Report-Capstone (Week 2).ipynb

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   "source": [
    "# Capstone Project - Car accident severity\n",
    "### Applied Data Science  Capstone by Marquise Piton\n",
    "### September 18, 2020\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Table of contents\n",
    "* [1. Introduction: Business Problem](#Introduction)\n",
    "* [2. Data](#Data)\n",
    "* [3. Methodology](#Methodology)\n",
    "* [4. Analysis](#Analysis)\n",
    "* [5. Result and Discussion](#Result)\n",
    "* [6. Conclusion](#Conclusion)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.Introduction: Business Problem <a name=\"Introduction\"></a>\n",
    "###  Background"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Driving is an essential task in most adult's lives. On January 29th, 1886, the first automobile was created, which revolutionized our lives in every way. You drive to work, buy groceries from the store, travel to other places for vacation, and many more examples. Knowing the importance of driving in our daily lives, we must also consider the dangers of getting behind the wheel and examine the various causes of accidents. "
   ]
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   "metadata": {},
   "source": [
    "###  Problem"
   ]
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    "Many of the weather conditions and road conditions can affect the driver's ability. On the other hand, the driver can't control the conditions of the weather or roads. Still, suppose if there was a study that can inform the driver of the possibilities of getting into an accident base on the road and weather conditions. In that case, this could have a beneficial effect on the driver's ability to drive in these conditions and how severe the accident will be if you were in an accident. "
   ]
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   "source": [
    "###  Interest "
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Anyone that uses a car for professional or casual reasons would be very interested in theses studied predictions. This study could save lives, money, or even prevent traffic jams in the future. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Data <a name=\"Data\"></a>\n",
    "### Data sources"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data I will be using will come from the \"Applied Data Science Capstone\" course under the \"Introduction to Capstone Project.\" Here is a link that will take you to the data source: https://www.coursera.org/learn/applied-data-science-capstone/supplement/Nh5uS/downloading-example-dataset. The data showcase the different types of accidents and the various conditions (weather and road). With this data, we could calculate the possible severity of an accident based on situations and inform the driver, possibly implementing safer driving decisions."
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Methodology <a name=\"Methodology\"></a>"
   ]
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this project, we will look at the road and light conditions to see if there is a correlation between them.  Then examine the location of where these accidents are taking place. \n",
    "\n",
    "In the first step, we will isolate the data light and road condition into one group and examine the combination using charts. \n",
    "\n",
    "For the second step, the location of each case will viewed using \"X\" (Longitude) and  \"Y\" (Latitude) data frame."
   ]
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   "source": [
    "## 4. Analysis <a name=\"Analysis\"></a>"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Road Conditions and Severity of Accidents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let start by importing the needed libraries to help with our analytics.\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import math "
   ]
  },
  {
   "cell_type": "code",
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   "metadata": {},
   "outputs": [
    {
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     "text": [
      "Data as been uploaded...\n"
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       "      <td>23000</td>\n",
       "      <td>23000</td>\n",
       "      <td>537838</td>\n",
       "      <td>Matched</td>\n",
       "      <td>Block</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Dry</td>\n",
       "      <td>Daylight</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4016025.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14</td>\n",
       "      <td>From same direction - both going straight - on...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2</td>\n",
       "      <td>-122.328270</td>\n",
       "      <td>47.571420</td>\n",
       "      <td>24</td>\n",
       "      <td>330833</td>\n",
       "      <td>332333</td>\n",
       "      <td>EA29752</td>\n",
       "      <td>Matched</td>\n",
       "      <td>Block</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Dry</td>\n",
       "      <td>Daylight</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14</td>\n",
       "      <td>From same direction - both going straight - on...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1</td>\n",
       "      <td>-122.383802</td>\n",
       "      <td>47.583715</td>\n",
       "      <td>25</td>\n",
       "      <td>97100</td>\n",
       "      <td>97100</td>\n",
       "      <td>2894590</td>\n",
       "      <td>Matched</td>\n",
       "      <td>Block</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Dry</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9152035.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32</td>\n",
       "      <td>One parked--one moving</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2</td>\n",
       "      <td>-122.292403</td>\n",
       "      <td>47.732847</td>\n",
       "      <td>26</td>\n",
       "      <td>1347</td>\n",
       "      <td>1347</td>\n",
       "      <td>3608880</td>\n",
       "      <td>Matched</td>\n",
       "      <td>Block</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Dry</td>\n",
       "      <td>Dark - Street Lights On</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14</td>\n",
       "      <td>From same direction - both going straight - on...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2</td>\n",
       "      <td>-122.313786</td>\n",
       "      <td>47.708535</td>\n",
       "      <td>28</td>\n",
       "      <td>1323</td>\n",
       "      <td>1323</td>\n",
       "      <td>3502831</td>\n",
       "      <td>Matched</td>\n",
       "      <td>Intersection</td>\n",
       "      <td>36505.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Dry</td>\n",
       "      <td>Daylight</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>Entering at angle</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1</td>\n",
       "      <td>-122.318169</td>\n",
       "      <td>47.615837</td>\n",
       "      <td>29</td>\n",
       "      <td>80000</td>\n",
       "      <td>80000</td>\n",
       "      <td>2882620</td>\n",
       "      <td>Matched</td>\n",
       "      <td>Block</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Dry</td>\n",
       "      <td>Dark - Street Lights On</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8200013.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32</td>\n",
       "      <td>One parked--one moving</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1</td>\n",
       "      <td>-122.337486</td>\n",
       "      <td>47.589746</td>\n",
       "      <td>31</td>\n",
       "      <td>28700</td>\n",
       "      <td>28700</td>\n",
       "      <td>1213894</td>\n",
       "      <td>Matched</td>\n",
       "      <td>Block</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4256026.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>51</td>\n",
       "      <td>Other object</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2</td>\n",
       "      <td>-122.279658</td>\n",
       "      <td>47.553405</td>\n",
       "      <td>33</td>\n",
       "      <td>1268</td>\n",
       "      <td>1268</td>\n",
       "      <td>3672152</td>\n",
       "      <td>Matched</td>\n",
       "      <td>Intersection</td>\n",
       "      <td>33499.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Dry</td>\n",
       "      <td>Daylight</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Y</td>\n",
       "      <td>14</td>\n",
       "      <td>From same direction - both going straight - on...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2</td>\n",
       "      <td>-122.312857</td>\n",
       "      <td>47.599218</td>\n",
       "      <td>34</td>\n",
       "      <td>320932</td>\n",
       "      <td>322432</td>\n",
       "      <td>E926429</td>\n",
       "      <td>Matched</td>\n",
       "      <td>Block</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Dry</td>\n",
       "      <td>Daylight</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>Vehicle Strikes Pedalcyclist</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1</td>\n",
       "      <td>-122.330730</td>\n",
       "      <td>47.615450</td>\n",
       "      <td>35</td>\n",
       "      <td>113300</td>\n",
       "      <td>113300</td>\n",
       "      <td>3346338</td>\n",
       "      <td>Matched</td>\n",
       "      <td>Intersection</td>\n",
       "      <td>29865.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Wet</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10317016.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28</td>\n",
       "      <td>From opposite direction - one left turn - one ...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1</td>\n",
       "      <td>-122.385859</td>\n",
       "      <td>47.581191</td>\n",
       "      <td>36</td>\n",
       "      <td>64700</td>\n",
       "      <td>64700</td>\n",
       "      <td>2798260</td>\n",
       "      <td>Matched</td>\n",
       "      <td>Block</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Dry</td>\n",
       "      <td>Daylight</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7204015.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>Entering at angle</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1</td>\n",
       "      <td>-122.304990</td>\n",
       "      <td>47.611474</td>\n",
       "      <td>37</td>\n",
       "      <td>1083</td>\n",
       "      <td>1083</td>\n",
       "      <td>3605909</td>\n",
       "      <td>Matched</td>\n",
       "      <td>Block</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Dry</td>\n",
       "      <td>Daylight</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23</td>\n",
       "      <td>From same direction - all others</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>38</td>\n",
       "      <td>61500</td>\n",
       "      <td>61500</td>\n",
       "      <td>2607270</td>\n",
       "      <td>Matched</td>\n",
       "      <td>Block</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Dry</td>\n",
       "      <td>Daylight</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6320040.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32</td>\n",
       "      <td>One parked--one moving</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>N</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",
       "      <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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194643</th>\n",
       "      <td>1</td>\n",
       "      <td>-122.345863</td>\n",
       "      <td>47.612991</td>\n",
       "      <td>219510</td>\n",
       "      <td>307577</td>\n",
       "      <td>308857</td>\n",
       "      <td>3811871</td>\n",
       "      <td>Matched</td>\n",
       "      <td>Block</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Dry</td>\n",
       "      <td>Dark - Street Lights On</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20</td>\n",
       "      <td>One car leaving parked position</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194644</th>\n",
       "      <td>1</td>\n",
       "      <td>-122.330298</td>\n",
       "      <td>47.603233</td>\n",
       "      <td>219511</td>\n",
       "      <td>312117</td>\n",
       "      <td>313537</td>\n",
       "      <td>E885893</td>\n",
       "      <td>Matched</td>\n",
       "      <td>Intersection</td>\n",
       "      <td>30479.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Dry</td>\n",
       "      <td>Dusk</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>Entering at angle</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194645</th>\n",
       "      <td>2</td>\n",
       "      <td>-122.328079</td>\n",
