task_05.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# _Importing libraries_\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# _Importing data_\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Source</th>\n",
       "      <th>Severity</th>\n",
       "      <th>Start_Time</th>\n",
       "      <th>End_Time</th>\n",
       "      <th>Start_Lat</th>\n",
       "      <th>Start_Lng</th>\n",
       "      <th>End_Lat</th>\n",
       "      <th>End_Lng</th>\n",
       "      <th>Distance(mi)</th>\n",
       "      <th>...</th>\n",
       "      <th>Roundabout</th>\n",
       "      <th>Station</th>\n",
       "      <th>Stop</th>\n",
       "      <th>Traffic_Calming</th>\n",
       "      <th>Traffic_Signal</th>\n",
       "      <th>Turning_Loop</th>\n",
       "      <th>Sunrise_Sunset</th>\n",
       "      <th>Civil_Twilight</th>\n",
       "      <th>Nautical_Twilight</th>\n",
       "      <th>Astronomical_Twilight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A-2047758</td>\n",
       "      <td>Source2</td>\n",
       "      <td>2</td>\n",
       "      <td>12/06/2019 10:10:56</td>\n",
       "      <td>12/06/2019 10:55:58</td>\n",
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       "      <th>1</th>\n",
       "      <td>A-4694324</td>\n",
       "      <td>Source1</td>\n",
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       "      <td>Source1</td>\n",
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       "      <td>20/08/2022 15:22:45</td>\n",
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       "      <td>34.661189</td>\n",
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       "      <td>False</td>\n",
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       "      <td>Day</td>\n",
       "      <td>Day</td>\n",
       "      <td>Day</td>\n",
       "      <td>Day</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A-4237356</td>\n",
       "      <td>Source1</td>\n",
       "      <td>2</td>\n",
       "      <td>21/02/2022 17:43:04</td>\n",
       "      <td>21/02/2022 19:43:23</td>\n",
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       "      <th>4</th>\n",
       "      <td>A-6690583</td>\n",
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       "      <td>04/12/2020 4:13:09</td>\n",
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       "      <td>Source1</td>\n",
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       "      <td>Source1</td>\n",
       "      <td>2</td>\n",
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       "      <td>19/12/2021 17:40:37</td>\n",
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       "      <td>Day</td>\n",
       "      <td>Day</td>\n",
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       "      <td>Day</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499997</th>\n",
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       "      <td>Source1</td>\n",
       "      <td>2</td>\n",
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       "      <td>Day</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499998</th>\n",
       "      <td>A-7030381</td>\n",
       "      <td>Source1</td>\n",
       "      <td>3</td>\n",
       "      <td>15/05/2020 17:20:56</td>\n",
       "      <td>15/05/2020 17:50:56</td>\n",
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       "      <td>Source1</td>\n",
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       "      <td>03/04/2022 0:49:48</td>\n",
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       "      <td>False</td>\n",
       "      <td>Night</td>\n",
       "      <td>Night</td>\n",
       "      <td>Night</td>\n",
       "      <td>Night</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>500000 rows × 46 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               ID   Source  Severity           Start_Time  \\\n",
       "0       A-2047758  Source2         2  12/06/2019 10:10:56   \n",
       "1       A-4694324  Source1         2  03/12/2022 23:37:14   \n",
       "2       A-5006183  Source1         2  20/08/2022 13:13:00   \n",
       "3       A-4237356  Source1         2  21/02/2022 17:43:04   \n",
       "4       A-6690583  Source1         2   04/12/2020 1:46:00   \n",
       "...           ...      ...       ...                  ...   \n",
       "499995  A-6077227  Source1         2   15/12/2021 7:30:00   \n",
       "499996  A-6323243  Source1         2  19/12/2021 16:25:00   \n",
       "499997  A-3789256  Source1         2  13/04/2022 19:28:29   \n",
       "499998  A-7030381  Source1         3  15/05/2020 17:20:56   \n",
       "499999  A-5438901  Source1         2  02/04/2022 23:23:13   \n",
       "\n",
       "                   End_Time  Start_Lat   Start_Lng    End_Lat     End_Lng  \\\n",
       "0       12/06/2019 10:55:58  30.641211  -91.153481        NaN         NaN   \n",
       "1        04/12/2022 1:56:53  38.990562  -77.399070  38.990037  -77.398282   \n",
       "2       20/08/2022 15:22:45  34.661189 -120.492822  34.661189 -120.492442   \n",
       "3       21/02/2022 19:43:23  43.680592  -92.993317  43.680574  -92.972223   \n",
       "4        04/12/2020 4:13:09  35.395484 -118.985176  35.395476 -118.985995   \n",
       "...                     ...        ...         ...        ...         ...   \n",
       "499995   15/12/2021 7:50:30  45.522510 -123.084104  45.520225 -123.084211   \n",
