casualties_temporal_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a6a3cfcc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import requests\n",
    "import json\n",
    "import geopandas as gpd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "013bfdb4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data exported successfully.\n"
     ]
    }
   ],
   "source": [
    "# URL of the JSON file\n",
    "url = \"https://cycling.data.tfl.gov.uk/CyclingInfrastructure/data/lines/advanced_stop_line.json\"\n",
    "\n",
    "# Fetch the content from the URL\n",
    "response = requests.get(url)\n",
    "\n",
    "# Check if the request was successful\n",
    "if response.status_code == 200:\n",
    "    # Parse the JSON data\n",
    "    data = response.json()\n",
    "    \n",
    "    # Convert JSON data to pandas DataFrame\n",
    "    df_adv_stop_line = pd.DataFrame(data)\n",
    "    \n",
    "    # Export DataFrame to CSV\n",
    "    df_adv_stop_line.to_csv('advanced_stop_line_data.csv', index=False)\n",
    "    \n",
    "    # Export DataFrame to Excel\n",
    "    # df.to_excel('signal_data.xlsx', index=False)\n",
    "    \n",
    "    print(\"Data exported successfully.\")\n",
    "else:\n",
    "    print(\"Failed to fetch data. Status code:\", response.status_code)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c5fc05c1",
   "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>type</th>\n",
       "      <th>name</th>\n",
       "      <th>features</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FeatureCollection</td>\n",
       "      <td>advanced_stop_line</td>\n",
       "      <td>{'type': 'Feature', 'geometry': {'type': 'Line...</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                type                name  \\\n",
       "0  FeatureCollection  advanced_stop_line   \n",
       "\n",
       "                                            features geometry  \n",
       "0  {'type': 'Feature', 'geometry': {'type': 'Line...     None  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read in advanced stop line data\n",
    "rs = pd.read_csv(\"advanced_stop_line_data.csv\")\n",
    "rs.head()\n",
    "\n",
    "# Convert to geopandas\n",
    "grs = gpd.read_file(\"advanced_stop_line_data.csv\")\n",
    "grs.head(1)\n",
    "\n",
    "# Plot grs\n",
    "# rs.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7b574d35",
   "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>_Collision Id</th>\n",
       "      <th>Borough Name</th>\n",
       "      <th>Borough Number</th>\n",
       "      <th>Easting</th>\n",
       "      <th>Northing</th>\n",
       "      <th>_Casualty Id</th>\n",
       "      <th>_Casualty Class</th>\n",
       "      <th>Casualty Gender</th>\n",
       "      <th>Casualty Age (5 Year Bands)</th>\n",
       "      <th>Casualty Age</th>\n",
       "      <th>_Casualty Count</th>\n",
       "      <th>_Casualty Severity</th>\n",
       "      <th>Pedestrian Location</th>\n",
       "      <th>Pedestrian Movement</th>\n",
       "      <th>Casualty Mode of Travel</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1210287148</td>\n",
       "      <td>Barnet</td>\n",
       "      <td>30</td>\n",
       "      <td>521508</td>\n",
       "      <td>193079</td>\n",
       "      <td>1</td>\n",
       "      <td>Driver/Rider</td>\n",
       "      <td>Male</td>\n",
       "      <td>15-19</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Slight</td>\n",
       "      <td>Not Applicable</td>\n",
       "      <td>Not Applicable</td>\n",
       "      <td>Car</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1210287149</td>\n",
       "      <td>Tower Hamlets</td>\n",
       "      <td>5</td>\n",
       "      <td>535379</td>\n",
       "      <td>180783</td>\n",
       "      <td>1</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>Male</td>\n",
       "      <td>25-29</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Slight</td>\n",
       "      <td>Not Applicable</td>\n",
       "      <td>Not Applicable</td>\n",
       "      <td>Car</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1210287149</td>\n",
       "      <td>Tower Hamlets</td>\n",
       "      <td>5</td>\n",
       "      <td>535379</td>\n",
       "      <td>180783</td>\n",
       "      <td>2</td>\n",
       "      <td>Driver/Rider</td>\n",
       "      <td>Male</td>\n",
       "      <td>20-24</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Slight</td>\n",
       "      <td>Not Applicable</td>\n",
       "      <td>Not Applicable</td>\n",
       "      <td>Car</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1210287149</td>\n",
       "      <td>Tower Hamlets</td>\n",
       "      <td>5</td>\n",
       "      <td>535379</td>\n",
       "      <td>180783</td>\n",
       "      <td>3</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>Male</td>\n",
       "      <td>15-19</td>\n",
       "      <td>17.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Serious</td>\n",
       "      <td>Not Applicable</td>\n",
       "      <td>Not Applicable</td>\n",
       "      <td>Car</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1210287151</td>\n",
       "      <td>Lambeth</td>\n",
       "      <td>9</td>\n",
       "      <td>529701</td>\n",
       "      <td>170398</td>\n",
       "      <td>1</td>\n",
       "      <td>Driver/Rider</td>\n",
       "      <td>Male</td>\n",
       "      <td>25-29</td>\n",
       "      <td>27.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Slight</td>\n",
       "      <td>Not Applicable</td>\n",
       "      <td>Not Applicable</td>\n",
       "      <td>Car</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   _Collision Id   Borough Name  Borough Number  Easting  Northing  \\\n",
       "0     1210287148         Barnet              30   521508    193079   \n",
       "1     1210287149  Tower Hamlets               5   535379    180783   \n",
       "2     1210287149  Tower Hamlets               5   535379    180783   \n",
       "3     1210287149  Tower Hamlets               5   535379    180783   \n",
       "4     1210287151        Lambeth               9   529701    170398   \n",
       "\n",
       "   _Casualty Id _Casualty Class Casualty Gender Casualty Age (5 Year Bands)  \\\n",
       "0             1    Driver/Rider            Male                       15-19   \n",
       "1             1       Passenger            Male                       25-29   \n",
       "2             2    Driver/Rider            Male                       20-24   \n",
       "3             3       Passenger            Male                       15-19   \n",
       "4             1    Driver/Rider            Male                       25-29   \n",
       "\n",
       "   Casualty Age  _Casualty Count _Casualty Severity Pedestrian Location  \\\n",
       "0          19.0                1             Slight      Not Applicable   \n",
       "1          25.0                1             Slight      Not Applicable   \n",
       "2          21.0                1             Slight      Not Applicable   \n",
       "3          17.0                1            Serious      Not Applicable   \n",
       "4          27.0                1             Slight      Not Applicable   \n",
       "\n",
       "  Pedestrian Movement Casualty Mode of Travel  \n",
       "0      Not Applicable                     Car  \n",
       "1      Not Applicable                     Car  \n",
       "2      Not Applicable                     Car  \n",
       "3      Not Applicable                     Car  \n",
       "4      Not Applicable                     Car  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2021 collision dataset\n",
    "\n",
    "# Read the xlsx file into a DataFrame\n",
    "df2 = pd.read_csv(\"jan-dec-2021-gla-data-extract-casualty.csv\", header=1)\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e3d17b1f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot count of casualties per borough\n",
    "\n",
    "# Filter to only show Pedal Cycles casualties\n",
    "borough_casualties = df2[df2[\"Casualty Mode of Travel\"] == \"Pedal Cycle\"]\n",
    "\n",
    "# Count casualties per borough\n",
    "borough_casualties_count = borough_casualties[\"Borough Name\"].value_counts()\n",
    "\n",
