n",
+ "result24"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 122,
+ "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": [
+ "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=result24.sort_values('road_name')\n",
+ "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": 123,
+ "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": [
+ "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",
+ "dangeorusroadtype = A2018.groupby('Road_Type','first_road_class').agg(F.count(A2018.accident_index).alias('Total accidents'))\n",
+ "dangeorusroadtype_df=dangeorusroadtype.toPandas()\n",
+ "\n",
+ "dangeorusroadtype_df=dangeorusroadtype_df.rename(columns={\"first_road_class\": \"road_name\"})\n",
+ "\n",
+ "result30=pd.merge(dangeorusroadtype_df, road_length_traffic, on=['road_name'])\n",
+ "\n",
+ "result30[\"Accident Probability\"] = result30[\"Total accidents\"] / result30[\"Trafficvolume\"]\n",
+ "result30=result30.drop(['Total accidents', 'Trafficvolume'], axis=1)\n",
+ "result30=result30.drop(['road_name'], axis=1)\n",
+ "result30_df = result30.groupby('Road_Type', sort=False)[\"Accident Probability\"].sum().reset_index(name ='Accident Probability')\n",
+ "result30_df=result30_df.drop(labels=[6],axis=0)\n",
+ "result30_df=result30_df.sort_values('Road_Type')\n",
+ "ax=result30_df.plot.bar('Road_Type','Accident Probability', rot=90,title=\"Accidents probabilty over road type \",figsize=(20, 10),color=\"Orange\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [],
+ "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",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "roadtype1=A2018.select(col(\"Road_Type\"),col(\"longitude\"),col(\"latitude\"))\n",
+ "Accident_Information20052019points=roadtype1.select(col(\"Longitude\"),col(\"Latitude\"),col(\"Year\"),concat(Accident_Information20052019points['1st_Road_Class'],Accident_Information20052019points['1st_Road_Number']).alias(\"road_name\"))\n",
+ "Accident_Information20052019points=Accident_Information20052019points.filter(Accident_Information20052019points.road_name==\"A51\")\n",
+ "Accident_Information20052019points.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "+------------------+---------+---------+---------------+\n",
+ "| Road_Type|longitude| latitude|Total accidents|\n",
+ "+------------------+---------+---------+---------------+\n",
+ "|Single carriageway|-0.170782|51.501819| 1|\n",
+ "|Single carriageway|-0.206827|51.523238| 2|\n",
+ "|Single carriageway|-0.201912|51.497984| 2|\n",
+ "| One way street|-0.135473|51.498475| 1|\n",
+ "|Single carriageway|-0.167087|51.529279| 1|\n",
+ "|Single carriageway|-0.193582|51.526452| 3|\n",
+ "| One way street|-0.151663|51.518515| 2|\n",
+ "|Single carriageway|-0.193442| 51.52636| 4|\n",
+ "| One way street|-0.133996|51.509962| 1|\n",
+ "| One way street|-0.130422| 51.51629| 4|\n",
+ "|Single carriageway|-0.125741|51.496881| 15|\n",
+ "| Dual carriageway|-0.154789|51.508942| 1|\n",
+ "|Single carriageway|-0.131944| 51.49671| 1|\n",
+ "|Single carriageway|-0.147967|51.506586| 5|\n",
+ "|Single carriageway|-0.090806|51.543349| 1|\n",
+ "|Single carriageway|-0.168891|51.538209| 1|\n",
+ "|Single carriageway|-0.141031|51.553059| 1|\n",
+ "|Single carriageway|-0.135949| 51.52564| 1|\n",
+ "| One way street|-0.234388|51.492722| 2|\n",
+ "|Single carriageway|-0.032297| 51.53231| 5|\n",
+ "+------------------+---------+---------+---------------+\n",
+ "only showing top 20 rows\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "roadtype = A2018.groupby('Road_Type','longitude','latitude').agg(F.count(A2018.accident_index).alias('Total accidents'))\n",
+ "roadtype.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 39,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "DataFrame[Road_Type: string, longitude: string, latitude: string, Total accidents: bigint]"
+ ]
+ },
+ "execution_count": 39,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "roadtype_spark=roadtype.toPandas()\n",
+ "roadtype"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 40,
+ "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>longitude</th>\n",
+ " <th>latitude</th>\n",
+ " <th>Total accidents</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>Single carriageway</td>\n",
+ " <td>-0.170782</td>\n",
+ " <td>51.501819</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>Single carriageway</td>\n",
+ " <td>-0.206827</td>\n",
+ " <td>51.523238</td>\n",
+ " <td>2</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>Single carriageway</td>\n",
+ " <td>-0.201912</td>\n",
+ " <td>51.497984</td>\n",
+ " <td>2</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>One way street</td>\n",
+ " <td>-0.135473</td>\n",
+ " <td>51.498475</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>Single carriageway</td>\n",
+ " <td>-0.167087</td>\n",
+ " <td>51.529279</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>...</th>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2015739</th>\n",
+ " <td>Single carriageway</td>\n",
+ " <td>-4.287365</td>\n",
+ " <td>55.872205</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2015740</th>\n",
+ " <td>Single carriageway</td>\n",
+ " <td>-4.25897</td>\n",
+ " <td>55.828063</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2015741</th>\n",
+ " <td>Single carriageway</td>\n",
+ " <td>-4.288574</td>\n",
+ " <td>55.882689</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2015742</th>\n",
+ " <td>Single carriageway</td>\n",
+ " <td>-4.175269</td>\n",
+ " <td>55.85861</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2015743</th>\n",
+ " <td>Single carriageway</td>\n",
+ " <td>-3.87831</td>\n",
+ " <td>55.785517</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>2015744 rows × 4 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " Road_Type longitude latitude Total accidents\n",
+ "0 Single carriageway -0.170782 51.501819 1\n",
+ "1 Single carriageway -0.206827 51.523238 2\n",
+ "2 Single carriageway -0.201912 51.497984 2\n",
+ "3 One way street -0.135473 51.498475 1\n",
+ "4 Single carriageway -0.167087 51.529279 1\n",
+ "... ... ... ... ...\n",
+ "2015739 Single carriageway -4.287365 55.872205 1\n",
+ "2015740 Single carriageway -4.25897 55.828063 1\n",
+ "2015741 Single carriageway -4.288574 55.882689 1\n",
+ "2015742 Single carriageway -4.175269 55.85861 1\n",
+ "2015743 Single carriageway -3.87831 55.785517 1\n",
+ "\n",
+ "[2015744 rows x 4 columns]"
+ ]
+ },
+ "execution_count": 40,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "roadtype_spark"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "+--------------+----+---------+-----------+---------------+------------------+---------------------+--------------------+---------+-------------+---------+------------------------+----------------------+-------+--------+------------+------------+--------------+-------------------+-----------------+-------------------------------------------------+------------+--------------------------+--------------+-----------------+-----+-----------------+-----------------+-------------------------+----------------------------+-----------------------+-----------------------+--------+------------------+\n",