       "499996  19/12/2021 17:40:37  26.702570  -80.111169  26.703141  -80.111133   \n",
       "499997  13/04/2022 21:33:44  34.561862 -112.259620  34.566822 -112.267150   \n",
       "499998  15/05/2020 17:50:56  38.406680  -78.619310  38.406680  -78.619310   \n",
       "499999   03/04/2022 0:49:48  35.069358  -85.234410  35.070505  -85.233836   \n",
       "\n",
       "        Distance(mi)  ... Roundabout Station   Stop Traffic_Calming  \\\n",
       "0              0.000  ...      False   False  False           False   \n",
       "1              0.056  ...      False   False  False           False   \n",
       "2              0.022  ...      False   False  False           False   \n",
       "3              1.054  ...      False   False  False           False   \n",
       "4              0.046  ...      False   False  False           False   \n",
       "...              ...  ...        ...     ...    ...             ...   \n",
       "499995         0.158  ...      False   False  False           False   \n",
       "499996         0.040  ...      False   False  False           False   \n",
       "499997         0.549  ...      False   False  False           False   \n",
       "499998         0.000  ...      False   False  False           False   \n",
       "499999         0.086  ...      False   False  False           False   \n",
       "\n",
       "       Traffic_Signal Turning_Loop Sunrise_Sunset Civil_Twilight  \\\n",
       "0                True        False            Day            Day   \n",
       "1               False        False          Night          Night   \n",
       "2                True        False            Day            Day   \n",
       "3               False        False            Day            Day   \n",
       "4               False        False          Night          Night   \n",
       "...               ...          ...            ...            ...   \n",
       "499995          False        False          Night            Day   \n",
       "499996          False        False            Day            Day   \n",
       "499997           True        False          Night          Night   \n",
       "499998           True        False            Day            Day   \n",
       "499999          False        False          Night          Night   \n",
       "\n",
       "       Nautical_Twilight Astronomical_Twilight  \n",
       "0                    Day                   Day  \n",
       "1                  Night                 Night  \n",
       "2                    Day                   Day  \n",
       "3                    Day                   Day  \n",
       "4                  Night                 Night  \n",
       "...                  ...                   ...  \n",
       "499995               Day                   Day  \n",
       "499996               Day                   Day  \n",
       "499997               Day                   Day  \n",
       "499998               Day                   Day  \n",
       "499999             Night                 Night  \n",
       "\n",
       "[500000 rows x 46 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\n",
    "    r\"C:\\Users\\22831\\Downloads\\US_Accidents_March23_sampled_500k\\US_Accidents_March23_sampled_500k.csv\"\n",
    ")\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# _EDA_\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(500000, 46)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Civil_Twilight</th>\n",
       "      <th>Nautical_Twilight</th>\n",
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       "      <td>Night</td>\n",
       "      <td>Night</td>\n",
       "      <td>Night</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A-5006183</td>\n",
       "      <td>Source1</td>\n",
       "      <td>2</td>\n",
       "      <td>20/08/2022 13:13:00</td>\n",
       "      <td>20/08/2022 15:22:45</td>\n",
       "      <td>34.661189</td>\n",
       "      <td>-120.492822</td>\n",
       "      <td>34.661189</td>\n",
       "      <td>-120.492442</td>\n",
       "      <td>0.022</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>Day</td>\n",
       "      <td>Day</td>\n",
       "      <td>Day</td>\n",
       "      <td>Day</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A-4237356</td>\n",
       "      <td>Source1</td>\n",
       "      <td>2</td>\n",
       "      <td>21/02/2022 17:43:04</td>\n",
       "      <td>21/02/2022 19:43:23</td>\n",
       "      <td>43.680592</td>\n",
       "      <td>-92.993317</td>\n",
       "      <td>43.680574</td>\n",
       "      <td>-92.972223</td>\n",
       "      <td>1.054</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Day</td>\n",
       "      <td>Day</td>\n",
       "      <td>Day</td>\n",
       "      <td>Day</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A-6690583</td>\n",
       "      <td>Source1</td>\n",
       "      <td>2</td>\n",
       "      <td>04/12/2020 1:46:00</td>\n",
       "      <td>04/12/2020 4:13:09</td>\n",
       "      <td>35.395484</td>\n",
       "      <td>-118.985176</td>\n",
       "      <td>35.395476</td>\n",
       "      <td>-118.985995</td>\n",
       "      <td>0.046</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Night</td>\n",
       "      <td>Night</td>\n",
       "      <td>Night</td>\n",
       "      <td>Night</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 46 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          ID   Source  Severity           Start_Time             End_Time  \\\n",
