notebooks/milestone-3.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.append('../src')\n",
    "from data_loading import *\n",
    "from population_pyramid_helpers import *\n",
    "from vehicle_type_helpers import *\n",
    "from introduction_helpers import *\n",
    "from map_france_helpers import *\n",
    "from car_injuries_helpers import *\n",
    "from security_equipments_helper import *\n",
    "from weather_road_conditions_helpers import *\n",
    "from stats_helpers import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Driving kills but can data help you survive"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The goal of our data story will be to explore some key insights of the dataset by making a story about how to reduce your chance of dying in a road accident. By reading the story, the reader will be able to see what are the main factors of road accidents regarding different aspects about drivers (age, sexe, ..), places where accidents occurs (type of roads, departements, ..), moments when accidents occurs (during the year, month, day or with specific weather conditions) and security equipements (if the driver had its belt, the impact of security equipment on the gravity of the accident, ..).\n",
    "\n",
    "At the end, the story will depict the \"perfect\" ride to avoid an accident by looking at the best possible case for each insight (the one having the less fatal accidents rate)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The demand for road transportation and the rate of motorization have continued to increase in all cities around the world. This increasing demand and the preference for private transport are at the root of many problems, among which road accidents. In fact, the number of accidents has continued to increase, with disastrous consequences. With our story, we aim to analyze the causes and effects of this phenomenon. \n",
    "\n",
    "We would like to investigate the correlation between the gravity level of a road accident and the components of the traffic system composed of the road, the driver and the vehicule. These components can be described by variables illustrating the drivers behavior, the attributes of the vehicle operated, the quality of the road and the climatic conditions. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Number of accidents"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In 10 years, from 2008 to 2018, the number of accident per year lies between 57783 (in 2018) and 76767 (in 2008). The general trend is a decrease in the number of accident per year."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Accidents per years"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From 2008 to 2012, we notice that the number of accidents per year has been reduced linearly by 25% and then stabilizing around 60000 accidents per year until 2018. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_accidents_per_year = get_number_accidents_per_years(2008, 2018)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2008    76767\n",
       "2009    74409\n",
       "2010    69379\n",
       "2011    66974\n",
       "2012    62250\n",
       "2013    58397\n",
       "2014    59854\n",
       "2015    58654\n",
       "2016    59432\n",
       "2017    60701\n",
       "2018    57783\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_accidents_per_year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_number_accidents_per_years(df_accidents_per_year)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Accidents per months"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We plotted the monthly distribution of accidents in the most recent year provided (2018). The number of accidents lays between 3458 accidents in February and 5698 accidents in October. Two main spikes can be noticed, one around the summer holidays (May to July) and one around the back-to-school period. Let us see if this trend is generalized over the years. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "year = 2018\n",
    "data_2018 = YearLoader(year)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_accidents_per_months_2018 = get_number_accidents_per_months(data_2018)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Jan     4350\n",
       "Feb     3458\n",
       "Mar     4152\n",
       "Apr     4833\n",
       "May     5055\n",
       "June    5579\n",
       "July    5241\n",
       "Aug     4336\n",
       "Sept    5532\n",
       "Oct     5698\n",
       "Nov     4883\n",
       "Dec     4666\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_accidents_per_months_2018"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_number_accidents_per_months(df_accidents_per_months_2018)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also dig deeper and take into account the different months. Indeed the number of accidents vary and depends of the period of the year. On the following plot we can observe that some months like October, June or July have a high number of accidents while August and September are safer on average.\n",
    "\n",
    "Some results were unexpected. In February for example, with the winter, we could have expected more accidents. In August, because of the holiday break and the departures, we could have expected more accidents."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evolution over the years"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We notice that around the years, the above trend is repeated, with February being the month with the least accidents and October being the month with the most frequent ones."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_all_accidents_per_months = get_number_accidents_per_months_all_years(2008, 2018)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_all_years_accidents_per_months(df_all_accidents_per_months)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Accidents gravity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The gravity of accidents is measured according to the level of damage done on humans (no damage (unscathed), slightly injured, injured, dead). The number of accidents in each class is inversely proportional to the level of damage (there are much more accidents where there has been no damage (around 60000 accidents/year) than the number of accidents where death has occurred (around 4000 accidents). The number of accidents related to death has been stable over the years. In the other classes (no damage and injury), the number of accidents follow the same trend : a clear decrease from 2008 to 2013, then a slight increase until 2018."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_all_years_accidents_gravity = get_accidents_gravity_all_years(2008, 2018)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_all_years_accidents_gravity(df_all_years_accidents_gravity)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Number of people per accidents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_people_per_accidents_all = get_number_people_per_accidents_all_years(2008, 2018)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    76767.000000\n",
       "mean         2.226999\n",
       "std          1.182808\n",
       "min          1.000000\n",
       "25%          2.000000\n",
       "50%          2.000000\n",
       "75%          2.000000\n",
       "max         58.000000\n",
       "Name: Num_Acc, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_people_per_accidents_all.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_number_people_per_accidents(df_people_per_accidents_all, multiple_years=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As the upper graph shows, most of the accidents involve less than 4 people. Indeed, most of the time people take the car alone, such as to get to work. As a result, accidents involve few people in average and most of the time, only one vehicle."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Be the right person .."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this part, we were curious to know if gender and age have an impact on the road accidents and on their gravities. In our dataset, we could not differentiate between the passengers and the drivers, but still we could get an indication of the consequences of accidents on people."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>age_group</th>\n",
       "      <th>male_dead</th>\n",
       "      <th>male_alive</th>\n",
       "      <th>female_dead</th>\n",
       "      <th>female_alive</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>00-10</td>\n",
       "      <td>34</td>\n",
