Luxury_cars.ipynb

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    "% matplotlib inline\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import missingno as msno"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Are Luxury Cars Less Likely To Get Into Accidents?\n",
    "\n",
    "It goes without saying that the rich love their luxurious cars. Does this mean that they will go through extra lengths to take care of them from accidents? To answer this question we will be using two datasets.\n",
    "\n",
    "## The Data\n",
    "\n",
    "### Motor Vehicle Crashes in New York State\n",
    "Shows all the motor vehicle crashes within the New York State from 2012 to 2014. Includes information such as the vehicle's make, contributing factors to the incident, and actions prior to the incident.\n",
    "\n",
    "The data can be found [here](https://catalog.data.gov/dataset/motor-vehicle-crashes-vehicle-information-beginning-2009/resource/6b83cc59-6e65-443c-8b97-9300891c1333)."
   ]
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Case Vehicle ID</th>\n",
       "      <th>Vehicle Body Type</th>\n",
       "      <th>Registration Class</th>\n",
       "      <th>Action Prior to Accident</th>\n",
       "      <th>Type / Axles of Truck or Bus</th>\n",
       "      <th>Direction of Travel</th>\n",
       "      <th>Fuel Type</th>\n",
       "      <th>Vehicle Year</th>\n",
       "      <th>State of Registration</th>\n",
       "      <th>Number of Occupants</th>\n",
       "      <th>Engine Cylinders</th>\n",
       "      <th>Vehicle Make</th>\n",
       "      <th>Contributing Factor 1</th>\n",
       "      <th>Contributing Factor 1 Description</th>\n",
       "      <th>Contributing Factor 2</th>\n",
       "      <th>Contributing Factor 2 Description</th>\n",
       "      <th>Event Type</th>\n",
       "      <th>Partial VIN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013</td>\n",
       "      <td>11256149</td>\n",
       "      <td>2 DOOR SEDAN</td>\n",
       "      <td>Not Entered</td>\n",
       "      <td>Backing</td>\n",
       "      <td>Not Entered</td>\n",
       "      <td>South</td>\n",
       "      <td>Not Entered</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>NY</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>HOND</td>\n",
       "      <td>HUMAN</td>\n",
       "      <td>Backing Unsafely</td>\n",
       "      <td>HUMAN</td>\n",
       "      <td>Driver Inattention/Distraction*</td>\n",
       "      <td>Not Applicable</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013</td>\n",
       "      <td>11330798</td>\n",
       "      <td>4 DOOR SEDAN</td>\n",
       "      <td>Not Entered</td>\n",
       "      <td>Overtaking/Passing</td>\n",
       "      <td>Not Entered</td>\n",
       "      <td>North</td>\n",
       "      <td>Not Entered</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>VA</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DODG</td>\n",
       "      <td>HUMAN</td>\n",
       "      <td>Unsafe Lane Changing</td>\n",
       "      <td>HUMAN</td>\n",
       "      <td>Passing or Lane Usage Improper</td>\n",
       "      <td>Overturned, Non-Collision</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013</td>\n",
       "      <td>11053659</td>\n",
       "      <td>2 DOOR SEDAN</td>\n",
       "      <td>Not Entered</td>\n",
       "      <td>Going Straight Ahead</td>\n",
       "      <td>Not Entered</td>\n",
       "      <td>East</td>\n",
       "      <td>Not Entered</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>PA</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NISS</td>\n",
       "      <td>ENVMT</td>\n",
       "      <td>Animal's Action</td>\n",
       "      <td>HUMAN</td>\n",
       "      <td>Not Applicable</td>\n",
       "      <td>Not Applicable</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013</td>\n",
       "      <td>10831549</td>\n",
       "      <td>POLICE VEHICLE</td>\n",
       "      <td>Not Entered</td>\n",
       "      <td>Going Straight Ahead</td>\n",
       "      <td>Not Entered</td>\n",
       "      <td>East</td>\n",
       "      <td>Not Entered</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>NY</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FORD</td>\n",
       "      <td>ENVMT</td>\n",
       "      <td>Animal's Action</td>\n",
       "      <td>HUMAN</td>\n",
       "      <td>Not Applicable</td>\n",
       "      <td>Not Applicable</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013</td>\n",
       "      <td>10890741</td>\n",
       "      <td>SUBURBAN</td>\n",
       "      <td>Not Entered</td>\n",
       "      <td>Changing Lanes</td>\n",
       "      <td>Not Entered</td>\n",
       "      <td>North</td>\n",
       "      <td>Not Entered</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>NJ</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DODG</td>\n",
       "      <td>ENVMT</td>\n",
       "      <td>Pavement Slippery</td>\n",
       "      <td>HUMAN</td>\n",
       "      <td>Not Applicable</td>\n",
       "      <td>Not Applicable</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  Case Vehicle ID Vehicle Body Type Registration Class  \\\n",
       "0  2013         11256149      2 DOOR SEDAN        Not Entered   \n",
       "1  2013         11330798      4 DOOR SEDAN        Not Entered   \n",
       "2  2013         11053659      2 DOOR SEDAN        Not Entered   \n",
       "3  2013         10831549    POLICE VEHICLE        Not Entered   \n",
       "4  2013         10890741          SUBURBAN        Not Entered   \n",
       "\n",
       "  Action Prior to Accident Type / Axles of Truck or Bus Direction of Travel  \\\n",
       "0                  Backing                  Not Entered               South   \n",
       "1       Overtaking/Passing                  Not Entered               North   \n",
       "2     Going Straight Ahead                  Not Entered                East   \n",
       "3     Going Straight Ahead                  Not Entered                East   \n",
       "4           Changing Lanes                  Not Entered               North   \n",
       "\n",
       "     Fuel Type  Vehicle Year State of Registration  Number of Occupants  \\\n",
       "0  Not Entered        2013.0                    NY                  1.0   \n",
       "1  Not Entered        2013.0                    VA                  1.0   \n",
       "2  Not Entered        2010.0                    PA                  1.0   \n",
       "3  Not Entered        2012.0                    NY                  1.0   \n",
       "4  Not Entered        2006.0                    NJ                  3.0   \n",
       "\n",
       "   Engine Cylinders Vehicle Make Contributing Factor 1  \\\n",
       "0               NaN         HOND                 HUMAN   \n",
       "1               NaN         DODG                 HUMAN   \n",
       "2               NaN         NISS                 ENVMT   \n",
       "3               NaN         FORD                 ENVMT   \n",
       "4               NaN         DODG                 ENVMT   \n",
       "\n",
       "  Contributing Factor 1 Description Contributing Factor 2  \\\n",
       "0                  Backing Unsafely                 HUMAN   \n",
       "1              Unsafe Lane Changing                 HUMAN   \n",
       "2                   Animal's Action                 HUMAN   \n",
       "3                   Animal's Action                 HUMAN   \n",
       "4                 Pavement Slippery                 HUMAN   \n",
       "\n",
       "  Contributing Factor 2 Description                 Event Type Partial VIN  \n",
       "0   Driver Inattention/Distraction*             Not Applicable         NaN  \n",
       "1    Passing or Lane Usage Improper  Overturned, Non-Collision         NaN  \n",
       "2                    Not Applicable             Not Applicable         NaN  \n",
       "3                    Not Applicable             Not Applicable         NaN  \n",
       "4                    Not Applicable             Not Applicable         NaN  "
      ]
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     "execution_count": 6,
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   ],
   "source": [
    "crashes_df = pd.read_csv('../Data/Motor_Vehicle_Crashes_-_Vehicle_Information__Three_Year_Window.csv')\n",
    "crashes_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### U.S. 2013-2014 New Vehicle Sales Volume\n",
    "This dataset contains the sales volume of different car brands and models for the years 2013 and 2014. \n",
    "\n",
    "Data was downloaded from [here](http://www.goodcarbadcar.net/p/downloads.html)"
   ]
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  {
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     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>U.S. 2014 Calendar Year New Vehicle Sales Volume  \n",
       "All Vehicles &amp; All Automakers – Two Sheets \n",
       "© GoodCarBadCar.net</th>\n",
       "      <th>Unnamed: 1</th>\n",
       "      <th>Unnamed: 2</th>\n",
       "      <th>Unnamed: 3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Vehicle/Brand/Manufacturer</td>\n",
       "      <td>2014</td>\n",
       "      <td>2013</td>\n",
       "      <td>% Change</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Total New Vehicle Sales ¥</td>\n",
       "      <td>16,516,116</td>\n",
       "      <td>15,595,820</td>\n",
       "      <td>5.9%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Passenger Cars Ø</td>\n",
       "      <td>7,935,161</td>\n",
       "      <td>7,801,870</td>\n",
       "      <td>1.7%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  U.S. 2014 Calendar Year New Vehicle Sales Volume  \\nAll Vehicles & All Automakers – Two Sheets \\n© GoodCarBadCar.net  \\\n",
       "0                                                NaN                                                                     \n",
       "1                                                NaN                                                                     \n",
       "2                         Vehicle/Brand/Manufacturer                                                                     \n",
       "3                          Total New Vehicle Sales ¥                                                                     \n",