       "0  A-2047758  Source2         2  12/06/2019 10:10:56  12/06/2019 10:55:58   \n",
       "1  A-4694324  Source1         2  03/12/2022 23:37:14   04/12/2022 1:56:53   \n",
       "2  A-5006183  Source1         2  20/08/2022 13:13:00  20/08/2022 15:22:45   \n",
       "3  A-4237356  Source1         2  21/02/2022 17:43:04  21/02/2022 19:43:23   \n",
       "4  A-6690583  Source1         2   04/12/2020 1:46:00   04/12/2020 4:13:09   \n",
       "\n",
       "   Start_Lat   Start_Lng    End_Lat     End_Lng  Distance(mi)  ... Roundabout  \\\n",
       "0  30.641211  -91.153481        NaN         NaN         0.000  ...      False   \n",
       "1  38.990562  -77.399070  38.990037  -77.398282         0.056  ...      False   \n",
       "2  34.661189 -120.492822  34.661189 -120.492442         0.022  ...      False   \n",
       "3  43.680592  -92.993317  43.680574  -92.972223         1.054  ...      False   \n",
       "4  35.395484 -118.985176  35.395476 -118.985995         0.046  ...      False   \n",
       "\n",
       "  Station   Stop Traffic_Calming Traffic_Signal Turning_Loop Sunrise_Sunset  \\\n",
       "0   False  False           False           True        False            Day   \n",
       "1   False  False           False          False        False          Night   \n",
       "2   False  False           False           True        False            Day   \n",
       "3   False  False           False          False        False            Day   \n",
       "4   False  False           False          False        False          Night   \n",
       "\n",
       "  Civil_Twilight Nautical_Twilight Astronomical_Twilight  \n",
       "0            Day               Day                   Day  \n",
       "1          Night             Night                 Night  \n",
       "2            Day               Day                   Day  \n",
       "3            Day               Day                   Day  \n",
       "4          Night             Night                 Night  \n",
       "\n",
       "[5 rows x 46 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Source</th>\n",
       "      <th>Severity</th>\n",
       "      <th>Start_Time</th>\n",
       "      <th>End_Time</th>\n",
       "      <th>Start_Lat</th>\n",
       "      <th>Start_Lng</th>\n",
       "      <th>End_Lat</th>\n",
       "      <th>End_Lng</th>\n",
       "      <th>Distance(mi)</th>\n",
       "      <th>...</th>\n",
       "      <th>Roundabout</th>\n",
       "      <th>Station</th>\n",
       "      <th>Stop</th>\n",
       "      <th>Traffic_Calming</th>\n",
       "      <th>Traffic_Signal</th>\n",
       "      <th>Turning_Loop</th>\n",
       "      <th>Sunrise_Sunset</th>\n",
       "      <th>Civil_Twilight</th>\n",
       "      <th>Nautical_Twilight</th>\n",
       "      <th>Astronomical_Twilight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>499995</th>\n",
       "      <td>A-6077227</td>\n",
       "      <td>Source1</td>\n",
       "      <td>2</td>\n",
       "      <td>15/12/2021 7:30:00</td>\n",
       "      <td>15/12/2021 7:50:30</td>\n",
       "      <td>45.522510</td>\n",
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       "      <td>45.520225</td>\n",
       "      <td>-123.084211</td>\n",
       "      <td>0.158</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Night</td>\n",
       "      <td>Day</td>\n",
       "      <td>Day</td>\n",
       "      <td>Day</td>\n",
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       "    <tr>\n",
       "      <th>499996</th>\n",
       "      <td>A-6323243</td>\n",
       "      <td>Source1</td>\n",
       "      <td>2</td>\n",
       "      <td>19/12/2021 16:25:00</td>\n",
       "      <td>19/12/2021 17:40:37</td>\n",
       "      <td>26.702570</td>\n",
       "      <td>-80.111169</td>\n",
       "      <td>26.703141</td>\n",
       "      <td>-80.111133</td>\n",
       "      <td>0.040</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Day</td>\n",
       "      <td>Day</td>\n",
       "      <td>Day</td>\n",
       "      <td>Day</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499997</th>\n",
       "      <td>A-3789256</td>\n",
       "      <td>Source1</td>\n",
       "      <td>2</td>\n",
       "      <td>13/04/2022 19:28:29</td>\n",
       "      <td>13/04/2022 21:33:44</td>\n",
       "      <td>34.561862</td>\n",
       "      <td>-112.259620</td>\n",
       "      <td>34.566822</td>\n",
       "      <td>-112.267150</td>\n",
       "      <td>0.549</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>Night</td>\n",
       "      <td>Night</td>\n",
       "      <td>Day</td>\n",
       "      <td>Day</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499998</th>\n",
       "      <td>A-7030381</td>\n",
       "      <td>Source1</td>\n",
       "      <td>3</td>\n",
       "      <td>15/05/2020 17:20:56</td>\n",
       "      <td>15/05/2020 17:50:56</td>\n",
       "      <td>38.406680</td>\n",
       "      <td>-78.619310</td>\n",
       "      <td>38.406680</td>\n",
       "      <td>-78.619310</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>Day</td>\n",
       "      <td>Day</td>\n",
       "      <td>Day</td>\n",
       "      <td>Day</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499999</th>\n",
       "      <td>A-5438901</td>\n",
       "      <td>Source1</td>\n",
       "      <td>2</td>\n",
