ges_data_wrangling.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "GES data wrangling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pysal as ps\n",
    "import scipy.spatial as spatial\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read in GES data from csv (2015) or txt (all other years) files "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/rlrognstad/anaconda3/lib/python3.6/site-packages/IPython/core/interactiveshell.py:2821: DtypeWarning: Columns (25,26) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  if self.run_code(code, result):\n"
     ]
    },
    {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CASENUM</th>\n",
       "      <th>VEH_NO</th>\n",
       "      <th>REGION</th>\n",
       "      <th>STRATUM</th>\n",
       "      <th>PJ</th>\n",
       "      <th>PSU</th>\n",
       "      <th>PSUSTRAT</th>\n",
       "      <th>VE_FORMS</th>\n",
       "      <th>NUMOCCS</th>\n",
       "      <th>MONTH</th>\n",
       "      <th>...</th>\n",
       "      <th>HITRUN_IM</th>\n",
       "      <th>BDYTYP_IM</th>\n",
       "      <th>MDLYR_IM</th>\n",
       "      <th>IMPACT1_IM</th>\n",
       "      <th>VEVENT_IM</th>\n",
       "      <th>PCRASH1_IM</th>\n",
       "      <th>MXVSEV_IM</th>\n",
       "      <th>NUMINJ_IM</th>\n",
       "      <th>V_ALCH_IM</th>\n",
       "      <th>YEAR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>201510443751</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
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       "      <td>3</td>\n",
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       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2004</td>\n",
       "      <td>12</td>\n",
       "      <td>59</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>201510443757</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>31</td>\n",
       "      <td>2005</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>201510443760</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2005</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>201510443760</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>66</td>\n",
       "      <td>1999</td>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>201510443763</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>39</td>\n",
       "      <td>1996</td>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 83 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        CASENUM  VEH_NO  REGION  STRATUM  PJ  PSU  PSUSTRAT  VE_FORMS  \\\n",
       "0  201510443751       1       1        4   3    2         3         1   \n",
       "1  201510443757       1       1        6   4    2         3         1   \n",
       "2  201510443760       1       1        4   4    2         3         2   \n",
       "3  201510443760       2       1        4   4    2         3         2   \n",
       "4  201510443763       1       1        4  10    2         3         2   \n",
       "\n",
       "   NUMOCCS  MONTH  ...   HITRUN_IM  BDYTYP_IM  MDLYR_IM  IMPACT1_IM  \\\n",
       "0        1      1  ...           0          4      2004          12   \n",
       "1        1      1  ...           0         31      2005           0   \n",
       "2        2      1  ...           0          4      2005          12   \n",
       "3        2      1  ...           0         66      1999           6   \n",
       "4        1      1  ...           0         39      1996           6   \n",
       "\n",
       "   VEVENT_IM  PCRASH1_IM  MXVSEV_IM  NUMINJ_IM  V_ALCH_IM  YEAR  \n",
       "0         59           1          0          0          2  2015  \n",
       "1          1           1          0          0          2  2015  \n",
       "2         12           1          0          0          2  2015  \n",
       "3         12           2          0          0          2  2015  \n",
       "4         12           5          0          0          2  2015  \n",
       "\n",
       "[5 rows x 83 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accident_15 = pd.DataFrame.from_csv('nhtsa-ges-data/GES_2015/ACCIDENT.CSV', index_col=None)\n",
    "accident_15['YEAR'] = 2015 #add column to specify year, needed when compling multiyear dataset\n",