       "      <td>2283</td>\n",
       "      <td>25</td>\n",
       "      <td>1695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>10-20</td>\n",
       "      <td>201</td>\n",
       "      <td>9258</td>\n",
       "      <td>59</td>\n",
       "      <td>4614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>20-30</td>\n",
       "      <td>582</td>\n",
       "      <td>21350</td>\n",
       "      <td>116</td>\n",
       "      <td>9552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>30-40</td>\n",
       "      <td>395</td>\n",
       "      <td>16570</td>\n",
       "      <td>77</td>\n",
       "      <td>6990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>40-50</td>\n",
       "      <td>361</td>\n",
       "      <td>13621</td>\n",
       "      <td>67</td>\n",
       "      <td>5832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>50-60</td>\n",
       "      <td>364</td>\n",
       "      <td>11273</td>\n",
       "      <td>73</td>\n",
       "      <td>5177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>60-70</td>\n",
       "      <td>261</td>\n",
       "      <td>6086</td>\n",
       "      <td>92</td>\n",
       "      <td>3514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>70-80</td>\n",
       "      <td>196</td>\n",
       "      <td>3238</td>\n",
       "      <td>103</td>\n",
       "      <td>2281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>80-90</td>\n",
       "      <td>174</td>\n",
       "      <td>1636</td>\n",
       "      <td>136</td>\n",
       "      <td>1312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>90-100</td>\n",
       "      <td>46</td>\n",
       "      <td>260</td>\n",
       "      <td>29</td>\n",
       "      <td>197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>100-110</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>?</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age_group  male_dead  male_alive  female_dead  female_alive\n",
       "0      00-10         34        2283           25          1695\n",
       "1      10-20        201        9258           59          4614\n",
       "2      20-30        582       21350          116          9552\n",
       "3      30-40        395       16570           77          6990\n",
       "4      40-50        361       13621           67          5832\n",
       "5      50-60        364       11273           73          5177\n",
       "6      60-70        261        6086           92          3514\n",
       "7      70-80        196        3238          103          2281\n",
       "8      80-90        174        1636          136          1312\n",
       "9     90-100         46         260           29           197\n",
       "10   100-110          1           4            0             4\n",
       "11         ?          0          24            0             6"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_2018 = YearLoader(2018)\n",
    "data_dead_alive = get_dead_alive_gender_df(data_2018)\n",
    "\n",
    "data_dead_alive"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Population Pyramids"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As an example, population pyramids for year 2018 :"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The men and woman pyramid shows that the 20-30 years old are the people that are the most prone to be involved in accidents. 70% of the whole population involved in accidents are male. 3% of the male population dies in these accidents. The female pyramid shows two spikes in the number of dead women : 15% of the overall dead women at 20-30 years old and another 15% at 80-90 years old. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Male population pyramid (dead vs alive)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_pyramid_dead_alive_gender(data_dead_alive, male=True, figsize=(10,10), xlim = None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Female drivers population pyramid (dead vs alive)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_pyramid_dead_alive_gender(data_dead_alive, male=False, female=True, figsize=(10,10), xlim = None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Normalized pyramids (alive vs dead)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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wrnK36BHAxnJq9TvA8RExvtxscHxpkyRJGtMGfUq0+HpETACeBD6YmRsi4kLgmoiYBzwInNqoY0T8JfAx4P8BVkTEDSXM3UDP9XCrgSeA9wBk5vqI+ARwRxni/MxcP8T6JUmSqjekwJaZRzdoWwfM2om+lwKXNmhP4IP99Pki8MWBVypJkjR6+UsHkiRJlTOwSZIkVc7AJkmSVDkDmyRJUuUMbJIkSZUzsEmSJFXOwCZJklQ5A5skSVLlDGySJEmVM7BJkiRVzsAmSZJUOQObJElS5Yb04++S1Eq5ILdpi3OjBZU0ycyZ0NHR6iokVcAjbJIkSZUzsEmSJFXOwCZJklQ5A5skSVLlDGySJEmVM7BJkiRVzsAmSZJUOQObJElS5fziXEnDJs4bQ19aW4POTgjfU2lUy22/4HswPMImSZJUOQObJElS5QxskiRJlTOwSZIkVc7AJkmSVDkDmyRJUuUMbJIkSZUzsEmSJFXOwCZJklQ5A5skSVLlDGySJEmVa1pgi4iPRMTdEXFXRFwZEbtHxJSIuC0iVkXE1RHx3H76nhYRK0r/f+zVvlvpt7qMM7lZ9UuSJNWiKYEtIiYCfwm0Z+argF2A04GLgEsycyqwAZjXoO8E4J+AWZl5MLBvRMwqs+cBGzLz5cAlZTxJkqQxrZmnRMcBz4uIccDzgTXAscC1Zf5i4OQG/V4G/CIzu8vrfwPeVqZPKv0o48yKiGhC7ZIkSdVoSmDLzF8BFwMP0hPUNgKdwKOZubks1gVMbNB9NXBgREwuYe9kYP8ybyLwUFnH5jLuhGZsgyRJUi2adUp0PD1Hw6YALwFeALyxwaK5TUPmBuADwNXAD4AHgC0hr9HRtG3GiIj5EdERER3d3d0NukhSnbbaf7W6GEnVaNYp0T8D7s/M7sx8EvgGcCSwVzlqBjAJeDgidomI5eVxPkBmfiszX5OZrwXuAVaVPl2Uo21lnD2B9X1XnpkLM7M9M9vb2tqatImSNPy22n+1uhhJ1Ri340UG5UHgiIh4PvB7YBbQAdwMnAJcBcwFrs/Mp4DpvTtHxD6ZubYcqTsT+H/LrCWl34/KON/LzG2OsEmSJI0lTQlsmXlbRFwL/ISe05nLgIXA/wGuiohPlrZF/QzxvyNiWpk+PzN/UaYXAV+OiNX0HFk7vRn1S5Ik1aRZR9jIzAXAgj7N9wGH70Tft/fTvgk4dejVSZIkjR7+0oEkSVLlDGySJEmVM7BJkiRVzsAmSZJUOQObJElS5QxskiRJlTOwSZIkVc7AJkmSVDkDmyRJUuUMbJIkSZUzsEmSJFXOwCZJklS5pv34u6Rnn1yQrS6BODdaXcLwmTkTOjpaXYWkCniETZIkqXIGNkmSpMoZ2CRJkipnYJMkSaqcgU2SJKlyBjZJkqTKGdgkSZIq5/ewSc8icd4Y+o6yZ4POTgg/M41B2frvbBxtPMImSZJUOQObJElS5QxskiRJlTOwSZIkVc7AJkmSVDkDmyRJUuUMbJIkSZUzsEmSJFXOwCZJklQ5A5skSVLlDGySJEmVM7BJkiRVrimBLSJeERHLez1+GxF/FRF7R8RNEbGqPI/vp/+siPhJ6fvvEfHy0r5bRFwdEasj4raImNyM+iVJkmrSlMCWmfdk5vTMnA7MBJ4ArgPOApZm5lRgaXndyGeBd5T+XwX+rrTPAzZk5suBS4CLmlG/JElSTUbilOgs4N7M/CVwErC4tC8GTu6nTwIvKtN7Ag+X6d79rwVmRUQMe8WSJEkVGTcC6zgduLJM75uZawAyc01E7NNPnzOAGyLi98BvgSNK+0TgodJ/c0RsBCYAv2lW8ZIkSa3W1CNsEfFcYA7wtQF2/QjwpsycBHwJ+NSWIRssmw3WOz8iOiKio7u7e4CrlqTW2Wr/1epiJFWj2adE3wj8JDMfKa8fiYj9AMrz2jL9nXKDwRciog2Ylpm3lT5XA0eW6S5g/9JnHD2nS9f3XWlmLszM9sxsb2tra9a2SdKw22r/1epiJFWj2YHt7fzxdCjAEmBumZ4LXA+QmbPLTQpnABuAPSPiT8tyxwErG/Q/BfheZm5zhE2SJGksado1bBHxfHrC1vt7NV8IXBMR84AHgVP79ivXpr0P+HpEPE1PgHtvmb0I+HJErKbnyNrpzapfkiSpFk0LbJn5BD03BPRuW0fPXaM76nsdPV8D0rd9Ew1CniRJ0ljmLx1IkiRVzsAmSZJUOQObJElS5QxskiRJlTOwSZIkVc7AJkmSVDkDmyRJUuUMbJIkSZUzsEmSJFXOwCZJklQ5A5skSVLlDGySJEmVa9qPv0uqTy7IVpfQdHFutLqE4TNzJnR0tLoKSRXwCJskSVLlDGySJEmVM7BJkiRVzsAmSZJUOQObJElS5QxskiRJlTOwSZIkVc7vYZNGUJw3hr4jTM3X2QnhvxlVIMf+dzjWziNskiRJlTOwSZIkVc7AJkmSVDkDmyRJUuUMbJIkSZUzsEmSJFXOwCZJklQ5A5skSVLlDGySJEmVM7BJkiRVzsAmSZJUOQObJElS5ZoW2CJir4i4NiJ+HhErI+K1EbF3RNwUEavK8/h++v4gIpaXx8MR8c3SHhFxaUSsjogVEXFos+qXJEmqRTOPsP1v4NuZeSAwDVgJnAUszcypwNLyehuZeXRmTs/M6cCPgG+UWW8EppbHfOCzTaxfkiSpCk0JbBHxIuD1wCKAzPzPzHwUOAlYXBZbDJy8g3H2AI4FvlmaTgIuzx4/BvaKiP2asAmSJEnVaNYRtpcB3cCXImJZRHwhIl4A7JuZawDK8z47GOe/0nNE7rfl9UTgoV7zu0qbJEnSmNWswDYOOBT4bGbOAB6nn9OfO/B24Mper6PBMtm3ISLmR0RHRHR0d3cPYrWS1Bpb7b9aXYykajQrsHUBXZl5W3l9LT0B7pEtpzDL89oy/Z1yg8EXtgwQEROAw4H/02fc/Xu9ngQ83HflmbkwM9szs72trW0YN0uSmmur/Veri5FUjaYEtsz8NfBQRLyiNM0CfgYsAeaWtrnA9WX52eUmgzN6DXMq8K+ZualX2xLgXeVu0SOAjVtOsUqSJI1V45o49n8HroiI5wL3Ae+hJyBeExHzgAfpCWX9OR24sE/bDcCbgNXAE2VMSZKkMa1pgS0zlwPtDWbN2sn+b2jQlsAHh1aZJEnS6OIvHUiSJFXOwCZJklQ5A5skSVLlDGySJEmVM7BJkiRVzsAmSZJUOQObJElS5QxskiRJlTOwSZIkVc7AJkmSVDkDmyRJUuUMbJIkSZVr2o+/S9pWLshWlzDmxbnR6hKGz8yZ0NHR6iokVcAjbJIkSZUzsEmSJFXOwCZJklQ5A5skSVLlDGySJEmVM7BJkiRVzsAmSZJUOb+HTWNCnDeGvntL2qKzE8J/28966fc3yiNskiRJ1TOwSZIkVc7AJkmSVDkDmyRJUuUMbJIkSZUzsEmSJFXOwCZJklQ5A5skSVLlDGySJEmVM7BJkiRVzsAmSZJUOQObJElS5ZoW2CLigYj4aUQsj4iO0rZ3RNwUEavK8/h++kZEXBARv4iIlRHxl73aL42I1RGxIiIObVb9kiRJtWj2EbZjMnN6ZraX12cBSzNzKrC0vG7k3cD+wIGZeRBwVWl/IzC1POYDn21W4ZIkSbUY6VOiJwGLy/Ri4OR+lvsAcH5mPg2QmWt79b88e/wY2Csi9mtmwZIkSa3WzMCWwHcjojMi5pe2fTNzDUB53qefvn8CnBYRHRFxY0RMLe0TgYd6LddV2iRJksascU0c+3WZ+XBE7APcFBE/H0Df3YBNmdkeEW8FvggcDUSDZbNvQwmI8wEOOOCAgVcuSS2y1f6rxbVIqkfTjrBl5sPleS1wHXA48MiWU5jleW2Z/k65OeELpXsX8PUyfR1wSK/2/XutZhLwcIN1L8zM9sxsb2trG94Nk6Qm2mr/1epiJFWjKYEtIl4QEXtsmQaOB+4ClgBzy2JzgesBMnN2uTnhjDLvm8CxZfq/AL8o00uAd5W7RY8ANm45xSpJkjRWNeuU6L7AdRGxZR1fzcxvR8QdwDURMQ94EDi1n/4XAldExEeAx4AtQe4G4E3AauAJ4D1Nql+SJKkaTQlsmXkfMK1B+zpg1k70fxR4c4P2BD44HDVKkiSNFv7SgSRJUuUMbJIkSZUzsEmSJFXOwCZJklQ5A5skSVLlDGySJEmVM7BJkiRVzsAmSZJUOQObJElS5QxskiRJlTOwSZIkVc7AJkmSVLmm/Pi7NNJyQba6BFUizo1WlzB8Zs6Ejo5WVyGpAh5hkyRJqpyBTZIkqXIGNkmSpMoZ2CRJkipnYJMkSaqcgU2SJKlyBjZJkqTK+T1sqkKcN4a+O0saLp2dEP5tjEnpd0dqYDzCJkmSVDkDmyRJUuUMbJIkSZUzsEmSJFXOwCZJklQ5A5skSVLlDGySJEmVM7BJkiRVzsAmSZJUOQObJElS5QxskiRJlTOwSZIkVa5pgS0idomIZRHxr+X1lIi4LSJWRcTVEfHcfvrdEhH3RMTy8tintO9W+q0u40xuVu2SJEk1aeYRtg8DK3u9vgi4JDOnAhuAedvp+47MnF4ea0vbPGBDZr4cuKSMJ0mSNOY1JbBFxCTgzcAXyusAjgWuLYssBk4e4LAnlX6UcWaVcSVJksa0Zh1h+zTwMeDp8noC8Ghmbi6vu4CJ2+n/pXI69JxeoWwi8BBAGWdjGVeSJGlMG/bAFhEnAmszs7N3c4NFs58h3pGZrwaOLo8/H+gYETE/IjoioqO7u3snK5ek1ttq/9XqYiRVoxlH2F4HzImIB4Cr6DkV+mlgr4gYV5aZBDxcbkzYcnPB+QCZ+avy/Dvgq8DhpU8XsD9AGWdPYH2jAjJzYWa2Z2Z7W1tbEzZRkppjq/1Xq4uRVI1hD2yZeXZmTsrMycDpwPcy8x3AzcApZbG5wPWZ+VSvmwv+PiLGRcSLASJiV+BE4K7SZ0npRxnne5nZ31E6SZKkMWPcjhcZNn8DXBURnwSWAYsaLLMb8J0S1nYB/g34fJm3CPhyRKym58ja6c0vWZIkqfWaGtgy8xbgljJ9H388vdnf8o8DM/uZtwk4dXgrlCRJqp+/dCBJklQ5A5skSVLlDGySJEmVM7BJkiRVzsAmSZJUOQObJElS5QxskiRJlTOwSZIkVc7AJkmSVDkDmyRJUuUMbJIkSZUzsEmSJFWuqT/+Lu2sXJCtLkFjRJwbrS5h+MycCR0dra5CUgU8wiZJklQ5A5skSVLlDGySJEmVM7BJkiRVzsAmSZJUOQObJElS5QxskiRJlfN72LSNOG8MfY+VNJp1dkL496jtSL/D8tnCI2ySJEmVM7BJkiRVzsAmSZJUOQObJElS5QxskiRJlTOwSZIkVc7AJkmSVDkDmyRJUuUMbJIkSZUzsEmSJFXOwCZJklQ5A5skSVLlmhLYImL3iLg9Iu6MiLsj4rzSPiUibouIVRFxdUQ8dwfjLImIu3q93jsibir9b4qI8c2oX5IkqSbNOsL2B+DYzJwGTAdOiIgjgIuASzJzKrABmNffABHxVuCxPs1nAUtL/6XltSRJ0pjWlMCWPbaErV3LI4FjgWtL+2Lg5Eb9I+KFwEeBT/aZdVLpt93+kiRJY0nTrmGLiF0iYjmwFrgJuBd4NDM3l0W6gIn9dP8E8L+AJ/q075uZawDK8z7DXrgkSVJlmhbYMvOpzJwOTAIOBw5qtFjfhoiYDrw8M68b7LojYn5EdERER3d392CHkaQRt9X+q9XFSKpG0+8SzcxHgVuAI4C9ImJcmTUJeHjLkbjyOB94LTAzIh4A/h3404i4pfR5JCL2AyjPa/tZ58LMbM/M9ra2tmZtmiQNu632X60uRlI1mnWXaFtE7FWmnwf8GbASuBk4pSw2F7h+y5G48vj7zPxsZr4kMycDRwG/yMw3lD5LSr9n+jejfkmSpJqM2/Eig7IfsDgidqEnFF6Tmf8aET8DroqITwLLgEUDHPdC4JqImAc8CJw6nEVLkiTVqCmBLTNXADMatN9Hz/VsOzvOA8Crer1eB8wahhIlSZJGDX/pQJIkqXIGNkmSpMoZ2CRJkipnYJMkSaqcgU2SJKlyBjZJkqTKGdgkSZIqZ2CTJEmqnIFNkiSpcvPWwDEAAA+BSURBVAY2SZKkyhnYJEmSKtesH3/XKJYLstUlSIMW50arSxg+M2dCR0erq5BUAY+wSZIkVc7AJkmSVDkDmyRJUuUMbJIkSZUzsEmSJFXOwCZJklQ5A5skSVLlDGySJEmV84tz+4jzxtCXbkoa3To7IdwnSQOSY/PL3z3CJkmSVDkDmyRJUuUMbJIkSZUzsEmSJFXOwCZJklQ5A5skSVLlDGySJEmVM7BJkiRVzsAmSZJUOQObJElS5QxskiRJlTOwSZIkVa4pgS0i9o+ImyNiZUTcHREfLu17R8RNEbGqPI/vp/+iiLgzIlZExLUR8cLSvltEXB0RqyPitoiY3Iz6JUmSatKsI2ybgf+RmQcBRwAfjIhXAmcBSzNzKrC0vG7kI5k5LTMPAR4EPlTa5wEbMvPlwCXARU2qX5IkqRpNCWyZuSYzf1KmfwesBCYCJwGLy2KLgZP76f9bgIgI4HlAllm9+18LzCrLSJIkjVlNv4atnLacAdwG7JuZa6An1AH7bKffl4BfAwcCnynNE4GHSv/NwEZgQpNKlyRJqkJTA1u59uzrwF9tOWq2szLzPcBL6Dk6d9qWIRst2mC98yOiIyI6uru7B1i1JLXOVvuvVhcjqRpNC2wRsSs9Ye2KzPxGaX4kIvYr8/cD1pbp70TE8oj4Qu8xMvMp4GrgbaWpC9i/9BkH7Ams77vuzFyYme2Z2d7W1jb8GydJTbLV/qvVxUiqRrPuEg1gEbAyMz/Va9YSYG6ZngtcD5CZszNzemaeET1e3muctwA/b9D/FOB7mbnNETZJkqSxZFyTxn0d8OfATyNieWn7W+BC4JqImEfP3Z+nNugbwOKIeFGZvhP4QJm3CPhyRKym58ja6U2qX5IkqRpNCWyZ+e80vt4MYNYO+j5NT+BrNG8TjUOeJEnSmOUvHUiSJFXOwCZJklQ5A5skSVLlDGySJEmVM7BJkiRVzsAmSZJUOQObJElS5QxskiRJlTOwSZIkVc7AJkmSVDkDmyRJUuWa9ePvo1YuyFaXIGkI4tz+fsZ4FJo5Ezo6Wl2FpAp4hE2SJKlyBjZJkqTKGdgkSZIqZ2CTJEmqnIFNkiSpcgY2SZKkyhnYJEmSKmdgkyRJqpxfnKtnvThvDH3RqsaWzk4I/31WLf2ydY0Mj7BJkiRVzsAmSZJUOQObJElS5QxskiRJlTOwSZIkVc7AJkmSVDkDmyRJUuUMbJIkSZUzsEmSJFXOwCZJklQ5A5skSVLlmhLYIuKLEbE2Iu7q1bZ3RNwUEavK8/h++l4REfdExF1lnF1Le0TEpRGxOiJWRMShzahdkiSpNs06wnYZcEKftrOApZk5FVhaXjdyBXAg8GrgecAZpf2NwNTymA98dnhLliRJqlNTAltm3gqs79N8ErC4TC8GTu6n7w1ZALcDk3r1v7zM+jGwV0TsN/zVS5Ik1WUkr2HbNzPXAJTnfba3cDkV+ufAt0vTROChXot0lTZJkqQxreabDv4FuDUzf1BeR4NlslHHiJgfER0R0dHd3d20AiVpuG21/2p1MZKqMZKB7ZEtpzDL89oy/Z2IWB4RX9iyYEQsANqAj/bq3wXs3+v1JODhRivKzIWZ2Z6Z7W1tbcO8GZLUPFvtv1pdjKRqjGRgWwLMLdNzgesBMnN2Zk7PzDMAIuIMYDbw9sx8uk//d5W7RY8ANm45xSpJkjSWNetrPa4EfgS8IiK6ImIecCFwXESsAo4rrxv5HLAv8KNy5O3vS/sNwH3AauDzwJnNqF2SJKk245oxaGa+vZ9Zs3aib8Oayl2jHxxKXZIkSaNRzTcdSJIkCQObJElS9QxskiRJlTOwSZIkVc7AJkmSVDkDmyRJUuUMbJIkSZUzsEmSJFXOwCZJklQ5A5skSVLlDGySJEmVM7BJkiRVrik//i6NJrkgW12ChlGcG60uYfjMnAkdHa2uQlIFPMImSZJUOQObJElS5QxskiRJlTOwSZIkVc7AJkmSVDkDmyRJUuUMbJIkSZUzsEmSJFXOL86VtENx3hj6MtrRpLMTYhS99+mXUEvN4hE2SZKkyhnYJEmSKmdgkyRJqpyBTZIkqXIGNkmSpMoZ2CRJkipnYJMkSaqcgU2SJKlyBjZJkqTKGdgkSZIqZ2CTJEmqXNMCW0ScEBH3RMTqiDirtE2JiNsiYlVEXB0Rz+2n7wUR8VBEPNanfbfSb3UZZ3Kz6pckSapFUwJbROwC/H/AG4FXAm+PiFcCFwGXZOZUYAMwr58hvgUc3qB9HrAhM18OXFLGkyRJGtOadYTtcGB1Zt6Xmf8JXAWcBBwLXFuWWQyc3KhzZv44M9c0mHVS6UcZZ1ZExLBWLkmSVJlmBbaJwEO9XneVtkczc3OftkGNW8bZCEwYWqmSJEl1a1Zga3TUa5cGbTkM424zRkTMj4iOiOjo7u4e4CokqXW22n+1uhhJ1WhWYOsC9u/1ehLwILBXRIzr1fZwROwSEcvL4/ydHbeMsyewvu9CmbkwM9szs72trW2o2yJJI2ar/Veri5FUjXE7XmRQ7gCmRsQU4FfA6cB/Aw4FTqHnmra5wPWZ+RQwfSfHXVL6/aiM873MHOhROkmSpFGlKUfYyvVlHwK+A6wErsnMu4G/AT4aEavpufZsUaP+EfGPEdEFPD8iuiLi3DJrETCh9P8ocFYz6pckSapJs46wkZk3ADf0abuPxl/X0bfvx4CPNWjfBJw6XDVKkiSNBv7SgSRJUuUMbJIkSZUzsEmSJFXOwCZJklQ5A5skSVLlDGySJEmVM7BJkiRVzsAmSZJUOQObJElS5QxskiRJlTOwSZIkVc7AJkmSVLmm/fi7pLEjF2SrS9hpcW60uoThM3MmdHS0ugpJFfAImyRJUuUMbJIkSZUzsEmSJFXOwCZJklQ5A5skSVLlDGySJEmVM7BJkiRVzsAmSZJUOQObJElS5QxskiRJlTOwSZIkVc7AJkmSVDkDmyRJUuUMbJIkSZUzsEmSJFXOwCZJklQ5A5skSVLlDGySJEmVM7BJkiRVzsAmSZJUOQObJElS5QxskiRJlTOwSZIkVc7AJkmSVLnIzFbX0FQR0Q38stV17MCLgd+0uohh5jaNDmNxm16RmXu0uojhEBG/A+5pdR191Ppvpsa6rGnn1FgTtK6ul2ZmW9/GcS0oZEQ12ujaRERHZra3uo7h5DaNDmN1m1pdwzC6p7bPp9Z/MzXWZU07p8aaoL66PCUqSZJUOQObJElS5QxsdVjY6gKawG0aHdymutW4LTXWBHXWZU07p8aaoLK6xvxNB5IkSaOdR9gkSZIqZ2Brsog4ISLuiYjVEXFWg/kfjYifRcSKiFgaES/tNW9uRKwqj7kjW3n/dmKbXh8RP4mIzRFxSp95T0XE8vJYMnJVb98Qt2m0fk67RcTVZf5tETG5tE+OiN/3+pw+N9K192ew21TmnV3a74mI2aO13v7GjIgpZYxVZcznVlLXZRFxf69/T9NHsKYvRsTaiLirz1h7R8RN5b26KSLGV1DTuRHxq17v05tGoqaI2D8ibo6IlRFxd0R8eKDvUwvqatV7tXtE3B4Rd5aazuu1/JTYyb+/QctMH016ALsA9wIvA54L3Am8ss8yxwDPL9MfAK4u03sD95Xn8WV6/CjZpsnAIcDlwCl95j3W6m0Yzm0a5Z/TmcDnyvTpvf7tTQbuavU2DPM2vbIsvxswpYyzy2ird3tjAtcAp5fpzwEfqKSuy/ruB0bqswVeDxza998z8I/AWWX6LOCiCmo6F/ifLfg3tR9waFlmD+AXvT67Hb5PLaqrVe9VAC8sy+wK3AYcMZC/v6E8PMLWXIcDqzPzvsz8T+Aq4KTeC2TmzZn5RHn5Y2BSmZ4N3JSZ6zNzA3ATcMII1b09O7NND2TmCuDpVhQ4CEPZplH7OZXXi8v0tcCsiIgRrHGghrJNJwFXZeYfMvN+YHUZb7TV23DM0ufYMgZlzJNbXddOvk/NqonMvBVY32B9vcfq770a6Zp2xrDXlJlrMvMnpbbfASuBiQ3GGtF/Uzuoa2c0o6bMzMfK8ruWRw7w72/QDGzNNRF4qNfrLrb/D24ecOMg+46Uoda1e0R0RMSPI2LY/0EP0lC2aTR/Ts8sk5mbgY3AhDJvSkQsi4jvR8TRzS52Jw1lm1rxOTWj3v7aJwCPljH6W1cr6trigui57OOSiNhthGrann0zc00Zaw2wTwU1AXyovE9f7Of0Y1NrKqcEZ9Bz5Ah27n1qRV3QovcqInaJiOXAWnr+Z/02Bvb3N2gGtuZqdLSi4W25EfFOoB34p4H2HWFDreuA7Pnm6P8GfDoi/mR4yhqSoWzTaP6c+ltmDT2f0wzgo8BXI+JFw1zfYAxlm1rxOTWj3uHYvpGsC+Bs4EDgMHouHfibEappqEa6ps8CfwJMp+dv8H+NZE0R8ULg68BfZeZvd1Brq+tq2XuVmU9l5nR6zoYdHhGv2sl1DZmBrbm6gP17vZ4EPNx3oYj4M+DjwJzM/MNA+rbAkOrKzIfL833ALfT8X1OrDWWbRvPn9MwyETEO2BNYX04DrAPIzE56rt/406ZXvGOD3qad7DvcmlFvf+2/AfYqY/S3rlbURTm1lWXf9iUan4oe6c/2kYjYr4y1Hz1HS1paU2Y+UsLA08DnGcH3KSJ2pScUXZGZ3+i1zM68TyNeVyvfq141PErPf8NOYGB/f4M3HBfC+ej3osdx9FyEPoU/XvR4cJ9lZtDzH8Spfdr3Bu6n50L28WV679GwTb2WvYytL9AfD+xWpl8MrKLPRaCjcJtG7ecEfJCtL7i9pky38ccLpF8G/GoMbNPBbH0R8X00/6aDYa93e2MCX2Pri57PrKSu/cpzAJ8GLhzJz5YGN9HQcyaj98X0/1hBTfv1mv4IPddQjcRnF/TcTPXpBuvb4fvUorpa9V61AXuVZZ4H/AA4cSB/f0Papwz3gD62+UfzJnrubrkX+HhpO5+eo2kA/wY8AiwvjyW9+r6XnosdVwPvafW2DGCbDqPn/1AeB9YBd5f2I4Gflj+EnwLzWr0tQ92mUf457V52MquB24GXlfa3AXeXz+knwFtavS1D3aYy7+Ol3z3AG0drvY3GLO0vK2OsLmPuVkld3yt/73cBX6HcZTdCNV1JzymzJ+n5+51X2icAS+n5n8al9PM/JCNc05fL+7QCWEKvUNLMmoCj6Dl9t4I//nfoTQN5n1pQV6veq0OAZWW9dwF/P5i/v8E+/KUDSZKkynkNmyRJUuUMbJIkSZUzsEmSJFXOwCZJklQ5A5skSVLlDGySJEmVM7BJkiRVzsAmSZJUuf8LWaOC/li+EQoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pyramid_data = data_dead_alive\n",
    "plot_norm_pyramid_dead_alive_gender(data_dead_alive, male=True, female=True, xlim= None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The age of a person influences both the probability to be in an accident and to die within. For the men, both probabilities decrease with age. The men and women pyramid shows that the 20-30 years old are the people that are the most prone to be involved in accidents and to die in an accident. For the women the number of death increase after 50 years, which can be due to the vulnerability of people at this age."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "contingency_table :-\n",
      " grav      1     2      3      4\n",
      "sexe                           \n",
      "1     39028  2615  15487  31088\n",
      "2     15220   777   6682  19272\n",
      "Observed Values :-\n",
      " [[39028  2615 15487 31088]\n",
      " [15220   777  6682 19272]]\n",
      "Expected Values :-\n",
      " [[36764.89843204  2298.82273045 15024.35174273 34129.92709478]\n",
      " [17483.10156796  1093.17726955  7144.64825727 16230.07290522]]\n",
      "Degree of Freedom:- 1\n",
      "chi-square statistic:- 567.1888411192656\n",
      "critical_value: 3.841458820694124\n",
      "p-value: 0.0\n",
      "Significance level:  0.05\n",
      "Degree of Freedom:  1\n",
      "chi-square statistic: 567.1888411192656\n",
      "critical_value: 3.841458820694124\n",
