temporal patterns analysis year 1439.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import geopandas as gpd\n",
    "import matplotlib.pyplot as plt\n",
    "import shapely as shp\n",
    "import seaborn as sns\n",
    "% matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The general distribution of data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>month</th>\n",
       "      <th>month_name</th>\n",
       "      <th>region</th>\n",
       "      <th>sum_injuries</th>\n",
       "      <th>male_injuries</th>\n",
       "      <th>female_injuries</th>\n",
       "      <th>urban_injuries</th>\n",
       "      <th>suburban_injuries</th>\n",
       "      <th>injuries_0_18</th>\n",
       "      <th>injuries_18_30</th>\n",
       "      <th>...</th>\n",
       "      <th>cause_illegal_turn</th>\n",
       "      <th>cause_illegal_crossing</th>\n",
       "      <th>cause_traffic_light_break</th>\n",
       "      <th>cause_speeding</th>\n",
       "      <th>cause_drug_use</th>\n",
       "      <th>cause_other</th>\n",
       "      <th>injuries_total</th>\n",
       "      <th>dead_total</th>\n",
       "      <th>intact_total</th>\n",
       "      <th>??? ??????? ??? ??? ????? ???????</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Moharram</td>\n",
       "      <td>Ar Riyad</td>\n",
       "      <td>393.0</td>\n",
       "      <td>364.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>291.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>...</td>\n",
       "      <td>974.0</td>\n",
       "      <td>891.0</td>\n",
       "      <td>1208.0</td>\n",
       "      <td>1304.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>393.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>12435.0</td>\n",
       "      <td>1.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>Safar</td>\n",
       "      <td>Ar Riyad</td>\n",
       "      <td>433.0</td>\n",
       "      <td>387.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>276.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1940.0</td>\n",
       "      <td>898.0</td>\n",
       "      <td>739.0</td>\n",
       "      <td>997.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>433.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>9110.0</td>\n",
       "      <td>1.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>Rabeea Awal</td>\n",
       "      <td>Ar Riyad</td>\n",
       "      <td>455.0</td>\n",
       "      <td>361.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>284.0</td>\n",
       "      <td>171.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>124.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1418.0</td>\n",
       "      <td>1255.0</td>\n",
       "      <td>1380.0</td>\n",
       "      <td>2444.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>455.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>15385.0</td>\n",
       "      <td>1.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>Rabeea Thany</td>\n",
       "      <td>Ar Riyad</td>\n",
       "      <td>427.0</td>\n",
       "      <td>378.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>295.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1608.0</td>\n",
       "      <td>1512.0</td>\n",
       "      <td>1366.0</td>\n",
       "      <td>1992.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>427.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>15181.0</td>\n",
       "      <td>1.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>Jamad Awal</td>\n",
       "      <td>Ar Riyad</td>\n",
       "      <td>358.0</td>\n",
       "      <td>311.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>262.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1511.0</td>\n",
       "      <td>1318.0</td>\n",
       "      <td>1426.0</td>\n",
       "      <td>2228.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>358.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>15437.0</td>\n",
       "      <td>1.19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 88 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   month    month_name    region  sum_injuries  male_injuries  \\\n",
       "0    1.0      Moharram  Ar Riyad         393.0          364.0   \n",
       "1    2.0         Safar  Ar Riyad         433.0          387.0   \n",
       "2    3.0   Rabeea Awal  Ar Riyad         455.0          361.0   \n",
       "3    4.0  Rabeea Thany  Ar Riyad         427.0          378.0   \n",
       "4    5.0    Jamad Awal  Ar Riyad         358.0          311.0   \n",
       "\n",
       "   female_injuries  urban_injuries  suburban_injuries  injuries_0_18  \\\n",
       "0             29.0           291.0              102.0           66.0   \n",
       "1             46.0           276.0              157.0           44.0   \n",
       "2             94.0           284.0              171.0          115.0   \n",
       "3             49.0           295.0              132.0          132.0   \n",
       "4             47.0           262.0               96.0           61.0   \n",
       "\n",
       "   injuries_18_30  ...  cause_illegal_turn  cause_illegal_crossing  \\\n",
       "0            97.0  ...               974.0                   891.0   \n",
       "1           117.0  ...              1940.0                   898.0   \n",
       "2           124.0  ...              1418.0                  1255.0   \n",
       "3           119.0  ...              1608.0                  1512.0   \n",
       "4           111.0  ...              1511.0                  1318.0   \n",
       "\n",
       "   cause_traffic_light_break  cause_speeding  cause_drug_use  cause_other  \\\n",
       "0                     1208.0          1304.0             0.0          0.0   \n",
       "1                      739.0           997.0             0.0          0.0   \n",
       "2                     1380.0          2444.0             0.0          0.0   \n",
       "3                     1366.0          1992.0             0.0          0.0   \n",
       "4                     1426.0          2228.0             0.0          0.0   \n",
       "\n",
       "   injuries_total  dead_total  intact_total  ??? ??????? ??? ??? ????? ???????  \n",
       "0           393.0       100.0       12435.0                               1.33  \n",
       "1           433.0        98.0        9110.0                               1.26  \n",
       "2           455.0       105.0       15385.0                               1.21  \n",
       "3           427.0        91.0       15181.0                               1.15  \n",
       "4           358.0        96.0       15437.0                               1.19  \n",
       "\n",
       "[5 rows x 88 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('TASA.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['month', 'month_name', 'region', 'sum_injuries', 'male_injuries',\n",
       "       'female_injuries', 'urban_injuries', 'suburban_injuries',\n",
       "       'injuries_0_18', 'injuries_18_30', 'injuries_30_40', 'injuries_40_50',\n",
       "       'injuries_50', 'injuries_saudi', 'injuries_foreign', 'sum_dead',\n",
       "       'male_dead', 'female_dead', 'urban_dead', 'suburban_dead', 'dead_0_18',\n",
       "       'dead_18_30', 'dead_30_40', 'dead_40_50', 'dead_50', 'dead_saudi',\n",
       "       'dead_foreign', 'sum_accidents', 'sum_involved', 'acc_damages',\n",
       "       'acc_injuries', 'acc_dead', 'Saturday', 'Sunday', 'Monday', 'Tuesday',\n",
       "       'Wednesday', 'Thursday', 'Friday', 'day', 'night', 'urban', 'suburban',\n",
       "       'small_car', 'jeep', 'bus', 'pickup', 'truck', 'water_truck', 'other',\n",
       "       'involved_0_18', 'involved_18_30', 'involved_30_40', 'involved_40_50',\n",
       "       'involved_50', 'involved_saudi', 'involved_foreign', 'involved_married',\n",
       "       'involved_single', 'involved_litrate', 'involved_illitrate',\n",
       "       'private_license', 'public_license', 'machine_license', 'bike_license',\n",
       "       'no_license', 'sum_license', 'valid_license', 'expired_license',\n",
       "       'type_car_hit', 'type_object_hit', 'type_pedestrain_hit',\n",
       "       'type_animal_hit', 'type_Fire', 'type_rollover',\n",
       "       'type_offroad_diviation', 'type_other', 'cause_illegal_parking_stop',\n",
       "       'cause_illegal_turn', 'cause_illegal_crossing',\n",
       "       'cause_traffic_light_break', 'cause_speeding', 'cause_drug_use',\n",
       "       'cause_other', 'injuries_total', 'dead_total', 'intact_total',\n",
       "       '??? ??????? ??? ??? ????? ???????'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Daily comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "days = df[['Sunday', 'Monday', 'Tuesday',\n",
    "       'Wednesday', 'Thursday', 'Friday','Saturday']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sunday</th>\n",
       "      <th>Monday</th>\n",
       "      <th>Tuesday</th>\n",
       "      <th>Wednesday</th>\n",
       "      <th>Thursday</th>\n",
       "      <th>Friday</th>\n",
       "      <th>Saturday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>781.0</td>\n",
       "      <td>625.0</td>\n",
       "      <td>814.0</td>\n",
       "      <td>702.0</td>\n",
       "      <td>775.0</td>\n",
       "      <td>783.0</td>\n",
       "      <td>919.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1407.0</td>\n",
       "      <td>1434.0</td>\n",
       "      <td>857.0</td>\n",
       "      <td>421.0</td>\n",
       "      <td>388.0</td>\n",
       "      <td>340.0</td>\n",
       "      <td>1427.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1435.0</td>\n",
       "      <td>1121.0</td>\n",
       "      <td>1122.0</td>\n",
       "      <td>1140.0</td>\n",
       "      <td>1063.0</td>\n",
       "      <td>1076.0</td>\n",
       "      <td>1209.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1436.0</td>\n",
       "      <td>1094.0</td>\n",
       "      <td>1054.0</td>\n",
       "      <td>1085.0</td>\n",
       "      <td>1076.0</td>\n",
       "      <td>1053.0</td>\n",
       "      <td>1368.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1298.0</td>\n",
       "      <td>1014.0</td>\n",
       "      <td>1088.0</td>\n",
       "      <td>1110.0</td>\n",
       "      <td>1125.0</td>\n",
       "      <td>1196.0</td>\n",
       "      <td>1410.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Sunday  Monday  Tuesday  Wednesday  Thursday  Friday  Saturday\n",
       "0   781.0   625.0    814.0      702.0     775.0   783.0     919.0\n",
       "1  1407.0  1434.0    857.0      421.0     388.0   340.0    1427.0\n",
       "2  1435.0  1121.0   1122.0     1140.0    1063.0  1076.0    1209.0\n",
       "3  1436.0  1094.0   1054.0     1085.0    1076.0  1053.0    1368.0\n",
       "4  1298.0  1014.0   1088.0     1110.0    1125.0  1196.0    1410.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "days.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 800.0)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
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    {
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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots(figsize = (10,7))\n",
    "\n",
    "colors = ['#3d6c6f','#72a2ac','#cb9573','#ef7b4f','#bf3f27']\n",
    "ax.set_prop_cycle(color = colors )\n",
    "sns.boxplot(data = days, ax=ax, color='#72a2ac')\n",
    "sns.swarmplot(data=days, ax=ax, color='#bf3f27')\n",
    "plt.ylim(0,800)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots(figsize = (10,7))\n",
    "\n",
    "sns.swarmplot(data=days, color = '#72a2ac', ax=ax,  zorder = 2)\n",
    "\n",
    "plt.plot(range(len(days.mean().values)), days.mean().values, color ='#bf3f27', label = 'Mean',\n",
    "        linewidth = 3 )\n",
    "#for i in range(len(days.mean().values)):\n",
    "    #plt.annotate(text = int(np.round(days.mean().values[i])) , xy = (range(len(days.mean().values))[i] , days.mean().values[i]),\n",