    "# Sort borough casualties count in descending order\n",
    "borough_casualties_count = borough_casualties_count.sort_values(ascending=False)\n",
    "\n",
    "# Plot the count of casualties per borough\n",
    "borough_casualties_count.plot(kind=\"bar\")\n",
    "plt.xlabel(\"Borough\")\n",
    "plt.ylabel(\"Count of Pedal Cycle Casualties\")\n",
    "plt.title(\"Count of Pedal Cycle Casualties per London Borough\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "ef3c3d5a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Casualty Mode of Travel\n",
      "Car                  9100\n",
      "Powered 2 Wheeler    6006\n",
      "Pedal Cycle          5277\n",
      "Pedestrian           3837\n",
      "Bus Or Coach          866\n",
      "Goods Vehicle         495\n",
      "Other Vehicle         491\n",
      "Taxi                  342\n",
      "Private Hire          257\n",
      "dtype: int64\n",
      "26671\n"
     ]
    }
   ],
   "source": [
    "casualties_per_mode_2021 = df2.groupby(\"Casualty Mode of Travel\").size().sort_values(ascending = False)\n",
    "print(casualties_per_mode_2021)\n",
    "print(casualties_per_mode_2021.sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a5317c47",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Accident Ref.</th>\n",
       "      <th>Borough</th>\n",
       "      <th>Boro</th>\n",
       "      <th>Easting</th>\n",
       "      <th>Northing</th>\n",
       "      <th>CREFNO</th>\n",
       "      <th>Casualty Class</th>\n",
       "      <th>Casualty Sex</th>\n",
       "      <th>Casualty Age (Banded)</th>\n",
       "      <th>Casualty Age</th>\n",
       "      <th>No. of Casualties</th>\n",
       "      <th>Casualty Severity</th>\n",
       "      <th>Ped. Location</th>\n",
       "      <th>Ped. Movement</th>\n",
       "      <th>Mode of Travel</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1200240171</td>\n",
       "      <td>CITY OF LONDON</td>\n",
       "      <td>0</td>\n",
       "      <td>531188</td>\n",
       "      <td>181609</td>\n",
       "      <td>1</td>\n",
       "      <td>1 DRIVER/RIDER</td>\n",
       "      <td>1 MALE</td>\n",
       "      <td>25-59</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3 SLIGHT</td>\n",
       "      <td>'-2 NOT APPLICABLE</td>\n",
       "      <td>'-2 NOT APPLICABLE</td>\n",
       "      <td>3 POWERED 2 WHEELER</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1200246170</td>\n",
       "      <td>CITY OF LONDON</td>\n",
       "      <td>0</td>\n",
       "      <td>533386</td>\n",
       "      <td>181909</td>\n",
       "      <td>1</td>\n",
       "      <td>1 DRIVER/RIDER</td>\n",
       "      <td>1 MALE</td>\n",
       "      <td>25-59</td>\n",
       "      <td>29.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3 SLIGHT</td>\n",
       "      <td>'-2 NOT APPLICABLE</td>\n",
       "      <td>'-2 NOT APPLICABLE</td>\n",
       "      <td>2 PEDAL CYCLE</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1200246170</td>\n",
       "      <td>CITY OF LONDON</td>\n",
       "      <td>0</td>\n",
       "      <td>533386</td>\n",
       "      <td>181909</td>\n",
       "      <td>2</td>\n",
       "      <td>1 DRIVER/RIDER</td>\n",
       "      <td>'-1 UNKNOWN</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>3 SLIGHT</td>\n",
       "      <td>'-2 NOT APPLICABLE</td>\n",
       "      <td>'-2 NOT APPLICABLE</td>\n",
       "      <td>2 PEDAL CYCLE</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1200248479</td>\n",
       "      <td>CITY OF LONDON</td>\n",
       "      <td>0</td>\n",
       "      <td>533359</td>\n",
       "      <td>181760</td>\n",
       "      <td>1</td>\n",
       "      <td>1 DRIVER/RIDER</td>\n",
       "      <td>1 MALE</td>\n",
       "      <td>25-59</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3 SLIGHT</td>\n",
       "      <td>'-2 NOT APPLICABLE</td>\n",
       "      <td>'-2 NOT APPLICABLE</td>\n",
       "      <td>2 PEDAL CYCLE</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1200251644</td>\n",
       "      <td>CITY OF LONDON</td>\n",
       "      <td>0</td>\n",
       "      <td>532122</td>\n",
       "      <td>181853</td>\n",
       "      <td>1</td>\n",
       "      <td>1 DRIVER/RIDER</td>\n",
       "      <td>2 FEMALE</td>\n",
       "      <td>25-59</td>\n",
       "      <td>27.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3 SLIGHT</td>\n",
       "      <td>'-2 NOT APPLICABLE</td>\n",
       "      <td>'-2 NOT APPLICABLE</td>\n",
       "      <td>2 PEDAL CYCLE</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Accident Ref.         Borough  Boro  Easting  Northing  CREFNO  \\\n",
       "0     1200240171  CITY OF LONDON     0   531188    181609       1   \n",
       "1     1200246170  CITY OF LONDON     0   533386    181909       1   \n",
       "2     1200246170  CITY OF LONDON     0   533386    181909       2   \n",
       "3     1200248479  CITY OF LONDON     0   533359    181760       1   \n",
       "4     1200251644  CITY OF LONDON     0   532122    181853       1   \n",
       "\n",
       "   Casualty Class Casualty Sex Casualty Age (Banded)  Casualty Age  \\\n",
       "0  1 DRIVER/RIDER       1 MALE                 25-59          30.0   \n",
       "1  1 DRIVER/RIDER       1 MALE                 25-59          29.0   \n",
       "2  1 DRIVER/RIDER  '-1 UNKNOWN               UNKNOWN           NaN   \n",
       "3  1 DRIVER/RIDER       1 MALE                 25-59          30.0   \n",
       "4  1 DRIVER/RIDER     2 FEMALE                 25-59          27.0   \n",
       "\n",
       "   No. of Casualties Casualty Severity       Ped. Location  \\\n",
       "0                  1          3 SLIGHT  '-2 NOT APPLICABLE   \n",
       "1                  1          3 SLIGHT  '-2 NOT APPLICABLE   \n",
       "2                  1          3 SLIGHT  '-2 NOT APPLICABLE   \n",
       "3                  1          3 SLIGHT  '-2 NOT APPLICABLE   \n",
       "4                  1          3 SLIGHT  '-2 NOT APPLICABLE   \n",
       "\n",
       "        Ped. Movement       Mode of Travel      \n",
       "0  '-2 NOT APPLICABLE  3 POWERED 2 WHEELER NaN  \n",
       "1  '-2 NOT APPLICABLE        2 PEDAL CYCLE NaN  \n",
       "2  '-2 NOT APPLICABLE        2 PEDAL CYCLE NaN  \n",
       "3  '-2 NOT APPLICABLE        2 PEDAL CYCLE NaN  \n",
       "4  '-2 NOT APPLICABLE        2 PEDAL CYCLE NaN  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2020 collision dataset\n",
    "\n",
    "# Read the file into a DataFrame\n",
    "df3 = pd.read_csv(\"2020-gla-data-extract-casualty.csv\")\n",
    "df3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c2e691c3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mode of Travel\n",
      "4 CAR                  8852\n",
      "3 POWERED 2 WHEELER    5012\n",
      "2 PEDAL CYCLE          4789\n",
      "1 PEDESTRIAN           3677\n",
      "6 BUS OR COACH          645\n",
      "7 GOODS VEHICLE         485\n",
      "5 TAXI                  346\n",
      "8 OTHER VEHICLE         308\n",
      "9 PRIVATE HIRE          231\n",
      "dtype: int64\n",
      "24345\n"
     ]
    }
   ],
   "source": [
    "casualties_per_mode_2020 = df3.groupby(\"Mode of Travel\").size().sort_values(ascending = False)\n",
    "print(casualties_per_mode_2020)\n",
    "print(casualties_per_mode_2020.sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c4da8f01",
   "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>AREFNO</th>\n",
       "      <th>Borough</th>\n",
       "      <th>Boro</th>\n",
       "      <th>Easting</th>\n",
       "      <th>Northing</th>\n",
       "      <th>CREFNO</th>\n",
       "      <th>Casualty Class</th>\n",
       "      <th>Casualty Sex</th>\n",
       "      <th>Casualty Age (Banded)</th>\n",
       "      <th>Casualty Age</th>\n",
       "      <th>No. of Casualties</th>\n",
       "      <th>Casualty Severity</th>\n",
       "      <th>Ped Location</th>\n",
       "      <th>Ped Movement</th>\n",
       "      <th>Mode of Travel</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1190128300</td>\n",
       "      <td>WESTMINSTER</td>\n",
       "      <td>1</td>\n",
       "      <td>528218</td>\n",
       "      <td>180407</td>\n",
       "      <td>1</td>\n",
       "      <td>1 DRIVER/RIDER</td>\n",
