V_UM_BARS_REPLACE_AVG.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "VZ = pd.read_excel('C:/Users/mjc341/desktop/Plotly/PY_2011-18_CRASH.xlsx', dtype =object)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "avg_dr_ag = VZ['DR_AGE'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "35.01211568525274"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avg_dr_ag"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "avg_spd = VZ['VEH_SPEED'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15.317092235539343"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avg_spd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "VZ_AGE = VZ[['CRASHNUM','DR_AGE']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRASHNUM</th>\n",
       "      <th>DR_AGE</th>\n",
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       "      <td>49</td>\n",
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       "      <td>810256720</td>\n",
       "      <td>31</td>\n",
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       "      <td>823135060</td>\n",
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       "      <td>40</td>\n",
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       "      <td>820628480</td>\n",
       "      <td>72</td>\n",
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       "      <td>820776640</td>\n",
       "      <td>63</td>\n",
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       "      <td>823197430</td>\n",
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       "      <td>825060440</td>\n",
       "      <td>34</td>\n",
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       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>818599160</td>\n",
       "      <td>42</td>\n",
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       "      <td>835355990</td>\n",
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       "      <td>835356340</td>\n",
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       "      <td>818641420</td>\n",
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       "      <td>865872740</td>\n",
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       "      <td>50</td>\n",
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       "    <tr>\n",
       "      <th>7656</th>\n",
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       "      <td>82</td>\n",
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       "    <tr>\n",
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       "      <td>868907800</td>\n",
       "      <td>27</td>\n",
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       "    <tr>\n",
       "      <th>7658</th>\n",
       "      <td>865860200</td>\n",
       "      <td>39</td>\n",
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       "    <tr>\n",
       "      <th>7659</th>\n",
       "      <td>859897480</td>\n",
       "      <td>26</td>\n",
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       "    <tr>\n",
       "      <th>7660</th>\n",
       "      <td>843657280</td>\n",
       "      <td>36</td>\n",
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       "    <tr>\n",
       "      <th>7661</th>\n",
       "      <td>856514560</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>7662</th>\n",
       "      <td>843604230</td>\n",
       "      <td>55</td>\n",
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       "    <tr>\n",
       "      <th>7663</th>\n",
       "      <td>867001600</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>7664</th>\n",
       "      <td>843626820</td>\n",
       "      <td>36</td>\n",
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       "    <tr>\n",
       "      <th>7665</th>\n",
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       "      <td>837299550</td>\n",
       "      <td>54</td>\n",
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       "      <td>853936930</td>\n",
       "      <td>62</td>\n",
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       "      <th>7669</th>\n",
       "      <td>838076440</td>\n",
       "      <td>17</td>\n",
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       "      <th>7670</th>\n",
       "      <td>838094850</td>\n",
       "      <td>33</td>\n",
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       "      <th>7671</th>\n",
       "      <td>837989820</td>\n",
       "      <td>0</td>\n",
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       "      <td>837989920</td>\n",
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      ],
      "text/plain": [
       "       CRASHNUM DR_AGE\n",
       "0     818743120     40\n",
       "1     817219370      0\n",
       "2     818746210     29\n",
       "3     817219810     58\n",
       "4     815569390     50\n",
       "5     819717700     24\n",
       "6     825060430      0\n",
       "7     820411630     64\n",
       "8     824950420     21\n",
       "9     820293400     24\n",