       "      <td>47.604161</td>\n",
       "      <td>219512</td>\n",
       "      <td>307692</td>\n",
       "      <td>308972</td>\n",
       "      <td>E851047</td>\n",
       "      <td>Matched</td>\n",
       "      <td>Intersection</td>\n",
       "      <td>29973.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Dry</td>\n",
       "      <td>Dark - Street Lights On</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16</td>\n",
       "      <td>From same direction - one right turn - one str...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194646</th>\n",
       "      <td>1</td>\n",
       "      <td>-122.320756</td>\n",
       "      <td>47.608656</td>\n",
       "      <td>219513</td>\n",
       "      <td>312289</td>\n",
       "      <td>313709</td>\n",
       "      <td>E872044</td>\n",
       "      <td>Unmatched</td>\n",
       "      <td>Block</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194647</th>\n",
       "      <td>2</td>\n",
       "      <td>-122.355449</td>\n",
       "      <td>47.704720</td>\n",
       "      <td>219514</td>\n",
       "      <td>308575</td>\n",
       "      <td>309855</td>\n",
       "      <td>E872078</td>\n",
       "      <td>Matched</td>\n",
       "      <td>Block</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Wet</td>\n",
       "      <td>Dusk</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>Vehicle going straight hits pedestrian</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194648</th>\n",
       "      <td>1</td>\n",
       "      <td>-122.357624</td>\n",
       "      <td>47.635956</td>\n",
       "      <td>219515</td>\n",
       "      <td>307792</td>\n",
       "      <td>309072</td>\n",
       "      <td>3578593</td>\n",
       "      <td>Matched</td>\n",
       "      <td>Block</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Wet</td>\n",
       "      <td>Dark - Street Lights On</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>Entering at angle</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194649</th>\n",
       "      <td>2</td>\n",
       "      <td>-122.302382</td>\n",
       "      <td>47.626759</td>\n",
       "      <td>219516</td>\n",
       "      <td>307985</td>\n",
       "      <td>309265</td>\n",
       "      <td>E869075</td>\n",
       "      <td>Matched</td>\n",
       "      <td>Intersection</td>\n",
       "      <td>29328.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Dry</td>\n",
       "      <td>Dark - Street Lights On</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28</td>\n",
       "      <td>From opposite direction - one left turn - one ...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194650</th>\n",
       "      <td>2</td>\n",
       "      <td>-122.355298</td>\n",
       "      <td>47.684382</td>\n",
       "      <td>219517</td>\n",
       "      <td>310417</td>\n",
       "      <td>311717</td>\n",
       "      <td>3751221</td>\n",
       "      <td>Matched</td>\n",
       "      <td>Block</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Dry</td>\n",
       "      <td>Daylight</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32</td>\n",
       "      <td>One parked--one moving</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194651</th>\n",
       "      <td>1</td>\n",
       "      <td>-122.320780</td>\n",
       "      <td>47.614076</td>\n",
       "      <td>219519</td>\n",
       "      <td>309594</td>\n",
       "      <td>310874</td>\n",
       "      <td>E881227</td>\n",
       "      <td>Matched</td>\n",
       "      <td>Intersection</td>\n",
       "      <td>29745.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Wet</td>\n",
       "      <td>Dark - Street Lights On</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>Vehicle going straight hits pedestrian</td>\n",
       "      <td>0</td>\n",
       "      <td>522257</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194652</th>\n",
       "      <td>1</td>\n",
       "      <td>-122.335527</td>\n",
       "      <td>47.617434</td>\n",
       "      <td>219520</td>\n",
       "      <td>308698</td>\n",
       "      <td>309978</td>\n",
       "      <td>3812450</td>\n",
       "      <td>Matched</td>\n",
       "      <td>Block</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Wet</td>\n",
       "      <td>Dusk</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32</td>\n",
       "      <td>One parked--one moving</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194653</th>\n",
       "      <td>2</td>\n",
       "      <td>-122.322097</td>\n",
       "      <td>47.649615</td>\n",
       "      <td>219521</td>\n",
       "      <td>308342</td>\n",
       "      <td>309622</td>\n",
       "      <td>3693206</td>\n",
       "      <td>Matched</td>\n",
       "      <td>Block</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Wet</td>\n",
       "      <td>Dark - Street Lights On</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32</td>\n",
       "      <td>One parked--one moving</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194654</th>\n",
       "      <td>1</td>\n",
       "      <td>-122.312679</td>\n",
       "      <td>47.719414</td>\n",
       "      <td>219522</td>\n",
       "      <td>311117</td>\n",
       "      <td>312437</td>\n",
       "      <td>E881587</td>\n",
       "      <td>Unmatched</td>\n",
       "      <td>Intersection</td>\n",
       "      <td>36427.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194655</th>\n",
       "      <td>2</td>\n",
       "      <td>-122.380016</td>\n",
       "      <td>47.664879</td>\n",
       "      <td>219523</td>\n",
       "      <td>310911</td>\n",
       "      <td>312231</td>\n",
       "      <td>3751425</td>\n",
       "      <td>Unmatched</td>\n",
       "      <td>Intersection</td>\n",
       "      <td>25174.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>650595</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194656</th>\n",
       "      <td>2</td>\n",
       "      <td>-122.340474</td>\n",
       "      <td>47.614496</td>\n",
       "      <td>219524</td>\n",
       "      <td>312179</td>\n",
       "      <td>313599</td>\n",
       "      <td>E885580</td>\n",
       "      <td>Matched</td>\n",
       "      <td>Intersection</td>\n",
       "      <td>29545.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Dry</td>\n",
       "      <td>Daylight</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>Vehicle turning left hits pedestrian</td>\n",
       "      <td>0</td>\n",
       "      <td>523322</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194657</th>\n",
       "      <td>1</td>\n",
       "      <td>-122.337137</td>\n",
       "      <td>47.610709</td>\n",
       "      <td>219525</td>\n",
       "      <td>307834</td>\n",
       "      <td>309114</td>\n",
       "      <td>3811279</td>\n",
       "      <td>Matched</td>\n",
       "      <td>Block</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Wet</td>\n",
       "      <td>Dark - Street Lights On</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11</td>\n",
       "      <td>From same direction - both going straight - bo...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194658</th>\n",
       "      <td>1</td>\n",
       "      <td>-122.302554</td>\n",
       "      <td>47.584099</td>\n",
       "      <td>219528</td>\n",
       "      <td>311697</td>\n",
       "      <td>313057</td>\n",
       "      <td>E886941</td>\n",
       "      <td>Matched</td>\n",
       "      <td>Block</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Wet</td>\n",
       "      <td>Dark - Street Lights On</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14</td>\n",
       "      <td>From same direction - both going straight - on...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194659</th>\n",
       "      <td>1</td>\n",
       "      <td>-122.298955</td>\n",
       "      <td>47.717456</td>\n",
       "      <td>219529</td>\n",
       "      <td>308019</td>\n",
       "      <td>309299</td>\n",
       "      <td>3767486</td>\n",
       "      <td>Matched</td>\n",
       "      <td>Block</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Wet</td>\n",
       "      <td>Dark - Street Lights On</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32</td>\n",
       "      <td>One parked--one moving</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194660</th>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>219531</td>\n",
       "      <td>308990</td>\n",
       "      <td>310270</td>\n",
       "      <td>3811749</td>\n",
       "      <td>Matched</td>\n",
       "      <td>Block</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Wet</td>\n",
       "      <td>Other</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>52</td>\n",
       "      <td>Vehicle overturned</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194661</th>\n",
       "      <td>2</td>\n",
       "      <td>-122.306092</td>\n",
       "      <td>47.617881</td>\n",
       "      <td>219532</td>\n",
       "      <td>308532</td>\n",
       "      <td>309812</td>\n",
       "      <td>3814599</td>\n",
       "      <td>Matched</td>\n",
       "      <td>Block</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Dry</td>\n",
       "      <td>Daylight</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29</td>\n",
       "      <td>From opposite direction - one left turn - one ...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194662</th>\n",
       "      <td>1</td>\n",
       "      <td>-122.337880</td>\n",
       "      <td>47.625793</td>\n",
       "      <td>219535</td>\n",
       "      <td>307802</td>\n",
       "      <td>309082</td>\n",
       "      <td>E869008</td>\n",
       "      <td>Matched</td>\n",
       "      <td>Block</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Dry</td>\n",
       "      <td>Dusk</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32</td>\n",
       "      <td>One parked--one moving</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194663</th>\n",
       "      <td>2</td>\n",
       "      <td>-122.299160</td>\n",
       "      <td>47.579673</td>\n",
       "      <td>219536</td>\n",
       "      <td>309335</td>\n",
       "      <td>310615</td>\n",
       "      <td>E880807</td>\n",
       "      <td>Matched</td>\n",
       "      <td>Block</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Wet</td>\n",
       "      <td>Daylight</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>Entering at angle</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194664</th>\n",
       "      <td>1</td>\n",
       "      <td>-122.325887</td>\n",
       "      <td>47.643191</td>\n",
       "      <td>219537</td>\n",
       "      <td>309222</td>\n",
       "      <td>310502</td>\n",
       "      <td>E879537</td>\n",
       "      <td>Matched</td>\n",
       "      <td>Intersection</td>\n",
       "      <td>28300.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Dry</td>\n",
       "      <td>Daylight</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>Entering at angle</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194665</th>\n",
       "      <td>1</td>\n",
       "      <td>-122.304217</td>\n",
       "      <td>47.669537</td>\n",
       "      <td>219538</td>\n",
       "      <td>308480</td>\n",
       "      <td>309760</td>\n",
       "      <td>3642620</td>\n",
       "      <td>Matched</td>\n",
       "      <td>Intersection</td>\n",
       "      <td>26005.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Dry</td>\n",
       "      <td>Daylight</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>Entering at angle</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194666</th>\n",
       "      <td>2</td>\n",
       "      <td>-122.344569</td>\n",
       "      <td>47.694547</td>\n",
       "      <td>219539</td>\n",
       "      <td>309170</td>\n",
       "      <td>310450</td>\n",
       "      <td>E879712</td>\n",
       "      <td>Matched</td>\n",
       "      <td>Block</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Wet</td>\n",
       "      <td>Daylight</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>Entering at angle</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194667</th>\n",
       "      <td>1</td>\n",
       "      <td>-122.361672</td>\n",
       "      <td>47.556722</td>\n",
       "      <td>219541</td>\n",
       "      <td>307804</td>\n",
       "      <td>309084</td>\n",
       "      <td>3745813</td>\n",
       "      <td>Matched</td>\n",
       "      <td>Block</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Wet</td>\n",
