exploring_data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 21,
   "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": 5,
   "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": 6,
   "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": 6,
     "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": 36,
   "id": "6e4eccd9",
   "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>26666</th>\n",
       "      <td>48211153885</td>\n",
       "      <td>City Of London</td>\n",
       "      <td>0</td>\n",
       "      <td>532307</td>\n",
       "      <td>181385</td>\n",
       "      <td>1</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>Male</td>\n",
       "      <td>20-24</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Slight</td>\n",
       "      <td>Not Applicable</td>\n",
       "      <td>Not Applicable</td>\n",
       "      <td>Taxi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26667</th>\n",
       "      <td>48211153891</td>\n",
       "      <td>City Of London</td>\n",
       "      <td>0</td>\n",
       "      <td>533203</td>\n",
       "      <td>181439</td>\n",
       "      <td>1</td>\n",
       "      <td>Driver/Rider</td>\n",
       "      <td>Male</td>\n",
       "      <td>30-34</td>\n",
       "      <td>34.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Slight</td>\n",
       "      <td>Not Applicable</td>\n",
       "      <td>Not Applicable</td>\n",
       "      <td>Pedal Cycle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26668</th>\n",
       "      <td>48211153896</td>\n",
       "      <td>City Of London</td>\n",
       "      <td>0</td>\n",
       "      <td>533034</td>\n",
       "      <td>180826</td>\n",
       "      <td>1</td>\n",
       "      <td>Driver/Rider</td>\n",
       "      <td>Male</td>\n",
       "      <td>35-39</td>\n",
       "      <td>36.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Serious</td>\n",
       "      <td>Not Applicable</td>\n",
       "      <td>Not Applicable</td>\n",
       "      <td>Pedal Cycle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26669</th>\n",
       "      <td>48211153967</td>\n",
       "      <td>City Of London</td>\n",
       "      <td>0</td>\n",
       "      <td>532776</td>\n",
       "      <td>181424</td>\n",
       "      <td>1</td>\n",
       "      <td>Driver/Rider</td>\n",
       "      <td>Female</td>\n",
       "      <td>30-34</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Slight</td>\n",
       "      <td>Not Applicable</td>\n",
       "      <td>Not Applicable</td>\n",
       "      <td>Pedal Cycle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26670</th>\n",
       "      <td>48211153971</td>\n",
       "      <td>City Of London</td>\n",
       "      <td>0</td>\n",
       "      <td>533809</td>\n",
       "      <td>180967</td>\n",
       "      <td>1</td>\n",
       "      <td>Driver/Rider</td>\n",
       "      <td>Male</td>\n",
       "      <td>50-54</td>\n",
       "      <td>54.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Slight</td>\n",
       "      <td>Not Applicable</td>\n",
       "      <td>Not Applicable</td>\n",
       "      <td>Powered 2 Wheeler</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       _Collision Id    Borough Name  Borough Number  Easting  Northing  \\\n",
       "26666    48211153885  City Of London               0   532307    181385   \n",
       "26667    48211153891  City Of London               0   533203    181439   \n",
       "26668    48211153896  City Of London               0   533034    180826   \n",
       "26669    48211153967  City Of London               0   532776    181424   \n",
       "26670    48211153971  City Of London               0   533809    180967   \n",
       "\n",
       "       _Casualty Id _Casualty Class Casualty Gender  \\\n",
       "26666             1       Passenger            Male   \n",
       "26667             1    Driver/Rider            Male   \n",
       "26668             1    Driver/Rider            Male   \n",
       "26669             1    Driver/Rider          Female   \n",
       "26670             1    Driver/Rider            Male   \n",
       "\n",
       "      Casualty Age (5 Year Bands)  Casualty Age  _Casualty Count  \\\n",
       "26666                       20-24          20.0                1   \n",
       "26667                       30-34          34.0                1   \n",
       "26668                       35-39          36.0                1   \n",
       "26669                       30-34          31.0                1   \n",
       "26670                       50-54          54.0                1   \n",
       "\n",
       "      _Casualty Severity Pedestrian Location Pedestrian Movement  \\\n",