      "p-value: 0.0\n",
      "Chi-square test result : Reject H0, There is a relationship between 2 categorical variables\n",
      "Reject H0 with 95% level of confidence.\n"
     ]
    }
   ],
   "source": [
    "data = YearLoader(2018).get_dataframe(\"passengers\")\n",
    "\n",
    "gender_stats = ChiSquare(data['sexe'],data['grav'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_gender_vs_gravity_contingency(data_2018)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On another hand, looking at the risks of injuries, we notice that even though there are more men exposed to accidents than women, there are less women that end up completely safe compared to men, and there are more risks of injuries as a woman than as a man (62% of chance to be injured as a woman versus 53% as a man)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Types of vehicles"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The graph distribution of vehicule types shows that the car is the vehicule where most accidents occur since 90% of the vehicules involved in accidents are cars. It is followed by the motorcycle with around 8% of overall number of vehicules."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_vehicles = get_vehicles_type_df(data_2018, 8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "vehicles repartition over categories:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_vehicles_cat(data_vehicles, (15,15))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. At the right place .."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Accidents per regions/departments (map of France)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From this map, we observe that the French departments with the highest number of accidents are located where big cities are, where the population density is the biggest, for example around Paris and Marseille."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>dep</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>75</td>\n",
       "      <td>5591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>3413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>93</td>\n",
       "      <td>2735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>94</td>\n",
       "      <td>2548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>92</td>\n",
       "      <td>2520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>95</td>\n",
       "      <td>09</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>96</td>\n",
       "      <td>48</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>97</td>\n",
       "      <td>46</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>98</td>\n",
       "      <td>23</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>99</td>\n",
       "      <td>90</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   dep  count\n",
       "0   75   5591\n",
       "1   13   3413\n",
       "2   93   2735\n",
       "3   94   2548\n",
       "4   92   2520\n",
       "..  ..    ...\n",
       "95  09     90\n",
       "96  48     76\n",
       "97  46     75\n",
       "98  23     61\n",
       "99  90     58\n",
       "\n",
       "[100 rows x 2 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_dep_count_df(data_2018)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The map of france displaying accidents per departments is interactive"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![SegmentLocal](map_france.gif \"segment\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Injury visualization in the car (Heatmap from the car top -> data story milestone)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Car place number:\n",
    "![car place](car.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All injuries :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>place</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1.0</td>\n",
       "      <td>96011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2.0</td>\n",
       "      <td>14465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3.0</td>\n",
       "      <td>2475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4.0</td>\n",
       "      <td>2049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9.0</td>\n",
       "      <td>1435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7.0</td>\n",
       "      <td>1297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8.0</td>\n",
       "      <td>688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5.0</td>\n",
       "      <td>610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6.0</td>\n",
       "      <td>179</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     place\n",
       "1.0  96011\n",
       "2.0  14465\n",
       "3.0   2475\n",
       "4.0   2049\n",
       "9.0   1435\n",
       "7.0   1297\n",
       "8.0    688\n",
       "5.0    610\n",
       "6.0    179"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_car_injuries_df(data_2018, dead_only=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Death only:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>2411</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>318</td>\n",
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       "      <td>9.0</td>\n",
       "      <td>37</td>\n",
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       "      <td>7.0</td>\n",
       "      <td>34</td>\n",
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       "      <td>4.0</td>\n",
       "      <td>33</td>\n",
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       "      <td>3.0</td>\n",
       "      <td>29</td>\n",
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       "      <td>14</td>\n",
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       "      <td>7</td>\n",
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      "text/plain": [
       "     place\n",
       "1.0   2411\n",
       "2.0    318\n",
       "9.0     37\n",
       "7.0     34\n",
       "4.0     33\n",
       "3.0     29\n",
       "8.0     14\n",
       "5.0      7\n",
       "6.0      1"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_car_injuries_df(data_2018, dead_only=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. At the right moment .."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Weather conditions\n",
    "\n",
    "Accidents and percentage of the accidents given the weather for all year between 2008 and 2018"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most accidents happen during normal weather conditions (80%) and 10% happen during light rainfall surprisingly. This could be due to the fact that during good weather conditions, the driver is less focused on the road than when the weather conditions are bad, also the speed of the cars is greater in normal weather conditions which leads to a greater risk of accidents. For a deeper analysis, this graph should be weighted with the frequency of the weather condition during the year.\n",
    "\n",
    "Looking at the percentage of killed people, the worst case would be driving under fog."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>accidents</th>\n",
       "      <th>perc</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weather</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Normal</td>\n",
       "      <td>568065</td>\n",
       "      <td>80.630693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Light rainfall</td>\n",
       "      <td>74927</td>\n",
       "      <td>10.635079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Heavy rainfall</td>\n",
       "      <td>15767</td>\n",
       "      <td>2.237955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Snow - hail</td>\n",
       "      <td>4134</td>\n",
       "      <td>0.586777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Fog - smoke</td>\n",
       "      <td>4531</td>\n",
       "      <td>0.643127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Strong wind - storm</td>\n",
       "      <td>1680</td>\n",
       "      <td>0.238458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Dazzling weather</td>\n",
       "      <td>7787</td>\n",
       "      <td>1.105281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Covered weather</td>\n",
       "      <td>22697</td>\n",
       "      <td>3.221594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Other</td>\n",
       "      <td>4939</td>\n",
       "      <td>0.701038</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     accidents       perc\n",
       "weather                                  \n",
       "Normal                  568065  80.630693\n",