    "                #color = 'k', ha = 'center', va = 'bottom', size = 12, weight=\"bold\")    \n",
    "\n",
    "\n",
    "plt.plot(range(len(days.median().values)), days.median().values, color ='#ef7b4f', label = 'Median',\n",
    "        linewidth = 3)\n",
    "#for i in range(len(days.median().values)):\n",
    "    #plt.annotate(text = int(np.round(days.median().values[i])) ,\n",
    "                 #xy = (range(len(days.median().values))[i] , days.median().values[i]),\n",
    "                #color = 'k', ha = 'center', va = 'bottom', size = 12, weight=\"bold\")    \n",
    "\n",
    "plt.title('RTA count per week days', size = 16)\n",
    "plt.ylim(0,1750)\n",
    "plt.legend()\n",
    "plt.tight_layout()\n",
    "#plt.savefig('./output/weekdays.jpg', dpi = 300)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Weekly comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "week = pd.DataFrame({'Work days': (days.Sunday+days.Monday+days.Tuesday+days.Wednesday)/4 ,\n",
    "                     'Weekends': (days.Thursday+days.Friday+days.Saturday)/3})\n",
    "\n",
    "fig,ax = plt.subplots(figsize = (6,8))\n",
    "sns.swarmplot(data = week, color= '#72a2ac', ax=ax,  zorder = 2)\n",
    "\n",
    "plt.scatter(x = range(len(week.mean().values)), y = week.mean().values, color ='#bf3f27', zorder=5)\n",
    "plt.plot(range(len(week.mean().values)), week.mean().values, color ='#bf3f27', label = 'Mean',\n",
    "        linewidth = 3 )\n",
    "\n",
    "for i in range(len(week.mean().values)):\n",
    "    plt.annotate(text = int(np.round(week.mean().values[i])) ,\n",
    "                 xy = (range(len(week.mean().values))[i]+0.15 , week.mean().values[i]),\n",
    "                color = '#bf3f27', ha = 'center', va = 'bottom', size = 12)\n",
    "\n",
    "\n",
    "plt.scatter(x = range(len(week.median().values)), y = week.median().values, color ='#ef7b4f', zorder=5)\n",
    "plt.plot(range(len(week.median().values)), week.median().values, color ='#ef7b4f', label = 'Median',\n",
    "        linewidth = 3)\n",
    "\n",
    "for i in range(len(week.median().values)):\n",
    "    plt.annotate(text = int(np.round(week.median().values[i])) ,\n",
    "                 xy = (range(len(week.median().values))[i]+0.15 , week.median().values[i]),\n",
    "                color = '#ef7b4f', ha = 'center', va = 'bottom', size = 12)    \n",
    "\n",
    "\n",
    "plt.title('Average RTA count per week days', size = 16)\n",
    "plt.legend()\n",
    "plt.ylim(0,600)\n",
    "plt.tight_layout()\n",
    "#plt.savefig('./output/workdays vs weekend.jpg', dpi = 300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## export the tables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def describe_with_sum(df):\n",
    "    x = df.copy()\n",
    "    x = x.describe()\n",
    "    summary = list(x.describe().index)\n",
    "    summary.append('sum')\n",
    "    x = x.append(df.sum(),True)\n",
    "    x['summary']=summary\n",
    "    x.set_index(x.summary, drop=True, inplace = True)\n",
    "    x.drop(columns='summary',inplace=True)\n",
    "    return np.round(x,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#describe_with_sum(days).to_csv('./output/days.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#describe_with_sum(week).to_csv('./output/workdays vs weekend.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Day and night comparison"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## loading the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>NAME</th>\n",
       "      <th>Total injuries</th>\n",
       "      <th>Total mortalities</th>\n",
       "      <th>Number of traffic accidents</th>\n",
       "      <th>Number of people involved in traffic accidents</th>\n",
       "      <th>Number of Accidents including damages</th>\n",
       "      <th>Number of Accidents including injuries</th>\n",
       "      <th>Number of Accidents including mortalities</th>\n",
       "      <th>Percentage of Accidents including damages</th>\n",
       "      <th>Percentage of Accidents including injuries</th>\n",
       "      <th>Percentage of Accidents including mortalities</th>\n",
       "      <th>Accidents per 1000 population</th>\n",
       "      <th>Involvment in traffic accidents per 1000 population</th>\n",
       "      <th>Injuries 1000 population</th>\n",
       "      <th>Mortalities per 1000 population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Makkah</td>\n",
       "      <td>3400.0</td>\n",
       "      <td>609.0</td>\n",
       "      <td>44915.0</td>\n",
       "      <td>88472.0</td>\n",
       "      <td>43146.0</td>\n",
       "      <td>1311.0</td>\n",
       "      <td>458.0</td>\n",
       "      <td>96.061449</td>\n",
       "      <td>2.918847</td>\n",
       "      <td>1.019704</td>\n",
       "      <td>13.770946</td>\n",
       "      <td>27.125529</td>\n",
       "      <td>1.042441</td>\n",
       "      <td>0.186719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Aseer</td>\n",
       "      <td>2192.0</td>\n",
       "      <td>696.0</td>\n",
       "      <td>25964.0</td>\n",
       "      <td>50085.0</td>\n",
       "      <td>24608.0</td>\n",
       "      <td>771.0</td>\n",
       "      <td>585.0</td>\n",
       "      <td>94.777384</td>\n",
       "      <td>2.969496</td>\n",
       "      <td>2.253120</td>\n",
       "      <td>11.467712</td>\n",
       "      <td>22.121412</td>\n",
       "      <td>0.968157</td>\n",
       "      <td>0.307407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Tabouk</td>\n",
       "      <td>977.0</td>\n",
       "      <td>293.0</td>\n",
       "      <td>10034.0</td>\n",
       "      <td>9905.0</td>\n",
       "      <td>9247.0</td>\n",
       "      <td>573.0</td>\n",
       "      <td>214.0</td>\n",
       "      <td>92.156667</td>\n",
       "      <td>5.710584</td>\n",
       "      <td>2.132749</td>\n",
       "      <td>10.420215</td>\n",
       "      <td>10.286250</td>\n",
       "      <td>1.014605</td>\n",
       "      <td>0.304278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Jazan</td>\n",
       "      <td>2112.0</td>\n",
       "      <td>563.0</td>\n",
       "      <td>19572.0</td>\n",
       "      <td>33268.0</td>\n",
       "      <td>18046.0</td>\n",
       "      <td>1098.0</td>\n",
       "      <td>428.0</td>\n",
       "      <td>92.203147</td>\n",
       "      <td>5.610055</td>\n",
       "      <td>2.186797</td>\n",
       "      <td>11.906906</td>\n",
       "      <td>20.239063</td>\n",
       "      <td>1.284865</td>\n",
       "      <td>0.342509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Al-Baha</td>\n",
       "      <td>935.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>1764.0</td>\n",
       "      <td>3918.0</td>\n",
       "      <td>1193.0</td>\n",
       "      <td>490.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>67.630385</td>\n",
       "      <td>27.777778</td>\n",
       "      <td>4.591837</td>\n",
       "      <td>3.524814</td>\n",
       "      <td>7.828923</td>\n",
       "      <td>1.868311</td>\n",
       "      <td>0.221799</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0     NAME  Total injuries  Total mortalities  \\\n",
       "0           0   Makkah          3400.0              609.0   \n",
       "1           1    Aseer          2192.0              696.0   \n",
       "2           2   Tabouk           977.0              293.0   \n",
       "3           3    Jazan          2112.0              563.0   \n",
       "4           4  Al-Baha           935.0              111.0   \n",
       "\n",
       "   Number of traffic accidents  \\\n",
       "0                      44915.0   \n",
       "1                      25964.0   \n",
       "2                      10034.0   \n",
       "3                      19572.0   \n",
       "4                       1764.0   \n",
       "\n",
       "   Number of people involved in traffic accidents  \\\n",
       "0                                         88472.0   \n",
       "1                                         50085.0   \n",
       "2                                          9905.0   \n",
       "3                                         33268.0   \n",
       "4                                          3918.0   \n",
       "\n",
       "   Number of Accidents including damages  \\\n",
       "0                                43146.0   \n",
       "1                                24608.0   \n",
       "2                                 9247.0   \n",
       "3                                18046.0   \n",
       "4                                 1193.0   \n",
       "\n",
       "   Number of Accidents including injuries  \\\n",
       "0                                  1311.0   \n",
       "1                                   771.0   \n",
       "2                                   573.0   \n",
       "3                                  1098.0   \n",
       "4                                   490.0   \n",
       "\n",
       "   Number of Accidents including mortalities  \\\n",
       "0                                      458.0   \n",
       "1                                      585.0   \n",
       "2                                      214.0   \n",
       "3                                      428.0   \n",
       "4                                       81.0   \n",
       "\n",
       "   Percentage of Accidents including damages  \\\n",
       "0                                  96.061449   \n",
       "1                                  94.777384   \n",
       "2                                  92.156667   \n",
       "3                                  92.203147   \n",
       "4                                  67.630385   \n",
       "\n",
       "   Percentage of Accidents including injuries  \\\n",
       "0                                    2.918847   \n",
       "1                                    2.969496   \n",
       "2                                    5.710584   \n",
       "3                                    5.610055   \n",
       "4                                   27.777778   \n",
       "\n",
       "   Percentage of Accidents including mortalities  \\\n",
       "0                                       1.019704   \n",
       "1                                       2.253120   \n",
       "2                                       2.132749   \n",
       "3                                       2.186797   \n",
       "4                                       4.591837   \n",
       "\n",
       "   Accidents per 1000 population  \\\n",
       "0                      13.770946   \n",
       "1                      11.467712   \n",
       "2                      10.420215   \n",
       "3                      11.906906   \n",
       "4                       3.524814   \n",
       "\n",
       "   Involvment in traffic accidents per 1000 population  \\\n",
       "0                                          27.125529     \n",
       "1                                          22.121412     \n",
       "2                                          10.286250     \n",
       "3                                          20.239063     \n",
       "4                                           7.828923     \n",
       "\n",
       "   Injuries 1000 population  Mortalities per 1000 population  \n",
       "0                  1.042441                         0.186719  \n",
       "1                  0.968157                         0.307407  \n",
       "2                  1.014605                         0.304278  \n",
       "3                  1.284865                         0.342509  \n",
       "4                  1.868311                         0.221799  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "overview = pd.read_csv('overview.csv')\n",
    "overview.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### recalculating the population"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NAME</th>\n",