    "#accident_15.head()\n",
    "person_15 = pd.DataFrame.from_csv('nhtsa-ges-data/GES_2015/PERSON.CSV', index_col=None)\n",
    "person_15['YEAR'] = 2015 #add column to specify year, needed when compling multiyear dataset\n",
    "#person_15.head()\n",
    "\n",
    "vehicle_15 = pd.DataFrame.from_csv('nhtsa-ges-data/GES_2015/VEHICLE.CSV', index_col=None, encoding = 'latin-1')\n",
    "vehicle_15['YEAR'] = 2015 #add column to specify year, needed when compling multiyear dataset\n",
    "vehicle_15.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read in .txt files, which are tab-delimited and convert to data frames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/rlrognstad/anaconda3/lib/python3.6/site-packages/IPython/core/interactiveshell.py:2717: DtypeWarning: Columns (34,35) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    }
   ],
   "source": [
    "accident_14 = pd.read_csv('nhtsa-ges-data/GES_2014/ACCIDENT.TXT',sep='\\t',header=(0))\n",
    "accident_14['YEAR'] = 2014 #add column to specify year, needed when compling multiyear dataset\n",
    "#accident_14.head()\n",
    "person_14 = pd.read_csv('nhtsa-ges-data/GES_2014/PERSON.TXT',sep='\\t',header=(0))\n",
    "person_14['YEAR'] = 2014 #add column to specify year, needed when compling multiyear dataset\n",
    "#person_14.head()\n",
    "\n",
    "vehicle_14 = pd.read_csv('nhtsa-ges-data/GES_2014/VEHICLE.TXT',sep='\\t',header=(0))\n",
    "vehicle_14['YEAR'] = 2014 #add column to specify year, needed when compling multiyear dataset\n",
    "#vehicle_14.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/rlrognstad/anaconda3/lib/python3.6/site-packages/IPython/core/interactiveshell.py:2717: DtypeWarning: Columns (34,35) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CASENUM</th>\n",
       "      <th>VEH_NO</th>\n",
       "      <th>HITRUN_IM</th>\n",
       "      <th>BDYTYP_IM</th>\n",
       "      <th>MDLYR_IM</th>\n",
       "      <th>IMPACT1_IM</th>\n",
       "      <th>VEVENT_IM</th>\n",
       "      <th>PCRASH1_IM</th>\n",
       "      <th>MXVSEV_IM</th>\n",
       "      <th>NUMINJ_IM</th>\n",
       "      <th>...</th>\n",
       "      <th>VSURCOND</th>\n",
       "      <th>VTRAFCON</th>\n",
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       "      <th>P_CRASH2</th>\n",
       "      <th>P_CRASH3</th>\n",
       "      <th>PCRASH4</th>\n",
       "      <th>PCRASH5</th>\n",
       "      <th>ACC_TYPE</th>\n",
       "      <th>YEAR</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "<p>5 rows × 82 columns</p>\n",
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      ],
      "text/plain": [
       "        CASENUM  VEH_NO  HITRUN_IM  BDYTYP_IM  MDLYR_IM  IMPACT1_IM  \\\n",
       "0  201310436487       1          0          4      2010          12   \n",
       "1  201310436580       1          0          4      2011          12   \n",
       "2  201310436595       1          0          4      2004           6   \n",
       "3  201310436595       2          0          4      2004          12   \n",
       "4  201310436599       1          0         19      2001          12   \n",
       "\n",
       "   VEVENT_IM  PCRASH1_IM  MXVSEV_IM  NUMINJ_IM  ...   VSURCOND  VTRAFCON  \\\n",
       "0         11           1          0          0  ...          1         0   \n",
       "1         48           1          0          0  ...          1         0   \n",
       "2         12           5          0          0  ...          1         3   \n",
       "3         12           1          0          0  ...          1         3   \n",
       "4         11           1          0          0  ...          1         0   \n",
       "\n",
       "   VTCONT_F  P_CRASH1  P_CRASH2  P_CRASH3  PCRASH4  PCRASH5  ACC_TYPE  YEAR  \n",
       "0         0         1        87        99        1        1        13  2013  \n",
       "1         0         1        13        99        1        4         1  2013  \n",
       "2         3         5        53         1        1        1        21  2013  \n",
       "3         3         1        50         4        1        1        20  2013  \n",
       "4         0         1        87        98        1        1        13  2013  \n",
       "\n",
