US-Accidents-Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "active-extraction",
   "metadata": {},
   "source": [
    "# <center>US Accidents Exploratory Data Analysis</center>\n",
    "\n",
    "TODO - talk about the dataset (source, what it contains, how it will be useful)\n",
    "  - Kaggle\n",
    "  - informaiton about accidents\n",
    "  - can use useful to prevent accidents\n",
    "  - mention that this does not contain data about New York"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "offshore-bernard",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Please provide your Kaggle credentials to download this dataset. Learn more: http://bit.ly/kaggle-creds\n",
      "Your Kaggle username: "
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      " hassanaftab93\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your Kaggle Key: "
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      " ································\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0.00/290M [00:00<?, ?B/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading us-accidents.zip to ./us-accidents\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 290M/290M [01:22<00:00, 3.68MB/s] \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "import opendatasets as od\n",
    "\n",
    "download_url = 'https://www.kaggle.com/sobhanmoosavi/us-accidents'\n",
    "\n",
    "od.download(download_url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "decent-wallace",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_filename = './us-accidents/US_Accidents_Dec20_Updated.csv'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "incident-residence",
   "metadata": {},
   "source": [
    "## Data Preparation and Cleaning\n",
    "\n",
    "1. Load the file using Pandas\n",
    "2. Look at some information about the data & the columns\n",
    "3. Fix any missing or incorrect values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "norman-pocket",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "developed-sherman",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(data_filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "composite-african",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</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>Description</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-1</td>\n",
       "      <td>2</td>\n",
       "      <td>2019-05-21 08:29:55</td>\n",
       "      <td>2019-05-21 09:29:40</td>\n",
       "      <td>34.808868</td>\n",
       "      <td>-82.269157</td>\n",
       "      <td>34.808868</td>\n",
       "      <td>-82.269157</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Accident on Tanner Rd at Pennbrooke Ln.</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>1</th>\n",
       "      <td>A-2</td>\n",
       "      <td>2</td>\n",
       "      <td>2019-10-07 17:43:09</td>\n",
       "      <td>2019-10-07 19:42:50</td>\n",
       "      <td>35.090080</td>\n",
       "      <td>-80.745560</td>\n",
       "      <td>35.090080</td>\n",
       "      <td>-80.745560</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Accident on Houston Branch Rd at Providence Br...</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>2</th>\n",
       "      <td>A-3</td>\n",
       "      <td>2</td>\n",
       "      <td>2020-12-13 21:53:00</td>\n",
       "      <td>2020-12-13 22:44:00</td>\n",
       "      <td>37.145730</td>\n",
       "      <td>-121.985052</td>\n",
       "      <td>37.165850</td>\n",
       "      <td>-121.988062</td>\n",
       "      <td>1.4</td>\n",
       "      <td>Stationary traffic on CA-17 from Summit Rd (CA...</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",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A-4</td>\n",
       "      <td>2</td>\n",
       "      <td>2018-04-17 16:51:23</td>\n",
       "      <td>2018-04-17 17:50:46</td>\n",
       "      <td>39.110390</td>\n",
       "      <td>-119.773781</td>\n",
       "      <td>39.110390</td>\n",
       "      <td>-119.773781</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Accident on US-395 Southbound at Topsy Ln.</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>4</th>\n",
       "      <td>A-5</td>\n",
       "      <td>3</td>\n",
       "      <td>2016-08-31 17:40:49</td>\n",
       "      <td>2016-08-31 18:10:49</td>\n",
       "      <td>26.102942</td>\n",
       "      <td>-80.265091</td>\n",
       "      <td>26.102942</td>\n",
       "      <td>-80.265091</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Accident on I-595 Westbound at Exit 4 / Pine I...</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",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 47 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    ID  Severity           Start_Time             End_Time  Start_Lat  \\\n",
       "0  A-1         2  2019-05-21 08:29:55  2019-05-21 09:29:40  34.808868   \n",
       "1  A-2         2  2019-10-07 17:43:09  2019-10-07 19:42:50  35.090080   \n",
       "2  A-3         2  2020-12-13 21:53:00  2020-12-13 22:44:00  37.145730   \n",
