n1_EDA_precrash.ipynb

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   "source": [
    "# Project5: First Responders   \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5136934",
   "metadata": {},
   "source": [
    "### **Problem Satatement**\n",
    "First Responders want to be better prepared by the time they reach an accident site  \n",
    "Based on vehicle information, personal data, time of day, weather conditions, how many fatalities and what level of injury severity to expect?  \n",
    "What are the worst days/times when they should be appropriately staffed?  \n",
    "What can be done to reduce the number of fatalities and severity of injuries?  \n",
    "\n",
    "In spite of all the advances in technology, safety features  - multiple airbags, collision avoidance warning lights - and better enforcement of seat-belt use, helmets for motorcycles, bicycles - the number of fatalities has been increasing over the years.   \n",
    "\n",
    "Are there certain days or times of day when the fatalities are larger? \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb1caadb",
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   "source": [
    "### **Data Source**\n",
    "\n",
    "**National Highway Traffic Safety Administration’s (NHTSA) Fatality Analysis Reporting System (FARS)/Crash Report Sampling System (CRSS)**    \n",
    "FARS contains data derived from a census of fatal motor vehicle traffic crashes in the 50 States, the District of Columbia, and Puerto Rico.   \n",
    "To be included in FARS, a crash must involve a motor vehicle traveling on a trafficway customarily open to the public and must result in the death of at least one person (occupant of a vehicle or a non-motorist) within 30 days of the crash. \n",
    "FARS was conceived, designed, and developed by NHTSA’s National Center for Statistics and Analysis (NCSA) in 1975 to provide an overall measure of highway safety, to help identify traffic safety problems, to suggest solutions, and to help provide an objective basis to evaluate the effectiveness of motor vehicle safety standards and highway safety programs.  \n",
    "\n",
    "**FARS data ‘dictionary’:** \n",
    "Fatality Analysis Reporting System (FARS) Analytical User’s Manual, 1975-2020: \n",
    "This multi-year analytical user’s manual provides documentation on the historical coding practices of FARS from 1975 to 2020. In other words, this manual presents the evolution of FARS coding from inception through present. The manual includes the data elements that are contained in FARS and other useful information that will enable the users to become familiar with the data system.\n",
    "Fatalities and Coding and Validation manual \n",
    "Provides more detailed definitions for each data element and attribute for a given year. \n",
    "US Census Bureau, Dept. of Labor, : for county population and location (latitude and longitude) data\n",
    "\n",
    "\n"
   ]
  },
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   "source": [
    "### **Data summary**\n",
    "Downloaded FARS data from 2010-2020 (2.3 GB of data!)  \n",
    "Each year comprised 23-36 .csv files   \n",
    "200+ features across all files  \n",
    "\n",
    "Types of data available:  \n",
    "Crash-level: Date/Time, GPS, Work Zone, EMS Arrival, Highway, etc.  \n",
    "Vehicle-level: Type, Make/Model, Traveling Speed, Registration, Rollover, etc.  \n",
    "Driver-level: Presence, License State/Zip/Status, Height, Speeding, etc.  \n",
    "Precrash-level: Speed Limit, Roadway Grade, Distracted, Vision Obscured, etc.  \n",
    "Person-level (MVO): Age, Sex, Seating Position, Air Bag, Ejection, etc.  \n",
    "Person-level (NaMVO): Location, Alcohol test, Safety Equipment, etc.  \n",
    "\n",
    "Automated merging of .csv files based on desired features  \n"
   ]
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    "#### Focus\n",
    "\n",
    "I focused on pre-crash analysis and the aident data. \n",
    "\n",
    "Precrash data analysis: PC 1-23  \n",
    "Dig into the following information from NHTSA's FARS data files (and corresponding csv file name)  \n",
    "vehicle info: vehicle.csv   \n",
    "driver's vision obscured by: vision.csv   \n",
    "driver distracted: distract.csv  \n",
    "driver maneuver to avoid: maneuver.csv  \n",
    "weather at time of crash: weather.csv  \n",
    "factors involved (tires, brakes): factor.csv  \n",
    "\n",
    "Crash data is in the accident.csv file. It has data aggregated (by NHTSA) from the other files\n",
    "\n",
    "**Main areas** :\n",
    "1. Do EDA on pre-crash factors\n",
    "2. Create categories for the 99 Precrash events for better visualizations\n",
    "3. Create date_time index for accident\n",
    "4. Create 3 hour (and 2 hour and 4 hour) windows since these are the 'seasonality frequencies' for accidents and especially drunk drivers. \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "import numpy as np  \n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import datetime as dt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c215970",
   "metadata": {},
   "source": [
    "### Get the data from the NHTSA FARS website, extract files from the zips for years 2010-2020"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "173f5ed4",
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   "outputs": [
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     "text": [
      "/Users/sarniraj/opt/anaconda3/lib/python3.8/site-packages/IPython/core/interactiveshell.py:3172: DtypeWarning: Columns (14,56,58,179,181) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n"
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     "text": [
      "2010 2010_fars/\n",
      "2011 2011_fars/\n",
      "2012 2012_fars/\n",
      "2013 2013_fars/\n",
      "2014 2014_fars/\n",
      "2015 2015_fars/\n",
      "2016 2016_fars/\n",
      "2017 2017_fars/\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sarniraj/opt/anaconda3/lib/python3.8/site-packages/IPython/core/interactiveshell.py:3172: DtypeWarning: Columns (40,42) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2018 2018_fars/\n",
      "2019 2019_fars/\n",
      "2020 2020_fars/\n"
     ]
    }
   ],
   "source": [
    "# Run kehinde_gather_data.ipynb first which downloads the data from the NHTSA FARS website and extracts the .zip files\n",
    "# into the folders by year. \n",
    "\n",
    "#folder = \"FARS2020NationalCSV/\"\n",
    "\n",
    "# Get data for these files for only 1 year\n",
    "folder = \"2020_fars/\"\n",
    "\n",
    "df_vehicle = pd.read_csv(\"./data/\" + folder + \"VEHICLE.CSV\")\n",
    "df_vision = pd.read_csv(\"./data/\" + folder + \"VISION.CSV\")\n",
    "df_distract = pd.read_csv(\"./data/\" + folder + \"DISTRACT.CSV\")\n",
    "df_maneuver = pd.read_csv(\"./data/\" + folder + \"maneuver.CSV\")\n",
    "df_weather = pd.read_csv(\"./data/\" + folder + \"weather.CSV\")\n",
    "df_factor = pd.read_csv(\"./data/\" + folder + \"factor.CSV\")\n",
    "#df_person = pd.read_csv(\"./data/\" + folder + \"PERSON.CSV\")\n",
    "\n",
    "# Get accident data for all years\n",
    "\n",
    "#path = f'./data/{year}_fars/'\n",
    "#folderFARS = \"FARS\" \n",
    "#folderType = \"NationalCSV/\"\n",
    "\n",
    "folderFARS = \"_fars\" \n",
    "\n",
    "df_accident = pd.DataFrame()\n",
    "\n",
    "for i in range( 2010, 2021):\n",
    "    folder_name =  str(i) + folderFARS + \"/\"\n",
    "    print(i, folder_name)\n",
    "    df_accident = df_accident.append(pd.read_csv(\"./data/\" + folder_name + \"accident.CSV\"))\n",
    "\n",
    "# Make column names lowercase\n",
    "df_vehicle.columns = df_vehicle.columns.str.lower()\n",
    "df_vision.columns = df_vision.columns.str.lower()\n",
    "df_distract.columns = df_distract.columns.str.lower()\n",
    "df_maneuver.columns = df_maneuver.columns.str.lower()\n",
    "df_weather.columns = df_weather.columns.str.lower()\n",
    "df_accident.columns = df_accident.columns.str.lower()\n",
    "df_factor.columns = df_factor.columns.str.lower()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "55929848",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2020    35766\n",
       "2016    34748\n",
       "2017    34560\n",
       "2018    33919\n",
       "2019    33487\n",
       "2015    32538\n",
       "2012    31006\n",
       "2010    30296\n",
       "2013    30202\n",
       "2014    30056\n",
       "2011    29867\n",
       "Name: year, dtype: int64"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check all years are showing\n",
    "df_accident['year'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "533e12b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reset index to sort across all years\n",
    "df_accident.reset_index(drop=True, inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "300d2c73",
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   "source": [
    "# Keep only required columns from accident to conserve space, memory, time\n",
    "# C7, C17, C18, 19, C20, C24-C30...: 47 columns\n",
    "\n",
    "accident_cols = [ 'YEAR', \"MONTH\", \"MONTHNAME\", \"DAY\", \"DAY_WEEK\", \"DAY_WEEKNAME\", \"HOUR\", \"HOURNAME\", \"MINUTE\", \n",
    "    \"STATE\", \"STATENAME\", \"COUNTY\", \"COUNTYNAME\", \"ST_CASE\", \"VE_TOTAL\", \"VE_FORMS\", \"PVH_INVL\", \"PEDS\", \"PERSONS\", \n",
    "    \"CITY\", \"CITYNAME\", \"LATITUDE\", \"LONGITUD\", \"HARM_EV\", \"HARM_EVNAME\", \"MAN_COLL\", \"MAN_COLLNAME\", \n",
    "    \"WRK_ZONE\", \"WRK_ZONENAME\", \"REL_ROAD\", \"REL_ROADNAME\", \"LGT_COND\", \"LGT_CONDNAME\", \"WEATHER\", \"WEATHERNAME\", \n",
    "    \"SCH_BUS\", \"SCH_BUSNAME\", \"NOT_HOUR\", \"NOT_HOURNAME\", \"NOT_MIN\", \"NOT_MINNAME\", \"ARR_HOUR\", \"ARR_HOURNAME\", \n",
    "    \"ARR_MIN\", \"ARR_MINNAME\", \"FATALS\", \"DRUNK_DR\"]\n",
    "\n",
    "# Convert to lower case \n",
    "accident_cols = [ c.lower() for c in accident_cols]\n",
    "\n",
    "# Keep only these columns\n",
    "df_accident = df_accident[accident_cols]"
   ]
  },
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   "cell_type": "code",
   "execution_count": 163,
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 356445 entries, 0 to 356444\n",