+ "|Count_point_id|year|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 |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",
+ "|51 |2005|1 |South West |E12000009 |1 |Isles of Scilly |E06000053 |A3111 |PA |Major |Pierhead, Hugh Town |A3112 |90200 |10585 |49.915022920|-6.317072964|.30 |.19000000000000000 |Estimated |Estimated using previous year's AADF on this link|205 |35 |470 |30 |323 |83 |6 |0 |1 |0 |0 |90 |948 |\n",
+ "|52 |2005|1 |South West |E12000009 |1 |Isles of Scilly |E06000053 |A3112 |PA |Major |A3111 |A3110 |91000 |10240 |49.912342659|-6.305685679|2.00 |1.24000000000000000|Estimated |Estimated using previous year's AADF on this link|66 |104 |543 |40 |182 |13 |1 |0 |0 |0 |0 |14 |883 |\n",
+ "|53 |2005|1 |South West |E12000009 |1 |Isles of Scilly |E06000053 |A3111 |PA |Major |A3112 |A3110 |91000 |10775 |49.917140810|-6.306113756|1.20 |.75000000000000000 |Estimated |Estimated using previous year's AADF on this link|184 |125 |914 |53 |283 |33 |0 |0 |0 |0 |0 |33 |1408 |\n",
+ "|54 |2005|1 |South West |E12000009 |1 |Isles of Scilly |E06000053 |A3110 |PA |Major |A3111 |A3112 |91515 |10820 |49.917810282|-6.298996455|.20 |.12000000000000000 |Estimated |Estimated using previous year's AADF on this link|68 |97 |517 |12 |257 |13 |0 |0 |0 |0 |0 |13 |896 |\n",
+ "|55 |2005|1 |South West |E12000009 |1 |Isles of Scilly |E06000053 |A3110 |PA |Major |A3111 |A3112 |91800 |10890 |49.918585039|-6.295093656|4.00 |2.49000000000000000|Counted |Manual count |42 |54 |183 |8 |67 |12 |3 |0 |0 |0 |0 |15 |327 |\n",
+ "|501 |2005|4 |Wales |W92000004 |6 |Newport |W06000022 |M4 |TM |Major |28 |27 |328570 |187000 |51.577320306|-3.032184269|2.20 |1.37000000000000000|Estimated |Estimated using previous year's AADF on this link|0 |395 |89870 |472 |13396|2667 |441 |366 |1040 |3874 |2283 |10671 |114804 |\n",
+ "|502 |2005|4 |Wales |W92000004 |7 |Bridgend |W06000013 |M4 |TM |Major |LA Boundary |37 |280770 |182100 |51.525189997|-3.720044465|2.90 |1.80000000000000000|Counted |Manual count |0 |143 |47789 |271 |7636 |1989 |539 |672 |374 |1625 |1153 |6352 |62191 |\n",
+ "|503 |2005|4 |Wales |W92000004 |8 |Swansea |W06000011 |M4 |TM |Major |47 |46 |263100 |199260 |51.675378776|-3.981299876|2.90 |1.80000000000000000|Estimated |Estimated using previous year's AADF on this link|0 |189 |42993 |113 |8423 |1721 |374 |256 |334 |906 |685 |4276 |55994 |\n",
+ "|505 |2005|4 |Wales |W92000004 |10 |Gwynedd |W06000002 |A5 |PA |Major |A487 |A4087 |256000 |371400 |53.220081427|-4.158239803|2.90 |1.80000000000000000|Estimated |Estimated using previous year's AADF on this link|128 |182 |10960 |127 |1214 |73 |9 |1 |2 |1 |0 |86 |12569 |\n",
+ "|506 |2005|4 |Wales |W92000004 |11 |Conwy |W06000003 |A5 |TA |Major |LA Boundary |A4086 |266150 |360260 |53.122661912|-4.001657116|8.40 |5.22000000000000000|Estimated |Estimated using previous year's AADF on this link|0 |19 |2561 |22 |456 |63 |13 |13 |4 |7 |10 |110 |3168 |\n",
+ "|508 |2005|4 |Wales |W92000004 |12 |Denbighshire |W06000004 |A5 |TA |Major |A539 |LA Boundary |324887 |341121 |52.962175094|-3.119702564|4.80 |2.98000000000000000|Estimated |Estimated using previous year's AADF on this link|1 |93 |4670 |50 |708 |178 |31 |25 |17 |59 |72 |382 |5903 |\n",
+ "|509 |2005|4 |Wales |W92000004 |13 |Monmouthshire |W06000021 |A40 |TA |Major |A449 |B4233 |350000 |211160 |51.796842440|-2.726434945|9.70 |6.03000000000000000|Counted |Manual count |0 |111 |18793 |108 |3373 |1227 |148 |63 |413 |1511 |1088 |4450 |26835 |\n",
+ "|510 |2005|4 |Wales |W92000004 |13 |Monmouthshire |W06000021 |A40 |TA |Major |LA Boundary |Pentre Rd, Abergavenny|325000 |216300 |51.840248870|-3.090006725|4.30 |2.67000000000000000|Counted |Manual count |13 |138 |7159 |54 |906 |130 |34 |8 |11 |46 |51 |280 |8537 |\n",
+ "|512 |2005|4 |Wales |W92000004 |14 |Powys |W06000023 |A40 |TA |Major |LA Boundary |A4067 |290000 |229321 |51.951468998|-3.601999230|11.50 |7.15000000000000000|Estimated |Estimated using previous year's AADF on this link|2 |84 |2347 |40 |694 |241 |30 |24 |5 |23 |69 |392 |3557 |\n",
+ "|514 |2005|4 |Wales |W92000004 |15 |Carmarthenshire |W06000010 |A40 |TA |Major |A485 |B4130 |245000 |221137 |51.867218741|-4.252634039|6.40 |3.98000000000000000|Counted |Manual count |6 |48 |5881 |80 |772 |123 |46 |21 |63 |40 |57 |350 |7131 |\n",
+ "|515 |2005|4 |Wales |W92000004 |16 |Pembrokeshire |W06000009 |A40 |TA |Major |A478 |LA Boundary |215000 |216780 |51.818943568|-4.685548935|6.80 |4.23000000000000000|Counted |Manual count |1 |82 |7668 |49 |1510 |428 |116 |36 |43 |176 |114 |913 |10222 |\n",
+ "|516 |2005|4 |Wales |W92000004 |16 |Pembrokeshire |W06000009 |A40 |TA |Major |A4219 |B4331 |194270 |230000 |51.930392758|-4.993778463|6.00 |3.73000000000000000|Counted |Manual count |1 |32 |4753 |68 |719 |192 |41 |12 |8 |58 |18 |329 |5901 |\n",
+ "|520 |2005|4 |Wales |W92000004 |17 |Neath Port Talbot |W06000012 |A48 |PA |Major |M4 jn38 |LA Boundary |281140 |185000 |51.551331439|-3.715693758|4.60 |2.86000000000000000|Estimated |Estimated using previous year's AADF on this link|78 |191 |7712 |93 |1351 |189 |21 |34 |4 |9 |10 |267 |9614 |\n",
+ "|521 |2005|4 |Wales |W92000004 |18 |The Vale of Glamorgan|W06000014 |A48 |PA |Major |LA boundary |A4222 Gibbet's Hill |295000 |176435 |51.477099176|-3.513319138|7.67 |4.77000000000000000|Counted |Manual count |12 |156 |11063 |80 |1129 |198 |48 |63 |16 |39 |36 |400 |12828 |\n",
+ "|522 |2005|4 |Wales |W92000004 |19 |Cardiff |W06000015 |A48 |PA |Major |A4232 / A4050 |A4161 |312000 |175000 |51.467103059|-3.268243720|4.50 |2.80000000000000000|Estimated |Estimated using previous year's AADF on this link|44 |124 |18249 |292 |1957 |248 |55 |14 |8 |15 |27 |367 |20989 |\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_aadf.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": 17,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "+--------------+----+---------+-----------+---------------+------------------+--------------------+--------------------+---------+-------------+---------+------------------------+----------------------+-------+--------+------------+------------+--------------+-------------------+-----------------+--------------------------+------------+--------------------------+--------------+-----------------+-----+-----------------+-----------------+-------------------------+----------------------------+-----------------------+-----------------------+--------+------------------+\n",
+ "|Count_point_id|year|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|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",
+ "| 51|2005| 1| South West| E12000009| 1| Isles of Scilly| E06000053| A3111| PA| Major| Pierhead, Hugh Town| A3112| 90200| 10585|49.915022920|-6.317072964| .30| .19000000000000000| Estimated| Estimated using p...| 205| 35| 470| 30| 323| 83| 6| 0| 1| 0| 0| 90| 948|\n",
+ "| 52|2005| 1| South West| E12000009| 1| Isles of Scilly| E06000053| A3112| PA| Major| A3111| A3110| 91000| 10240|49.912342659|-6.305685679| 2.00|1.24000000000000000| Estimated| Estimated using p...| 66| 104| 543| 40| 182| 13| 1| 0| 0| 0| 0| 14| 883|\n",