       "      <td>Dark - Street Lights On</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50</td>\n",
       "      <td>Fixed object</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194668</th>\n",
       "      <td>2</td>\n",
       "      <td>-122.290826</td>\n",
       "      <td>47.565408</td>\n",
       "      <td>219543</td>\n",
       "      <td>309534</td>\n",
       "      <td>310814</td>\n",
       "      <td>E871089</td>\n",
       "      <td>Matched</td>\n",
       "      <td>Block</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Dry</td>\n",
       "      <td>Daylight</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24</td>\n",
       "      <td>From opposite direction - both moving - head-on</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194669</th>\n",
       "      <td>1</td>\n",
       "      <td>-122.344526</td>\n",
       "      <td>47.690924</td>\n",
       "      <td>219544</td>\n",
       "      <td>309085</td>\n",
       "      <td>310365</td>\n",
       "      <td>E876731</td>\n",
       "      <td>Matched</td>\n",
       "      <td>Block</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Wet</td>\n",
       "      <td>Daylight</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13</td>\n",
       "      <td>From same direction - both going straight - bo...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194670</th>\n",
       "      <td>2</td>\n",
       "      <td>-122.306689</td>\n",
       "      <td>47.683047</td>\n",
       "      <td>219545</td>\n",
       "      <td>311280</td>\n",
       "      <td>312640</td>\n",
       "      <td>3809984</td>\n",
       "      <td>Matched</td>\n",
       "      <td>Intersection</td>\n",
       "      <td>24760.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Dry</td>\n",
       "      <td>Daylight</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28</td>\n",
       "      <td>From opposite direction - one left turn - one ...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194671</th>\n",
       "      <td>2</td>\n",
       "      <td>-122.355317</td>\n",
       "      <td>47.678734</td>\n",
       "      <td>219546</td>\n",
       "      <td>309514</td>\n",
       "      <td>310794</td>\n",
       "      <td>3810083</td>\n",
       "      <td>Matched</td>\n",
       "      <td>Intersection</td>\n",
       "      <td>24349.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Dry</td>\n",
       "      <td>Dusk</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>Vehicle Strikes Pedalcyclist</td>\n",
       "      <td>4308</td>\n",
       "      <td>0</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194672</th>\n",
       "      <td>1</td>\n",
       "      <td>-122.289360</td>\n",
       "      <td>47.611017</td>\n",
       "      <td>219547</td>\n",
       "      <td>308220</td>\n",
       "      <td>309500</td>\n",
       "      <td>E868008</td>\n",
       "      <td>Matched</td>\n",
       "      <td>Block</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Wet</td>\n",
       "      <td>Daylight</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14</td>\n",
       "      <td>From same direction - both going straight - on...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>194673 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Severity Level   Longitude   Latitude  Object ID  INC Key  COLDETKEY  \\\n",
       "0                    2 -122.323148  47.703140          1     1307       1307   \n",
       "1                    1 -122.347294  47.647172          2    52200      52200   \n",
       "2                    1 -122.334540  47.607871          3    26700      26700   \n",
       "3                    1 -122.334803  47.604803          4     1144       1144   \n",
       "4                    2 -122.306426  47.545739          5    17700      17700   \n",
       "5                    1 -122.387598  47.690575          6   320840     322340   \n",
       "6                    1 -122.338485  47.618534          7    83300      83300   \n",
       "7                    2 -122.320780  47.614076          9   330897     332397   \n",
       "8                    1 -122.335930  47.611904         10    63400      63400   \n",
       "9                    2 -122.384700  47.528475         12    58600      58600   \n",
       "10                   1         NaN        NaN         14    48900      48900   \n",
       "11                   1 -122.333831  47.547371         15    38800      38800   \n",
       "12                   1 -122.356273  47.571375         16     2771       2771   \n",
       "13                   1 -122.323966  47.606374         17    32800      32800   \n",
       "14                   2 -122.317414  47.664028         19     1212       1212   \n",
       "15                   1 -122.337663  47.617510         20   330878     332378   \n",
       "16                   2 -122.344539  47.692012         21    46300      46300   \n",
       "17                   1         NaN        NaN         23    23000      23000   \n",
       "18                   2 -122.328270  47.571420         24   330833     332333   \n",
       "19                   1 -122.383802  47.583715         25    97100      97100   \n",
       "20                   2 -122.292403  47.732847         26     1347       1347   \n",
       "21                   2 -122.313786  47.708535         28     1323       1323   \n",
       "22                   1 -122.318169  47.615837         29    80000      80000   \n",
       "23                   1 -122.337486  47.589746         31    28700      28700   \n",
       "24                   2 -122.279658  47.553405         33     1268       1268   \n",
       "25                   2 -122.312857  47.599218         34   320932     322432   \n",
       "26                   1 -122.330730  47.615450         35   113300     113300   \n",
       "27                   1 -122.385859  47.581191         36    64700      64700   \n",
       "28                   1 -122.304990  47.611474         37     1083       1083   \n",
       "29                   1         NaN        NaN         38    61500      61500   \n",
       "...                ...         ...        ...        ...      ...        ...   \n",
       "194643               1 -122.345863  47.612991     219510   307577     308857   \n",
       "194644               1 -122.330298  47.603233     219511   312117     313537   \n",
       "194645               2 -122.328079  47.604161     219512   307692     308972   \n",
       "194646               1 -122.320756  47.608656     219513   312289     313709   \n",
       "194647               2 -122.355449  47.704720     219514   308575     309855   \n",
       "194648               1 -122.357624  47.635956     219515   307792     309072   \n",
       "194649               2 -122.302382  47.626759     219516   307985     309265   \n",
       "194650               2 -122.355298  47.684382     219517   310417     311717   \n",
       "194651               1 -122.320780  47.614076     219519   309594     310874   \n",
       "194652               1 -122.335527  47.617434     219520   308698     309978   \n",
       "194653               2 -122.322097  47.649615     219521   308342     309622   \n",
       "194654               1 -122.312679  47.719414     219522   311117     312437   \n",
       "194655               2 -122.380016  47.664879     219523   310911     312231   \n",
       "194656               2 -122.340474  47.614496     219524   312179     313599   \n",
       "194657               1 -122.337137  47.610709     219525   307834     309114   \n",
       "194658               1 -122.302554  47.584099     219528   311697     313057   \n",
       "194659               1 -122.298955  47.717456     219529   308019     309299   \n",
       "194660               2         NaN        NaN     219531   308990     310270   \n",
       "194661               2 -122.306092  47.617881     219532   308532     309812   \n",
       "194662               1 -122.337880  47.625793     219535   307802     309082   \n",
       "194663               2 -122.299160  47.579673     219536   309335     310615   \n",
       "194664               1 -122.325887  47.643191     219537   309222     310502   \n",
       "194665               1 -122.304217  47.669537     219538   308480     309760   \n",
       "194666               2 -122.344569  47.694547     219539   309170     310450   \n",
       "194667               1 -122.361672  47.556722     219541   307804     309084   \n",
       "194668               2 -122.290826  47.565408     219543   309534     310814   \n",
       "194669               1 -122.344526  47.690924     219544   309085     310365   \n",
       "194670               2 -122.306689  47.683047     219545   311280     312640   \n",
       "194671               2 -122.355317  47.678734     219546   309514     310794   \n",
       "194672               1 -122.289360  47.611017     219547   308220     309500   \n",
       "\n",
       "       Report #     Status  Address Type  INT Key  ... Road Condition  \\\n",
       "0       3502005    Matched  Intersection  37475.0  ...            Wet   \n",
       "1       2607959    Matched         Block      NaN  ...            Wet   \n",
       "2       1482393    Matched         Block      NaN  ...            Dry   \n",
       "3       3503937    Matched         Block      NaN  ...            Dry   \n",
       "4       1807429    Matched  Intersection  34387.0  ...            Wet   \n",
       "5       E919477    Matched  Intersection  36974.0  ...            Dry   \n",
       "6       3282542    Matched  Intersection  29510.0  ...            Wet   \n",
       "7       EA30304    Matched  Intersection  29745.0  ...            Dry   \n",
       "8       2071243    Matched         Block      NaN  ...            Dry   \n",
       "9       2072105    Matched  Intersection  34679.0  ...            Dry   \n",
       "10      2024040    Matched         Alley      NaN  ...            Dry   \n",
       "11      C654800    Matched  Intersection  33194.0  ...            Dry   \n",
       "12      1211870  Unmatched         Block      NaN  ...            Wet   \n",
       "13      2128498    Matched         Block      NaN  ...            Wet   \n",
       "14      3507861    Matched         Block      NaN  ...            Dry   \n",
       "15      3838086  Unmatched         Block      NaN  ...            NaN   \n",
       "16      2023080    Matched  Intersection  37365.0  ...            Dry   \n",
       "17       537838    Matched         Block      NaN  ...            Dry   \n",
       "18      EA29752    Matched         Block      NaN  ...            Dry   \n",
       "19      2894590    Matched         Block      NaN  ...            Dry   \n",
       "20      3608880    Matched         Block      NaN  ...            Dry   \n",
       "21      3502831    Matched  Intersection  36505.0  ...            Dry   \n",
       "22      2882620    Matched         Block      NaN  ...            Dry   \n",
       "23      1213894    Matched         Block      NaN  ...        Unknown   \n",
       "24      3672152    Matched  Intersection  33499.0  ...            Dry   \n",
       "25      E926429    Matched         Block      NaN  ...            Dry   \n",
       "26      3346338    Matched  Intersection  29865.0  ...            Wet   \n",
       "27      2798260    Matched         Block      NaN  ...            Dry   \n",
       "28      3605909    Matched         Block      NaN  ...            Dry   \n",
       "29      2607270    Matched         Block      NaN  ...            Dry   \n",
       "...         ...        ...           ...      ...  ...            ...   \n",
       "194643  3811871    Matched         Block      NaN  ...            Dry   \n",
       "194644  E885893    Matched  Intersection  30479.0  ...            Dry   \n",
       "194645  E851047    Matched  Intersection  29973.0  ...            Dry   \n",
       "194646  E872044  Unmatched         Block      NaN  ...            NaN   \n",
       "194647  E872078    Matched         Block      NaN  ...            Wet   \n",
       "194648  3578593    Matched         Block      NaN  ...            Wet   \n",
       "194649  E869075    Matched  Intersection  29328.0  ...            Dry   \n",
       "194650  3751221    Matched         Block      NaN  ...            Dry   \n",
       "194651  E881227    Matched  Intersection  29745.0  ...            Wet   \n",
       "194652  3812450    Matched         Block      NaN  ...            Wet   \n",
       "194653  3693206    Matched         Block      NaN  ...            Wet   \n",
       "194654  E881587  Unmatched  Intersection  36427.0  ...            NaN   \n",
       "194655  3751425  Unmatched  Intersection  25174.0  ...            NaN   \n",