       "      <th>Total injuries</th>\n",
       "      <th>Injuries 1000 population</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Makkah</td>\n",
       "      <td>3400.0</td>\n",
       "      <td>1.042441</td>\n",
       "      <td>3261577.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Aseer</td>\n",
       "      <td>2192.0</td>\n",
       "      <td>0.968157</td>\n",
       "      <td>2264096.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Tabouk</td>\n",
       "      <td>977.0</td>\n",
       "      <td>1.014605</td>\n",
       "      <td>962936.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Jazan</td>\n",
       "      <td>2112.0</td>\n",
       "      <td>1.284865</td>\n",
       "      <td>1643752.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Al-Baha</td>\n",
       "      <td>935.0</td>\n",
       "      <td>1.868311</td>\n",
       "      <td>500452.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      NAME  Total injuries  Injuries 1000 population  population\n",
       "0   Makkah          3400.0                  1.042441   3261577.0\n",
       "1    Aseer          2192.0                  0.968157   2264096.0\n",
       "2   Tabouk           977.0                  1.014605    962936.0\n",
       "3    Jazan          2112.0                  1.284865   1643752.0\n",
       "4  Al-Baha           935.0                  1.868311    500452.0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population = overview[['NAME','Total injuries','Injuries 1000 population']].copy()\n",
    "population['population'] = (population['Total injuries']*1000)/population['Injuries 1000 population']\n",
    "population.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "regions_time = df.groupby('region').mean()[['Saturday', 'Sunday', 'Monday', 'Tuesday',\n",
    "                                            'Wednesday', 'Thursday', 'Friday', 'day', 'night']]\n",
    "regions_time.sort_values('day', inplace = True)\n",
    "regions_time.reset_index(inplace = True)\n",
    "\n",
    "regions_time = pd.merge(regions_time,population[['NAME','population']], left_on='region', right_on='NAME').drop(columns='NAME')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### calculating the day/night accidents per capita"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>region</th>\n",
       "      <th>Saturday</th>\n",
       "      <th>Sunday</th>\n",
       "      <th>Monday</th>\n",
       "      <th>Tuesday</th>\n",
       "      <th>Wednesday</th>\n",
       "      <th>Thursday</th>\n",
       "      <th>Friday</th>\n",
       "      <th>day</th>\n",
       "      <th>night</th>\n",
       "      <th>population</th>\n",
       "      <th>day_10000</th>\n",
       "      <th>night_10000</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Al-Qurayyat</td>\n",
       "      <td>40.583333</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>44.500000</td>\n",
       "      <td>49.166667</td>\n",
       "      <td>46.333333</td>\n",
       "      <td>45.666667</td>\n",
       "      <td>32.333333</td>\n",
       "      <td>68.333333</td>\n",
       "      <td>240.250000</td>\n",
       "      <td>179831.0</td>\n",
       "      <td>3.799864</td>\n",
       "      <td>13.359766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Al-Baha</td>\n",
       "      <td>19.500000</td>\n",
       "      <td>23.083333</td>\n",
       "      <td>23.250000</td>\n",
       "      <td>21.416667</td>\n",
       "      <td>20.833333</td>\n",
       "      <td>20.583333</td>\n",
       "      <td>18.333333</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>500452.0</td>\n",
       "      <td>1.978212</td>\n",
       "      <td>0.959133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Najran</td>\n",
       "      <td>38.500000</td>\n",
       "      <td>45.083333</td>\n",
       "      <td>34.416667</td>\n",
       "      <td>39.583333</td>\n",
       "      <td>35.583333</td>\n",
       "      <td>28.416667</td>\n",
       "      <td>38.750000</td>\n",
       "      <td>163.583333</td>\n",
       "      <td>96.750000</td>\n",
       "      <td>617873.0</td>\n",
       "      <td>2.647524</td>\n",
       "      <td>1.565856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Al-Jouf</td>\n",
       "      <td>45.583333</td>\n",
       "      <td>45.416667</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>44.500000</td>\n",
       "      <td>42.250000</td>\n",
       "      <td>41.416667</td>\n",
       "      <td>40.166667</td>\n",
       "      <td>179.666667</td>\n",
       "      <td>124.666667</td>\n",
       "      <td>357976.0</td>\n",
       "      <td>5.018958</td>\n",
       "      <td>3.482543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Hail</td>\n",
       "      <td>111.000000</td>\n",
       "      <td>95.666667</td>\n",
       "      <td>90.333333</td>\n",
       "      <td>81.500000</td>\n",
       "      <td>70.666667</td>\n",
       "      <td>67.166667</td>\n",
       "      <td>63.250000</td>\n",
       "      <td>347.750000</td>\n",
       "      <td>231.833333</td>\n",
       "      <td>728034.0</td>\n",
       "      <td>4.776563</td>\n",
       "      <td>3.184375</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        region    Saturday     Sunday     Monday    Tuesday  Wednesday  \\\n",
       "0  Al-Qurayyat   40.583333  50.000000  44.500000  49.166667  46.333333   \n",
       "1      Al-Baha   19.500000  23.083333  23.250000  21.416667  20.833333   \n",
       "2       Najran   38.500000  45.083333  34.416667  39.583333  35.583333   \n",
       "3      Al-Jouf   45.583333  45.416667  45.000000  44.500000  42.250000   \n",
       "4         Hail  111.000000  95.666667  90.333333  81.500000  70.666667   \n",
       "\n",
       "    Thursday     Friday         day       night  population  day_10000  \\\n",
       "0  45.666667  32.333333   68.333333  240.250000    179831.0   3.799864   \n",
       "1  20.583333  18.333333   99.000000   48.000000    500452.0   1.978212   \n",
       "2  28.416667  38.750000  163.583333   96.750000    617873.0   2.647524   \n",
       "3  41.416667  40.166667  179.666667  124.666667    357976.0   5.018958   \n",
       "4  67.166667  63.250000  347.750000  231.833333    728034.0   4.776563   \n",
       "\n",
       "   night_10000  \n",
       "0    13.359766  \n",
       "1     0.959133  \n",
       "2     1.565856  \n",
       "3     3.482543  \n",
       "4     3.184375  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regions_time['day_10000'] = (regions_time.day*10000)/regions_time.population\n",
    "regions_time['night_10000'] = (regions_time.night*10000)/regions_time.population\n",
    "regions_time.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### visualizing the difference between day/night accidents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\aaaa\\anaconda3\\envs\\gis\\lib\\site-packages\\ipykernel_launcher.py:15: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  from ipykernel import kernelapp as app\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots(figsize = (8*2,10), ncols = 2, nrows = 1, facecolor = 'w')\n",
    "\n",
    "colors = ['#3d6c6f','#72a2ac','#cb9573','#ef7b4f','#bf3f27']\n",
    "\n",
    "ax[0].scatter(x=regions_time.day, y = regions_time.region, color = colors[0], zorder = 2, label = 'Day')\n",
    "ax[0].scatter(x=regions_time.night, y = regions_time.region, color = colors[-1], zorder = 2, label = 'Night')\n",
    "for idx, row in regions_time.iterrows():\n",
    "    if row.day > row.night:\n",
    "        ax[0].plot([row.day,row.night], [row.region,row.region], color = colors[0], zorder = 1 )\n",
    "    else:\n",
    "        ax[0].plot([row.day,row.night], [row.region,row.region], color = colors[-1], zorder = 1 )\n",
    "\n",
    "ax[0].grid(axis = 'x', color = 'k', alpha = 0.05)\n",
    "ax[0].set_xlabel('Number of accidents', fontsize = 14)\n",
    "ax[0].set_yticklabels(regions_time.region.values, fontsize = 16)\n",
    "#ax[0].set_title(label = 'Average monthly difference between \\nday and night RTA occurance'label = 'Average monthly difference between \\nday and night RTA occurance', size = 18,\n",
    "#                size = 16, color = 'grey')\n",
    "\n",
    "\n",
    "\n",
    "ax[1].scatter(x=regions_time.day_10000, y = regions_time.region, color = colors[0], zorder = 2, label = 'Day')\n",
    "ax[1].scatter(x=regions_time.night_10000, y = regions_time.region, color = colors[-1], zorder = 2, label = 'Night')\n",
    "for idx, row in regions_time.iterrows():\n",
    "    if row.day_10000 > row.night_10000:\n",
    "        ax[1].plot([row.day_10000,row.night_10000], [row.region,row.region], color = colors[0], zorder = 1 )\n",
    "    else:\n",
    "        ax[1].plot([row.day_10000,row.night_10000], [row.region,row.region], color = colors[-1], zorder = 1 )\n",
    "\n",
    "ax[1].grid(axis = 'x', color = 'k', alpha = 0.05)\n",
    "ax[1].set_xlabel('Number of accidents per 10,000 population', fontsize = 16)\n",
    "ax[1].set_yticklabels('')\n",
    "#ax[1].set_title(label = 'Average monthly difference between \\nday and night RTA occurance per 10,000 population',\n",
    "#                size = 16, color = 'grey')\n",
    "\n",
    "fig.suptitle(t = 'Average monthly difference between day and night RTA occurance', size = 18, y = 0.99 )\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.legend(fontsize = 14, markerscale = 1.5)\n",
    "#ax.spines['right'].set_visible(False)\n",
    "#ax.spines['top'].set_visible(False)\n",
    "\n",
    "#plt.savefig('./output/Day and night.jpg', dpi = 300)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### calculating the difference between day/night accidents count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "dayn = np.round(regions_time[['region','day','night']].copy())\n",
    "dayn.sort_values(by='day', inplace = True, ascending=False)"
   ]
  },
  {
   "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>region</th>\n",
       "      <th>day</th>\n",
       "      <th>night</th>\n",
       "      <th>change</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Ar Riyad</td>\n",
       "      <td>3553.0</td>\n",
       "      <td>3104.0</td>\n",
       "      <td>-12.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Jiddah</td>\n",
       "      <td>2891.0</td>\n",
       "      <td>506.0</td>\n",
       "      <td>-82.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Makkah</td>\n",
       "      <td>2460.0</td>\n",
       "      <td>1283.0</td>\n",
       "      <td>-47.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Ash Sharqiyah</td>\n",
       "      <td>2343.0</td>\n",
       "      <td>1414.0</td>\n",
       "      <td>-39.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Aseer</td>\n",
       "      <td>1497.0</td>\n",
       "      <td>667.0</td>\n",
       "      <td>-55.44</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           region     day   night  change\n",
       "15       Ar Riyad  3553.0  3104.0  -12.64\n",
       "14         Jiddah  2891.0   506.0  -82.50\n",
       "13         Makkah  2460.0  1283.0  -47.85\n",
       "12  Ash Sharqiyah  2343.0  1414.0  -39.65\n",
       "11          Aseer  1497.0   667.0  -55.44"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dayn['change'] = np.round((((dayn.night-dayn.day)/dayn.day)*100),2)\n",
    "dayn.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#dayn.to_csv('./dayn.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Difference between weekdays and weekends"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### loading the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>month</th>\n",
       "      <th>month_name</th>\n",
       "      <th>region</th>\n",
       "      <th>sum_injuries</th>\n",
       "      <th>male_injuries</th>\n",
       "      <th>female_injuries</th>\n",