       "10    824974400     55\n",
       "11    823203380     48\n",
       "12    825061280     62\n",
       "13    823217930     30\n",
       "14    825089410     22\n",
       "15    822436940     50\n",
       "16    823236110     26\n",
       "17    823174170     18\n",
       "18    825012840     49\n",
       "19    824306280      0\n",
       "20    810256720     31\n",
       "21    823135060      0\n",
       "22    820801220     40\n",
       "23    820628480     72\n",
       "24    820776640     63\n",
       "25    823134370     52\n",
       "26    823137860     46\n",
       "27    823197430     25\n",
       "28    825060440     34\n",
       "29    818599160     42\n",
       "...         ...    ...\n",
       "7646  835355990      0\n",
       "7647  835356340      0\n",
       "7648  843662660     46\n",
       "7649  869013870     23\n",
       "7650  859660360     25\n",
       "7651  869919710     44\n",
       "7652  869914840     29\n",
       "7653  818641420     32\n",
       "7654  865872740     51\n",
       "7655  868891980     50\n",
       "7656  859743400     82\n",
       "7657  868907800     27\n",
       "7658  865860200     39\n",
       "7659  859897480     26\n",
       "7660  843657280     36\n",
       "7661  856514560      0\n",
       "7662  843604230     55\n",
       "7663  867001600      0\n",
       "7664  843626820     36\n",
       "7665  856643990      0\n",
       "7666  837573570     66\n",
       "7667  837299550     54\n",
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       "7670  838094850     33\n",
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       "7672  837989920     21\n",
       "7673  837046640     46\n",
       "7674  838074970      0\n",
       "7675  854644470     62\n",
       "\n",
       "[7676 rows x 2 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "VZ_AGE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
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   },
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th>CRASHNUM</th>\n",
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       "      <th>7</th>\n",
       "      <td>820411630</td>\n",
       "      <td>64.000000</td>\n",
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       "      <th>8</th>\n",
       "      <td>824950420</td>\n",
       "      <td>21.000000</td>\n",
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       "      <th>9</th>\n",
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       "      <td>24.000000</td>\n",
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       "      <th>10</th>\n",
       "      <td>824974400</td>\n",
       "      <td>55.000000</td>\n",
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       "      <th>11</th>\n",
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       "      <td>48.000000</td>\n",
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       "      <td>62.000000</td>\n",
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       "      <td>823217930</td>\n",
       "      <td>30.000000</td>\n",
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       "      <th>14</th>\n",
       "      <td>825089410</td>\n",
       "      <td>22.000000</td>\n",
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       "    <tr>\n",
       "      <th>15</th>\n",
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       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>823236110</td>\n",
       "      <td>26.000000</td>\n",
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       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>823174170</td>\n",
       "      <td>18.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>825012840</td>\n",
       "      <td>49.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>824306280</td>\n",
       "      <td>35.012116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>810256720</td>\n",
       "      <td>31.000000</td>\n",
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       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>823135060</td>\n",
       "      <td>35.012116</td>\n",
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       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>820801220</td>\n",
       "      <td>40.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>820628480</td>\n",
       "      <td>72.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>820776640</td>\n",
       "      <td>63.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>823134370</td>\n",
       "      <td>52.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>823137860</td>\n",
       "      <td>46.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>823197430</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>825060440</td>\n",
       "      <td>34.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>818599160</td>\n",
       "      <td>42.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7646</th>\n",