       "3  A-4         2  2018-04-17 16:51:23  2018-04-17 17:50:46  39.110390   \n",
       "4  A-5         3  2016-08-31 17:40:49  2016-08-31 18:10:49  26.102942   \n",
       "\n",
       "    Start_Lng    End_Lat     End_Lng  Distance(mi)  \\\n",
       "0  -82.269157  34.808868  -82.269157           0.0   \n",
       "1  -80.745560  35.090080  -80.745560           0.0   \n",
       "2 -121.985052  37.165850 -121.988062           1.4   \n",
       "3 -119.773781  39.110390 -119.773781           0.0   \n",
       "4  -80.265091  26.102942  -80.265091           0.0   \n",
       "\n",
       "                                         Description  ...  Roundabout Station  \\\n",
       "0            Accident on Tanner Rd at Pennbrooke Ln.  ...       False   False   \n",
       "1  Accident on Houston Branch Rd at Providence Br...  ...       False   False   \n",
       "2  Stationary traffic on CA-17 from Summit Rd (CA...  ...       False   False   \n",
       "3         Accident on US-395 Southbound at Topsy Ln.  ...       False   False   \n",
       "4  Accident on I-595 Westbound at Exit 4 / Pine I...  ...       False   False   \n",
       "\n",
       "    Stop Traffic_Calming Traffic_Signal Turning_Loop Sunrise_Sunset  \\\n",
       "0  False           False          False        False            Day   \n",
       "1  False           False          False        False            Day   \n",
       "2  False           False          False        False          Night   \n",
       "3  False           False           True        False            Day   \n",
       "4  False           False           True        False            Day   \n",
       "\n",
       "  Civil_Twilight Nautical_Twilight Astronomical_Twilight  \n",
       "0            Day               Day                   Day  \n",
       "1            Day               Day                   Day  \n",
       "2          Night             Night                 Night  \n",
       "3            Day               Day                   Day  \n",
       "4            Day               Day                   Day  \n",
       "\n",
       "[5 rows x 47 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "interstate-eating",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2906610 entries, 0 to 2906609\n",
      "Data columns (total 47 columns):\n",
      " #   Column                 Dtype  \n",
      "---  ------                 -----  \n",
      " 0   ID                     object \n",
      " 1   Severity               int64  \n",
      " 2   Start_Time             object \n",
      " 3   End_Time               object \n",
      " 4   Start_Lat              float64\n",
      " 5   Start_Lng              float64\n",
      " 6   End_Lat                float64\n",
      " 7   End_Lng                float64\n",
      " 8   Distance(mi)           float64\n",
      " 9   Description            object \n",
      " 10  Number                 float64\n",
      " 11  Street                 object \n",
      " 12  Side                   object \n",
      " 13  City                   object \n",
      " 14  County                 object \n",
      " 15  State                  object \n",
      " 16  Zipcode                object \n",
      " 17  Country                object \n",
      " 18  Timezone               object \n",
      " 19  Airport_Code           object \n",
      " 20  Weather_Timestamp      object \n",
      " 21  Temperature(F)         float64\n",
      " 22  Wind_Chill(F)          float64\n",
      " 23  Humidity(%)            float64\n",
      " 24  Pressure(in)           float64\n",
      " 25  Visibility(mi)         float64\n",
      " 26  Wind_Direction         object \n",
      " 27  Wind_Speed(mph)        float64\n",
      " 28  Precipitation(in)      float64\n",
      " 29  Weather_Condition      object \n",
      " 30  Amenity                bool   \n",
      " 31  Bump                   bool   \n",
      " 32  Crossing               bool   \n",
      " 33  Give_Way               bool   \n",
      " 34  Junction               bool   \n",
      " 35  No_Exit                bool   \n",
      " 36  Railway                bool   \n",
      " 37  Roundabout             bool   \n",
      " 38  Station                bool   \n",
      " 39  Stop                   bool   \n",
      " 40  Traffic_Calming        bool   \n",
      " 41  Traffic_Signal         bool   \n",
      " 42  Turning_Loop           bool   \n",
      " 43  Sunrise_Sunset         object \n",
      " 44  Civil_Twilight         object \n",
      " 45  Nautical_Twilight      object \n",
      " 46  Astronomical_Twilight  object \n",
      "dtypes: bool(13), float64(13), int64(1), object(20)\n",
      "memory usage: 790.0+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "excess-prototype",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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>Number</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>2.906610e+06</td>\n",
       "      <td>2.906610e+06</td>\n",
       "      <td>2.906610e+06</td>\n",
       "      <td>2.623789e+06</td>\n",