       "      <th>urban_injuries</th>\n",
       "      <th>suburban_injuries</th>\n",
       "      <th>injuries_0_18</th>\n",
       "      <th>injuries_18_30</th>\n",
       "      <th>...</th>\n",
       "      <th>cause_illegal_turn</th>\n",
       "      <th>cause_illegal_crossing</th>\n",
       "      <th>cause_traffic_light_break</th>\n",
       "      <th>cause_speeding</th>\n",
       "      <th>cause_drug_use</th>\n",
       "      <th>cause_other</th>\n",
       "      <th>injuries_total</th>\n",
       "      <th>dead_total</th>\n",
       "      <th>intact_total</th>\n",
       "      <th>??? ??????? ??? ??? ????? ???????</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Moharram</td>\n",
       "      <td>Ar Riyad</td>\n",
       "      <td>393.0</td>\n",
       "      <td>364.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>291.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>...</td>\n",
       "      <td>974.0</td>\n",
       "      <td>891.0</td>\n",
       "      <td>1208.0</td>\n",
       "      <td>1304.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>393.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>12435.0</td>\n",
       "      <td>1.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>Safar</td>\n",
       "      <td>Ar Riyad</td>\n",
       "      <td>433.0</td>\n",
       "      <td>387.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>276.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1940.0</td>\n",
       "      <td>898.0</td>\n",
       "      <td>739.0</td>\n",
       "      <td>997.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>433.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>9110.0</td>\n",
       "      <td>1.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>Rabeea Awal</td>\n",
       "      <td>Ar Riyad</td>\n",
       "      <td>455.0</td>\n",
       "      <td>361.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>284.0</td>\n",
       "      <td>171.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>124.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1418.0</td>\n",
       "      <td>1255.0</td>\n",
       "      <td>1380.0</td>\n",
       "      <td>2444.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>455.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>15385.0</td>\n",
       "      <td>1.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>Rabeea Thany</td>\n",
       "      <td>Ar Riyad</td>\n",
       "      <td>427.0</td>\n",
       "      <td>378.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>295.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1608.0</td>\n",
       "      <td>1512.0</td>\n",
       "      <td>1366.0</td>\n",
       "      <td>1992.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>427.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>15181.0</td>\n",
       "      <td>1.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>Jamad Awal</td>\n",
       "      <td>Ar Riyad</td>\n",
       "      <td>358.0</td>\n",
       "      <td>311.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>262.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1511.0</td>\n",
       "      <td>1318.0</td>\n",
       "      <td>1426.0</td>\n",
       "      <td>2228.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>358.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>15437.0</td>\n",
       "      <td>1.19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 88 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   month    month_name    region  sum_injuries  male_injuries  \\\n",
       "0    1.0      Moharram  Ar Riyad         393.0          364.0   \n",
       "1    2.0         Safar  Ar Riyad         433.0          387.0   \n",
       "2    3.0   Rabeea Awal  Ar Riyad         455.0          361.0   \n",
       "3    4.0  Rabeea Thany  Ar Riyad         427.0          378.0   \n",
       "4    5.0    Jamad Awal  Ar Riyad         358.0          311.0   \n",
       "\n",
       "   female_injuries  urban_injuries  suburban_injuries  injuries_0_18  \\\n",
       "0             29.0           291.0              102.0           66.0   \n",
       "1             46.0           276.0              157.0           44.0   \n",
       "2             94.0           284.0              171.0          115.0   \n",
       "3             49.0           295.0              132.0          132.0   \n",
       "4             47.0           262.0               96.0           61.0   \n",
       "\n",
       "   injuries_18_30  ...  cause_illegal_turn  cause_illegal_crossing  \\\n",
       "0            97.0  ...               974.0                   891.0   \n",
       "1           117.0  ...              1940.0                   898.0   \n",
       "2           124.0  ...              1418.0                  1255.0   \n",
       "3           119.0  ...              1608.0                  1512.0   \n",
       "4           111.0  ...              1511.0                  1318.0   \n",
       "\n",
       "   cause_traffic_light_break  cause_speeding  cause_drug_use  cause_other  \\\n",
       "0                     1208.0          1304.0             0.0          0.0   \n",
       "1                      739.0           997.0             0.0          0.0   \n",
       "2                     1380.0          2444.0             0.0          0.0   \n",
       "3                     1366.0          1992.0             0.0          0.0   \n",
       "4                     1426.0          2228.0             0.0          0.0   \n",
       "\n",
       "   injuries_total  dead_total  intact_total  ??? ??????? ??? ??? ????? ???????  \n",
       "0           393.0       100.0       12435.0                               1.33  \n",
       "1           433.0        98.0        9110.0                               1.26  \n",
       "2           455.0       105.0       15385.0                               1.21  \n",
       "3           427.0        91.0       15181.0                               1.15  \n",
       "4           358.0        96.0       15437.0                               1.19  \n",
       "\n",
       "[5 rows x 88 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('TASA.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "        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>Saturday</th>\n",
       "      <th>Sunday</th>\n",
       "      <th>Monday</th>\n",
       "      <th>Tuesday</th>\n",
       "      <th>Wednesday</th>\n",
       "      <th>Thursday</th>\n",
       "      <th>Friday</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>region</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Al-Baha</th>\n",
       "      <td>234.0</td>\n",
       "      <td>277.0</td>\n",
       "      <td>279.0</td>\n",
       "      <td>257.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>247.0</td>\n",
       "      <td>220.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Al-Jouf</th>\n",
       "      <td>547.0</td>\n",
       "      <td>545.0</td>\n",
       "      <td>540.0</td>\n",
       "      <td>534.0</td>\n",
       "      <td>507.0</td>\n",
       "      <td>497.0</td>\n",
       "      <td>482.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Al-Qurayyat</th>\n",
       "      <td>487.0</td>\n",
       "      <td>600.0</td>\n",
       "      <td>534.0</td>\n",
       "      <td>590.0</td>\n",
       "      <td>556.0</td>\n",
       "      <td>548.0</td>\n",
       "      <td>388.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ar Riyad</th>\n",
       "      <td>13117.0</td>\n",
       "      <td>12735.0</td>\n",
       "      <td>11448.0</td>\n",
       "      <td>10905.0</td>\n",
       "      <td>10474.0</td>\n",
       "      <td>10326.0</td>\n",
       "      <td>10879.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aseer</th>\n",
       "      <td>4984.0</td>\n",
       "      <td>3052.0</td>\n",
       "      <td>3263.0</td>\n",
       "      <td>2864.0</td>\n",
       "      <td>2951.0</td>\n",
       "      <td>4073.0</td>\n",
       "      <td>4777.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ash Sharqiyah</th>\n",
       "      <td>6693.0</td>\n",
       "      <td>5966.0</td>\n",
       "      <td>6297.0</td>\n",
       "      <td>6211.0</td>\n",
       "      <td>6079.0</td>\n",
       "      <td>6702.0</td>\n",
       "      <td>7129.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>At-Taif</th>\n",
       "      <td>2637.0</td>\n",
       "      <td>2561.0</td>\n",
       "      <td>2675.0</td>\n",
       "      <td>2621.0</td>\n",
       "      <td>2761.0</td>\n",
       "      <td>2563.0</td>\n",
       "      <td>2481.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hail</th>\n",
       "      <td>1332.0</td>\n",
       "      <td>1148.0</td>\n",
       "      <td>1084.0</td>\n",
       "      <td>978.0</td>\n",
       "      <td>848.0</td>\n",
       "      <td>806.0</td>\n",
       "      <td>759.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jazan</th>\n",
       "      <td>2395.0</td>\n",
       "      <td>3169.0</td>\n",
       "      <td>3544.0</td>\n",
       "      <td>2825.0</td>\n",
       "      <td>2752.0</td>\n",
       "      <td>2730.0</td>\n",
       "      <td>2157.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jiddah</th>\n",
       "      <td>5331.0</td>\n",
       "      <td>7359.0</td>\n",
       "      <td>6192.0</td>\n",
       "      <td>5681.0</td>\n",
       "      <td>5594.0</td>\n",
       "      <td>5886.0</td>\n",
       "      <td>4716.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Madinah</th>\n",
       "      <td>2587.0</td>\n",
       "      <td>2832.0</td>\n",
       "      <td>2543.0</td>\n",
       "      <td>2923.0</td>\n",
       "      <td>3205.0</td>\n",
       "      <td>3482.0</td>\n",
       "      <td>2975.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Makkah</th>\n",
       "      <td>6726.0</td>\n",
       "      <td>6478.0</td>\n",
       "      <td>6438.0</td>\n",
       "      <td>6058.0</td>\n",
       "      <td>6300.0</td>\n",
       "      <td>6903.0</td>\n",
       "      <td>6012.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Najran</th>\n",
       "      <td>462.0</td>\n",
       "      <td>541.0</td>\n",
       "      <td>413.0</td>\n",
       "      <td>475.0</td>\n",
       "      <td>427.0</td>\n",
       "      <td>341.0</td>\n",
       "      <td>465.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Northern Region</th>\n",
       "      <td>1486.0</td>\n",
       "      <td>1569.0</td>\n",
       "      <td>1530.0</td>\n",
       "      <td>1530.0</td>\n",
       "      <td>1537.0</td>\n",
       "      <td>1638.0</td>\n",
       "      <td>1415.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Qaseem</th>\n",
       "      <td>2619.0</td>\n",
       "      <td>2271.0</td>\n",
       "      <td>2268.0</td>\n",
       "      <td>2247.0</td>\n",
       "      <td>2262.0</td>\n",
       "      <td>2862.0</td>\n",
       "      <td>2981.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tabouk</th>\n",
       "      <td>934.0</td>\n",
       "      <td>982.0</td>\n",
       "      <td>1808.0</td>\n",
       "      <td>1584.0</td>\n",
       "      <td>1579.0</td>\n",
       "      <td>1613.0</td>\n",
       "      <td>1534.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Saturday   Sunday   Monday  Tuesday  Wednesday  Thursday  \\\n",
       "region                                                                      \n",
       "Al-Baha             234.0    277.0    279.0    257.0      250.0     247.0   \n",
       "Al-Jouf             547.0    545.0    540.0    534.0      507.0     497.0   \n",
       "Al-Qurayyat         487.0    600.0    534.0    590.0      556.0     548.0   \n",
       "Ar Riyad          13117.0  12735.0  11448.0  10905.0    10474.0   10326.0   \n",
       "Aseer              4984.0   3052.0   3263.0   2864.0     2951.0    4073.0   \n",