       "Light rainfall           74927  10.635079\n",
       "Heavy rainfall           15767   2.237955\n",
       "Snow - hail               4134   0.586777\n",
       "Fog - smoke               4531   0.643127\n",
       "Strong wind - storm       1680   0.238458\n",
       "Dazzling weather          7787   1.105281\n",
       "Covered weather          22697   3.221594\n",
       "Other                     4939   0.701038"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_accident_per_weather_years(list(range(2008, 2019)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Impact of the weather on the gravity of the accidents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>counts</th>\n",
       "      <th>perc</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>atm</th>\n",
       "      <th>grav</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <td rowspan=\"4\" valign=\"top\">Normal</td>\n",
       "      <td>Unharmed</td>\n",
       "      <td>521212</td>\n",
       "      <td>41.197840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Killed</td>\n",
       "      <td>32855</td>\n",
       "      <td>2.596938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Injured and hospitalized</td>\n",
       "      <td>257930</td>\n",
       "      <td>20.387403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Slightly injured</td>\n",
       "      <td>453147</td>\n",
       "      <td>35.817820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"4\" valign=\"top\">Light rainfall</td>\n",
       "      <td>Unharmed</td>\n",
       "      <td>65647</td>\n",
       "      <td>40.003047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Killed</td>\n",
       "      <td>3753</td>\n",
       "      <td>2.286950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Injured and hospitalized</td>\n",
       "      <td>30620</td>\n",
       "      <td>18.658786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Slightly injured</td>\n",
       "      <td>64085</td>\n",
       "      <td>39.051217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"4\" valign=\"top\">Heavy rainfall</td>\n",
       "      <td>Unharmed</td>\n",
       "      <td>14039</td>\n",
       "      <td>39.364625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Killed</td>\n",
       "      <td>1108</td>\n",
       "      <td>3.106774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Injured and hospitalized</td>\n",
       "      <td>7783</td>\n",
       "      <td>21.823127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Slightly injured</td>\n",
       "      <td>12734</td>\n",
       "      <td>35.705473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"4\" valign=\"top\">Snow - hail</td>\n",
       "      <td>Unharmed</td>\n",
       "      <td>4241</td>\n",
       "      <td>41.738018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Killed</td>\n",
       "      <td>332</td>\n",
       "      <td>3.267395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Injured and hospitalized</td>\n",
       "      <td>2257</td>\n",
       "      <td>22.212381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Slightly injured</td>\n",
       "      <td>3331</td>\n",
       "      <td>32.782206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"4\" valign=\"top\">Fog - smoke</td>\n",
       "      <td>Unharmed</td>\n",
       "      <td>3966</td>\n",
       "      <td>36.817675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Killed</td>\n",
       "      <td>701</td>\n",
       "      <td>6.507612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Injured and hospitalized</td>\n",
       "      <td>3283</td>\n",
       "      <td>30.477163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Slightly injured</td>\n",
       "      <td>2822</td>\n",
       "      <td>26.197549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"4\" valign=\"top\">Strong wind - storm</td>\n",
       "      <td>Unharmed</td>\n",
       "      <td>1274</td>\n",
       "      <td>35.556796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Killed</td>\n",
       "      <td>213</td>\n",
       "      <td>5.944739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Injured and hospitalized</td>\n",
       "      <td>1034</td>\n",
       "      <td>28.858498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Slightly injured</td>\n",
       "      <td>1062</td>\n",
       "      <td>29.639967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"4\" valign=\"top\">Dazzling weather</td>\n",
       "      <td>Unharmed</td>\n",
       "      <td>7730</td>\n",
       "      <td>42.906306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Killed</td>\n",
       "      <td>823</td>\n",
       "      <td>4.568162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Injured and hospitalized</td>\n",
       "      <td>5211</td>\n",
       "      <td>28.924290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Slightly injured</td>\n",
       "      <td>4252</td>\n",
       "      <td>23.601243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"4\" valign=\"top\">Covered weather</td>\n",
       "      <td>Unharmed</td>\n",
       "      <td>20100</td>\n",
       "      <td>39.474459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Killed</td>\n",
       "      <td>1953</td>\n",
       "      <td>3.835503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Injured and hospitalized</td>\n",
       "      <td>11441</td>\n",
       "      <td>22.469019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Slightly injured</td>\n",
       "      <td>17425</td>\n",
       "      <td>34.221018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"4\" valign=\"top\">Other</td>\n",
       "      <td>Unharmed</td>\n",
       "      <td>3783</td>\n",
       "      <td>35.547829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Killed</td>\n",
       "      <td>510</td>\n",
       "      <td>4.792332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Injured and hospitalized</td>\n",
       "      <td>2821</td>\n",
       "      <td>26.508175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Slightly injured</td>\n",
       "      <td>3528</td>\n",
       "      <td>33.151663</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              counts       perc\n",
       "atm                 grav                                       \n",
       "Normal              Unharmed                  521212  41.197840\n",
       "                    Killed                     32855   2.596938\n",
       "                    Injured and hospitalized  257930  20.387403\n",
       "                    Slightly injured          453147  35.817820\n",
       "Light rainfall      Unharmed                   65647  40.003047\n",
       "                    Killed                      3753   2.286950\n",
       "                    Injured and hospitalized   30620  18.658786\n",
       "                    Slightly injured           64085  39.051217\n",
       "Heavy rainfall      Unharmed                   14039  39.364625\n",
       "                    Killed                      1108   3.106774\n",
       "                    Injured and hospitalized    7783  21.823127\n",
       "                    Slightly injured           12734  35.705473\n",
       "Snow - hail         Unharmed                    4241  41.738018\n",
       "                    Killed                       332   3.267395\n",
       "                    Injured and hospitalized    2257  22.212381\n",
       "                    Slightly injured            3331  32.782206\n",
       "Fog - smoke         Unharmed                    3966  36.817675\n",
       "                    Killed                       701   6.507612\n",
       "                    Injured and hospitalized    3283  30.477163\n",
       "                    Slightly injured            2822  26.197549\n",
       "Strong wind - storm Unharmed                    1274  35.556796\n",
       "                    Killed                       213   5.944739\n",
       "                    Injured and hospitalized    1034  28.858498\n",
       "                    Slightly injured            1062  29.639967\n",
       "Dazzling weather    Unharmed                    7730  42.906306\n",
       "                    Killed                       823   4.568162\n",
       "                    Injured and hospitalized    5211  28.924290\n",
       "                    Slightly injured            4252  23.601243\n",
       "Covered weather     Unharmed                   20100  39.474459\n",