+ "| 53|2005| 1| South West| E12000009| 1| Isles of Scilly| E06000053| A3111| PA| Major| A3112| A3110| 91000| 10775|49.917140810|-6.306113756| 1.20| .75000000000000000| Estimated| Estimated using p...| 184| 125| 914| 53| 283| 33| 0| 0| 0| 0| 0| 33| 1408|\n",
+ "| 54|2005| 1| South West| E12000009| 1| Isles of Scilly| E06000053| A3110| PA| Major| A3111| A3112| 91515| 10820|49.917810282|-6.298996455| .20| .12000000000000000| Estimated| Estimated using p...| 68| 97| 517| 12| 257| 13| 0| 0| 0| 0| 0| 13| 896|\n",
+ "| 55|2005| 1| South West| E12000009| 1| Isles of Scilly| E06000053| A3110| PA| Major| A3111| A3112| 91800| 10890|49.918585039|-6.295093656| 4.00|2.49000000000000000| Counted| Manual count| 42| 54| 183| 8| 67| 12| 3| 0| 0| 0| 0| 15| 327|\n",
+ "| 501|2005| 4| Wales| W92000004| 6| Newport| W06000022| M4| TM| Major| 28| 27| 328570| 187000|51.577320306|-3.032184269| 2.20|1.37000000000000000| Estimated| Estimated using p...| 0| 395| 89870| 472|13396| 2667| 441| 366| 1040| 3874| 2283| 10671| 114804|\n",
+ "| 502|2005| 4| Wales| W92000004| 7| Bridgend| W06000013| M4| TM| Major| LA Boundary| 37| 280770| 182100|51.525189997|-3.720044465| 2.90|1.80000000000000000| Counted| Manual count| 0| 143| 47789| 271| 7636| 1989| 539| 672| 374| 1625| 1153| 6352| 62191|\n",
+ "| 503|2005| 4| Wales| W92000004| 8| Swansea| W06000011| M4| TM| Major| 47| 46| 263100| 199260|51.675378776|-3.981299876| 2.90|1.80000000000000000| Estimated| Estimated using p...| 0| 189| 42993| 113| 8423| 1721| 374| 256| 334| 906| 685| 4276| 55994|\n",
+ "| 505|2005| 4| Wales| W92000004| 10| Gwynedd| W06000002| A5| PA| Major| A487| A4087| 256000| 371400|53.220081427|-4.158239803| 2.90|1.80000000000000000| Estimated| Estimated using p...| 128| 182| 10960| 127| 1214| 73| 9| 1| 2| 1| 0| 86| 12569|\n",
+ "| 506|2005| 4| Wales| W92000004| 11| Conwy| W06000003| A5| TA| Major| LA Boundary| A4086| 266150| 360260|53.122661912|-4.001657116| 8.40|5.22000000000000000| Estimated| Estimated using p...| 0| 19| 2561| 22| 456| 63| 13| 13| 4| 7| 10| 110| 3168|\n",
+ "| 508|2005| 4| Wales| W92000004| 12| Denbighshire| W06000004| A5| TA| Major| A539| LA Boundary| 324887| 341121|52.962175094|-3.119702564| 4.80|2.98000000000000000| Estimated| Estimated using p...| 1| 93| 4670| 50| 708| 178| 31| 25| 17| 59| 72| 382| 5903|\n",
+ "| 509|2005| 4| Wales| W92000004| 13| Monmouthshire| W06000021| A40| TA| Major| A449| B4233| 350000| 211160|51.796842440|-2.726434945| 9.70|6.03000000000000000| Counted| Manual count| 0| 111| 18793| 108| 3373| 1227| 148| 63| 413| 1511| 1088| 4450| 26835|\n",
+ "| 510|2005| 4| Wales| W92000004| 13| Monmouthshire| W06000021| A40| TA| Major| LA Boundary| Pentre Rd, Aberga...| 325000| 216300|51.840248870|-3.090006725| 4.30|2.67000000000000000| Counted| Manual count| 13| 138| 7159| 54| 906| 130| 34| 8| 11| 46| 51| 280| 8537|\n",
+ "| 512|2005| 4| Wales| W92000004| 14| Powys| W06000023| A40| TA| Major| LA Boundary| A4067| 290000| 229321|51.951468998|-3.601999230| 11.50|7.15000000000000000| Estimated| Estimated using p...| 2| 84| 2347| 40| 694| 241| 30| 24| 5| 23| 69| 392| 3557|\n",
+ "| 514|2005| 4| Wales| W92000004| 15| Carmarthenshire| W06000010| A40| TA| Major| A485| B4130| 245000| 221137|51.867218741|-4.252634039| 6.40|3.98000000000000000| Counted| Manual count| 6| 48| 5881| 80| 772| 123| 46| 21| 63| 40| 57| 350| 7131|\n",
+ "| 515|2005| 4| Wales| W92000004| 16| Pembrokeshire| W06000009| A40| TA| Major| A478| LA Boundary| 215000| 216780|51.818943568|-4.685548935| 6.80|4.23000000000000000| Counted| Manual count| 1| 82| 7668| 49| 1510| 428| 116| 36| 43| 176| 114| 913| 10222|\n",
+ "| 516|2005| 4| Wales| W92000004| 16| Pembrokeshire| W06000009| A40| TA| Major| A4219| B4331| 194270| 230000|51.930392758|-4.993778463| 6.00|3.73000000000000000| Counted| Manual count| 1| 32| 4753| 68| 719| 192| 41| 12| 8| 58| 18| 329| 5901|\n",
+ "| 520|2005| 4| Wales| W92000004| 17| Neath Port Talbot| W06000012| A48| PA| Major| M4 jn38| LA Boundary| 281140| 185000|51.551331439|-3.715693758| 4.60|2.86000000000000000| Estimated| Estimated using p...| 78| 191| 7712| 93| 1351| 189| 21| 34| 4| 9| 10| 267| 9614|\n",
+ "| 521|2005| 4| Wales| W92000004| 18|The Vale of Glamo...| W06000014| A48| PA| Major| LA boundary| A4222 Gibbet's Hill| 295000| 176435|51.477099176|-3.513319138| 7.67|4.77000000000000000| Counted| Manual count| 12| 156| 11063| 80| 1129| 198| 48| 63| 16| 39| 36| 400| 12828|\n",
+ "| 522|2005| 4| Wales| W92000004| 19| Cardiff| W06000015| A48| PA| Major| A4232 / A4050| A4161| 312000| 175000|51.467103059|-3.268243720| 4.50|2.80000000000000000| Estimated| Estimated using p...| 44| 124| 18249| 292| 1957| 248| 55| 14| 8| 15| 27| 367| 20989|\n",
+ "+--------------+----+---------+-----------+---------------+------------------+--------------------+--------------------+---------+-------------+---------+------------------------+----------------------+-------+--------+------------+------------+--------------+-------------------+-----------------+--------------------------+------------+--------------------------+--------------+-----------------+-----+-----------------+-----------------+-------------------------+----------------------------+-----------------------+-----------------------+--------+------------------+\n",
+ "only showing top 20 rows\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "Trafficvolume.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "+--------------+------------+------------+-------------------+------------------+\n",
+ "|Count_point_id| longitude| latitude| Link_length_miles|All_motor_vehicles|\n",
+ "+--------------+------------+------------+-------------------+------------------+\n",
+ "| 51|-6.317072964|49.915022920| .19000000000000000| 948|\n",
+ "| 52|-6.305685679|49.912342659|1.24000000000000000| 883|\n",
+ "| 53|-6.306113756|49.917140810| .75000000000000000| 1408|\n",
+ "| 54|-6.298996455|49.917810282| .12000000000000000| 896|\n",
+ "| 55|-6.295093656|49.918585039|2.49000000000000000| 327|\n",
+ "| 501|-3.032184269|51.577320306|1.37000000000000000| 114804|\n",
+ "| 502|-3.720044465|51.525189997|1.80000000000000000| 62191|\n",
+ "| 503|-3.981299876|51.675378776|1.80000000000000000| 55994|\n",
+ "| 505|-4.158239803|53.220081427|1.80000000000000000| 12569|\n",
+ "| 506|-4.001657116|53.122661912|5.22000000000000000| 3168|\n",
+ "| 508|-3.119702564|52.962175094|2.98000000000000000| 5903|\n",
+ "| 509|-2.726434945|51.796842440|6.03000000000000000| 26835|\n",
+ "| 510|-3.090006725|51.840248870|2.67000000000000000| 8537|\n",
+ "| 512|-3.601999230|51.951468998|7.15000000000000000| 3557|\n",
+ "| 514|-4.252634039|51.867218741|3.98000000000000000| 7131|\n",
+ "| 515|-4.685548935|51.818943568|4.23000000000000000| 10222|\n",
+ "| 516|-4.993778463|51.930392758|3.73000000000000000| 5901|\n",
+ "| 520|-3.715693758|51.551331439|2.86000000000000000| 9614|\n",
+ "| 521|-3.513319138|51.477099176|4.77000000000000000| 12828|\n",
+ "| 522|-3.268243720|51.467103059|2.80000000000000000| 20989|\n",
+ "+--------------+------------+------------+-------------------+------------------+\n",