       "4                                   Passenger Cars Ø                                                                     \n",
       "\n",
       "   Unnamed: 1  Unnamed: 2 Unnamed: 3  \n",
       "0         NaN         NaN        NaN  \n",
       "1         NaN         NaN        NaN  \n",
       "2        2014        2013   % Change  \n",
       "3  16,516,116  15,595,820       5.9%  \n",
       "4   7,935,161   7,801,870       1.7%  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales_df = pd.read_csv('car_sales.csv')\n",
    "sales_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Motor Vehicle Crashes Dataset\n",
    "\n",
    "We'll start cleaning this dataset up first then do some exploration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
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    {
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8np6eJXwU/7tDhw4xevRoGjRogK+vLwCnTp1i8uTJ9O/fn6CgoFv+3c2tbGrV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MibPPPjsWL14cf/jDH6Jz586xYsWKdpkHa0Rq6623jk022SRuvvnmmD59eixdujS22WabePvt\nt2PatGnxk5/8JJIkicsvvzw6duzYbgtQ8tBELlLykPLMSPXq1St22223eP755+O9996LrbbaqpKH\n9raBD2UdkvJwaIiW3fN333133HTTTXH55ZdH//79sw6tKh588MH49re/HfPnz690Anft2jU6deoU\nl19+eQwYMCDrEKtuyZIlsXDhwrjyyitj0aJFce2117bLLwFrMnfu3Lj44ovjpZdeiuOOOy5OOeWU\niGgfs04/iR/96EcxadKkmDRpUmyzzTb/doOiFpRnGq5YsSImTJgQXbp0iauuuqpmrouP3hvjx4+P\nH/zgB3HHHXfErbfeGp/5zGeyDrEqrBGrf9a333473nzzzbjqqqviH//4R/zyl79s9wW35r72ta/F\nkiVLYvfdd49SqRQzZsyIurq6uO2226Jz5865PoytNdwbqeZ5OP7442P06NFxxhlnxKJFi+L3v/99\npbjSHt/ALLNGNHn00UfjkksuiX/961/RtWvXWL58eWy66aax1VZbxdVXX93i4PT2TB6ayEVKHlKe\nGal/9x2iln970n4pyrOa5oX5d999t913yX/UG2+8EdOnT48nnngiVq5cGYMHD4699tqrXc3Kbo3m\n10NE2v1bC18GyubOnRuXXHJJvPLKK3HooYfG17/+9axDqrryNbBkyZIYM2ZMfOYzn4mrr74667Ay\nV/5xsHLlyspM9VoqOJW/ML/66qtRV1cXr776atx6662x0047ZR1aVVkj1mzVqlWxZMmSdnHw87pa\nuXJl/P73v49f/OIXsWTJkujfv38MHTo0zjrrrOjUqVNNPTsj3Btl5TzMnj073n777dhkk01i8uTJ\nNVNc+XdqcY2ISDclXnzxxZg+fXp069Ytdtxxx9hzzz0roxBr5XqQhyZykZKHlGdGqvw7Y86cOXH4\n4YfHV7/61axDgk+FojxrVEuFJdau+fVQi9fG3Llz46yzzorNN988fvzjH9fc5y9bsWJFnHfeeTF9\n+vT43e9+167Hk6yr5h0btdi9MW/evDjvvPPipZdeoHIAMQAAIABJREFUihtuuCEGDhyYdUiZsEa0\nVIv3QnOLFi2KpUuXRs+ePaN79+4RETXR4bcm7o3U3Llz45xzzom6urr41a9+VZObNM3V+hqxJnKS\nkocmcpGqxTx4ZqTmzp0b5513Xrz//vtxww03VM7sgfZEUR7WoNaL0KzuzTffjI022qjmuqE/as6c\nObFq1arYaqutsg6FNuL111+PJEmiUChkHUqmrBH8O7V+Pbg3Us1HI9ZicYUmtXwfNCcPTeQiJQ9N\nPDNSr732WkREbLbZZhlHAp8ORXmAVqjFbg1g3VkjYM3cGyl5AGBdeWZA+6YoDwAAAAAAVWLL7T/w\n+uuvx+677x433nhj1qEAAAAAAJADivKf0Lvvvhtf/epXY8mSJVmHAgAAAABATijKfwJz586NL37x\ni/H0009nHQoAAAAAADmiKN9KN954Y4wePTr+8Y9/xN577511OAAAAAAA5IiifCvddNNNscUWW8Qt\nt9wSn/3sZ7MOBwAAAACAHOmUdQB5c9FFF8W+++4bdXV18fLLL2cdDgAAAAAAOaIo30oHHHBA1iEA\nAAAAAJBTivJVVl9fn3UImSsWixER0dDQkHEk2ZKHJnKRkoeUPKTkISUPTeQiJQ8peUgVi8UoFApZ\nh9EmlEol14P7okIuUvKQkoeUPKTkoaXGxsasQ6gZBx54YNYhtHD//fdn+t+vKA8AAAAANUQxGrLl\noFcAAAAAAKgSnfIAAAAAUEOMV055Y4CsKMoDAAAAQA1RjIZsGV8DAAAAAABVolMeAAAAAGqI8TUp\nbwyQFUV5AAAAAKghitFERDz33HMxf/78SJIkIqLyr80lSRIrVqyI7373u9UOr11TlP8PjB07NsaO\nHZt1GAAAAACwznTKp2p5c2Ls2LGxYMGCrMOoWWbKAwAAAADUEAX5bCnKAwAAAABQE374wx9mHYLx\nNQAAkEeFQqGmX7kGAIBP4txzz437778/0xgU5QEAIIdKpVI0NDRkHUamisViFAqFrMMAAIBWUZQH\nAMiBcuFRZ3RajEanPC25HlLWB4B145kB2VKUBwCAHNIpr1MeWDsbVk3kISUPQFugKA8AkAPl7k9F\n2GLWIUCbZJPG+sCauTea7g15kIcIefgomzRkpWPWAQAAAAAAQK1QlAcAAAAAgCoxvgYAAHLInGQA\nAGi93r17Zx2CojwAAOSROckOegUAoPUWLVqUdQjG1wAAAAAAQLUoygMAAAAAQJUYXwMAkAPlER1m\niKdjW4CWnDGQsj4ArBvPDPbaa6+YNm1a1mHULEV5AIAcKBeazBAvZh0CtEnOGLA+ALRGfX191iG0\nCbW8OaEgny3jawAAAAAAoEoU5QEAAAAAoEoU5QEAAAAAoEoU5QEAAAAAoEoU5QEAAAAAoEo6ZR0A\nAADQeoVCIRobG7MOAwAAaCVFeQCAHCgUChERirARUSqVsg6hTSiVStHQ0JB1GJkqFouVe6PW2aRJ\nWR8A1o1nBmRLUR4AIAfKhSZF2GLWIUCbZJPG+gDQGvX19VmH0CbYnCArivIAADmgU74leUjJA2U6\n5VM65QHWjWcGZEtRHgAgB3TKp8qdsPIgDxHG1zSnU16nPEBr6JRP2ZwgK4ryAAA5oFO+JXnQEUxL\nOuVT7guAdeOZAdlSlAcAyAGd8ikd4qlyh7gf1JTplNcpD9AaOuVTvkuRlY5ZBwAAAAAAALVCpzwA\nAOSQzmgz5QEAyCed8gAAAAAAUCU65QEAcsBBry3JQ0oeKHPGQMpBrwDrxjMDsqUoDwCQAw56TTno\nNSUPKeNrmhhn5KBXgNZw0GvK5gRZMb4GAAAAAACqRKc8AEAOGF/TxHiKlHElNOd6SFkf+Cj3RhN5\nSMlDSh4gW4ryAAA5YHxNyniKJsaVGF/TnOvB+sCauTeMPCuTh5Q8tGRzgqwYXwMAAAAAAFWiKA8A\nAAAAAFWiKA8AAAAAAFWiKA8AAAAAAFWiKA8AAAAAAFWiKA8AAAAAAFWiKA8AAAAAAFWiKA8AAAAA\nAFXSKesAAABYu0KhEBERjY2N2QbSRshDSh4oKxQKroeIKJVKWYcAkAueGZAtRXkAgBwoF5oaGhoy\njiRbxWIxIuShWCxWNmogIl0j3BfFrEMAyI36+vqsQ2gTbE6QFUV5AADIIUVYmxMAAOSTmfIAAAAA\nAFAlivIAAAAAAFAlivIAAAAAAFAlivIAAAAAAFAlivIAAAAAAFAlivIAAAAAAFAlnbIOAACAtSsU\nChER0djYmG0gbUCpVMo6BGhzCoWC9SGsDwDryjMDsqUoDwCQA+VCU0NDQ8aRZKtYLGYdArRJpVLJ\n+mB9AFhn9fX1WYfQJticICvG1wAAAAAAQJUoygMAAAAA1JAePXpkHUJNU5QHAAAAAKghS5YsyTqE\nmqYoDwAAAAAAVaIoDwAAAAAAVaIoDwAAAAAAVaIoDwAAAAAAVaIoDwAAAAAAVaIoDwAAAAAAVaIo\nDwAAAAAAVaIoDwAAAAAAVdIp6wAAAFi7QqEQERGNjY3ZBtIGlEqlrEOgDSmVStHQ0JB1GJkqFotR\nKBSsD2F9AFhXnhmQLUV5AAByRfExpfiYcj0AAJA3ivIAADlQLsDqCC5GhDzojOajvDHQtD4AsHb1\n9fVZh9Am+C5FVhTlAQAghxRhmzYnAAAgTxz0CgAAAAAAVaJTHgAAcsj4GgAAyCdFeQAAyCHja4yv\nAQAgn4yvAQAAAACAKtEpDwCQA+VuYONKUvKQkgfKjDNKlUqlrEMA2jBrZRN5gGwpygMA5EC50GRc\nSTEi5EEeUsbXNDHOqOm+APh3rJW+Q3yUzQmyYnwNAAAAAABUiaI8AAAAAABUifE1AAA5YKZ8S/KQ\nkgfKzElOmSkPsG48MyBbivIAADlgpnzKHNSUPKTMlG9iTrKZ8gCtUV9fn3UIbYLNCbJifA0AAAAA\nADVh4sSJWYegKA8AAAAAQG046aSTsg5BUR4AAAAAAKpFUR4AAAAAAKrEQa8AADlQPszSYVQpeUjJ\nA2WFQsH1EE2HYgPw8TwzIFuK8gAAOVAuNDU0NGQcSbaKxWJEyEOxWKxs1EBEuka4L4pZhwCQG/X1\n9VmH0CbYnCArivIAAJBDirA2JwAAyCdFeQCAHDC+ponxFLA642tS1geAdeOZAdlSlAcAyAHja1LG\nU8CaeXPC+gDQGsbXpGxOkJWOWQcAAAAAAAC1Qqc8AAAAAO2eUV9AW6EoDwAAAEC7Z9SXUV/QVijK\nAwDkgINemzjIEVan+zNlfQBYN54ZkC1FeQCAHHDQa0p3F6yZ7k/rA0BrOOg1ZXOCrCjKAwDkgE75\nluQhJQ+U6ZRP6ZQHWDeeGZCtjlkHAAAAAAAAtUKnPABADhhfkyqPp5AHeYhI81B+i6TWGV9jfA1A\naxhfk/LGAFnRKQ8AAAAAAFWiKA8AAAAAAFWiKA8AAAAAAFWiKA8AAAAAAFWiKA8AAAAAAFWiKA8A\nAAAAAFXSKesAAABYu0KhEBERjY2N2QbSRshDSh4oKxQKroeIKJVKWYcAkAueGdSyE044IesQFOUB\nAPKgXGhqaGjIOJJsFYvFiJAHeUgVi8XKhlWtK5VKrocP7wsA1q6+vj7rENoEmxO16bbbbov//u//\nzjQGRXkAAMghndEAAJBPivIAAJBDOqN1ygMAkE8OegUAAAAAgCrRKQ8AkAMOem1JHlLyQJlxRikH\nvQKsG88MyJaiPAAAuWJsi4Ney4yv4aNsTrQkFyl5SMlDSh6AtkBRHgAgB8rdn4qwxaxDgDbJZpXN\nqubkIiUPKXlIyUNKHlqySUNWFOUBAHLA+JqW5MGYDlrSIZ5yXwCsG88MyJaiPABADuiUT+nuSnlj\ngI/SKe++AGiN+vr6rENoE2xOkJWOWQcAAAAAAAC1Qqc8AEAOGF/Tkjyk5IEy42tSxtcArBvPDMiW\nojwAQA4YX5MyviYlD6lisVjZsKp1xtcYXwPQGsbXpGxOkBXjawAAAAAAoEp0ygMA5IDxNS3JgzEd\ntGR8Tcp9AbBuPDMgW4ryAAA5YHxNytiWlDEdfJTxNe4LACA/FOUBAAAAoIaYKZ/yxgBZUZQHAMgB\n42takoeUPFBmfE3K+BqAdeOZwc477xwzZ87MOoyapSgPAECuGNNhjE9ZsVisbFgBALDuFi9enHUI\nNU1RHgAgB8yUT5kZDWtms8r6ANAaxtekavmNgTlz5mQdQk3rmHUAAAAAAABUj7cNs6UoDwAAAABQ\nQ5zDki1FeQAAAAAAqBJFeQAAAAAAqBIHvQIA5EB55mMtH0ZV5lXbVKFQcD1Q4XpIWR8A1o1nBmRL\nUR4AgFxRfAQAAPJMUR4AIAfK3Z8NDQ0ZR5KtYrEYEfIgD6lisVh5i6TWlUol18OH9wUAa1dfX591\nCG2CRg+yYqY8AAAAAABUiaI8AAAAAABUiaI8AAAAAABUiaI8AAAAAABUiYNeAQAAAKCGOOAUsqUo\nDwCQA4VCISL8gCqTh5Q8UFYoFFwPEVEqlbIOAQBgrRTlAQByoFxoamhoyDiSbBWLxYiQB3lIFYvF\nyoZVrSuVSq6HD+8LANauvr4+6xDaBBvaZMVMeQAAAAAAqBKd8gAAkEPGlQAA8En16dMnFi5cmHUY\nNUtRHgAgB8yUb0keoCWbNCkz5QHWjWcGCvLZMr4GAAAAAACqRKc8AEAOOOg15YDTlANO+SgHvTro\nFaA1HPSa8sYAWVGUBwCAHFKEtTkBAEA+GV8DAAAAAABVoigPAAAAAABVoigPAAAAAABVoigPAAAA\nAABVoigPAAAAAABVoigPAAAAAABVoigPAAAAAABVoigPAAAAAABV0inrAAAAAADg01YoFKKxsTHr\nMAAU5QEA8qBQKERE+CH5IXlIyQNlCk2pUqmUdQhAG1YqlaKhoSHrMDJVLBYjwncIyJqiPABADpQL\nTX5Ipj8k5UEeItI8lDesap1CU9N9AcDa1dfXZx1Cm2BzgqyYKQ8AAAAAAFWiKA8AAAAAAFWiKA8A\nAAAAAFWiKA8AAAAAAFXioFcAgBwoH2bpMKqUPKTkgbJCoeB6iKZDsQH4eJ4ZdO7cOT744IOsw6hZ\nivIAADlQLjQ1NDRkHEm2isViRMiDPKSKxWJlw6rWlUol18OH9wUAa1dfX591CG1CLW9O1HJBvlu3\nblmHoCgPAJAHOuWb6ISF1emUT1kfANaNZwa1bOnSpVmHoCgPAJAHOuVTOmFhzXTKWx8AWkOnfMrm\nBFlRlAcAyAGd8i3JQ0oeKNMpn9IpD7BuPDMgW4ryAACQQzqjzZQHACCfFOUBAHLA+JqUA05TxnTw\nUTZp3BcArWF8TcobA2SlY9YBAAAAAABANfTu3TvrEBTlAQAAAACoDYsWLco6BEV5AAAAAACoFkV5\nAAAAAACoEkV5AAAAAACoEkV5AAAAAACoEkV5AAAAAACoEkV5AAAAAACoEkV5AAAAAACokk5ZBwAA\nAADwaSgUCtHY2Jh1GG2CPKTkAdh8882zDkFRHgAgDwqFQkT4IVkmDyl5oEzhMVUqlbIOgTamVCpF\nQ0ND1mFkqlgsRkTIgzxERFMePDOoZfPmzcs6BEV5AIA8KBea/JD0gzpCHsqKxWJlw6rWKTw23RcA\nrF19fX3WIbQJNifIiqI8AEAO6JRvohM2pTOa5lwPKesDwLrxzIBsKcoDAJArio8AAECeKcoDAOSA\n8TUpY1tS8pAyvqaJ8TXG1wC0hvE1KY0eZKVj1gEAAAAAAFA9J510UtYh1DRFeQAAAACAGjJx4sSs\nQ6hpivIAAAAAAFAlivIAAAAAAFAlivIAAAAAAFAlivIAAAAAAFAlnbIOAACAtSsUChER0djYmG0g\nbUCpVMo6hDahUCi4HqhwPaSsDwDrxjMDsqUoDwCQA+VCU0NDQ8aRZKtYLGYdQptRKpVcD8ViZcOq\n1rkerA8ArVFfX591CG2CzQmyYnwNAAAAAABUiU55AADIIeNKAAAgnxTlAQBywEz5luQBWrJJkzJT\nHmDdeGZAthTlAQBywEz5VHlmtDzIQ4SZ8s2ZKW+mPEBrmCmfsjlBVsyUBwAAAACAKlGUBwAAAACA\nKlGUBwAAAACAKlGUBwAAAACgJuy3335Zh6AoDwAAAABAbejZs2fWIUSnrAMAAAAAgE9boVCIxsbG\nrMMAMjZlypQ477zzMo1BUR4AAACAdq9UKkVDQ0PWYWSqWCxmHQIQxtcAAAAAAEDV6JQHAMiBQqEQ\nEeGV60i73ICWjGRIWR8A1o1nBmRLUR4AIAfKhSavXHvlGtbESAbrA0Br1NfXZx1Cm2BzgqwYXwMA\nAAAAAFWiKA8AAAAAQE0YMmRI1iEYXwMAkAdmyrckDyl5oMxM+ZSZ8gDrxjODWvb0009nHYKiPABA\nHpgpnyrPjJYHeYhI81DesKp1ZsqbKQ/QGmbKp2xOkBVFeQAAAADaPW8VAW2FojwAQA4YX9OSPKTk\ngTKFppTxNcDH8VZR01tFnhmQLUV5AIAcML4mZWxLSh5Sxtc0UWgyvgagNYyvSdmcICsdsw4AAAAA\nAABqhU55AIAcML6mifEUsDrja1LWB4B145kB2VKUBwDIAeNrUsZTwJoZX2N9AGgN42tSNifIiqI8\nAEAO6JRvSR5S8kCZTvmUTnmAdeOZAdlSlAcAyAGd8ikHnKbkIeWg1yY65XXKA7SGTvmUzQmy4qBX\nAAAAAACoEp3yAAA5YHxNE+MpYHXG16SsDwDrxjMDsqUoDwCQA8bXpIyngDUzvsb6ANAaxtekbE6Q\nFeNrAAAAAABqSO/evbMOoabplAcAyAHja1qSh5Q8UGZ8Tcr4Gj7KvdFEHlLykJIHFi1alHUINU1R\nHgCAXDGmo2lMhzwUKxtWAGvimeGZUSYPqXIePD8hW4ryAAA5YKZ8ysxoWDOFR+sDQGuYKZ/yxgBZ\nMVMeAAAAAACqRFEeAAAAAACqRFEeAAAAAACqRFEeAAAAAACqRFEeAAAAAACqRFEeAAAAAACqRFEe\nAAAAAACqRFEeAAAAAACqRFEeAAAAAACqRFEeAAAAAACqpFPWAQAAsHaFQiEiIhobG7MNpI2Qh5Q8\nUFYoFFwPEVEqlbIOASAXPDMgW4ryAADkSqlUioaGhqzDyFSxWKxs1AAAAPmiKA8AkAPl7k/F6GLW\nIUCbZLPK+gDQGvX19VmH0CZ4Y4CsKMoDAEAOKcJ6YwAAgHxSlAcAyAEz5ZuYGQ2rM1M+ZX0AWDee\nGdSyDTbYIOsQFOUBAMgXxccm8gAAAK2zYMGCrENQlAcAyAMz5VPlmdHyIA8Rxtc0Z5yRmfIArWGm\nfKqWGxy6du0ay5YtyzqMmtUx6wAAAAAAAKgeBflsKcoDAAAAAECVKMoDAAAAAECVmCkPAAA55MBb\nAADIJ0V5AADIIQd7OugVAIB8Mr4GAAAAAACqRKc8AEAOlLuBjStJO8SBlowzSlkfANaNZwZkS1Ee\nAABySBEWAADySVEeACAHyt2fZogXsw4B2iRnDFgfAFqjvr4+6xDaBA0OZEVRHgAAckgR1kGvALSO\nt8yAtkJRHgAAAIB2z4a2t4qgreiYdQAAAAAAAFArFOUBAAAAAKBKFOUBAAAAAKBKFOUBAAAAAKBK\nFOUBAAAAAKBKFOUBAAAAAKBKFOUBAAAAAKBKFOUBAAAAAKBKOmUdAAAAa1coFCIiorGxMdtA2gh5\nSMkDZYVCwfUQEaVSKesQAHLBMwOypSgPAJAD5UJTQ0NDxpFkq1gsRoQ8yEOqWCxWNqxqXalUcj18\neF8AsHb19fVZh9Am2JwgK8bXAAAAAADUkP79+2cdQk1TlAcAAAAAqCGvvvpq1iHUNONrAAAAAKCG\nGNsC2VKUBwAAAIAaYqZ8yuYEWTG+BgAAAAAAqkRRHgAAAAAAqkRRHgAAAACAmjBixIisQzBTHgAg\nDwqFQkSYexkRUSqVsg4B2pxCoWB9COsDwLryzKCWbLfddhER0aFDh1hvvfViwoQJGUekKA8AQM4o\nPjaRBwAA+Hgvvvhii39/8803xxlnnJFRNClFeQAAcqVUKkVDQ0PWYWSqWCxGRMhDsVh