      "Data columns (total 47 columns):\n",
      " #   Column        Non-Null Count   Dtype  \n",
      "---  ------        --------------   -----  \n",
      " 0   year          356445 non-null  int64  \n",
      " 1   month         356445 non-null  int64  \n",
      " 2   monthname     205018 non-null  object \n",
      " 3   day           356445 non-null  int64  \n",
      " 4   day_week      356445 non-null  int64  \n",
      " 5   day_weekname  205018 non-null  object \n",
      " 6   hour          356445 non-null  int64  \n",
      " 7   hourname      205018 non-null  object \n",
      " 8   minute        356445 non-null  int64  \n",
      " 9   state         356445 non-null  int64  \n",
      " 10  statename     205018 non-null  object \n",
      " 11  county        356445 non-null  int64  \n",
      " 12  countyname    172480 non-null  object \n",
      " 13  st_case       356445 non-null  int64  \n",
      " 14  ve_total      356445 non-null  int64  \n",
      " 15  ve_forms      356445 non-null  int64  \n",
      " 16  pvh_invl      326149 non-null  float64\n",
      " 17  peds          356445 non-null  int64  \n",
      " 18  persons       356445 non-null  int64  \n",
      " 19  city          356445 non-null  int64  \n",
      " 20  cityname      172480 non-null  object \n",
      " 21  latitude      356445 non-null  float64\n",
      " 22  longitud      356445 non-null  float64\n",
      " 23  harm_ev       356445 non-null  int64  \n",
      " 24  harm_evname   205018 non-null  object \n",
      " 25  man_coll      356445 non-null  int64  \n",
      " 26  man_collname  205018 non-null  object \n",
      " 27  wrk_zone      356445 non-null  int64  \n",
      " 28  wrk_zonename  205018 non-null  object \n",
      " 29  rel_road      356445 non-null  int64  \n",
      " 30  rel_roadname  205018 non-null  object \n",
      " 31  lgt_cond      356445 non-null  int64  \n",
      " 32  lgt_condname  205018 non-null  object \n",
      " 33  weather       356445 non-null  int64  \n",
      " 34  weathername   205018 non-null  object \n",
      " 35  sch_bus       356445 non-null  int64  \n",
      " 36  sch_busname   205018 non-null  object \n",
      " 37  not_hour      356445 non-null  int64  \n",
      " 38  not_hourname  205018 non-null  object \n",
      " 39  not_min       356445 non-null  int64  \n",
      " 40  not_minname   205018 non-null  object \n",
      " 41  arr_hour      356445 non-null  int64  \n",
      " 42  arr_hourname  205018 non-null  object \n",
      " 43  arr_min       356445 non-null  int64  \n",
      " 44  arr_minname   205018 non-null  object \n",
      " 45  fatals        356445 non-null  int64  \n",
      " 46  drunk_dr      356445 non-null  int64  \n",
      "dtypes: float64(3), int64(27), object(17)\n",
      "memory usage: 127.8+ MB\n"
     ]
    }
   ],
   "source": [
    "\n",
    "df_accident.info() # 47 cols, 35MB for 3 years; 120MB for 11 years, saved half memory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "cc3e2e2f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Only accident (crash) is done afor all years. For remaining, just do for latest year for EDA\n",
    "# Precrash - from vehicle csv File\n",
    "# PC5-13, 17, 19-23: 15 items in vehicle.csv\n",
    "# Not in vehicle file: 14, 15, 16, 18 (in other .csvs)\n",
    "\n",
    "# These column names are in the XLS and as per the Validation and User Manauls\n",
    "# Subset columns for easier viewing\n",
    "cols = [\"VTRAFWAY\", \"VNUM_LAN\", \"VSPD_LIM\", \"VALIGN\", \"VPROFILE\", \"VPAVETYP\", \"VSURCOND\", \"VTRAFCON\", \"VTCONT_F\", \"P_CRASH1\", \"P_CRASH2\", \"P_CRASH3\", \"PCRASH4\", \"PCRASH5\", \"ACC_TYPE\"]\n",
    "\n",
    "# code column header and code name column header from XLS files (names are NOT mentioned in the manuals)\n",
    "cols_with_names = [\"VTRAFWAY\", \"VTRAFWAYNAME\", \"VNUM_LAN\", \"VNUM_LANNAME\", \"VSPD_LIM\", \"VSPD_LIMNAME\", \"VALIGN\", \"VALIGNNAME\", \"VPROFILE\", \"VPROFILENAME\", \n",
    "                   \"VPAVETYP\", \"VSURCOND\", \"VTRAFCON\", \"VTCONT_F\", \"P_CRASH1\", \"P_CRASH2\", \"P_CRASH3\", \"PCRASH4\", \"PCRASH5\", \"ACC_TYPE\", \"ACC_TYPENAME\"]\n",
    "\n",
    "# Subset of names for display purposes\n",
    "cols_main_names= [\"VTRAFWAY\", \"VNUM_LAN\", \"VSPD_LIM\", \"VALIGN\", \"VPROFILE\", \n",
    "                   \"VPAVETYP\", \"VSURCOND\", \"VTRAFCON\", \"VTCONT_F\", \"P_CRASH1\", \"P_CRASH2\", \"P_CRASH2NAME\", \"P_CRASH3\", \"PCRASH4\", \"PCRASH5\", \"ACC_TYPE\", \"ACC_TYPENAME\"]\n",
    "\n",
    "cols_veh_prev_hist = [\"DEATHS\", \"DR_DRINK\", \n",
    "                      \"PREV_ACC\", \"PREV_ACCNAME\", \"PREV_SUS1\", \"PREV_SUS1NAME\", \"PREV_SUS2\", \"PREV_SUS2NAME\", \"PREV_SUS3\", \"PREV_SUS3NAME\", \"PREV_DWI\", \n",
    "                      \"PREV_DWINAME\", \"PREV_SPD\", \"PREV_SPDNAME\", \"PREV_OTH\", \"PREV_OTHNAME\"]\n",
    "               \n",
    "# Convert to lower case       \n",
    "cols = [ c.lower() for c in cols]\n",
    "cols_with_names = [ c.lower() for c in cols_with_names]\n",
    "cols_main_names = [ c.lower() for c in cols_main_names]\n",
    "cols_veh_prev_hist = [ c.lower() for c in cols_veh_prev_hist]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "b4e9a415",
   "metadata": {},
   "outputs": [],
   "source": [
    "#df_accident.loc[df_accident[\"year\"]==2020][[\"peds\", \"fatals\", \"persons\", \"ve_total\"]].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0e5a314",
   "metadata": {},
   "source": [
    "### Data Cleanup and EDA\n",
    "There are many columns in this detailed dataset. Look through the obvious ones to see if there is anything obvious for further investigation. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "ba0d0a86",
   "metadata": {},
   "outputs": [
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       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32384</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32385</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32386</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32387</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32389</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32390</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32383</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32369</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32368</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32366</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32349</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32350</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32351</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32352</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32353</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32355</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32356</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32357</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32358</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32360</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32361</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       deaths  dr_drink  prev_dwi  prev_acc  prev_sus1  prev_sus2  prev_sus3  \\\n",
       "31          1         0         0        98          0          0          0   \n",
       "32374       1         0         0        98          0          0          0   \n",
       "32375       1         0         0        98          0          0          0   \n",
       "32376       0         0         0        98          0          0          0   \n",
       "32378       0         0         0        98          0          0          0   \n",
       "32380       1         1         0        98          0          0          0   \n",
       "32381       1         1         0        98          0          0          0   \n",
       "32372       0         0         0        98          0          0          0   \n",
       "32382       2         0         0        98          0          0          0   \n",
       "32384       1         1         0        98          0          0          0   \n",
       "32385       1         0         0        98          0          0          0   \n",
       "32386       0         0         0        98          0          0          0   \n",
       "32387       0         0         0        98          0          0          0   \n",
       "32389       0         0         0        98          0          0          0   \n",
       "32390       1         0         0        98          0          0          0   \n",
       "32383       1         1         0        98          0          0          0   \n",
       "32369       0         0         0        98          0          0          0   \n",
       "32368       0         0         0        98          0          0          0   \n",
       "32366       1         0         0        98          0          0          0   \n",
       "32349       1         0         0        98          0          0          0   \n",
       "32350       2         0         0        98          0          0          0   \n",
       "32351       1         0         0        98          0          0          0   \n",
       "32352       0         0         0        98          0          0          0   \n",
       "32353       1         1         0        98          0          0          0   \n",
       "32355       1         1         0        98          0          0          0   \n",
       "32356       1         0         0        98          0          0          0   \n",
       "32357       0         0         0        98          0          0          0   \n",
       "32358       1         0         0        98          0          0          0   \n",
       "32360       1         0         0        98          0          0          0   \n",
       "32361       0         0         0        98          0          0          0   \n",
       "\n",
       "       prev_spd  \n",
       "31            0  \n",
       "32374         1  \n",
       "32375         0  \n",
       "32376         0  \n",
       "32378         1  \n",
       "32380         1  \n",