+ "only showing top 20 rows\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "Trafficvolumepoints=Trafficvolume.select(col(\"Count_point_id\"),col(\"longitude\"),col(\"latitude\"),col(\"Link_length_miles\"),col(\"All_motor_vehicles\"))\n",
+ "Trafficvolumepoints.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead 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>Count_point_id</th>\n",
+ " <th>longitude</th>\n",
+ " <th>latitude</th>\n",
+ " <th>Link_length_miles</th>\n",
+ " <th>All_motor_vehicles</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>329793</th>\n",
+ " <td>1000</td>\n",
+ " <td>-4.139833</td>\n",
+ " <td>55.869526</td>\n",
+ " <td>1.12000000000000000</td>\n",
+ " <td>87649</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>211127</th>\n",
+ " <td>1000</td>\n",
+ " <td>-4.139833</td>\n",
+ " <td>55.869528</td>\n",
+ " <td>1.12000000000000000</td>\n",
+ " <td>88207</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>233871</th>\n",
+ " <td>1000</td>\n",
+ " <td>-4.139833</td>\n",
+ " <td>55.869528</td>\n",
+ " <td>1.12000000000000000</td>\n",
+ " <td>88858</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>121187</th>\n",
+ " <td>1000</td>\n",
+ " <td>-4.139833</td>\n",
+ " <td>55.869528</td>\n",
+ " <td>1.12000000000000000</td>\n",
+ " <td>120384</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>66505</th>\n",
+ " <td>1000</td>\n",
+ " <td>-4.139833</td>\n",
+ " <td>55.869528</td>\n",
+ " <td>1.12000000000000000</td>\n",
+ " <td>111253</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>...</th>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>121028</th>\n",
+ " <td>999999</td>\n",
+ " <td>-1.332606</td>\n",
+ " <td>51.355174</td>\n",
+ " <td>None</td>\n",
+ " <td>6886</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>94338</th>\n",
+ " <td>999999</td>\n",
+ " <td>-1.332606</td>\n",
+ " <td>51.355174</td>\n",
+ " <td>None</td>\n",
+ " <td>7726</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>66346</th>\n",
+ " <td>999999</td>\n",
+ " <td>-1.332606</td>\n",
+ " <td>51.355174</td>\n",
+ " <td>None</td>\n",
+ " <td>7203</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>44343</th>\n",
+ " <td>999999</td>\n",
+ " <td>-1.332606</td>\n",
+ " <td>51.355174</td>\n",
+ " <td>None</td>\n",
+ " <td>7204</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>22119</th>\n",
+ " <td>999999</td>\n",
+ " <td>-1.332606</td>\n",
+ " <td>51.355174</td>\n",
+ " <td>None</td>\n",
+ " <td>6892</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>362550 rows × 5 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " Count_point_id longitude latitude Link_length_miles \\\n",
+ "329793 1000 -4.139833 55.869526 1.12000000000000000 \n",
+ "211127 1000 -4.139833 55.869528 1.12000000000000000 \n",
+ "233871 1000 -4.139833 55.869528 1.12000000000000000 \n",
+ "121187 1000 -4.139833 55.869528 1.12000000000000000 \n",
+ "66505 1000 -4.139833 55.869528 1.12000000000000000 \n",
+ "... ... ... ... ... \n",
+ "121028 999999 -1.332606 51.355174 None \n",
+ "94338 999999 -1.332606 51.355174 None \n",
+ "66346 999999 -1.332606 51.355174 None \n",
+ "44343 999999 -1.332606 51.355174 None \n",
+ "22119 999999 -1.332606 51.355174 None \n",
+ "\n",
+ " All_motor_vehicles \n",
+ "329793 87649 \n",
+ "211127 88207 \n",
+ "233871 88858 \n",
+ "121187 120384 \n",
+ "66505 111253 \n",
+ "... ... \n",
+ "121028 6886 \n",
+ "94338 7726 \n",
+ "66346 7203 \n",
+ "44343 7204 \n",
+ "22119 6892 \n",
+ "\n",
+ "[362550 rows x 5 columns]"
+ ]
+ },
+ "execution_count": 24,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "Trafficvolumepoints=Trafficvolumepoints.toPandas()\n",
+ "Trafficvolumepoints['longitude'] = Trafficvolumepoints['longitude'].astype(float)\n",
+ "Trafficvolumepoints['latitude'] = Trafficvolumepoints['latitude'].astype(float)\n",
+ "Trafficvolumepoints\n",
+ "Trafficvolumepoints.sort_values(by=['Count_point_id'])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "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>Count_point_id</th>\n",
+ " <th>longitude</th>\n",
+ " <th>latitude</th>\n",
+ " <th>number of times</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>3066</th>\n",
+ " <td>1000</td>\n",
+ " <td>-4.139833</td>\n",
+ " <td>55.869528</td>\n",
+ " <td>13</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>34024</th>\n",
+ " <td>1000</td>\n",
+ " <td>-4.139833</td>\n",
+ " <td>55.869526</td>\n",
+ " <td>2</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>55057</th>\n",
+ " <td>1001</td>\n",
+ " <td>-4.243721</td>\n",
+ " <td>55.869385</td>\n",
+ " <td>13</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>46011</th>\n",
+ " <td>1001</td>\n",
+ " <td>-4.243720</td>\n",
+ " <td>55.869383</td>\n",
+ " <td>2</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>853</th>\n",
+ " <td>1004</td>\n",
+ " <td>-4.096664</td>\n",
+ " <td>55.839624</td>\n",
+ " <td>2</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>...</th>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2486</th>\n",
+ " <td>999994</td>\n",
+ " <td>-0.108731</td>\n",
+ " <td>51.368788</td>\n",
+ " <td>5</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>35394</th>\n",
+ " <td>999995</td>\n",
+ " <td>-2.686455</td>\n",
+ " <td>52.964380</td>\n",
+ " <td>3</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1805</th>\n",
+ " <td>999996</td>\n",
+ " <td>0.395456</td>\n",
+ " <td>51.874254</td>\n",
+ " <td>4</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>17036</th>\n",
+ " <td>999997</td>\n",
+ " <td>-0.506198</td>\n",
+ " <td>50.807358</td>\n",
+ " <td>4</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>59923</th>\n",
+ " <td>999999</td>\n",
+ " <td>-1.332606</td>\n",
+ " <td>51.355174</td>\n",
+ " <td>5</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>69080 rows × 4 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " Count_point_id longitude latitude number of times\n",
+ "3066 1000 -4.139833 55.869528 13\n",
+ "34024 1000 -4.139833 55.869526 2\n",
+ "55057 1001 -4.243721 55.869385 13\n",
+ "46011 1001 -4.243720 55.869383 2\n",
+ "853 1004 -4.096664 55.839624 2\n",
+ "... ... ... ... ...\n",
+ "2486 999994 -0.108731 51.368788 5\n",
+ "35394 999995 -2.686455 52.964380 3\n",
+ "1805 999996 0.395456 51.874254 4\n",
+ "17036 999997 -0.506198 50.807358 4\n",
+ "59923 999999 -1.332606 51.355174 5\n",
+ "\n",
+ "[69080 rows x 4 columns]"
+ ]
+ },
+ "execution_count": 27,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "Trafficvolumepoints=Trafficvolume.select(col(\"Count_point_id\"),col(\"longitude\"),col(\"latitude\"),col(\"Link_length_miles\"),col(\"All_motor_vehicles\"))\n",
+ "Trafficvolumepoints = Trafficvolumepoints.groupby('Count_point_id','longitude','latitude').agg(F.count(Trafficvolumepoints.Count_point_id).alias('number of times'))\n",
+ "Trafficvolumepoints.sort(col('Count_point_id').desc())\n",
+ "Trafficvolumepoints=Trafficvolumepoints.toPandas()\n",
+ "Trafficvolumepoints['longitude'] = Trafficvolumepoints['longitude'].astype(float)\n",
+ "Trafficvolumepoints['latitude'] = Trafficvolumepoints['latitude'].astype(float)\n",
+ "Trafficvolumepoints\n",