5iwQAANbF\nvHnzsg7BTHkAAAAAAGrD448/nnUIOuUBAPKgPCdZZ3Qx6xCgTfIGifUBoDXq6+uzDqFNMAqQrOiU\nBwAAAACAKlGUBwAAAACAKlGUBwAAAACAKjFTHgAAcqhQKJiDCgAAOaRTHgAAAAAAqkSnPABADhQK\nhYgIndERUSqVsg6hTSiVStHQ0JB1GJkqFouVe6PWeXMiZX0AWDeeGZAtRXkAgBwoF5oUYYtZhwBt\nkk0a6wNAa9TX12cdQptgc4KsGF8DAAAAAABVolMeAAByyLgSAADIJ0V5AADIIeNKzJQHACCfjK8B\nAAAAAIAqUZQHAAAAAIAqUZQHAAAAAIAqUZQHAAAAAIAqUZQHAAAAAIAq6ZR1AAAArF2hUIiIiMbG\nxmwDaQNKpVLWIUCbUygUrA9hfQBYV54ZkC1FeQCAHCgXmhoaGjKOJFvFYjHrEKBNKpVK1gfrA8A6\nq6+vzzqENsHmBFkxvgYAAAAAAKpEpzwAAOSQcSUAAJBPivIAAJBDxpWk40rK5y0AAEBeGF8DAAAA\nAABVoigPAAAAAABVoigPAAAAAABVoigPAAAAAABV4qBXAADIoUKhEI2NjVmHAQAAtJKiPAAA5FCp\nVIqGhoasw8hUsViMQqGQdRgAANAqxtcAAAAAAECVKMoDAAAAAECVGF8DAAAAADXEuTSQLUV5AADI\nIQe9AgCfVH19fdYhtAm+S5EVRXkAAMghB7066BUAgHxSlAcAyIFy4VE3T1qMBlry5kTK+gCwbjwz\nIFuK8gAAOVAuNOmMLmYdArRJ3pywPgC0hvE1KZsTZEVRHgAAAABqiGI0ZEtRHgAAAABqiE75lM2J\n2tS7d++sQ4iOWQcAAAAAAADVsGjRoqxDUJQHAAAAAIBqMb4GAAAAgHavUCgYVwK0CYryAAAAALR7\npVIpGhoasg4jU8ViMesQgDC+BgAAAAAAqkanPABADhQKhYgIr1xH2uUGtGQkQ8r6ALBuPDMgW4ry\nAAA5UC40eeXaK9dlirA0ZySD9QEAyA9FeQAAyCFF2LQIW36LBADWxoZ2k/r6+qxDaBNcD2RFUR4A\nIAeMr2liPAWsTqEpZX0APo4N7aa3ijwzIFuK8gAA5IriYxN5AACA/FGUBwDIATPlU+XuLnmQhwjj\na5rT/WmmPEBrGF+T0uBAVjpmHQAAAAAAANQKnfIAAJBDxvgAAEA+KcoDAEAOGVdifA0AAPlkfA0A\nAAAAAFSJojwAAAAAAFSJojwAAAAAAFSJojwAAAAAAFSJg14BACCHCoVCNDY2Zh0GAADQSoryAAA5\nUCgUIiIUYT8kD9CSTZpUqVTKOgSAXPDMgGwpygMA5EC50NTQ0JBxJNkqFosRIQ/ykCoWi5UNq1pX\nKpVcDx/eFwCsXX19fdYhtAk2J8iKmfIAAAAAAFAlOuUBAHLA+JqW5CElD5QZX5MyvgZg3XhmwP9v\n796DrarL/4G/QbDQ/Nqo0Z7I0igo77dQ83aiKJ0Encb0iKGO6NSYlGWTpYhj3vIyGhoz1eh4gWpn\nWmQzmFF2gC4KzoTmhVIbyqStooU3vCG/P5b7HE8c4Zx+utbZ7Ndrhj/Ye+3NwzN7rzXzXp/9fKol\nlAcAaAHG1xSMbSnoQ8H4mh7G1xhfAzAQxtcU3JygKsbXAAAAAABASYTyAAAAAABQEqE8AAAAAACU\nRCgPAAAAAAAlEcoDAAAAAEBJhPIAAAAAAFCSYVUXAAAADFytVktXV1fVZQAAAAMklAcAgBbUaDTS\n2dlZdRmVqtfrqdVqVZcBAAADYnwNAAAAAACURCgPAAAAAAAlEcoDAAAAAEBJhPIAAAAAAFASoTwA\nAAAAAJRkWNUFAACwYbVaLUnS1dVVbSGDhD4U9IGmWq3m85Ck0WhUXQJAS3DNgGoJ5QEAWkAzaOrs\n7Ky4kmrV6/Uk+qAPhXq93n3Dqt01Gg2fh1e/FwAAg51QHgAAAADaSEdHR9UlDAp+MUBVzJQHAAAA\nAICSWCkPAADARsFs/d70oqAPBX0o6AMwGAjlAQAA2CiYrV+w70RBHwr6UNCHgv03YHAwvgYAAAAA\ngLaw3XbbVV2CUB4AAAAAgPawfPnyqkswvgYAoBXUarUk5qAmxXgKoDez1AvODwD945oB1RLKAwC0\ngGbQZA6qOajQF7PUnR8ABqKjo6PqEgYFNyeoilAeAKAFWCnfmz4U9IEmK+ULVsoD9I9rBu1s6NDq\nJ7oL5QEAaClWBPesCNaHevcNKwAA6I9XXnml6hKE8gAArcD4moLxFNA3N6ucHwAGwviagl8MUJXq\n1+oDAAAAAECbEMoDAAAAAEBJhPIAAAAAAFASM+UBAKAF1Wo1c1ABAKAFCeUBAKAF2diz2NizVqtV\nXQYAAAyIUB4AoAU0g0crowv6UITy0OSXEwXfC4D+cc2AagnlAQBaQDNosjK6nkQfmn2AJr+c8L0A\nGIiOjo6qSxgU3JygKjZ6BQAAAACAklgpDwDQAoyv6U0fCvpAk/E1BeNrAPrHNQOqZaU8AAAAAACU\nxEp5AIAWYKZ8wUz5gj4U6vV6969I2p2Z8mbKAwyEmfIFvxigKkJ5AABoQcaVAABAaxLKAwC0ADPl\ne9MH6M1NmoKZ8vw3340e+lDQh4I+QLWE8gAALcD4moKxLQV9KBhf08P4GuNr6JvvhmtGkz4U9KE3\nNyfa00EHHVR1CTZ6BQAAAACgPSxYsKDqEoTyAAAAAABQFqE8AAAAAACURCgPAAAAAEBb2Hrrrasu\nwUavAACtoLmZpc2oCvpQ0AeaarWaz0N6NsUGYP1cM2hnTzzxRNUlWCkPAAAAAABlsVIeAKAFNFd/\ndnZ2VlxJter1ehJ90IdCvV7v/hVJu2s0Gj4Pr34vANiwjo6OqksYFPxigKpYKQ8AAAAAACURygMA\nAAAAQEmMrwEAgBZkY08AAGhNQnkAAGhBZohfROcXAAAYD0lEQVSbKQ8AQGsSygMAtIBm8GhldM+m\nt0APv5woOD8A9I9rBlRLKA8A0AKaQZOV0fWqS4BByS8nnB8ABqKjo6PqEgYFNyeoio1eAQAAAABo\nC7vuumvVJQjlAQAAAABoD/vvv3/VJQjlAQAAAADaySGHHFJ1CZWZNWtW1SWYKQ8A0Aps9NqbPhT0\ngSYbvRZs9ArQP64Z3HLLLVWX0NaE8gAALcBGr4XmRo76oA9J0YfmDat2Z6NXG70CDISNXgtuTlAV\noTwAALQgK6MBAKA1CeUBAKAFWRltpTwAAK3JRq8AAAAAAFASoTwAAAAAAJREKA8AAAAAACURygMA\nAAAAQEmE8gAAAAAAUBKhPAAAAAAAlEQoDwAAAAAAJRHKAwAAAABASYTyAAAAAABQEqE8AAAAAACU\nZFjVBQAAAANXq9XS1dVVdRkAAMAACeUBAKAFNRqNdHZ2Vl1Gper1emq1WtVlAADAgBhfAwAAAAAA\nJRHKAwAAAAC0ka233rrqEtqa8TUAAC2gOaLDDPGCPhT0gSZ7DBQajUbVJQC0BNcMnnjiiapLaGtC\neQCAFtAMmswQryfRB30omCnfwx4DPd8LADaso6Oj6hIGBTcnqIpQHgCgBVgp38NK2IKV0byWz0PB\n+QGgf1wzaGcHH3xw1SUI5QEAoBVZGW2lPAAAA/fLX/4yp59+eqU12OgVAAAAAABKYqU8AEALMFO+\nYGY09M0vJ5wfAAbCTPmCMT5UxUp5AAAAAAAoiVAeAAAAAABKIpQHAAAAAICSmCkPAAAAwEavVquZ\nIQ4MCkJ5AAAAADZ6NsW2KTYMFkJ5AABoQVb7AQBAaxLKAwBAC7Lar1jtV6vVqi4DAAAGxEavAAAA\nAABQEivlAQBaQHM1sHElxQpxoDfjjArODwD945oB1RLKAwC0gGbQZFyJzcmgL8YZOT8ADERHR0fV\nJQwKbk5QFaE8AEALsFK+h5WwsC4r5QvODwD945oB1RLKAwC0ACvlC1bCQt+slHd+ABgIK+ULbk5Q\nFRu9AgAAAABASayUBwBoAcbX9DCeAtZlfE3B+QGgf1wzoFpWygMAAAAAQEmslAcAoKVYEdxDHwAA\noPUI5QEAWoCNXgvNjRz1QR+Sog/N0U7tzkavNnoFAFqHUB4AAAAA2khHR0fVJQwKfnVIVcyUBwAA\nAACAkgjlAQAAAACgJEJ5AAAAAAAoiVAeAAAAAABKIpQHAAAAAKAtjBgxouoSMqzqAgAAgIGr1Wrp\n6uqqugwAAGgpq1evrroEoTwAQCuo1WpJIoR9lT5Ab27SFBqNRtUlALQE1wyollAeAICW0mg00tnZ\nWXUZlarX60miD/V69w0rAABoFUJ5AIAW0Fz9KYStV10CDEpuVjk/AAxER0dH1SUMCn4xQFVs9AoA\nAAAAACWxUh4AoAWYKd/DzGhYl5nyBecHgP5xzYBqCeUBAFqA8TUF4ymgb8bXOD8ADITxNQU3J6iK\n8TUAAAAAAFASoTwAAAAAAJREKA8AAAAAACURygMAAAAA0BYuuOCCqkuw0SsAQCuo1WpJbEaV9Gx6\nC/So1WrOD3F+AOgv1wza2RlnnJHf/va3ldYglAcAaAHNoKmzs7PiSqpVr9erLgEGpUaj4fzg/ADQ\nbx0dHVWXMCi4OUFVjK8BAAAAAICSCOUBAAAAAKAkQnkAAAAAACiJUB4AAAAAAEoilAcAAAAAgJII\n5QEAAAAAoCTDqi4AAIANq9VqSZKurq5qCxkk9KGgDzTVajWfhySNRqPqEgBagmsGVEsoDwDQAppB\nU2dnZ8WVVKteryfRB30o1Ov17htW7a7RaPg8vPq9AGDDOjo6qi5hUHBzgqoI5QEAWoCV8j2shIV1\nWSlfcH4A6B/XDKiWUB4AoAVYKV+wEhb6ZqW88wPAQFgpX3BzgqrY6BUAAAAAAEpipTwAQAswvqY3\nfSjoA03G1xSMrwHoH9cMqJZQHgCAlmJMhw1OAQCglQnlAQBagJnyBTOje7g54ebEa/k8OD8ADISZ\n8gW/GKAqZsoDAAAAAEBJrJQHAABalhXiVogDMHBWiEO1hPIAAC3ARq89bORYsLFnD30o6IPzAwDQ\nGoTyAAAtwEz5ghXBPawQN1P+tXwenB8ABsJM+YIb2lTFTHkAAAAAACiJlfIAAC3A+JoexlPAuowz\nKjg/APSPawZUSygPANACjK8pGE8BfTO+xvkBYCCMrym4OUFVjK8BAAAAAICSWCkPANACjK/pYTwF\nrMv4mh760EMvCvpQ0IeCPhT0AaollAcAaAHG1xSMp4C+GV/Tc35o9z4ketGkDwV9KOhDQR96c3OC\nqhhfAwAAAAAAJRHKAwAAAAC0kc0337zqEtqaUB4AAAAAoI08++yzVZfQ1oTyAAAAAABQEhu9AgAA\nsFGo1Wo27XsNvSjoQ0EfCvoADAZCeQAAADYKjUYjnZ2dVZdRuXq9niRt3wt9KOhDQR8KzT4A1TK+\nBgAAAAAASiKUBwAAAACAkgjlAQAAAACgJGbKAwC0gFqtlsTmZE36UNAHmmxwWmg0GlWXANASXDOg\nWkJ5AIAW0AyabE5mk7ZEH5rq9Xr3Dat2Z4NTmxcCDERHR0fVJQwKbk5QFeNrAAAAAACgJEJ5AAAA\nAAAoifE1AAAAANBGjG2BagnlAQAAgI2STZB76IPNoF/LTPmC7wVVEcoDAAAAGyWbINscvMlm0MBg\nYqY8AAAAAACURCgPAAAAAAAlEcoDAAAAAEBJhPIAAAAAAFASG70CAAAAG6VarZaurq6qyxgU9KGg\nD8BgIJQHAAAANkqNRiOdnZ1Vl1Gper2eJPqgD0l6+gBUy/gaAAAAAAAoiVAeAAAAAABKIpQHAAAA\nAICSmCkPAAAAAG3EhrdQLaE8AAAAALSRjo6OqksYFNycoCrG1wAAAAAAQEmE8gAAAAAAUBKhPAAA\nAAAAlEQoDwAAAAAAJRHKAwAAAABASYZVXQAAABtWq9WSJF1dXdUWMgg0Go2qS4BBp1arOT/E+QGg\nv1wzoFpCeQCAFtAMmjo7OyuupFr1er3qEmBQajQazg/ODwD91tHRUXUJg4KbE+3p3e9+d9UlGF8D\nAAAAAEB7+Oc//1l1CVbKAwC0AuNretMHYzrozfiagu8FQP+4ZkC1hPIAAC3A+JpCczyFPhjTQW/G\n1/heAAyE8TUFNyeoivE1AAAAAAC0hS222KLqEoTyAAAAAAC0h6effrrqEoTyAAAAAABQFqE8AAAA\nAACUxEavAAAtoFarJbEZVZM+FPSBplqt5vPwKn3ooRcFfSjoQ0EfCvoA1RLKAwC0gEajkSTp7Oys\nuJJq1ev1JPqgD4V6vd59w6rdNRoNnwffi256UdCHgj4U9KGgD725OUFVjK8BAAAAAICSCOUBAAAA\nAKAkQnkAAAAAACiJUB4AAAAAAEoilAcAAAAAgJIMq7oAAAAAAKA8XV1dVZcAbU0oDwAAAABtpKOj\no+oSBgU3J6iK8TUAAAAAAFASK+UBAFpArVZLYjVPkz4kjUaj6hIYRGq1mu9FfC8A+ss1A6ollAcA\naAHNoKmzs7PiSqpVr9eT6EOzD9DUaDR8L3wvAPrN+JqCmxNUxfgaAAAAAAAoiVAeAAAAAABKIpQH\nAAAAAICSCOUBAAAAAKAkQnkAAAAAACiJUB4AAAAAAEoy4FD+0UcfzZ577plrr722z+cXLlyYKVOm\nZPfdd8/ee++dqVOn5u67797g+3Z1dWXs2LG58sor+3z+Jz/5SSZNmpSdd945e+21Vz7/+c9n2bJl\nfR774IMP5uSTT86+++6bPffcM1OnTs29997b57FPPfVUvvWtb+WjH/1odtlllxx88MGZNWtWXnjh\nhfXWu2bNmhx55JEZO3bsBv9vAAAAAABvlFWrVuWrX/1qxo0blwMOOCCXXnpp1qxZU3VZLWHLLbf8\nn1/74osv5tBDD80f/vCH7sfuv//+TJ48ObvvvnsOP/zwLFq0aIPvM6BQ/tlnn820adPyzDPP9Pn8\nDTfckJNOOinLly/PEUcckfHjx2fJkiWZPHnyeoP5Z555JjNmzHjd5y+//PJMnz49q1atylFHHZXx\n48fnd7/7XTo7O3PPPff0Ovahhx7K0UcfnTvuuCOf/OQnM2nSpCxdujRHH330OjU89dRTmTx5cq69\n9tqMGTMmxxxzTDbbbLNcccUVOe2009bbi+uuuy533XXXeo8BAAAAAHijnXPOOXn00UczZ86cXHLJ\nJZk7d26uueaaqstqCatWrfqfXvfCCy/kK1/5Sh544IHux5588skcd9xx2XbbbXPjjTdmypQpmTZt\nWv785z+v972G9fcffeSRRzJt2rTXXXG+YsWKnH/++Rk9enTmzJmTrbbaKknS2dmZzs7OXHrppbn+\n+uv7fO1FF12URx99tM/nVq5cmauuuiqjRo3K3Llz83//939JkkmTJmXq1Km56KKLMnv27O7jzz//\n/Dz33HO58cYb86EPfShJcvTRR+fII4/MOeeck5tuuqn72G9/+9t54IEHMmPGjBxzzDFJkldeeSUn\nn3xy5s+fnyVLluTDH/7wOjX9/e9/z8yZMzfUMgAAAACAN9yCBQty0UUXZcyYMUmSQw89NLfffntO\nPPHEiivbOD344IM57bTTsnbt2l6Pz507N5tvvnnOO++8DB8+PKNHj85dd92Va665Jpdddtnrvl+/\nVspfe+21mThxYpYtW5Z99tmnz2NuvPHGPP/885k+fXp3IJ8ku+66a0488cTugPy//fGPf8wNN9yQ\ngw46qM/n77vvvrz88suZMGFCdyCfJPvvv39GjRrVa7X68uXL8/vf/z4f+9jHev17Y8aMyaRJk3LP\nPffk/vvvT5I899xz+elPf5o99tijO5BPkqFDh+bkk0/Opz/96bz88svr1LN27dpMnz49I0eOzHbb\nbddnzQAAAAAAb5a3v/3tufnmm7N69eo8+uijWbRoUXbccceqy9poLV68OHvvvXd+/OMf93r84Ycf\nzo477pjhw4d3P/bBD34wS5cuXe/79SuUv/766zNq1KjMmTMnhx12WJ/HLFy4MFtuuWWfof1pp52W\nb3zjG+s8vnr16px11lkZN25cPvOZz/T5vm9/+9uTFCvxX+v555/PqlWret0AWLJkSZJk7733Xud9\nmo8tXry4+9jVq1fnk5/85DrH7rLLLrnwwguz7777rvNcvV7P4sWLc+655+atb31rnzUDAAAAALxZ\nzj777CxevDh77LFHDjzwwGyzzTaZNm1av1773zlrO/rSl740oD5Mnjw5Z5xxRkaMGNHr8a233jqN\nRqPXYytWrMi///3v9b5fv0L5c845J3Pnzs0ee+zR5/Nr167NQw89lPe97315/PHHc/rpp2efffbJ\nbrvtlqlTp3avTv9vl112WR577LGcd955GTJkSJ/H7Lzzztlxxx0zf/78XHfddXnqqaeyYsWKfO1r\nX8szzzyT448/vvvYhx9+OEmy7bbbrvM+o0aNSlKspk+Sv/71r0mS97///Zk7d24OP/zw7LLLLhk/\nfnxmzZrV5yr5f/3rX7nkkktyxBFHvO4vBgAAAAAA3kz/+Mc/ssMOO2TOnDn5/ve/n0ceeSQXXXRR\nv17b3+M2Znffffcb0oeDDz449913X+bMmZOXXnopS5cuzU033ZSXXnppva/r10z5Aw44YL3PP/30\n03nuuefywgsv5DOf+UxGjBiRQw89NI8//njmz5+fyZMn5/rrr8/OO+/c/Zo//elPmTNnTr785S/n\nve99b68B+a81ZMiQXH311Zk+fXouuOCCXHDBBd2PT58+PVOmTOk+9j//+U+S9Bpz07TFFlt015ok\njz32WJJkzpw5WbhwYSZMmJC99torixYtyhVXXJEHH3wwl19+ea/3mDFjRjbbbLOcfvrp6+3H+nR1\ndf3Pr93Y6EVBH3roRUEfCvpQ0IeCPvTQi4I+FPSBplqt5vPwKn3ooRcFfSjoQ0EfCvrQ2v7xj3/k\nggsuyG233ZZarZYkectb3pITTjghn/vc57LNNtus9/X/vWfoJptskl//+tdvWr2Dxcc//vGsWbOm\n+++vt3fqQLz//e/PhRdemHPPPTfnn39+3vOe9+TYY4/Ntddeu97X9Xuj1/VZvXp1kmL++7777pvv\nfve73aNdfvOb3+Tkk0/OjBkz8rOf/SxJ8uKLL+bMM8/MBz/4wZxwwgkbfP/Zs2dnwYIFGT16dPbb\nb7+sWrUq8+fPz5VXXpntt98++++/f5J034HYdNNN13mP5mMvvvhir5q7urpy1VVXdb/H6tWrM3Xq\n1MybNy+f+tSn8vGPfzxJMbR/4cKFueKKK/oM/QEAAAAA3mz33HNPtthii+5APkl22mmnrFmzJitW\nrNhgKN8OAXxf3qz/92GHHZaJEydm5cqVecc73pEf/vCH3VNbXk+/xtdsyGtHz3z961/vNWv9Yx/7\nWMaNG5f77ruve3TMrFmzsnz58px33nkZNmz99wV+/vOfZ9asWTnooIMyd+7cnHnmmbn44oszd+7c\nDBkyJNOmTcuTTz6ZJN3/bl8/D2iG8c25P0OHDu2urxnIN58/9dRTkyS33HJLkmTlypW58MILM2HC\nhD5n0AMAAAAAlGHkyJF56qmnuieBJMlDDz2UJHn3u99dVVlt6Y477sgXv/jFDB06NCNHjsyQIUNy\n22239bnn6Wu9IaF8czTM8OHDM2bMmHWe/9CHPpSk+GnFfffdl6uuuirHH398v3YEbq6u//rXv95r\nBfx73/venHjiiXnuuee6w/PmCvbmiJrXaj7WrPVtb3tbkvRZQ7Pe5oz6b37zm1mzZk1mzJixwXoB\nAAAAAN4su+22W8aMGZOvfe1rWbZsWZYuXZqzzjorhx12WLbaaquqy2sr22+/fRYtWpTrrrsuDz/8\ncGbOnJm77rorxx133Hpf94aE8iNGjMjIkSOzZs2aXnN5mpqbpo4YMSK/+c1v8vLLL+fqq6/O2LFj\nu/984QtfSJJ85zvfydixY/PTn/40SbG56qabbtrn5q0f+MAHuo9JiiYkyT//+c91jm0+1jxmu+22\nS9L3qvrmY82V97feemuefvrpHHDAAb1qXrZsWZJk7NixGT9+/Ab7BAAAAADw/2PYsGH5/ve/ny23\n3DLHHXdcTjnllIwbNy7f/OY3qy6t7YwcOTIzZ87MDTfckIkTJ+b222/PNddc02u0UF/ekJnySbLX\nXntl3rx5WbJkST7ykY/0eu7ee+/NsGHDMnr06Lzyyis55ZRT1nn93/72t8ybNy/jxo3LuHHjuler\nb7PNNlm+fHlWrFiRd73rXb1e0xyH05yTtOeeeyZJlixZks7Ozl7HLl68OElxJ+m1x95+++350pe+\n1OvYe+65J0kRtifps94kqdfrWblyZU455ZTuFfgAAAAAAG+md77znZk5c2bVZbSlv/zlL73+fuCB\nB+bAAw8c0Hu8YaH8kUcemXnz5uWSSy7J7Nmzu8fDzJs3L0uXLs2ECROy1VZbZe+99+5zps6vf/3r\n7lB+2rRp3Y8fcsghufPOO3PxxRfn0ksv7Z5B32g0ctVVV2X48OH5xCc+kSTZdttts8cee+TWW2/N\n8ccfn5133jlJ8te//jU333xzdtppp+5xNaNHj85ee+2VO++8MzfffHMmTZqUJHn22Wczc+bMDB06\nNJ/+9KeTpFc9/13zypUrX/d5AAAAAAB4rTcslN93330zZcqUzJ49OxMnTswnPvGJNBqN/OpXv8o2\n22yTb3zjG//T+x511FG57bbbcsstt+SBBx7IgQcemFWrVmX+/Pl5+umnc/bZZ/daQX/mmWfms5/9\nbI499thMnDgxm2yySW6++easXbs2Z599dq/3Pvfcc3PMMcfk9NNPz6233ppRo0ZlwYIFWb58eU46\n6aTu1foAAAAAAPBGeMNC+SSZPn16dthhh8yZMyc/+tGPsvnmm+fQQw/NqaeemlGjRv1P7zl8+PB8\n73vfyzXXXJOf//znmT17djbddNPssssuOemkk7Lffvv1On6nnXbKD37wg1x22WX5xS9+keHDh2e3\n3XbLqaee2r1yvul973tfbrrpplx55ZVZsGBBFi1alO233z7nn39+jjjiiP+5DwAAAAAA0Jcha9eu\nXVt1EQAAAAAA0A6GVl0AAAAAAAC0C6E8AAAAAACURCgPAAAAAAAlEcoDAAAAAEBJhPIAAAAAAFAS\noTwAAAAAAJREKA8AAAAAACURygMAAAAAQEmE8gAAAAAAUJL/B5dki5P3sDHpAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110bae0b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "msno.matrix(crashes_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "In order to measure the number of luxury vehicles, we would need to know the car's make.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tail of the Vehicle Make column: ['ALFA' 'THMOA' 'CHVRL' 'VURM' 'SUPRM' 'TESL' 'TOYOA' 'KING' 'HYSTE'\n",