       "Ash Sharqiyah      6693.0   5966.0   6297.0   6211.0     6079.0    6702.0   \n",
       "At-Taif            2637.0   2561.0   2675.0   2621.0     2761.0    2563.0   \n",
       "Hail               1332.0   1148.0   1084.0    978.0      848.0     806.0   \n",
       "Jazan              2395.0   3169.0   3544.0   2825.0     2752.0    2730.0   \n",
       "Jiddah             5331.0   7359.0   6192.0   5681.0     5594.0    5886.0   \n",
       "Madinah            2587.0   2832.0   2543.0   2923.0     3205.0    3482.0   \n",
       "Makkah             6726.0   6478.0   6438.0   6058.0     6300.0    6903.0   \n",
       "Najran              462.0    541.0    413.0    475.0      427.0     341.0   \n",
       "Northern Region    1486.0   1569.0   1530.0   1530.0     1537.0    1638.0   \n",
       "Qaseem             2619.0   2271.0   2268.0   2247.0     2262.0    2862.0   \n",
       "Tabouk              934.0    982.0   1808.0   1584.0     1579.0    1613.0   \n",
       "\n",
       "                  Friday  \n",
       "region                    \n",
       "Al-Baha            220.0  \n",
       "Al-Jouf            482.0  \n",
       "Al-Qurayyat        388.0  \n",
       "Ar Riyad         10879.0  \n",
       "Aseer             4777.0  \n",
       "Ash Sharqiyah     7129.0  \n",
       "At-Taif           2481.0  \n",
       "Hail               759.0  \n",
       "Jazan             2157.0  \n",
       "Jiddah            4716.0  \n",
       "Madinah           2975.0  \n",
       "Makkah            6012.0  \n",
       "Najran             465.0  \n",
       "Northern Region   1415.0  \n",
       "Qaseem            2981.0  \n",
       "Tabouk            1534.0  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subset = df[['region','Saturday', 'Sunday', 'Monday', 'Tuesday',\n",
    "       'Wednesday', 'Thursday', 'Friday']].copy()\n",
    "week_d = subset.groupby('region').sum()\n",
    "week_d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>region</th>\n",
       "      <th>Saturday</th>\n",
       "      <th>Sunday</th>\n",
       "      <th>Monday</th>\n",
       "      <th>Tuesday</th>\n",
       "      <th>Wednesday</th>\n",
       "      <th>Thursday</th>\n",
       "      <th>Friday</th>\n",
       "      <th>work_days</th>\n",
       "      <th>weekends</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Al-Baha</td>\n",
       "      <td>234.0</td>\n",
       "      <td>277.0</td>\n",
       "      <td>279.0</td>\n",
       "      <td>257.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>247.0</td>\n",
       "      <td>220.0</td>\n",
       "      <td>1310.0</td>\n",
       "      <td>454.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Al-Jouf</td>\n",
       "      <td>547.0</td>\n",
       "      <td>545.0</td>\n",
       "      <td>540.0</td>\n",
       "      <td>534.0</td>\n",
       "      <td>507.0</td>\n",
       "      <td>497.0</td>\n",
       "      <td>482.0</td>\n",
       "      <td>2623.0</td>\n",
       "      <td>1029.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Al-Qurayyat</td>\n",
       "      <td>487.0</td>\n",
       "      <td>600.0</td>\n",
       "      <td>534.0</td>\n",
       "      <td>590.0</td>\n",
       "      <td>556.0</td>\n",
       "      <td>548.0</td>\n",
       "      <td>388.0</td>\n",
       "      <td>2828.0</td>\n",
       "      <td>875.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ar Riyad</td>\n",
       "      <td>13117.0</td>\n",
       "      <td>12735.0</td>\n",
       "      <td>11448.0</td>\n",
       "      <td>10905.0</td>\n",
       "      <td>10474.0</td>\n",
       "      <td>10326.0</td>\n",
       "      <td>10879.0</td>\n",
       "      <td>55888.0</td>\n",
       "      <td>23996.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Aseer</td>\n",
       "      <td>4984.0</td>\n",
       "      <td>3052.0</td>\n",
       "      <td>3263.0</td>\n",
       "      <td>2864.0</td>\n",
       "      <td>2951.0</td>\n",
       "      <td>4073.0</td>\n",
       "      <td>4777.0</td>\n",
       "      <td>16203.0</td>\n",
       "      <td>9761.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        region  Saturday   Sunday   Monday  Tuesday  Wednesday  Thursday  \\\n",
       "0      Al-Baha     234.0    277.0    279.0    257.0      250.0     247.0   \n",
       "1      Al-Jouf     547.0    545.0    540.0    534.0      507.0     497.0   \n",
       "2  Al-Qurayyat     487.0    600.0    534.0    590.0      556.0     548.0   \n",
       "3     Ar Riyad   13117.0  12735.0  11448.0  10905.0    10474.0   10326.0   \n",
       "4        Aseer    4984.0   3052.0   3263.0   2864.0     2951.0    4073.0   \n",
       "\n",
       "    Friday  work_days  weekends  \n",
       "0    220.0     1310.0     454.0  \n",
       "1    482.0     2623.0    1029.0  \n",
       "2    388.0     2828.0     875.0  \n",
       "3  10879.0    55888.0   23996.0  \n",
       "4   4777.0    16203.0    9761.0  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "week_d['work_days'] = week_d.Sunday + week_d.Thursday + week_d.Monday+ week_d.Tuesday+ week_d.Wednesday\n",
    "week_d['weekends'] = week_d.Friday + week_d.Saturday\n",
    "week_d.reset_index(inplace=True)\n",
    "week_d.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### retrieving the population data from the another dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NAME</th>\n",
       "      <th>TOTPOP_CY</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>Total injuries</th>\n",
       "      <th>Total mortalities</th>\n",
       "      <th>Number of traffic accidents</th>\n",
       "      <th>Number of people involved in traffic accidents</th>\n",
       "      <th>Number of Accidents including damages</th>\n",
       "      <th>Number of Accidents including injuries</th>\n",
       "      <th>Number of Accidents including mortalities</th>\n",
       "      <th>Percentage of Accidents including damages</th>\n",
       "      <th>Percentage of Accidents including injuries</th>\n",
       "      <th>Percentage of Accidents including mortalities</th>\n",
       "      <th>Accidents per 1000 population</th>\n",
       "      <th>Involvment in traffic accidents per 1000 population</th>\n",
       "      <th>Injuries 1000 population</th>\n",
       "      <th>Mortalities per 1000 population</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Makkah</td>\n",
       "      <td>3261577</td>\n",
       "      <td>-398222.616706</td>\n",
       "      <td>2.366203e+06</td>\n",
       "      <td>3400.0</td>\n",
       "      <td>609.0</td>\n",
       "      <td>44915.0</td>\n",
       "      <td>88472.0</td>\n",
       "      <td>43146.0</td>\n",
       "      <td>1311.0</td>\n",
       "      <td>458.0</td>\n",
       "      <td>96.061449</td>\n",
       "      <td>2.918847</td>\n",
       "      <td>1.019704</td>\n",
       "      <td>13.770946</td>\n",
       "      <td>27.125529</td>\n",
       "      <td>1.042441</td>\n",
       "      <td>0.186719</td>\n",
       "      <td>MULTIPOLYGON Z (((-456710.530 2650896.255 0.00...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Aseer</td>\n",
       "      <td>2264096</td>\n",
       "      <td>-352045.193083</td>\n",
       "      <td>2.127645e+06</td>\n",
       "      <td>2192.0</td>\n",
       "      <td>696.0</td>\n",
       "      <td>25964.0</td>\n",
       "      <td>50085.0</td>\n",
       "      <td>24608.0</td>\n",
       "      <td>771.0</td>\n",
       "      <td>585.0</td>\n",
       "      <td>94.777384</td>\n",
       "      <td>2.969496</td>\n",
       "      <td>2.253120</td>\n",
       "      <td>11.467712</td>\n",
       "      <td>22.121412</td>\n",
       "      <td>0.968157</td>\n",
       "      <td>0.307407</td>\n",
       "      <td>MULTIPOLYGON Z (((-308858.199 2319738.780 0.00...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Tabouk</td>\n",
       "      <td>962936</td>\n",
       "      <td>-698604.846811</td>\n",
       "      <td>3.091249e+06</td>\n",
       "      <td>977.0</td>\n",
       "      <td>293.0</td>\n",
       "      <td>10034.0</td>\n",
       "      <td>9905.0</td>\n",
       "      <td>9247.0</td>\n",
       "      <td>573.0</td>\n",
       "      <td>214.0</td>\n",
       "      <td>92.156667</td>\n",
       "      <td>5.710584</td>\n",
       "      <td>2.132749</td>\n",
       "      <td>10.420215</td>\n",
       "      <td>10.286250</td>\n",
       "      <td>1.014605</td>\n",
       "      <td>0.304278</td>\n",
       "      <td>MULTIPOLYGON Z (((-789203.443 3344744.803 0.00...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Jazan</td>\n",
       "      <td>1643752</td>\n",
       "      <td>-377562.520722</td>\n",
       "      <td>1.923485e+06</td>\n",
       "      <td>2112.0</td>\n",
       "      <td>563.0</td>\n",
       "      <td>19572.0</td>\n",
       "      <td>33268.0</td>\n",
       "      <td>18046.0</td>\n",
       "      <td>1098.0</td>\n",
       "      <td>428.0</td>\n",
       "      <td>92.203147</td>\n",
       "      <td>5.610055</td>\n",
       "      <td>2.186797</td>\n",
       "      <td>11.906906</td>\n",
       "      <td>20.239063</td>\n",
       "      <td>1.284865</td>\n",
       "      <td>0.342509</td>\n",
       "      <td>MULTIPOLYGON Z (((-419719.167 1993300.107 0.00...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Al-Baha</td>\n",
       "      <td>500452</td>\n",
       "      <td>-509430.926111</td>\n",
       "      <td>2.256117e+06</td>\n",
       "      <td>935.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>1764.0</td>\n",
       "      <td>3918.0</td>\n",
       "      <td>1193.0</td>\n",
       "      <td>490.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>67.630385</td>\n",
       "      <td>27.777778</td>\n",
       "      <td>4.591837</td>\n",
       "      <td>3.524814</td>\n",
       "      <td>7.828923</td>\n",
       "      <td>1.868311</td>\n",
       "      <td>0.221799</td>\n",
       "      <td>POLYGON Z ((-497258.929 2331363.256 0.000, -49...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      NAME  TOTPOP_CY              x             y  Total injuries  \\\n",
       "0   Makkah    3261577 -398222.616706  2.366203e+06          3400.0   \n",
       "1    Aseer    2264096 -352045.193083  2.127645e+06          2192.0   \n",
       "2   Tabouk     962936 -698604.846811  3.091249e+06           977.0   \n",
       "3    Jazan    1643752 -377562.520722  1.923485e+06          2112.0   \n",
       "4  Al-Baha     500452 -509430.926111  2.256117e+06           935.0   \n",
       "\n",
       "   Total mortalities  Number of traffic accidents  \\\n",
       "0              609.0                      44915.0   \n",
       "1              696.0                      25964.0   \n",
       "2              293.0                      10034.0   \n",
       "3              563.0                      19572.0   \n",
       "4              111.0                       1764.0   \n",
       "\n",
       "   Number of people involved in traffic accidents  \\\n",
       "0                                         88472.0   \n",
       "1                                         50085.0   \n",
       "2                                          9905.0   \n",
       "3                                         33268.0   \n",
       "4                                          3918.0   \n",
       "\n",
       "   Number of Accidents including damages  \\\n",
       "0                                43146.0   \n",
       "1                                24608.0   \n",
       "2                                 9247.0   \n",
       "3                                18046.0   \n",
       "4                                 1193.0   \n",
       "\n",
       "   Number of Accidents including injuries  \\\n",
       "0                                  1311.0   \n",
       "1                                   771.0   \n",
       "2                                   573.0   \n",
       "3                                  1098.0   \n",