       "[5 rows x 82 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accident_13 = pd.read_csv('nhtsa-ges-data/GES_2013/ACCIDENT.TXT',sep='\\t',header=(0))\n",
    "accident_13['YEAR'] = 2013 #add column to specify year, needed when compling multiyear dataset\n",
    "#accident_13.head()\n",
    "person_13 = pd.read_csv('nhtsa-ges-data/GES_2013/PERSON.TXT',sep='\\t',header=(0))\n",
    "person_13['YEAR'] = 2013 #add column to specify year, needed when compling multiyear dataset\n",
    "#person_13.head()\n",
    "\n",
    "vehicle_13 = pd.read_csv('nhtsa-ges-data/GES_2013/VEHICLE.TXT',sep='\\t',header=(0))\n",
    "vehicle_13['YEAR'] = 2013 #add column to specify year, needed when compling multiyear dataset\n",
    "vehicle_13.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/rlrognstad/anaconda3/lib/python3.6/site-packages/IPython/core/interactiveshell.py:2717: DtypeWarning: Columns (34,35) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    }
   ],
   "source": [
    "accident_12 = pd.read_csv('nhtsa-ges-data/GES_2012/ACCIDENT.TXT',sep='\\t',header=(0))\n",
    "accident_12['YEAR'] = 2012 #add column to specify year, needed when compling multiyear dataset\n",
    "#accident_12.head()\n",
    "person_12 = pd.read_csv('nhtsa-ges-data/GES_2012/PERSON.TXT',sep='\\t',header=(0))\n",
    "person_12['YEAR'] = 2012 #add column to specify year, needed when compling multiyear dataset\n",
    "#person_12.head()\n",
    "\n",
    "vehicle_12 = pd.read_csv('nhtsa-ges-data/GES_2012/VEHICLE.TXT',sep='\\t',header=(0))\n",
    "vehicle_12['YEAR'] = 2012 #add column to specify year, needed when compling multiyear dataset\n",
    "#vehicle_12.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "accident_11 = pd.read_csv('nhtsa-ges-data/GES_2011/ACCIDENT.TXT',sep='\\t',header=(0))\n",
    "accident_11['YEAR'] = 2011 #add column to specify year, needed when compling multiyear dataset\n",
    "#accident_11.head()\n",
    "person_11 = pd.read_csv('nhtsa-ges-data/GES_2011/PERSON.TXT',sep='\\t',header=(0))\n",
    "person_11['YEAR'] = 2011 #add column to specify year, needed when compling multiyear dataset\n",
    "#person_11.head()\n",
    "\n",
    "vehicle_11 = pd.read_csv('nhtsa-ges-data/GES_2011/VEHICLE.TXT',sep='\\t',header=(0))\n",
    "vehicle_11['YEAR'] = 2011 #add column to specify year, needed when compling multiyear dataset\n",
    "#vehicle_11.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ALCHL_IM</th>\n",
       "      <th>ALCOHOL</th>\n",
       "      <th>CASENUM</th>\n",
       "      <th>CF1</th>\n",
       "      <th>CF2</th>\n",
       "      <th>CF3</th>\n",
       "      <th>DAY_WEEK</th>\n",
       "      <th>EVENT1_IM</th>\n",
       "      <th>HARM_EV</th>\n",
       "      <th>HOUR</th>\n",
       "      <th>...</th>\n",
       "      <th>VE_FORMS</th>\n",
       "      <th>VE_TOTAL</th>\n",
       "      <th>WEATHER</th>\n",
       "      <th>WEATHER1</th>\n",
       "      <th>WEATHER2</th>\n",
       "      <th>WEATHR_IM</th>\n",
       "      <th>WEIGHT</th>\n",
       "      <th>WKDY_IM</th>\n",
       "      <th>WRK_ZONE</th>\n",
       "      <th>YEAR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>201510443751</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7</td>\n",
       "      <td>59</td>\n",
       "      <td>59</td>\n",
       "      <td>14</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>217.16800</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>201510443757</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>107.77342</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>201510443760</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>215.54684</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>201510443763</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>179.70652</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>201510443802</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>15</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>217.16800</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 50 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   ALCHL_IM  ALCOHOL       CASENUM  CF1  CF2  CF3  DAY_WEEK  EVENT1_IM  \\\n",
       "0         2        2  201510443751  0.0  0.0  0.0         7         59   \n",