       "      <td>1 MALE</td>\n",
       "      <td>25-59</td>\n",
       "      <td>58.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3 SLIGHT</td>\n",
       "      <td>'-2 NOT APPLICABLE</td>\n",
       "      <td>'-2 NOT APPLICABLE</td>\n",
       "      <td>4 CAR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1190128300</td>\n",
       "      <td>WESTMINSTER</td>\n",
       "      <td>1</td>\n",
       "      <td>528218</td>\n",
       "      <td>180407</td>\n",
       "      <td>2</td>\n",
       "      <td>2 PASSENGER</td>\n",
       "      <td>2 FEMALE</td>\n",
       "      <td>U</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>3 SLIGHT</td>\n",
       "      <td>'-2 NOT APPLICABLE</td>\n",
       "      <td>'-2 NOT APPLICABLE</td>\n",
       "      <td>4 CAR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1190128300</td>\n",
       "      <td>WESTMINSTER</td>\n",
       "      <td>1</td>\n",
       "      <td>528218</td>\n",
       "      <td>180407</td>\n",
       "      <td>3</td>\n",
       "      <td>2 PASSENGER</td>\n",
       "      <td>2 FEMALE</td>\n",
       "      <td>U</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>3 SLIGHT</td>\n",
       "      <td>'-2 NOT APPLICABLE</td>\n",
       "      <td>'-2 NOT APPLICABLE</td>\n",
       "      <td>4 CAR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1190152270</td>\n",
       "      <td>LAMBETH</td>\n",
       "      <td>9</td>\n",
       "      <td>530219</td>\n",
       "      <td>172463</td>\n",
       "      <td>1</td>\n",
       "      <td>1 DRIVER/RIDER</td>\n",
       "      <td>2 FEMALE</td>\n",
       "      <td>16-24</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3 SLIGHT</td>\n",
       "      <td>'-2 NOT APPLICABLE</td>\n",
       "      <td>'-2 NOT APPLICABLE</td>\n",
       "      <td>4 CAR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1190155191</td>\n",
       "      <td>CAMDEN</td>\n",
       "      <td>2</td>\n",
       "      <td>530222</td>\n",
       "      <td>182543</td>\n",
       "      <td>1</td>\n",
       "      <td>2 PASSENGER</td>\n",
       "      <td>2 FEMALE</td>\n",
       "      <td>16-24</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3 SLIGHT</td>\n",
       "      <td>'-2 NOT APPLICABLE</td>\n",
       "      <td>'-2 NOT APPLICABLE</td>\n",
       "      <td>2 PEDAL CYCLE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       AREFNO      Borough  Boro  Easting  Northing  CREFNO  Casualty Class  \\\n",
       "0  1190128300  WESTMINSTER     1   528218    180407       1  1 DRIVER/RIDER   \n",
       "1  1190128300  WESTMINSTER     1   528218    180407       2     2 PASSENGER   \n",
       "2  1190128300  WESTMINSTER     1   528218    180407       3     2 PASSENGER   \n",
       "3  1190152270      LAMBETH     9   530219    172463       1  1 DRIVER/RIDER   \n",
       "4  1190155191       CAMDEN     2   530222    182543       1     2 PASSENGER   \n",
       "\n",
       "  Casualty Sex Casualty Age (Banded)  Casualty Age  No. of Casualties  \\\n",
       "0       1 MALE                 25-59          58.0                  3   \n",
       "1     2 FEMALE                     U           NaN                  3   \n",
       "2     2 FEMALE                     U           NaN                  3   \n",
       "3     2 FEMALE                 16-24          24.0                  1   \n",
       "4     2 FEMALE                 16-24          21.0                  1   \n",
       "\n",
       "  Casualty Severity        Ped Location        Ped Movement Mode of Travel  \n",
       "0          3 SLIGHT  '-2 NOT APPLICABLE  '-2 NOT APPLICABLE          4 CAR  \n",
       "1          3 SLIGHT  '-2 NOT APPLICABLE  '-2 NOT APPLICABLE          4 CAR  \n",
       "2          3 SLIGHT  '-2 NOT APPLICABLE  '-2 NOT APPLICABLE          4 CAR  \n",
       "3          3 SLIGHT  '-2 NOT APPLICABLE  '-2 NOT APPLICABLE          4 CAR  \n",
       "4          3 SLIGHT  '-2 NOT APPLICABLE  '-2 NOT APPLICABLE  2 PEDAL CYCLE  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2019 collision dataset\n",
    "\n",
    "# Read the file into a DataFrame\n",
    "df4 = pd.read_csv(\"2019-gla-data-extract-casualty.csv\")\n",
    "df4.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3c0300eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mode of Travel\n",
      "4 CAR                  11457\n",
      "1 PEDESTRIAN            5662\n",
      "3 POWERED 2 WHEELER     5391\n",
      "2 PEDAL CYCLE           4634\n",
      "6 BUS OR COACH          1174\n",
      "7 GOODS VEHICLE          594\n",
      "5 TAXI                   521\n",
      "9 PRIVATE HIRE           425\n",
      "8 OTHER VEHICLE          149\n",
      "dtype: int64\n",
      "30007\n"
     ]
    }
   ],
   "source": [
    "casualties_per_mode_2019 = df4.groupby(\"Mode of Travel\").size().sort_values(ascending = False)\n",
    "print(casualties_per_mode_2019)\n",
    "print(casualties_per_mode_2019.sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6f182aa0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Accident Ref.</th>\n",
       "      <th>Borough</th>\n",
       "      <th>Boro</th>\n",
       "      <th>Easting</th>\n",
       "      <th>Northing</th>\n",
       "      <th>CREFNO</th>\n",
       "      <th>Casualty Class</th>\n",
       "      <th>Casualty Sex</th>\n",
       "      <th>Casualty Age (Banded)</th>\n",
       "      <th>Casualty Age</th>\n",
       "      <th>No. of Casualties</th>\n",
       "      <th>Casualty Severity</th>\n",
       "      <th>Ped. Location</th>\n",
       "      <th>Ped. Movement</th>\n",
       "      <th>Mode of Travel</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1180087514</td>\n",
       "      <td>CITY OF LONDON</td>\n",
       "      <td>0</td>\n",
       "      <td>533090</td>\n",
       "      <td>181120</td>\n",
       "      <td>1</td>\n",
       "      <td>3 Pedestrian</td>\n",
       "      <td>2 Female</td>\n",
       "      <td>16-24</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>3 Slight</td>\n",
       "      <td>06 On Footpath - Verge</td>\n",
       "      <td>9 Unknown Or Other</td>\n",
       "      <td>1 Pedestrian</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1180104854</td>\n",
       "      <td>CITY OF LONDON</td>\n",
       "      <td>0</td>\n",
       "      <td>533650</td>\n",
       "      <td>180200</td>\n",
       "      <td>1</td>\n",
       "      <td>1 Driver/Rider</td>\n",
       "      <td>2 Female</td>\n",
       "      <td>16-24</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>3 Slight</td>\n",
       "      <td>-2 Unknown</td>\n",
       "      <td>-2 N/A</td>\n",
       "      <td>4 Car</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1180104878</td>\n",
       "      <td>CITY OF LONDON</td>\n",
       "      <td>0</td>\n",
       "      <td>531860</td>\n",
       "      <td>181830</td>\n",
       "      <td>1</td>\n",
       "      <td>1 Driver/Rider</td>\n",
       "      <td>1 Male</td>\n",
       "      <td>25-59</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>3 Slight</td>\n",
       "      <td>-2 Unknown</td>\n",
       "      <td>-2 N/A</td>\n",
       "      <td>2 Pedal Cycle</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1180108670</td>\n",
       "      <td>CITY OF LONDON</td>\n",
       "      <td>0</td>\n",
       "      <td>531109</td>\n",
       "      <td>180810</td>\n",
       "      <td>1</td>\n",
       "      <td>1 Driver/Rider</td>\n",
       "      <td>1 Male</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3 Slight</td>\n",
       "      <td>-2 Unknown</td>\n",
       "      <td>-2 N/A</td>\n",
       "      <td>2 Pedal Cycle</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1180118053</td>\n",
       "      <td>CITY OF LONDON</td>\n",
       "      <td>0</td>\n",
       "      <td>533834</td>\n",
       "      <td>180877</td>\n",
       "      <td>1</td>\n",
       "      <td>1 Driver/Rider</td>\n",
       "      <td>1 Male</td>\n",
       "      <td>25-59</td>\n",
       "      <td>57</td>\n",
       "      <td>1</td>\n",
       "      <td>3 Slight</td>\n",
       "      <td>-2 Unknown</td>\n",
       "      <td>-2 N/A</td>\n",
       "      <td>2 Pedal Cycle</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Accident Ref.         Borough  Boro  Easting  Northing  CREFNO  \\\n",