+ "Trafficvolumepoints.sort_values(by=['Count_point_id'])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "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>Count_point_id</th>\n",
+ " <th>longitude</th>\n",
+ " <th>latitude</th>\n",
+ " <th>number of times</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>1119</td>\n",
+ " <td>-4.720427</td>\n",
+ " <td>57.367016</td>\n",
+ " <td>13</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>6871</td>\n",
+ " <td>-0.104098</td>\n",
+ " <td>51.498207</td>\n",
+ " <td>13</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>7123</td>\n",
+ " <td>-1.588269</td>\n",
+ " <td>52.280898</td>\n",
+ " <td>13</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>7499</td>\n",
+ " <td>-1.630728</td>\n",
+ " <td>55.068909</td>\n",
+ " <td>13</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>7551</td>\n",
+ " <td>-0.178150</td>\n",
+ " <td>50.830306</td>\n",
+ " <td>13</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>...</th>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>69075</th>\n",
+ " <td>811341</td>\n",
+ " <td>-4.723405</td>\n",
+ " <td>55.277716</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>69076</th>\n",
+ " <td>811564</td>\n",
+ " <td>-4.131090</td>\n",
+ " <td>56.598905</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>69077</th>\n",
+ " <td>811569</td>\n",
+ " <td>-2.990135</td>\n",
+ " <td>56.484969</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>69078</th>\n",
+ " <td>812043</td>\n",
+ " <td>-1.187613</td>\n",
+ " <td>60.353261</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>69079</th>\n",
+ " <td>941849</td>\n",
+ " <td>1.026896</td>\n",
+ " <td>52.922097</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>69080 rows × 4 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " Count_point_id longitude latitude number of times\n",
+ "0 1119 -4.720427 57.367016 13\n",
+ "1 6871 -0.104098 51.498207 13\n",
+ "2 7123 -1.588269 52.280898 13\n",
+ "3 7499 -1.630728 55.068909 13\n",
+ "4 7551 -0.178150 50.830306 13\n",
+ "... ... ... ... ...\n",
+ "69075 811341 -4.723405 55.277716 1\n",
+ "69076 811564 -4.131090 56.598905 1\n",
+ "69077 811569 -2.990135 56.484969 1\n",
+ "69078 812043 -1.187613 60.353261 1\n",
+ "69079 941849 1.026896 52.922097 1\n",
+ "\n",
+ "[69080 rows x 4 columns]"
+ ]
+ },
+ "execution_count": 28,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "Trafficvolumepoints"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "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>Count_point_id</th>\n",
+ " <th>longitude</th>\n",
+ " <th>latitude</th>\n",
+ " <th>coordinates</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>1119</td>\n",
+ " <td>-4.720427</td>\n",
+ " <td>57.367016</td>\n",
+ " <td>(-4.720427161, 57.367015644)</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>6871</td>\n",
+ " <td>-0.104098</td>\n",
+ " <td>51.498207</td>\n",
+ " <td>(-0.104098441, 51.498207386)</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>7123</td>\n",
+ " <td>-1.588269</td>\n",
+ " <td>52.280898</td>\n",
+ " <td>(-1.588269169, 52.280898317)</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>7499</td>\n",
+ " <td>-1.630728</td>\n",
+ " <td>55.068909</td>\n",
+ " <td>(-1.630728357, 55.068908918)</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>7551</td>\n",
+ " <td>-0.178150</td>\n",
+ " <td>50.830306</td>\n",
+ " <td>(-0.178149938, 50.830305808)</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>...</th>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>69075</th>\n",
+ " <td>811341</td>\n",
+ " <td>-4.723405</td>\n",
+ " <td>55.277716</td>\n",
+ " <td>(-4.7234053, 55.277716)</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>69076</th>\n",
+ " <td>811564</td>\n",
+ " <td>-4.131090</td>\n",
+ " <td>56.598905</td>\n",
+ " <td>(-4.1310899, 56.598905)</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>69077</th>\n",
+ " <td>811569</td>\n",
+ " <td>-2.990135</td>\n",
+ " <td>56.484969</td>\n",
+ " <td>(-2.9901353, 56.484969)</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>69078</th>\n",
+ " <td>812043</td>\n",
+ " <td>-1.187613</td>\n",
+ " <td>60.353261</td>\n",
+ " <td>(-1.1876129, 60.353261)</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>69079</th>\n",
+ " <td>941849</td>\n",
+ " <td>1.026896</td>\n",
+ " <td>52.922097</td>\n",
+ " <td>(1.0268962, 52.922097)</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>69080 rows × 4 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " Count_point_id longitude latitude coordinates\n",
+ "0 1119 -4.720427 57.367016 (-4.720427161, 57.367015644)\n",
+ "1 6871 -0.104098 51.498207 (-0.104098441, 51.498207386)\n",
+ "2 7123 -1.588269 52.280898 (-1.588269169, 52.280898317)\n",
+ "3 7499 -1.630728 55.068909 (-1.630728357, 55.068908918)\n",
+ "4 7551 -0.178150 50.830306 (-0.178149938, 50.830305808)\n",
+ "... ... ... ... ...\n",
+ "69075 811341 -4.723405 55.277716 (-4.7234053, 55.277716)\n",
+ "69076 811564 -4.131090 56.598905 (-4.1310899, 56.598905)\n",
+ "69077 811569 -2.990135 56.484969 (-2.9901353, 56.484969)\n",
+ "69078 812043 -1.187613 60.353261 (-1.1876129, 60.353261)\n",
+ "69079 941849 1.026896 52.922097 (1.0268962, 52.922097)\n",
+ "\n",
+ "[69080 rows x 4 columns]"
+ ]
+ },
+ "execution_count": 29,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "Trafficvolumepoints=Trafficvolumepoints.drop(columns=['number of times'])\n",
+ "Trafficvolumepoints\n",
+ "Trafficvolumepoints[\"coordinates\"] = list(zip(Trafficvolumepoints[\"longitude\"] , Trafficvolumepoints[\"latitude\"]))\n",
+ "Trafficvolumepoints"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "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>Count_point_id</th>\n",
+ " <th>longitude</th>\n",
+ " <th>latitude</th>\n",
+ " <th>coordinates</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>1119</td>\n",
+ " <td>-4.720427</td>\n",
+ " <td>57.367016</td>\n",
+ " <td>(-4.720427161, 57.367015644)</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>6871</td>\n",
+ " <td>-0.104098</td>\n",
+ " <td>51.498207</td>\n",
+ " <td>(-0.104098441, 51.498207386)</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>7123</td>\n",
+ " <td>-1.588269</td>\n",
+ " <td>52.280898</td>\n",
+ " <td>(-1.588269169, 52.280898317)</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>7499</td>\n",
+ " <td>-1.630728</td>\n",
+ " <td>55.068909</td>\n",
+ " <td>(-1.630728357, 55.068908918)</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>7551</td>\n",
+ " <td>-0.178150</td>\n",
+ " <td>50.830306</td>\n",
+ " <td>(-0.178149938, 50.830305808)</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>...</th>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>69075</th>\n",
+ " <td>811341</td>\n",
+ " <td>-4.723405</td>\n",
+ " <td>55.277716</td>\n",
+ " <td>(-4.7234053, 55.277716)</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>69076</th>\n",
+ " <td>811564</td>\n",
+ " <td>-4.131090</td>\n",
+ " <td>56.598905</td>\n",
+ " <td>(-4.1310899, 56.598905)</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>69077</th>\n",
+ " <td>811569</td>\n",
+ " <td>-2.990135</td>\n",