       "32381         1  \n",
       "32372         0  \n",
       "32382         0  \n",
       "32384         0  \n",
       "32385         0  \n",
       "32386         2  \n",
       "32387         0  \n",
       "32389         0  \n",
       "32390         1  \n",
       "32383         1  \n",
       "32369         0  \n",
       "32368         0  \n",
       "32366         0  \n",
       "32349         0  \n",
       "32350         0  \n",
       "32351         0  \n",
       "32352         0  \n",
       "32353         0  \n",
       "32355         0  \n",
       "32356         0  \n",
       "32357         0  \n",
       "32358         0  \n",
       "32360         0  \n",
       "32361         3  "
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_vehicle.loc[ df_vehicle[\"prev_acc\"]== 98 ][[\"deaths\", \"dr_drink\", \"prev_dwi\",\"prev_acc\", \"prev_sus1\",  \n",
    "                                                                        \"prev_sus2\", \"prev_sus3\", \"prev_spd\"  ]].sort_values(by = \"prev_dwi\", ascending = True).head(30)\n",
    "\n",
    "# set \"prev_acc\"== 98 or 99 to 0 since they don't matter for DWI \n",
    "\n",
    "# Clean up prev_accidents\n",
    "#df_vehicle['prev_acc'] =  [0 if ((a==98) | (a==99)) else a for a in df_vehicle['prev_acc']]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "97f6af91",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      50715\n",
       "99      3112\n",
       "998      349\n",
       "1         69\n",
       "2         15\n",
       "3          4\n",
       "4          4\n",
       "5          1\n",
       "6          1\n",
       "9          1\n",
       "10         1\n",
       "Name: prev_sus1, dtype: int64"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Clean up prev_sus1\n",
    "df_vehicle['prev_sus1'].value_counts() #69 had 1 prev underage suspension for zero-tolerance alcohol violations; 96 had 1 or more underage suspensions; 12 people had 3 or more\n",
    "#df_vehicle['prev_sus1'] = [0 if ((a==99) | (a==998)) else a for a in df_vehicle['prev_sus1']]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "6862c047",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     53234\n",
       "1       704\n",
       "2       219\n",
       "3        61\n",
       "4        22\n",
       "5        13\n",
       "6         3\n",
       "18        3\n",
       "10        3\n",
       "15        1\n",
       "20        1\n",
       "19        1\n",
       "17        1\n",
       "40        1\n",
       "12        1\n",
       "9         1\n",
       "8         1\n",
       "7         1\n",
       "25        1\n",
       "Name: prev_sus2, dtype: int64"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Clean up prev_sus2\n",
    "\n",
    "df_vehicle['prev_sus2'] = [0 if ((a==99) | (a==998)) else a for a in df_vehicle['prev_sus2']]\n",
    "\n",
    "df_vehicle['prev_sus2'].value_counts() #704 had 1 prev BAC above a specified limit (not BAC violations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "53858bad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     47654\n",
       "1      3276\n",
       "2      1404\n",
       "3       725\n",
       "4       383\n",
       "5       229\n",
       "6       160\n",
       "7       103\n",
       "8        82\n",
       "9        60\n",
       "10       40\n",
       "11       20\n",
       "14       17\n",
       "12       17\n",
       "13       16\n",
       "18       14\n",
       "15       14\n",
       "17       10\n",
       "20        9\n",
       "19        8\n",
       "16        6\n",
       "23        3\n",
       "28        2\n",
       "27        2\n",
       "26        2\n",
       "21        2\n",
       "31        2\n",
       "33        2\n",
       "34        2\n",
       "42        2\n",
       "35        1\n",
       "45        1\n",
       "24        1\n",
       "32        1\n",
       "60        1\n",
       "36        1\n",
       "Name: prev_sus3, dtype: int64"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Clean up prev_sus3\n",
    "df_vehicle['prev_sus3'] = [0 if ((a==99) | (a==998)) else a for a in df_vehicle['prev_sus3']]\n",
    "df_vehicle['prev_sus3'].value_counts() #3276 had 1 prev drug violation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "8985dc36",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# (1,98) are values with prior violations/DWI\n",
    "\n",
    "df_vehicle.loc[ ( df_vehicle[\"prev_dwi\"] > 4 ) & (df_vehicle[\"prev_dwi\"] < 99 ) ][[\"deaths\", \"dr_drink\", \"prev_dwi\",\"prev_acc\", \"prev_sus1\",  \n",
    "                        \"prev_sus2\", \"prev_sus3\", \"prev_spd\"  ]].sort_values(by = \"prev_dwi\", ascending = False).plot(\n",
    "                        kind=\"barh\", title=\"Drivers with more than 4 previous DWI convictions\");\n",
    "#df_vehicle.loc[ ( df_vehicle[\"prev_dwi\"] > 1 ) & (df_vehicle[\"prev_dwi\"] < 99 ) ][[\"deaths\", \"dr_drink\", \"prev_dwi\",\"prev_acc\", \"prev_sus1\",  \n",
    "#                                                                        \"prev_sus2\", \"prev_sus3\", \"prev_spd\"  ]].sort_values(by = \"prev_dwi\", ascending = False).plot(kind=\"barh\")\n",
    "\n",
    "\n",
    "#1739 drivers had prev dwi convictions in past 5 years; ; 294 had more than 1 prior DWI; 52 had more than 2(44 deaths, ); 18 more than 3, 14 deaths; 852 drinking drivers; 1358 deaths \n",
    "\n",
    "\n",
    "#df_vehicle.loc[ ( df_vehicle[\"prev_dwi\"] > 0 ) & (df_vehicle[\"prev_dwi\"] < 99 ) ][[\"deaths\", \"dr_drink\", \"prev_acc\", \"prev_sus1\", \"prev_dwi\" ]].sum().plot(kind=\"barh\")\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "d9c4bb3b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>p_crash2</th>\n",
       "      <th>p_crash2name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13</td>\n",
       "      <td>Off the edge of the road on the right side</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15</td>\n",
       "      <td>Turning Left</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>62</td>\n",
       "      <td>From opposite direction  over left lane line</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>62</td>\n",
       "      <td>From opposite direction  over left lane line</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>62</td>\n",
       "      <td>From opposite direction  over left lane line</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>13</td>\n",
       "      <td>Off the edge of the road on the right side</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>66</td>\n",
       "      <td>From crossing street, across path</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>12</td>\n",
       "      <td>Off the edge of the road on the left side</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>12</td>\n",
       "      <td>Off the edge of the road on the left side</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>Over the lane line on left side of travel lane</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   p_crash2                                    p_crash2name\n",
       "0        13      Off the edge of the road on the right side\n",
       "1        15                                    Turning Left\n",
       "2        62    From opposite direction  over left lane line\n",
       "3        62    From opposite direction  over left lane line\n",
       "4        62    From opposite direction  over left lane line\n",
       "5        13      Off the edge of the road on the right side\n",
       "6        66               From crossing street, across path\n",
       "7        12       Off the edge of the road on the left side\n",
       "8        12       Off the edge of the road on the left side\n",
       "9        10  Over the lane line on left side of travel lane"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pre-crash reasons\n",
    "df_vehicle[[\"p_crash2\", \"p_crash2name\"]].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "5374a1f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create categories for Pre-crash crit events. \n",
    "# This categorical data is much more meaningful (and useful) for modeling. \n",
    "\n",
    "\n",
    "''' Values and descriptions of the categories (matches the Manuals)\n",
    "1 This Vehicle Loss of Control Due to:\n",
    "2 This Vehicle Traveling\n",
    "3 Other Motor Vehicle in Lane\n",
    "4 Other Motor Vehicle Encroaching into Lane\n",
    "5 Pedestrian or Pedalcyclist or Other Non-Motorist\n",
    "6 Object or Animal\n",
    "7 Other\n",
    "9 Unknown\n",
    "'''\n",
    "\n",
    "for i in range( df_vehicle.shape[0]):\n",
    "    e = df_vehicle.loc[i, 'p_crash2']\n",
    "    \n",
    "    #print( {i}, {e})\n",
    "    if e >=1 and e <= 9:\n",
    "        df_vehicle.loc[i, 'crit_event_category'] = 1\n",
    "        df_vehicle.loc[i, 'crit_event_cat_name'] = \"This lost control\"\n",
    "    elif e >=10 and e <= 21: \n",
    "        df_vehicle.loc[i, 'crit_event_category'] = 2\n",
    "        df_vehicle.loc[i, 'crit_event_cat_name'] = \"This traveling\"\n",
    "    elif e >=50 and e <= 59: \n",
    "        df_vehicle.loc[i, 'crit_event_category'] = 3\n",
    "        df_vehicle.loc[i, 'crit_event_cat_name'] = \"Other Motor Vehicle in Lane\"\n",
    "    elif e >=60 and e <= 78: \n",
    "        df_vehicle.loc[i, 'crit_event_category'] = 4\n",
    "        df_vehicle.loc[i, 'crit_event_cat_name'] = \"Other Motor Vehicle Encroaching into Lane\"\n",
    "    elif e >=80 and e <= 85: \n",
    "        df_vehicle.loc[i, 'crit_event_category'] = 5\n",
    "        df_vehicle.loc[i, 'crit_event_cat_name'] = \"Pedestrian or Pedalcyclist or Other Non-Motorist\"\n",
    "    elif e >=87 and e <= 92: \n",
    "        df_vehicle.loc[i, 'crit_event_category'] = 6\n",
    "        df_vehicle.loc[i, 'crit_event_cat_name'] = \"Object or Animal\"\n",
    "    elif e == 98: \n",
    "        df_vehicle.loc[i, 'crit_event_category'] = 7\n",
    "        df_vehicle.loc[i, 'crit_event_cat_name'] = \"Other\"\n",
    "    else: #e == 99: \n",
    "        df_vehicle.loc[i, 'crit_event_category'] = 9\n",
    "        df_vehicle.loc[i, 'crit_event_cat_name'] = \"Unknown\"\n",