       "0     1180087514  CITY OF LONDON     0   533090    181120       1   \n",
       "1     1180104854  CITY OF LONDON     0   533650    180200       1   \n",
       "2     1180104878  CITY OF LONDON     0   531860    181830       1   \n",
       "3     1180108670  CITY OF LONDON     0   531109    180810       1   \n",
       "4     1180118053  CITY OF LONDON     0   533834    180877       1   \n",
       "\n",
       "   Casualty Class Casualty Sex Casualty Age (Banded)  Casualty Age  \\\n",
       "0    3 Pedestrian     2 Female                 16-24            24   \n",
       "1  1 Driver/Rider     2 Female                 16-24            21   \n",
       "2  1 Driver/Rider       1 Male                 25-59            32   \n",
       "3  1 Driver/Rider       1 Male               Unknown             0   \n",
       "4  1 Driver/Rider       1 Male                 25-59            57   \n",
       "\n",
       "   No. of Casualties Casualty Severity           Ped. Location  \\\n",
       "0                  1          3 Slight  06 On Footpath - Verge   \n",
       "1                  1          3 Slight              -2 Unknown   \n",
       "2                  1          3 Slight              -2 Unknown   \n",
       "3                  1          3 Slight              -2 Unknown   \n",
       "4                  1          3 Slight              -2 Unknown   \n",
       "\n",
       "        Ped. Movement Mode of Travel     \n",
       "0  9 Unknown Or Other   1 Pedestrian     \n",
       "1              -2 N/A          4 Car     \n",
       "2              -2 N/A  2 Pedal Cycle     \n",
       "3              -2 N/A  2 Pedal Cycle     \n",
       "4              -2 N/A  2 Pedal Cycle     "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2018 collision dataset\n",
    "\n",
    "# Read the file into a DataFrame\n",
    "df5 = pd.read_csv(\"2018-data-files-casualty.csv\")\n",
    "df5.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7ece4c92",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mode of Travel\n",
      "4 Car                  11804\n",
      "1 Pedestrian            5762\n",
      "3 Powered 2 Wheeler     5122\n",
      "2 Pedal Cycle           4755\n",
      "6 Bus Or Coach          1451\n",
      "5 Taxi                   957\n",
      "7 Goods Vehicle          634\n",
      "8 Other Vehicle          106\n",
      "dtype: int64\n",
      "30591\n"
     ]
    }
   ],
   "source": [
    "casualties_per_mode_2018 = df5.groupby(\"Mode of Travel\").size().sort_values(ascending = False)\n",
    "print(casualties_per_mode_2018)\n",
    "print(casualties_per_mode_2018.sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "139820bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Accident Ref.</th>\n",
       "      <th>Borough</th>\n",
       "      <th>Boro</th>\n",
       "      <th>Easting</th>\n",
       "      <th>Northing</th>\n",
       "      <th>CREFNO</th>\n",
       "      <th>Casualty Class</th>\n",
       "      <th>Casualty Sex</th>\n",
       "      <th>Casualty Age (Banded)</th>\n",
       "      <th>Casualty Age</th>\n",
       "      <th>No. of Casualties</th>\n",
       "      <th>Casualty Severity</th>\n",
       "      <th>Ped. Location</th>\n",
       "      <th>Ped. Movement</th>\n",
       "      <th>Mode of Travel</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1170014045</td>\n",
       "      <td>CITY OF LONDON</td>\n",
       "      <td>0</td>\n",
       "      <td>532780</td>\n",
       "      <td>180350</td>\n",
       "      <td>1</td>\n",
       "      <td>1 Driver/Rider</td>\n",
       "      <td>1 Male</td>\n",
       "      <td>25-59</td>\n",
       "      <td>34</td>\n",
       "      <td>1</td>\n",
       "      <td>3 Slight</td>\n",
       "      <td>-2 Unknown</td>\n",
       "      <td>-2 N/A</td>\n",
       "      <td>2 Pedal Cycle</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1170014113</td>\n",
       "      <td>CITY OF LONDON</td>\n",
       "      <td>0</td>\n",
       "      <td>533841</td>\n",
       "      <td>180787</td>\n",
       "      <td>1</td>\n",
       "      <td>1 Driver/Rider</td>\n",
       "      <td>2 Female</td>\n",
       "      <td>25-59</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>3 Slight</td>\n",
       "      <td>-2 Unknown</td>\n",
       "      <td>-2 N/A</td>\n",
       "      <td>2 Pedal Cycle</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1170017232</td>\n",
       "      <td>CITY OF LONDON</td>\n",
       "      <td>0</td>\n",
       "      <td>533630</td>\n",
       "      <td>181200</td>\n",
       "      <td>1</td>\n",
       "      <td>3 Pedestrian</td>\n",
       "      <td>1 Male</td>\n",
       "      <td>25-59</td>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>3 Slight</td>\n",
       "      <td>01 Crossing Road On Ped Xing</td>\n",
       "      <td>9 Unknown Or Other</td>\n",
       "      <td>1 Pedestrian</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1170019554</td>\n",
       "      <td>CITY OF LONDON</td>\n",
       "      <td>0</td>\n",
       "      <td>531130</td>\n",
       "      <td>181110</td>\n",
       "      <td>1</td>\n",
       "      <td>3 Pedestrian</td>\n",
       "      <td>1 Male</td>\n",
       "      <td>25-59</td>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>2 Serious</td>\n",
       "      <td>01 Crossing Road On Ped Xing</td>\n",
       "      <td>3 From Drivers O/Side</td>\n",
       "      <td>1 Pedestrian</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1170020053</td>\n",
       "      <td>CITY OF LONDON</td>\n",
       "      <td>0</td>\n",
       "      <td>531900</td>\n",
       "      <td>181840</td>\n",
       "      <td>1</td>\n",
       "      <td>3 Pedestrian</td>\n",
       "      <td>1 Male</td>\n",
       "      <td>25-59</td>\n",
       "      <td>36</td>\n",
       "      <td>1</td>\n",
       "      <td>2 Serious</td>\n",
       "      <td>09 In Road - Not Crossing</td>\n",
       "      <td>7 In Rd Facing Traffic</td>\n",
       "      <td>1 Pedestrian</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Accident Ref.         Borough  Boro  Easting  Northing  CREFNO  \\\n",
       "0     1170014045  CITY OF LONDON     0   532780    180350       1   \n",
       "1     1170014113  CITY OF LONDON     0   533841    180787       1   \n",
       "2     1170017232  CITY OF LONDON     0   533630    181200       1   \n",
       "3     1170019554  CITY OF LONDON     0   531130    181110       1   \n",
       "4     1170020053  CITY OF LONDON     0   531900    181840       1   \n",
       "\n",
       "   Casualty Class Casualty Sex Casualty Age (Banded)  Casualty Age  \\\n",
       "0  1 Driver/Rider       1 Male                 25-59            34   \n",
       "1  1 Driver/Rider     2 Female                 25-59            35   \n",
       "2    3 Pedestrian       1 Male                 25-59            37   \n",
       "3    3 Pedestrian       1 Male                 25-59            29   \n",
       "4    3 Pedestrian       1 Male                 25-59            36   \n",
       "\n",
       "   No. of Casualties Casualty Severity                 Ped. Location  \\\n",
       "0                  1          3 Slight                    -2 Unknown   \n",
       "1                  1          3 Slight                    -2 Unknown   \n",
       "2                  1          3 Slight  01 Crossing Road On Ped Xing   \n",
       "3                  1         2 Serious  01 Crossing Road On Ped Xing   \n",
       "4                  1         2 Serious     09 In Road - Not Crossing   \n",
       "\n",
       "            Ped. Movement Mode of Travel     \n",
       "0                  -2 N/A  2 Pedal Cycle     \n",
       "1                  -2 N/A  2 Pedal Cycle     \n",
       "2      9 Unknown Or Other   1 Pedestrian     \n",
       "3   3 From Drivers O/Side   1 Pedestrian     \n",
       "4  7 In Rd Facing Traffic   1 Pedestrian     "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2017 collision dataset\n",
    "\n",
    "# Read the file into a DataFrame\n",
    "df6 = pd.read_csv(\"2017-data-casualty.csv\")\n",
    "df6.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "c3f6bd5e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mode of Travel\n",
      "4 Car                  12375\n",