       "194656  E885580    Matched  Intersection  29545.0  ...            Dry   \n",
       "194657  3811279    Matched         Block      NaN  ...            Wet   \n",
       "194658  E886941    Matched         Block      NaN  ...            Wet   \n",
       "194659  3767486    Matched         Block      NaN  ...            Wet   \n",
       "194660  3811749    Matched         Block      NaN  ...            Wet   \n",
       "194661  3814599    Matched         Block      NaN  ...            Dry   \n",
       "194662  E869008    Matched         Block      NaN  ...            Dry   \n",
       "194663  E880807    Matched         Block      NaN  ...            Wet   \n",
       "194664  E879537    Matched  Intersection  28300.0  ...            Dry   \n",
       "194665  3642620    Matched  Intersection  26005.0  ...            Dry   \n",
       "194666  E879712    Matched         Block      NaN  ...            Wet   \n",
       "194667  3745813    Matched         Block      NaN  ...            Wet   \n",
       "194668  E871089    Matched         Block      NaN  ...            Dry   \n",
       "194669  E876731    Matched         Block      NaN  ...            Wet   \n",
       "194670  3809984    Matched  Intersection  24760.0  ...            Dry   \n",
       "194671  3810083    Matched  Intersection  24349.0  ...            Dry   \n",
       "194672  E868008    Matched         Block      NaN  ...            Wet   \n",
       "\n",
       "                Light Condition PEDROWNOTGRNT  SDOTCOLNUM SPEEDING ST_COLCODE  \\\n",
       "0                      Daylight           NaN         NaN      NaN         10   \n",
       "1       Dark - Street Lights On           NaN   6354039.0      NaN         11   \n",
       "2                      Daylight           NaN   4323031.0      NaN         32   \n",
       "3                      Daylight           NaN         NaN      NaN         23   \n",
       "4                      Daylight           NaN   4028032.0      NaN         10   \n",
       "5                      Daylight           NaN         NaN      NaN         10   \n",
       "6                      Daylight           NaN   8344002.0      NaN         10   \n",
       "7                      Daylight           NaN         NaN      NaN          5   \n",
       "8                      Daylight           NaN   6166014.0      NaN         32   \n",
       "9                      Daylight           NaN   6079001.0      NaN         10   \n",
       "10                     Daylight           NaN   6145029.0      NaN         22   \n",
       "11                     Daylight           NaN   5223041.0      NaN         10   \n",
       "12      Dark - Street Lights On           NaN   3137016.0      NaN         14   \n",
       "13      Dark - No Street Lights           NaN   5356027.0      NaN         32   \n",
       "14      Dark - Street Lights On           NaN         NaN      NaN         30   \n",
       "15                          NaN           NaN         NaN      NaN              \n",
       "16                     Daylight           NaN   5182022.0      NaN         28   \n",
       "17                     Daylight           NaN   4016025.0      NaN         14   \n",
       "18                     Daylight           NaN         NaN      NaN         14   \n",
       "19                      Unknown           NaN   9152035.0      NaN         32   \n",
       "20      Dark - Street Lights On           NaN         NaN      NaN         14   \n",
       "21                     Daylight           NaN         NaN      NaN         10   \n",
       "22      Dark - Street Lights On           NaN   8200013.0      NaN         32   \n",
       "23                      Unknown           NaN   4256026.0      NaN         51   \n",
       "24                     Daylight           NaN         NaN        Y         14   \n",
       "25                     Daylight           NaN         NaN      NaN          5   \n",
       "26                      Unknown           NaN  10317016.0      NaN         28   \n",
       "27                     Daylight           NaN   7204015.0      NaN         10   \n",
       "28                     Daylight           NaN         NaN      NaN         23   \n",
       "29                     Daylight           NaN   6320040.0      NaN         32   \n",
       "...                         ...           ...         ...      ...        ...   \n",
       "194643  Dark - Street Lights On           NaN         NaN      NaN         20   \n",
       "194644                     Dusk           NaN         NaN      NaN         10   \n",
       "194645  Dark - Street Lights On           NaN         NaN      NaN         16   \n",
       "194646                      NaN           NaN         NaN      NaN              \n",
       "194647                     Dusk           NaN         NaN      NaN          0   \n",
       "194648  Dark - Street Lights On           NaN         NaN      NaN         10   \n",
       "194649  Dark - Street Lights On           NaN         NaN      NaN         28   \n",
       "194650                 Daylight           NaN         NaN      NaN         32   \n",
       "194651  Dark - Street Lights On           NaN         NaN      NaN          0   \n",
       "194652                     Dusk           NaN         NaN      NaN         32   \n",
       "194653  Dark - Street Lights On           NaN         NaN      NaN         32   \n",
       "194654                      NaN           NaN         NaN      NaN              \n",
       "194655                      NaN           NaN         NaN      NaN              \n",
       "194656                 Daylight           NaN         NaN      NaN          2   \n",
       "194657  Dark - Street Lights On           NaN         NaN      NaN         11   \n",
       "194658  Dark - Street Lights On           NaN         NaN      NaN         14   \n",
       "194659  Dark - Street Lights On           NaN         NaN      NaN         32   \n",
       "194660                    Other           NaN         NaN      NaN         52   \n",
       "194661                 Daylight           NaN         NaN      NaN         29   \n",
       "194662                     Dusk           NaN         NaN      NaN         32   \n",
       "194663                 Daylight           NaN         NaN      NaN         10   \n",
       "194664                 Daylight           NaN         NaN      NaN         10   \n",
       "194665                 Daylight           NaN         NaN      NaN         10   \n",
       "194666                 Daylight           NaN         NaN      NaN         10   \n",
       "194667  Dark - Street Lights On           NaN         NaN      NaN         50   \n",
       "194668                 Daylight           NaN         NaN      NaN         24   \n",
       "194669                 Daylight           NaN         NaN      NaN         13   \n",
       "194670                 Daylight           NaN         NaN      NaN         28   \n",
       "194671                     Dusk           NaN         NaN      NaN          5   \n",
       "194672                 Daylight           NaN         NaN      NaN         14   \n",
       "\n",
       "                                               ST_COLDESC  SEGLANEKEY  \\\n",
       "0                                       Entering at angle           0   \n",
       "1       From same direction - both going straight - bo...           0   \n",
       "2                                  One parked--one moving           0   \n",
       "3                        From same direction - all others           0   \n",
       "4                                       Entering at angle           0   \n",
       "5                                       Entering at angle           0   \n",
       "6                                       Entering at angle           0   \n",
       "7                            Vehicle Strikes Pedalcyclist        6855   \n",
       "8                                  One parked--one moving           0   \n",
       "9                                       Entering at angle           0   \n",
       "10                        One car leaving driveway access           0   \n",
       "11                                      Entering at angle           0   \n",
       "12      From same direction - both going straight - on...           0   \n",
       "13                                 One parked--one moving           0   \n",
       "14                   From opposite direction - all others           0   \n",
       "15                                                    NaN           0   \n",
       "16      From opposite direction - one left turn - one ...           0   \n",
       "17      From same direction - both going straight - on...           0   \n",
       "18      From same direction - both going straight - on...           0   \n",
       "19                                 One parked--one moving           0   \n",
       "20      From same direction - both going straight - on...           0   \n",
       "21                                      Entering at angle           0   \n",
       "22                                 One parked--one moving           0   \n",
       "23                                           Other object           0   \n",
       "24      From same direction - both going straight - on...           0   \n",
       "25                           Vehicle Strikes Pedalcyclist           0   \n",
       "26      From opposite direction - one left turn - one ...           0   \n",
       "27                                      Entering at angle           0   \n",
       "28                       From same direction - all others           0   \n",
       "29                                 One parked--one moving           0   \n",
       "...                                                   ...         ...   \n",
       "194643                    One car leaving parked position           0   \n",
       "194644                                  Entering at angle           0   \n",
       "194645  From same direction - one right turn - one str...           0   \n",
       "194646                                                NaN           0   \n",
       "194647             Vehicle going straight hits pedestrian           0   \n",
       "194648                                  Entering at angle           0   \n",
       "194649  From opposite direction - one left turn - one ...           0   \n",
       "194650                             One parked--one moving           0   \n",
       "194651             Vehicle going straight hits pedestrian           0   \n",
       "194652                             One parked--one moving           0   \n",
       "194653                             One parked--one moving           0   \n",
       "194654                                                NaN           0   \n",
       "194655                                                NaN           0   \n",
       "194656               Vehicle turning left hits pedestrian           0   \n",
       "194657  From same direction - both going straight - bo...           0   \n",
       "194658  From same direction - both going straight - on...           0   \n",
       "194659                             One parked--one moving           0   \n",
       "194660                                 Vehicle overturned           0   \n",
       "194661  From opposite direction - one left turn - one ...           0   \n",
       "194662                             One parked--one moving           0   \n",
       "194663                                  Entering at angle           0   \n",
       "194664                                  Entering at angle           0   \n",
       "194665                                  Entering at angle           0   \n",
       "194666                                  Entering at angle           0   \n",
       "194667                                       Fixed object           0   \n",
       "194668    From opposite direction - both moving - head-on           0   \n",
       "194669  From same direction - both going straight - bo...           0   \n",
       "194670  From opposite direction - one left turn - one ...           0   \n",
       "194671                       Vehicle Strikes Pedalcyclist        4308   \n",
       "194672  From same direction - both going straight - on...           0   \n",
       "\n",
       "        CROSSWALKKEY  HITPARKEDCAR  \n",
       "0                  0             N  \n",
       "1                  0             N  \n",
       "2                  0             N  \n",
       "3                  0             N  \n",