       "      <td>02/04/2022 23:23:13</td>\n",
       "      <td>03/04/2022 0:49:48</td>\n",
       "      <td>35.069358</td>\n",
       "      <td>-85.234410</td>\n",
       "      <td>35.070505</td>\n",
       "      <td>-85.233836</td>\n",
       "      <td>0.086</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Night</td>\n",
       "      <td>Night</td>\n",
       "      <td>Night</td>\n",
       "      <td>Night</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 46 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               ID   Source  Severity           Start_Time  \\\n",
       "499995  A-6077227  Source1         2   15/12/2021 7:30:00   \n",
       "499996  A-6323243  Source1         2  19/12/2021 16:25:00   \n",
       "499997  A-3789256  Source1         2  13/04/2022 19:28:29   \n",
       "499998  A-7030381  Source1         3  15/05/2020 17:20:56   \n",
       "499999  A-5438901  Source1         2  02/04/2022 23:23:13   \n",
       "\n",
       "                   End_Time  Start_Lat   Start_Lng    End_Lat     End_Lng  \\\n",
       "499995   15/12/2021 7:50:30  45.522510 -123.084104  45.520225 -123.084211   \n",
       "499996  19/12/2021 17:40:37  26.702570  -80.111169  26.703141  -80.111133   \n",
       "499997  13/04/2022 21:33:44  34.561862 -112.259620  34.566822 -112.267150   \n",
       "499998  15/05/2020 17:50:56  38.406680  -78.619310  38.406680  -78.619310   \n",
       "499999   03/04/2022 0:49:48  35.069358  -85.234410  35.070505  -85.233836   \n",
       "\n",
       "        Distance(mi)  ... Roundabout Station   Stop Traffic_Calming  \\\n",
       "499995         0.158  ...      False   False  False           False   \n",
       "499996         0.040  ...      False   False  False           False   \n",
       "499997         0.549  ...      False   False  False           False   \n",
       "499998         0.000  ...      False   False  False           False   \n",
       "499999         0.086  ...      False   False  False           False   \n",
       "\n",
       "       Traffic_Signal Turning_Loop Sunrise_Sunset Civil_Twilight  \\\n",
       "499995          False        False          Night            Day   \n",
       "499996          False        False            Day            Day   \n",
       "499997           True        False          Night          Night   \n",
       "499998           True        False            Day            Day   \n",
       "499999          False        False          Night          Night   \n",
       "\n",
       "       Nautical_Twilight Astronomical_Twilight  \n",
       "499995               Day                   Day  \n",
       "499996               Day                   Day  \n",
       "499997               Day                   Day  \n",
       "499998               Day                   Day  \n",
       "499999             Night                 Night  \n",
       "\n",
       "[5 rows x 46 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ID', 'Source', 'Severity', 'Start_Time', 'End_Time', 'Start_Lat',\n",
       "       'Start_Lng', 'End_Lat', 'End_Lng', 'Distance(mi)', 'Description',\n",
       "       'Street', 'City', 'County', 'State', 'Zipcode', 'Country', 'Timezone',\n",
       "       'Airport_Code', 'Weather_Timestamp', 'Temperature(F)', 'Wind_Chill(F)',\n",
       "       'Humidity(%)', 'Pressure(in)', 'Visibility(mi)', 'Wind_Direction',\n",
       "       'Wind_Speed(mph)', 'Precipitation(in)', 'Weather_Condition', 'Amenity',\n",
       "       'Bump', 'Crossing', 'Give_Way', 'Junction', 'No_Exit', 'Railway',\n",
       "       'Roundabout', 'Station', 'Stop', 'Traffic_Calming', 'Traffic_Signal',\n",
       "       'Turning_Loop', 'Sunrise_Sunset', 'Civil_Twilight', 'Nautical_Twilight',\n",
       "       'Astronomical_Twilight'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Severity</th>\n",
       "      <th>Start_Lat</th>\n",
       "      <th>Start_Lng</th>\n",
       "      <th>End_Lat</th>\n",
       "      <th>End_Lng</th>\n",
       "      <th>Distance(mi)</th>\n",
       "      <th>Temperature(F)</th>\n",
       "      <th>Wind_Chill(F)</th>\n",
       "      <th>Humidity(%)</th>\n",
       "      <th>Pressure(in)</th>\n",
       "      <th>Visibility(mi)</th>\n",
       "      <th>Wind_Speed(mph)</th>\n",
       "      <th>Precipitation(in)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>500000.000000</td>\n",
       "      <td>500000.000000</td>\n",
       "      <td>500000.000000</td>\n",
       "      <td>279623.000000</td>\n",
       "      <td>279623.000000</td>\n",
       "      <td>500000.000000</td>\n",
       "      <td>489534.000000</td>\n",
       "      <td>370983.000000</td>\n",
       "      <td>488870.000000</td>\n",
       "      <td>491072.000000</td>\n",
       "      <td>488709.000000</td>\n",
       "      <td>463013.000000</td>\n",
       "      <td>357384.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.212748</td>\n",
       "      <td>36.206421</td>\n",
       "      <td>-94.736583</td>\n",
       "      <td>36.273192</td>\n",
       "      <td>-95.776553</td>\n",
       "      <td>0.564317</td>\n",
       "      <td>61.646254</td>\n",
       "      <td>58.229028</td>\n",
       "      <td>64.834921</td>\n",
       "      <td>29.536621</td>\n",
       "      <td>9.091540</td>\n",
       "      <td>7.681347</td>\n",
       "      <td>0.008289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.486661</td>\n",
       "      <td>5.071411</td>\n",