      "1 Pedestrian            6652\n",
      "3 Powered 2 Wheeler     5577\n",
      "2 Pedal Cycle           4521\n",
      "6 Bus Or Coach          1752\n",
      "5 Taxi                   904\n",
      "7 Goods Vehicle          611\n",
      "8 Other Vehicle          175\n",
      "dtype: int64\n",
      "32567\n"
     ]
    }
   ],
   "source": [
    "casualties_per_mode_2017 = df6.groupby(\"Mode of Travel\").size().sort_values(ascending = False)\n",
    "print(casualties_per_mode_2017)\n",
    "print(casualties_per_mode_2017.sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "6b428cb9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Casualty Mode of Travel\n",
      "Car                  9002\n",
      "Powered 2 Wheeler    6151\n",
      "Pedal Cycle          5091\n",
      "Pedestrian           4555\n",
      "Bus Or Coach         1015\n",
      "Goods Vehicle         435\n",
      "Other Vehicle         381\n",
      "Taxi                  319\n",
      "Private Hire          258\n",
      "dtype: int64\n",
      "27207\n"
     ]
    }
   ],
   "source": [
    "# 2022 collision dataset\n",
    "\n",
    "# Read the file into a DataFrame\n",
    "df7 = pd.read_csv(\"jan-dec-2022-gla-data-extract-casualties.csv\")\n",
    "df7.head()\n",
    "\n",
    "casualties_per_mode_2022 = df7.groupby(\"Casualty Mode of Travel\").size().sort_values(ascending = False)\n",
    "print(casualties_per_mode_2022)\n",
    "print(casualties_per_mode_2022.sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "34ee3991",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Speed Limit\n",
      "20    12526\n",
      "30    12268\n",
      "40     1343\n",
      "50      786\n",
      "70      179\n",
      "60       44\n",
      "10       35\n",
      "5        17\n",
      "15        4\n",
      "3         2\n",
      "12        1\n",
      "25        1\n",
      "38        1\n",
      "dtype: int64\n",
      "27207\n"
     ]
    }
   ],
   "source": [
    "df_attendant_2022 = pd.read_csv(\"jan-dec-2022-gla-data-extract-attendant.csv\")\n",
    "df_attendant_2022.head()\n",
    "\n",
    "# Join 2022 datasets\n",
    "df2022 = pd.merge(df7, df_attendant_2022, on='_Collision Id')\n",
    "df2022.head()\n",
    "\n",
    "# Group number of casualties by speed limit\n",
    "\n",
    "sl_casualties2022 = df2022.groupby(\"Speed Limit\").size().sort_values(ascending = False)\n",
    "print(sl_casualties2022)\n",
    "print(sl_casualties2022.sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "6826a59c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['_Collision Id' 'Borough Name_x' 'Borough Number_x' 'Easting_x'\n",
      " 'Northing_x' '_Casualty Id' '_Casualty Class' 'Casualty Gender'\n",
      " 'Casualty Age (5 Year Bands)' 'Casualty Age' '_Casualty Count_x'\n",
      " '_Casualty Severity' 'Pedestrian Location' 'Pedestrian Movement'\n",
      " 'Casualty Mode of Travel' 'Borough Name_y' 'Borough Number_y' 'Easting_y'\n",
      " 'Northing_y' 'Collision Location' '_Collision Severity'\n",
      " '_Casualty Count_y' '_Vehicle Count' '_Collision Date' 'Day Name' 'Time'\n",
      " 'Highway Authority' 'First Road Class' 'First Road Number' 'Road Type'\n",
      " 'Speed Limit' 'Junction Detail' 'Junction Control' 'Second Road Class'\n",
      " 'Second Road Number' 'Pedestrian Crossing Facilities' 'Light Conditions'\n",
      " 'Weather Details' 'Road Surface Condition' 'Special Conditions at Site'\n",
      " 'Carriageway Hazards' 'Place Collision Reported']\n"
     ]
    }
   ],
   "source": [
    "print(df2022.columns.values)\n",
    "df2022[\"_Collision Date\"].head(10)\n",
    "\n",
    "# Filter out \"Bus Or Coach\", \"Goods Vehicle\" and \"Other Vehicle\"\n",
    "unwanted_values = [\"Bus Or Coach\", \"Goods Vehicle\", \"Other Vehicle\"]\n",
    "df2022_filtered = df2022[~df2022[\"Casualty Mode of Travel\"].isin(unwanted_values)]\n",
    "\n",
    "# Count of casualties by unique date\n",
    "df2022_casualties_day = df2022_filtered.groupby('_Collision Date')[[\"Casualty Mode of Travel\"]].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "fa5f48da",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mode of Travel\n",
      "4 Car                  11891\n",
      "1 Pedestrian            5549\n",
      "3 Powered 2 Wheeler     5255\n",
      "2 Pedal Cycle           4424\n",
      "6 Bus Or Coach          1593\n",
      "5 Taxi                   789\n",
      "7 Goods Vehicle          623\n",
      "8 Other Vehicle          146\n",
      "dtype: int64\n",
      "30270\n"
     ]
    }
   ],
   "source": [
    "# 2016 collision dataset\n",
    "\n",
    "# Read the file into a DataFrame\n",
    "df8 = pd.read_csv(\"2016-gla-data-extract-casualty.csv\")\n",
    "df8.head()\n",
    "\n",
    "casualties_per_mode_2016 = df8.groupby(\"Mode of Travel\").size().sort_values(ascending = False)\n",
    "print(casualties_per_mode_2016)\n",
    "print(casualties_per_mode_2016.sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "550e4193",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Speed Limit\n",
      "2 30 MPH       25013\n",
      "1 <= 20 MPH     2784\n",
      "3 40 MPH        1387\n",
      "4 50 MPH         822\n",
      "6 70 MPH         232\n",
      "5 60 MPH          32\n",
      "dtype: int64\n",
      "30270\n"
     ]
    }
   ],
   "source": [
    "df_attendant_2016 = pd.read_csv(\"2016-gla-data-extract-attendant.csv\")\n",
    "df_attendant_2016.head()\n",
    "\n",
    "# Join 2016 datasets\n",
    "df2016 = pd.merge(df8, df_attendant_2016, on='Accident Ref.')\n",
    "df2016.head()\n",
    "\n",
    "# Group number of casualties by speed limit\n",
    "\n",
    "sl_casualties2016 = df2016.groupby(\"Speed Limit\").size().sort_values(ascending = False)\n",
    "print(sl_casualties2016)\n",
    "print(sl_casualties2016.sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "e5591654",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a DataFrame\n",
    "total_casualties = pd.DataFrame({\n",
    "    \"Year\": [\"2016\", \"2017\", \"2018\", \"2019\", \"2020\", \"2021\", \"2022\"],\n",
    "    \"Total Casualties\": [30270, 32567, 30591, 30007, 24345, 26671, 27207],\n",
    "    \"Total Casualties - Pedal Cycle\": [4424, 4521, 4755, 4634, 4789, 5277, 5091],\n",
    "    \"Total Km Road Travel\": [20083026279, 20055359109, 19992861706, 20264369690, 16613638242, 18453291284, 19089347093],\n",
    "    \"Total Km Pedal Cycle Travel\": [506307274.6, 500504499.9, 518009547.1, 467836359.5, 697725718.5, 647245366.3, 605772496.9],\n",
    "    \"Total Casualties - Speed Limit <= 20 MPH\": [2784, 6274, 7156, 8505, 8747, 11471, 12585],\n",
    "    \"Total Casualties - Speed Limit 30 MPH\": [25013, 23138, 20374, 18581, 13340, 12831, 12268],\n",
    "    \"Total Casualties - Speed Limit 40 MPH\": [1387, 1776, 1690, 1650, 1335, 1307, 1343],\n",
    "    \"Total Casualties - Speed Limit 50 MPH\": [822, 975, 979, 910, 689, 795, 786],\n",
    "    \"Total Casualties - Speed Limit 60 MPH\": [32, 88, 69, 65, 49, 48, 44],\n",
    "    \"Total Casualties - Speed Limit 70 MPH\": [232, 315, 323, 292, 180, 205, 179]\n",
    "})\n",
    "\n",
    "# Calculate Total Road Accidents per Million Km Road Travel\n",
    "total_casualties[\"Total Road Accidents per Million Km Road Travel\"] = (total_casualties[\"Total Casualties\"] / total_casualties[\"Total Km Road Travel\"]) * 1000000\n",
    "total_casualties[\"Total Casualties per Million Km Road Travel - Speed Limit <= 20 MPH\"] = (total_casualties[\"Total Casualties - Speed Limit <= 20 MPH\"] / total_casualties[\"Total Km Road Travel\"]) * 1000000\n",
    "total_casualties[\"Total Casualties - Pedal Cycle per Million Km Road Travel\"] = (total_casualties[\"Total Casualties - Pedal Cycle\"] / total_casualties[\"Total Km Pedal Cycle Travel\"]) * 1000000\n",
    "\n",
    "# Plot the DataFrame\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(total_casualties[\"Year\"], total_casualties[\"Total Road Accidents per Million Km Road Travel\"], color=\"g\", marker=\"x\")\n",
    "ax.set_xlabel(\"Year\")\n",
    "ax.set_ylabel(\"Casualties per Million Km Road Travel\", color=\"green\")\n",
    "ax2 = ax.twinx()\n",
    "ax2.plot(total_casualties[\"Year\"], total_casualties[\"Total Casualties per Million Km Road Travel - Speed Limit <= 20 MPH\"], color=\"r\", marker=\".\")\n",