       "4                  0             N  \n",
       "5                  0             N  \n",
       "6                  0             N  \n",
       "7                  0             N  \n",
       "8                  0             N  \n",
       "9                  0             N  \n",
       "10                 0             N  \n",
       "11                 0             N  \n",
       "12                 0             N  \n",
       "13                 0             N  \n",
       "14                 0             N  \n",
       "15                 0             N  \n",
       "16                 0             N  \n",
       "17                 0             N  \n",
       "18                 0             N  \n",
       "19                 0             N  \n",
       "20                 0             N  \n",
       "21                 0             N  \n",
       "22                 0             N  \n",
       "23                 0             N  \n",
       "24                 0             N  \n",
       "25                 0             N  \n",
       "26                 0             N  \n",
       "27                 0             N  \n",
       "28                 0             N  \n",
       "29                 0             N  \n",
       "...              ...           ...  \n",
       "194643             0             N  \n",
       "194644             0             N  \n",
       "194645             0             N  \n",
       "194646             0             N  \n",
       "194647             0             N  \n",
       "194648             0             N  \n",
       "194649             0             N  \n",
       "194650             0             Y  \n",
       "194651        522257             N  \n",
       "194652             0             Y  \n",
       "194653             0             Y  \n",
       "194654             0             N  \n",
       "194655        650595             N  \n",
       "194656        523322             N  \n",
       "194657             0             N  \n",
       "194658             0             N  \n",
       "194659             0             Y  \n",
       "194660             0             N  \n",
       "194661             0             N  \n",
       "194662             0             Y  \n",
       "194663             0             N  \n",
       "194664             0             N  \n",
       "194665             0             N  \n",
       "194666             0             N  \n",
       "194667             0             N  \n",
       "194668             0             N  \n",
       "194669             0             N  \n",
       "194670             0             N  \n",
       "194671             0             N  \n",
       "194672             0             N  \n",
       "\n",
       "[194673 rows x 38 columns]"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The dataset we will be using\n",
    "df = pd.read_csv('datacollisions.csv')\n",
    "print(\"Data as been uploaded...\")\n",
    "# lets change some of the column names so it would be eaiser to identify the info the columns show.\n",
    "dataframe_renamed = df.rename(columns={'SEVERITYCODE': \"Severity Level\" ,'X': 'Longitude', \n",
    "                               'Y': 'Latitude', 'OBJECTID':'Object ID','INCKEY':'INC Key','REPORTNO': 'Report #', \n",
    "                                       'STATUS':'Status', 'ADDRTYPE': 'Address Type', 'INTKEY':'INT Key','ROADCOND': 'Road Condition', 'LIGHTCOND': 'Light Condition'})\n",
    "dataframe_renamed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Base on what I seen from the dataset, I wanted to see if there were a correlation between the Road conditon and light conditon. On how to do this, I will write a function that will count all the possible combine conditons and then I will graph them base on the output. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        vertical-align: top;\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Road Condition</th>\n",
       "      <th>Light Condition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Wet</td>\n",
       "      <td>Daylight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Wet</td>\n",
       "      <td>Dark - Street Lights On</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Dry</td>\n",
       "      <td>Daylight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Dry</td>\n",
       "      <td>Daylight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Wet</td>\n",
       "      <td>Daylight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Dry</td>\n",
       "      <td>Daylight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Wet</td>\n",
       "      <td>Daylight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Dry</td>\n",
       "      <td>Daylight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Dry</td>\n",
       "      <td>Daylight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Dry</td>\n",
       "      <td>Daylight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Dry</td>\n",
       "      <td>Daylight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Dry</td>\n",
       "      <td>Daylight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Wet</td>\n",
       "      <td>Dark - Street Lights On</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Wet</td>\n",
       "      <td>Dark - No Street Lights</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Dry</td>\n",
       "      <td>Dark - Street Lights On</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Dry</td>\n",
       "      <td>Daylight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Dry</td>\n",
       "      <td>Daylight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Dry</td>\n",
       "      <td>Daylight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Dry</td>\n",
       "      <td>Unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Dry</td>\n",
       "      <td>Dark - Street Lights On</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Dry</td>\n",
       "      <td>Daylight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Dry</td>\n",
       "      <td>Dark - Street Lights On</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Dry</td>\n",
       "      <td>Daylight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Dry</td>\n",
       "      <td>Daylight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Wet</td>\n",
       "      <td>Unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Dry</td>\n",
       "      <td>Daylight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Dry</td>\n",
       "      <td>Daylight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Dry</td>\n",
       "      <td>Daylight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194643</th>\n",
       "      <td>Dry</td>\n",
       "      <td>Dark - Street Lights On</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194644</th>\n",
       "      <td>Dry</td>\n",
       "      <td>Dusk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194645</th>\n",
       "      <td>Dry</td>\n",
       "      <td>Dark - Street Lights On</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194646</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194647</th>\n",
       "      <td>Wet</td>\n",
       "      <td>Dusk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194648</th>\n",
       "      <td>Wet</td>\n",
       "      <td>Dark - Street Lights On</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194649</th>\n",
       "      <td>Dry</td>\n",
       "      <td>Dark - Street Lights On</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194650</th>\n",
       "      <td>Dry</td>\n",
       "      <td>Daylight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194651</th>\n",
       "      <td>Wet</td>\n",
       "      <td>Dark - Street Lights On</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194652</th>\n",
       "      <td>Wet</td>\n",
       "      <td>Dusk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194653</th>\n",
       "      <td>Wet</td>\n",
       "      <td>Dark - Street Lights On</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194654</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194655</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194656</th>\n",
       "      <td>Dry</td>\n",
       "      <td>Daylight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194657</th>\n",
       "      <td>Wet</td>\n",
       "      <td>Dark - Street Lights On</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194658</th>\n",
       "      <td>Wet</td>\n",
       "      <td>Dark - Street Lights On</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194659</th>\n",
       "      <td>Wet</td>\n",
       "      <td>Dark - Street Lights On</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194660</th>\n",
       "      <td>Wet</td>\n",
       "      <td>Other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194661</th>\n",
       "      <td>Dry</td>\n",
       "      <td>Daylight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194662</th>\n",
       "      <td>Dry</td>\n",
       "      <td>Dusk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194663</th>\n",
       "      <td>Wet</td>\n",
       "      <td>Daylight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194664</th>\n",
       "      <td>Dry</td>\n",
       "      <td>Daylight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194665</th>\n",
       "      <td>Dry</td>\n",
       "      <td>Daylight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194666</th>\n",
       "      <td>Wet</td>\n",
       "      <td>Daylight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194667</th>\n",
       "      <td>Wet</td>\n",
       "      <td>Dark - Street Lights On</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194668</th>\n",
       "      <td>Dry</td>\n",
       "      <td>Daylight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194669</th>\n",
       "      <td>Wet</td>\n",
       "      <td>Daylight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194670</th>\n",
       "      <td>Dry</td>\n",
       "      <td>Daylight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194671</th>\n",
       "      <td>Dry</td>\n",
       "      <td>Dusk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194672</th>\n",
       "      <td>Wet</td>\n",
       "      <td>Daylight</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>194673 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Road Condition          Light Condition\n",
       "0                 Wet                 Daylight\n",
       "1                 Wet  Dark - Street Lights On\n",
       "2                 Dry                 Daylight\n",
       "3                 Dry                 Daylight\n",
       "4                 Wet                 Daylight\n",
       "5                 Dry                 Daylight\n",
       "6                 Wet                 Daylight\n",
       "7                 Dry                 Daylight\n",
       "8                 Dry                 Daylight\n",
       "9                 Dry                 Daylight\n",
       "10                Dry                 Daylight\n",
       "11                Dry                 Daylight\n",
       "12                Wet  Dark - Street Lights On\n",
       "13                Wet  Dark - No Street Lights\n",
       "14                Dry  Dark - Street Lights On\n",
       "15                NaN                      NaN\n",
       "16                Dry                 Daylight\n",
       "17                Dry                 Daylight\n",
       "18                Dry                 Daylight\n",
       "19                Dry                  Unknown\n",
       "20                Dry  Dark - Street Lights On\n",
       "21                Dry                 Daylight\n",
       "22                Dry  Dark - Street Lights On\n",
       "23            Unknown                  Unknown\n",
       "24                Dry                 Daylight\n",
       "25                Dry                 Daylight\n",
       "26                Wet                  Unknown\n",
       "27                Dry                 Daylight\n",
       "28                Dry                 Daylight\n",
       "29                Dry                 Daylight\n",
       "...               ...                      ...\n",
       "194643            Dry  Dark - Street Lights On\n",
       "194644            Dry                     Dusk\n",
       "194645            Dry  Dark - Street Lights On\n",
       "194646            NaN                      NaN\n",
       "194647            Wet                     Dusk\n",
       "194648            Wet  Dark - Street Lights On\n",