    "    #print( i, e, df_vehicle.loc[i, 'crit_event_category'], df_vehicle.loc[i, 'crit_event_cat_name'])\n",
    "        \n",
    "# This takes a minute to run, even for 54k records"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "c442a950",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>p_crash2</th>\n",
       "      <th>crit_event_cat_name</th>\n",
       "      <th>crit_event_category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13</td>\n",
       "      <td>This traveling</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15</td>\n",
       "      <td>This traveling</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>62</td>\n",
       "      <td>Other Motor Vehicle Encroaching into Lane</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>62</td>\n",
       "      <td>Other Motor Vehicle Encroaching into Lane</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>62</td>\n",
       "      <td>Other Motor Vehicle Encroaching into Lane</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>13</td>\n",
       "      <td>This traveling</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>66</td>\n",
       "      <td>Other Motor Vehicle Encroaching into Lane</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>12</td>\n",
       "      <td>This traveling</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>12</td>\n",
       "      <td>This traveling</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>This traveling</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>62</td>\n",
       "      <td>Other Motor Vehicle Encroaching into Lane</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>80</td>\n",
       "      <td>Pedestrian or Pedalcyclist or Other Non-Motorist</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>51</td>\n",
       "      <td>Other Motor Vehicle in Lane</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>53</td>\n",
       "      <td>Other Motor Vehicle in Lane</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>52</td>\n",
       "      <td>Other Motor Vehicle in Lane</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>53</td>\n",
       "      <td>Other Motor Vehicle in Lane</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>54</td>\n",
       "      <td>Other Motor Vehicle in Lane</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>54</td>\n",
       "      <td>Other Motor Vehicle in Lane</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>17</td>\n",
       "      <td>This traveling</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>66</td>\n",
       "      <td>Other Motor Vehicle Encroaching into Lane</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    p_crash2                               crit_event_cat_name  \\\n",
       "0         13                                    This traveling   \n",
       "1         15                                    This traveling   \n",
       "2         62         Other Motor Vehicle Encroaching into Lane   \n",
       "3         62         Other Motor Vehicle Encroaching into Lane   \n",
       "4         62         Other Motor Vehicle Encroaching into Lane   \n",
       "5         13                                    This traveling   \n",
       "6         66         Other Motor Vehicle Encroaching into Lane   \n",
       "7         12                                    This traveling   \n",
       "8         12                                    This traveling   \n",
       "9         10                                    This traveling   \n",
       "10        62         Other Motor Vehicle Encroaching into Lane   \n",
       "11        80  Pedestrian or Pedalcyclist or Other Non-Motorist   \n",
       "12        51                       Other Motor Vehicle in Lane   \n",
       "13        53                       Other Motor Vehicle in Lane   \n",
       "14        52                       Other Motor Vehicle in Lane   \n",
       "15        53                       Other Motor Vehicle in Lane   \n",
       "16        54                       Other Motor Vehicle in Lane   \n",
       "17        54                       Other Motor Vehicle in Lane   \n",
       "18        17                                    This traveling   \n",
       "19        66         Other Motor Vehicle Encroaching into Lane   \n",
       "\n",
       "    crit_event_category  \n",
       "0                   2.0  \n",
       "1                   2.0  \n",
       "2                   4.0  \n",
       "3                   4.0  \n",
       "4                   4.0  \n",
       "5                   2.0  \n",
       "6                   4.0  \n",
       "7                   2.0  \n",
       "8                   2.0  \n",
       "9                   2.0  \n",
       "10                  4.0  \n",
       "11                  5.0  \n",
       "12                  3.0  \n",
       "13                  3.0  \n",
       "14                  3.0  \n",
       "15                  3.0  \n",
       "16                  3.0  \n",
       "17                  3.0  \n",
       "18                  2.0  \n",
       "19                  4.0  "
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the mapping from event to category is done properly\n",
    "#df_vehicle[[\"crit_event_category\", \"p_crash2\"]].head(20)\n",
    "df_vehicle[[\"p_crash2\", \"crit_event_cat_name\", \"crit_event_category\"]].head(20)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "748d6432",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the critical categories\n",
    "\n",
    "plt.figure( figsize=(4,4));\n",
    "\n",
    "df_vehicle[\"crit_event_cat_name\"].value_counts(normalize = True, ascending=True).plot(kind=\"barh\", title=\"All Precrash Critical Categories\");\n",
    "plt.xlabel(\"Proportion of each category\");\n",
    "plt.ylabel(\"Precrash category\");\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "b890371a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot all the Precrash events. Showing both the event code and number for easier comparison\n",
    "\n",
    "plt.figure( figsize=(10,12))\n",
    "df_vehicle[[\"p_crash2\", \"p_crash2name\"]].value_counts(normalize=True, ascending=True).plot(kind=\"barh\",\n",
    "                                                                title=\"All Pre-crash Critical Events\");\n",
    "plt.xlabel(\"Proportion of each critical event\", fontsize=12);\n",
    "plt.ylabel(\"Precrash critical event\", fontsize=12);\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "944f2adb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "vsurcond  vsurcondname                     \n",
       "1         Dry                                  83.833284\n",
       "2         Wet                                  11.499484\n",
       "98        Not Reported                          1.488797\n",
       "0         Non-Trafficway or Driveway Access     0.790463\n",
       "4         Ice/Frost                             0.663325\n",
       "3         Snow                                  0.495652\n",
       "99        Reported as Unknown                   0.401680\n",
       "11        Mud, Dirt or Gravel                   0.322450\n",
       "6         Water (Standing or Moving)            0.195312\n",
       "10        Slush                                 0.175044\n",
       "8         Other                                 0.105027\n",
       "5         Sand                                  0.022111\n",
       "7         Oil                                   0.007370\n",
       "dtype: float64"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_vehicle[[\"vsurcond\", \"vsurcondname\"]].value_counts(normalize=True)*100\n",
    "\n",
    "# This might be interesting for further deep dive. Wet conditions: 11%, Entering a trafficway or was in Driveway: 0.8%, Ice 0.6%, \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "ddf5e204",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot all the roadway surface conditions. Showing both the event code and number for easier comparison\n",
    "\n",
    "plt.figure( figsize=(6,4))\n",
    "df_vehicle[[\"vsurcond\", \"vsurcondname\"]].value_counts(normalize=True, ascending=True).plot(kind=\"barh\",\n",
    "                                                                title=\"Roadway surface condition prior to Critical Precrash Event\");\n",
    "plt.xlabel(\"Proportion of each critical event\", fontsize=12);\n",
    "plt.ylabel(\"Roadway surface condition\", fontsize=12);\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "14bea7ea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 54325 entries, 0 to 54324\n",
      "Data columns (total 6 columns):\n",
      " #   Column      Non-Null Count  Dtype \n",
      "---  ------      --------------  ----- \n",
      " 0   state       54325 non-null  int64 \n",
      " 1   statename   54325 non-null  object\n",
      " 2   st_case     54325 non-null  int64 \n",
      " 3   veh_no      54325 non-null  int64 \n",
      " 4   vision      54325 non-null  int64 \n",
      " 5   visionname  54325 non-null  object\n",
      "dtypes: int64(4), object(2)\n",
      "memory usage: 2.5+ MB\n"
     ]
    }
   ],
   "source": [
    "# Now check if drivers vison was obscured from vision df\n",
    "df_vision.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "id": "7b62ee2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "vision  visionname                                      \n",
       "0       No Obstruction Noted                                93.087897\n",
       "99      Reported as Unknown                                  3.350207\n",
       "1       Rain, Snow, Fog, Smoke, Sand, Dust                   0.727105\n",
       "95      No Driver Present/Unknown if Driver present          0.642430\n",
       "98      Other Visual Obstruction                             0.539347\n",
       "2       Reflected Glare, Bright Sunlight, Headlights         0.416015\n",
       "6       In-Transport Motor Vehicle (including load)          0.410492\n",
       "3       Curve, Hill or Other Roadway Design Feature          0.283479\n",
       "97      Vision Obscured - No Details                         0.165670\n",
       "5       Trees, Crops, Vegetation                             0.145421\n",