       "      <td>2.623789e+06</td>\n",
       "      <td>2.906610e+06</td>\n",
       "      <td>1.014938e+06</td>\n",
       "      <td>2.839386e+06</td>\n",
       "      <td>1.722751e+06</td>\n",
       "      <td>2.835340e+06</td>\n",
       "      <td>2.849702e+06</td>\n",
       "      <td>2.834532e+06</td>\n",
       "      <td>2.599447e+06</td>\n",
       "      <td>1.605284e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.288649e+00</td>\n",
       "      <td>3.653027e+01</td>\n",
       "      <td>-9.642676e+01</td>\n",
       "      <td>3.651733e+01</td>\n",
       "      <td>-9.620367e+01</td>\n",
       "      <td>3.980541e-01</td>\n",
       "      <td>6.789728e+03</td>\n",
       "      <td>6.098873e+01</td>\n",
       "      <td>5.499048e+01</td>\n",
       "      <td>6.537758e+01</td>\n",
       "      <td>2.965685e+01</td>\n",
       "      <td>9.116766e+00</td>\n",
       "      <td>7.818111e+00</td>\n",
       "      <td>1.119459e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5.541618e-01</td>\n",
       "      <td>5.013964e+00</td>\n",
       "      <td>1.775412e+01</td>\n",
       "      <td>5.016609e+00</td>\n",
       "      <td>1.765971e+01</td>\n",
       "      <td>1.592556e+00</td>\n",
       "      <td>1.697225e+04</td>\n",
       "      <td>1.845258e+01</td>\n",
       "      <td>2.219542e+01</td>\n",
       "      <td>2.287854e+01</td>\n",
       "      <td>9.094133e-01</td>\n",
       "      <td>2.837271e+00</td>\n",
       "      <td>5.431171e+00</td>\n",
       "      <td>1.540812e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>2.455527e+01</td>\n",
       "      <td>-1.246238e+02</td>\n",
       "      <td>2.455527e+01</td>\n",
       "      <td>-1.246238e+02</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-8.900000e+01</td>\n",
       "      <td>-8.900000e+01</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>3.366453e+01</td>\n",
       "      <td>-1.178232e+02</td>\n",
       "      <td>3.364659e+01</td>\n",
       "      <td>-1.177020e+02</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>9.650000e+02</td>\n",
       "      <td>4.890000e+01</td>\n",
       "      <td>3.900000e+01</td>\n",
       "      <td>4.900000e+01</td>\n",
       "      <td>2.959000e+01</td>\n",
       "      <td>1.000000e+01</td>\n",
       "      <td>4.600000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>3.609977e+01</td>\n",
       "      <td>-9.116690e+01</td>\n",
       "      <td>3.605898e+01</td>\n",
       "      <td>-9.105163e+01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>3.093000e+03</td>\n",
       "      <td>6.300000e+01</td>\n",
       "      <td>5.800000e+01</td>\n",
       "      <td>6.800000e+01</td>\n",
       "      <td>2.992000e+01</td>\n",
       "      <td>1.000000e+01</td>\n",
       "      <td>7.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.000000e+00</td>\n",
       "      <td>4.037505e+01</td>\n",
       "      <td>-8.085814e+01</td>\n",
       "      <td>4.033133e+01</td>\n",
       "      <td>-8.084679e+01</td>\n",
       "      <td>2.790000e-01</td>\n",
       "      <td>7.976000e+03</td>\n",
       "      <td>7.500000e+01</td>\n",
       "      <td>7.200000e+01</td>\n",
       "      <td>8.500000e+01</td>\n",
       "      <td>3.007000e+01</td>\n",
       "      <td>1.000000e+01</td>\n",
       "      <td>1.040000e+01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4.000000e+00</td>\n",
       "      <td>4.900220e+01</td>\n",
       "      <td>-6.711317e+01</td>\n",
       "      <td>4.907500e+01</td>\n",
       "      <td>-6.710924e+01</td>\n",
       "      <td>3.336300e+02</td>\n",
       "      <td>9.999997e+06</td>\n",
       "      <td>2.030000e+02</td>\n",
       "      <td>1.740000e+02</td>\n",
       "      <td>1.000000e+02</td>\n",
       "      <td>5.804000e+01</td>\n",
       "      <td>1.400000e+02</td>\n",
       "      <td>9.840000e+02</td>\n",
       "      <td>2.400000e+01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Severity     Start_Lat     Start_Lng       End_Lat       End_Lng  \\\n",
       "count  2.906610e+06  2.906610e+06  2.906610e+06  2.623789e+06  2.623789e+06   \n",
       "mean   2.288649e+00  3.653027e+01 -9.642676e+01  3.651733e+01 -9.620367e+01   \n",
       "std    5.541618e-01  5.013964e+00  1.775412e+01  5.016609e+00  1.765971e+01   \n",
       "min    1.000000e+00  2.455527e+01 -1.246238e+02  2.455527e+01 -1.246238e+02   \n",
       "25%    2.000000e+00  3.366453e+01 -1.178232e+02  3.364659e+01 -1.177020e+02   \n",
       "50%    2.000000e+00  3.609977e+01 -9.116690e+01  3.605898e+01 -9.105163e+01   \n",
       "75%    3.000000e+00  4.037505e+01 -8.085814e+01  4.033133e+01 -8.084679e+01   \n",
       "max    4.000000e+00  4.900220e+01 -6.711317e+01  4.907500e+01 -6.710924e+01   \n",
       "\n",
       "       Distance(mi)        Number  Temperature(F)  Wind_Chill(F)  \\\n",
       "count  2.906610e+06  1.014938e+06    2.839386e+06   1.722751e+06   \n",
       "mean   3.980541e-01  6.789728e+03    6.098873e+01   5.499048e+01   \n",
       "std    1.592556e+00  1.697225e+04    1.845258e+01   2.219542e+01   \n",