    "ax2.set_ylabel(\"Speed Limit <=20MPH Roads' Casualties per Million Km Road Travel\", color=\"red\")\n",
    "\n",
    "# Add a title and labels\n",
    "#plt.title(\"Count of Total Road Accidents per Million Km Road Travel by Year\")\n",
    "#plt.xlabel(\"Year\")\n",
    "#plt.ylabel(\"Total Road Accidents per Million Km Road Travel\")\n",
    "\n",
    "#fig.set_size_inches([])\n",
    "fig.savefig(\"casualties and speed limit casualties normalised.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "6abb8114",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Total Casualties - Speed Limit\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(total_casualties[\"Year\"], total_casualties[\"Total Casualties - Speed Limit <= 20 MPH\"], color=\"r\", marker=\".\")\n",
    "\n",
    "# Add a title and labels\n",
    "plt.title(\"Count of Total Road Accidents where the speed limit is <= 20 MPH\")\n",
    "plt.xlabel(\"Year\")\n",
    "plt.ylabel(\"Total Casualties - Speed Limit <= 20 MPH\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "ef36fafe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot total casualties x total pedal cycle casualties\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(total_casualties[\"Year\"], total_casualties[\"Total Road Accidents per Million Km Road Travel\"], color=\"g\", marker=\"x\")\n",
    "ax.set_xlabel(\"Year\")\n",
    "ax.set_ylabel(\"Casualties per Million Km Road Travel\", color=\"green\")\n",
    "ax2 = ax.twinx()\n",
    "ax2.plot(total_casualties[\"Year\"], total_casualties[\"Total Casualties - Pedal Cycle per Million Km Road Travel\"], marker=\"o\")\n",
    "ax2.set_ylabel(\"Pedal Cycle Casualties per Million Km Road Travel\", color=\"blue\")\n",
    "fig.savefig(\"casualties and cycling casualties normalised.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "d188179f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Bar chart of casualties per speed limit by year\n",
    "fig, ax = plt.subplots()\n",
    "ax.bar(total_casualties[\"Year\"], total_casualties[\"Total Casualties - Speed Limit <= 20 MPH\"], label=\"<= 20MPH\")\n",
    "ax.bar(total_casualties[\"Year\"], total_casualties[\"Total Casualties - Speed Limit 30 MPH\"], bottom=total_casualties[\"Total Casualties - Speed Limit <= 20 MPH\"], label=\"30MPH\")\n",
    "ax.bar(total_casualties[\"Year\"], total_casualties[\"Total Casualties - Speed Limit 40 MPH\"], bottom=total_casualties[\"Total Casualties - Speed Limit <= 20 MPH\"] + total_casualties[\"Total Casualties - Speed Limit 30 MPH\"], label=\"40MPH\")\n",
    "ax.bar(total_casualties[\"Year\"], total_casualties[\"Total Casualties - Speed Limit 50 MPH\"], bottom=total_casualties[\"Total Casualties - Speed Limit <= 20 MPH\"] + total_casualties[\"Total Casualties - Speed Limit 30 MPH\"] + total_casualties[\"Total Casualties - Speed Limit 40 MPH\"], label=\"50MPH\")\n",
    "#ax.bar(total_casualties[\"Year\"], total_casualties[\"Total Casualties - Speed Limit 60 MPH\"], bottom=total_casualties[\"Total Casualties - Speed Limit <= 20 MPH\"] + total_casualties[\"Total Casualties - Speed Limit 30 MPH\"] + total_casualties[\"Total Casualties - Speed Limit 40 MPH\"] + total_casualties[\"Total Casualties - Speed Limit 50 MPH\"], label=\"60MPH\")\n",
    "#ax.bar(total_casualties[\"Year\"], total_casualties[\"Total Casualties - Speed Limit 70 MPH\"], bottom=total_casualties[\"Total Casualties - Speed Limit <= 20 MPH\"] + total_casualties[\"Total Casualties - Speed Limit 30 MPH\"] + total_casualties[\"Total Casualties - Speed Limit 40 MPH\"] + total_casualties[\"Total Casualties - Speed Limit 50 MPH\"] + total_casualties[\"Total Casualties - Speed Limit 60 MPH\"], label=\"70MPH\")\n",
    "ax.set_xlabel(\"Year\")\n",
    "ax.set_ylabel(\"Count of Casualties\")\n",
    "plt.title(\"Count of Casualties according to Road Speed Limit\")\n",
    "ax.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "5cd0717b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Speed Limit\n",
      "2 30 MPH       20374\n",
      "1 <= 20 MPH     7156\n",
      "3 40 MPH        1690\n",
      "4 50 MPH         979\n",
      "6 70 MPH         323\n",
      "5 60 MPH          69\n",
      "dtype: int64\n",
      "30591\n"
     ]
    }
   ],
   "source": [
    "df_attendant_2018 = pd.read_csv(\"2018-data-files-attendant.csv\")\n",
    "df_attendant_2018.head()\n",
    "\n",
    "# Join 2018 datasets\n",
    "df2018 = pd.merge(df5, df_attendant_2018, on='Accident Ref.')\n",
    "df2018.head()\n",
    "\n",
    "# Group number of casualties by speed limit\n",
    "# print(df2018.groupby(\"Speed Limit\").sum())\n",
    "\n",
    "sl_casualties2018 = df2018.groupby(\"Speed Limit\").size().sort_values(ascending = False)\n",
    "print(sl_casualties2018)\n",
    "print(sl_casualties2018.sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "3a8ef9ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Accident Ref.' 'Borough_x' 'Boro_x' 'Easting_x' 'Northing_x' 'CREFNO'\n",
      " 'Casualty Class' 'Casualty Sex' 'Casualty Age (Banded)' 'Casualty Age'\n",
      " 'No. of Casualties' 'Casualty Severity' 'Ped. Location' 'Ped. Movement'\n",
      " 'Mode of Travel' ' ' 'Borough_y' 'Boro_y' 'Easting_y' 'Northing_y'\n",
      " 'Location' 'Accident Severity' 'No. of Casualties in Acc.'\n",
      " 'No. of Vehicles in Acc.' 'Accident Date' 'Day' 'Time ' 'Highway'\n",
      " 'Road Class 1' 'Road No. 1' 'Road Type' 'Speed Limit' 'Junction Detail'\n",
      " 'Junction Control' 'Road Class 2' 'Road No. 2' 'Ped. Crossing Decoded'\n",
      " 'Light Conditions (Banded)' 'Weather' 'Road Surface' 'Special Conditions'\n",
      " 'C/W Hazard']\n",
      "Accident Date  Mode of Travel     \n",
      "01-Apr-18      4 Car                  31\n",
      "               1 Pedestrian            7\n",
      "               2 Pedal Cycle           6\n",
      "               3 Powered 2 Wheeler     5\n",
      "               5 Taxi                  4\n",
      "                                      ..\n",
      "31-Oct-18      4 Car                  31\n",
      "               1 Pedestrian           24\n",
      "               2 Pedal Cycle          16\n",
      "               3 Powered 2 Wheeler    15\n",
      "               5 Taxi                  2\n",
      "Name: count, Length: 1769, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df2018.columns.values)\n",
    "\n",
    "# Filter out \"Bus Or Coach\", \"Goods Vehicle\" and \"Other Vehicle\"\n",
    "unwanted_values = [\"6 Bus Or Coach\", \"7 Goods Vehicle\", \"8 Other Vehicle\"]\n",
    "df2018_filtered = df2018[~df2018[\"Mode of Travel\"].isin(unwanted_values)]\n",
    "\n",
    "# Count of casualties by unique date\n",
    "df2018_casualties_day = df2018_filtered.groupby('Accident Date')[[\"Mode of Travel\"]].value_counts()\n",
    "print(df2018_casualties_day)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "73e5721b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Speed Limit\n",
      "30    18581\n",
      "20     8434\n",
      "40     1650\n",
      "50      910\n",
      "70      292\n",
      "60       65\n",
      "10       48\n",
      "5        17\n",
      "15        6\n",
      "25        4\n",
      "dtype: int64\n",
      "30007\n"
     ]
    }
   ],
   "source": [
    "df_attendant_2019 = pd.read_csv(\"2019-gla-data-extract-attendant.csv\")\n",
    "df_attendant_2019.head()\n",
    "\n",
    "# Rename column\n",
    "df_attendant_2019 = df_attendant_2019.rename(columns={'Accident Ref': 'AREFNO'})\n",
    "\n",
    "# Join 2019 datasets\n",
    "df2019 = pd.merge(df4, df_attendant_2019, on='AREFNO')\n",
    "df2019.head()\n",
    "\n",
    "# Group number of casualties by speed limit\n",
    "\n",
    "sl_casualties2019 = df2019.groupby(\"Speed Limit\").size().sort_values(ascending = False)\n",
    "print(sl_casualties2019)\n",
    "print(sl_casualties2019.sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "0fe9787d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['AREFNO' 'Borough_x' 'Boro_x' 'Easting_x' 'Northing_x' 'CREFNO'\n",
      " 'Casualty Class' 'Casualty Sex' 'Casualty Age (Banded)' 'Casualty Age'\n",
      " 'No. of Casualties' 'Casualty Severity' 'Ped Location' 'Ped Movement'\n",
      " 'Mode of Travel' 'Borough_y' 'Boro_y' 'Easting_y' 'Northing_y' 'Location'\n",
