Data_Analysis_Cars.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import libraries:\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Download the updated dataset\n",
    "\n",
    "import asyncio\n",
    "import aiohttp\n",
    "\n",
    "async def download(url, filename):\n",
    "    async with aiohttp.ClientSession() as session:\n",
    "        async with session.get(url) as response:\n",
    "            if response.status == 200:\n",
    "                with open(filename, 'wb') as f:\n",
    "                    f.write(await response.read())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "file_path= \"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DA0101EN-SkillsNetwork/labs/Data%20files/automobileEDA.csv\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "await download(file_path, \"usedcars.csv\")\n",
    "file_name=\"usedcars.csv\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(file_name, header=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symboling</th>\n",
       "      <th>normalized-losses</th>\n",
       "      <th>make</th>\n",
       "      <th>aspiration</th>\n",
       "      <th>num-of-doors</th>\n",
       "      <th>body-style</th>\n",
       "      <th>drive-wheels</th>\n",
       "      <th>engine-location</th>\n",
       "      <th>wheel-base</th>\n",
       "      <th>length</th>\n",
       "      <th>...</th>\n",
       "      <th>compression-ratio</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>peak-rpm</th>\n",
       "      <th>city-mpg</th>\n",
       "      <th>highway-mpg</th>\n",
       "      <th>price</th>\n",
       "      <th>city-L/100km</th>\n",
       "      <th>horsepower-binned</th>\n",
       "      <th>diesel</th>\n",
       "      <th>gas</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>122</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>88.6</td>\n",
       "      <td>0.811148</td>\n",
       "      <td>...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>13495.0</td>\n",
       "      <td>11.190476</td>\n",
       "      <td>Medium</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>122</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>88.6</td>\n",
       "      <td>0.811148</td>\n",
       "      <td>...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>16500.0</td>\n",
       "      <td>11.190476</td>\n",
       "      <td>Medium</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>122</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>94.5</td>\n",
       "      <td>0.822681</td>\n",
       "      <td>...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>19</td>\n",
       "      <td>26</td>\n",
       "      <td>16500.0</td>\n",
       "      <td>12.368421</td>\n",
       "      <td>Medium</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>164</td>\n",
       "      <td>audi</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>99.8</td>\n",
       "      <td>0.848630</td>\n",
       "      <td>...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>24</td>\n",
       "      <td>30</td>\n",
       "      <td>13950.0</td>\n",
       "      <td>9.791667</td>\n",
       "      <td>Medium</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>164</td>\n",
       "      <td>audi</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>4wd</td>\n",
       "      <td>front</td>\n",
       "      <td>99.4</td>\n",
       "      <td>0.848630</td>\n",
       "      <td>...</td>\n",
       "      <td>8.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>18</td>\n",
       "      <td>22</td>\n",
       "      <td>17450.0</td>\n",
       "      <td>13.055556</td>\n",
       "      <td>Medium</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   symboling  normalized-losses         make aspiration num-of-doors  \\\n",
       "0          3                122  alfa-romero        std          two   \n",
       "1          3                122  alfa-romero        std          two   \n",