       "26666             Slight      Not Applicable      Not Applicable   \n",
       "26667             Slight      Not Applicable      Not Applicable   \n",
       "26668            Serious      Not Applicable      Not Applicable   \n",
       "26669             Slight      Not Applicable      Not Applicable   \n",
       "26670             Slight      Not Applicable      Not Applicable   \n",
       "\n",
       "      Casualty Mode of Travel  \n",
       "26666                    Taxi  \n",
       "26667             Pedal Cycle  \n",
       "26668             Pedal Cycle  \n",
       "26669             Pedal Cycle  \n",
       "26670       Powered 2 Wheeler  "
      ]
     },
     "execution_count": 36,
     "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": 37,
   "id": "93d4130b",
   "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": 53,
   "id": "a96284ad",
   "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": 42,
   "id": "dc574500",
   "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": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2020 collision dataset\n",
    "\n",
    "# Read the xlsx file into a DataFrame\n",
    "df3 = pd.read_csv(\"2020-gla-data-extract-casualty.csv\")\n",
    "df3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "da1aeb0b",
   "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": 48,
   "id": "dad7e5df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\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": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2019 collision dataset\n",
    "\n",
    "# Read the xlsx file into a DataFrame\n",
    "df4 = pd.read_csv(\"2019-gla-data-extract-casualty.csv\")\n",
    "df4.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "54af0a54",
   "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": 55,
   "id": "75326449",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\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": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2018 collision dataset\n",
    "\n",
    "# Read the xlsx file into a DataFrame\n",
    "df5 = pd.read_csv(\"2018-data-files-casualty.csv\")\n",
    "df5.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "291ef279",
   "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": 59,
   "id": "dd60f6ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "<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": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2017 collision dataset\n",
    "\n",
    "# Read the xlsx file into a DataFrame\n",
    "df6 = pd.read_csv(\"2017-data-casualty.csv\")\n",
    "df6.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "4b11d0fe",
   "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": 67,
   "id": "5bd93a76",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a DataFrame\n",
    "total_casualties = pd.DataFrame({\n",
    "    \"Year\": [\"2017\", \"2018\", \"2019\", \"2020\", \"2021\"],\n",
    "    \"Total Casualties\": [32567, 30591, 30007, 24345, 26671]\n",
    "    \"Model of Travel\": [\"Car\", \"Pedestrian\", \"Powered 2 Wheeler\", \"\"]\n",
    "})\n",
    "\n",
    "# Plot the DataFrame\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(total_casualties[\"Year\"], total_casualties[\"Total Casualties\"], color=\"g\", marker=\"x\")\n",
    "\n",
    "# Add a title and labels\n",
    "plt.title(\"Count of Total Road Accidents per Year\")\n",
    "plt.xlabel(\"Year\")\n",
    "plt.ylabel(\"Total Casualties\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "f0823911",
   "metadata": {},
   "outputs": [
    {
     "ename": "UnicodeDecodeError",
     "evalue": "'utf-8' codec can't decode byte 0xa3 in position 15: invalid start byte",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mUnicodeDecodeError\u001b[0m                        Traceback (most recent call last)",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\_libs\\parsers.pyx:1090\u001b[0m, in \u001b[0;36mpandas._libs.parsers.TextReader._convert_tokens\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\_libs\\parsers.pyx:1242\u001b[0m, in \u001b[0;36mpandas._libs.parsers.TextReader._convert_with_dtype\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\_libs\\parsers.pyx:1255\u001b[0m, in \u001b[0;36mpandas._libs.parsers.TextReader._string_convert\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\_libs\\parsers.pyx:1503\u001b[0m, in \u001b[0;36mpandas._libs.parsers._string_box_utf8\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mUnicodeDecodeError\u001b[0m: 'utf-8' codec can't decode byte 0xa3 in position 15: invalid start byte",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mUnicodeDecodeError\u001b[0m                        Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[72], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m df_attendant_2017 \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mread_csv(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m2017-data-attendant.csv\u001b[39m\u001b[38;5;124m\"\u001b[39m, encoding\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mutf-8\u001b[39m\u001b[38;5;124m'\u001b[39m, parse_dates\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m      2\u001b[0m df_attendant_2017\u001b[38;5;241m.