       "      <td>835355990</td>\n",
       "      <td>35.012116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7647</th>\n",
       "      <td>835356340</td>\n",
       "      <td>35.012116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7648</th>\n",
       "      <td>843662660</td>\n",
       "      <td>46.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7649</th>\n",
       "      <td>869013870</td>\n",
       "      <td>23.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7650</th>\n",
       "      <td>859660360</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7651</th>\n",
       "      <td>869919710</td>\n",
       "      <td>44.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7652</th>\n",
       "      <td>869914840</td>\n",
       "      <td>29.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7653</th>\n",
       "      <td>818641420</td>\n",
       "      <td>32.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7654</th>\n",
       "      <td>865872740</td>\n",
       "      <td>51.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7655</th>\n",
       "      <td>868891980</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7656</th>\n",
       "      <td>859743400</td>\n",
       "      <td>82.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7657</th>\n",
       "      <td>868907800</td>\n",
       "      <td>27.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7658</th>\n",
       "      <td>865860200</td>\n",
       "      <td>39.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7659</th>\n",
       "      <td>859897480</td>\n",
       "      <td>26.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7660</th>\n",
       "      <td>843657280</td>\n",
       "      <td>36.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7661</th>\n",
       "      <td>856514560</td>\n",
       "      <td>35.012116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7662</th>\n",
       "      <td>843604230</td>\n",
       "      <td>55.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7663</th>\n",
       "      <td>867001600</td>\n",
       "      <td>35.012116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7664</th>\n",
       "      <td>843626820</td>\n",
       "      <td>36.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7665</th>\n",
       "      <td>856643990</td>\n",
       "      <td>35.012116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7666</th>\n",
       "      <td>837573570</td>\n",
       "      <td>66.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7667</th>\n",
       "      <td>837299550</td>\n",
       "      <td>54.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7668</th>\n",
       "      <td>853936930</td>\n",
       "      <td>62.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7669</th>\n",
       "      <td>838076440</td>\n",
       "      <td>17.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7670</th>\n",
       "      <td>838094850</td>\n",
       "      <td>33.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7671</th>\n",
       "      <td>837989820</td>\n",
       "      <td>35.012116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7672</th>\n",
       "      <td>837989920</td>\n",
       "      <td>21.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7673</th>\n",
       "      <td>837046640</td>\n",
       "      <td>46.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7674</th>\n",
       "      <td>838074970</td>\n",
       "      <td>35.012116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7675</th>\n",
       "      <td>854644470</td>\n",
       "      <td>62.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7676 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       CRASHNUM     DR_AGE\n",
       "0     818743120  40.000000\n",
       "1     817219370  35.012116\n",
       "2     818746210  29.000000\n",
       "3     817219810  58.000000\n",
       "4     815569390  50.000000\n",
       "5     819717700  24.000000\n",
       "6     825060430  35.012116\n",
       "7     820411630  64.000000\n",
       "8     824950420  21.000000\n",
       "9     820293400  24.000000\n",
       "10    824974400  55.000000\n",
       "11    823203380  48.000000\n",
       "12    825061280  62.000000\n",
       "13    823217930  30.000000\n",
       "14    825089410  22.000000\n",
       "15    822436940  50.000000\n",
       "16    823236110  26.000000\n",
       "17    823174170  18.000000\n",
       "18    825012840  49.000000\n",
       "19    824306280  35.012116\n",
       "20    810256720  31.000000\n",
       "21    823135060  35.012116\n",
       "22    820801220  40.000000\n",
       "23    820628480  72.000000\n",
       "24    820776640  63.000000\n",
       "25    823134370  52.000000\n",
       "26    823137860  46.000000\n",
       "27    823197430  25.000000\n",
       "28    825060440  34.000000\n",
       "29    818599160  42.000000\n",
