06_Speed Limit Project - FINAL.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d0df529b",
   "metadata": {},
   "source": [
    "# Road Safety Project"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd56af36",
   "metadata": {},
   "source": [
    "The data below presents the number of road accidents by the speed zones and vehicle manoeuvring just before the incident.\n",
    "\n",
    "It appears from chart 1 that most accidents occurred in regions where there was a 30mph speed limit.\n",
    "\n",
    "Chart 2 shows that most accidents occurred when vehicles were apparently \"going ahead\" of another vehicle. This suggests that the cars were in their lanes or perhaps on single carriageways. Turning right is the next vehicle manoeuvring type that led to an incident. This could relate to vehicles turning right at a junction or into a road.\n",
    "\n",
    "There might be a relationship between vehicles changing lanes to right and overtaking moving vehicles - offside, however the number of accidents recorded for such incidents are a lot less, which could suggest increased driver alertness before such manoeuvring."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "dfb2287a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "89c2ccc3",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\arshad\\anaconda3\\envs\\jupyterenv\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3441: DtypeWarning: Columns (1) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  exec(code_obj, self.user_global_ns, self.user_ns)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\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>Unnamed: 0</th>\n",
       "      <th>Accident_Index</th>\n",
       "      <th>1st_Road_Class</th>\n",
       "      <th>Accident_Severity</th>\n",
       "      <th>Year</th>\n",
       "      <th>Date</th>\n",
       "      <th>Day_of_Week</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Light_Conditions</th>\n",
       "      <th>Local_Authority_(District)</th>\n",
       "      <th>...</th>\n",
       "      <th>Journey_Purpose_of_Driver</th>\n",
       "      <th>Junction_Location</th>\n",
       "      <th>make</th>\n",
       "      <th>model</th>\n",
       "      <th>Propulsion_Code</th>\n",
       "      <th>Sex_of_Driver</th>\n",
       "      <th>Towing_and_Articulation</th>\n",
       "      <th>Vehicle_Manoeuvre</th>\n",
       "      <th>Vehicle_Type</th>\n",
       "      <th>Was_Vehicle_Left_Hand_Drive</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>478587</td>\n",
       "      <td>201001BS70003</td>\n",
       "      <td>B</td>\n",
       "      <td>Slight</td>\n",
       "      <td>2010</td>\n",
       "      <td>2010-01-11</td>\n",
       "      <td>Monday</td>\n",
       "      <td>51.484087</td>\n",
       "      <td>Daylight</td>\n",
       "      <td>Kensington and Chelsea</td>\n",
       "      <td>...</td>\n",
       "      <td>Commuting to/from work</td>\n",
       "      <td>Mid Junction - on roundabout or on main road</td>\n",
       "      <td>CITROEN</td>\n",
       "      <td>BERLINGO FIRST 600</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Female</td>\n",
       "      <td>No tow/articulation</td>\n",
       "      <td>Turning right</td>\n",
       "      <td>Van / Goods 3.5 tonnes mgw or under</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>478588</td>\n",
       "      <td>201001BS70004</td>\n",
       "      <td>A</td>\n",
       "      <td>Slight</td>\n",
       "      <td>2010</td>\n",
       "      <td>2010-01-11</td>\n",
       "      <td>Monday</td>\n",
       "      <td>51.509212</td>\n",
       "      <td>Darkness - lights lit</td>\n",
       "      <td>Kensington and Chelsea</td>\n",
       "      <td>...</td>\n",
       "      <td>Journey as part of work</td>\n",
       "      <td>Mid Junction - on roundabout or on main road</td>\n",
       "      <td>RENAULT</td>\n",
       "      <td>SCENIC DYN DCI 130</td>\n",
       "      <td>Heavy oil</td>\n",
       "      <td>Male</td>\n",
       "      <td>No tow/articulation</td>\n",
       "      <td>Going ahead other</td>\n",
       "      <td>Car</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>478589</td>\n",
       "      <td>201001BS70007</td>\n",
       "      <td>Unclassified</td>\n",
       "      <td>Slight</td>\n",
       "      <td>2010</td>\n",
       "      <td>2010-01-02</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>51.513314</td>\n",