+ " <td>56.484969</td>\n",
+ " <td>(-2.9901353, 56.484969)</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>69078</th>\n",
+ " <td>812043</td>\n",
+ " <td>-1.187613</td>\n",
+ " <td>60.353261</td>\n",
+ " <td>(-1.1876129, 60.353261)</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>69079</th>\n",
+ " <td>941849</td>\n",
+ " <td>1.026896</td>\n",
+ " <td>52.922097</td>\n",
+ " <td>(1.0268962, 52.922097)</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>69080 rows × 4 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " Count_point_id longitude latitude coordinates\n",
+ "0 1119 -4.720427 57.367016 (-4.720427161, 57.367015644)\n",
+ "1 6871 -0.104098 51.498207 (-0.104098441, 51.498207386)\n",
+ "2 7123 -1.588269 52.280898 (-1.588269169, 52.280898317)\n",
+ "3 7499 -1.630728 55.068909 (-1.630728357, 55.068908918)\n",
+ "4 7551 -0.178150 50.830306 (-0.178149938, 50.830305808)\n",
+ "... ... ... ... ...\n",
+ "69075 811341 -4.723405 55.277716 (-4.7234053, 55.277716)\n",
+ "69076 811564 -4.131090 56.598905 (-4.1310899, 56.598905)\n",
+ "69077 811569 -2.990135 56.484969 (-2.9901353, 56.484969)\n",
+ "69078 812043 -1.187613 60.353261 (-1.1876129, 60.353261)\n",
+ "69079 941849 1.026896 52.922097 (1.0268962, 52.922097)\n",
+ "\n",
+ "[69080 rows x 4 columns]"
+ ]
+ },
+ "execution_count": 22,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "Trafficvolumepoints=Trafficvolumepoints.drop(columns=['number of times'])\n",
+ "Trafficvolumepoints\n",
+ "Trafficvolumepoints[\"coordinates\"] = list(zip(Trafficvolumepoints[\"longitude\"] , Trafficvolumepoints[\"latitude\"]))\n",
+ "Trafficvolumepoints"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 42,
+ "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>longitude</th>\n",
+ " <th>latitude</th>\n",
+ " <th>Total accidents</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>Single carriageway</td>\n",
+ " <td>-0.170782</td>\n",
+ " <td>51.501819</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>Single carriageway</td>\n",
+ " <td>-0.206827</td>\n",
+ " <td>51.523238</td>\n",
+ " <td>2</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>Single carriageway</td>\n",
+ " <td>-0.201912</td>\n",
+ " <td>51.497984</td>\n",
+ " <td>2</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>One way street</td>\n",
+ " <td>-0.135473</td>\n",
+ " <td>51.498475</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>Single carriageway</td>\n",
+ " <td>-0.167087</td>\n",
+ " <td>51.529279</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>...</th>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2015739</th>\n",
+ " <td>Single carriageway</td>\n",
+ " <td>-4.287365</td>\n",
+ " <td>55.872205</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2015740</th>\n",
+ " <td>Single carriageway</td>\n",
+ " <td>-4.25897</td>\n",
+ " <td>55.828063</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2015741</th>\n",
+ " <td>Single carriageway</td>\n",
+ " <td>-4.288574</td>\n",
+ " <td>55.882689</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2015742</th>\n",
+ " <td>Single carriageway</td>\n",
+ " <td>-4.175269</td>\n",
+ " <td>55.85861</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2015743</th>\n",
+ " <td>Single carriageway</td>\n",
+ " <td>-3.87831</td>\n",
+ " <td>55.785517</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>2015744 rows × 4 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " Road_Type longitude latitude Total accidents\n",
+ "0 Single carriageway -0.170782 51.501819 1\n",
+ "1 Single carriageway -0.206827 51.523238 2\n",
+ "2 Single carriageway -0.201912 51.497984 2\n",
+ "3 One way street -0.135473 51.498475 1\n",
+ "4 Single carriageway -0.167087 51.529279 1\n",
+ "... ... ... ... ...\n",
+ "2015739 Single carriageway -4.287365 55.872205 1\n",
+ "2015740 Single carriageway -4.25897 55.828063 1\n",
+ "2015741 Single carriageway -4.288574 55.882689 1\n",
+ "2015742 Single carriageway -4.175269 55.85861 1\n",
+ "2015743 Single carriageway -3.87831 55.785517 1\n",
+ "\n",
+ "[2015744 rows x 4 columns]"
+ ]
+ },
+ "execution_count": 42,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "roadtype_spark"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 45,
+ "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>longitude</th>\n",
+ " <th>latitude</th>\n",
+ " <th>Total accidents</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>Single carriageway</td>\n",
+ " <td>-0.170782</td>\n",
+ " <td>51.5018</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>Single carriageway</td>\n",
+ " <td>-0.206827</td>\n",
+ " <td>51.5232</td>\n",
+ " <td>2</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>Single carriageway</td>\n",
+ " <td>-0.201912</td>\n",
+ " <td>51.498</td>\n",
+ " <td>2</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>One way street</td>\n",
+ " <td>-0.135473</td>\n",
+ " <td>51.4985</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>Single carriageway</td>\n",
+ " <td>-0.167087</td>\n",
+ " <td>51.5293</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>...</th>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2015739</th>\n",
+ " <td>Single carriageway</td>\n",
+ " <td>-4.287365</td>\n",
+ " <td>55.872205</td>\n",
+ " <td>1</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>1</th>\n",
- " <td>M</td>\n",
+ " <th>2015740</th>\n",
+ " <td>Single carriageway</td>\n",
+ " <td>-4.25897</td>\n",
+ " <td>55.828063</td>\n",
+ " <td>1</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>2</th>\n",
- " <td>U</td>\n",
+ " <th>2015741</th>\n",
+ " <td>Single carriageway</td>\n",
+ " <td>-4.288574</td>\n",
+ " <td>55.882689</td>\n",
+ " <td>1</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>3</th>\n",
- " <td>C</td>\n",
+ " <th>2015742</th>\n",
+ " <td>Single carriageway</td>\n",
+ " <td>-4.175269</td>\n",
+ " <td>55.85861</td>\n",
+ " <td>1</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>4</th>\n",
- " <td>A</td>\n",
+ " <th>2015743</th>\n",
+ " <td>Single carriageway</td>\n",
+ " <td>-3.87831</td>\n",
+ " <td>55.785517</td>\n",
+ " <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
+ "<p>2015744 rows × 4 columns</p>\n",
"</div>"
],
"text/plain": [
- " road_name\n",
- "0 B\n",
- "1 M\n",
- "2 U\n",
- "3 C\n",
- "4 A"
+ " Road_Type longitude latitude Total accidents\n",
+ "0 Single carriageway -0.170782 51.5018 1\n",
+ "1 Single carriageway -0.206827 51.5232 2\n",
+ "2 Single carriageway -0.201912 51.498 2\n",
+ "3 One way street -0.135473 51.4985 1\n",
+ "4 Single carriageway -0.167087 51.5293 1\n",
+ "... ... ... ... ...\n",
+ "2015739 Single carriageway -4.287365 55.872205 1\n",
+ "2015740 Single carriageway -4.25897 55.828063 1\n",
+ "2015741 Single carriageway -4.288574 55.882689 1\n",
+ "2015742 Single carriageway -4.175269 55.85861 1\n",
+ "2015743 Single carriageway -3.87831 55.785517 1\n",
+ "\n",
+ "[2015744 rows x 4 columns]"
]
},
- "execution_count": 116,
+ "execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "road_length_traffic"
+ "roadtype_spark = roadtype_spark.dropna()\n",
+ "roadtype_spark"
]
},
{
"cell_type": "code",