       "...         ...        ...\n",
       "7646  835355990  35.012116\n",
       "7647  835356340  35.012116\n",
       "7648  843662660  46.000000\n",
       "7649  869013870  23.000000\n",
       "7650  859660360  25.000000\n",
       "7651  869919710  44.000000\n",
       "7652  869914840  29.000000\n",
       "7653  818641420  32.000000\n",
       "7654  865872740  51.000000\n",
       "7655  868891980  50.000000\n",
       "7656  859743400  82.000000\n",
       "7657  868907800  27.000000\n",
       "7658  865860200  39.000000\n",
       "7659  859897480  26.000000\n",
       "7660  843657280  36.000000\n",
       "7661  856514560  35.012116\n",
       "7662  843604230  55.000000\n",
       "7663  867001600  35.012116\n",
       "7664  843626820  36.000000\n",
       "7665  856643990  35.012116\n",
       "7666  837573570  66.000000\n",
       "7667  837299550  54.000000\n",
       "7668  853936930  62.000000\n",
       "7669  838076440  17.000000\n",
       "7670  838094850  33.000000\n",
       "7671  837989820  35.012116\n",
       "7672  837989920  21.000000\n",
       "7673  837046640  46.000000\n",
       "7674  838074970  35.012116\n",
       "7675  854644470  62.000000\n",
       "\n",
       "[7676 rows x 2 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "VZ_AGE.replace(0, avg_dr_ag)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['CRASHNUM', 'DR_AGE'], dtype='object')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "VZ_AGE.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "VZ_AGE2 = VZ_AGE.rename(columns = {'DR_AGE':'DR_AGE_AVG'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "VZ_AGE3 = VZ_AGE2.replace(0, avg_dr_ag)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "VZJ = pd.concat([VZ, VZ_AGE3], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRASHNUM</th>\n",
       "      <th>COUNT</th>\n",
       "      <th>DR_AGE</th>\n",
       "      <th>DR_GENDER</th>\n",
       "      <th>VEH_SPEED</th>\n",
       "      <th>Speed_LIMIT</th>\n",
       "      <th>CALYEAR</th>\n",
       "      <th>CRASH_DATE</th>\n",
       "      <th>CRASHTIME</th>\n",
       "      <th>DAYOFWEEK</th>\n",
       "      <th>...</th>\n",
       "      <th>Ped_GENDER</th>\n",
       "      <th>CrashYear</th>\n",
       "      <th>Month</th>\n",
       "      <th>Holiday</th>\n",
       "      <th>Time Range</th>\n",
       "      <th>LATITUDE</th>\n",
       "      <th>LONGITUDE</th>\n",
       "      <th>NEIGHBORHOOD</th>\n",
       "      <th>CRASHNUM</th>\n",
       "      <th>DR_AGE_AVG</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>818743120</td>\n",
       "      <td>1</td>\n",
       "      <td>40</td>\n",
       "      <td>Female</td>\n",
       "      <td>35</td>\n",
       "      <td>40</td>\n",
       "      <td>2011</td>\n",
       "      <td>2011-01-15</td>\n",
       "      <td>1834</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>...</td>\n",
       "      <td>Female</td>\n",
       "      <td>2011</td>\n",
       "      <td>January</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6PM to 9PM</td>\n",
       "      <td>25.7502</td>\n",
       "      <td>-80.2627</td>\n",
       "      <td>Coral Gables</td>\n",
       "      <td>818743120</td>\n",
       "      <td>40.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>817219370</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>n.a.</td>\n",
       "      <td>5</td>\n",
       "      <td>35</td>\n",
       "      <td>2011</td>\n",
       "      <td>2011-01-02</td>\n",
       "      <td>921</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>...</td>\n",
       "      <td>Female</td>\n",
       "      <td>2011</td>\n",
       "      <td>January</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9AM to 12PM</td>\n",
       "      <td>25.926</td>\n",
       "      <td>-80.1559</td>\n",
       "      <td>North Miami Beach</td>\n",
       "      <td>817219370</td>\n",
       "      <td>35.012116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>818746210</td>\n",
       "      <td>1</td>\n",
       "      <td>29</td>\n",
       "      <td>Male</td>\n",
       "      <td>10</td>\n",
       "      <td>30</td>\n",
       "      <td>2011</td>\n",
       "      <td>2011-02-18</td>\n",
       "      <td>2345</td>\n",
       "      <td>Friday</td>\n",
       "      <td>...</td>\n",
       "      <td>Female</td>\n",
       "      <td>2011</td>\n",
       "      <td>February</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9PM to 12AM</td>\n",
       "      <td>25.7502</td>\n",
       "      <td>-80.2627</td>\n",
       "      <td>Coral Gables</td>\n",
       "      <td>818746210</td>\n",
       "      <td>29.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    CRASHNUM COUNT DR_AGE DR_GENDER VEH_SPEED Speed_LIMIT CALYEAR CRASH_DATE  \\\n",
       "0  818743120     1     40    Female        35          40    2011 2011-01-15   \n",
       "1  817219370     1      0      n.a.         5          35    2011 2011-01-02   \n",