      " 'Accident Severity' 'No. of Casualties in Acc.' 'No. of Vehicles in Acc.'\n",
      " 'Accident Date' 'Day' 'Time' 'Highway' 'Road Class 1' 'Road No. 1'\n",
      " 'Road Type' 'Speed Limit' 'Junction Detail' 'Junction Control'\n",
      " 'Road Class 2' 'Road No 2' 'Ped. Crossing Decoded'\n",
      " 'Light Condtions (Banded)' 'Weather' 'Road Surface' 'Special Conditions'\n",
      " 'C/W Hazard' 'ADATES_FULL' 'APOLICER_DECODED']\n",
      "Accident Date  Mode of Travel     \n",
      "01/01/2019     4 CAR                  26\n",
      "               3 POWERED 2 WHEELER    13\n",
      "               1 PEDESTRIAN           12\n",
      "               2 PEDAL CYCLE           5\n",
      "               5 TAXI                  4\n",
      "                                      ..\n",
      "31/12/2019     4 CAR                  29\n",
      "               3 POWERED 2 WHEELER    17\n",
      "               1 PEDESTRIAN           10\n",
      "               2 PEDAL CYCLE           3\n",
      "               5 TAXI                  1\n",
      "Name: count, Length: 1926, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df2019.columns.values)\n",
    "\n",
    "# Filter out \"Bus Or Coach\", \"Goods Vehicle\" and \"Other Vehicle\"\n",
    "unwanted_values = [\"6 BUS OR COACH\", \"7 GOODS VEHICLE\", \"8 OTHER VEHICLE\"]\n",
    "df2019_filtered = df2019[~df2019[\"Mode of Travel\"].isin(unwanted_values)]\n",
    "\n",
    "# Count of casualties by unique date\n",
    "df2019_casualties_day = df2019_filtered.groupby('Accident Date')[[\"Mode of Travel\"]].value_counts()\n",
    "print(df2019_casualties_day)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "aa5d4b4f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Speed Limit\n",
      "30    13340\n",
      "20     8658\n",
      "40     1335\n",
      "50      689\n",
      "70      180\n",
      "10       50\n",
      "60       49\n",
      "5        29\n",
      "15        7\n",
      "25        5\n",
      "1         1\n",
      "2         1\n",
      "18        1\n",
      "dtype: int64\n",
      "24345\n"
     ]
    }
   ],
   "source": [
    "df_attendant_2020 = pd.read_csv(\"2020-gla-data-extract-attendant.csv\")\n",
    "df_attendant_2020.head()\n",
    "\n",
    "# Rename column\n",
    "df_attendant_2020 = df_attendant_2020.rename(columns={'Accident Ref': 'Accident Ref.'})\n",
    "\n",
    "# Join 2020 datasets\n",
    "df2020 = pd.merge(df3, df_attendant_2020, on='Accident Ref.')\n",
    "df2020.head()\n",
    "\n",
    "# Group number of casualties by speed limit\n",
    "\n",
    "sl_casualties2020 = df2020.groupby(\"Speed Limit\").size().sort_values(ascending = False)\n",
    "print(sl_casualties2020)\n",
    "print(sl_casualties2020.sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "300e5bd2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Accident Ref.' 'Borough_x' 'Boro_x' 'Easting_x' 'Northing_x' 'CREFNO'\n",
      " 'Casualty Class' 'Casualty Sex' 'Casualty Age (Banded)' 'Casualty Age'\n",
      " 'No. of Casualties' 'Casualty Severity' 'Ped. Location' 'Ped. Movement'\n",
      " 'Mode of Travel' ' ' 'Borough_y' 'Boro_y' 'Easting_y' 'Northing_y'\n",
      " 'Location' 'Accident Severity' 'No. of Casualties in Acc.'\n",
      " 'No. of Vehicles in Acc.' 'Accident Date' 'Day' 'Time' 'Highway'\n",
      " 'Road Class 1' 'Road No. 1' 'Road Type' 'Speed Limit' 'Junction Detail'\n",
      " 'Junction Control' 'Road Class 2' 'Road No 2' 'Ped. Crossing Decoded'\n",
      " 'Light Condtions (Banded)' 'Weather' 'Road Surface' 'Special Conditions'\n",
      " 'C/W Hazard' 'ADATES_FULL' 'APOLICER_DECODED']\n",
      "Accident Date  Mode of Travel     \n",
      "01/01/2020     4 CAR                  35\n",
      "               1 PEDESTRIAN           12\n",
      "               3 POWERED 2 WHEELER     6\n",
      "               9 PRIVATE HIRE          6\n",
      "               5 TAXI                  2\n",
      "                                      ..\n",
      "31/12/2020     4 CAR                  29\n",
      "               3 POWERED 2 WHEELER    16\n",
      "               2 PEDAL CYCLE           9\n",
      "               1 PEDESTRIAN            7\n",
      "               5 TAXI                  2\n",
      "Name: count, Length: 1786, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df2020.columns.values)\n",
    "\n",
    "# Filter out \"Bus Or Coach\", \"Goods Vehicle\" and \"Other Vehicle\"\n",
    "unwanted_values = [\"6 BUS OR COACH\", \"7 GOODS VEHICLE\", \"8 OTHER VEHICLE\"]\n",
    "df2020_filtered = df2020[~df2020[\"Mode of Travel\"].isin(unwanted_values)]\n",
    "\n",
    "# Count of casualties by unique date\n",
    "df2020_casualties_day = df2020_filtered.groupby('Accident Date')[[\"Mode of Travel\"]].value_counts()\n",
    "print(df2020_casualties_day)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "c24badc8",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_attendant_2021 = pd.read_csv(\"jan-dec-2021-gla-data-extract-attendant.csv\", header=1)\n",
    "df_attendant_2021.head()\n",
    "\n",
    "# Drop the last row = \n",
    "df_attendant_2021 = df_attendant_2021.iloc[:-1]\n",
    "\n",
    "df_attendant_2021[\"_Collision Id\"] = df_attendant_2021[\"_Collision Id\"].apply(pd.to_numeric)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "c1c27082",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Speed Limit\n",
      "30.0     12831\n",
      "20.0     11351\n",
      "40.0      1307\n",
      "50.0       795\n",
      "70.0       205\n",
      "10.0        54\n",
      "60.0        48\n",
      "5.0         37\n",
      "15.0        21\n",
      "25.0         4\n",
      "13.0         3\n",
      "38.0         2\n",
      "1.0          2\n",
      "36.0         1\n",
      "2.0          1\n",
      "29.0         1\n",
      "45.0         1\n",
      "21.0         1\n",
      "14.0         1\n",
      "8.0          1\n",
      "80.0         1\n",
      "220.0        1\n",
      "300.0        1\n",
      "301.0        1\n",
      "dtype: int64\n",
      "26671\n"
     ]
    }
   ],
   "source": [
    "# Join 2021 datasets\n",
    "df2021 = pd.merge(df2, df_attendant_2021, on=\"_Collision Id\")\n",
    "df2021.head()\n",
    "\n",
    "# Group number of casualties by speed limit\n",
    "\n",
    "sl_casualties2021 = df2021.groupby(\"Speed Limit\").size().sort_values(ascending = False)\n",
    "print(sl_casualties2021)\n",
    "print(sl_casualties2021.sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "451924c8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['_Collision Id' 'Borough Name_x' 'Borough Number_x' 'Easting_x'\n",
      " 'Northing_x' '_Casualty Id' '_Casualty Class' 'Casualty Gender'\n",
      " 'Casualty Age (5 Year Bands)' 'Casualty Age' '_Casualty Count_x'\n",
      " '_Casualty Severity' 'Pedestrian Location' 'Pedestrian Movement'\n",
      " 'Casualty Mode of Travel' 'Borough Name_y' 'Borough Number_y' 'Easting_y'\n",
      " 'Northing_y' 'Collision Location' '_Collision Severity'\n",
      " '_Casualty Count_y' '_Vehicle Count' '_Collision Date' 'Day Name' 'Time'\n",
      " 'Highway Authority' 'First Road Class' 'First Road Number' 'Road Type'\n",
      " 'Speed Limit' 'Junction Detail' 'Junction Control' 'Second Road Class'\n",
      " 'Second Road Number' 'Pedestrian Crossing Facilities' 'Light Conditions'\n",
      " 'Weather Details' 'Road Surface Condition' 'Special Conditions at Site'\n",
      " 'Carriageway Hazards' 'Place Collision Reported']\n",
      "_Collision Date   Casualty Mode of Travel\n",
      "01/01/2021 00:00  Car                        19\n",
      "                  Powered 2 Wheeler           5\n",
      "                  Pedal Cycle                 5\n",
      "                  Pedestrian                  3\n",
      "                  Goods Vehicle               1\n",
      "                                             ..\n",
      "31/12/2021 00:00  Car                        20\n",
      "                  Powered 2 Wheeler          11\n",
      "                  Pedal Cycle                 6\n",
      "                  Pedestrian                  4\n",
      "                  Bus Or Coach                3\n",
      "Name: count, Length: 2602, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df2021.columns.values)\n",