- "execution_count": 102,
+ "execution_count": 68,
"metadata": {},
- "outputs": [
- {
- "ename": "KeyError",
- "evalue": "'Trafficvolume'",
- "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: 'Trafficvolume'",
- "\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-102-20660c43864d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 14\u001b[0m \u001b[0mresult24\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_df_notyear_df\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mroad_length_traffic\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mon\u001b[0m\u001b[0;34m=\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[0;32m---> 15\u001b[0;31m \u001b[0mresult24\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[0mresult24\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[0mresult24\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[0m\u001b[1;32m 16\u001b[0m \u001b[0mresult24\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mresult24\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 17\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mresult24\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'road_name'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'Accident Probability'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrot\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"Accidents probabilty over road type \"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"Orange\"\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/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: 'Trafficvolume'"
- ]
- }
- ],
+ "outputs": [],
"source": [
- "A2018t_df_notyear = A2018.groupby(\"first_road_class\").agg(F.count(A2018.accident_index).alias('Total accidents'))\n",
- "A2018t_df_notyear = A2018t_df_notyear.withColumnRenamed(\"first_road_class\", \"road_name\")\n",
- "A2018t_df_notyear_df=A2018t_df_notyear.toPandas()\n",
- "\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_df=TrafficvolumeGrouped_notyear.toPandas()\n",
- "\n",
- "\n",
- "\n",
- "\n"
+ "roadtype_spark1=roadtype_spark\n",
+ "roadtype_spark1=roadtype_spark1.dropna().reset_index(drop=True)\n"
]
},
{
"cell_type": "code",
- "execution_count": 118,
+ "execution_count": 77,
"metadata": {},
"outputs": [],
- "source": []
+ "source": [
+ "roadtype_spark1=roadtype_spark1[roadtype_spark1['longitude']!=\"NULL\"]"
+ ]
},
{
"cell_type": "code",
- "execution_count": 120,
+ "execution_count": 78,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+ "Try using .loc[row_indexer,col_indexer] = value instead\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " \"\"\"Entry point for launching an IPython kernel.\n",
+ "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+ "Try using .loc[row_indexer,col_indexer] = value instead\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " \n"
+ ]
+ }
+ ],
+ "source": [
+ "roadtype_spark1['longitude'] = roadtype_spark1['longitude'].astype(float)\n",
+ "roadtype_spark1['latitude'] = roadtype_spark1['latitude'].astype(float)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 79,
"metadata": {},
"outputs": [
{
@@ -4822,144 +8240,250 @@
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
- " <th>road_name</th>\n",
+ " <th>Road_Type</th>\n",
+ " <th>longitude</th>\n",
+ " <th>latitude</th>\n",
" <th>Total accidents</th>\n",
- " <th>Trafficvolume</th>\n",
+ " <th>coordinates</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
- " <td>B</td>\n",
- " <td>286824</td>\n",
- " <td>2.057755e+12</td>\n",
+ " <td>Single carriageway</td>\n",
+ " <td>-0.170782</td>\n",
+ " <td>51.5018</td>\n",
+ " <td>1</td>\n",
+ " <td>(-0.170782, 51.501819)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
- " <td>M</td>\n",
- " <td>86106</td>\n",
- " <td>8.846338e+11</td>\n",
+ " <td>Single carriageway</td>\n",
+ " <td>-0.206827</td>\n",
+ " <td>51.5232</td>\n",
+ " <td>2</td>\n",
+ " <td>(-0.206827, 51.523238)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
- " <td>U</td>\n",
- " <td>687752</td>\n",
- " <td>1.175274e+13</td>\n",
+ " <td>Single carriageway</td>\n",
+ " <td>-0.201912</td>\n",
+ " <td>51.498</td>\n",
+ " <td>2</td>\n",
+ " <td>(-0.201912, 51.497984)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
- " <td>C</td>\n",
- " <td>188025</td>\n",
- " <td>3.374912e+12</td>\n",
+ " <td>One way street</td>\n",
+ " <td>-0.135473</td>\n",
+ " <td>51.4985</td>\n",
+ " <td>1</td>\n",
+ " <td>(-0.135473, 51.498475)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
- " <td>A</td>\n",
- " <td>1038720</td>\n",
- " <td>3.126184e+13</td>\n",
+ " <td>Single carriageway</td>\n",
+ " <td>-0.167087</td>\n",
+ " <td>51.5293</td>\n",
+ " <td>1</td>\n",
+ " <td>(-0.167087, 51.529279)</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>...</th>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2015739</th>\n",
+ " <td>Single carriageway</td>\n",
+ " <td>-4.287365</td>\n",
+ " <td>55.872205</td>\n",
+ " <td>1</td>\n",
+ " <td>(-4.287365, 55.872205)</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2015740</th>\n",
+ " <td>Single carriageway</td>\n",
+ " <td>-4.25897</td>\n",
+ " <td>55.828063</td>\n",
+ " <td>1</td>\n",
+ " <td>(-4.25897, 55.828063)</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2015741</th>\n",
+ " <td>Single carriageway</td>\n",
+ " <td>-4.288574</td>\n",
+ " <td>55.882689</td>\n",
+ " <td>1</td>\n",
+ " <td>(-4.288574, 55.882689)</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2015742</th>\n",
+ " <td>Single carriageway</td>\n",
+ " <td>-4.175269</td>\n",
+ " <td>55.85861</td>\n",
+ " <td>1</td>\n",
+ " <td>(-4.175269, 55.85861)</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2015743</th>\n",
+ " <td>Single carriageway</td>\n",
+ " <td>-3.87831</td>\n",
+ " <td>55.785517</td>\n",
+ " <td>1</td>\n",
+ " <td>(-3.87831, 55.785517)</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
+ "<p>2015744 rows × 5 columns</p>\n",
"</div>"
],
"text/plain": [
- " road_name Total accidents Trafficvolume\n",
- "0 B 286824 2.057755e+12\n",
- "1 M 86106 8.846338e+11\n",
- "2 U 687752 1.175274e+13\n",
- "3 C 188025 3.374912e+12\n",
- "4 A 1038720 3.126184e+13"
+ " Road_Type longitude latitude Total accidents \\\n",
+ "0 Single carriageway -0.170782 51.5018 1 \n",
+ "1 Single carriageway -0.206827 51.5232 2 \n",
+ "2 Single carriageway -0.201912 51.498 2 \n",
+ "3 One way street -0.135473 51.4985 1 \n",
+ "4 Single carriageway -0.167087 51.5293 1 \n",
+ "... ... ... ... ... \n",
+ "2015739 Single carriageway -4.287365 55.872205 1 \n",
+ "2015740 Single carriageway -4.25897 55.828063 1 \n",
+ "2015741 Single carriageway -4.288574 55.882689 1 \n",
+ "2015742 Single carriageway -4.175269 55.85861 1 \n",
+ "2015743 Single carriageway -3.87831 55.785517 1 \n",
+ "\n",
+ " coordinates \n",
+ "0 (-0.170782, 51.501819) \n",
+ "1 (-0.206827, 51.523238) \n",
+ "2 (-0.201912, 51.497984) \n",
+ "3 (-0.135473, 51.498475) \n",
+ "4 (-0.167087, 51.529279) \n",
+ "... ... \n",
+ "2015739 (-4.287365, 55.872205) \n",
+ "2015740 (-4.25897, 55.828063) \n",
+ "2015741 (-4.288574, 55.882689) \n",
+ "2015742 (-4.175269, 55.85861) \n",
+ "2015743 (-3.87831, 55.785517) \n",