       "2  818746210     1     29      Male        10          30    2011 2011-02-18   \n",
       "\n",
       "  CRASHTIME DAYOFWEEK    ...     Ped_GENDER CrashYear     Month Holiday  \\\n",
       "0      1834  Saturday    ...         Female      2011   January     NaN   \n",
       "1       921    Sunday    ...         Female      2011   January     NaN   \n",
       "2      2345    Friday    ...         Female      2011  February     NaN   \n",
       "\n",
       "    Time Range LATITUDE LONGITUDE       NEIGHBORHOOD   CRASHNUM DR_AGE_AVG  \n",
       "0   6PM to 9PM  25.7502  -80.2627       Coral Gables  818743120  40.000000  \n",
       "1  9AM to 12PM   25.926  -80.1559  North Miami Beach  817219370  35.012116  \n",
       "2  9PM to 12AM  25.7502  -80.2627       Coral Gables  818746210  29.000000  \n",
       "\n",
       "[3 rows x 23 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "VZJ.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "VZ_SPD = VZ[['CRASHNUM','VEH_SPEED']]\n",
    "VZ_SPD2 = VZ_SPD.rename(columns = {'VEH_SPEED':'VEH_SPEED_AVG'})\n",
    "VZ_SPD3 =VZ_SPD2.replace(0, avg_spd)\n",
    "VZJ2 = pd.concat([VZJ, VZ_SPD3], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>COUNT</th>\n",
       "      <th>DR_AGE</th>\n",
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       "      <th>VEH_SPEED</th>\n",
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       "      <th>...</th>\n",
       "      <th>Month</th>\n",
       "      <th>Holiday</th>\n",
       "      <th>Time Range</th>\n",
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       "      <th>LONGITUDE</th>\n",
       "      <th>NEIGHBORHOOD</th>\n",
       "      <th>CRASHNUM</th>\n",
       "      <th>DR_AGE_AVG</th>\n",
       "      <th>CRASHNUM</th>\n",
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       "  <tbody>\n",
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       "      <td>Saturday</td>\n",
       "      <td>...</td>\n",
       "      <td>January</td>\n",
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       "      <td>6PM to 9PM</td>\n",
       "      <td>25.7502</td>\n",
       "      <td>-80.2627</td>\n",
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       "      <td>2011</td>\n",
       "      <td>2011-01-02</td>\n",
       "      <td>921</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>...</td>\n",
       "      <td>January</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9AM to 12PM</td>\n",
       "      <td>25.926</td>\n",
       "      <td>-80.1559</td>\n",
       "      <td>North Miami Beach</td>\n",
       "      <td>817219370</td>\n",
       "      <td>35.012116</td>\n",
       "      <td>817219370</td>\n",
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       "      <td>29</td>\n",
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       "      <td>30</td>\n",
       "      <td>2011</td>\n",
       "      <td>2011-02-18</td>\n",
       "      <td>2345</td>\n",
       "      <td>Friday</td>\n",
       "      <td>...</td>\n",
       "      <td>February</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9PM to 12AM</td>\n",
       "      <td>25.7502</td>\n",
       "      <td>-80.2627</td>\n",
       "      <td>Coral Gables</td>\n",
       "      <td>818746210</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>818746210</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    CRASHNUM COUNT DR_AGE DR_GENDER VEH_SPEED Speed_LIMIT CALYEAR CRASH_DATE  \\\n",
       "0  818743120     1     40    Female        35          40    2011 2011-01-15   \n",
       "1  817219370     1      0      n.a.         5          35    2011 2011-01-02   \n",
       "2  818746210     1     29      Male        10          30    2011 2011-02-18   \n",
       "\n",
       "  CRASHTIME DAYOFWEEK      ...          Month Holiday   Time Range LATITUDE  \\\n",
       "0      1834  Saturday      ...        January     NaN   6PM to 9PM  25.7502   \n",
       "1       921    Sunday      ...        January     NaN  9AM to 12PM   25.926   \n",
       "2      2345    Friday      ...       February     NaN  9PM to 12AM  25.7502   \n",
       "\n",
       "  LONGITUDE       NEIGHBORHOOD   CRASHNUM DR_AGE_AVG   CRASHNUM VEH_SPEED_AVG  \n",
       "0  -80.2627       Coral Gables  818743120  40.000000  818743120          35.0  \n",
       "1  -80.1559  North Miami Beach  817219370  35.012116  817219370           5.0  \n",
       "2  -80.2627       Coral Gables  818746210  29.000000  818746210          10.0  \n",
       "\n",
       "[3 rows x 25 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "VZJ2.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['CRASHNUM', 'COUNT', 'DR_AGE', 'DR_GENDER', 'VEH_SPEED', 'Speed_LIMIT',\n",
       "       'CALYEAR', 'CRASH_DATE', 'CRASHTIME', 'DAYOFWEEK', 'WEATH', 'LTCOND',\n",
       "       'Ped_AGE', 'Ped_GENDER', 'CrashYear', 'Month', 'Holiday', 'Time Range',\n",
       "       'LATITUDE', 'LONGITUDE', 'NEIGHBORHOOD', 'CRASHNUM', 'DR_AGE_AVG',\n",