       "min    0.000000e+00  0.000000e+00   -8.900000e+01  -8.900000e+01   \n",
       "25%    0.000000e+00  9.650000e+02    4.890000e+01   3.900000e+01   \n",
       "50%    0.000000e+00  3.093000e+03    6.300000e+01   5.800000e+01   \n",
       "75%    2.790000e-01  7.976000e+03    7.500000e+01   7.200000e+01   \n",
       "max    3.336300e+02  9.999997e+06    2.030000e+02   1.740000e+02   \n",
       "\n",
       "        Humidity(%)  Pressure(in)  Visibility(mi)  Wind_Speed(mph)  \\\n",
       "count  2.835340e+06  2.849702e+06    2.834532e+06     2.599447e+06   \n",
       "mean   6.537758e+01  2.965685e+01    9.116766e+00     7.818111e+00   \n",
       "std    2.287854e+01  9.094133e-01    2.837271e+00     5.431171e+00   \n",
       "min    1.000000e+00  0.000000e+00    0.000000e+00     0.000000e+00   \n",
       "25%    4.900000e+01  2.959000e+01    1.000000e+01     4.600000e+00   \n",
       "50%    6.800000e+01  2.992000e+01    1.000000e+01     7.000000e+00   \n",
       "75%    8.500000e+01  3.007000e+01    1.000000e+01     1.040000e+01   \n",
       "max    1.000000e+02  5.804000e+01    1.400000e+02     9.840000e+02   \n",
       "\n",
       "       Precipitation(in)  \n",
       "count       1.605284e+06  \n",
       "mean        1.119459e-02  \n",
       "std         1.540812e-01  \n",
       "min         0.000000e+00  \n",
       "25%         0.000000e+00  \n",
       "50%         0.000000e+00  \n",
       "75%         0.000000e+00  \n",
       "max         2.400000e+01  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "twenty-lounge",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numerics = ['int16', 'int32', 'int64', 'float16', 'float32', 'float64']\n",
    "\n",
    "numeric_df = df.select_dtypes(include=numerics)\n",
    "len(numeric_df.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "involved-manual",
   "metadata": {},
   "source": [
    "Percentage of missing values per column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "conscious-still",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Number                   0.650817\n",
       "Precipitation(in)        0.447713\n",
       "Wind_Chill(F)            0.407299\n",
       "Wind_Speed(mph)          0.105677\n",
       "End_Lat                  0.097303\n",
       "End_Lng                  0.097303\n",
       "Visibility(mi)           0.024798\n",
       "Weather_Condition        0.024720\n",
       "Humidity(%)              0.024520\n",
       "Temperature(F)           0.023128\n",
       "Wind_Direction           0.021838\n",
       "Pressure(in)             0.019579\n",
       "Weather_Timestamp        0.016141\n",
       "Airport_Code             0.002273\n",
       "Timezone                 0.001180\n",
       "Zipcode                  0.000383\n",
       "Sunrise_Sunset           0.000038\n",
       "Civil_Twilight           0.000038\n",
       "Nautical_Twilight        0.000038\n",
       "Astronomical_Twilight    0.000038\n",
       "City                     0.000037\n",
       "Country                  0.000000\n",
       "Junction                 0.000000\n",
       "Start_Time               0.000000\n",
       "End_Time                 0.000000\n",
       "Start_Lat                0.000000\n",
       "Turning_Loop             0.000000\n",
       "Traffic_Signal           0.000000\n",
       "Traffic_Calming          0.000000\n",
       "Stop                     0.000000\n",
       "Station                  0.000000\n",
       "Roundabout               0.000000\n",
       "Railway                  0.000000\n",
       "No_Exit                  0.000000\n",
       "Give_Way                 0.000000\n",
       "State                    0.000000\n",
       "Crossing                 0.000000\n",
       "Bump                     0.000000\n",
       "Amenity                  0.000000\n",
       "Start_Lng                0.000000\n",
       "Distance(mi)             0.000000\n",
       "Description              0.000000\n",
       "Street                   0.000000\n",
       "Side                     0.000000\n",
       "Severity                 0.000000\n",
       "County                   0.000000\n",
       "ID                       0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_percentages = df.isna().sum().sort_values(ascending=False) / len(df)\n",
    "missing_percentages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "right-placement",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "missing_percentages[missing_percentages != 0].plot(kind='barh')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "logical-impression",
   "metadata": {},
   "source": [
    "## Exploratory Analysis and Visualization\n",
    "\n",
    "Columns we'll analyze:\n",
    "\n",
    "1. City\n",
    "2. Start Time\n",
    "3. Start Lat, Start Lng\n",
    "4. Temperature\n",
    "5. Weather Condition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "residential-austin",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ID', 'Severity', 'Start_Time', 'End_Time', 'Start_Lat', 'Start_Lng',\n",