       "      <td>17.405761</td>\n",
       "      <td>5.265333</td>\n",
       "      <td>18.120211</td>\n",
       "      <td>1.774872</td>\n",
       "      <td>19.000133</td>\n",
       "      <td>22.352246</td>\n",
       "      <td>22.826158</td>\n",
       "      <td>1.008666</td>\n",
       "      <td>2.708083</td>\n",
       "      <td>5.431361</td>\n",
       "      <td>0.101865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>24.562117</td>\n",
       "      <td>-124.497420</td>\n",
       "      <td>24.570110</td>\n",
       "      <td>-124.497419</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-77.800000</td>\n",
       "      <td>-53.200000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.120000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>33.416823</td>\n",
       "      <td>-117.233047</td>\n",
       "      <td>33.474773</td>\n",
       "      <td>-117.778324</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>29.370000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>4.600000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>35.832147</td>\n",
       "      <td>-87.794365</td>\n",
       "      <td>36.192669</td>\n",
       "      <td>-88.039013</td>\n",
       "      <td>0.029000</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>67.000000</td>\n",
       "      <td>29.860000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>40.082443</td>\n",
       "      <td>-80.359601</td>\n",
       "      <td>40.181341</td>\n",
       "      <td>-80.252449</td>\n",
       "      <td>0.465000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>84.000000</td>\n",
       "      <td>30.030000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.400000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>48.999569</td>\n",
       "      <td>-67.484130</td>\n",
       "      <td>48.998901</td>\n",
       "      <td>-67.484130</td>\n",
       "      <td>193.479996</td>\n",
       "      <td>207.000000</td>\n",
       "      <td>207.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>38.440000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>822.800000</td>\n",
       "      <td>10.130000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Severity      Start_Lat      Start_Lng        End_Lat  \\\n",
       "count  500000.000000  500000.000000  500000.000000  279623.000000   \n",
       "mean        2.212748      36.206421     -94.736583      36.273192   \n",
       "std         0.486661       5.071411      17.405761       5.265333   \n",
       "min         1.000000      24.562117    -124.497420      24.570110   \n",
       "25%         2.000000      33.416823    -117.233047      33.474773   \n",
       "50%         2.000000      35.832147     -87.794365      36.192669   \n",
       "75%         2.000000      40.082443     -80.359601      40.181341   \n",
       "max         4.000000      48.999569     -67.484130      48.998901   \n",
       "\n",
       "             End_Lng   Distance(mi)  Temperature(F)  Wind_Chill(F)  \\\n",
       "count  279623.000000  500000.000000   489534.000000  370983.000000   \n",
       "mean      -95.776553       0.564317       61.646254      58.229028   \n",
       "std        18.120211       1.774872       19.000133      22.352246   \n",
       "min      -124.497419       0.000000      -77.800000     -53.200000   \n",
       "25%      -117.778324       0.000000       49.000000      43.000000   \n",
       "50%       -88.039013       0.029000       64.000000      62.000000   \n",
       "75%       -80.252449       0.465000       76.000000      75.000000   \n",
       "max       -67.484130     193.479996      207.000000     207.000000   \n",
       "\n",
       "         Humidity(%)   Pressure(in)  Visibility(mi)  Wind_Speed(mph)  \\\n",
       "count  488870.000000  491072.000000   488709.000000    463013.000000   \n",
       "mean       64.834921      29.536621        9.091540         7.681347   \n",
       "std        22.826158       1.008666        2.708083         5.431361   \n",
       "min         1.000000       0.120000        0.000000         0.000000   \n",
       "25%        48.000000      29.370000       10.000000         4.600000   \n",
       "50%        67.000000      29.860000       10.000000         7.000000   \n",
       "75%        84.000000      30.030000       10.000000        10.400000   \n",
       "max       100.000000      38.440000      130.000000       822.800000   \n",
       "\n",
       "       Precipitation(in)  \n",
       "count      357384.000000  \n",
       "mean            0.008289  \n",
       "std             0.101865  \n",
       "min             0.000000  \n",
       "25%             0.000000  \n",
       "50%             0.000000  \n",
       "75%             0.000000  \n",
       "max            10.130000  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 500000 entries, 0 to 499999\n",
      "Data columns (total 46 columns):\n",
      " #   Column                 Non-Null Count   Dtype  \n",
      "---  ------                 --------------   -----  \n",
      " 0   ID                     500000 non-null  object \n",
      " 1   Source                 500000 non-null  object \n",
      " 2   Severity               500000 non-null  int64  \n",
      " 3   Start_Time             500000 non-null  object \n",
      " 4   End_Time               500000 non-null  object \n",
      " 5   Start_Lat              500000 non-null  float64\n",