       "       'CRASHNUM', 'VEH_SPEED_AVG'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "VZJ2.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "VZN = VZJ2[['CRASHNUM','DR_AGE_AVG','VEH_SPEED_AVG','DR_GENDER','CALYEAR','NEIGHBORHOOD',]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>CRASHNUM</th>\n",
       "      <th>CRASHNUM</th>\n",
       "      <th>CRASHNUM</th>\n",
       "      <th>DR_AGE_AVG</th>\n",
       "      <th>VEH_SPEED_AVG</th>\n",
       "      <th>DR_GENDER</th>\n",
       "      <th>CALYEAR</th>\n",
       "      <th>NEIGHBORHOOD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>818743120</td>\n",
       "      <td>818743120</td>\n",
       "      <td>818743120</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>35.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>2011</td>\n",
       "      <td>Coral Gables</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>817219370</td>\n",
       "      <td>817219370</td>\n",
       "      <td>817219370</td>\n",
       "      <td>35.012116</td>\n",
       "      <td>5.0</td>\n",
       "      <td>n.a.</td>\n",
       "      <td>2011</td>\n",
       "      <td>North Miami Beach</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>818746210</td>\n",
       "      <td>818746210</td>\n",
       "      <td>818746210</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>10.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>2011</td>\n",
       "      <td>Coral Gables</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    CRASHNUM   CRASHNUM   CRASHNUM  DR_AGE_AVG  VEH_SPEED_AVG DR_GENDER  \\\n",
       "0  818743120  818743120  818743120   40.000000           35.0    Female   \n",
       "1  817219370  817219370  817219370   35.012116            5.0      n.a.   \n",
       "2  818746210  818746210  818746210   29.000000           10.0      Male   \n",
       "\n",
       "  CALYEAR       NEIGHBORHOOD  \n",
       "0    2011       Coral Gables  \n",
       "1    2011  North Miami Beach  \n",
       "2    2011       Coral Gables  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "VZN.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "VZN.columns = ['CRASHNUM','CRASHNUM2','CRASHMUN3','AGE','SPEED','GENDER','YEAR','NEGHBORHOOD']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['CRASHNUM', 'CRASHNUM2', 'CRASHMUN3', 'AGE', 'SPEED', 'GENDER', 'YEAR',\n",
       "       'NEGHBORHOOD'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "VZN.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "VZN2= VZN[['CRASHNUM','AGE', 'SPEED', 'GENDER', 'YEAR',\n",
    "       'NEGHBORHOOD']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['CRASHNUM', 'AGE', 'SPEED', 'GENDER', 'YEAR', 'NEGHBORHOOD'], dtype='object')"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "VZN2.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['CRASHNUM', 'COUNT', 'DR_AGE', 'DR_GENDER', 'VEH_SPEED', 'Speed_LIMIT',\n",
       "       'CALYEAR', 'CRASH_DATE', 'CRASHTIME', 'DAYOFWEEK', 'WEATH', 'LTCOND',\n",
       "       'Ped_AGE', 'Ped_GENDER', 'CrashYear', 'Month', 'Holiday', 'Time Range',\n",
       "       'LATITUDE', 'LONGITUDE', 'NEIGHBORHOOD'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "VZ.columns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x15ffa4546d8>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(figsize = (10,8))\n",
    "#count of incidents\n",
    "dw =sns.countplot(x='DAYOFWEEK',data = VZ, palette = 'ocean')\n",
    "dw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "ename": "IndentationError",
     "evalue": "unexpected indent (<ipython-input-51-d3e5ebf41441>, line 4)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-51-d3e5ebf41441>\"\u001b[1;36m, line \u001b[1;32m4\u001b[0m\n\u001b[1;33m    palette = {'Male': 'g', 'Female' :'m', 'n.a.': 'y'},\u001b[0m\n\u001b[1;37m    ^\u001b[0m\n\u001b[1;31mIndentationError\u001b[0m\u001b[1;31m:\u001b[0m unexpected indent\n"
     ]
    }
   ],
   "source": [
    "f, ax = plt.subplots(figsize = (10,8))\n",
    "sns.set_style('darkgrid')\n",
    "pp = sns.pointplot(x = 'YEAR', y = ['AGE']), hue = 'GENDER', data = VZN2,\n",
    "    palette = {'Male': 'g', 'Female' :'m', 'n.a.': 'y'},\n",
    "    markers = ['o','^','o'], linestyles = ['-','--','-'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x15ffbbf6898>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(figsize = (10,8))\n",
    "#count of incidents\n",
    "dy =sns.countplot(x='CALYEAR',data = VZ, palette = 'ocean')\n",
    "dy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#f, ax = plt.subplots(figsize = (10,8))\n",
    "#sns.set_style('darkgrid')\n",
    "#VZ['SPD']=VZ['VEH_SPEED'] !=\"unknown\"\n",
    "#st = sns.swarmplot( x= 'CALYEAR', y = 'VEH_SPEED', data= VZ, hue= 'DR_GENDER',size = 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:Anaconda3]",
   "language": "python",
   "name": "conda-env-Anaconda3-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}