       "       'End_Lat', 'End_Lng', 'Distance(mi)', 'Description', 'Number', 'Street',\n",
       "       'Side', '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": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "sustained-processor",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                Greenville\n",
       "1                 Charlotte\n",
       "2                 Los Gatos\n",
       "3               Carson City\n",
       "4           Fort Lauderdale\n",
       "                 ...       \n",
       "2906605             Houston\n",
       "2906606              Colton\n",
       "2906607               Miami\n",
       "2906608      Salt Lake City\n",
       "2906609    Colorado Springs\n",
       "Name: City, Length: 2906610, dtype: object"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.City"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "posted-classic",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11790"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cities = df.City.unique()\n",
    "len(cities)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "regulated-power",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Los Angeles        68411\n",
       "Houston            68265\n",
       "Charlotte          56176\n",
       "Miami              49965\n",
       "Dallas             48525\n",
       "                   ...  \n",
       "Aroda                  1\n",
       "East Rockaway          1\n",
       "East Longmeadow        1\n",
       "South Hampton          1\n",
       "Peetz                  1\n",
       "Name: City, Length: 11789, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cities_by_accident = df.City.value_counts()\n",
    "cities_by_accident"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "retired-gilbert",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Los Angeles      68411\n",
       "Houston          68265\n",
       "Charlotte        56176\n",
       "Miami            49965\n",
       "Dallas           48525\n",
       "Austin           38808\n",
       "Raleigh          31355\n",
       "Atlanta          29244\n",
       "Sacramento       28984\n",
       "Orlando          28092\n",
       "Nashville        25277\n",
       "Baton Rouge      25080\n",
       "Minneapolis      22469\n",
       "San Diego        22329\n",
       "Phoenix          21370\n",
       "Oklahoma City    21292\n",
       "Portland         19432\n",
       "Richmond         18343\n",
       "Seattle          17384\n",
       "Saint Paul       17266\n",
       "Name: City, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cities_by_accident.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "passing-barrier",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cities_by_accident[:20].plot(kind='barh')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "functional-pollution",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "sns.set_style(\"darkgrid\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "senior-works",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='City', ylabel='Count'>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(cities_by_accident, log_scale=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "labeled-asian",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Lublin             1\n",
       "Sobieski           1\n",
       "Township 9         1\n",
       "Annada             1\n",
       "Adell              1\n",
       "                  ..\n",
       "Aroda              1\n",
       "East Rockaway      1\n",
       "East Longmeadow    1\n",
       "South Hampton      1\n",
       "Peetz              1\n",
       "Name: City, Length: 1306, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cities_by_accident[cities_by_accident == 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "chubby-caution",
   "metadata": {},
   "source": [
    "### Start Time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "lonely-penalty",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          2019-05-21 08:29:55\n",
       "1          2019-10-07 17:43:09\n",
       "2          2020-12-13 21:53:00\n",
       "3          2018-04-17 16:51:23\n",
       "4          2016-08-31 17:40:49\n",
       "                  ...         \n",
       "2906605    2018-06-28 08:49:01\n",
       "2906606    2019-01-10 02:01:01\n",
       "2906607    2020-11-23 12:52:00\n",
       "2906608    2019-12-29 22:38:00\n",
       "2906609    2018-03-26 13:35:03\n",
       "Name: Start_Time, Length: 2906610, dtype: object"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Start_Time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "functional-triangle",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.Start_Time = pd.to_datetime(df.Start_Time)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "spoken-floating",
   "metadata": {},
   "source": [
    "- Figure out how to show percentages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "published-orbit",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Start_Time'>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df.Start_Time.dt.hour, bins=24, kde=False, norm_hist=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "presidential-productivity",