      " 6   Start_Lng              500000 non-null  float64\n",
      " 7   End_Lat                279623 non-null  float64\n",
      " 8   End_Lng                279623 non-null  float64\n",
      " 9   Distance(mi)           500000 non-null  float64\n",
      " 10  Description            499999 non-null  object \n",
      " 11  Street                 499309 non-null  object \n",
      " 12  City                   499981 non-null  object \n",
      " 13  County                 500000 non-null  object \n",
      " 14  State                  500000 non-null  object \n",
      " 15  Zipcode                499884 non-null  object \n",
      " 16  Country                500000 non-null  object \n",
      " 17  Timezone               499493 non-null  object \n",
      " 18  Airport_Code           498554 non-null  object \n",
      " 19  Weather_Timestamp      492326 non-null  object \n",
      " 20  Temperature(F)         489534 non-null  float64\n",
      " 21  Wind_Chill(F)          370983 non-null  float64\n",
      " 22  Humidity(%)            488870 non-null  float64\n",
      " 23  Pressure(in)           491072 non-null  float64\n",
      " 24  Visibility(mi)         488709 non-null  float64\n",
      " 25  Wind_Direction         488803 non-null  object \n",
      " 26  Wind_Speed(mph)        463013 non-null  float64\n",
      " 27  Precipitation(in)      357384 non-null  float64\n",
      " 28  Weather_Condition      488899 non-null  object \n",
      " 29  Amenity                500000 non-null  bool   \n",
      " 30  Bump                   500000 non-null  bool   \n",
      " 31  Crossing               500000 non-null  bool   \n",
      " 32  Give_Way               500000 non-null  bool   \n",
      " 33  Junction               500000 non-null  bool   \n",
      " 34  No_Exit                500000 non-null  bool   \n",
      " 35  Railway                500000 non-null  bool   \n",
      " 36  Roundabout             500000 non-null  bool   \n",
      " 37  Station                500000 non-null  bool   \n",
      " 38  Stop                   500000 non-null  bool   \n",
      " 39  Traffic_Calming        500000 non-null  bool   \n",
      " 40  Traffic_Signal         500000 non-null  bool   \n",
      " 41  Turning_Loop           500000 non-null  bool   \n",
      " 42  Sunrise_Sunset         498517 non-null  object \n",
      " 43  Civil_Twilight         498517 non-null  object \n",
      " 44  Nautical_Twilight      498517 non-null  object \n",
      " 45  Astronomical_Twilight  498517 non-null  object \n",
      "dtypes: bool(13), float64(12), int64(1), object(20)\n",
      "memory usage: 132.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID                        object\n",
       "Source                    object\n",
       "Severity                   int64\n",
       "Start_Time                object\n",
       "End_Time                  object\n",
       "Start_Lat                float64\n",
       "Start_Lng                float64\n",
       "End_Lat                  float64\n",
       "End_Lng                  float64\n",
       "Distance(mi)             float64\n",
       "Description               object\n",
       "Street                    object\n",
       "City                      object\n",
       "County                    object\n",
       "State                     object\n",
       "Zipcode                   object\n",
       "Country                   object\n",
       "Timezone                  object\n",
       "Airport_Code              object\n",
       "Weather_Timestamp         object\n",
       "Temperature(F)           float64\n",
       "Wind_Chill(F)            float64\n",
       "Humidity(%)              float64\n",
       "Pressure(in)             float64\n",
       "Visibility(mi)           float64\n",
       "Wind_Direction            object\n",
       "Wind_Speed(mph)          float64\n",
       "Precipitation(in)        float64\n",
       "Weather_Condition         object\n",
       "Amenity                     bool\n",
       "Bump                        bool\n",
       "Crossing                    bool\n",
       "Give_Way                    bool\n",
       "Junction                    bool\n",
       "No_Exit                     bool\n",
       "Railway                     bool\n",
       "Roundabout                  bool\n",
       "Station                     bool\n",
       "Stop                        bool\n",
       "Traffic_Calming             bool\n",
       "Traffic_Signal              bool\n",
       "Turning_Loop                bool\n",
       "Sunrise_Sunset            object\n",
       "Civil_Twilight            object\n",
       "Nautical_Twilight         object\n",
       "Astronomical_Twilight     object\n",
       "dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 3, 4, 1], dtype=int64)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Severity\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['LA', 'VA', 'CA', 'MN', 'MA', 'OR', 'FL', 'TX', 'IN', 'MT', 'AL',\n",
       "       'AZ', 'NY', 'NC', 'SC', 'KS', 'MI', 'CO', 'GA', 'MD', 'IL', 'PA',\n",
       "       'NM', 'NV', 'NE', 'NJ', 'TN', 'UT', 'RI', 'MS', 'IA', 'WA', 'MO',\n",
       "       'OH', 'OK', 'CT', 'DC', 'AR', 'WV', 'KY', 'WI', 'NH', 'WY', 'DE',\n",
       "       'VT', 'ID', 'ME', 'ND', 'SD'], dtype=object)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"State\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         LA\n",
       "1         VA\n",