      " 'HURST' 'BUSO' 'MNRC' 'MACKQ' 'SPPR' 'KNTY' 'ETNYR' 'MAZ' 'FODR' 'LARO'\n",
      " 'LEXES' 'KALM' 'PACKA' 'BOCS' 'TRUMP' 'PR/ON' 'PONSC' 'SEAR' 'WHI' 'YAMAG'\n",
      " 'SPCNS']\n",
      "\n",
      "\n",
      "Number of unique brand names: 2000\n"
     ]
    }
   ],
   "source": [
    "print('Tail of the Vehicle Make column: ' + str(crashes_df['Vehicle Make'].unique()[-30:]))\n",
    "print('\\n\\nNumber of unique brand names: ' + str(len(crashes_df['Vehicle Make'].unique())))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the visible typos, we can assume that this column is close to uncleanable (2000 unique brand names!). Thankfully we can then look at the Vehicular Information Number of the car. Although the VIN provided is only provided in part for privacy purposes, it is enough for us to use for our data.\n",
    "\n",
    "There are still rows where the 'Partial VIN' is null. Let's see if the rows where VIN is null, it can then be replaced by the original 'Vehicle Make' column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "610"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vin_missing = crashes_df[(crashes_df['Vehicle Make'].notnull()) & crashes_df['Partial VIN'].isnull()]\n",
    "vin_missing_unique = vin_missing['Vehicle Make'].unique()\n",
    "len(vin_missing_unique)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['HOND', 'DODG', 'NISS', 'FORD', 'TOYT', 'DODGE', 'CHRY', 'MAC',\n",
       "       'ME/BE', 'CHEV', 'VOLV', 'HYUN', 'PONT', 'HONDA', 'CADI', 'SUBA',\n",
       "       'FRHT', 'JEEP', 'SCIO', 'JAGU'], dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vin_missing['Vehicle Make'].unique()[:20]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It seems that even as a backup, the column 'Vehicle Make' has too many mistakes and typos. We will not use it at all. Any rows where there are null values in 'Partial VIN' will now be dropped."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "crashes_df = crashes_df.dropna(axis=0,subset=['Partial VIN'])\n",
    "crashes_df = crashes_df.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "crashes_df = crashes_df.drop(labels=['index'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Case Vehicle ID</th>\n",
       "      <th>Vehicle Body Type</th>\n",
       "      <th>Registration Class</th>\n",
       "      <th>Action Prior to Accident</th>\n",
       "      <th>Type / Axles of Truck or Bus</th>\n",
       "      <th>Direction of Travel</th>\n",
       "      <th>Fuel Type</th>\n",
       "      <th>Vehicle Year</th>\n",
       "      <th>State of Registration</th>\n",
       "      <th>Number of Occupants</th>\n",
       "      <th>Engine Cylinders</th>\n",
       "      <th>Vehicle Make</th>\n",
       "      <th>Contributing Factor 1</th>\n",
       "      <th>Contributing Factor 1 Description</th>\n",
       "      <th>Contributing Factor 2</th>\n",
       "      <th>Contributing Factor 2 Description</th>\n",
       "      <th>Event Type</th>\n",
       "      <th>Partial VIN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013</td>\n",
       "      <td>10950427</td>\n",
       "      <td>4 DOOR SEDAN</td>\n",
       "      <td>Not Entered</td>\n",
       "      <td>Parked</td>\n",
       "      <td>Not Entered</td>\n",
       "      <td>South</td>\n",
       "      <td>Not Entered</td>\n",
       "      <td>1998.0</td>\n",
       "      <td>NY</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MAZD</td>\n",
       "      <td>HUMAN</td>\n",
       "      <td>Not Applicable</td>\n",
       "      <td>HUMAN</td>\n",
       "      <td>Not Applicable</td>\n",
       "      <td>Not Applicable</td>\n",
       "      <td>1YVGF22D3W5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013</td>\n",
       "      <td>11245573</td>\n",
       "      <td>TRACTOR</td>\n",
       "      <td>Not Entered</td>\n",
       "      <td>Going Straight Ahead</td>\n",
       "      <td>Not Entered</td>\n",
       "      <td>East</td>\n",
       "      <td>Not Entered</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>NJ</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FRHT</td>\n",
       "      <td>HUMAN</td>\n",
       "      <td>Unsafe Speed</td>\n",
       "      <td>HUMAN</td>\n",
       "      <td>Not Applicable</td>\n",
       "      <td>Not Applicable</td>\n",
       "      <td>IU201417285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013</td>\n",
       "      <td>11202923</td>\n",
       "      <td>4 DOOR SEDAN</td>\n",
       "      <td>Not Entered</td>\n",
       "      <td>Slowing or Stopping</td>\n",
       "      <td>Not Entered</td>\n",
       "      <td>North</td>\n",
       "      <td>Not Entered</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>IL</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FORD</td>\n",
       "      <td>HUMAN</td>\n",
       "      <td>Not Applicable</td>\n",
       "      <td>HUMAN</td>\n",
       "      <td>Not Applicable</td>\n",
       "      <td>Not Applicable</td>\n",
       "      <td>1FADP3F28DL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013</td>\n",
       "      <td>10944884</td>\n",
       "      <td>DELIVERY TRUCK</td>\n",
       "      <td>Not Entered</td>\n",
       "      <td>Going Straight Ahead</td>\n",
       "      <td>Not Entered</td>\n",
       "      <td>North</td>\n",
       "      <td>Not Entered</td>\n",
       "      <td>1988.0</td>\n",
       "      <td>UN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GRUM</td>\n",
       "      <td>HUMAN</td>\n",
       "      <td>Not Applicable</td>\n",
       "      <td>HUMAN</td>\n",
       "      <td>Not Applicable</td>\n",
       "      <td>Not Applicable</td>\n",
       "      <td>1GBBS10EXK2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2012</td>\n",
       "      <td>11143993</td>\n",
       "      <td>TRACTOR</td>\n",
       "      <td>SEMI-TRAILER</td>\n",
       "      <td>Making Right Turn</td>\n",
       "      <td>Not Entered</td>\n",
       "      <td>South</td>\n",
       "      <td>None</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>NY</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>AM/HA</td>\n",
       "      <td>HUMAN</td>\n",
       "      <td>Not Entered</td>\n",
       "      <td>HUMAN</td>\n",
       "      <td>Not Entered</td>\n",
       "      <td>Not Entered</td>\n",
       "      <td>5N6200F20C1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  Case Vehicle ID Vehicle Body Type Registration Class  \\\n",
       "0  2013         10950427      4 DOOR SEDAN        Not Entered   \n",
       "1  2013         11245573           TRACTOR        Not Entered   \n",
       "2  2013         11202923      4 DOOR SEDAN        Not Entered   \n",
       "3  2013         10944884    DELIVERY TRUCK        Not Entered   \n",
       "4  2012         11143993           TRACTOR       SEMI-TRAILER   \n",
       "\n",
       "  Action Prior to Accident Type / Axles of Truck or Bus Direction of Travel  \\\n",
       "0                   Parked                  Not Entered               South   \n",
       "1     Going Straight Ahead                  Not Entered                East   \n",
       "2      Slowing or Stopping                  Not Entered               North   \n",
       "3     Going Straight Ahead                  Not Entered               North   \n",
       "4        Making Right Turn                  Not Entered               South   \n",
       "\n",
       "     Fuel Type  Vehicle Year State of Registration  Number of Occupants  \\\n",
       "0  Not Entered        1998.0                    NY                  0.0   \n",
       "1  Not Entered        2000.0                    NJ                  1.0   \n",
       "2  Not Entered        2013.0                    IL                  2.0   \n",
       "3  Not Entered        1988.0                    UN                  1.0   \n",
       "4         None        2012.0                    NY                  1.0   \n",
       "\n",
       "   Engine Cylinders Vehicle Make Contributing Factor 1  \\\n",
       "0               NaN         MAZD                 HUMAN   \n",
       "1               NaN         FRHT                 HUMAN   \n",
       "2               NaN         FORD                 HUMAN   \n",
       "3               NaN         GRUM                 HUMAN   \n",
       "4               0.0        AM/HA                 HUMAN   \n",
       "\n",
       "  Contributing Factor 1 Description Contributing Factor 2  \\\n",
       "0                    Not Applicable                 HUMAN   \n",
       "1                      Unsafe Speed                 HUMAN   \n",
       "2                    Not Applicable                 HUMAN   \n",
       "3                    Not Applicable                 HUMAN   \n",
       "4                       Not Entered                 HUMAN   \n",