       "2          1                122  alfa-romero        std          two   \n",
       "3          2                164         audi        std         four   \n",
       "4          2                164         audi        std         four   \n",
       "\n",
       "    body-style drive-wheels engine-location  wheel-base    length  ...  \\\n",
       "0  convertible          rwd           front        88.6  0.811148  ...   \n",
       "1  convertible          rwd           front        88.6  0.811148  ...   \n",
       "2    hatchback          rwd           front        94.5  0.822681  ...   \n",
       "3        sedan          fwd           front        99.8  0.848630  ...   \n",
       "4        sedan          4wd           front        99.4  0.848630  ...   \n",
       "\n",
       "   compression-ratio  horsepower  peak-rpm city-mpg highway-mpg    price  \\\n",
       "0                9.0       111.0    5000.0       21          27  13495.0   \n",
       "1                9.0       111.0    5000.0       21          27  16500.0   \n",
       "2                9.0       154.0    5000.0       19          26  16500.0   \n",
       "3               10.0       102.0    5500.0       24          30  13950.0   \n",
       "4                8.0       115.0    5500.0       18          22  17450.0   \n",
       "\n",
       "  city-L/100km  horsepower-binned  diesel  gas  \n",
       "0    11.190476             Medium       0    1  \n",
       "1    11.190476             Medium       0    1  \n",
       "2    12.368421             Medium       0    1  \n",
       "3     9.791667             Medium       0    1  \n",
       "4    13.055556             Medium       0    1  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 201 entries, 0 to 200\n",
      "Data columns (total 29 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   symboling          201 non-null    int64  \n",
      " 1   normalized-losses  201 non-null    int64  \n",
      " 2   make               201 non-null    object \n",
      " 3   aspiration         201 non-null    object \n",
      " 4   num-of-doors       201 non-null    object \n",
      " 5   body-style         201 non-null    object \n",
      " 6   drive-wheels       201 non-null    object \n",
      " 7   engine-location    201 non-null    object \n",
      " 8   wheel-base         201 non-null    float64\n",
      " 9   length             201 non-null    float64\n",
      " 10  width              201 non-null    float64\n",
      " 11  height             201 non-null    float64\n",
      " 12  curb-weight        201 non-null    int64  \n",
      " 13  engine-type        201 non-null    object \n",
      " 14  num-of-cylinders   201 non-null    object \n",
      " 15  engine-size        201 non-null    int64  \n",
      " 16  fuel-system        201 non-null    object \n",
      " 17  bore               201 non-null    float64\n",
      " 18  stroke             197 non-null    float64\n",
      " 19  compression-ratio  201 non-null    float64\n",
      " 20  horsepower         201 non-null    float64\n",
      " 21  peak-rpm           201 non-null    float64\n",
      " 22  city-mpg           201 non-null    int64  \n",
      " 23  highway-mpg        201 non-null    int64  \n",
      " 24  price              201 non-null    float64\n",
      " 25  city-L/100km       201 non-null    float64\n",
      " 26  horsepower-binned  200 non-null    object \n",
      " 27  diesel             201 non-null    int64  \n",
      " 28  gas                201 non-null    int64  \n",
      "dtypes: float64(11), int64(8), object(10)\n",
      "memory usage: 45.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# What is the data type of the column \"peak-rpm\"?\n",
    "df['peak-rpm'].dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bore</th>\n",
       "      <th>stroke</th>\n",
       "      <th>compression-ratio</th>\n",