       "2         CA\n",
       "3         MN\n",
       "4         CA\n",
       "          ..\n",
       "499995    OR\n",
       "499996    FL\n",
       "499997    AZ\n",
       "499998    VA\n",
       "499999    TN\n",
       "Name: State, Length: 500000, dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"State\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(500000, 46)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## _Checking for null :_\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID                        0.0000\n",
       "Source                    0.0000\n",
       "Severity                  0.0000\n",
       "Start_Time                0.0000\n",
       "End_Time                  0.0000\n",
       "Start_Lat                 0.0000\n",
       "Start_Lng                 0.0000\n",
       "End_Lat                  44.0754\n",
       "End_Lng                  44.0754\n",
       "Distance(mi)              0.0000\n",
       "Description               0.0002\n",
       "Street                    0.1382\n",
       "City                      0.0038\n",
       "County                    0.0000\n",
       "State                     0.0000\n",
       "Zipcode                   0.0232\n",
       "Country                   0.0000\n",
       "Timezone                  0.1014\n",
       "Airport_Code              0.2892\n",
       "Weather_Timestamp         1.5348\n",
       "Temperature(F)            2.0932\n",
       "Wind_Chill(F)            25.8034\n",
       "Humidity(%)               2.2260\n",
       "Pressure(in)              1.7856\n",
       "Visibility(mi)            2.2582\n",
       "Wind_Direction            2.2394\n",
       "Wind_Speed(mph)           7.3974\n",
       "Precipitation(in)        28.5232\n",
       "Weather_Condition         2.2202\n",
       "Amenity                   0.0000\n",
       "Bump                      0.0000\n",
       "Crossing                  0.0000\n",
       "Give_Way                  0.0000\n",
       "Junction                  0.0000\n",
       "No_Exit                   0.0000\n",
       "Railway                   0.0000\n",
       "Roundabout                0.0000\n",
       "Station                   0.0000\n",
       "Stop                      0.0000\n",
       "Traffic_Calming           0.0000\n",
       "Traffic_Signal            0.0000\n",
       "Turning_Loop              0.0000\n",
       "Sunrise_Sunset            0.2966\n",
       "Civil_Twilight            0.2966\n",
       "Nautical_Twilight         0.2966\n",
       "Astronomical_Twilight     0.2966\n",
       "dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum() / len(df) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\22831\\AppData\\Local\\Temp\\ipykernel_7164\\1639216733.py:1: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
      "\n",
      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
      "\n",
      "\n",
      "  df[\"End_Lat\"].fillna(df[\"End_Lat\"].mean(), inplace=True)\n"
     ]
    }
   ],
   "source": [
    "df[\"End_Lat\"].fillna(df[\"End_Lat\"].mean(), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\22831\\AppData\\Local\\Temp\\ipykernel_7164\\130499968.py:1: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
      "\n",
      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
      "\n",
      "\n",
      "  df[\"End_Lng\"].fillna(df[\"End_Lng\"].mean(), inplace=True)\n"
     ]
    }
   ],
   "source": [
    "df[\"End_Lng\"].fillna(df[\"End_Lng\"].mean(), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID                        0.0000\n",
       "Source                    0.0000\n",
       "Severity                  0.0000\n",
       "Start_Time                0.0000\n",
       "End_Time                  0.0000\n",
       "Start_Lat                 0.0000\n",
       "Start_Lng                 0.0000\n",
       "End_Lat                   0.0000\n",
       "End_Lng                   0.0000\n",
       "Distance(mi)              0.0000\n",
       "Description               0.0002\n",
       "Street                    0.1382\n",
       "City                      0.0038\n",
       "County                    0.0000\n",
       "State                     0.0000\n",
       "Zipcode                   0.0232\n",
       "Country                   0.0000\n",
       "Timezone                  0.1014\n",
       "Airport_Code              0.2892\n",
       "Weather_Timestamp         1.5348\n",
       "Temperature(F)            2.0932\n",
       "Wind_Chill(F)            25.8034\n",
       "Humidity(%)               2.2260\n",
       "Pressure(in)              1.7856\n",
       "Visibility(mi)            2.2582\n",
       "Wind_Direction            2.2394\n",
       "Wind_Speed(mph)           7.3974\n",
       "Precipitation(in)        28.5232\n",
       "Weather_Condition         2.2202\n",
       "Amenity                   0.0000\n",
       "Bump                      0.0000\n",
       "Crossing                  0.0000\n",
       "Give_Way                  0.0000\n",
       "Junction                  0.0000\n",
       "No_Exit                   0.0000\n",
       "Railway                   0.0000\n",
       "Roundabout                0.0000\n",
       "Station                   0.0000\n",
       "Stop                      0.0000\n",
       "Traffic_Calming           0.0000\n",
       "Traffic_Signal            0.0000\n",
       "Turning_Loop              0.0000\n",
       "Sunrise_Sunset            0.2966\n",