       "\n",
       "  Contributing Factor 2 Description      Event Type  Partial VIN  \n",
       "0                    Not Applicable  Not Applicable  1YVGF22D3W5  \n",
       "1                    Not Applicable  Not Applicable  IU201417285  \n",
       "2                    Not Applicable  Not Applicable  1FADP3F28DL  \n",
       "3                    Not Applicable  Not Applicable  1GBBS10EXK2  \n",
       "4                       Not Entered     Not Entered  5N6200F20C1  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crashes_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that the dataset is cleaned up, we can use 'Partial VIN' to obtain the Vehicle Make and other information. Because the dataset is so large, I decided to write a script outside of this notebook, writing the data to SQLite database. The data was then saved to *'vin_info.csv'* upon completion."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "vin_info = pd.read_csv('../vin_info.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>car_id</th>\n",
       "      <th>year</th>\n",
       "      <th>make</th>\n",
       "      <th>manufacturer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1998.0</td>\n",
       "      <td>MAZDA</td>\n",
       "      <td>AUTO ALLIANCE INTERNATIONAL, USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>FORD</td>\n",
       "      <td>FORD MOTOR COMPANY, USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1989.0</td>\n",
       "      <td>CHEVROLET</td>\n",
       "      <td>GENERAL MOTORS LLC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>AMERICAN HAULER</td>\n",
       "      <td>AMERICAN HAULER INDUSTRIES, LLC.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  car_id    year             make  \\\n",
       "0           0       0  1998.0            MAZDA   \n",
       "1           1       1     NaN              NaN   \n",
       "2           2       2  2013.0             FORD   \n",
       "3           3       3  1989.0        CHEVROLET   \n",
       "4           4       4  2012.0  AMERICAN HAULER   \n",
       "\n",
       "                       manufacturer  \n",
       "0  AUTO ALLIANCE INTERNATIONAL, USA  \n",
       "1                               NaN  \n",
       "2           FORD MOTOR COMPANY, USA  \n",
       "3                GENERAL MOTORS LLC  \n",
       "4  AMERICAN HAULER INDUSTRIES, LLC.  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vin_info.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Car Brands DataFrame\n",
    "Now that we have no more typos, we can make a dataframe revolving around car brands."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>car_id</th>\n",
       "      <th>year</th>\n",
       "      <th>manufacturer</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>make</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>TOYOTA</th>\n",
       "      <td>159310</td>\n",
       "      <td>159310</td>\n",
       "      <td>159298</td>\n",
       "      <td>159310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FORD</th>\n",
       "      <td>155295</td>\n",
       "      <td>155295</td>\n",
       "      <td>155286</td>\n",
       "      <td>155295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HONDA</th>\n",
       "      <td>141798</td>\n",
       "      <td>141798</td>\n",
       "      <td>141794</td>\n",
       "      <td>141798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CHEVROLET</th>\n",
       "      <td>141320</td>\n",
       "      <td>141320</td>\n",
       "      <td>141314</td>\n",
       "      <td>141320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NISSAN</th>\n",
       "      <td>105793</td>\n",
       "      <td>105793</td>\n",
       "      <td>105792</td>\n",
       "      <td>105793</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Unnamed: 0  car_id    year  manufacturer\n",
       "make                                               \n",
       "TOYOTA         159310  159310  159298        159310\n",
       "FORD           155295  155295  155286        155295\n",
       "HONDA          141798  141798  141794        141798\n",
       "CHEVROLET      141320  141320  141314        141320\n",
       "NISSAN         105793  105793  105792        105793"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_brands_df = vin_info.groupby(by='make').count()\n",
    "car_brands_df = car_brands_df.sort_values(by='car_id',ascending=False)\n",
    "car_brands_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Num of Accidents</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>make</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>TOYOTA</th>\n",
       "      <td>159310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FORD</th>\n",
       "      <td>155295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HONDA</th>\n",
       "      <td>141798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CHEVROLET</th>\n",
       "      <td>141320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NISSAN</th>\n",
       "      <td>105793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DODGE</th>\n",
       "      <td>64312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HYUNDAI</th>\n",
       "      <td>55938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JEEP</th>\n",
       "      <td>49458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SUBARU</th>\n",
       "      <td>30908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CHRYSLER</th>\n",
       "      <td>29504</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Num of Accidents\n",
       "make                       \n",
       "TOYOTA               159310\n",
       "FORD                 155295\n",
       "HONDA                141798\n",
       "CHEVROLET            141320\n",
       "NISSAN               105793\n",
       "DODGE                 64312\n",
       "HYUNDAI               55938\n",
       "JEEP                  49458\n",
       "SUBARU                30908\n",
       "CHRYSLER              29504"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_brands_df = pd.DataFrame(car_brands_df.iloc[:,0])\n",
    "car_brands_df = car_brands_df.rename(index=str,columns={'Unnamed: 0':'Num of Accidents'})\n",
    "car_brands_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1126feb38>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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tAnt7uwd4lHXn4WE+Z0PDzJbXkPI2pCz3wtbyAsxsDbaWF7B+ZquUAW9vb2zb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\nohJarg4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1126fc160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = car_brands_df.iloc[:10].plot(kind='bar')\n",
    "ax.set_title('# of Car Accidents in NYS by Make from 2012-2015')\n",
    "ax.set_ylabel('# of Accidents')\n",
    "ax.set_xlabel('Car Make')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that Toyota takes the top, having the most accidents out of all the car brands. However, Toyota is one of the most popular car brands and would have a larger volume of cars on the road. To get an idea of how luxury cars are more likely to be in an accident, we need to observe a metric like accidents per 1000 cars.\n",
    "\n",
    "There is no data on the volume of car brands on the New York City roads nor is there any data on what kind of cars New Yorkers have. \n",
    "\n",
    "This is where our second dataset comes in. I decided to use the dataset containing 2013/2014 Car Vehicle Sales (other years were blocked by a paywall). From individual car brand sales, we can obtain an idea of the popularity of each car brand in America. This directly correlates with the number of each car brand on New York roads."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sales DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>U.S. 2014 Calendar Year New Vehicle Sales Volume  \n",
       "All Vehicles &amp; All Automakers – Two Sheets \n",
       "© GoodCarBadCar.net</th>\n",
       "      <th>Unnamed: 1</th>\n",
       "      <th>Unnamed: 2</th>\n",
       "      <th>Unnamed: 3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Vehicle/Brand/Manufacturer</td>\n",
       "      <td>2014</td>\n",
       "      <td>2013</td>\n",
       "      <td>% Change</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Total New Vehicle Sales ¥</td>\n",
       "      <td>16,516,116</td>\n",
       "      <td>15,595,820</td>\n",
       "      <td>5.9%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Passenger Cars Ø</td>\n",
       "      <td>7,935,161</td>\n",
       "      <td>7,801,870</td>\n",
       "      <td>1.7%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>SUVs &amp; Crossovers Ø</td>\n",
       "      <td>5,314,878</td>\n",
       "      <td>4,747,988</td>\n",
       "      <td>11.9%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Pickup Trucks</td>\n",
       "      <td>2,320,219</td>\n",
       "      <td>2,175,633</td>\n",
       "      <td>6.6%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Minivans</td>\n",
       "      <td>556,548</td>\n",
       "      <td>532,357</td>\n",
       "      <td>4.5%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Commercial Vans</td>\n",
       "      <td>356,814</td>\n",
       "      <td>312,045</td>\n",
       "      <td>14.3%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>BMW Group °</td>\n",
       "      <td>395,850</td>\n",
       "      <td>375,782</td>\n",
       "      <td>5.3%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>BMW Brand Total</td>\n",
       "      <td>339,738</td>\n",
       "      <td>309,280</td>\n",
       "      <td>9.8%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>BMW Car</td>\n",
       "      <td>229,470</td>\n",
       "      <td>206,778</td>\n",
       "      <td>11.0%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>BMW 2-Series (&amp; 1-Series)</td>\n",
       "      <td>7,345</td>\n",
       "      <td>7,220</td>\n",
       "      <td>1.7%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>BMW 3-Series &amp; 4-Series</td>\n",
       "      <td>142,232</td>\n",
       "      <td>119,521</td>\n",
       "      <td>19.0%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>BMW 5-Series</td>\n",
       "      <td>52,704</td>\n",
       "      <td>56,863</td>\n",