       "4                                   490.0   \n",
       "\n",
       "   Number of Accidents including mortalities  \\\n",
       "0                                      458.0   \n",
       "1                                      585.0   \n",
       "2                                      214.0   \n",
       "3                                      428.0   \n",
       "4                                       81.0   \n",
       "\n",
       "   Percentage of Accidents including damages  \\\n",
       "0                                  96.061449   \n",
       "1                                  94.777384   \n",
       "2                                  92.156667   \n",
       "3                                  92.203147   \n",
       "4                                  67.630385   \n",
       "\n",
       "   Percentage of Accidents including injuries  \\\n",
       "0                                    2.918847   \n",
       "1                                    2.969496   \n",
       "2                                    5.710584   \n",
       "3                                    5.610055   \n",
       "4                                   27.777778   \n",
       "\n",
       "   Percentage of Accidents including mortalities  \\\n",
       "0                                       1.019704   \n",
       "1                                       2.253120   \n",
       "2                                       2.132749   \n",
       "3                                       2.186797   \n",
       "4                                       4.591837   \n",
       "\n",
       "   Accidents per 1000 population  \\\n",
       "0                      13.770946   \n",
       "1                      11.467712   \n",
       "2                      10.420215   \n",
       "3                      11.906906   \n",
       "4                       3.524814   \n",
       "\n",
       "   Involvment in traffic accidents per 1000 population  \\\n",
       "0                                          27.125529     \n",
       "1                                          22.121412     \n",
       "2                                          10.286250     \n",
       "3                                          20.239063     \n",
       "4                                           7.828923     \n",
       "\n",
       "   Injuries 1000 population  Mortalities per 1000 population  \\\n",
       "0                  1.042441                         0.186719   \n",
       "1                  0.968157                         0.307407   \n",
       "2                  1.014605                         0.304278   \n",
       "3                  1.284865                         0.342509   \n",
       "4                  1.868311                         0.221799   \n",
       "\n",
       "                                            geometry  \n",
       "0  MULTIPOLYGON Z (((-456710.530 2650896.255 0.00...  \n",
       "1  MULTIPOLYGON Z (((-308858.199 2319738.780 0.00...  \n",
       "2  MULTIPOLYGON Z (((-789203.443 3344744.803 0.00...  \n",
       "3  MULTIPOLYGON Z (((-419719.167 1993300.107 0.00...  \n",
       "4  POLYGON Z ((-497258.929 2331363.256 0.000, -49...  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "overview = gpd.read_file('./overview.geojson')\n",
    "overview.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>region</th>\n",
       "      <th>Saturday</th>\n",
       "      <th>Sunday</th>\n",
       "      <th>Monday</th>\n",
       "      <th>Tuesday</th>\n",
       "      <th>Wednesday</th>\n",
       "      <th>Thursday</th>\n",
       "      <th>Friday</th>\n",
       "      <th>work_days</th>\n",
       "      <th>weekends</th>\n",
       "      <th>TOTPOP_CY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Al-Baha</td>\n",
       "      <td>234.0</td>\n",
       "      <td>277.0</td>\n",
       "      <td>279.0</td>\n",
       "      <td>257.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>247.0</td>\n",
       "      <td>220.0</td>\n",
       "      <td>1310.0</td>\n",
       "      <td>454.0</td>\n",
       "      <td>500452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Al-Jouf</td>\n",
       "      <td>547.0</td>\n",
       "      <td>545.0</td>\n",
       "      <td>540.0</td>\n",
       "      <td>534.0</td>\n",
       "      <td>507.0</td>\n",
       "      <td>497.0</td>\n",
       "      <td>482.0</td>\n",
       "      <td>2623.0</td>\n",
       "      <td>1029.0</td>\n",
       "      <td>357976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Al-Qurayyat</td>\n",
       "      <td>487.0</td>\n",
       "      <td>600.0</td>\n",
       "      <td>534.0</td>\n",
       "      <td>590.0</td>\n",
       "      <td>556.0</td>\n",
       "      <td>548.0</td>\n",
       "      <td>388.0</td>\n",
       "      <td>2828.0</td>\n",
       "      <td>875.0</td>\n",
       "      <td>179831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ar Riyad</td>\n",
       "      <td>13117.0</td>\n",
       "      <td>12735.0</td>\n",
       "      <td>11448.0</td>\n",
       "      <td>10905.0</td>\n",
       "      <td>10474.0</td>\n",
       "      <td>10326.0</td>\n",
       "      <td>10879.0</td>\n",
       "      <td>55888.0</td>\n",
       "      <td>23996.0</td>\n",
       "      <td>8431083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Aseer</td>\n",
       "      <td>4984.0</td>\n",
       "      <td>3052.0</td>\n",
       "      <td>3263.0</td>\n",
       "      <td>2864.0</td>\n",
       "      <td>2951.0</td>\n",
       "      <td>4073.0</td>\n",
       "      <td>4777.0</td>\n",
       "      <td>16203.0</td>\n",
       "      <td>9761.0</td>\n",
       "      <td>2264096</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        region  Saturday   Sunday   Monday  Tuesday  Wednesday  Thursday  \\\n",
       "0      Al-Baha     234.0    277.0    279.0    257.0      250.0     247.0   \n",
       "1      Al-Jouf     547.0    545.0    540.0    534.0      507.0     497.0   \n",
       "2  Al-Qurayyat     487.0    600.0    534.0    590.0      556.0     548.0   \n",
       "3     Ar Riyad   13117.0  12735.0  11448.0  10905.0    10474.0   10326.0   \n",
       "4        Aseer    4984.0   3052.0   3263.0   2864.0     2951.0    4073.0   \n",
       "\n",
       "    Friday  work_days  weekends  TOTPOP_CY  \n",
       "0    220.0     1310.0     454.0     500452  \n",
       "1    482.0     2623.0    1029.0     357976  \n",
       "2    388.0     2828.0     875.0     179831  \n",
       "3  10879.0    55888.0   23996.0    8431083  \n",
       "4   4777.0    16203.0    9761.0    2264096  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "week_d = pd.merge(week_d, overview[['NAME','TOTPOP_CY']], left_on='region', right_on='NAME').drop(columns = 'NAME')\n",
    "week_d.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>region</th>\n",
       "      <th>Saturday</th>\n",
       "      <th>Sunday</th>\n",
       "      <th>Monday</th>\n",
       "      <th>Tuesday</th>\n",
       "      <th>Wednesday</th>\n",
       "      <th>Thursday</th>\n",
       "      <th>Friday</th>\n",
       "      <th>work_days</th>\n",
       "      <th>weekends</th>\n",
       "      <th>TOTPOP_CY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Al-Baha</td>\n",
       "      <td>234.0</td>\n",
       "      <td>277.0</td>\n",
       "      <td>279.0</td>\n",
       "      <td>257.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>247.0</td>\n",
       "      <td>220.0</td>\n",
       "      <td>262.0</td>\n",
       "      <td>227.0</td>\n",
       "      <td>500452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Al-Jouf</td>\n",
       "      <td>547.0</td>\n",
       "      <td>545.0</td>\n",
       "      <td>540.0</td>\n",
       "      <td>534.0</td>\n",
       "      <td>507.0</td>\n",
       "      <td>497.0</td>\n",
       "      <td>482.0</td>\n",
       "      <td>524.6</td>\n",
       "      <td>514.5</td>\n",
       "      <td>357976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Al-Qurayyat</td>\n",
       "      <td>487.0</td>\n",
       "      <td>600.0</td>\n",
       "      <td>534.0</td>\n",
       "      <td>590.0</td>\n",
       "      <td>556.0</td>\n",
       "      <td>548.0</td>\n",
       "      <td>388.0</td>\n",
       "      <td>565.6</td>\n",
       "      <td>437.5</td>\n",
       "      <td>179831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ar Riyad</td>\n",
       "      <td>13117.0</td>\n",
       "      <td>12735.0</td>\n",
       "      <td>11448.0</td>\n",
       "      <td>10905.0</td>\n",
       "      <td>10474.0</td>\n",
       "      <td>10326.0</td>\n",
       "      <td>10879.0</td>\n",
       "      <td>11177.6</td>\n",
       "      <td>11998.0</td>\n",
       "      <td>8431083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Aseer</td>\n",
       "      <td>4984.0</td>\n",
       "      <td>3052.0</td>\n",
       "      <td>3263.0</td>\n",
       "      <td>2864.0</td>\n",
       "      <td>2951.0</td>\n",
       "      <td>4073.0</td>\n",
       "      <td>4777.0</td>\n",
       "      <td>3240.6</td>\n",
       "      <td>4880.5</td>\n",
       "      <td>2264096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Ash Sharqiyah</td>\n",
       "      <td>6693.0</td>\n",
       "      <td>5966.0</td>\n",
       "      <td>6297.0</td>\n",
       "      <td>6211.0</td>\n",
       "      <td>6079.0</td>\n",
       "      <td>6702.0</td>\n",
       "      <td>7129.0</td>\n",
       "      <td>6251.0</td>\n",
       "      <td>6911.0</td>\n",
       "      <td>4494027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>At-Taif</td>\n",
       "      <td>2637.0</td>\n",
       "      <td>2561.0</td>\n",
       "      <td>2675.0</td>\n",
       "      <td>2621.0</td>\n",
       "      <td>2761.0</td>\n",
       "      <td>2563.0</td>\n",
       "      <td>2481.0</td>\n",
       "      <td>2636.2</td>\n",
       "      <td>2559.0</td>\n",
       "      <td>1079365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Hail</td>\n",
       "      <td>1332.0</td>\n",
       "      <td>1148.0</td>\n",
       "      <td>1084.0</td>\n",
       "      <td>978.0</td>\n",
       "      <td>848.0</td>\n",
       "      <td>806.0</td>\n",
       "      <td>759.0</td>\n",
       "      <td>972.8</td>\n",
       "      <td>1045.5</td>\n",
       "      <td>728034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Jazan</td>\n",
       "      <td>2395.0</td>\n",
       "      <td>3169.0</td>\n",
       "      <td>3544.0</td>\n",
       "      <td>2825.0</td>\n",
       "      <td>2752.0</td>\n",
       "      <td>2730.0</td>\n",
       "      <td>2157.0</td>\n",
       "      <td>3004.0</td>\n",
       "      <td>2276.0</td>\n",
       "      <td>1643752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Jiddah</td>\n",
       "      <td>5331.0</td>\n",
       "      <td>7359.0</td>\n",
       "      <td>6192.0</td>\n",
       "      <td>5681.0</td>\n",
       "      <td>5594.0</td>\n",
       "      <td>5886.0</td>\n",
       "      <td>4716.0</td>\n",
       "      <td>6142.4</td>\n",
       "      <td>5023.5</td>\n",
       "      <td>4362920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Madinah</td>\n",
       "      <td>2587.0</td>\n",
       "      <td>2832.0</td>\n",
       "      <td>2543.0</td>\n",
       "      <td>2923.0</td>\n",
       "      <td>3205.0</td>\n",
       "      <td>3482.0</td>\n",
       "      <td>2975.0</td>\n",
       "      <td>2997.0</td>\n",
       "      <td>2781.0</td>\n",
       "      <td>2193344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Makkah</td>\n",
       "      <td>6726.0</td>\n",
       "      <td>6478.0</td>\n",
       "      <td>6438.0</td>\n",
       "      <td>6058.0</td>\n",
       "      <td>6300.0</td>\n",
       "      <td>6903.0</td>\n",
       "      <td>6012.0</td>\n",
       "      <td>6435.4</td>\n",
       "      <td>6369.0</td>\n",