       "      <td>Darkness - lights lit</td>\n",
       "      <td>Kensington and Chelsea</td>\n",
       "      <td>...</td>\n",
       "      <td>Other/Not known (2005-10)</td>\n",
       "      <td>Mid Junction - on roundabout or on main road</td>\n",
       "      <td>NISSAN</td>\n",
       "      <td>PRIMERA SVE CVT</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Female</td>\n",
       "      <td>No tow/articulation</td>\n",
       "      <td>Going ahead right-hand bend</td>\n",
       "      <td>Car</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>478590</td>\n",
       "      <td>201001BS70007</td>\n",
       "      <td>Unclassified</td>\n",
       "      <td>Slight</td>\n",
       "      <td>2010</td>\n",
       "      <td>2010-01-02</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>51.513314</td>\n",
       "      <td>Darkness - lights lit</td>\n",
       "      <td>Kensington and Chelsea</td>\n",
       "      <td>...</td>\n",
       "      <td>Other/Not known (2005-10)</td>\n",
       "      <td>Mid Junction - on roundabout or on main road</td>\n",
       "      <td>MERCEDES</td>\n",
       "      <td>A140 ELEGANCE</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Female</td>\n",
       "      <td>No tow/articulation</td>\n",
       "      <td>Going ahead other</td>\n",
       "      <td>Car</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>478591</td>\n",
       "      <td>201001BS70008</td>\n",
       "      <td>A</td>\n",
       "      <td>Slight</td>\n",
       "      <td>2010</td>\n",
       "      <td>2010-01-04</td>\n",
       "      <td>Monday</td>\n",
       "      <td>51.484361</td>\n",
       "      <td>Darkness - lights lit</td>\n",
       "      <td>Kensington and Chelsea</td>\n",
       "      <td>...</td>\n",
       "      <td>Journey as part of work</td>\n",
       "      <td>Mid Junction - on roundabout or on main road</td>\n",
       "      <td>VAUXHALL</td>\n",
       "      <td>ZAFIRA ELEGANCE DTI</td>\n",
       "      <td>Heavy oil</td>\n",
       "      <td>Male</td>\n",
       "      <td>No tow/articulation</td>\n",
       "      <td>Turning right</td>\n",
       "      <td>Taxi/Private hire car</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0 Accident_Index 1st_Road_Class Accident_Severity  Year  \\\n",
       "0      478587  201001BS70003              B            Slight  2010   \n",
       "1      478588  201001BS70004              A            Slight  2010   \n",
       "2      478589  201001BS70007   Unclassified            Slight  2010   \n",
       "3      478590  201001BS70007   Unclassified            Slight  2010   \n",
       "4      478591  201001BS70008              A            Slight  2010   \n",
       "\n",
       "         Date Day_of_Week   Latitude       Light_Conditions  \\\n",
       "0  2010-01-11      Monday  51.484087               Daylight   \n",
       "1  2010-01-11      Monday  51.509212  Darkness - lights lit   \n",
       "2  2010-01-02    Saturday  51.513314  Darkness - lights lit   \n",
       "3  2010-01-02    Saturday  51.513314  Darkness - lights lit   \n",
       "4  2010-01-04      Monday  51.484361  Darkness - lights lit   \n",
       "\n",
       "  Local_Authority_(District)  ...  Journey_Purpose_of_Driver  \\\n",
       "0     Kensington and Chelsea  ...     Commuting to/from work   \n",
       "1     Kensington and Chelsea  ...    Journey as part of work   \n",
       "2     Kensington and Chelsea  ...  Other/Not known (2005-10)   \n",
       "3     Kensington and Chelsea  ...  Other/Not known (2005-10)   \n",
       "4     Kensington and Chelsea  ...    Journey as part of work   \n",
       "\n",
       "                              Junction_Location      make  \\\n",
       "0  Mid Junction - on roundabout or on main road   CITROEN   \n",
       "1  Mid Junction - on roundabout or on main road   RENAULT   \n",
       "2  Mid Junction - on roundabout or on main road    NISSAN   \n",
       "3  Mid Junction - on roundabout or on main road  MERCEDES   \n",
       "4  Mid Junction - on roundabout or on main road  VAUXHALL   \n",
       "\n",
       "                 model Propulsion_Code Sex_of_Driver  Towing_and_Articulation  \\\n",
       "0   BERLINGO FIRST 600          Petrol        Female      No tow/articulation   \n",
       "1   SCENIC DYN DCI 130       Heavy oil          Male      No tow/articulation   \n",