   "metadata": {},
   "source": [
    "- A high percentage of accidents occur between 6 am to 10 am (probably people in a hurry to get to work)\n",
    "- Next higest percentage is 3 pm to 6 pm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "ruled-sport",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Start_Time'>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df.Start_Time.dt.dayofweek, bins=7, kde=False, norm_hist=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "played-biology",
   "metadata": {},
   "source": [
    "Is the distribution of accidents by hour the same on weekends as on weekdays?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "unusual-thompson",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Start_Time'>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sundays_start_time = df.Start_Time[df.Start_Time.dt.dayofweek == 6]\n",
    "sns.distplot(sundays_start_time.dt.hour, bins=24, kde=False, norm_hist=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "correct-lawsuit",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Start_Time'>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEECAYAAADTdnSRAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAXbUlEQVR4nO3dfVBU1/3H8c+yyyKwq5tUtA8GqyjVJKWI6UPqkA6xNNVJH+KkaGlxGp3WcabThmrE2gYNUkSj9iGxzJgSnVJboJrpOO1kOmOqocEkTZmhFFOsxY6Z1kyDk6DsAruwe39/8HMTEnBZZFk4+379xXLO4X7vYfPhenLvWZtlWZYAAEZJincBAICJR7gDgIEIdwAwEOEOAAYi3AHAQI54F3BdKBRSMDi+G3fsdtu4x5qEeRjCPAxhHoaYPA/JyfZR26ZMuAeDlrq7e8c11uNJG/dYkzAPQ5iHIczDEJPnISPDPWobyzIAYCDCHQAMRLgDgIEIdwAwEOEOAAYi3AHAQIQ7ABiIcAcAAxHuAGCgKfOEKszit6TegWBUY9KS7UqxxaggIMEQ7oiJ3oGgTne8EdWYgiVzlOIcfa8MAGPHsgwAGIhwBwADsSyDMYl2Dd3QHVaBaYNwx5hEu4Z+9+KMGFYDIBKWZQDAQIQ7ABiIcAcAAxHuAGAgwh0ADES4A4CBCHcAMFDE+9xDoZB27dql8+fPy+l0qrKyUvPnzw+3NzY2qr6+Xg6HQ5s3b1ZBQYEuX76sbdu2ybIszZo1SwcOHFBqampMTwQA8LaIV+6nTp1SIBBQQ0ODtmzZourq6nBbV1eX6urqVF9fr9raWh08eFCBQEBHjx7VqlWrdOzYMS1evFjHjx+P6UkAAIaLGO4tLS3Kz8+XJOXm5qq9vT3c1tbWpmXLlsnpdMrtdiszM1MdHR1aunSprl27Jknyer1yOHgQFgAmU8TU9Xq9crlc4dd2u12Dg4NyOBzyer1yu93htvT0dHm9Xr3//e/XgQMH9Pvf/16BQEDf/va3IxZit9vk8aSN6yTs9qRxjzVJLOeh72q/0lKdY+7vsCdF1V+SZqQkyzNrRrSlvQfvhyHMw5BEnYeI4e5yueTz+cKvQ6FQ+Er83W0+n09ut1vl5eXas2eP8vPzdebMGZWVlenw4cM3PE4waKm7u3dcJ+HxpI17rEliOQ/9gaB6+wJj7j8YDEXVX5L6/QPq7g5FW9p78H4YwjwMMXkeMjLco7ZFXJbJy8tTU1OTJKm1tVXZ2dnhtpycHLW0tMjv96unp0ednZ3Kzs7WzJkzw1f0c+bMCS/RAAAmR8Qr98LCQjU3N2vdunWyLEtVVVU6cuSIMjMztXLlSpWUlKi4uFiWZam0tFQpKSl69NFHVVFRoVAoJMuyVF5ePhnnAgD4fzbLsqbEztsDA0GWZW5SLOfhrUD0W/6+eKErqmMULJmjWybgY/Z4PwxhHoaYPA83tSwDAJh+CHcAMBDhDgAGItwBwECEOwAYiH0BAIzKbw19OHo00pLtSrHFqCCMGeEOJJBowzpoSU3nx34LrDR0S2vKBNzSiptDuAMJpHcg+ucVMD2x5g4ABiLcAcBAhDsAGIhwBwADEe4AYCDCHQAMRLgDgIEIdwAwEOEOAAYi3AHAQIQ7ABiIcAcAAxHuAGAgwh0ADES4A4CBCHcAMBDhDgAGItwBwECEOwAYiHAHAAMR7gBgIMIdAAxEuAOAgQh3ADCQI94FABgfvyX1DgRHbe+72q/+wPD2oBXrqjBVEO7ANNU7ENTpjjdGbU9Ldaq3LzDse3cvzoh1WZgiWJYBAAMR7gBgIMIdAAxEuAOAgQh3ADAQ4Q4ABooY7qFQSOXl5Vq7dq1KSkp06dKlYe2NjY1as2aNioqKdPr0aUlSb2+vtm3bpuLiYn3lK19RW1tbbKoHAIwo4n3up06dUiAQUENDg1pbW1VdXa2amhpJUldXl+rq6nTixAn5/X4VFxdrxYoVqq2t1eLFi7Vv3z51dHSoo6NDOTk5MT8ZAMCQiOHe0tKi/Px8SVJubq7a29vDbW1tbVq2bJmcTqecTqcyMzPV0dGhF154QatWrdLGjRuVnp6unTt3RizEbrfJ40kb10nY7UnjHmuSWM5D39V+paU6x9zfYU+Kqr8kzUhJlmfWjGhLe49EeT9E+p0kJdne0x7t7yWev8eJkijvh3eLGO5er1culyv82m63a3BwUA6HQ16vV263O9yWnp4ur9ert956S9euXVNtba1+97vfae/evdq3b98NjxMMWuru7h3XSXg8aeMea5JYzkN/IPiepx1vZDAYiqq/JPX7B9TdHYq2tPdIlPdDpN/JSE+oRvt7iefvcaKY/H7IyHCP2hZxzd3lcsnn84Vfh0IhORyOEdt8Pp/cbrc8Ho/uvfdeSVJBQcGwq30AQOxFDPe8vDw1NTVJklpbW5WdnR1uy8nJUUtLi/x+v3p6etTZ2ans7GwtX75czz//vCTplVde0aJFi2JUPgBgJBGXZQoLC9Xc3Kx169bJsixVVVXpyJEjyszM1MqVK1VSUqLi4mJZlqXS0lKlpKRo06ZN+uEPf6i1a9fK4XBo7969k3EuAKYAm82mtwKj71Y5krRku1JsMSooQUUM96SkJFVUVAz7XlZWVvjroqIiFRUVDWv3eDx68sknJ6hETLRIW8WOhK1iMVZ9gyG9eKErqjEFS+YoxWmPUUWJiS1/E1CkrWJHwlaxwPTCE6oAYCDCHQAMRLgDgIEIdwAwEOEOAAYi3AHAQNwKCUwBPHuAiUa4A1MAzx5gorEsAwAG4sodUwZ7kgATh3DHlMGeJMDEYVkGAAxEuAOAgQh3ADAQ4Q4ABiLcAcBA3C0DxEC0T5zytCkmGuEOxEC0T5zytCkmGssyAGAgwh0ADES4A4CBCHcAMBDhDgAGItwBwEDcColpbaRtgvuu9qt/lK2D2SIYiYJwx7Q20jbBaalO9fYFRux/79K56rWie2KIPwiYjgh3JBT2jEeiINyBCMbzCVFsJ4B4I9yBCMZztc92Aog37pYBAAMR7gBgIMIdAAxEuAOAgQh3ADAQ4Q4ABiLcAcBAhDsAGIhwBwADEe4AYKCI4R4KhVReXq61a9eqpKREly5dGtbe2NioNWvWqKioSKdPnx7W9pe//EWf+cxnJrZiAEBEEfeWOXXqlAKBgBoaGtTa2qrq6mrV1NRIkrq6ulRXV6cTJ07I7/eruLhYK1askNPp1Ouvv64jR45ocHAw5icBABguYri3tLQoPz9fkpSbm6v29vZwW1tbm5YtWyan0ymn06nMzEx1dHToIx/5iHbu3Kndu3drzZo1YyrEbrfJ40kb10nY7UnjHmuSsc5D39V+paU6o/rZDntSVGOi7T+RY5KSbKP+nHjWNdnHGGkepkJdI0l2OtRnG/um+ekpds2ckTymvomaDxHD3ev1yuVyhV/b7XYNDg7K4XDI6/XK7XaH29LT0+X1elVRUaENGzZo7ty5Yy4kGLTU3d0bZflDPJ60cY81yVjnoT8QHPXDLEYzGAxFNSba/hM55kYf1hHPuib7GCPNw1SoayQ9fQNR7bxZsGSOQv0DY+prcj5kZLhHbYu45u5yueTz+cKvQ6GQHA7HiG0+n0/Jycn661//qkOHDqmkpERXr15VaWnpzdQPAIhSxCv3vLw8nT59WqtXr1Zra6uys7PDbTk5OfrJT34iv9+vQCCgzs5O5eTk6I9//GO4z4oVK/TjH/84NtUDAEYUMdwLCwvV3NysdevWybIsVVVV6ciRI8rMzNTKlStVUlKi4uJiWZal0tJSpaSkTEbdAIAbiBjuSUlJqqioGPa9rKys8NdFRUUqKioadXxzc/NNlAcAGA8eYgIAAxHuAGAgwh0ADES4A4CBCHcAMBDhDgAGItwBwECEOwAYiHAHAAMR7gBgIMIdAAwUcW8ZTG1+S+odCEoa+