       "                    Killed                      1953   3.835503\n",
       "                    Injured and hospitalized   11441  22.469019\n",
       "                    Slightly injured           17425  34.221018\n",
       "Other               Unharmed                    3783  35.547829\n",
       "                    Killed                       510   4.792332\n",
       "                    Injured and hospitalized    2821  26.508175\n",
       "                    Slightly injured            3528  33.151663"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grav_weather_over_years(list(range(2008, 2019)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "contingency_table :-\n",
      " grav       1      2       3       4\n",
      "atm                                \n",
      "1     521212  32855  257930  453147\n",
      "2      65647   3753   30620   64085\n",
      "3      14039   1108    7783   12734\n",
      "4       4241    332    2257    3331\n",
      "5       3966    701    3283    2822\n",
      "6       1274    213    1034    1062\n",
      "7       7730    823    5211    4252\n",
      "8      20100   1953   11441   17425\n",
      "9       3783    510    2821    3528\n",
      "Observed Values :-\n",
      " [[521212  32855 257930 453147]\n",
      " [ 65647   3753  30620  64085]\n",
      " [ 14039   1108   7783  12734]\n",
      " [  4241    332   2257   3331]\n",
      " [  3966    701   3283   2822]\n",
      " [  1274    213   1034   1062]\n",
      " [  7730    823   5211   4252]\n",
      " [ 20100   1953  11441  17425]\n",
      " [  3783    510   2821   3528]]\n",
      "Expected Values :-\n",
      " [[5.17660434e+05 3.40660289e+04 2.59946184e+05 4.53471353e+05]\n",
      " [6.71470327e+04 4.41879001e+03 3.37182712e+04 5.88209061e+04]\n",
      " [1.45926801e+04 9.60310332e+02 7.32779882e+03 1.27832107e+04]\n",
      " [4.15758812e+03 2.73601202e+02 2.08775695e+03 3.64205372e+03]\n",
      " [4.40759170e+03 2.90053356e+02 2.21329769e+03 3.86105725e+03]\n",
      " [1.46606025e+03 9.64780147e+01 7.36190646e+02 1.28427109e+03]\n",
      " [7.37162756e+03 4.85109661e+02 3.70170546e+03 6.45755732e+03]\n",
      " [2.08345861e+04 1.37107564e+03 1.04622081e+04 1.82511302e+04]\n",
      " [4.35439945e+03 2.86552898e+02 2.18658690e+03 3.81446076e+03]]\n",
      "Degree of Freedom:- 1\n",
      "chi-square statistic:- 1826.341993778747\n",
      "critical_value: 3.841458820694124\n",
      "p-value: 0.0\n",
      "Significance level:  0.05\n",
      "Degree of Freedom:  1\n",
      "chi-square statistic: 1826.341993778747\n",
      "critical_value: 3.841458820694124\n",
      "p-value: 0.0\n",
      "Chi-square test result : Reject H0, There is a relationship between 2 categorical variables\n",
      "Reject H0 with 95% level of confidence.\n"
     ]
    }
   ],
   "source": [
    "years = list(range(2008, 2019)) # All years\n",
    "\n",
    "data = stats_df_weather_gravity(years)\n",
    "\n",
    "Chi_square_gravity_vs_seatbelt = ChiSquare(data['atm'],data['grav'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_weather_vs_gravity_contingency()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We showed previously that the number of accident varies during the year. This may be due to the weather that also impacts the road conditions. We showed that while driving, the worst conditions are fog, strong winds and dazzling weather, when the visibility on the road is the less. The weather condition with the least percentage of death per accident is during light rainfall. Even with heavy rainfall, this percentage is just above normal conditions. In these conditions, people tend to be more careful."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Road conditions\n",
    "\n",
    "Accidents and percentage of the accidents given the road conditions for all year between 2008 and 2018"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The observations of the above graph match the road condition graphs. During the worst visible weather conditions more lethal accidents occures."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>accidents</th>\n",
       "      <th>perc</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>surface</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Normal</td>\n",
       "      <td>544611</td>\n",
       "      <td>77.505280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Wet</td>\n",
       "      <td>121543</td>\n",
       "      <td>17.297161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Puddles</td>\n",
       "      <td>1111</td>\n",
       "      <td>0.158110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Flooded</td>\n",
       "      <td>410</td>\n",
       "      <td>0.058348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Snowy</td>\n",
       "      <td>2028</td>\n",
       "      <td>0.288611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Muddy</td>\n",
       "      <td>388</td>\n",
       "      <td>0.055217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Icy</td>\n",
       "      <td>4381</td>\n",
       "      <td>0.623474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Greasy substance - oil</td>\n",
       "      <td>1658</td>\n",
       "      <td>0.235955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Other</td>\n",
       "      <td>26546</td>\n",
       "      <td>3.777844</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        accidents       perc\n",
       "surface                                     \n",
       "Normal                     544611  77.505280\n",
       "Wet                        121543  17.297161\n",
       "Puddles                      1111   0.158110\n",
       "Flooded                       410   0.058348\n",
       "Snowy                        2028   0.288611\n",
       "Muddy                         388   0.055217\n",
       "Icy                          4381   0.623474\n",
       "Greasy substance - oil       1658   0.235955\n",
       "Other                       26546   3.777844"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_accident_per_road_conditions_years(list(range(2008, 2019)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Impact of the road conditions on the gravity of the accidents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\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></th>\n",
       "      <th>counts</th>\n",
       "      <th>perc</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>surf</th>\n",
       "      <th>grav</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td rowspan=\"4\" valign=\"top\">Flooded</td>\n",
       "      <td>Injured and hospitalized</td>\n",
       "      <td>230.0</td>\n",
       "      <td>25.302530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Killed</td>\n",
       "      <td>64.0</td>\n",
       "      <td>7.040704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Slightly injured</td>\n",
       "      <td>286.0</td>\n",
       "      <td>31.463146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Unharmed</td>\n",
       "      <td>329.0</td>\n",
       "      <td>36.193619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"4\" valign=\"top\">Greasy substance - oil</td>\n",
       "      <td>Injured and hospitalized</td>\n",
       "      <td>712.0</td>\n",
       "      <td>23.451910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Killed</td>\n",
       "      <td>83.0</td>\n",
       "      <td>2.733860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Slightly injured</td>\n",
       "      <td>1471.0</td>\n",
       "      <td>48.451910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Unharmed</td>\n",
       "      <td>770.0</td>\n",
       "      <td>25.362319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"4\" valign=\"top\">Icy</td>\n",
       "      <td>Injured and hospitalized</td>\n",
       "      <td>2747.0</td>\n",
       "      <td>29.477412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Killed</td>\n",
       "      <td>452.0</td>\n",
       "      <td>4.850306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Slightly injured</td>\n",
       "      <td>3086.0</td>\n",
       "      <td>33.115141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Unharmed</td>\n",
       "      <td>3034.0</td>\n",
       "      <td>32.557141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"4\" valign=\"top\">Muddy</td>\n",
       "      <td>Injured and hospitalized</td>\n",
       "      <td>263.0</td>\n",
       "      <td>38.506589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Killed</td>\n",