       "2      PRIMERA SVE CVT          Petrol        Female      No tow/articulation   \n",
       "3        A140 ELEGANCE          Petrol        Female      No tow/articulation   \n",
       "4  ZAFIRA ELEGANCE DTI       Heavy oil          Male      No tow/articulation   \n",
       "\n",
       "             Vehicle_Manoeuvre                         Vehicle_Type  \\\n",
       "0                Turning right  Van / Goods 3.5 tonnes mgw or under   \n",
       "1            Going ahead other                                  Car   \n",
       "2  Going ahead right-hand bend                                  Car   \n",
       "3            Going ahead other                                  Car   \n",
       "4                Turning right                Taxi/Private hire car   \n",
       "\n",
       "  Was_Vehicle_Left_Hand_Drive  \n",
       "0                          No  \n",
       "1                          No  \n",
       "2                          No  \n",
       "3                          No  \n",
       "4                          No  \n",
       "\n",
       "[5 rows x 33 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# filepath\n",
    "file_one = \"Resources/all.csv\"\n",
    "file_one_df = pd.read_csv(file_one)\n",
    "file_one_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "2cf36812",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Unnamed: 0', 'Accident_Index', '1st_Road_Class', 'Accident_Severity',\n",
       "       'Year', 'Date', 'Day_of_Week', 'Latitude', 'Light_Conditions',\n",
       "       'Local_Authority_(District)', 'Longitude', 'Number_of_Casualties',\n",
       "       'Number_of_Vehicles', 'Road_Surface_Conditions', 'Road_Type',\n",
       "       'Special_Conditions_at_Site', 'Speed_limit', 'Time',\n",
       "       'Urban_or_Rural_Area', 'Weather_Conditions', 'Age_Band_of_Driver',\n",
       "       'Age_of_Vehicle', 'Engine_Capacity_.CC.', 'Journey_Purpose_of_Driver',\n",
       "       'Junction_Location', 'make', 'model', 'Propulsion_Code',\n",
       "       'Sex_of_Driver', 'Towing_and_Articulation', 'Vehicle_Manoeuvre',\n",
       "       'Vehicle_Type', 'Was_Vehicle_Left_Hand_Drive'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# list column headers\n",
    "file_one_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3fd5e97e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([30, 60, 40, 70, 50, 20, 15, 10], dtype='int64')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create chart 1 of speed limit against the number of incidents\n",
    "df2= file_one_df[\"Speed_limit\"].value_counts()\n",
    "df2.values\n",
    "df2.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c3be8f4a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=df2.index, y=df2.values)\n",
    "\n",
    "\n",
    "plt.title(\"Speed Limit Zones at the time of the incident\")\n",
    "plt.xlabel(\"Speed Limit Zone (mph)\")\n",
    "plt.ylabel(\"Count\")\n",
    "\n",
    "\n",
    "plt.xticks(rotation=0)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1bcec08d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Going ahead other', 'Turning right', 'Slowing or stopping',\n",
       "       'Waiting to go - held up', 'Moving off', 'Parked',\n",
       "       'Going ahead right-hand bend', 'Going ahead left-hand bend',\n",
       "       'Turning left', 'Overtaking moving vehicle - offside',\n",
       "       'Waiting to turn right', 'Reversing',\n",
       "       'Overtaking static vehicle - offside', 'U-turn',\n",
       "       'Changing lane to right', 'Changing lane to left',\n",
       "       'Waiting to turn left', 'Overtaking - nearside',\n",
       "       'Data missing or out of range'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create chart 2 of vehicle manoeuvre against the number of incidents\n",
    "df= file_one_df[\"Vehicle_Manoeuvre\"].value_counts()\n",
    "df.values\n",
    "df.index\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "082a9e26",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(y=df.index, x=df.values)\n",
    "\n",
    "\n",
    "plt.title(\"Vehicle_Manoeuvre Type at the time of the incident\")\n",
    "plt.xlabel(\"Count\")\n",
    "plt.ylabel(\"Vehicle_Manoeuvre Type\")\n",
    "\n",
    "\n",
    "plt.xticks(rotation=0)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "10b7135e",
   "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": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}