       "7       Not In-Transport Motor Vehicle (parked, working)     0.104924\n",
       "4       Building, Billboard, Other Structure                 0.040497\n",
       "11      Obstruction Interior to the Vehicle                  0.018408\n",
       "13      Broken or Improperly Cleaned Windshield              0.018408\n",
       "14      Obstructing Angles on Vehicle                        0.018408\n",
       "9       Inadequate Defrost or Defog System                   0.016567\n",
       "12      External Mirrors                                     0.007363\n",
       "8       Splash or Spray of Passing Vehicle                   0.003682\n",
       "10      Inadequate Vehicle Lighting System                   0.003682\n",
       "dtype: float64"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_vision[[\"vision\", \"visionname\"]].value_counts(normalize=True)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "db8e3e1a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot. Showing both the event code and number for easier comparison\n",
    "\n",
    "plt.figure( figsize=(6,4))\n",
    "df_vision[[\"vision\", \"visionname\"]].value_counts( ascending=True).plot(kind=\"barh\",\n",
    "                                                                title=\"Driver's Vision Obscured By\");\n",
    "plt.xlabel(\"Number of each event\", fontsize=12);\n",
    "plt.ylabel(\"Obscured By\", fontsize=12);\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "e224ed35",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "drdistract  drdistractname                                                \n",
       "96          Not Reported                                                      48.348713\n",
       "0           Not Distracted                                                    31.688493\n",
       "99          Reported as Unknown if Distracted                                 13.816286\n",
       "93          Inattention (Inattentive), Details Unknown                         2.120057\n",
       "92          Distraction (Distracted), Details Unknown                          1.007533\n",
       "16          No Driver Present/Unknown if Driver present                        0.642832\n",
       "12          Distracted by Outside Person, Object or Event                      0.443904\n",
       "17          Distraction/Inattention                                            0.335231\n",
       "15          Other Mobile Phone Related                                         0.257870\n",
       "3           By Other Occupant(s)                                               0.248660\n",
       "98          Other Distraction [Specify:]                                       0.215505\n",
       "6           While Manipulating Mobile Phone                                    0.211821\n",
       "5           While Talking or Listening to Mobile Phone                         0.193402\n",
       "10          While Using or Reaching for Device/Object Brought into Vehicle     0.165773\n",
       "9           While Using Other Component/Controls Integral to Vehicle           0.073677\n",
       "7           Adjusting Audio Or Climate Controls                                0.068151\n",
       "13          Eating or Drinking                                                 0.064467\n",
       "97          Lost in Thought / Day dreaming                                     0.040522\n",
       "4           By a Moving Object in Vehicle                                      0.025787\n",
       "19          Careless/Inattentive                                               0.022103\n",
       "14          Smoking Related                                                    0.007368\n",
       "18          Distraction/Careless                                               0.001842\n",
       "dtype: float64"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check if Driver was distracted \n",
    "df_distract[[\"drdistract\", \"drdistractname\"]].value_counts(normalize=True)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "id": "bd65627f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "maneuver  maneuvername                                     \n",
       "98        Not Reported                                         64.089385\n",
       "0         Driver Did Not Maneuver to Avoid                     22.309421\n",
       "4         Contact Motor Vehicle (In this crash)                 6.536237\n",
       "99        Reported as Unknown                                   2.910725\n",
       "5         Pedestrian, Pedalcyclist or Other Non-Motorist        1.860654\n",
       "92        Phantom/Non-Contact Motor Vehicle                     1.197450\n",
       "95        No Driver Present/Unknown if Driver present           0.642939\n",
       "3         Live Animal                                           0.237648\n",
       "1         Object                                                0.163959\n",
       "2         Poor Road Conditions (Puddle, Ice, Pothole, etc.)     0.051582\n",
       "dtype: float64"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The thing(s) the driver attempted to avoid while the vehicle was on the road portion of the trafficway, \n",
    "# just prior to the first harmful event for this vehicle.\n",
    "#PC15. Maneuver.MANEUVER\n",
    "\n",
    "df_maneuver[[\"maneuver\", \"maneuvername\"]].value_counts(normalize=True)*100\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "id": "7bc9d056",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "vehiclecc  vehicleccname                             \n",
       "0          None Noted                                    92.042611\n",
       "99         Reported as Unknown                            5.720121\n",
       "1          Tires                                          1.107595\n",
       "97         Other                                          0.283338\n",
       "2          Brake System                                   0.228142\n",
       "7          Head Lights                                    0.141669\n",
       "17         Vehicle Contributing Factors - No Details      0.075434\n",
       "9          Other Lights                                   0.075434\n",
       "5          Power Train                                    0.057036\n",
       "3          Steering                                       0.057036\n",
       "8          Signal Lights                                  0.033117\n",
       "16         Safety Systems                                 0.033117\n",
       "15         Truck Coupling/Trailer Hitch/Safety Chains     0.031278\n",
       "13         Windows/Windshield                             0.029438\n",
       "11         Wheels                                         0.027598\n",
       "14         Body, Doors                                    0.025758\n",
       "4          Suspension                                     0.016559\n",
       "6          Exhaust System                                 0.005520\n",
       "10         Wipers                                         0.005520\n",
       "12         Mirrors                                        0.003680\n",
       "dtype: float64"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# PC4 - Contributing Circumstances, Motor Vehicle\n",
    "# Factor.VEHICLECC\n",
    "\n",
    "df_factor[[\"vehiclecc\", \"vehicleccname\"]].value_counts(normalize=True)*100\n",
    "\n",
    "# Tires contribute 1.1% to all fatal accidents!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "id": "bd4be57f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "weather  weathername             \n",
       "1        Clear                       69.126606\n",
       "10       Cloudy                      13.602127\n",
       "2        Rain                         7.313358\n",
       "98       Not Reported                 6.814909\n",
       "5        Fog, Smog, Smoke             1.052282\n",
       "4        Snow                         0.783673\n",
       "99       Reported as Unknown          0.722751\n",
       "6        Severe Crosswinds            0.177226\n",
       "12       Freezing Rain or Drizzle     0.119074\n",
       "11       Blowing Snow                 0.113536\n",
       "3        Sleet or Hail                0.088613\n",
       "8        Other                        0.060922\n",
       "7        Blowing Sand, Soil, Dirt     0.024922\n",
       "dtype: float64"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# What was the weather when the crash occured\n",
    "\n",
    "df_weather[[\"weather\", \"weathername\"]].value_counts(normalize=True)*100"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6cde588",
   "metadata": {},
   "source": [
    "### Done with basic investigation of the pre-crash reasons. \n",
    "Now let's look at accident data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "id": "ea3159f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1     331008\n",
       "2      21264\n",
       "3       3061\n",
       "4        796\n",
       "5        225\n",
       "6         56\n",
       "7         16\n",
       "8          8\n",
       "9          3\n",
       "10         2\n",
       "13         2\n",
       "15         2\n",
       "11         1\n",
       "20         1\n",
       "Name: fatals, dtype: int64"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# number of fatals. None of the records have zero fatals - i.e. the dataset ONLY contains accidents with at least one fatality\n",
    "df_accident['fatals'].value_counts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "id": "7c560f4a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "wrk_zone  wrk_zonename           \n",
       "0         None                       97.914817\n",
       "1         Construction                1.293545\n",
       "4         Work Zone, Type Unknown     0.617507\n",
       "2         Maintenance                 0.145841\n",
       "3         Utility                     0.028290\n",
       "dtype: float64"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Construction/Work zones \n",