       "      <td>35.0</td>\n",
       "      <td>5.124451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Slightly injured</td>\n",
       "      <td>200.0</td>\n",
       "      <td>29.282577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Unharmed</td>\n",
       "      <td>185.0</td>\n",
       "      <td>27.086384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"4\" valign=\"top\">Normal</td>\n",
       "      <td>Injured and hospitalized</td>\n",
       "      <td>250630.0</td>\n",
       "      <td>20.540193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Killed</td>\n",
       "      <td>32375.0</td>\n",
       "      <td>2.653269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Slightly injured</td>\n",
       "      <td>431205.0</td>\n",
       "      <td>35.339082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Unharmed</td>\n",
       "      <td>505983.0</td>\n",
       "      <td>41.467456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"4\" valign=\"top\">Other</td>\n",
       "      <td>Injured and hospitalized</td>\n",
       "      <td>11255.0</td>\n",
       "      <td>20.622618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Killed</td>\n",
       "      <td>1190.0</td>\n",
       "      <td>2.180446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Slightly injured</td>\n",
       "      <td>20795.0</td>\n",
       "      <td>38.102829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Unharmed</td>\n",
       "      <td>21336.0</td>\n",
       "      <td>39.094107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"4\" valign=\"top\">Puddles</td>\n",
       "      <td>Injured and hospitalized</td>\n",
       "      <td>620.0</td>\n",
       "      <td>29.779059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Killed</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Slightly injured</td>\n",
       "      <td>771.0</td>\n",
       "      <td>37.031700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Unharmed</td>\n",
       "      <td>691.0</td>\n",
       "      <td>33.189241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"4\" valign=\"top\">Snowy</td>\n",
       "      <td>Injured and hospitalized</td>\n",
       "      <td>1112.0</td>\n",
       "      <td>21.846758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Killed</td>\n",
       "      <td>169.0</td>\n",
       "      <td>3.320236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Slightly injured</td>\n",
       "      <td>1665.0</td>\n",
       "      <td>32.711198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Unharmed</td>\n",
       "      <td>2144.0</td>\n",
       "      <td>42.121807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"4\" valign=\"top\">Wet</td>\n",
       "      <td>Injured and hospitalized</td>\n",
       "      <td>53660.0</td>\n",
       "      <td>19.964952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Killed</td>\n",
       "      <td>7718.0</td>\n",
       "      <td>2.871590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Slightly injured</td>\n",
       "      <td>101690.0</td>\n",
       "      <td>37.835183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Unharmed</td>\n",
       "      <td>105703.0</td>\n",
       "      <td>39.328276</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   counts       perc\n",
       "surf                   grav                                         \n",
       "Flooded                Injured and hospitalized     230.0  25.302530\n",
       "                       Killed                        64.0   7.040704\n",
       "                       Slightly injured             286.0  31.463146\n",
       "                       Unharmed                     329.0  36.193619\n",
       "Greasy substance - oil Injured and hospitalized     712.0  23.451910\n",
       "                       Killed                        83.0   2.733860\n",
       "                       Slightly injured            1471.0  48.451910\n",
       "                       Unharmed                     770.0  25.362319\n",
       "Icy                    Injured and hospitalized    2747.0  29.477412\n",
       "                       Killed                       452.0   4.850306\n",
       "                       Slightly injured            3086.0  33.115141\n",
       "                       Unharmed                    3034.0  32.557141\n",
       "Muddy                  Injured and hospitalized     263.0  38.506589\n",
       "                       Killed                        35.0   5.124451\n",
       "                       Slightly injured             200.0  29.282577\n",
       "                       Unharmed                     185.0  27.086384\n",
       "Normal                 Injured and hospitalized  250630.0  20.540193\n",
       "                       Killed                     32375.0   2.653269\n",
       "                       Slightly injured          431205.0  35.339082\n",
       "                       Unharmed                  505983.0  41.467456\n",
       "Other                  Injured and hospitalized   11255.0  20.622618\n",
       "                       Killed                      1190.0   2.180446\n",
       "                       Slightly injured           20795.0  38.102829\n",
       "                       Unharmed                   21336.0  39.094107\n",
       "Puddles                Injured and hospitalized     620.0  29.779059\n",
       "                       Killed                         NaN        NaN\n",
       "                       Slightly injured             771.0  37.031700\n",
       "                       Unharmed                     691.0  33.189241\n",
       "Snowy                  Injured and hospitalized    1112.0  21.846758\n",
       "                       Killed                       169.0   3.320236\n",
       "                       Slightly injured            1665.0  32.711198\n",
       "                       Unharmed                    2144.0  42.121807\n",
       "Wet                    Injured and hospitalized   53660.0  19.964952\n",
       "                       Killed                      7718.0   2.871590\n",
       "                       Slightly injured          101690.0  37.835183\n",
       "                       Unharmed                  105703.0  39.328276"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grav_surface_over_years(list(range(2008, 2019)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. And keep it secure"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since less data was collected on people not wearing a seatbelt, we cannot compare these tabs horizontally. Considering that the ratio of people in each class over the whole population is the same for both plots, we can say that the proportion of killed people is decrease by 17% when a seatbelt is worn and the proportion of injured and hospitalized people is decrease by 20%. \n",
    "Considering the motorcycles equipment, wearing a helmet prevents death and injury decreasing the proportion of death by 4% and that of injury by 12%.\n",
    "Considering the reflective devices, having one prevents death and injury decreasing the proportion of death by 6% and that of injury by 20%."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Security equipment usage\n",
    "#### Presence of seatbelt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "code_seatbelt    = [11] # Seatbelt attached\n",
    "code_no_seatbelt = [12] # No setbelt\n",
    "years = list(range(2008, 2019)) # All years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>counts</th>\n",
       "      <th>perc</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>grav</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Unharmed</td>\n",
       "      <td>506165</td>\n",
       "      <td>57.268007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Killed</td>\n",
       "      <td>15951</td>\n",
       "      <td>1.804712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Injured and hospitalized</td>\n",
       "      <td>119810</td>\n",
       "      <td>13.555422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Slightly injured</td>\n",
       "      <td>241927</td>\n",
       "      <td>27.371859</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          counts       perc\n",
       "grav                                       \n",
       "Unharmed                  506165  57.268007\n",
       "Killed                     15951   1.804712\n",
       "Injured and hospitalized  119810  13.555422\n",