       "      <th>horsepower</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>bore</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.055390</td>\n",
       "      <td>0.001263</td>\n",
       "      <td>0.566936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stroke</th>\n",
       "      <td>-0.055390</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.187923</td>\n",
       "      <td>0.098462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>compression-ratio</th>\n",
       "      <td>0.001263</td>\n",
       "      <td>0.187923</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.214514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>horsepower</th>\n",
       "      <td>0.566936</td>\n",
       "      <td>0.098462</td>\n",
       "      <td>-0.214514</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       bore    stroke  compression-ratio  horsepower\n",
       "bore               1.000000 -0.055390           0.001263    0.566936\n",
       "stroke            -0.055390  1.000000           0.187923    0.098462\n",
       "compression-ratio  0.001263  0.187923           1.000000   -0.214514\n",
       "horsepower         0.566936  0.098462          -0.214514    1.000000"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the correlation between the following columns: bore, stroke, compression-ratio, and horsepower.\n",
    "\n",
    "df[[\"bore\", \"stroke\", \"compression-ratio\", \"horsepower\"]].corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Continuous Numerical Variables:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 53503.30078562048)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Positive Linear Relationship\n",
    "\n",
    "# Engine size as potential predictor variable of price\n",
    "sns.regplot(x=\"engine-size\", y=\"price\", data=df)\n",
    "plt.ylim(0,)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### As the engine-size goes up, the price goes up: this indicates a positive direct correlation between these two variables. Engine size seems like a pretty good predictor of price since the regression line is almost a perfect diagonal line."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>engine-size</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>engine-size</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.872335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>price</th>\n",
       "      <td>0.872335</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             engine-size     price\n",
       "engine-size     1.000000  0.872335\n",
       "price           0.872335  1.000000"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We can examine the correlation between 'engine-size' and 'price'\n",
    "\n",
    "df[[\"engine-size\", \"price\"]].corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 48169.69704431989)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's find the scatterplot of \"highway-mpg\" and \"price\"\n",
    "\n",
    "sns.regplot(x=\"highway-mpg\", y=\"price\", data=df)\n",
    "plt.ylim(0,)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### As highway-mpg goes up, the price goes down: this indicates an inverse/negative relationship between these two variables. Highway mpg could potentially be a predictor of price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>highway-mpg</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>highway-mpg</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.704692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>price</th>\n",
       "      <td>-0.704692</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             highway-mpg     price\n",
       "highway-mpg     1.000000 -0.704692\n",
       "price          -0.704692  1.000000"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We can examine the correlation between 'highway-mpg' and 'price'\n",
    "\n",
    "df[['highway-mpg', 'price']].corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='peak-rpm', ylabel='price'>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Weak Linear Relationship\n",