       "1         2        2  201510443757  0.0  0.0  0.0         7          1   \n",
       "2         2        2  201510443760  0.0  0.0  0.0         5         12   \n",
       "3         2        2  201510443763  0.0  0.0  0.0         7         12   \n",
       "4         2        2  201510443802  0.0  0.0  0.0         7         30   \n",
       "\n",
       "   HARM_EV  HOUR  ...   VE_FORMS  VE_TOTAL  WEATHER  WEATHER1  WEATHER2  \\\n",
       "0       59    14  ...          1         1        4         4         0   \n",
       "1        1    15  ...          1         1        4         4         0   \n",
       "2       12    13  ...          2         2        1         1         0   \n",
       "3       12    13  ...          2         2        4         4         0   \n",
       "4       30    15  ...          1         1        4         4         0   \n",
       "\n",
       "   WEATHR_IM     WEIGHT  WKDY_IM  WRK_ZONE  YEAR  \n",
       "0          4  217.16800        7         0  2015  \n",
       "1          4  107.77342        7         0  2015  \n",
       "2          1  215.54684        5         0  2015  \n",
       "3          4  179.70652        7         0  2015  \n",
       "4          4  217.16800        7         0  2015  \n",
       "\n",
       "[5 rows x 50 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#make combined dataframes\n",
    "a_frames = [accident_15, accident_14, accident_13, accident_12, accident_11]\n",
    "accident_all = pd.concat(a_frames)\n",
    "\n",
    "accident_all.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AGE</th>\n",
       "      <th>AGE_IM</th>\n",
       "      <th>AIR_BAG</th>\n",
       "      <th>ALC_RES</th>\n",
       "      <th>ALC_STATUS</th>\n",
       "      <th>ATST_TYP</th>\n",
       "      <th>BODY_TYP</th>\n",
       "      <th>CASENUM</th>\n",
       "      <th>DRINKING</th>\n",
       "      <th>DRUGRES1</th>\n",
       "      <th>...</th>\n",
       "      <th>SEX</th>\n",
       "      <th>SEX_IM</th>\n",
       "      <th>SPEC_USE</th>\n",
       "      <th>STRATUM</th>\n",
       "      <th>STR_VEH</th>\n",
       "      <th>TOW_VEH</th>\n",
       "      <th>VEH_NO</th>\n",
       "      <th>VE_FORMS</th>\n",
       "      <th>WEIGHT</th>\n",
       "      <th>YEAR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>52</td>\n",
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       "      <td>995</td>\n",
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       "      <td>95</td>\n",
       "      <td>49.0</td>\n",
       "      <td>201510443751</td>\n",
       "      <td>0</td>\n",
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       "      <td>217.16800</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>98</td>\n",
       "      <td>995</td>\n",
       "      <td>8</td>\n",
       "      <td>95</td>\n",
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       "      <td>0</td>\n",
       "      <td>95</td>\n",
       "      <td>...</td>\n",
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       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>107.77342</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>61</td>\n",
       "      <td>61</td>\n",
       "      <td>98</td>\n",
       "      <td>995</td>\n",
       "      <td>8</td>\n",
       "      <td>95</td>\n",
       "      <td>49.0</td>\n",
       "      <td>201510443760</td>\n",
       "      <td>0</td>\n",
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       "      <td>215.54684</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>98</td>\n",
       "      <td>996</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>201510443760</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
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       "      <td>0</td>\n",
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       "      <td>2</td>\n",
       "      <td>215.54684</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>98</td>\n",
       "      <td>995</td>\n",
       "      <td>8</td>\n",
       "      <td>95</td>\n",
       "      <td>66.0</td>\n",
       "      <td>201510443760</td>\n",
       "      <td>0</td>\n",