       "      <td>-7.3%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>BMW 6-Series</td>\n",
       "      <td>8,647</td>\n",
       "      <td>9,762</td>\n",
       "      <td>-11.4%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>BMW 7-Series</td>\n",
       "      <td>9,744</td>\n",
       "      <td>10,932</td>\n",
       "      <td>-10.9%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>BMW i3</td>\n",
       "      <td>6,092</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>BMW i8</td>\n",
       "      <td>555</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   U.S. 2014 Calendar Year New Vehicle Sales Volume  \\nAll Vehicles & All Automakers – Two Sheets \\n© GoodCarBadCar.net  \\\n",
       "0                                                 NaN                                                                     \n",
       "1                                                 NaN                                                                     \n",
       "2                          Vehicle/Brand/Manufacturer                                                                     \n",
       "3                           Total New Vehicle Sales ¥                                                                     \n",
       "4                                    Passenger Cars Ø                                                                     \n",
       "5                                 SUVs & Crossovers Ø                                                                     \n",
       "6                                       Pickup Trucks                                                                     \n",
       "7                                            Minivans                                                                     \n",
       "8                                     Commercial Vans                                                                     \n",
       "9                                                 NaN                                                                     \n",
       "10                                        BMW Group °                                                                     \n",
       "11                                    BMW Brand Total                                                                     \n",
       "12                                            BMW Car                                                                     \n",
       "13                          BMW 2-Series (& 1-Series)                                                                     \n",
       "14                            BMW 3-Series & 4-Series                                                                     \n",
       "15                                       BMW 5-Series                                                                     \n",
       "16                                       BMW 6-Series                                                                     \n",
       "17                                       BMW 7-Series                                                                     \n",
       "18                                             BMW i3                                                                     \n",
       "19                                             BMW i8                                                                     \n",
       "\n",
       "    Unnamed: 1  Unnamed: 2 Unnamed: 3  \n",
       "0          NaN         NaN        NaN  \n",
       "1          NaN         NaN        NaN  \n",
       "2         2014        2013   % Change  \n",
       "3   16,516,116  15,595,820       5.9%  \n",
       "4    7,935,161   7,801,870       1.7%  \n",
       "5    5,314,878   4,747,988      11.9%  \n",
       "6    2,320,219   2,175,633       6.6%  \n",
       "7      556,548     532,357       4.5%  \n",
       "8      356,814     312,045      14.3%  \n",
       "9          NaN         NaN        NaN  \n",
       "10     395,850     375,782       5.3%  \n",
       "11     339,738     309,280       9.8%  \n",
       "12     229,470     206,778      11.0%  \n",
       "13       7,345       7,220       1.7%  \n",
       "14     142,232     119,521      19.0%  \n",
       "15      52,704      56,863      -7.3%  \n",
       "16       8,647       9,762     -11.4%  \n",
       "17       9,744      10,932     -10.9%  \n",
       "18       6,092           0       0.0%  \n",
       "19         555           0       0.0%  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales_df.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "long_column_name = 'U.S. 2014 Calendar Year New Vehicle Sales Volume  \\nAll Vehicles & All Automakers – Two Sheets \\n© GoodCarBadCar.net'\n",
    "sales_df = sales_df.rename(index=str,columns={long_column_name: 'Make',\n",
    "                                              'Unnamed: 1': '2014',\n",
    "                                              'Unnamed: 2': '2013',\n",
    "                                              'Unnamed: 3': '% Change'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We don't need individual car models. We do need, as the dataset puts it, the \"Brand Total\". So we attempt to remove everything else."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Checks if 'Brand Total' is in a string.\n",
    "def brand_total(x):\n",
    "    return ('Brand Total' in str(x)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "sales_df = sales_df[sales_df['Make'].map(brand_total)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2014</th>\n",
       "      <th>2013</th>\n",
       "      <th>% Change</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Make</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BMW</th>\n",
       "      <td>339,738</td>\n",
       "      <td>309,280</td>\n",
       "      <td>9.8%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MINI</th>\n",
       "      <td>56,112</td>\n",
       "      <td>66,502</td>\n",
       "      <td>-15.6%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CHRYSLER</th>\n",
       "      <td>308,785</td>\n",
       "      <td>302,492</td>\n",
       "      <td>2.1%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DODGE</th>\n",
       "      <td>574,155</td>\n",
       "      <td>596,343</td>\n",
       "      <td>-3.7%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JEEP</th>\n",
       "      <td>692,348</td>\n",
       "      <td>490,454</td>\n",
       "      <td>41.2%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             2014     2013 % Change\n",
       "Make                               \n",
       "BMW       339,738  309,280     9.8%\n",
       "MINI       56,112   66,502   -15.6%\n",
       "CHRYSLER  308,785  302,492     2.1%\n",
       "DODGE     574,155  596,343    -3.7%\n",
       "JEEP      692,348  490,454    41.2%"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Formatting 'Make' column such that it is the same with our crashes dataset, then making it the index.\n",
    "sales_df['Make'] = sales_df['Make'].map(lambda x: x.replace(\" Brand Total\",\"\").upper())\n",
    "sales_df = sales_df.set_index(sales_df['Make']).drop('Make',axis=1)\n",
    "sales_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Turning the strings to ints\n",
    "sales_df.iloc[:,0] = sales_df.iloc[:,0].map(lambda x: int(x.replace(\",\",\"\")))\n",
    "sales_df.iloc[:,1] = sales_df.iloc[:,1].map(lambda x: int(x.replace(\",\",\"\")))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are given 2013 and 2014 sales data. I decided to take the mean of the two to better represent the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2014</th>\n",
       "      <th>2013</th>\n",
       "      <th>% Change</th>\n",
       "      <th>Mean Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Make</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BMW</th>\n",
       "      <td>339738</td>\n",
       "      <td>309280</td>\n",
       "      <td>9.8%</td>\n",
       "      <td>324509.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MINI</th>\n",
       "      <td>56112</td>\n",
       "      <td>66502</td>\n",
       "      <td>-15.6%</td>\n",
       "      <td>61307.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CHRYSLER</th>\n",
       "      <td>308785</td>\n",
       "      <td>302492</td>\n",
       "      <td>2.1%</td>\n",
       "      <td>305638.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DODGE</th>\n",
       "      <td>574155</td>\n",
       "      <td>596343</td>\n",
       "      <td>-3.7%</td>\n",
       "      <td>585249.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JEEP</th>\n",
       "      <td>692348</td>\n",
       "      <td>490454</td>\n",
       "      <td>41.2%</td>\n",
       "      <td>591401.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            2014    2013 % Change  Mean Sales\n",
       "Make                                         \n",
       "BMW       339738  309280     9.8%    324509.0\n",
       "MINI       56112   66502   -15.6%     61307.0\n",
       "CHRYSLER  308785  302492     2.1%    305638.5\n",
       "DODGE     574155  596343    -3.7%    585249.0\n",
       "JEEP      692348  490454    41.2%    591401.0"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales_df['Mean Sales'] = (sales_df.iloc[:,0] + sales_df.iloc[:,1])/2\n",
    "sales_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Merging Sales dataframe with the Car Brands Dataframe\n",
    "\n",
    "This is where we put two and two together. By dividing the number of accidents by the mean sales of each car brand, we will get a number that is heavily correlated with accidents per car brand volume. I will name this number 'Accident Score' where the bigger the number is, the higher likelihood of being in a car crash."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "car_brands_df['Mean Sales'] = sales_df.ix[car_brands_df.index,'Mean Sales']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Num of Accidents</th>\n",
       "      <th>Mean Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>make</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>TOYOTA</th>\n",
       "      <td>159310</td>\n",
       "      <td>1949123.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FORD</th>\n",
       "      <td>155295</td>\n",