    "df_accident[[\"wrk_zone\", \"wrk_zonename\"]].value_counts(normalize=True)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "78ea96ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "lgt_cond  lgt_condname           \n",
       "1         Daylight                   46.997337\n",
       "2         Dark - Not Lighted         27.514169\n",
       "3         Dark - Lighted             19.843136\n",
       "5         Dusk                        2.403204\n",
       "4         Dawn                        1.908613\n",
       "6         Dark - Unknown Lighting     0.736521\n",
       "9         Reported as Unknown         0.242418\n",
       "          Unknown                     0.205348\n",
       "8         Not Reported                0.105844\n",
       "7         Other                       0.043411\n",
       "dtype: float64"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#LGT_COND\n",
    "df_accident[[\"lgt_cond\", \"lgt_condname\"]].value_counts(normalize=True)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "id": "78ea96ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "weather  weathername             \n",
       "1        Clear                       69.379274\n",
       "10       Cloudy                      14.565550\n",
       "2        Rain                         7.377889\n",
       "98       Not Reported                 5.479031\n",
       "5        Fog, Smog, Smoke             1.116000\n",
       "4        Snow                         0.972598\n",
       "99       Reported as Unknown          0.274610\n",
       "         Unknown                      0.228760\n",
       "3        Sleet or Hail                0.172668\n",
       "6        Severe Crosswinds            0.141939\n",
       "8        Other                        0.113649\n",
       "12       Freezing Rain or Drizzle     0.077066\n",
       "11       Blowing Snow                 0.069262\n",
       "7        Blowing Sand, Soil, Dirt     0.031705\n",
       "dtype: float64"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#WEATHER\n",
    "df_accident[[\"weather\", \"weathername\"]].value_counts(normalize=True)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "id": "1c4ccea4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rel_road  rel_roadname                              \n",
       "1         On Roadway                                    61.368270\n",
       "4         On Roadside                                   29.582768\n",
       "3         On Median                                      3.761621\n",
       "5         Outside Trafficway                             2.134447\n",
       "2         On Shoulder                                    1.485723\n",
       "8         Gore                                           0.463374\n",
       "6         Off Roadway-Location Unknown                   0.298023\n",
       "10        Separator                                      0.283390\n",
       "7         In Parking Lane/Zone                           0.257538\n",
       "99        Reported as Unknown                            0.107308\n",
       "11        Continuous Left - Turn Lane                    0.077554\n",
       "99        Unknown                                        0.073652\n",
       "98        Not Reported                                   0.062921\n",
       "12        Pedestrian Refuge Island or Traffic Island     0.043411\n",
       "dtype: float64"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#\"REL_ROAD\", \"REL_ROADNAME\"\n",
    "df_accident[[\"rel_road\", \"rel_roadname\"]].value_counts(normalize=True)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "2836d9cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18    0.057585\n",
       "20    0.055568\n",
       "17    0.055405\n",
       "21    0.054721\n",
       "19    0.054387\n",
       "16    0.051814\n",
       "15    0.051051\n",
       "22    0.048100\n",
       "14    0.046394\n",
       "23    0.043693\n",
       "13    0.042857\n",
       "12    0.039336\n",
       "0     0.039097\n",
       "2     0.038943\n",
       "1     0.037206\n",
       "6     0.036525\n",
       "11    0.035526\n",
       "7     0.033753\n",
       "10    0.031256\n",
       "5     0.030204\n",
       "3     0.028591\n",
       "9     0.028072\n",
       "8     0.027996\n",
       "4     0.024481\n",
       "99    0.007440\n",
       "Name: hour, dtype: float64"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# accidents per hour. No major standouts here. Hence we will break this up into chunks of multiple hours. \n",
    "df_accident['hour'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "id": "fc6b7adc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create datetime column. \n",
    "# This is the timeseries index used in modeling \n",
    "\n",
    "# Unfortunately, we cannot use the standard pd.to_datetime functions since the FARS data has values of 98 and 99 which are considered \n",
    "# valid values and mess up the calculations. \n",
    "# df_accident[\"date_time\"] = pd.to_datetime(df_accident[[\"year\", \"month\", \"day\", \"hour\", \"minute\"]], errors='coerce')\n",
    "\n",
    "# If hour = 99 or minute = 99 -> set to NaT, equivalent of NaN. We'll drop these rows later\n",
    "for i in range (df_accident.shape[0]) :\n",
    "    d = df_accident.iloc[i]\n",
    "    if (d['hour'] < 99) & (d['minute'] < 99): # valid values\n",
    "        dt = str(d[\"year\"]) + \"-\" + str( d[\"month\"]) + \"-\" + str( d[\"day\"]) + \" \" + str( d[\"hour\"]) + \":\" + str( d[\"minute\"]) \n",
    "        #print( i, dt )\n",
    "        df_accident.loc[i, \"date_time\"] = pd.to_datetime(dt)\n",
    "    else:\n",
    "        # don't create an entry if invalid hour or minute??\n",
    "        df_accident.loc[i, \"date_time\"] = pd.NaT # NaT is the equivalent of NaN for datetime\n",
    "        #print( i, d )\n",
    "    \n",
    "    \n",
    "# takes 10 minutes   for 10 years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "id": "58a04257",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>hour</th>\n",
       "      <th>minute</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date_time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-01-15 04:10:00</th>\n",
       "      <td>2010</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-11 06:00:00</th>\n",
       "      <td>2010</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-14 15:10:00</th>\n",
       "      <td>2010</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-21 01:16:00</th>\n",
       "      <td>2010</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-04 06:38:00</th>\n",
       "      <td>2010</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-12 16:36:00</th>\n",
       "      <td>2020</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>16</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-18 18:06:00</th>\n",
       "      <td>2020</td>\n",
       "      <td>12</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-11 17:30:00</th>\n",
       "      <td>2020</td>\n",
       "      <td>12</td>\n",
       "      <td>11</td>\n",
       "      <td>17</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-15 20:21:00</th>\n",
       "      <td>2020</td>\n",
       "      <td>12</td>\n",
       "      <td>15</td>\n",
       "      <td>20</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-24 09:25:00</th>\n",
       "      <td>2020</td>\n",
       "      <td>12</td>\n",
       "      <td>24</td>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>353764 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     year  month  day  hour  minute\n",
       "date_time                                          \n",
       "2010-01-15 04:10:00  2010      1   15     4      10\n",
       "2010-01-11 06:00:00  2010      1   11     6       0\n",
       "2010-01-14 15:10:00  2010      1   14    15      10\n",
       "2010-01-21 01:16:00  2010      1   21     1      16\n",
       "2010-01-04 06:38:00  2010      1    4     6      38\n",
       "...                   ...    ...  ...   ...     ...\n",
       "2020-12-12 16:36:00  2020     12   12    16      36\n",
       "2020-12-18 18:06:00  2020     12   18    18       6\n",
       "2020-12-11 17:30:00  2020     12   11    17      30\n",
       "2020-12-15 20:21:00  2020     12   15    20      21\n",
       "2020-12-24 09:25:00  2020     12   24     9      25\n",
       "\n",
       "[353764 rows x 5 columns]"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#df_accident\n",
    "df_accident[['year', \"month\", \"day\", \"hour\", \"minute\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "574beb3d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2681"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_accident[\"date_time\"].isnull().sum() # 315 have null (NaT) in 2020; 787 in 2018-2020; 2618 in 10 years. \n",
    "# Less than 1% so can drop these rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "id": "c7402e54",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop the nulls first before using this as the index. (Keep the nonnulls)\n",
    "df_accident = df_accident.loc[df_accident[\"date_time\"].notnull()]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "id": "8bbe18c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year                        0\n",
       "month                       0\n",
       "monthname              150299\n",
       "day                         0\n",
       "day_week                    0\n",
       "day_weekname           150299\n",
       "hour                        0\n",
       "hourname               150299\n",
       "minute                      0\n",
       "state                       0\n",
       "statename              150299\n",
       "county                      0\n",
       "countyname             182586\n",
       "st_case                     0\n",
       "ve_total                    0\n",
       "ve_forms                    0\n",
       "pvh_invl                30075\n",
       "peds                        0\n",
       "persons                     0\n",
       "city                        0\n",
       "cityname               182586\n",
       "latitude                    0\n",
       "longitud                    0\n",
       "harm_ev                     0\n",