       "      <td>95</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>215.54684</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 61 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   AGE  AGE_IM  AIR_BAG  ALC_RES  ALC_STATUS  ATST_TYP  BODY_TYP  \\\n",
       "0   52      52       98      995           8        95      49.0   \n",
       "1   27      27       98      995           8        95      49.0   \n",
       "2   61      61       98      995           8        95      49.0   \n",
       "3   60      60       98      996           0         0      49.0   \n",
       "4   25      25       98      995           8        95      66.0   \n",
       "\n",
       "        CASENUM  DRINKING  DRUGRES1  ...   SEX  SEX_IM  SPEC_USE  STRATUM  \\\n",
       "0  201510443751         0        95  ...     2       2       0.0        4   \n",
       "1  201510443757         0        95  ...     1       1       0.0        6   \n",
       "2  201510443760         0        95  ...     1       1       0.0        4   \n",
       "3  201510443760         8         0  ...     2       2       0.0        4   \n",
       "4  201510443760         0        95  ...     1       1       0.0        4   \n",
       "\n",
       "   STR_VEH  TOW_VEH  VEH_NO  VE_FORMS     WEIGHT  YEAR  \n",
       "0        0      0.0       1         1  217.16800  2015  \n",
       "1        0      0.0       1         1  107.77342  2015  \n",
       "2        0      0.0       1         2  215.54684  2015  \n",
       "3        0      0.0       1         2  215.54684  2015  \n",
       "4        0      1.0       2         2  215.54684  2015  \n",
       "\n",
       "[5 rows x 61 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_frames = [person_15, person_14, person_13, person_12, person_11]\n",
    "person_all = pd.concat(p_frames)\n",
    "\n",
    "person_all.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "      <th>ACC_TYPE</th>\n",
       "      <th>BDYTYP_IM</th>\n",
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       "      <th>BUS_USE</th>\n",
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       "      <th>CASENUM</th>\n",
       "      <th>DEFORMED</th>\n",
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       "      <th>DR_SF2</th>\n",
       "      <th>...</th>\n",
       "      <th>VPROFILE</th>\n",
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       "      <th>VSURCOND</th>\n",
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       "      <th>VTRAFCON</th>\n",
       "      <th>VTRAFWAY</th>\n",
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       "      <th>YEAR</th>\n",
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       "  </thead>\n",
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       "      <td>98</td>\n",
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       "      <td>2</td>\n",
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       "      <td>2015</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 84 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   ACC_TYPE  BDYTYP_IM  BODY_TYP  BUS_USE  CARGO_BT       CASENUM  DEFORMED  \\\n",
       "0         2          4        49        0         0  201510443751         8   \n",
       "1        98         31        49        0         0  201510443757         6   \n",
       "2        28          4        49        0         0  201510443760         8   \n",
       "3        29         66        66        0         2  201510443760         8   \n",
       "4        32         39        39        0         0  201510443763         8   \n",
       "\n",
       "   DR_PRES  DR_SF1  DR_SF2  ...   VPROFILE  VSPD_LIM  VSURCOND  VTCONT_F  \\\n",
       "0        1     0.0     0.0  ...          1        98         3         0   \n",
       "1        1     0.0     0.0  ...          1        98         4         0   \n",
       "2        1     0.0     0.0  ...          1        98         1         3   \n",
       "3        1     0.0     0.0  ...          1        98         1         3   \n",
       "4        1     0.0     0.0  ...          2        98         3         3   \n",
       "\n",
       "   VTRAFCON  VTRAFWAY  V_ALCH_IM  V_CONFIG     WEIGHT  YEAR  \n",
       "0         0         1          2         0  217.16800  2015  \n",