       "      <td>2390191.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HONDA</th>\n",
       "      <td>141798</td>\n",
       "      <td>1366452.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CHEVROLET</th>\n",
       "      <td>141320</td>\n",
       "      <td>1990283.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NISSAN</th>\n",
       "      <td>105793</td>\n",
       "      <td>1200765.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DODGE</th>\n",
       "      <td>64312</td>\n",
       "      <td>585249.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HYUNDAI</th>\n",
       "      <td>55938</td>\n",
       "      <td>723250.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JEEP</th>\n",
       "      <td>49458</td>\n",
       "      <td>591401.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SUBARU</th>\n",
       "      <td>30908</td>\n",
       "      <td>469188.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CHRYSLER</th>\n",
       "      <td>29504</td>\n",
       "      <td>305638.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VOLKSWAGEN</th>\n",
       "      <td>25645</td>\n",
       "      <td>387337.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GMC</th>\n",
       "      <td>24900</td>\n",
       "      <td>476377.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BMW</th>\n",
       "      <td>24399</td>\n",
       "      <td>324509.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MERCEDES-BENZ</th>\n",
       "      <td>23213</td>\n",
       "      <td>345240.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KIA</th>\n",
       "      <td>22039</td>\n",
       "      <td>557706.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PONTIAC</th>\n",
       "      <td>20860</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BUICK</th>\n",
       "      <td>19673</td>\n",
       "      <td>217236.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LEXUS</th>\n",
       "      <td>19656</td>\n",
       "      <td>292618.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACURA</th>\n",
       "      <td>19629</td>\n",
       "      <td>166639.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAZDA</th>\n",
       "      <td>18161</td>\n",
       "      <td>294873.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LINCOLN</th>\n",
       "      <td>17720</td>\n",
       "      <td>88084.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MERCURY</th>\n",
       "      <td>14285</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SATURN</th>\n",
       "      <td>13800</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MITSUBISHI</th>\n",
       "      <td>13400</td>\n",
       "      <td>69935.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INFINITI</th>\n",
       "      <td>12158</td>\n",
       "      <td>116892.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CADILLAC</th>\n",
       "      <td>11949</td>\n",
       "      <td>176646.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AUDI</th>\n",
       "      <td>10599</td>\n",
       "      <td>170036.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VOLVO</th>\n",
       "      <td>9266</td>\n",
       "      <td>58799.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SUZUKI</th>\n",
       "      <td>7631</td>\n",
       "      <td>2973.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INTERNATIONAL</th>\n",
       "      <td>7261</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>QIANJIANG MOTORCYCLES, UM MOTORCYCLES</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>QINGQI, SSR, PIONEER, HARDKNOCK</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>QUALITY STEEL &amp; ALUMINUM PRODUCTS</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MCT INDUSTRIES, INC.</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MCLAREN</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MANA</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KINROAD XINTIAN MOTORCYCLE MANUFACTURE CO.</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IVECO</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>J &amp; L'S CARGO EXPRESS, INC.</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JET COMPANY INC.</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JIALING</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JIANGSU SINSKI VEHICLE SCIENCE &amp; TECHNOLOGY CO.</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JIMMY'S CUSTOM BUILT TRAILERS &amp; FABRICATION</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JLG INDUSTRIES</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JOHNNY PAG MOTORCYCLES</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KAR-TOTE</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KINETIC ENGINEERING LTD.</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KROSS KOUNTRY</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MALON</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KUTZ FARM EQUIPMENT</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LEXUS, LEXUS</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LGS INDUSTRIES</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LIFESTYLE LUXURY</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LOAD TRAIL</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LONG CHIH INDUSTRIAL</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M.H. EBY, INC</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAGNUM PRODUCTS</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAJESTIC CORPORATION</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZONGSHEN INDUSTRIAL GROUP CO</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>450 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 Num of Accidents  Mean Sales\n",
       "make                                                                         \n",
       "TOYOTA                                                     159310   1949123.5\n",
       "FORD                                                       155295   2390191.5\n",
       "HONDA                                                      141798   1366452.5\n",
       "CHEVROLET                                                  141320   1990283.5\n",
       "NISSAN                                                     105793   1200765.0\n",
       "DODGE                                                       64312    585249.0\n",
       "HYUNDAI                                                     55938    723250.5\n",
       "JEEP                                                        49458    591401.0\n",
       "SUBARU                                                      30908    469188.0\n",
       "CHRYSLER                                                    29504    305638.5\n",
       "VOLKSWAGEN                                                  25645    387337.0\n",
       "GMC                                                         24900    476377.0\n",
       "BMW                                                         24399    324509.0\n",
       "MERCEDES-BENZ                                               23213    345240.0\n",
       "KIA                                                         22039    557706.5\n",
       "PONTIAC                                                     20860         NaN\n",
       "BUICK                                                       19673    217236.0\n",
       "LEXUS                                                       19656    292618.0\n",
       "ACURA                                                       19629    166639.5\n",
       "MAZDA                                                       18161    294873.5\n",
       "LINCOLN                                                     17720     88084.0\n",
       "MERCURY                                                     14285         NaN\n",
       "SATURN                                                      13800         NaN\n",
       "MITSUBISHI                                                  13400     69935.0\n",
       "INFINITI                                                    12158    116892.5\n",
       "CADILLAC                                                    11949    176646.5\n",
       "AUDI                                                        10599    170036.0\n",
       "VOLVO                                                        9266     58799.5\n",
       "SUZUKI                                                       7631      2973.0\n",
       "INTERNATIONAL                                                7261         NaN\n",
       "...                                                           ...         ...\n",
       "QIANJIANG MOTORCYCLES, UM MOTORCYCLES                           1         NaN\n",
       "QINGQI, SSR, PIONEER, HARDKNOCK                                 1         NaN\n",
       "QUALITY STEEL & ALUMINUM PRODUCTS                               1         NaN\n",
       "MCT INDUSTRIES, INC.                                            1         NaN\n",
       "MCLAREN                                                         1         NaN\n",
       "MANA                                                            1         NaN\n",
       "KINROAD XINTIAN MOTORCYCLE MANUFACTURE CO.                      1         NaN\n",
       "IVECO                                                           1         NaN\n",
       "J & L'S CARGO EXPRESS, INC.                                     1         NaN\n",
       "JET COMPANY INC.                                                1         NaN\n",
       "JIALING                                                         1         NaN\n",
       "JIANGSU SINSKI VEHICLE SCIENCE & TECHNOLOGY CO.                 1         NaN\n",
       "JIMMY'S CUSTOM BUILT TRAILERS & FABRICATION                     1         NaN\n",
       "JLG INDUSTRIES                                                  1         NaN\n",
       "JOHNNY PAG MOTORCYCLES                                          1         NaN\n",
       "KAR-TOTE                                                        1         NaN\n",
       "KINETIC ENGINEERING LTD.                                        1         NaN\n",
       "KROSS KOUNTRY                                                   1         NaN\n",
       "MALON                                                           1         NaN\n",