    "\n",
    "# Filter out \"Bus Or Coach\", \"Goods Vehicle\" and \"Other Vehicle\"\n",
    "unwanted_values = [\"6 Bus Or Coach\", \"7 Goods Vehicle\", \"8 Other Vehicle\"]\n",
    "df2021_filtered = df2021[~df2021[\"Casualty Mode of Travel\"].isin(unwanted_values)]\n",
    "\n",
    "# Count of casualties by unique date\n",
    "df2021_casualties_day = df2021_filtered.groupby('_Collision Date')[[\"Casualty Mode of Travel\"]].value_counts()\n",
    "print(df2021_casualties_day)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "2b8496c3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Speed Limit\n",
      "2 30 MPH       23138\n",
      "1 <= 20 MPH     6274\n",
      "3 40 MPH        1776\n",
      "4 50 MPH         975\n",
      "6 70 MPH         315\n",
      "5 60 MPH          88\n",
      "7 Not Coded        1\n",
      "dtype: int64\n",
      "32567\n"
     ]
    }
   ],
   "source": [
    "file_encoding = 'utf8'        # set file_encoding to the file encoding (utf8, latin1, etc.)\n",
    "input_fd = open(\"2017-data-attendant.csv\", encoding=file_encoding, errors = 'backslashreplace')\n",
    "df_attendant_2017 = pd.read_csv(input_fd)\n",
    "df_attendant_2017.head()\n",
    "\n",
    "# Join 2017 datasets\n",
    "df2017 = pd.merge(df6, df_attendant_2017, on=\"Accident Ref.\")\n",
    "df2017.head()\n",
    "\n",
    "# Group number of casualties by speed limit\n",
    "\n",
    "sl_casualties2017 = df2017.groupby(\"Speed Limit\").size().sort_values(ascending = False)\n",
    "print(sl_casualties2017)\n",
    "print(sl_casualties2017.sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "91e791ab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Accident Ref.' 'Borough_x' 'Boro_x' 'Easting_x' 'Northing_x' 'CREFNO'\n",
      " 'Casualty Class' 'Casualty Sex' 'Casualty Age (Banded)' 'Casualty Age'\n",
      " 'No. of Casualties' 'Casualty Severity' 'Ped. Location' 'Ped. Movement'\n",
      " 'Mode of Travel' ' ' 'Borough_y' 'Boro_y' 'Easting_y' 'Northing_y'\n",
      " 'Location' 'Accident Severity' 'No. of Casualties in Acc.'\n",
      " 'No. of Vehicles in Acc.' 'Accident Date' 'Day' 'Time' 'Highway'\n",
      " 'Road Class 1' 'Road No. 1' 'Road Type' 'Speed Limit' 'Junction Detail'\n",
      " 'Junction Control' 'Road Class 2' 'Road No. 2' 'Ped. Crossing Decoded'\n",
      " 'Light Conditions (Banded)' 'Weather' 'Road Surface' 'Special Conditions'\n",
      " 'C/W Hazard']\n",
      "Accident Date  Mode of Travel     \n",
      "01-Apr-17      4 Car                  50\n",
      "               3 Powered 2 Wheeler    20\n",
      "               1 Pedestrian           19\n",
      "               5 Taxi                  4\n",
      "               2 Pedal Cycle           4\n",
      "                                      ..\n",
      "31-Oct-17      3 Powered 2 Wheeler    24\n",
      "               4 Car                  20\n",
      "               2 Pedal Cycle          20\n",
      "               1 Pedestrian           17\n",
      "               5 Taxi                  2\n",
      "Name: count, Length: 1765, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df2017.columns.values)\n",
    "\n",
    "# Filter out \"Bus Or Coach\", \"Goods Vehicle\" and \"Other Vehicle\"\n",
    "unwanted_values = [\"6 Bus Or Coach\", \"7 Goods Vehicle\", \"8 Other Vehicle\"]\n",
    "df2017_filtered = df2017[~df2017[\"Mode of Travel\"].isin(unwanted_values)]\n",
    "\n",
    "# Count of casualties by unique date\n",
    "df2017_casualties_day = df2017_filtered.groupby('Accident Date')[[\"Mode of Travel\"]].value_counts()\n",
    "print(df2017_casualties_day)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "f8e2fb81",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accident Date  Mode of Travel     \n",
      "01-Apr-17      4 Car                  50\n",
      "               3 Powered 2 Wheeler    20\n",
      "               1 Pedestrian           19\n",
      "               5 Taxi                  4\n",
      "               2 Pedal Cycle           4\n",
      "                                      ..\n",
      "31-Oct-17      3 Powered 2 Wheeler    24\n",
      "               4 Car                  20\n",
      "               2 Pedal Cycle          20\n",
      "               1 Pedestrian           17\n",
      "               5 Taxi                  2\n",
      "Name: count, Length: 1765, dtype: int64\n",
      "Accident Date  Mode of Travel     \n",
      "01-Apr-18      4 Car                  31\n",
      "               1 Pedestrian            7\n",
      "               2 Pedal Cycle           6\n",
      "               3 Powered 2 Wheeler     5\n",
      "               5 Taxi                  4\n",
      "                                      ..\n",
      "31-Oct-18      4 Car                  31\n",
      "               1 Pedestrian           24\n",
      "               2 Pedal Cycle          16\n",
      "               3 Powered 2 Wheeler    15\n",
      "               5 Taxi                  2\n",
      "Name: count, Length: 1769, dtype: int64\n",
      "Accident Date  Mode of Travel     \n",
      "01/01/2019     4 CAR                  26\n",
      "               3 POWERED 2 WHEELER    13\n",
      "               1 PEDESTRIAN           12\n",
      "               2 PEDAL CYCLE           5\n",
      "               5 TAXI                  4\n",
      "                                      ..\n",
      "31/12/2019     4 CAR                  29\n",
      "               3 POWERED 2 WHEELER    17\n",
      "               1 PEDESTRIAN           10\n",
      "               2 PEDAL CYCLE           3\n",
      "               5 TAXI                  1\n",
      "Name: count, Length: 1926, dtype: int64\n",
      "Accident Date  Mode of Travel     \n",
      "01/01/2020     4 CAR                  35\n",
      "               1 PEDESTRIAN           12\n",
      "               3 POWERED 2 WHEELER     6\n",
      "               9 PRIVATE HIRE          6\n",
      "               5 TAXI                  2\n",
      "                                      ..\n",
      "31/12/2020     4 CAR                  29\n",
      "               3 POWERED 2 WHEELER    16\n",
      "               2 PEDAL CYCLE           9\n",
      "               1 PEDESTRIAN            7\n",
      "               5 TAXI                  2\n",
      "Name: count, Length: 1786, dtype: int64\n",
      "_Collision Date   Casualty Mode of Travel\n",
      "01/01/2021 00:00  Car                        19\n",
      "                  Powered 2 Wheeler           5\n",
      "                  Pedal Cycle                 5\n",
      "                  Pedestrian                  3\n",
      "                  Goods Vehicle               1\n",
      "                                             ..\n",
      "31/12/2021 00:00  Car                        20\n",
      "                  Powered 2 Wheeler          11\n",
      "                  Pedal Cycle                 6\n",
      "                  Pedestrian                  4\n",
      "                  Bus Or Coach                3\n",
      "Name: count, Length: 2602, dtype: int64\n",
      "_Collision Date   Casualty Mode of Travel\n",
      "01/01/2022 00:00  Car                        37\n",
      "                  Pedestrian                  7\n",
      "                  Powered 2 Wheeler           7\n",
      "                  Pedal Cycle                 3\n",
      "                  Private Hire                3\n",
      "                                             ..\n",
      "31/10/2022 00:00  Taxi                        1\n",
      "31/12/2022 00:00  Car                        20\n",
      "                  Pedestrian                 14\n",
      "                  Powered 2 Wheeler           7\n",
      "                  Pedal Cycle                 2\n",
      "Name: count, Length: 1801, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Dataframes with count of casualties per model of travel by day\n",
    "print(df2017_casualties_day)\n",
    "print(df2018_casualties_day)\n",
    "print(df2019_casualties_day)\n",
    "print(df2020_casualties_day)\n",
    "print(df2021_casualties_day)\n",
    "print(df2022_casualties_day)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "524ce02a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}