       "194649            Dry  Dark - Street Lights On\n",
       "194650            Dry                 Daylight\n",
       "194651            Wet  Dark - Street Lights On\n",
       "194652            Wet                     Dusk\n",
       "194653            Wet  Dark - Street Lights On\n",
       "194654            NaN                      NaN\n",
       "194655            NaN                      NaN\n",
       "194656            Dry                 Daylight\n",
       "194657            Wet  Dark - Street Lights On\n",
       "194658            Wet  Dark - Street Lights On\n",
       "194659            Wet  Dark - Street Lights On\n",
       "194660            Wet                    Other\n",
       "194661            Dry                 Daylight\n",
       "194662            Dry                     Dusk\n",
       "194663            Wet                 Daylight\n",
       "194664            Dry                 Daylight\n",
       "194665            Dry                 Daylight\n",
       "194666            Wet                 Daylight\n",
       "194667            Wet  Dark - Street Lights On\n",
       "194668            Dry                 Daylight\n",
       "194669            Wet                 Daylight\n",
       "194670            Dry                 Daylight\n",
       "194671            Dry                     Dusk\n",
       "194672            Wet                 Daylight\n",
       "\n",
       "[194673 rows x 2 columns]"
      ]
     },
     "execution_count": 245,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Obtaining only the Road and Light Condition data\n",
    "Road_and_Light_Conditions_df = dataframe_renamed[['Road Condition','Light Condition']]\n",
    "Road_and_Light_Conditions_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Road Condition  Light Condition         \n",
      "Dry             Dark - No Street Lights       857\n",
      "                Dark - Street Lights Off      675\n",
      "                Dark - Street Lights On     28184\n",
      "                Dark - Unknown Lighting         4\n",
      "                Dawn                         1268\n",
      "                Daylight                    87408\n",
      "                Dusk                         3869\n",
      "                Other                         106\n",
      "                Unknown                      2001\n",
      "Ice             Dark - No Street Lights        23\n",
      "                Dark - Street Lights Off       16\n",
      "                Dark - Street Lights On       469\n",
      "                Dawn                           62\n",
      "                Daylight                      548\n",
      "                Dusk                           19\n",
      "                Other                           1\n",
      "                Unknown                        70\n",
      "Oil             Dark - No Street Lights         5\n",
      "                Dark - Street Lights On        13\n",
      "                Dawn                            5\n",
      "                Daylight                       38\n",
      "                Dusk                            2\n",
      "                Unknown                         1\n",
      "Other           Dark - No Street Lights         2\n",
      "                Dark - Street Lights Off        2\n",
      "                Dark - Street Lights On        37\n",
      "                Dawn                            4\n",
      "                Daylight                       63\n",
      "                Dusk                            3\n",
      "                Other                           6\n",
      "                                            ...  \n",
      "Snow/Slush      Dawn                           26\n",
      "                Daylight                      344\n",
      "                Dusk                           21\n",
      "                Other                           6\n",
      "                Unknown                       133\n",
      "Standing Water  Dark - No Street Lights         2\n",
      "                Dark - Street Lights On        52\n",
      "                Dawn                            3\n",
      "                Daylight                       48\n",
      "                Dusk                            3\n",
      "                Other                           3\n",
      "                Unknown                         4\n",
      "Unknown         Dark - No Street Lights        92\n",
      "                Dark - Street Lights Off       53\n",
      "                Dark - Street Lights On      1222\n",
      "                Dark - Unknown Lighting         2\n",
      "                Dawn                           57\n",
      "                Daylight                     2786\n",
      "                Dusk                          180\n",
      "                Other                          20\n",
      "                Unknown                     10629\n",
      "Wet             Dark - No Street Lights       539\n",
      "                Dark - Street Lights Off      441\n",
      "                Dark - Street Lights On     18039\n",
      "                Dark - Unknown Lighting         5\n",
      "                Dawn                         1073\n",
      "                Daylight                    24843\n",
      "                Dusk                         1799\n",
      "                Other                          93\n",
      "                Unknown                       606\n",
      "Length: 70, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#This code will see all the different combination  of conditions\n",
    "Count = Road_and_Light_Conditions_df.groupby(['Road Condition', 'Light Condition']).size() \n",
    "print(Count)   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import Matplotlib to do some graphing with the data we have now.\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we will plot each colomn from the \"Road_and_Light_Conditions_df\" dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title('Different types of Road Conditions')\n",
    "plt.xlabel('Number of Accidents')\n",
    "plt.ylabel('Road Condition')\n",
    "Road_Condition = Road_and_Light_Conditions_df['Road Condition'].value_counts().head(30).plot(kind='barh')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1cb14306dd8>"
      ]
     },
     "execution_count": 278,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title('Different types of Light Conditions')\n",
    "plt.xlabel('Number of Accidents')\n",
    "plt.ylabel('Light Condition')\n",
    "Road_and_Light_Conditions_df['Light Condition'].value_counts().head(30).plot(kind='barh')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\quise\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "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>Road Condition</th>\n",
       "      <th>Light Condition</th>\n",
       "      <th>Road/Light Condition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Wet</td>\n",
       "      <td>Daylight</td>\n",
       "      <td>WetDaylight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Wet</td>\n",
       "      <td>Dark - Street Lights On</td>\n",
       "      <td>WetDark - Street Lights On</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Dry</td>\n",
       "      <td>Daylight</td>\n",
       "      <td>DryDaylight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Dry</td>\n",
       "      <td>Daylight</td>\n",
       "      <td>DryDaylight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Wet</td>\n",
       "      <td>Daylight</td>\n",
       "      <td>WetDaylight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Dry</td>\n",
       "      <td>Daylight</td>\n",
       "      <td>DryDaylight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Wet</td>\n",
       "      <td>Daylight</td>\n",
       "      <td>WetDaylight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Dry</td>\n",
       "      <td>Daylight</td>\n",
       "      <td>DryDaylight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Dry</td>\n",
       "      <td>Daylight</td>\n",
       "      <td>DryDaylight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Dry</td>\n",
       "      <td>Daylight</td>\n",
       "      <td>DryDaylight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Dry</td>\n",
       "      <td>Daylight</td>\n",
       "      <td>DryDaylight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Dry</td>\n",
       "      <td>Daylight</td>\n",
       "      <td>DryDaylight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Wet</td>\n",
       "      <td>Dark - Street Lights On</td>\n",
       "      <td>WetDark - Street Lights On</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Wet</td>\n",
       "      <td>Dark - No Street Lights</td>\n",
       "      <td>WetDark - No Street Lights</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Dry</td>\n",
       "      <td>Dark - Street Lights On</td>\n",
       "      <td>DryDark - Street Lights On</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Dry</td>\n",
       "      <td>Daylight</td>\n",
       "      <td>DryDaylight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Dry</td>\n",
       "      <td>Daylight</td>\n",
       "      <td>DryDaylight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Dry</td>\n",
       "      <td>Daylight</td>\n",
       "      <td>DryDaylight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Dry</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>DryUnknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Dry</td>\n",
       "      <td>Dark - Street Lights On</td>\n",
       "      <td>DryDark - Street Lights On</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Dry</td>\n",
       "      <td>Daylight</td>\n",
       "      <td>DryDaylight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Dry</td>\n",
       "      <td>Dark - Street Lights On</td>\n",
       "      <td>DryDark - Street Lights On</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>UnknownUnknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Dry</td>\n",
       "      <td>Daylight</td>\n",
       "      <td>DryDaylight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Dry</td>\n",
       "      <td>Daylight</td>\n",
       "      <td>DryDaylight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Wet</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>WetUnknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Dry</td>\n",
       "      <td>Daylight</td>\n",
       "      <td>DryDaylight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Dry</td>\n",
       "      <td>Daylight</td>\n",
       "      <td>DryDaylight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Dry</td>\n",
       "      <td>Daylight</td>\n",
       "      <td>DryDaylight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194643</th>\n",
       "      <td>Dry</td>\n",
       "      <td>Dark - Street Lights On</td>\n",
       "      <td>DryDark - Street Lights On</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194644</th>\n",
       "      <td>Dry</td>\n",
       "      <td>Dusk</td>\n",
       "      <td>DryDusk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194645</th>\n",
       "      <td>Dry</td>\n",
       "      <td>Dark - Street Lights On</td>\n",
       "      <td>DryDark - Street Lights On</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194646</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194647</th>\n",
       "      <td>Wet</td>\n",
       "      <td>Dusk</td>\n",
       "      <td>WetDusk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194648</th>\n",
       "      <td>Wet</td>\n",
       "      <td>Dark - Street Lights On</td>\n",
       "      <td>WetDark - Street Lights On</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194649</th>\n",
       "      <td>Dry</td>\n",
       "      <td>Dark - Street Lights On</td>\n",
       "      <td>DryDark - Street Lights On</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194650</th>\n",
       "      <td>Dry</td>\n",
       "      <td>Daylight</td>\n",
       "      <td>DryDaylight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194651</th>\n",
       "      <td>Wet</td>\n",
       "      <td>Dark - Street Lights On</td>\n",
       "      <td>WetDark - Street Lights On</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194652</th>\n",
       "      <td>Wet</td>\n",
       "      <td>Dusk</td>\n",
       "      <td>WetDusk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194653</th>\n",
       "      <td>Wet</td>\n",
       "      <td>Dark - Street Lights On</td>\n",