       "Civil_Twilight            0.2966\n",
       "Nautical_Twilight         0.2966\n",
       "Astronomical_Twilight     0.2966\n",
       "dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum() / len(df) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Weather_Condition\n",
       "Fair             166053\n",
       "Mostly Cloudy     65398\n",
       "Cloudy            53107\n",
       "Clear             52379\n",
       "Partly Cloudy     45125\n",
       "                  ...  \n",
       "Rain Shower           1\n",
       "Light Hail            1\n",
       "Light Haze            1\n",
       "Sleet / Windy         1\n",
       "Sand                  1\n",
       "Name: count, Length: 108, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_weather = df[\"Weather_Condition\"].value_counts()\n",
    "df_weather"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop([\"Description\", \"Zipcode\", \"Weather_Timestamp\"], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "City\n",
       "Miami            12141\n",
       "Houston          11031\n",
       "Los Angeles      10299\n",
       "Charlotte         8979\n",
       "Dallas            8245\n",
       "                 ...  \n",
       "Newcomerstown        1\n",
       "Lakehurst            1\n",
       "New Concord          1\n",
       "Smithmill            1\n",
       "Fair Haven           1\n",
       "Name: count, Length: 9488, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accident_by_cities = df[\"City\"].value_counts()\n",
    "accident_by_cities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "City\n",
       "Miami          12141\n",
       "Houston        11031\n",
       "Los Angeles    10299\n",
       "Charlotte       8979\n",
       "Dallas          8245\n",
       "Orlando         6985\n",
       "Austin          6269\n",
       "Raleigh         5553\n",
       "Nashville       4689\n",
       "Baton Rouge     4625\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accident_by_cities[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# _plot of cities vs no of accidents_\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(figure=(15, 9))\n",
    "accident_by_cities[:10].plot(kind=\"bar\")\n",
    "axes.set(title=\"Accident by cities\", xlabel=\"Cities\", ylabel=\"No of accidents\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# _Accidents by severity in pie chart_\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "severity = df[\"Severity\"].value_counts()\n",
    "label = [1, 2, 3, 4]\n",
    "plt.figure(figsize=(8, 8))\n",
    "plt.pie(\n",
    "    severity,\n",
    "    labels=label,  # type:ignore\n",
    "    autopct=\"%1.1f%%\",\n",
    "    colors=[\"#33c481\", \"#2285ba\", \"#ff4747\", \"#ffcc99\"],\n",
    "    pctdistance=0.85,\n",
    ")\n",
    "plt.title(\"Accidents by Severity\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "object\n",
      "object\n"
     ]
    }
   ],
   "source": [
    "print(df[\"Start_Time\"].dtypes)\n",
    "print(df[\"Start_Time\"].dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('<M8[ns]')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = df.astype({\"Start_Time\": \"datetime64[ns]\", \"End_Time\": \"datetime64[ns]\"})\n",
    "df1[\"Start_Time\"].dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2022-01-30 23:59:44')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1[\"Start_Time\"][2408]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2022-01-31 02:01:44')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1[\"End_Time\"][2408]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1[\"start_date\"] = [d.date() for d in df1[\"Start_Time\"]]\n",
    "df1[\"start_time\"] = [d.time() for d in df1[\"Start_Time\"]]\n",
    "df1[\"end_date\"] = [d.date() for d in df1[\"End_Time\"]]\n",
    "df1[\"end_time\"] = [d.time() for d in df1[\"End_Time\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         10:10:56\n",
       "1         23:37:14\n",
       "2         13:13:00\n",
       "3         17:43:04\n",
       "4         01:46:00\n",
       "            ...   \n",
       "499995    07:30:00\n",
       "499996    16:25:00\n",
       "499997    19:28:29\n",
       "499998    17:20:56\n",
       "499999    23:23:13\n",
       "Name: start_time, Length: 500000, dtype: object"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1[\"start_time\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# _No of accidents vs Time graph:_\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 5))\n",
    "sns.histplot(df1[\"Start_Time\"].dt.hour, bins=24)  # type: ignore\n",
    "\n",
    "plt.xlabel(\"Start Time\")\n",
    "plt.ylabel(\"Number of Occurence\")\n",
    "plt.title(\"Accidents Count By Time of Day\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 5))\n",
    "sns.histplot(df1[\"End_Time\"].dt.hour, bins=24)  # type: ignore\n",
    "\n",
    "plt.xlabel(\"End Time\")\n",
    "plt.ylabel(\"Number of Occurence\")\n",
    "plt.title(\"Accidents Count By Time of Day\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# _Plot of wether conditions to no of accidents_:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 5))\n",
    "df_weather.sort_values(ascending=False)[:20].plot(kind=\"bar\")\n",
    "ax.set(\n",
    "    title=\"Weather Conditions at Time of Accidents\",\n",
    "    xlabel=\"Weather\",\n",
    "    ylabel=\"Accidents Count\",\n",
    ")\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}