       "Slightly injured          241927  27.371859"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_gravity_over_years(code_seatbelt, years)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        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>counts</th>\n",
       "      <th>perc</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>grav</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Unharmed</td>\n",
       "      <td>5828</td>\n",
       "      <td>24.080654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Killed</td>\n",
       "      <td>4534</td>\n",
       "      <td>18.733989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Injured and hospitalized</td>\n",
       "      <td>8324</td>\n",
       "      <td>34.393852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Slightly injured</td>\n",
       "      <td>5516</td>\n",
       "      <td>22.791505</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          counts       perc\n",
       "grav                                       \n",
       "Unharmed                    5828  24.080654\n",
       "Killed                      4534  18.733989\n",
       "Injured and hospitalized    8324  34.393852\n",
       "Slightly injured            5516  22.791505"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_gravity_over_years(code_no_seatbelt, years)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "contingency_table :-\n",
      " grav      1     2      3      4\n",
      "secu                           \n",
      "11    52995  1686  13075  24615\n",
      "12      721   543   1054    638\n",
      "Observed Values :-\n",
      " [[52995  1686 13075 24615]\n",
      " [  721   543  1054   638]]\n",
      "Expected Values :-\n",
      " [[52050.31770642  2159.88082075 13690.87308947 24469.92838335]\n",
      " [ 1665.68229358    69.11917925   438.12691053   783.07161665]]\n",
      "Degree of Freedom:- 1\n",
      "chi-square statistic:- 3905.811786814595\n",
      "critical_value: 3.841458820694124\n",
      "p-value: 0.0\n",
      "Significance level:  0.05\n",
      "Degree of Freedom:  1\n",
      "chi-square statistic: 3905.811786814595\n",
      "critical_value: 3.841458820694124\n",
      "p-value: 0.0\n",
      "Chi-square test result : Reject H0, There is a relationship between 2 categorical variables\n",
      "Reject H0 with 95% level of confidence.\n"
     ]
    }
   ],
   "source": [
    "data = stats_df_security_gravity([11,12],years)\n",
    "\n",
    "Chi_square_gravity_vs_seatbelt = ChiSquare(data['secu'],data['grav'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_seatbelt_vs_gravity_contingency()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "More than **18%** killed when the seatbelt is not attached\n",
    "\n",
    "#### Presence of helmet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "code_helmet    = [21]\n",
    "code_no_helmet = [22]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>counts</th>\n",
       "      <th>perc</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>grav</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Unharmed</td>\n",
       "      <td>25085</td>\n",
       "      <td>8.709888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Killed</td>\n",
       "      <td>9777</td>\n",
       "      <td>3.394721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Injured and hospitalized</td>\n",
       "      <td>100725</td>\n",
       "      <td>34.973230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Slightly injured</td>\n",
       "      <td>152419</td>\n",
       "      <td>52.922161</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          counts       perc\n",
       "grav                                       \n",
       "Unharmed                   25085   8.709888\n",
       "Killed                      9777   3.394721\n",
       "Injured and hospitalized  100725  34.973230\n",
       "Slightly injured          152419  52.922161"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_gravity_over_years(code_helmet, years)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>counts</th>\n",
       "      <th>perc</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>grav</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Unharmed</td>\n",
       "      <td>1001</td>\n",
       "      <td>5.998322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Killed</td>\n",
       "      <td>1203</td>\n",
       "      <td>7.208773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Injured and hospitalized</td>\n",
       "      <td>7777</td>\n",
       "      <td>46.602349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Slightly injured</td>\n",
       "      <td>6707</td>\n",
       "      <td>40.190556</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          counts       perc\n",
       "grav                                       \n",
       "Unharmed                    1001   5.998322\n",
       "Killed                      1203   7.208773\n",
       "Injured and hospitalized    7777  46.602349\n",
       "Slightly injured            6707  40.190556"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_gravity_over_years(code_no_helmet, years)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "More killed and hospitalized passengers when the helmet was not present"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Presence of reflective device"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "code_refl_dev    = [41]\n",
    "code_no_refl_dev = [42]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>counts</th>\n",
       "      <th>perc</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>grav</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Unharmed</td>\n",
       "      <td>334</td>\n",
       "      <td>16.065416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Killed</td>\n",
       "      <td>112</td>\n",
       "      <td>5.387205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Injured and hospitalized</td>\n",
       "      <td>597</td>\n",
       "      <td>28.715729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Slightly injured</td>\n",
       "      <td>1036</td>\n",
       "      <td>49.831650</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          counts       perc\n",
       "grav                                       \n",
       "Unharmed                     334  16.065416\n",
       "Killed                       112   5.387205\n",
       "Injured and hospitalized     597  28.715729\n",
       "Slightly injured            1036  49.831650"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_gravity_over_years(code_refl_dev, years)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>counts</th>\n",
       "      <th>perc</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>grav</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Unharmed</td>\n",
       "      <td>75</td>\n",
       "      <td>4.564820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Killed</td>\n",
       "      <td>191</td>\n",
       "      <td>11.625076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Injured and hospitalized</td>\n",
       "      <td>740</td>\n",
       "      <td>45.039562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Slightly injured</td>\n",
       "      <td>637</td>\n",
       "      <td>38.770542</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          counts       perc\n",
       "grav                                       \n",
       "Unharmed                      75   4.564820\n",
       "Killed                       191  11.625076\n",
       "Injured and hospitalized     740  45.039562\n",
       "Slightly injured             637  38.770542"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_gravity_over_years(code_no_refl_dev, years)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**+6%** killed and almost **+20%** hospitalized when there was no relfective device"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using our data analysis, we can draw the perfect portail and setting for Mr Y and Mrs X in order for them to drive safely across France. Mr Y is between 20 and 50, and Mrs X is between 30 and 50. Mrs X is driving, and Mr Y is sitting next to her on the front seat, both wearing their seatbelts. They are cruising in the middle of France in the middle of nowhere, avoiding any big city. It is a little bit cloudy and it might rain lightly. In this case, being the best case scenario, we can affirm that they still have 1% of chance of dying in an accident."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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