hCO/kAw4pigFeuqAMQb4T7N9Q4EdbrjDUk3/pCKd7p7cUasywIQZyzLAICBCHcAMBDhDgAGItwBwED8D1UA047NZtNbY7gzTHr7LrK0ZLtSbDEubAoh3AFMO32DIb14oWtMfa/fRVawZI5SnPYYVzZ1sCwDAAYi3AHAQIQ7ABiIcAcAAxHuAGAgwh0ADES4A4CBCHcAMBDhDgAGItwBwECEOwAYiHAHAAMR7gBgIMIdAAzElr8AEkI0e8BfN533gCfcASSEaPaAv2467wHPsgwAGIgr9ynGb0m9A2P/p2PQimExQIKLdilnKi3jEO5TTO9AUKc73hhz/7sXZ8SwGiCxRbuUM5WWcViWAQADRbxyD4VC2rVrl86fPy+n06nKykrNnz8/3N7Y2Kj6+no5HA5t3rxZBQUFunz5snbs2KFgMCjLslRRUaGFCxfG9EQAAG+LeOV+6tQpBQIBNTQ0aMuWLaqurg63dXV1qa6uTvX19aqtrdXBgwcVCAT005/+VF//+tdVV1enTZs26eDBgzE9CQDAcBGv3FtaWpSfny9Jys3NVXt7e7itra1Ny5Ytk9PplNPpVGZmpjo6OlRWVia32y1JCgaDSklJiVH5AICRRAx3r9crl8sVfm232zU4OCiHwyGv1xsOcUlKT0+X1+vVrbfeKkm6ePGi9u7dq0OHDkUsxG63yeNJG885yG5PGvfYqabvar/SUp1j7u+wJ4X7JyXZxjT2nWPGc5xY9J/IMTeah3jWNdnHGGkepkJdEzEmmv7X52Ey6pqRkizPrBlRHSNWIoa7y+WSz+cLvw6FQnI4HCO2+Xy+cNi/9NJLeuyxx7Rv374xrbcHg5a6u3ujPgFJ8njSxj12qukPBNXbFxhz/8FgKNw/LdU5prHvHDOe48Si/0SOudE8xLOuyT7GSPMwFeqaiDHR9L8+D5NRV79/QN3doaiOcTMyMtyjtkVcc8/Ly1NTU5MkqbW1VdnZ2eG2nJwctbS0yO/3q6enR52dncrOztZLL72kH/3oR/rFL36hj370oxNwCgCAaES8ci8sLFRzc7PWrVsny7JUVVWlI0eOKDMzUytXrlRJSYmKi4tlWZZKS0uVkpKiqqoqDQwMaPv27ZKkBQsWqKKiIuYnAwAYEjHck5KS3hPMWVlZ4a+LiopUVFQ0rP3kyZMTVB4AYDx4QhUAJshU2nmScAeACTKVdp5k+wEAMBDhDgAGItwBwECEOwAYiHAHAAMR7gBgIMIdAAxkxH3u1/oHpu3nHAJALBgR7j5/dJ87OpU+5xAAYoFlGQAwEOEOAAYyYlkmWuPZ3MfpsCswODU2BAKASBIy3Mezuc/dizOiHnPv0rnqtayoxgSj6w4AI0rIcJ8s4/0jAgA3izV3ADAQ4Q4ABiLcAcBAhDsAGIhwBwADEe4AYCDCHQAMRLgDgIEIdwAwEOEOAAYi3AHAQIQ7ABiIcAcAAxHuAGAgwh0ADES4A4CBCHcAMBDhDgAGItwBwECEOwAYiHAHAAMR7gBgIMIdAAxEuAOAgRyROoRCIe3atUvnz5+X0+lUZWWl5s+fH25vbGxUfX29HA6HNm/erIKCAr355pvaunWr+vv7NWfOHO3Zs0epqakxPREAwNsiXrmfOnVKgUBADQ0N2rJli6qrq8NtXV1dqqurU319vWpra3Xw4EEFAgH9/Oc/1/33369f//rXuv3229XQ0BDTkwAADBcx3FtaWpSfny9Jys3NVXt7e7itra1Ny5Ytk9PplNvtVmZmpjo6OoaNueeee3T27NkYlQ8AGEnEZRmv1yuXyxV+bbfbNTg4KIfDIa/XK7fbHW5LT0+X1+sd9v309HT19PRELCQ52a6MDHfEfqPZtDI7qv45mbdEfYypOoa6qCuWY6gr9nXFQsQrd5fLJZ/PF34dCoXkcDhGbPP5fHK73cO+7/P5NHPmzImuGwBwAxHDPS8vT01NTZKk1tZWZWe/fYWck5OjlpYW+f1+9fT0qLOzU9nZ2crLy9Pzzz8vSWpqatLy5ctjVD4AYCQ2y7KsG3W4frfMP//5T1mWpaqqKjU1NSkzM1MrV65UY2OjGhoaZFmWNm3apPvuu09XrlxRWVmZfD6fbrnlFh04cEBpaWmTdU4AkPAihjsAYPrhISYAMBDhDgAGItwBwEAR73OfqiJti5BoHnjggfDzCPPmzdOePXviXNHk+tvf/qb9+/errq5Oly5d0vbt22Wz2bR48WLt3LlTSUmJcR3zznl49dVXtWnTJn34wx+WJH31q1/V6tWr41tgjA0MDGjHjh3673//q0AgoM2bN2vRokUJ+X6YtuH+zm0RWltbVV1drZqamniXFRd+v1+WZamuri7epcTFU089pZMnT4b3L9qzZ48efvhhffKTn1R5ebmee+45FRYWxrnK2Hv3PJw7d04PPfSQNmzYEOfKJs/Jkyfl8Xj0+OOPq7u7W1/+8pe1ZMmShHw/TNs/XzfaFiHRdHR0qK+vTxs2bND69evV2toa75ImVWZmpp544onw63PnzukTn/iEpMTa/uLd89De3q4zZ87oa1/7mnbs2CGv1xvH6ibH5z//eX33u9+VJFmWJbvdnrDvh2kb7qNti5CIZsyYoY0bN6q2tlaPPfaYtm7dmlBzcd9994WfmpaG/qO22WySxr79hQnePQ85OTnatm2bjh07pttuu02HDh2KY3WTIz09XS6XS16vV9/5znf08MMPJ+z7YdqG+422RUg0CxYs0Be/+EXZbDYtWLBAHo9HXV1d8S4rbt65nprI218UFhbqzjvvDH/96quvxrmiyfH6669r/fr1+tKXvqQvfOELCft+mLbhfqNtERLN8ePHw1sx/+9//5PX61VGRkacq4qf22+/XS+//LKkoe0v7rrrrjhXFB8bN25UW1ubJOnFF1/UHXfcEeeKYu/KlSvasGGDHnnkET344IOSEvf9MG2fUB1pW4SsrKx4lxUXgUBA3//+93X58mXZbDZt3bpVeXl58S5rUv3nP//R9773PTU2Nurf//63Hn30UQ0MDGjhwoWqrKyU3W6Pd4mT4p3zcO7cOe3evVvJycmaPXu2du/ePWwp00SVlZV69tlntXDhwvD3fvCDH6iysjLh3g/TNtwBAKObtssyAIDREe4AYCDCHQAMRLgDgIEIdwAwEOEOAAZKzEc6YZzDhw/r7NmzGhwclM1mU1lZmZKTk3Xt2jV9/OMfH9PPeOWVV+R2u7VkyZIR26urq3Xu3Dl1dXWpv79ft912m2655Rbdeeed+tSnPqWcnJyJPCXgphDumPb+9a9/6U9/+pN+85vfyGaz6R//+IfKyspUWFio2bNnjzncT5w4odWrV48a7tu3b5ckPfPMM7p48aK2bt06YecATDTCHdOe2+3W5cuXdfz4cd1zzz1aunSpampqVFJSouTkZN1xxx26fPmyjh07Fr6yf/LJJ3XhwgXt379fycnJ+vSnP60///nPOnfunBYtWqQPfvCDYz7+9u3btXr1al25ckWnT59Wf3+/urq6tH79ej333HO6cOGCtm3bps9+9rN69tlndfToUSUlJWn58uX8gUDMEO6Y9ubOnauamhr96le/0qFDhzRjxgyVlpbqgQce0OzZs5WTk6OzZ8/q8OHDSk1NVXl5uV544QXNnTtXfr9fv/3tbyUNPbq/evXqqIL93Xw+n55++mn94Q9/0NGjR9XY2KiXX35Zv/zlL3XXXXfpiSee0IkTJ5SamqpHHnlEzc3NWrFixURNBRBGuGPau3TpklwuV/jTp/7+97/rm9/8pu6//37Nnj1bkvS+971PZWVlSk9P18WLF5WbmytpaEfNibR06VJJQ/+ayMrKks1m06xZs+T3+/Xaa6/pzTff1Le+9S1JQ38IXnvtNcIdMUG4Y9o7f/68GhoaVFNTI6fTqQULFmjmzJnyeDwKhULq6enRz372M505c0aS9NBDD+n6lkrv3A7WZrPpZrdaur5v+EjmzZunD3zgA3r66aeVnJysZ555JvzHAJhohDumvc997nPq7OzUgw8+qLS0NFmWpW3btsnhcGjfvn3KyspSXl6e1q5dK4fDoZkzZ+qNN97QvHnzhv2cj33sY9q/f7/mzZsXkx1Gb731Vn3jG99QSUmJgsGgPvShD2nVqlUTfhxAYldIADASV+7Au+zatUudnZ3v+f5TTz2lGTNmxKEiIHpcuQOAgdh+AAAMRLgDgIEIdwAwEOEOAAb6P58EZZPny2FoAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "monday_start_time = df.Start_Time[df.Start_Time.dt.dayofweek == 0]\n",
    "sns.distplot(monday_start_time.dt.hour, bins=24, kde=False, norm_hist=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "elegant-pasta",
   "metadata": {},
   "source": [
    "### Start Latitude & Longitude"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "noticed-candidate",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          34.808868\n",
       "1          35.090080\n",
       "2          37.145730\n",
       "3          39.110390\n",
       "4          26.102942\n",
       "             ...    \n",
       "2906605    29.813824\n",
       "2906606    34.068890\n",
       "2906607    25.702200\n",
       "2906608    40.660140\n",
       "2906609    38.831749\n",