       "      <td>WetDark - Street Lights On</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194654</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194655</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194656</th>\n",
       "      <td>Dry</td>\n",
       "      <td>Daylight</td>\n",
       "      <td>DryDaylight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194657</th>\n",
       "      <td>Wet</td>\n",
       "      <td>Dark - Street Lights On</td>\n",
       "      <td>WetDark - Street Lights On</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194658</th>\n",
       "      <td>Wet</td>\n",
       "      <td>Dark - Street Lights On</td>\n",
       "      <td>WetDark - Street Lights On</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194659</th>\n",
       "      <td>Wet</td>\n",
       "      <td>Dark - Street Lights On</td>\n",
       "      <td>WetDark - Street Lights On</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194660</th>\n",
       "      <td>Wet</td>\n",
       "      <td>Other</td>\n",
       "      <td>WetOther</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194661</th>\n",
       "      <td>Dry</td>\n",
       "      <td>Daylight</td>\n",
       "      <td>DryDaylight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194662</th>\n",
       "      <td>Dry</td>\n",
       "      <td>Dusk</td>\n",
       "      <td>DryDusk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194663</th>\n",
       "      <td>Wet</td>\n",
       "      <td>Daylight</td>\n",
       "      <td>WetDaylight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194664</th>\n",
       "      <td>Dry</td>\n",
       "      <td>Daylight</td>\n",
       "      <td>DryDaylight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194665</th>\n",
       "      <td>Dry</td>\n",
       "      <td>Daylight</td>\n",
       "      <td>DryDaylight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194666</th>\n",
       "      <td>Wet</td>\n",
       "      <td>Daylight</td>\n",
       "      <td>WetDaylight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194667</th>\n",
       "      <td>Wet</td>\n",
       "      <td>Dark - Street Lights On</td>\n",
       "      <td>WetDark - Street Lights On</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194668</th>\n",
       "      <td>Dry</td>\n",
       "      <td>Daylight</td>\n",
       "      <td>DryDaylight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194669</th>\n",
       "      <td>Wet</td>\n",
       "      <td>Daylight</td>\n",
       "      <td>WetDaylight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194670</th>\n",
       "      <td>Dry</td>\n",
       "      <td>Daylight</td>\n",
       "      <td>DryDaylight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194671</th>\n",
       "      <td>Dry</td>\n",
       "      <td>Dusk</td>\n",
       "      <td>DryDusk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194672</th>\n",
       "      <td>Wet</td>\n",
       "      <td>Daylight</td>\n",
       "      <td>WetDaylight</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>194673 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Road Condition          Light Condition        Road/Light Condition\n",
       "0                 Wet                 Daylight                 WetDaylight\n",
       "1                 Wet  Dark - Street Lights On  WetDark - Street Lights On\n",
       "2                 Dry                 Daylight                 DryDaylight\n",
       "3                 Dry                 Daylight                 DryDaylight\n",
       "4                 Wet                 Daylight                 WetDaylight\n",
       "5                 Dry                 Daylight                 DryDaylight\n",
       "6                 Wet                 Daylight                 WetDaylight\n",
       "7                 Dry                 Daylight                 DryDaylight\n",
       "8                 Dry                 Daylight                 DryDaylight\n",
       "9                 Dry                 Daylight                 DryDaylight\n",
       "10                Dry                 Daylight                 DryDaylight\n",
       "11                Dry                 Daylight                 DryDaylight\n",
       "12                Wet  Dark - Street Lights On  WetDark - Street Lights On\n",
       "13                Wet  Dark - No Street Lights  WetDark - No Street Lights\n",
       "14                Dry  Dark - Street Lights On  DryDark - Street Lights On\n",
       "15                NaN                      NaN                         NaN\n",
       "16                Dry                 Daylight                 DryDaylight\n",
       "17                Dry                 Daylight                 DryDaylight\n",
       "18                Dry                 Daylight                 DryDaylight\n",
       "19                Dry                  Unknown                  DryUnknown\n",
       "20                Dry  Dark - Street Lights On  DryDark - Street Lights On\n",
       "21                Dry                 Daylight                 DryDaylight\n",
       "22                Dry  Dark - Street Lights On  DryDark - Street Lights On\n",
       "23            Unknown                  Unknown              UnknownUnknown\n",
       "24                Dry                 Daylight                 DryDaylight\n",
       "25                Dry                 Daylight                 DryDaylight\n",
       "26                Wet                  Unknown                  WetUnknown\n",
       "27                Dry                 Daylight                 DryDaylight\n",
       "28                Dry                 Daylight                 DryDaylight\n",
       "29                Dry                 Daylight                 DryDaylight\n",
       "...               ...                      ...                         ...\n",
       "194643            Dry  Dark - Street Lights On  DryDark - Street Lights On\n",
       "194644            Dry                     Dusk                     DryDusk\n",
       "194645            Dry  Dark - Street Lights On  DryDark - Street Lights On\n",
       "194646            NaN                      NaN                         NaN\n",
       "194647            Wet                     Dusk                     WetDusk\n",
       "194648            Wet  Dark - Street Lights On  WetDark - Street Lights On\n",
       "194649            Dry  Dark - Street Lights On  DryDark - Street Lights On\n",
       "194650            Dry                 Daylight                 DryDaylight\n",
       "194651            Wet  Dark - Street Lights On  WetDark - Street Lights On\n",
       "194652            Wet                     Dusk                     WetDusk\n",
       "194653            Wet  Dark - Street Lights On  WetDark - Street Lights On\n",
       "194654            NaN                      NaN                         NaN\n",
       "194655            NaN                      NaN                         NaN\n",
       "194656            Dry                 Daylight                 DryDaylight\n",
       "194657            Wet  Dark - Street Lights On  WetDark - Street Lights On\n",
       "194658            Wet  Dark - Street Lights On  WetDark - Street Lights On\n",
       "194659            Wet  Dark - Street Lights On  WetDark - Street Lights On\n",
       "194660            Wet                    Other                    WetOther\n",
       "194661            Dry                 Daylight                 DryDaylight\n",
       "194662            Dry                     Dusk                     DryDusk\n",
       "194663            Wet                 Daylight                 WetDaylight\n",
       "194664            Dry                 Daylight                 DryDaylight\n",
       "194665            Dry                 Daylight                 DryDaylight\n",
       "194666            Wet                 Daylight                 WetDaylight\n",
       "194667            Wet  Dark - Street Lights On  WetDark - Street Lights On\n",
       "194668            Dry                 Daylight                 DryDaylight\n",
       "194669            Wet                 Daylight                 WetDaylight\n",
       "194670            Dry                 Daylight                 DryDaylight\n",
       "194671            Dry                     Dusk                     DryDusk\n",
       "194672            Wet                 Daylight                 WetDaylight\n",
       "\n",
       "[194673 rows x 3 columns]"
      ]
     },
     "execution_count": 281,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Road_and_Light_Conditions_df['Road/Light Condition'] = Road_and_Light_Conditions_df['Road Condition'].str.cat(Road_and_Light_Conditions_df['Light Condition'])\n",
    "Road_and_Light_Conditions_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title('Accidents base on the Road and Light condition')\n",
    "plt.xlabel('Number of Accidents')\n",
    "plt.ylabel('Road Condition')\n",
    "Road_Condition = Road_and_Light_Conditions_df['Road/Light Condition'].value_counts().head(30).plot(kind='barh',figsize=(20,10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It appears that three times more accidents happen during the day time when it is dry. Let's take a look at the location of each accidents. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 342,
   "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>Longitude</th>\n",
       "      <th>Latitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-122.323148</td>\n",
       "      <td>47.703140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-122.347294</td>\n",
       "      <td>47.647172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-122.334540</td>\n",
       "      <td>47.607871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-122.334803</td>\n",
       "      <td>47.604803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-122.306426</td>\n",
       "      <td>47.545739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-122.387598</td>\n",
       "      <td>47.690575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-122.338485</td>\n",
       "      <td>47.618534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-122.320780</td>\n",
       "      <td>47.614076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-122.335930</td>\n",
       "      <td>47.611904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-122.384700</td>\n",
       "      <td>47.528475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>-122.333831</td>\n",
       "      <td>47.547371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>-122.356273</td>\n",
       "      <td>47.571375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>-122.323966</td>\n",
       "      <td>47.606374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>-122.317414</td>\n",
       "      <td>47.664028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>-122.337663</td>\n",
       "      <td>47.617510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>-122.344539</td>\n",
       "      <td>47.692012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>-122.328270</td>\n",
       "      <td>47.571420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>-122.383802</td>\n",
       "      <td>47.583715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>-122.292403</td>\n",
       "      <td>47.732847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>-122.313786</td>\n",
       "      <td>47.708535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>-122.318169</td>\n",
       "      <td>47.615837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>-122.337486</td>\n",
       "      <td>47.589746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>-122.279658</td>\n",
       "      <td>47.553405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>-122.312857</td>\n",
       "      <td>47.599218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>-122.330730</td>\n",
       "      <td>47.615450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>-122.385859</td>\n",
       "      <td>47.581191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>-122.304990</td>\n",
       "      <td>47.611474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194643</th>\n",
       "      <td>-122.345863</td>\n",
       "      <td>47.612991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194644</th>\n",
       "      <td>-122.330298</td>\n",
       "      <td>47.603233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194645</th>\n",
       "      <td>-122.328079</td>\n",
       "      <td>47.604161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194646</th>\n",