       "      <td>3261577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Najran</td>\n",
       "      <td>462.0</td>\n",
       "      <td>541.0</td>\n",
       "      <td>413.0</td>\n",
       "      <td>475.0</td>\n",
       "      <td>427.0</td>\n",
       "      <td>341.0</td>\n",
       "      <td>465.0</td>\n",
       "      <td>439.4</td>\n",
       "      <td>463.5</td>\n",
       "      <td>617873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Northern Region</td>\n",
       "      <td>1486.0</td>\n",
       "      <td>1569.0</td>\n",
       "      <td>1530.0</td>\n",
       "      <td>1530.0</td>\n",
       "      <td>1537.0</td>\n",
       "      <td>1638.0</td>\n",
       "      <td>1415.0</td>\n",
       "      <td>1560.8</td>\n",
       "      <td>1450.5</td>\n",
       "      <td>389870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Qaseem</td>\n",
       "      <td>2619.0</td>\n",
       "      <td>2271.0</td>\n",
       "      <td>2268.0</td>\n",
       "      <td>2247.0</td>\n",
       "      <td>2262.0</td>\n",
       "      <td>2862.0</td>\n",
       "      <td>2981.0</td>\n",
       "      <td>2382.0</td>\n",
       "      <td>2800.0</td>\n",
       "      <td>1491045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Tabouk</td>\n",
       "      <td>934.0</td>\n",
       "      <td>982.0</td>\n",
       "      <td>1808.0</td>\n",
       "      <td>1584.0</td>\n",
       "      <td>1579.0</td>\n",
       "      <td>1613.0</td>\n",
       "      <td>1534.0</td>\n",
       "      <td>1513.2</td>\n",
       "      <td>1234.0</td>\n",
       "      <td>962936</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             region  Saturday   Sunday   Monday  Tuesday  Wednesday  Thursday  \\\n",
       "0           Al-Baha     234.0    277.0    279.0    257.0      250.0     247.0   \n",
       "1           Al-Jouf     547.0    545.0    540.0    534.0      507.0     497.0   \n",
       "2       Al-Qurayyat     487.0    600.0    534.0    590.0      556.0     548.0   \n",
       "3          Ar Riyad   13117.0  12735.0  11448.0  10905.0    10474.0   10326.0   \n",
       "4             Aseer    4984.0   3052.0   3263.0   2864.0     2951.0    4073.0   \n",
       "5     Ash Sharqiyah    6693.0   5966.0   6297.0   6211.0     6079.0    6702.0   \n",
       "6           At-Taif    2637.0   2561.0   2675.0   2621.0     2761.0    2563.0   \n",
       "7              Hail    1332.0   1148.0   1084.0    978.0      848.0     806.0   \n",
       "8             Jazan    2395.0   3169.0   3544.0   2825.0     2752.0    2730.0   \n",
       "9            Jiddah    5331.0   7359.0   6192.0   5681.0     5594.0    5886.0   \n",
       "10          Madinah    2587.0   2832.0   2543.0   2923.0     3205.0    3482.0   \n",
       "11           Makkah    6726.0   6478.0   6438.0   6058.0     6300.0    6903.0   \n",
       "12           Najran     462.0    541.0    413.0    475.0      427.0     341.0   \n",
       "13  Northern Region    1486.0   1569.0   1530.0   1530.0     1537.0    1638.0   \n",
       "14           Qaseem    2619.0   2271.0   2268.0   2247.0     2262.0    2862.0   \n",
       "15           Tabouk     934.0    982.0   1808.0   1584.0     1579.0    1613.0   \n",
       "\n",
       "     Friday  work_days  weekends  TOTPOP_CY  \n",
       "0     220.0      262.0     227.0     500452  \n",
       "1     482.0      524.6     514.5     357976  \n",
       "2     388.0      565.6     437.5     179831  \n",
       "3   10879.0    11177.6   11998.0    8431083  \n",
       "4    4777.0     3240.6    4880.5    2264096  \n",
       "5    7129.0     6251.0    6911.0    4494027  \n",
       "6    2481.0     2636.2    2559.0    1079365  \n",
       "7     759.0      972.8    1045.5     728034  \n",
       "8    2157.0     3004.0    2276.0    1643752  \n",
       "9    4716.0     6142.4    5023.5    4362920  \n",
       "10   2975.0     2997.0    2781.0    2193344  \n",
       "11   6012.0     6435.4    6369.0    3261577  \n",
       "12    465.0      439.4     463.5     617873  \n",
       "13   1415.0     1560.8    1450.5     389870  \n",
       "14   2981.0     2382.0    2800.0    1491045  \n",
       "15   1534.0     1513.2    1234.0     962936  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "week_d['work_days'] = week_d['work_days']/5\n",
    "week_d['weekends'] = week_d['weekends']/2\n",
    "week_d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>region</th>\n",
       "      <th>Saturday</th>\n",
       "      <th>Sunday</th>\n",
       "      <th>Monday</th>\n",
       "      <th>Tuesday</th>\n",
       "      <th>Wednesday</th>\n",
       "      <th>Thursday</th>\n",
       "      <th>Friday</th>\n",
       "      <th>work_days</th>\n",
       "      <th>weekends</th>\n",
       "      <th>TOTPOP_CY</th>\n",
       "      <th>work_days_100000</th>\n",
       "      <th>weekends_100000</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Al-Baha</td>\n",
       "      <td>234.0</td>\n",
       "      <td>277.0</td>\n",
       "      <td>279.0</td>\n",
       "      <td>257.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>247.0</td>\n",
       "      <td>220.0</td>\n",
       "      <td>5.038462</td>\n",
       "      <td>4.365385</td>\n",
       "      <td>500452</td>\n",
       "      <td>1.006782</td>\n",
       "      <td>0.872288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Al-Jouf</td>\n",
       "      <td>547.0</td>\n",
       "      <td>545.0</td>\n",
       "      <td>540.0</td>\n",
       "      <td>534.0</td>\n",
       "      <td>507.0</td>\n",
       "      <td>497.0</td>\n",
       "      <td>482.0</td>\n",
       "      <td>10.088462</td>\n",
       "      <td>9.894231</td>\n",
       "      <td>357976</td>\n",
       "      <td>2.818195</td>\n",
       "      <td>2.763937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Al-Qurayyat</td>\n",
       "      <td>487.0</td>\n",
       "      <td>600.0</td>\n",
       "      <td>534.0</td>\n",
       "      <td>590.0</td>\n",
       "      <td>556.0</td>\n",
       "      <td>548.0</td>\n",
       "      <td>388.0</td>\n",
       "      <td>10.876923</td>\n",
       "      <td>8.413462</td>\n",
       "      <td>179831</td>\n",
       "      <td>6.048414</td>\n",
       "      <td>4.678538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ar Riyad</td>\n",
       "      <td>13117.0</td>\n",
       "      <td>12735.0</td>\n",
       "      <td>11448.0</td>\n",
       "      <td>10905.0</td>\n",
       "      <td>10474.0</td>\n",
       "      <td>10326.0</td>\n",
       "      <td>10879.0</td>\n",
       "      <td>214.953846</td>\n",
       "      <td>230.730769</td>\n",
       "      <td>8431083</td>\n",
       "      <td>2.549540</td>\n",
       "      <td>2.736668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Aseer</td>\n",
       "      <td>4984.0</td>\n",
       "      <td>3052.0</td>\n",
       "      <td>3263.0</td>\n",
       "      <td>2864.0</td>\n",
       "      <td>2951.0</td>\n",
       "      <td>4073.0</td>\n",
       "      <td>4777.0</td>\n",
       "      <td>62.319231</td>\n",
       "      <td>93.855769</td>\n",
       "      <td>2264096</td>\n",
       "      <td>2.752499</td>\n",
       "      <td>4.145397</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        region  Saturday   Sunday   Monday  Tuesday  Wednesday  Thursday  \\\n",
       "0      Al-Baha     234.0    277.0    279.0    257.0      250.0     247.0   \n",
       "1      Al-Jouf     547.0    545.0    540.0    534.0      507.0     497.0   \n",
       "2  Al-Qurayyat     487.0    600.0    534.0    590.0      556.0     548.0   \n",
       "3     Ar Riyad   13117.0  12735.0  11448.0  10905.0    10474.0   10326.0   \n",
       "4        Aseer    4984.0   3052.0   3263.0   2864.0     2951.0    4073.0   \n",
       "\n",
       "    Friday   work_days    weekends  TOTPOP_CY  work_days_100000  \\\n",
       "0    220.0    5.038462    4.365385     500452          1.006782   \n",
       "1    482.0   10.088462    9.894231     357976          2.818195   \n",
       "2    388.0   10.876923    8.413462     179831          6.048414   \n",
       "3  10879.0  214.953846  230.730769    8431083          2.549540   \n",
       "4   4777.0   62.319231   93.855769    2264096          2.752499   \n",
       "\n",
       "   weekends_100000  \n",
       "0         0.872288  \n",
       "1         2.763937  \n",
       "2         4.678538  \n",
       "3         2.736668  \n",
       "4         4.145397  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "week_d['work_days'] = week_d['work_days']/52\n",
    "week_d['weekends'] = week_d['weekends']/52\n",
    "week_d['work_days_100000'] = (week_d['work_days']/week_d['TOTPOP_CY'])*100000\n",
    "week_d['weekends_100000'] = (week_d['weekends']/week_d['TOTPOP_CY'])*100000\n",
    "week_d.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### visualizing the difference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "week_d.sort_values(by=\"work_days\", inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\aaaa\\anaconda3\\envs\\gis\\lib\\site-packages\\ipykernel_launcher.py:14: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  \n"
     ]
    },
    {
     "data": {
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xGHtj1sLm/Pnz6tGjh+rUqaO6desqLi7O0SHlq71791rHZw899JBKliyp9957z9Fh5atZs2apfv36atCggfr27atr167dfgUDyGepqalG9erVjb/++stITk42GjZsaOzatcvRYRUaVapUMc6cOWNT9sorrxhTp041DMMwpk6daowZM8YRod3Xvv32W+OHH34w6tevby3L6rjv2rXLaNiwoXHt2jXjwIEDRvXq1Y3U1FSHxH2/sXceJk6caLz99tuZ6nIe7p0TJ04YP/zwg2EYhnHx4kWjZs2axq5du3hPFHL23p+FTVbvjcIkPT3duHTpkmEYhnH9+nWjSZMmRlxcnIOjyn8zZ840+vbta3Ts2NHRoTiMvTFrYTNw4EBjwYIFhmEYRnJyspGYmOjYgBwoNTXV8Pb2Ng4dOuToUPLNsWPHjKpVqxpJSUmGYRhGz549jcWLF992Ha7gQb7bsWOHatSooerVq6to0aLq06ePPvvsM0eHVah99tlnGjRokCRp0KBBWrdunWMDug89+eSTKlOmjE1ZVsf9s88+U58+feTq6qpq1aqpRo0a2rFjR36HfF+ydx6ywnm4d3x9ffXII49IkkqUKKG6devq+PHjvCcKuZy8P+9XWb03ChOLxSIPDw9JUkpKilJSUmSxWBwcVf46duyYYmJiFBwc7OhQ4EAXL17Uli1b9Pzzz0uSihYtqtKlSzs2KAf673//qwcffFBVqlRxdCj5KjU1VVevXlVqaqqSkpJUoUKF29YnwYN8d/z4cT3wwAPW15UqVSp0gxdHslgsatu2rR599FFFRERIkhISEuTr6yvpxuDy9OnTjgyx0MjquPMeyX/z5s1Tw4YN9dxzz1mnBXEe8sehQ4f0008/qWnTprwngJvc/N4obNLS0vTQQw/Jy8tLgYGBhe4YjBw5UjNmzJCTU+H+qmZvzFqYHDhwQOXLl9fgwYP18MMPKzg4WFeuXHF0WA4TFRWlvn37OjqMfFWxYkW9/PLLqly5snx9fVWqVCm1bdv2tusU7k8NOIRh58Fthe0/M460bds2/fjjj/riiy/0/vvva8uWLY4OCbfgPZK/hg4dqr/++ks///yzfH19NXr0aEmch/xw+fJlPf3003rvvfdUsmTJLOtxLlDYZPe9cb8qUqSIfv75Zx07dkw7duzQ77//7uiQ8s2GDRvk5eWlRx991NGhOFxhH7Ompqbqxx9/1NChQ/XTTz+pePHihfbepdevX9f69evVs2dPR4eSrxITE/XZZ5/p4MGDOnHihK5cuaLly5ffdh0SPMh3lSpV0tGjR62vjx07dsdLzZB3Mo61l5eXunXrph07dsjb21snT56UJJ08eVJeXl6ODLHQyOq48x7JX97e3ipSpIicnJz0wgsvWKf+cB7urZSUFD399NPq37+/unfvLon3BCDZf28UVqVLl1aLFi20ceNGR4eSb7Zt26b169eratWq6tOnj77++msNGDDA0WE5hL0xa2FSqVIlVapUyXoFW48ePfTjjz86OCrH+OKLL/TII4/I29vb0aHkq02bNqlatWoqX768XFxc1L17d33//fe3XYcED/Jd48aNtX//fh08eFDXr19XVFSUOnfu7OiwCoUrV67o0qVL1t+/+uorNWjQQJ07d9aSJUskSUuWLFGXLl0cGWahkdVx79y5s6KiopScnKyDBw9q//79atKkiSNDva9lJBQkae3atdYn+HAe7h3DMPT888+rbt26GjVqlLWc9wQKu6zeG4XJmTNndP78eUnS1atXtWnTJtWpU8exQeWjqVOn6tixYzp06JCioqLUqlWrO/7H/n6U1Zi1MPHx8dEDDzygvXv3SrpxD5p69eo5OCrHWLlyZaGbniVJlStX1vbt25WUlCTDMPTf//5XdevWve06zvkUG2Dl7OysefPmqV27dkpLS9Nzzz2n+vXrOzqsQiEhIUHdunWTdOOyz379+ql9+/Zq3LixevXqpY8//liVK1fWp59+6uBI7z99+/bV5s2b9ffff6tSpUoKDw/Xa6+9Zve4169fX7169VK9evXk7Oys999/X0WKFHHwHtwf7J2HzZs36+eff5bFYlHVqlU1f/58SZyHe2nbtm1atmyZ9fG3kvTWW2/xnijk7L0/M24uWlhk9d7o0KGDYwPLRydPntSgQYOUlpam9PR09erVq1A/KrywymrMWtjMnTtX/fv31/Xr11W9enUtXrzY0SHlu6SkJMXGxlrHZ4VJ06ZN1aNHDz3yyCNydnbWww8/rJCQkNuuYzHsTWwHAAAAAACAaTBFCwAAAAAAwORI8AAAAAAAAJgcCR4AAAAAAACTI8EDAAAAAABgciR4AAAAAAAATI4ED4D7RmRkpCwWi0qXLq3ExESbZampqbJYLAoLC8v3uMLCwmSxWJSamprv286J9PR0jRw5Ur6+vnJyclLXrl0dHZIk6dlnn1XVqlXvWC/j/B86dOiexfLee+9pzZo196x9AAUHfcrdoU+5s/utT3n33XcVFBQkX1/fO74/FixYoDp16sjV1VW1a9fWRx99ZLfeunXr9PDDD8vNzU1VqlTRlClTlJaWlq14du3apbZt28rDw0Nly5bV4MGDde7cuUz1jh49qh49eqhUqVIqWbKkunfvriNHjmSql5iYqODgYJUrV07FixdXmzZt9Ntvv2UrloIuu++LW/38888KCwuze1wd9RlZ2JHgAXDfuXDhgqZPn+7oMExn9erVmj17tl555RVt27ZNM2bMcHRIkqTx48dr7dq1jg5D0v03GAdwZ/QpuUOfcmf3W5+yYMECnT59+o7JvAULFmjIkCF6+umntXHjRvXs2VPDhg3Thx9+aFPvyy+/1NNPP63GjRvriy++0IgRIzRlyhS98cYbd4zlxIkTatGiha5evarVq1fr/fff16ZNm9SpUyelp6db6yUlJalVq1bas2ePlixZomXLlmn//v1q2bKlrly5Yq1nGIY6d+6sjRs3au7cufrPf/6jlJQUtWzZUseOHcvZgbqP/PzzzwoPD7eb4ImLi1NwcLADoircnB0dAADktbZt22ru3LkaOXKkfHx8HB1OvkhOTparq+tdtfHHH39IkkaOHCknp4KT/3/wwQcdHQKAQow+JXfoU+4/aWlpMgxDzs72v0Lu2rVLTk5OSk1NzfKKnNTUVI0dO1bPPPOM3nzzTUlSy5YtdeLECY0fP17BwcFycXGRJL322mt6/PHHFRERYa13+fJlTZkyRaGhobd9P7799ttKSUlRdHS0SpcuLUmqUKGCAgICtG7dOnXv3l3SjWTTgQMHtHfvXtWoUUOS1LBhQ9WsWVPz58/XqFGjJEnr16/Xd999p6+//lotW7aUJDVr1kzVqlXTjBkzNGfOnJwcykLB39/f0SEUSgXn0xYA8si4ceMkyTpwyErGZe63uvUy1UOHDsliseijjz7S66+/Lh8fH5UoUUIDBgxQUlKS/vzzT7Vr104eHh6qUaOGlixZYnd7f/zxh1q2bCl3d3f5+vpqwoQJNv9FkqS///5bQ4cOVcWKFeXq6qo6depYBzYZMi4b37Jli3r27KnSpUuradOmt93XjRs3qlmzZipWrJhKlSqlrl27au/evdblVatWtV5GW6RIEVksFkVGRmbZ3rx589SsWTOVKVNGpUuXlr+/v2JiYjLVu3Llil577TU9+OCDcnV1lY+Pj55++mklJCRY6xw8eFDPPPOMfHx85OrqqurVq2vEiBHW5fYuGz5w4IA6duwod3d3lS9fXiNGjFBycrLdWBcsWKBGjRrJzc1N5cqV0/PPP5/pP00Wi0Xjxo3TnDlzVK1aNZUoUUIBAQHatWuXzTE6fPiwVqxYIYvFIovFomeffVaStG/fPnXr1k1eXl5yc3NT5cqV1bNnzwI/hQLAndGnZEafUvD6lIy/qw8++ECjRo2Sl5eX3N3d1alTJ7vTzLK7H2PHjtW0adNUrVo1FS1a9LZTkrKTyIuLi9OZM2c0YMAAm/JnnnlGZ8+e1XfffSfpxrSpn3/+2W69lJQUffHFF7fdzvr169WxY0drckeSnnzySVWuXFmfffaZTT1/f39rckeSqlWrpubNm2eqV6FCBWtyR5JKlSqloKAgm3r25OTcpKSkaNy4capataqKFi2qqlWraty4cUpJSclVe/amSWWsf7v3pCRNnDhRjzzyiEqVKqVy5cqpVatW2r59u3V5ZGSkBg8eLEmqWbOm9e84IwZ7277TZ4cktWjRQo8//rg2bdqkRx55RO7u7mrQoIHWrVt323hxAwkeAPcdX19fDR8+XBERETp8+HCetTt16lSdOHFCS5Ys0aRJk7Rq1Sr985//VLdu3dSxY0etXbtWDRs21ODBg20GcRm6du2qNm3aaN26derXr58mT56sSZMmWZdfvHhRzZs3V0xMjMLCwhQTE6OgoCANHTpUc+fOzdRe//79Va1aNa1evVrTpk3LMu6NGzeqY8eO8vDw0KpVq/Thhx/q999/1+OPP67jx49LktauXWsdWMbFxSkuLk4dO3bMss1Dhw4pODhYn376qVatWiU/Pz916tTJZsB1/fp1BQYGas6cOXr22We1YcMGzZs3T2XKlLHez+LgwYNq0qSJtmzZovDwcH3xxReaOHGi/v777yy3ndHuTz/9pPfff1+RkZE6ePCgpkyZkqnua6+9pmHDhqlNmzZav3693n77bW3cuFFPPfVUpjn8y5cvV0xMjGbPnq3FixfryJEj6tKli3VAvXbtWvn4+Khdu3bWYzR+/HhJUqdOnXT8+HF9+OGH+vLLLzVt2jS5urpm+rIFwHzoU2zRpxTsPmXq1Knav3+/Fi9erPfff18//PCD2rZta5MgyMl+REZGKiYmRu+8845iYmJUoUKFO8ZwOxl/yw0aNLApr1+/viRp9+7dt61XrVo1ubu7W+vZc/XqVR08eDDTuhnbuXndXbt23XW9I0eO6PLly1nGkyE752bQoEGaNm2aBg4cqA0bNmjw4MGaPn26Bg0alKv27sbx48cVGhqqdevWKTIyUl5eXnryySf166+/SpI6duxoTYB/+umn1r9jX19fu+1l57Mjw19//aURI0Zo1KhRWrNmjXx9fdWjRw/9+eefebJv9zUDAO4TixcvNiQZ+/fvN86ePWuUKlXKGDx4sGEYhpGSkmJIMiZOnGitP3HiRMPex+CgQYOMKlWqWF8fPHjQkGS0bNnSpl63bt0MScayZcusZefOnTOKFClihIWFZdrO1KlTbdYPDg42PDw8jMTERMMwDGPSpEmGq6ursW/fvkz1ypYta6SkpNjs58iRI7N1XB599FGjRo0a1vUNwzAOHDhgODs7G6GhodaysWPH2j0ed5KWlmakpKQYgYGBRufOna3lH3/8sSHJ+Oyzz7Jc95lnnjGKFy9uHD9+PMs6t56PiIgIQ5IRFxdnE0O9evUMScbBgwcNw7hx3pycnIzw8HCb9r777jtDkrF27VprmSSjRo0axvXr161ln376qSHJ2LZtm7WsSpUqRv/+/W3aO3PmzB33E4D50KfYR59SMPuUjL+runXrGmlpaZniW7hwYa72w9fX10hKSspRLPbeHxnefPNNQ5Jx9epVu+tMmjTJMAzDWLFihSHJ+OOPPzK1UbFiReO5557LcvvHjx83JBkffvhhpmX9+/c3qlevbn3t4uJivPrqq5nqjR071ihSpIj1dc2aNY3evXtnqrdgwQJDknHkyJEs48nuufntt9/sHrfJkycbkoxffvklR+0ZhmG3vYz1Fy9ebC279X1xq9TUVCMlJcWoVauW8dJLL1nLb/6cvNWt287uZ0dAQIDh7Oxs89mVkJBgODk5GW+++WaWMeIGruABcF8qU6aMRo8eraVLl2a69DO3nnrqKZvXderUkSS1a9fOWubp6SkvLy8dPXo00/q9evWyed2nTx9dvnxZv//+u6Qb/9lo2rSpqlWrptTUVOtPu3btdPbs2Uz/rerWrdsdY75y5Yp+/PFH9e7d22bOfMblx99+++0d27Dnhx9+UKdOneTt7S1nZ2e5uLgoNjbW5lh/9dVX8vHxUefOnbNs56uvvlKnTp1y9B/BuLg4PfDAAzZzu52cnDId39jYWKWnp6t///42x7Np06YqWbKktmzZYlM/MDDQOu9fkv7xj39Ikt0nadysbNmyql69ul577TUtWLBA+/fvz/a+ADAH+pQb6FMKfp/So0cPm6lSzZs3V6VKlRQXF5er/Wjfvr2KFSuWoxhuxzAMSbI7nTG79TKW5dW62alnGEauYrnZnc5NxrG/dVpaxutb3193au9ubdq0SS1btlTZsmWt78t9+/bl6jMwp58dNWvWVM2aNa2vvby85OXldcf3D5iiBeA+FhoaqjJlymjChAl50p6np6fN66JFi2ZZfu3atUzre3t7232dcVnq6dOntWXLFrm4uNj89OzZU5J09uxZm/WzugT2ZomJiTIMw25dHx8fu089uJOjR4+qdevWOnfunObOnavvv/9eO3fuVPv27W32++zZs6pYseJt2zp79qwqVaqUo+2fPHky07GUMh/f06dPS5Jq1KiR6ZhevHgx0/EsU6aMzeuMG4zaO5c3s1gsio2NlZ+fn15//XXVqlVL1atXz/Q0EADmRp9CnyIV/D4lq325+e8iJ/uRnb+LnMg4Lrf+rWS8zlieVT1JOn/+fKbjezNPT09ZLBa76yYmJtqs6+npmWW9m9+LZcqUybJeRjt3cqdzk9H+rcc842bSt27/Tu3djR9//FEdOnSQh4eHPv74Y23fvl07d+5Uo0aN7vg3bE9OPzvsnV9XV9dcbbuw4SlaAO5bHh4eev311zV69Gi98sormZa7ublJujH/PmNgLWUe9OaVhIQEVa9e3ea1JOuAtWzZsvLy8tLs2bPtrl+7dm2b13f675f0f4OcU6dOZVp26tQplS1bNtvxZ9i4caMuXLigTz75xGYgnZSUZFOvXLly1v8kZ6VcuXI5Hoj4+vravR/FzTfZlGTdt6+++sruwCs3+56V6tWra+nSpTIMQ7/88ovmzZunYcOGqWrVqpn+Sw/AnOhT6FOkgt+n3Bp3RtlDDz1kE2d29yM7fxc5kXGvnV27dtl82c+4oqxevXqZ6jVr1sxa79ChQ0pKSrLWs8fd3V1Vq1a1e153796tgIAAm3iyqnfzNurXr6+vvvrKbr3KlSvLw8Mjy3gy3OncZCQ1Tp06ZfO0t4z3263n5k7tSTeSItevX7epk53PpP/85z9ydnbWmjVrbK5ES0xMtLlxdXbdi88O2McVPADua8OGDVPFihWtN4G7WZUqVSTJZsB4/vx5ff/99/cklk8++cTmdVRUlDw8PKw37Wvfvr327NmjypUry8/PL9NPiRIlcrzN4sWL69FHH9Wnn35qc+PEw4cP6/vvv7cZ5GRXxqD75g5/37592rZtm029tm3b6tSpU4qOjs6yrbZt22rDhg06efJktrffrFkzHT161OZJDunp6ZmOb2BgoJycnHTkyBG7x7NatWrZ3mYGV1dXXb16NcvlFotFDz30kN59911JuuOXEQDmQp9Cn1LQ+5TVq1fb3Ix527ZtOnbsmDVJci/2IyeaNWumcuXKacWKFTbly5cvV5kyZdS8eXNJUuXKldWoUSO79VxcXO6Y6OrcubNiYmJ04cIFa9l3332nw4cP20zz69y5s7Zv364DBw5Yyw4dOqRt27Zlqnf8+HGbqUQXL15UdHT0bacN3uxO5ybj/RMVFWWzXsYxePLJJ3PUnnTjc+nWvxt7T6i7VVJSkvUJeBm+/vrrTFOkMq5Mu93fsXRvPjtgH1fwALivubq6asKECQoJCcm07KmnnlKpUqX0wgsvKDw8XMnJyZoxY0a2/guTGwsWLFB6eroaN26sL7/8UgsXLlRYWJj1PyGhoaFatWqVnnjiCYWGhqp27dq6cuWK9uzZo61bt97xMZxZmTx5sjp27KhOnTpp2LBhunz5siZOnKhSpUpp9OjROW6vTZs2cnZ21sCBAzV69GidPHlSEydOVOXKlW0GGgMGDNCCBQvUt29fvf7662ratKkuXbqkL7/8UiNHjlSdOnUUHh6umJgYPfbYY3rjjTdUo0YNHT9+XBs3btTy5cvtbj/jCRPdu3fXW2+9JS8vL3300Ue6ePGiTb0HH3xQr776qoYPH669e/cqICBAbm5uOnr0qGJjYxUcHGzzuNPsqFevnrZu3aoNGzbIx8dH5cqV08WLFzVixAj17t1bNWrUUFpamiIjI+Xs7KxWrVrl+PgCKLjoU+hTCnqfcunSJXXt2lVDhgzRmTNn9Prrr6tmzZoaOHDgPduPDPHx8Tp06JD1vO3evVurV6+WJHXo0EHu7u5ycXHR5MmTrcnSNm3a6Ouvv9aiRYs0d+5cm6vf3nrrLXXq1ElDhgxR37599dNPP2nKlCkaMWKEddqS9H+P6/7mm2/UokULSdIrr7yi5cuXq3Pnznr99dd14cIFjRkzRk2aNLG539QLL7ygefPmqUuXLpoyZYosFovGjx+vBx54QEOGDLHW69y5s5o1a6YBAwbo7bfflqenp6ZOnSrDMDRmzJhsHZ87nZv69eurb9++CgsLU2pqqh577DHFxcVp8uTJ6tu3rxo2bJij9qQb9+aaMmWK3nzzTfn7+2vr1q1auXLlHWNt37693nvvPT377LMaPHiw9u3bp8mTJ2eaJplxldP777+vQYMGycXFRQ0bNrQ5jxny+rMDWXDEnZ0B4F7I6k7+KSkpRs2aNe0+SWDr1q2Gn5+fUaxYMaNmzZrGsmXLsnziyYIFC2zWzXiSyc1PAzCMzE/FyKj322+/GS1atDDc3NwMb29vY9y4cTZPPzCMG09MGTlypFG1alXDxcXFKF++vPH4448bs2bNuuN+3s4XX3xh+Pv7G25ubkbJkiWNzp07G3v27LGpk5MnnqxatcqoXbu24erqatSrV89YuXKl3ScwXLp0yXj55ZeNypUrGy4uLoaPj4/x9NNPGwkJCdY6f/75p9GnTx+jbNmyRtGiRY1q1arZPM3FXrt//fWX8dRTTxnFihUzypUrZ7z00kvGRx99ZPPEkwxLly41mjZtari7uxvFixc36tSpY/zrX/8yjh49aq0jyRg7dqzNevaeMvHHH38Yjz/+uFGsWDFDkjFo0CAjISHBGDhwoFGzZk2jWLFihqenp/Hkk08aGzduzNaxBFAw0adkjT6l4PUpGe2///77RmhoqFGuXDmjWLFiRocOHYwDBw5kqp/b/bidQYMGGZLs/tx6HD/66COjZs2aRtGiRY0aNWoY77//vt02//Of/xgNGzY0ihYtajzwwANGeHi4kZqaalNn3rx5hiRj9+7dNuW//vqr0aZNG8Pd3d0oXbq0MWjQIOPvv//OtI3Dhw8b3bt3N0qUKGF4eHgYXbp0yRSvYRjG2bNnjcGDBxuenp5GsWLFjFatWhk///zzHY9LTs7N9evXjbFjxxqVK1c2nJ2djcqVKxtjx461eSJbTtq7evWq8dJLLxk+Pj6Gh4eH0atXL+N///tftp6iNWfOHKNq1aqGm5ub4efnZ8TGxhoBAQFGQECATb2wsDCjQoUKhpOTk825tvcZmZ3PjoCAAKN58+aZjmOVKlWMQYMGZX2gYRiGYVgMIwe3/gYAAAAAFCiHDh1StWrVtGDBAgUHBzs6nHzVr18/nT9/Xp9//rmjQ7Err89NYT7XuDOmaAEAAAAATGnLli2Z7pkEFFYkeAAAAAAApnTs2DFHhwAUGEzRAgAAAAAAMDkekw4AAAAAAGByJHgAAAAAAABMjgQPAAAAAACAyZHgAQAAAAAAMDkSPAAAAAAAACZHggcAAAAAAMDk/h/CdJq3SyS0JwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1152x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots(figsize = (8*2,10), ncols = 2, nrows = 1, facecolor = 'w')\n",
    "\n",
    "colors = ['#3d6c6f','#72a2ac','#cb9573','#ef7b4f','#bf3f27']\n",
    "\n",
    "ax[0].scatter(x=week_d.work_days, y = week_d.region, color = colors[0], zorder = 2, label = 'Work days')\n",
    "ax[0].scatter(x=week_d.weekends, y = week_d.region, color = colors[-1], zorder = 2, label = 'Weekends')\n",
    "for idx, row in week_d.iterrows():\n",
    "    if row.work_days > row.weekends:\n",
    "        ax[0].plot([row.work_days,row.weekends], [row.region,row.region], color = colors[0], zorder = 1 )\n",
    "    else:\n",
    "        ax[0].plot([row.work_days,row.weekends], [row.region,row.region], color = colors[-1], zorder = 1 )\n",
    "\n",
    "ax[0].grid(axis = 'x', color = 'k', alpha = 0.05)\n",
    "ax[0].set_yticklabels(week_d.region.values, fontsize = 16)\n",
    "ax[0].set_xlabel('Number of accidents', fontsize = 16)\n",
    "#ax[0].set_title(label = 'Average monthly difference between RTA \\noccurance in a work day and a weekend day', size = 14)\n",
    "\n",
    "\n",
    "\n",
    "ax[1].scatter(x=week_d.work_days_100000, y = week_d.region, color = colors[0], zorder = 2, label = 'Average Workday')\n",
    "ax[1].scatter(x=week_d.weekends_100000, y = week_d.region, color = colors[-1], zorder = 2, label = 'Average Weekend day')\n",
    "for idx, row in week_d.iterrows():\n",
    "    if row.work_days > row.weekends:\n",
    "        ax[1].plot([row.work_days_100000,row.weekends_100000], [row.region,row.region], color = colors[0], zorder = 1 )\n",
    "    else:\n",
    "        ax[1].plot([row.work_days_100000,row.weekends_100000], [row.region,row.region], color = colors[-1], zorder = 1 )\n",
    "\n",
    "ax[0].grid(axis = 'x', color = 'k', alpha = 0.05)\n",
    "ax[1].set_xlabel('Number of accidents per 100,000 population', fontsize = 16)\n",
    "ax[1].set_yticklabels('')\n",
    "#ax[1].set_title(label = 'Average monthly difference between RTA occurance \\nin a work day and a weekend day per 100,000 population', size = 14)\n",
    "\n",
    "fig.suptitle(t = 'Average monthly difference between workdays and weekend RTA occurance', size = 18, y = 0.99 )\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.legend()\n",
    "#ax.spines['right'].set_visible(False)\n",
    "#ax.spines['top'].set_visible(False)\n",
    "\n",
    "#plt.savefig('./output/workday vs weekend.jpg', dpi = 300)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### calculating the difference between workdays and weekends"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['region', 'Saturday', 'Sunday', 'Monday', 'Tuesday', 'Wednesday',\n",
       "       'Thursday', 'Friday', 'work_days', 'weekends', 'TOTPOP_CY',\n",
       "       'work_days_100000', 'weekends_100000'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "week_d.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "weekdif = np.round(week_d[['region','work_days','weekends']].copy())\n",
    "weekdif.sort_values(by=\"work_days\", ascending=False, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>region</th>\n",
       "      <th>work_days</th>\n",
       "      <th>weekends</th>\n",
       "      <th>difference</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ar Riyad</td>\n",
       "      <td>215.0</td>\n",
       "      <td>231.0</td>\n",
       "      <td>7.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Makkah</td>\n",
       "      <td>124.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>-1.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Ash Sharqiyah</td>\n",
       "      <td>120.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>10.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Jiddah</td>\n",
       "      <td>118.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>-17.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Aseer</td>\n",
       "      <td>62.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>51.61</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           region  work_days  weekends  difference\n",
       "3        Ar Riyad      215.0     231.0        7.44\n",
       "11         Makkah      124.0     122.0       -1.61\n",
       "5   Ash Sharqiyah      120.0     133.0       10.83\n",
       "9          Jiddah      118.0      97.0      -17.80\n",
       "4           Aseer       62.0      94.0       51.61"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weekdif['difference'] = np.round((((weekdif.weekends-weekdif.work_days)/weekdif.work_days)*100),2)\n",
    "weekdif.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "#weekdif.to_csv('./weekdif.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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