       "Name: Start_Lat, Length: 2906610, dtype: float64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Start_Lat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "sapphire-hampton",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          -82.269157\n",
       "1          -80.745560\n",
       "2         -121.985052\n",
       "3         -119.773781\n",
       "4          -80.265091\n",
       "              ...    \n",
       "2906605    -95.399437\n",
       "2906606   -117.342010\n",
       "2906607    -80.335556\n",
       "2906608   -111.952460\n",
       "2906609   -104.748161\n",
       "Name: Start_Lng, Length: 2906610, dtype: float64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Start_Lng"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "complex-going",
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_df = df.sample(int(0.1 * len(df)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "beginning-prize",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Start_Lng', ylabel='Start_Lat'>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x=sample_df.Start_Lng, y=sample_df.Start_Lat, size=0.001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "radio-blackberry",
   "metadata": {},
   "outputs": [],
   "source": [
    "import folium"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "exempt-bahamas",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(34.808868, -82.26915699999999)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lat, lon = df.Start_Lat[0], df.Start_Lng[0]\n",
    "lat, lon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "genetic-chapter",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2424119    33.386276\n",
      "250845     40.908306\n",
      "1241765    36.077113\n",
      "260956     34.030506\n",
      "1737378    38.270199\n",
      "             ...    \n",
      "1635488    37.170776\n",
      "2579010    41.876888\n",
      "826783     33.759472\n",
      "375334     34.084857\n",
      "1533121    32.789222\n",
      "Name: Start_Lat, Length: 100, dtype: float64\n",
      "2424119    -86.987930\n",
      "250845     -73.813591\n",
      "1241765    -79.253052\n",
      "260956     -81.096161\n",
      "1737378    -85.615646\n",
      "              ...    \n",
      "1635488   -113.359908\n",
      "2579010    -88.235178\n",
      "826783     -84.378876\n",
      "375334    -118.219338\n",
      "1533121    -96.587387\n",
      "Name: Start_Lng, Length: 100, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "for x in df[['Start_Lat', 'Start_Lng']].sample(100).iteritems():\n",
    "    print(x[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "narrow-newspaper",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<zip at 0x7f9b30104640>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zip(list(df.Start_Lat), list(df.Start_Lng))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "unable-passport",
   "metadata": {},
   "outputs": [],
   "source": [
    "from folium.plugins import HeatMap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "serious-matthew",
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_df = df.sample(int(0.001 * len(df)))\n",
    "lat_lon_pairs = list(zip(list(sample_df.Start_Lat), list(sample_df.Start_Lng)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "competitive-virgin",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><span style=\"color:#565656\">Make this Notebook Trusted to load map: File -> Trust Notebook</span><iframe src=\"about:blank\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" 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      ],
      "text/plain": [
       "<folium.folium.Map at 0x7f9ba3bada00>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "map = folium.Map()\n",
    "HeatMap(lat_lon_pairs).add_to(map)\n",
    "map"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "olympic-player",
   "metadata": {},
   "source": [
    "## Ask & answer questions\n",
    "\n",
    "1. Are there more accidents in warmer or colder areas?\n",
    "2. Which 5 states have the highest number of accidents? How about per capita?\n",
    "3. Does New York show up in the data? If yes, why is the count lower if this the most populated city.\n",
    "4. Among the top 100 cities in number of accidents, which states do they belong to most frequently.\n",
    "5. What time of the day are accidents most frequent in? - ANSWERED\n",
    "6. Which days of the week have the most accidents?\n",
    "7. Which months have the most accidents?\n",
    "8. What is the trend of accidents year over year (decreasing/increasing?)\n",
    "9. When is accidents per unit of traffic the highest."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "brief-complement",
   "metadata": {},
   "source": [
    "## Summary and Conclusion\n",
    "\n",
    "\n",
    "Insights:\n",
    "- No data from New York\n",
    "- The number of accidents per city decreases exponentially\n",
    "- Less than 5% of cities have more than 1000 yearly accidents.\n",
    "- Over 1200 cities have reported just one accident (need to investigate further)"
   ]
  }
 ],
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