\u001b[39mhead()\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:912\u001b[0m, in \u001b[0;36mread_csv\u001b[1;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)\u001b[0m\n\u001b[0;32m    899\u001b[0m kwds_defaults \u001b[38;5;241m=\u001b[39m _refine_defaults_read(\n\u001b[0;32m    900\u001b[0m     dialect,\n\u001b[0;32m    901\u001b[0m     delimiter,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    908\u001b[0m     dtype_backend\u001b[38;5;241m=\u001b[39mdtype_backend,\n\u001b[0;32m    909\u001b[0m )\n\u001b[0;32m    910\u001b[0m kwds\u001b[38;5;241m.\u001b[39mupdate(kwds_defaults)\n\u001b[1;32m--> 912\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _read(filepath_or_buffer, kwds)\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:583\u001b[0m, in \u001b[0;36m_read\u001b[1;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[0;32m    580\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m parser\n\u001b[0;32m    582\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m parser:\n\u001b[1;32m--> 583\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m parser\u001b[38;5;241m.\u001b[39mread(nrows)\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:1704\u001b[0m, in \u001b[0;36mTextFileReader.read\u001b[1;34m(self, nrows)\u001b[0m\n\u001b[0;32m   1697\u001b[0m nrows \u001b[38;5;241m=\u001b[39m validate_integer(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnrows\u001b[39m\u001b[38;5;124m\"\u001b[39m, nrows)\n\u001b[0;32m   1698\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m   1699\u001b[0m     \u001b[38;5;66;03m# error: \"ParserBase\" has no attribute \"read\"\u001b[39;00m\n\u001b[0;32m   1700\u001b[0m     (\n\u001b[0;32m   1701\u001b[0m         index,\n\u001b[0;32m   1702\u001b[0m         columns,\n\u001b[0;32m   1703\u001b[0m         col_dict,\n\u001b[1;32m-> 1704\u001b[0m     ) \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine\u001b[38;5;241m.\u001b[39mread(  \u001b[38;5;66;03m# type: ignore[attr-defined]\u001b[39;00m\n\u001b[0;32m   1705\u001b[0m         nrows\n\u001b[0;32m   1706\u001b[0m     )\n\u001b[0;32m   1707\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m:\n\u001b[0;32m   1708\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mclose()\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\io\\parsers\\c_parser_wrapper.py:234\u001b[0m, in \u001b[0;36mCParserWrapper.read\u001b[1;34m(self, nrows)\u001b[0m\n\u001b[0;32m    232\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m    233\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlow_memory:\n\u001b[1;32m--> 234\u001b[0m         chunks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reader\u001b[38;5;241m.\u001b[39mread_low_memory(nrows)\n\u001b[0;32m    235\u001b[0m         \u001b[38;5;66;03m# destructive to chunks\u001b[39;00m\n\u001b[0;32m    236\u001b[0m         data \u001b[38;5;241m=\u001b[39m _concatenate_chunks(chunks)\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\_libs\\parsers.pyx:814\u001b[0m, in \u001b[0;36mpandas._libs.parsers.TextReader.read_low_memory\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\_libs\\parsers.pyx:891\u001b[0m, in \u001b[0;36mpandas._libs.parsers.TextReader._read_rows\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\_libs\\parsers.pyx:1036\u001b[0m, in \u001b[0;36mpandas._libs.parsers.TextReader._convert_column_data\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\_libs\\parsers.pyx:1097\u001b[0m, in \u001b[0;36mpandas._libs.parsers.TextReader._convert_tokens\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\_libs\\parsers.pyx:1242\u001b[0m, in \u001b[0;36mpandas._libs.parsers.TextReader._convert_with_dtype\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\_libs\\parsers.pyx:1255\u001b[0m, in \u001b[0;36mpandas._libs.parsers.TextReader._string_convert\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\_libs\\parsers.pyx:1503\u001b[0m, in \u001b[0;36mpandas._libs.parsers._string_box_utf8\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mUnicodeDecodeError\u001b[0m: 'utf-8' codec can't decode byte 0xa3 in position 15: invalid start byte"
     ]
    }
   ],
   "source": [
    "df_attendant_2017 = pd.read_csv(\"2017-data-attendant.csv\")\n",
    "df_attendant_2017.head()"
   ]
  }
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