       "      <td>-122.320756</td>\n",
       "      <td>47.608656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194647</th>\n",
       "      <td>-122.355449</td>\n",
       "      <td>47.704720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194648</th>\n",
       "      <td>-122.357624</td>\n",
       "      <td>47.635956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194649</th>\n",
       "      <td>-122.302382</td>\n",
       "      <td>47.626759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194650</th>\n",
       "      <td>-122.355298</td>\n",
       "      <td>47.684382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194651</th>\n",
       "      <td>-122.320780</td>\n",
       "      <td>47.614076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194652</th>\n",
       "      <td>-122.335527</td>\n",
       "      <td>47.617434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194653</th>\n",
       "      <td>-122.322097</td>\n",
       "      <td>47.649615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194654</th>\n",
       "      <td>-122.312679</td>\n",
       "      <td>47.719414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194655</th>\n",
       "      <td>-122.380016</td>\n",
       "      <td>47.664879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194656</th>\n",
       "      <td>-122.340474</td>\n",
       "      <td>47.614496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194657</th>\n",
       "      <td>-122.337137</td>\n",
       "      <td>47.610709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194658</th>\n",
       "      <td>-122.302554</td>\n",
       "      <td>47.584099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194659</th>\n",
       "      <td>-122.298955</td>\n",
       "      <td>47.717456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194660</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194661</th>\n",
       "      <td>-122.306092</td>\n",
       "      <td>47.617881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194662</th>\n",
       "      <td>-122.337880</td>\n",
       "      <td>47.625793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194663</th>\n",
       "      <td>-122.299160</td>\n",
       "      <td>47.579673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194664</th>\n",
       "      <td>-122.325887</td>\n",
       "      <td>47.643191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194665</th>\n",
       "      <td>-122.304217</td>\n",
       "      <td>47.669537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194666</th>\n",
       "      <td>-122.344569</td>\n",
       "      <td>47.694547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194667</th>\n",
       "      <td>-122.361672</td>\n",
       "      <td>47.556722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194668</th>\n",
       "      <td>-122.290826</td>\n",
       "      <td>47.565408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194669</th>\n",
       "      <td>-122.344526</td>\n",
       "      <td>47.690924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194670</th>\n",
       "      <td>-122.306689</td>\n",
       "      <td>47.683047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194671</th>\n",
       "      <td>-122.355317</td>\n",
       "      <td>47.678734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194672</th>\n",
       "      <td>-122.289360</td>\n",
       "      <td>47.611017</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>194673 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Longitude   Latitude\n",
       "0      -122.323148  47.703140\n",
       "1      -122.347294  47.647172\n",
       "2      -122.334540  47.607871\n",
       "3      -122.334803  47.604803\n",
       "4      -122.306426  47.545739\n",
       "5      -122.387598  47.690575\n",
       "6      -122.338485  47.618534\n",
       "7      -122.320780  47.614076\n",
       "8      -122.335930  47.611904\n",
       "9      -122.384700  47.528475\n",
       "10             NaN        NaN\n",
       "11     -122.333831  47.547371\n",
       "12     -122.356273  47.571375\n",
       "13     -122.323966  47.606374\n",
       "14     -122.317414  47.664028\n",
       "15     -122.337663  47.617510\n",
       "16     -122.344539  47.692012\n",
       "17             NaN        NaN\n",
       "18     -122.328270  47.571420\n",
       "19     -122.383802  47.583715\n",
       "20     -122.292403  47.732847\n",
       "21     -122.313786  47.708535\n",
       "22     -122.318169  47.615837\n",
       "23     -122.337486  47.589746\n",
       "24     -122.279658  47.553405\n",
       "25     -122.312857  47.599218\n",
       "26     -122.330730  47.615450\n",
       "27     -122.385859  47.581191\n",
       "28     -122.304990  47.611474\n",
       "29             NaN        NaN\n",
       "...            ...        ...\n",
       "194643 -122.345863  47.612991\n",
       "194644 -122.330298  47.603233\n",
       "194645 -122.328079  47.604161\n",
       "194646 -122.320756  47.608656\n",
       "194647 -122.355449  47.704720\n",
       "194648 -122.357624  47.635956\n",
       "194649 -122.302382  47.626759\n",
       "194650 -122.355298  47.684382\n",
       "194651 -122.320780  47.614076\n",
       "194652 -122.335527  47.617434\n",
       "194653 -122.322097  47.649615\n",
       "194654 -122.312679  47.719414\n",
       "194655 -122.380016  47.664879\n",
       "194656 -122.340474  47.614496\n",
       "194657 -122.337137  47.610709\n",
       "194658 -122.302554  47.584099\n",
       "194659 -122.298955  47.717456\n",
       "194660         NaN        NaN\n",
       "194661 -122.306092  47.617881\n",
       "194662 -122.337880  47.625793\n",
       "194663 -122.299160  47.579673\n",
       "194664 -122.325887  47.643191\n",
       "194665 -122.304217  47.669537\n",
       "194666 -122.344569  47.694547\n",
       "194667 -122.361672  47.556722\n",
       "194668 -122.290826  47.565408\n",
       "194669 -122.344526  47.690924\n",
       "194670 -122.306689  47.683047\n",
       "194671 -122.355317  47.678734\n",
       "194672 -122.289360  47.611017\n",
       "\n",
       "[194673 rows x 2 columns]"
      ]
     },
     "execution_count": 342,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "long_lat_df = dataframe_renamed[['Longitude','Latitude']]\n",
    "\n",
    "long_lat_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 368,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "import numpy as np\n",
    "long_lat_df_test = long_lat_df[long_lat_df[\"Longitude\"]> 0].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 369,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = []\n",
    "y_true = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 373,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Cannot have number of splits n_splits=15 greater than the number of samples: n_samples=0.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-373-9bef27b84519>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmodel_selection\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mKFold\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mkf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mKFold\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn_splits\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m15\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrandom_state\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m42\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[1;32mfor\u001b[0m \u001b[0mtrain_index\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtest_index\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mkf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlong_lat_df_test\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m     \u001b[0mdf_test\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlong_lat_df_test\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mtest_index\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m     \u001b[0mdf_train\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlong_lat_df_test\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mtrain_index\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py\u001b[0m in \u001b[0;36msplit\u001b[1;34m(self, X, y, groups)\u001b[0m\n\u001b[0;32m    331\u001b[0m                 (\"Cannot have number of splits n_splits={0} greater\"\n\u001b[0;32m    332\u001b[0m                  \" than the number of samples: n_samples={1}.\")\n\u001b[1;32m--> 333\u001b[1;33m                 .format(self.n_splits, n_samples))\n\u001b[0m\u001b[0;32m    334\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    335\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mtrain\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtest\u001b[0m \u001b[1;32min\u001b[0m \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgroups\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: Cannot have number of splits n_splits=15 greater than the number of samples: n_samples=0."
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import KFold\n",
    "kf = KFold(n_splits=15, random_state = 42)\n",
    "for train_index, test_index in kf.split(long_lat_df_test):\n",
    "    df_test = long_lat_df_test.iloc[test_index]\n",
    "    df_train = long_lat_df_test.iloc[train_index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 348,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting package metadata (current_repodata.json): ...working... done\n",
      "Solving environment: ...working... done\n",
      "\n",
      "# All requested packages already installed.\n",
      "\n",
      "Folium installed and imported!\n"
     ]
    }
   ],
   "source": [
    "!conda install -c conda-forge folium=0.5.0 --yes\n",
    "import folium\n",
    "\n",
    "print('Folium installed and imported!')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 364,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><span style=\"color:#565656\">Make this Notebook Trusted to load map: File -> Trust Notebook</span><iframe src=\"about:blank\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" data-html=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 onload=\"this.contentDocument.open();this.contentDocument.write(atob(this.getAttribute('data-html')));this.contentDocument.close();\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
      ],
      "text/plain": [
       "<folium.folium.Map at 0x1cb1e7273c8>"
      ]
     },
     "execution_count": 364,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.scatter(x=long_lat_df['Longitude'], y=long_lat_df['Latitude'], s=400)\n",
    "plt.show()\n",
    "\n",
    "# define the world map centered around Canada with a higher zoom level\n",
    "world_map = folium.Map(location=[56.130, -106.35], zoom_start=8)\n",
    "\n",
    "\n",
    "#for i in long_lat_df.items():\n",
    " #   folium.Marker(location=i[1], popup=i[0]).add_to(map_cities)\n",
    "  #  print(i)\n",
    "\n",
    "\n",
    "# display world map\n",
    "world_map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Result and Discussion <a name=\"Result\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Base on our analysis, there is a high volume of accidents occurring when the Roads are dry, and it's daytime. Oddly the same, there is a less occurs of accidents occurring when it is wet and dark outside. Why is that?  Through the study, I came with a hypothesis that people are more aware when the conditions are more dangerous and less familiar when the conditions are less threatening.  But at the same time, the severity of accidents is higher when the conditions are worst. In other words, accidents happen a lot during normal conditions but not as severe. When the conditions are worst, fewer accidents happen, but they are more severe if there are accidents. "
   ]
  },
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   "source": [
    "## 6. Conclusion <a name=\"Conclusion\"></a>"
   ]
  },
  {
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   "source": [
    "This report aimed to understand why car frequently happens during a particular time of the day and the road conditions that may occur altogether. Driving is a vital task throughout our lives, and it's only natural to inform everyone of the dangers and build more awareness while cars. Now with this interpreted data, maybe the solutions will be more exact on how we can limit accidents. If accidents do happen, how we can help mitigate the severity of the casualties. I believe stakeholders, researchers, professionals, and civilians will find this study helpful. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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