       "KUTZ FARM EQUIPMENT                                             1         NaN\n",
       "2008                                                            1         NaN\n",
       "LEXUS, LEXUS                                                    1         NaN\n",
       "LGS INDUSTRIES                                                  1         NaN\n",
       "LIFESTYLE LUXURY                                                1         NaN\n",
       "LOAD TRAIL                                                      1         NaN\n",
       "LONG CHIH INDUSTRIAL                                            1         NaN\n",
       "M.H. EBY, INC                                                   1         NaN\n",
       "MAGNUM PRODUCTS                                                 1         NaN\n",
       "MAJESTIC CORPORATION                                            1         NaN\n",
       "ZONGSHEN INDUSTRIAL GROUP CO                                    1         NaN\n",
       "\n",
       "[450 rows x 2 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_brands_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "car_brands_df['Accident Score'] = car_brands_df['Num of Accidents']/car_brands_df['Mean Sales']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Num of Accidents</th>\n",
       "      <th>Mean Sales</th>\n",
       "      <th>Accident Score</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>make</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SUZUKI</th>\n",
       "      <td>7631</td>\n",
       "      <td>2973.0</td>\n",
       "      <td>2.566768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LINCOLN</th>\n",
       "      <td>17720</td>\n",
       "      <td>88084.0</td>\n",
       "      <td>0.201172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MITSUBISHI</th>\n",
       "      <td>13400</td>\n",
       "      <td>69935.0</td>\n",
       "      <td>0.191606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VOLVO</th>\n",
       "      <td>9266</td>\n",
       "      <td>58799.5</td>\n",
       "      <td>0.157586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACURA</th>\n",
       "      <td>19629</td>\n",
       "      <td>166639.5</td>\n",
       "      <td>0.117793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JAGUAR</th>\n",
       "      <td>1811</td>\n",
       "      <td>16362.5</td>\n",
       "      <td>0.110680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DODGE</th>\n",
       "      <td>64312</td>\n",
       "      <td>585249.0</td>\n",
       "      <td>0.109888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INFINITI</th>\n",
       "      <td>12158</td>\n",
       "      <td>116892.5</td>\n",
       "      <td>0.104010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HONDA</th>\n",
       "      <td>141798</td>\n",
       "      <td>1366452.5</td>\n",
       "      <td>0.103771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CHRYSLER</th>\n",
       "      <td>29504</td>\n",
       "      <td>305638.5</td>\n",
       "      <td>0.096532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BUICK</th>\n",
       "      <td>19673</td>\n",
       "      <td>217236.0</td>\n",
       "      <td>0.090560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NISSAN</th>\n",
       "      <td>105793</td>\n",
       "      <td>1200765.0</td>\n",
       "      <td>0.088105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JEEP</th>\n",
       "      <td>49458</td>\n",
       "      <td>591401.0</td>\n",
       "      <td>0.083629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TOYOTA</th>\n",
       "      <td>159310</td>\n",
       "      <td>1949123.5</td>\n",
       "      <td>0.081734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HYUNDAI</th>\n",
       "      <td>55938</td>\n",
       "      <td>723250.5</td>\n",
       "      <td>0.077342</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Num of Accidents  Mean Sales  Accident Score\n",
       "make                                                    \n",
       "SUZUKI                  7631      2973.0        2.566768\n",
       "LINCOLN                17720     88084.0        0.201172\n",
       "MITSUBISHI             13400     69935.0        0.191606\n",
       "VOLVO                   9266     58799.5        0.157586\n",
       "ACURA                  19629    166639.5        0.117793\n",
       "JAGUAR                  1811     16362.5        0.110680\n",
       "DODGE                  64312    585249.0        0.109888\n",
       "INFINITI               12158    116892.5        0.104010\n",
       "HONDA                 141798   1366452.5        0.103771\n",
       "CHRYSLER               29504    305638.5        0.096532\n",
       "BUICK                  19673    217236.0        0.090560\n",
       "NISSAN                105793   1200765.0        0.088105\n",
       "JEEP                   49458    591401.0        0.083629\n",
       "TOYOTA                159310   1949123.5        0.081734\n",
       "HYUNDAI                55938    723250.5        0.077342"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_brands_df = car_brands_df.sort_values(by='Accident Score',ascending=False)\n",
    "car_brands_df.head(15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Suzuki is a heavy outlier because despite the number of accidents being relatively low, its Accident Score is incredibly high. A quick Google search will tell us that Suzuki stopped U.S. car sales in 2012: one of the years this dataset starts. We will remove Suzuki for the sake of consistency in the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Num of Accidents</th>\n",
       "      <th>Mean Sales</th>\n",
       "      <th>Accident Score</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>make</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>LINCOLN</th>\n",
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       "      <td>88084.0</td>\n",
       "      <td>0.201172</td>\n",
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       "    <tr>\n",
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       "      <th>VOLVO</th>\n",
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       "    <tr>\n",
       "      <th>DODGE</th>\n",
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       "      <td>585249.0</td>\n",
       "      <td>0.109888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INFINITI</th>\n",
       "      <td>12158</td>\n",
       "      <td>116892.5</td>\n",
       "      <td>0.104010</td>\n",
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       "    <tr>\n",
       "      <th>CHRYSLER</th>\n",
       "      <td>29504</td>\n",
       "      <td>305638.5</td>\n",
       "      <td>0.096532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BUICK</th>\n",
       "      <td>19673</td>\n",
       "      <td>217236.0</td>\n",
       "      <td>0.090560</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Num of Accidents  Mean Sales  Accident Score\n",
       "make                                                    \n",
       "LINCOLN                17720     88084.0        0.201172\n",
       "MITSUBISHI             13400     69935.0        0.191606\n",
       "VOLVO                   9266     58799.5        0.157586\n",
       "ACURA                  19629    166639.5        0.117793\n",
       "JAGUAR                  1811     16362.5        0.110680\n",
       "DODGE                  64312    585249.0        0.109888\n",
       "INFINITI               12158    116892.5        0.104010\n",
       "HONDA                 141798   1366452.5        0.103771\n",
       "CHRYSLER               29504    305638.5        0.096532\n",
       "BUICK                  19673    217236.0        0.090560"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_brands_df = car_brands_df.drop('SUZUKI',axis=0)\n",
    "car_brands_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1061d1b00>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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HByMuLg6FhYVwdHREbGwsunfvjgkTJgAAxo8fX+EyAODj44NFixZhxYoVaNu2LQYMGCBX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\nUKlU8PT0RHJyMp588kmjjztDLpdDLpejoaEBCoU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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1061a26d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = car_brands_df.iloc[1:15,2].plot(kind='bar')\n",
    "ax.set_title('Accident Score of Different Car Brands')\n",
    "ax.set_ylabel('Accident Score')\n",
    "ax.set_xlabel('Car Make')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Right off the bat, luxury car brands within the top ten of our normalized accidents list include: Lincoln, Volvo, Acura, Jaguar, and Infiniti. This does not include the car brands who partly make luxury cars. Seeing that luxury cars dominate literally half of the top ten, we need to take our analysis further."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "luxury car accident score: 0.6912411863163387\n",
      "non-luxury car accident score: 0.5017979986382466\n"
     ]
    }
   ],
   "source": [
    "luxury = ['LINCOLN','VOLVO','ACURA','JAGUAR','INFINITI']\n",
    "non_lux = ['SUZUKI','MITSUBISHI','DODGE','HONDA','CHRYSLER']\n",
    "print('luxury car accident score: ' + str(car_brands_df.ix[luxury,2].sum()))\n",
    "print('non-luxury car accident score: ' + str(car_brands_df.ix[non_lux,2].sum()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion\n",
    "With luxury car brands having a higher Accident Score than non-luxury cars, we can say that luxury car brands face a slightly higher chance of accident in New York State than non-luxury cars. This is the complete opposite of our hypothesis.\n",
    "\n",
    "The higher Accident Score can be attributed to several causes:\n",
    "\n",
    "* Speeding is common with luxury cars\n",
    "* Cheaper cars means lower-income => Cannot afford to crash\n",
    "* Our Accident Score was not calculated correctly due to limited data"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
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   "language": "python",
   "name": "python3"
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  "language_info": {
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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