       "1         0         1          2         0  107.77342  2015  \n",
       "2         3         3          2         0  215.54684  2015  \n",
       "3         3         3          2         6  215.54684  2015  \n",
       "4         3         8          2         0  179.70652  2015  \n",
       "\n",
       "[5 rows x 84 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v_frames = [vehicle_15, vehicle_14, vehicle_13, vehicle_12, vehicle_11]\n",
    "vehicle_all = pd.concat(v_frames)\n",
    "\n",
    "vehicle_all.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#compare headers\n",
    "#list(vehicle_all)\n",
    "#list(person_all)\n",
    "#list(accident_all)\n",
    "\n",
    "#CITY, and COUNTY are only in accident\n",
    "#combine by STATE and ST_CASE?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import census data, 2011-2015"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SUMLEV</th>\n",
       "      <th>STATE</th>\n",
       "      <th>COUNTY</th>\n",
       "      <th>STNAME</th>\n",
       "      <th>CTYNAME</th>\n",
       "      <th>YEAR</th>\n",
       "      <th>AGEGRP</th>\n",
       "      <th>TOT_POP</th>\n",
       "      <th>TOT_MALE</th>\n",
       "      <th>TOT_FEMALE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Autauga County</td>\n",
       "      <td>2011</td>\n",
       "      <td>0</td>\n",
       "      <td>55253</td>\n",
       "      <td>26999</td>\n",
       "      <td>28254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Autauga County</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>3581</td>\n",
       "      <td>1864</td>\n",
       "      <td>1717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Autauga County</td>\n",
       "      <td>2011</td>\n",
       "      <td>2</td>\n",
       "      <td>3940</td>\n",
       "      <td>1954</td>\n",
       "      <td>1986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Autauga County</td>\n",
       "      <td>2011</td>\n",
       "      <td>3</td>\n",
       "      <td>4355</td>\n",
       "      <td>2223</td>\n",
       "      <td>2132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Autauga County</td>\n",
       "      <td>2011</td>\n",
       "      <td>4</td>\n",
       "      <td>3954</td>\n",
       "      <td>2033</td>\n",
       "      <td>1921</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    SUMLEV  STATE  COUNTY   STNAME         CTYNAME  YEAR  AGEGRP  TOT_POP  \\\n",
       "57      50      1       1  Alabama  Autauga County  2011       0    55253   \n",
       "58      50      1       1  Alabama  Autauga County  2011       1     3581   \n",
       "59      50      1       1  Alabama  Autauga County  2011       2     3940   \n",
       "60      50      1       1  Alabama  Autauga County  2011       3     4355   \n",
       "61      50      1       1  Alabama  Autauga County  2011       4     3954   \n",
       "\n",
       "    TOT_MALE  TOT_FEMALE  \n",
       "57     26999       28254  \n",
       "58      1864        1717  \n",
       "59      1954        1986  \n",
       "60      2223        2132  \n",
       "61      2033        1921  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "census = pd.DataFrame.from_csv('census_data/cc-est2015-subset.csv', index_col=None, encoding = 'utf-8')\n",
    "census = census[census['YEAR'] >= 4] #filter out desired census years, 2011:2015\n",
    "#convert coded census years to actual years, 4=2011; 5=2012; 6=2013; 7=2014; 8=2015\n",
    "census['YEAR'] = census['YEAR'] + 2007\n",
    "census.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Write output files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "accident_all.to_csv('wrangled_data/ges_accident_2011_2015.csv', index_label=False )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "person_all.to_csv('wrangled_data/ges_person_2011_2015.csv', index_label=False )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "vehicle_all.to_csv('wrangled_data/ges_vehicle_2011_2015.csv', index_label=False )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "census.to_csv('wrangled_data/census_2011_2015.csv', index_label=False )"
   ]
  }
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