+ "\n",
+ "[2015744 rows x 5 columns]"
]
},
- "execution_count": 120,
+ "execution_count": 79,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "road_length_total = pd.read_csv ('/Users/Asfandyar/Desktop/disertation/diseration_final/road_length.csv')\n",
- "road_length_traffic=pd.merge(TrafficvolumeGrouped_notyear_df, road_length_total, on=['road_name'])\n",
- "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)\n",
- "road_length_traffic[\"Trafficvolume\"] = road_length_traffic[\"all_motor_vehicles\"] * road_length_traffic[\"link_length_km\"]\n",
- "road_length_traffic=road_length_traffic.drop(['all_motor_vehicles', 'link_length_km'], axis=1)\n",
- "result24=pd.merge(A2018t_df_notyear_df, road_length_traffic, on=['road_name'])\n",
- "result24"
+ "\n",
+ "roadtype_spark[\"coordinates\"] = list(zip(roadtype_spark[\"longitude\"] , roadtype_spark[\"latitude\"]))\n",
+ "\n",
+ "roadtype_spark"
]
},
{
"cell_type": "code",
- "execution_count": 122,
+ "execution_count": 84,
"metadata": {},
"outputs": [
{
"data": {
- "image/png": "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",
"text/plain": [
- "<Figure size 1440x720 with 1 Axes>"
+ "float"
]
},
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
+ "execution_count": 84,
+ "metadata": {},
+ "output_type": "execute_result"
}
],
"source": [
- "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=result24.sort_values('road_name')\n",
- "ax=result24.plot.bar('road_name','Accident Probability', rot=0,title=\"Accidents probabilty over road type \",figsize=(20, 10),color=\"Orange\")"
+ "type(Trafficvolumepoints[\"coordinates\"][0][0])"
]
},
{
"cell_type": "code",
- "execution_count": 123,
+ "execution_count": 87,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "0 (-4.720427161, 57.367015644)\n",
+ "1 (-0.104098441, 51.498207386)\n",
+ "2 (-1.588269169, 52.280898317)\n",
+ "3 (-1.630728357, 55.068908918)\n",
+ "4 (-0.178149938, 50.830305808)\n",
+ " ... \n",
+ "69075 (-4.7234053, 55.277716)\n",
+ "69076 (-4.1310899, 56.598905)\n",
+ "69077 (-2.9901353, 56.484969)\n",
+ "69078 (-1.1876129, 60.353261)\n",
+ "69079 (1.0268962, 52.922097)\n",
+ "Name: coordinates, Length: 69080, dtype: object\n"
+ ]
+ },
+ {
+ "ename": "TypeError",
+ "evalue": "min() got an unexpected keyword argument 'key'",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m<ipython-input-87-af8b01514a79>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 20\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 21\u001b[0m points=nearest_neighbor_bf(\n\u001b[0;32m---> 22\u001b[0;31m \u001b[0mquery_points\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mquery_points\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mreference_points\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mreference_points\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 23\u001b[0m )\n\u001b[1;32m 24\u001b[0m \u001b[0mpoints\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m<ipython-input-87-af8b01514a79>\u001b[0m in \u001b[0;36mnearest_neighbor_bf\u001b[0;34m(query_points, reference_points)\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0mreference_points\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreference_points\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---> 12\u001b[0;31m key=lambda X: SED(X, query_p))\n\u001b[0m\u001b[1;32m 13\u001b[0m )\n\u001b[1;32m 14\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mdatad\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdatad2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;31mTypeError\u001b[0m: min() got an unexpected keyword argument 'key'"
+ ]
+ }
+ ],
+ "source": [
+ "def SED(X, Y):\n",
+ " #Squared Eucliden distance is computed between the x and y.\n",
+ " return sum((i-j)**2 for i, j in zip(X, Y))\n",
+ " \n",
+ "def nearest_neighbor_bf(*, query_points,reference_points):\n",
+ " #Nearest neighbor are found to the nearest coordinate\n",
+ " for query_p in query_points['coordinates']:\n",
+ " datad2.append(query_p)\n",
+ " datad.append( min(\n",
+ " reference_points,\n",
+ " print(reference_points),\n",
+ " key=lambda X: SED(X, query_p))\n",
+ " )\n",
+ " return datad,datad2\n",
+ "datad=[]\n",
+ "datad2=[]\n",
+ "reference_points =Trafficvolumepoints[\"coordinates\"]\n",
+ "query_points = roadtype_spark\n",
+ "count_point_id = Trafficvolumepoints[\"Count_point_id\"]\n",
+ "\n",
+ "points=nearest_neighbor_bf(\n",
+ " query_points = query_points,reference_points=reference_points\n",
+ ")\n",
+ "points"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 91,
"metadata": {},
"outputs": [
{
"data": {
- "image/png": "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",
+ "image/png": "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",
"text/plain": [
- "<Figure size 1440x720 with 1 Axes>"
+ "<IPython.core.display.Image object>"
]
},
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
+ "execution_count": 91,
+ "metadata": {},
+ "output_type": "execute_result"
}
],
"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",
"dangeorusroadtype = A2018.groupby('Road_Type','first_road_class').agg(F.count(A2018.accident_index).alias('Total accidents'))\n",
"dangeorusroadtype_df=dangeorusroadtype.toPandas()\n",
"\n",
@@ -4973,9 +8497,16 @@
"result30_df = result30.groupby('Road_Type', sort=False)[\"Accident Probability\"].sum().reset_index(name ='Accident Probability')\n",
"result30_df=result30_df.drop(labels=[6],axis=0)\n",
"result30_df=result30_df.sort_values('Road_Type')\n",
- "ax=result30_df.plot.bar('Road_Type','Accident Probability', rot=90,title=\"Accidents probabilty over road type \",figsize=(20, 10),color=\"Orange\")\n"
+ "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": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
{
"cell_type": "code",
"execution_count": 171,
@@ -5155,10 +8686,37 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 92,
"metadata": {},
- "outputs": [],
- "source": []
+ "outputs": [
+ {
+ "data": {
+ "image/png": "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",
+ "text/plain": [
+ "<IPython.core.display.Image object>"
+ ]
+ },
+ "execution_count": 92,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "dangeorusroadtype = A2018.groupby('Road_Type','first_road_class').agg(F.count(A2018.accident_index).alias('Total accidents'))\n",
+ "dangeorusroadtype_df=dangeorusroadtype.toPandas()\n",
+ "\n",
+ "dangeorusroadtype_df=dangeorusroadtype_df.rename(columns={\"first_road_class\": \"road_name\"})\n",
+ "\n",
+ "result30=pd.merge(dangeorusroadtype_df, road_length_traffic, on=['road_name'])\n",
+ "\n",
+ "result30[\"Accident Probability\"] = result30[\"Total accidents\"] / result30[\"Trafficvolume\"]\n",
+ "result30=result30.drop(['Total accidents', 'Trafficvolume'], axis=1)\n",
+ "result30=result30.drop(['road_name'], axis=1)\n",
+ "result30_df = result30.groupby('Road_Type', sort=False)[\"Accident Probability\"].sum().reset_index(name ='Accident Probability')\n",
+ "result30_df=result30_df.drop(labels=[6],axis=0)\n",
+ "result30_df=result30_df.sort_values('Road_Type')\n",
+ "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",