       "harm_evname            150299\n",
       "man_coll                    0\n",
       "man_collname           150299\n",
       "wrk_zone                    0\n",
       "wrk_zonename           150299\n",
       "rel_road                    0\n",
       "rel_roadname           150299\n",
       "lgt_cond                    0\n",
       "lgt_condname           150299\n",
       "weather                     0\n",
       "weathername            150299\n",
       "sch_bus                     0\n",
       "sch_busname            150299\n",
       "not_hour                    0\n",
       "not_hourname           150299\n",
       "not_min                     0\n",
       "not_minname            150299\n",
       "arr_hour                    0\n",
       "arr_hourname           150299\n",
       "arr_min                     0\n",
       "arr_minname            150299\n",
       "fatals                      0\n",
       "drunk_dr                    0\n",
       "day_3hr_window              0\n",
       "day_3hr_window_name    150299\n",
       "day_2hr_window              0\n",
       "day_2hr_window_name    150299\n",
       "day_4hr_window              0\n",
       "day_4hr_window_name    150299\n",
       "dtype: int64"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# confirm no nulls for date_time\n",
    "df_accident.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "id": "71e43bb2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_accident[\"date_time\"].isnull().sum() # finally, no nulls in date_time column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "id": "eba5b7b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set this new date_time column to the index (for lag, ACF, PACF plots for trend and seasonality, and for the time series modeling for AR and RNN)\n",
    "\n",
    "df_accident = df_accident.set_index( \"date_time\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "id": "144097fc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 353764 entries, 2010-01-15 04:10:00 to 2020-12-24 09:25:00\n",
      "Data columns (total 47 columns):\n",
      " #   Column        Non-Null Count   Dtype  \n",
      "---  ------        --------------   -----  \n",
      " 0   year          353764 non-null  int64  \n",
      " 1   month         353764 non-null  int64  \n",
      " 2   monthname     203465 non-null  object \n",
      " 3   day           353764 non-null  int64  \n",
      " 4   day_week      353764 non-null  int64  \n",
      " 5   day_weekname  203465 non-null  object \n",
      " 6   hour          353764 non-null  int64  \n",
      " 7   hourname      203465 non-null  object \n",
      " 8   minute        353764 non-null  int64  \n",
      " 9   state         353764 non-null  int64  \n",
      " 10  statename     203465 non-null  object \n",
      " 11  county        353764 non-null  int64  \n",
      " 12  countyname    171178 non-null  object \n",
      " 13  st_case       353764 non-null  int64  \n",
      " 14  ve_total      353764 non-null  int64  \n",
      " 15  ve_forms      353764 non-null  int64  \n",
      " 16  pvh_invl      323689 non-null  float64\n",
      " 17  peds          353764 non-null  int64  \n",
      " 18  persons       353764 non-null  int64  \n",
      " 19  city          353764 non-null  int64  \n",
      " 20  cityname      171178 non-null  object \n",
      " 21  latitude      353764 non-null  float64\n",
      " 22  longitud      353764 non-null  float64\n",
      " 23  harm_ev       353764 non-null  int64  \n",
      " 24  harm_evname   203465 non-null  object \n",
      " 25  man_coll      353764 non-null  int64  \n",
      " 26  man_collname  203465 non-null  object \n",
      " 27  wrk_zone      353764 non-null  int64  \n",
      " 28  wrk_zonename  203465 non-null  object \n",
      " 29  rel_road      353764 non-null  int64  \n",
      " 30  rel_roadname  203465 non-null  object \n",
      " 31  lgt_cond      353764 non-null  int64  \n",
      " 32  lgt_condname  203465 non-null  object \n",
      " 33  weather       353764 non-null  int64  \n",
      " 34  weathername   203465 non-null  object \n",
      " 35  sch_bus       353764 non-null  int64  \n",
      " 36  sch_busname   203465 non-null  object \n",
      " 37  not_hour      353764 non-null  int64  \n",
      " 38  not_hourname  203465 non-null  object \n",
      " 39  not_min       353764 non-null  int64  \n",
      " 40  not_minname   203465 non-null  object \n",
      " 41  arr_hour      353764 non-null  int64  \n",
      " 42  arr_hourname  203465 non-null  object \n",
      " 43  arr_min       353764 non-null  int64  \n",
      " 44  arr_minname   203465 non-null  object \n",
      " 45  fatals        353764 non-null  int64  \n",
      " 46  drunk_dr      353764 non-null  int64  \n",
      "dtypes: float64(3), int64(27), object(17)\n",
      "memory usage: 129.6+ MB\n"
     ]
    }
   ],
   "source": [
    "# Check info and make sure DatetimeIndex: 356445 entries, 2010-01-15 04:10:00 to 2020-12-24 09:25:00\n",
    "df_accident.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "id": "bd77f1cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>day_week</th>\n",
       "      <th>hour</th>\n",
       "      <th>day_weekname</th>\n",
       "      <th>day_2hr_window</th>\n",
       "      <th>day_2hr_window_name</th>\n",
       "      <th>day_3hr_window</th>\n",
       "      <th>day_3hr_window_name</th>\n",
       "      <th>day_4hr_window</th>\n",
       "      <th>day_4hr_window_name</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date_time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-01-15 04:10:00</th>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>63</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-11 06:00:00</th>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-14 15:10:00</th>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>56</td>\n",
       "      <td>NaN</td>\n",
       "      <td>38</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-21 01:16:00</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>49</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-04 06:38:00</th>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-12 16:36:00</th>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>81</td>\n",
       "      <td>Saturday 16:00 to 18:00</td>\n",
       "      <td>54</td>\n",
       "      <td>Saturday 15:00 to 18:00</td>\n",
       "      <td>41</td>\n",
       "      <td>Saturday 16:00 to 20:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-18 18:06:00</th>\n",
       "      <td>6</td>\n",
       "      <td>18</td>\n",
       "      <td>Friday</td>\n",
       "      <td>70</td>\n",
       "      <td>Friday 18:00 to 20:00</td>\n",
       "      <td>47</td>\n",
       "      <td>Friday 18:00 to 21:00</td>\n",
       "      <td>35</td>\n",
       "      <td>Friday 16:00 to 20:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-11 17:30:00</th>\n",
       "      <td>6</td>\n",
       "      <td>17</td>\n",
       "      <td>Friday</td>\n",
       "      <td>69</td>\n",
       "      <td>Friday 16:00 to 18:00</td>\n",
       "      <td>46</td>\n",
       "      <td>Friday 15:00 to 18:00</td>\n",
       "      <td>35</td>\n",
       "      <td>Friday 16:00 to 20:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-15 20:21:00</th>\n",
       "      <td>3</td>\n",
       "      <td>20</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>35</td>\n",
       "      <td>Tuesday 20:00 to 22:00</td>\n",
       "      <td>23</td>\n",
       "      <td>Tuesday 18:00 to 21:00</td>\n",
       "      <td>18</td>\n",
       "      <td>Tuesday 20:00 to 24:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-24 09:25:00</th>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>53</td>\n",
       "      <td>Thursday 8:00 to 10:00</td>\n",
       "      <td>36</td>\n",
       "      <td>Thursday 9:00 to 12:00</td>\n",
       "      <td>27</td>\n",
       "      <td>Thursday 8:00 to 12:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>353764 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     day_week  hour day_weekname  day_2hr_window  \\\n",
       "date_time                                                          \n",
       "2010-01-15 04:10:00         6     4          NaN              63   \n",
       "2010-01-11 06:00:00         2     6          NaN              16   \n",
       "2010-01-14 15:10:00         5    15          NaN              56   \n",
       "2010-01-21 01:16:00         5     1          NaN              49   \n",
       "2010-01-04 06:38:00         2     6          NaN              16   \n",
       "...                       ...   ...          ...             ...   \n",
       "2020-12-12 16:36:00         7    16     Saturday              81   \n",
       "2020-12-18 18:06:00         6    18       Friday              70   \n",
       "2020-12-11 17:30:00         6    17       Friday              69   \n",
       "2020-12-15 20:21:00         3    20      Tuesday              35   \n",
       "2020-12-24 09:25:00         5     9     Thursday              53   \n",
       "\n",
       "                         day_2hr_window_name  day_3hr_window  \\\n",
       "date_time                                                      \n",
       "2010-01-15 04:10:00                      NaN              42   \n",
       "2010-01-11 06:00:00                      NaN              11   \n",
       "2010-01-14 15:10:00                      NaN              38   \n",
       "2010-01-21 01:16:00                      NaN              33   \n",
       "2010-01-04 06:38:00                      NaN              11   \n",
       "...                                      ...             ...   \n",
       "2020-12-12 16:36:00  Saturday 16:00 to 18:00              54   \n",
       "2020-12-18 18:06:00    Friday 18:00 to 20:00              47   \n",
       "2020-12-11 17:30:00    Friday 16:00 to 18:00              46   \n",
       "2020-12-15 20:21:00   Tuesday 20:00 to 22:00              23   \n",
       "2020-12-24 09:25:00   Thursday 8:00 to 10:00              36   \n",
       "\n",
       "                         day_3hr_window_name  day_4hr_window  \\\n",
       "date_time                                                      \n",
       "2010-01-15 04:10:00                      NaN              32   \n",
       "2010-01-11 06:00:00                      NaN               8   \n",
       "2010-01-14 15:10:00                      NaN              28   \n",
       "2010-01-21 01:16:00                      NaN              25   \n",
       "2010-01-04 06:38:00                      NaN               8   \n",
       "...                                      ...             ...   \n",
       "2020-12-12 16:36:00  Saturday 15:00 to 18:00              41   \n",
       "2020-12-18 18:06:00    Friday 18:00 to 21:00              35   \n",
       "2020-12-11 17:30:00    Friday 15:00 to 18:00              35   \n",
       "2020-12-15 20:21:00   Tuesday 18:00 to 21:00              18   \n",
       "2020-12-24 09:25:00   Thursday 9:00 to 12:00              27   \n",
       "\n",
       "                         day_4hr_window_name  \n",
       "date_time                                     \n",
       "2010-01-15 04:10:00                      NaN  \n",
       "2010-01-11 06:00:00                      NaN  \n",
       "2010-01-14 15:10:00                      NaN  \n",
       "2010-01-21 01:16:00                      NaN  \n",
       "2010-01-04 06:38:00                      NaN  \n",
       "...                                      ...  \n",
       "2020-12-12 16:36:00  Saturday 16:00 to 20:00  \n",
       "2020-12-18 18:06:00    Friday 16:00 to 20:00  \n",
       "2020-12-11 17:30:00    Friday 16:00 to 20:00  \n",
       "2020-12-15 20:21:00   Tuesday 20:00 to 24:00  \n",
       "2020-12-24 09:25:00   Thursday 8:00 to 12:00  \n",
       "\n",
       "[353764 rows x 9 columns]"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a new column, which has the Day+hour with most accidents\n",
    "\n",
    "# Sun=1; Sat=7; \n",
    "# hour//3 for 3 hour window; \n",
    "# 3 hour window: 8 buckets of 3 hours each in a day; 7 days; buckets: 1 to 56\n",
    "# 2 hour window: 12 buckets of 2 hours each in a day; 7 days; buckets: 1 to 84\n",
    "# 4 hour window: 6 buckets of 4 hours each in a day; 7 days; buckets: 1 to 42\n",
    "\n",
    "# Create columns for 3 hour window and its name (for graphs)\n",
    "# Create 2 or 4 hour windows too \n",
    "\n",
    "# 3 hour window\n",
    "df_accident[\"day_3hr_window\"] = ((df_accident[\"day_week\"]-1) * 8) + ( df_accident[\"hour\"]//3) + 1 # starting from 1\n",
    "df_accident[\"day_3hr_window_name\"] = df_accident[\"day_weekname\"] + \" \" + (( df_accident[\"hour\"]//3)*3).astype(str) + \":00 to \" + ((( df_accident[\"hour\"]//3)*3)+3).astype(str) + \":00\"\n",
    "\n",
    "# 2 hour window\n",
    "df_accident[\"day_2hr_window\"] = ((df_accident[\"day_week\"]-1) * 12) + ( df_accident[\"hour\"]//2) + 1 # starting from 1\n",
    "df_accident[\"day_2hr_window_name\"] = df_accident[\"day_weekname\"] + \" \" + (( df_accident[\"hour\"]//2)*2).astype(str) + \":00 to \" + ((( df_accident[\"hour\"]//2)*2)+2).astype(str) + \":00\"\n",
    "\n",
    "# 4 hour window\n",
    "df_accident[\"day_4hr_window\"] = ((df_accident[\"day_week\"]-1) * 6) + ( df_accident[\"hour\"]//4) + 1 # starting from 1\n",
    "df_accident[\"day_4hr_window_name\"] = df_accident[\"day_weekname\"] + \" \" + (( df_accident[\"hour\"]//4)*4).astype(str) + \":00 to \" + ((( df_accident[\"hour\"]//4)*4)+4).astype(str) + \":00\"\n",
    "\n",
    "\n",
    "\n",
    "df_accident[[\"day_week\", \"hour\", \"day_weekname\", \"day_2hr_window\", \"day_2hr_window_name\", \"day_3hr_window\", \"day_3hr_window_name\", \"day_4hr_window\", \"day_4hr_window_name\"]]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "id": "4c77d2e5",
   "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>day_week</th>\n",
       "      <th>hour</th>\n",
       "      <th>day_weekname</th>\n",
       "      <th>day_2hr_window</th>\n",
       "      <th>day_2hr_window_name</th>\n",
       "      <th>day_3hr_window</th>\n",
       "      <th>day_3hr_window_name</th>\n",
       "      <th>day_4hr_window</th>\n",
       "      <th>day_4hr_window_name</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date_time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-01-03 13:50:00</th>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-10 04:37:00</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-17 21:35:00</th>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-10 21:55:00</th>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-17 01:20:00</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     day_week  hour day_weekname  day_2hr_window  \\\n",
       "date_time                                                          \n",
       "2010-01-03 13:50:00         1    13          NaN               7   \n",
       "2010-01-10 04:37:00         1     4          NaN               3   \n",
       "2010-01-17 21:35:00         1    21          NaN              11   \n",
       "2010-01-10 21:55:00         1    21          NaN              11   \n",
       "2010-01-17 01:20:00         1     1          NaN               1   \n",
       "\n",
       "                    day_2hr_window_name  day_3hr_window day_3hr_window_name  \\\n",
       "date_time                                                                     \n",
       "2010-01-03 13:50:00                 NaN               5                 NaN   \n",
       "2010-01-10 04:37:00                 NaN               2                 NaN   \n",
       "2010-01-17 21:35:00                 NaN               8                 NaN   \n",
       "2010-01-10 21:55:00                 NaN               8                 NaN   \n",
       "2010-01-17 01:20:00                 NaN               1                 NaN   \n",
       "\n",
       "                     day_4hr_window day_4hr_window_name  \n",
       "date_time                                                \n",
       "2010-01-03 13:50:00               4                 NaN  \n",
       "2010-01-10 04:37:00               2                 NaN  \n",
       "2010-01-17 21:35:00               6                 NaN  \n",
       "2010-01-10 21:55:00               6                 NaN  \n",
       "2010-01-17 01:20:00               1                 NaN  "
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check looks good for Sunday since calcualtion check is easier \n",
    "df_accident[(df_accident['day_week'] == 1)][[\"day_week\", \"hour\", \"day_weekname\", \"day_2hr_window\", \"day_2hr_window_name\", \"day_3hr_window\", \"day_3hr_window_name\", \"day_4hr_window\", \"day_4hr_window_name\"]].head()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "id": "b24b375c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>day_week</th>\n",
       "      <th>hour</th>\n",
       "      <th>day_weekname</th>\n",
       "      <th>day_2hr_window</th>\n",
       "      <th>day_2hr_window_name</th>\n",
       "      <th>day_3hr_window</th>\n",
       "      <th>day_3hr_window_name</th>\n",
       "      <th>day_4hr_window</th>\n",
       "      <th>day_4hr_window_name</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date_time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-01-15 04:10:00</th>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>63</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-11 06:00:00</th>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-14 15:10:00</th>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>56</td>\n",
       "      <td>NaN</td>\n",
       "      <td>38</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-21 01:16:00</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>49</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-04 06:38:00</th>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     day_week  hour day_weekname  day_2hr_window  \\\n",
       "date_time                                                          \n",
       "2010-01-15 04:10:00         6     4          NaN              63   \n",
       "2010-01-11 06:00:00         2     6          NaN              16   \n",
       "2010-01-14 15:10:00         5    15          NaN              56   \n",
       "2010-01-21 01:16:00         5     1          NaN              49   \n",
       "2010-01-04 06:38:00         2     6          NaN              16   \n",
       "\n",
       "                    day_2hr_window_name  day_3hr_window day_3hr_window_name  \\\n",
       "date_time                                                                     \n",
       "2010-01-15 04:10:00                 NaN              42                 NaN   \n",
       "2010-01-11 06:00:00                 NaN              11                 NaN   \n",
       "2010-01-14 15:10:00                 NaN              38                 NaN   \n",
       "2010-01-21 01:16:00                 NaN              33                 NaN   \n",
       "2010-01-04 06:38:00                 NaN              11                 NaN   \n",
       "\n",
       "                     day_4hr_window day_4hr_window_name  \n",
       "date_time                                                \n",
       "2010-01-15 04:10:00              32                 NaN  \n",
       "2010-01-11 06:00:00               8                 NaN  \n",
       "2010-01-14 15:10:00              28                 NaN  \n",
       "2010-01-21 01:16:00              25                 NaN  \n",
       "2010-01-04 06:38:00               8                 NaN  "
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check looks good for all days\n",
    "df_accident[[\"day_week\", \"hour\", \"day_weekname\", \"day_2hr_window\", \"day_2hr_window_name\", \"day_3hr_window\", \"day_3hr_window_name\", \"day_4hr_window\", \"day_4hr_window_name\"]].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "id": "2608fbf4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save the file (with date_time as index) for use in next notebook \n",
    "\n",
    "df_accident.to_csv(\"./data/accident_ts_10years.csv\" )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f5eefd7",
   "metadata": {},
   "source": [
    "### We're done with this file!! The next notebook has the EDA and seasonality, trends checks...\n"
   ]
  }
 ],
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