    "\n",
    "#Let's see if \"peak-rpm\" is a predictor variable of \"price\"\n",
    "\n",
    "sns.regplot(x=\"peak-rpm\", y=\"price\", data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Peak rpm does not seem like a good predictor of the price at all since the regression line is close to horizontal. Also, the data points are very scattered and far from the fitted line, showing lots of variability. Therefore, it's not a reliable variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>peak-rpm</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>peak-rpm</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.101616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>price</th>\n",
       "      <td>-0.101616</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          peak-rpm     price\n",
       "peak-rpm  1.000000 -0.101616\n",
       "price    -0.101616  1.000000"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We can examine the correlation between 'peak-rpm' and 'price'\n",
    "\n",
    "df[['peak-rpm','price']].corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>stroke</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>stroke</th>\n",
       "      <td>1.00000</td>\n",
       "      <td>0.08231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>price</th>\n",
       "      <td>0.08231</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         stroke    price\n",
       "stroke  1.00000  0.08231\n",
       "price   0.08231  1.00000"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the correlation between x=\"stroke\" and y=\"price\".\n",
    "\n",
    "df[['stroke','price']].corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='stroke', ylabel='price'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(x=\"stroke\", y=\"price\", data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Categorical Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='body-style', ylabel='price'>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=\"body-style\", y=\"price\", data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### We see that the distributions of price between the different body-style categories have a significant overlap, so body-style would not be a good predictor of price."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='engine-location', ylabel='price'>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=\"engine-location\", y=\"price\", data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Here we see that the distribution of price between these two engine-location categories, front and rear, are distinct enough to take engine-location as a potential good predictor of price."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='drive-wheels', ylabel='price'>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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MLVmyRD6fz+pWAQANGKEJlisuLtaxY8fUq1cvLVq0SD169FDTpk3Vo0cPLVq0SL169dLRo0dVXFxsdasAgAaM0ATL1YWhCRMmyGbz/0rabDZNmDDBrw4AACsQmhA0QkJCrG4BAIBLIjTBcn369JEkvfTSS6qtrfVbV1tbq3Xr1vnVAQBgBUITLNenTx9FRUWptLRUTzzxhMrKynTy5EmVlZXpiSeeUGlpqaKioghNAABLMU8TLBcaGqq0tDTNnz9fe/bsUWFhobkuPDxckpSWlsYklwAAS3GkCUEhKSlJCxYsUFRUlN/yli1basGCBUxuCQCwXIhhGIbVTdwIvF6vIiMj5fF4ZLfbrW7nusVtVAAA9elyfr85PQc/p0+fVkVFhaU9NGvWTM2aNZMkHThwwNJeEhISFBERYWkPAIDgQGiCn4qKCk2ZMsXqNoLGmjVr1LVrV6vbAAAEAUIT/CQkJGjNmjWW9nDw4EEtWrRITzzxhNq3b29pLwkJCZa+PwAgeBCa4CciIiJojqy0b98+aHoBAICr5wAAAAJAaAIAAAiApaFp1apVSkxMlN1ul91ul8Ph0F/+8hdz/bBhwxQSEuL3mDp1qt82KioqlJKSoqZNm6pNmzaaM2eOzp4961eTl5enfv36KTw8XF26dDFvy3GuFStWqEOHDoqIiNCgQYO0a9eua7LPAADg+mRpaGrbtq2eeeYZFRUV6YMPPtCdd96pe+65R2VlZWbNI488osrKSvOxePFic53P51NKSopqamq0c+dOrV+/XuvWrdP8+fPNmvLycqWkpOiOO+5QcXGxZs2apcmTJ2vbtm1mzauvvqq0tDRlZGRoz5496t27t5xOpw4fPlw/HwQAAAh+RpBp2bKlsXbtWsMwDGPo0KHGzJkzL1m7ZcsWw2azGW6321y2atUqw263G9XV1YZhGMbcuXONHj16+L1u3LhxhtPpNJ8PHDjQcLlc5nOfz2fEx8cbWVlZAfft8XgMSYbH4wn4Nbi4ffv2GUOHDjX27dtndSsAgBvc5fx+B82YJp/Pp1deeUUnTpyQw+Ewl2dnZ6t169bq2bOn5s2bp5MnT5rrCgsL1atXL8XExJjLnE6nvF6vebSqsLBQycnJfu/ldDrN+5vV1NSoqKjIr8Zmsyk5OdnvHmjnq66ultfr9XsAAIAbl+VTDpSWlsrhcOj06dNq3ry5Xn/9dXXv3l2S9MADD6h9+/aKj49XSUmJHn/8ce3bt09//vOfJUlut9svMEkyn7vd7m+t8Xq9OnXqlI4ePSqfz3fRmr17916y76ysLGVmZn6/nQcAANcNy0PTLbfcouLiYnk8Hv3xj3/U+PHjlZ+fr+7du/vNTN2rVy/FxcVp+PDhOnDggDp37mxh19K8efOUlpZmPvd6vWrXrp2FHQEAgGvJ8tAUFhamLl26SJL69++v3bt3a9myZXrhhRcuqB00aJAk6bPPPlPnzp0VGxt7wVVuVVVVkqTY2Fjzz7pl59bY7XY1adJEoaGhCg0NvWhN3TYuJjw8XOHh4Ze5twAA4HoVNGOa6tTW1qq6uvqi64qLiyVJcXFxkiSHw6HS0lK/q9xycnJkt9vNU3wOh0O5ubl+28nJyTHHTYWFhal///5+NbW1tcrNzfUbWwUAABo2S480zZs3TyNHjlRCQoKOHz+uDRs2KC8vT9u2bdOBAwe0YcMGjRo1Sq1atVJJSYlmz56tpKQkJSYmSpJGjBih7t2768EHH9TixYvldruVnp4ul8tlHgWaOnWqli9frrlz52rSpEnasWOHXnvtNW3evNnsIy0tTePHj9eAAQM0cOBALV26VCdOnNDEiRMt+VwAAEDwsTQ0HT58WA899JAqKysVGRmpxMREbdu2Tf/6r/+qQ4cO6a9//asZYNq1a6exY8cqPT3dfH1oaKg2bdqkadOmyeFwqFmzZho/frwWLFhg1nTs2FGbN2/W7NmztWzZMrVt21Zr166V0+k0a8aNG6evvvpK8+fPl9vtVp8+fbR169YLBocDAICGK8QwDMPqJm4EXq9XkZGR8ng8stvtVrdzXdu/f7+mTJmiNWvWcMNeAMA1dTm/30E3pgkAACAYEZoAAAACQGgCAAAIAKEJAAAgAIQmAACAABCaAAAAAkBoAgAACAChCQAAIACEJgAAgAAQmgAAAAJAaAIAAAgAoQkAACAAhCYAAIAAEJoAAAACQGgCAAAIAKEJAAAgAIQmAACAABCaAAAAAkBoAgAACAChCQAAIACEJgAAgAAQmgAAAAJAaAIAAAgAoQkAACAAhCYAAIAAEJoAAAACQGgCAAAIAKEJAAAgAIQmAACAABCaAAAAAkBoAgAACICloWnVqlVKTEyU3W6X3W6Xw+HQX/7yF3P96dOn5XK51KpVKzVv3lxjx45VVVWV3zYqKiqUkpKipk2bqk2bNpozZ47Onj3rV5OXl6d+/fopPDxcXbp00bp16y7oZcWKFerQoYMiIiI0aNAg7dq165rsMwAAuD5ZGpratm2rZ555RkVFRfrggw9055136p577lFZWZkkafbs2Xrrrbe0ceNG5efn68svv9SYMWPM1/t8PqWkpKimpkY7d+7U+vXrtW7dOs2fP9+sKS8vV0pKiu644w4VFxdr1qxZmjx5srZt22bWvPrqq0pLS1NGRob27Nmj3r17y+l06vDhw/X3YQAAgOBmBJmWLVsaa9euNY4dO2Y0btzY2Lhxo7nuk08+MSQZhYWFhmEYxpYtWwybzWa43W6zZtWqVYbdbjeqq6sNwzCMuXPnGj169PB7j3HjxhlOp9N8PnDgQMPlcpnPfT6fER8fb2RlZV2yz9OnTxsej8d8HDp0yJBkeDye7/cBwNi3b58xdOhQY9++fVa3AgC4wXk8noB/v4NmTJPP59Mrr7yiEydOyOFwqKioSGfOnFFycrJZc+uttyohIUGFhYWSpMLCQvXq1UsxMTFmjdPplNfrNY9WFRYW+m2jrqZuGzU1NSoqKvKrsdlsSk5ONmsuJisrS5GRkeajXbt23/9DAAAAQcvy0FRaWqrmzZsrPDxcU6dO1euvv67u3bvL7XYrLCxMUVFRfvUxMTFyu92SJLfb7ReY6tbXrfu2Gq/Xq1OnTukf//iHfD7fRWvqtnEx8+bNk8fjMR+HDh26ov0HAADXh0ZWN3DLLbeouLhYHo9Hf/zjHzV+/Hjl5+db3dZ3Cg8PV3h4uNVtAACAemJ5aAoLC1OXLl0kSf3799fu3bu1bNkyjRs3TjU1NTp27Jjf0aaqqirFxsZKkmJjYy+4yq3u6rpza86/4q6qqkp2u11NmjRRaGioQkNDL1pTtw0AAADLT8+dr7a2VtXV1erfv78aN26s3Nxcc92+fftUUVEhh8MhSXI4HCotLfW7yi0nJ0d2u13du3c3a87dRl1N3TbCwsLUv39/v5ra2lrl5uaaNQAAAJYeaZo3b55GjhyphIQEHT9+XBs2bFBeXp62bdumyMhIPfzww0pLS1N0dLTsdrtmzJghh8OhwYMHS5JGjBih7t2768EHH9TixYvldruVnp4ul8tlnjqbOnWqli9frrlz52rSpEnasWOHXnvtNW3evNnsIy0tTePHj9eAAQM0cOBALV26VCdOnNDEiRMt+VwAAEDwsTQ0HT58WA899JAqKysVGRmpxMREbdu2Tf/6r/8qSVqyZIlsNpvGjh2r6upqOZ1OrVy50nx9aGioNm3apGnTpsnhcKhZs2YaP368FixYYNZ07NhRmzdv1uzZs7Vs2TK1bdtWa9euldPpNGvGjRunr776SvPnz5fb7VafPn20devWCwaHAwCAhivEMAzD6iZuBF6vV5GRkfJ4PLLb7Va3c13bv3+/pkyZojVr1qhr165WtwMAuIFdzu930I1pAgAACEaEJgAAgAAQmgAAAAJAaAIAAAgAoQkAACAAhCYAAIAAEJoAAAACQGgCAAAIAKEJAAAgAIQmAACAABCaAAAAAkBoAgAACAChCQAAIACEJgAAgAAQmgAAAAJAaAIAAAgAoQkAACAAhCYAAIAAEJoAAAACcMWh6fe//72GDBmi+Ph4HTx4UJK0dOlSvfnmm1etOQAAgGBxRaFp1apVSktL06hRo3Ts2DH5fD5JUlRUlJYuXXo1+wMAAAgKVxSafvOb3+jFF1/UE088odDQUHP5gAEDVFpaetWaAwAACBZXFJrKy8vVt2/fC5aHh4frxIkT37spAACAYHNFoaljx44qLi6+YPnWrVvVrVu379sTAABA0Gl0JS9KS0uTy+XS6dOnZRiGdu3apT/84Q/KysrS2rVrr3aPAAAAlrui0DR58mQ1adJE6enpOnnypB544AHFx8dr2bJluu+++652jwAAAJa7otAkSampqUpNTdXJkyf19ddfq02bNlezLwAAgKByRaGpvLxcZ8+e1c0336ymTZuqadOmkqRPP/1UjRs3VocOHa5mjwAAAJa7ooHgEyZM0M6dOy9Y/v7772vChAnftycAAICgc0Wh6cMPP9SQIUMuWD548OCLXlUHAABwvbui0BQSEqLjx49fsNzj8ZizgwciKytLP/jBD9SiRQu1adNGo0eP1r59+/xqhg0bppCQEL/H1KlT/WoqKiqUkpKipk2bqk2bNpozZ47Onj3rV5OXl6d+/fopPDxcXbp00bp16y7oZ8WKFerQoYMiIiI0aNAg7dq1K+B9AQAAN7YrCk1JSUnKysryC0g+n09ZWVm6/fbbA95Ofn6+XC6X3nvvPeXk5OjMmTMaMWLEBRNkPvLII6qsrDQfixcv9nvflJQU1dTUaOfOnVq/fr3WrVun+fPnmzXl5eVKSUnRHXfcoeLiYs2aNUuTJ0/Wtm3bzJpXX31VaWlpysjI0J49e9S7d285nU4dPnz4Sj4iAABwozGuQFlZmdGqVSujc+fOxoQJE4wJEyYYnTt3Nm666SajtLT0SjZpGIZhHD582JBk5Ofnm8uGDh1qzJw585Kv2bJli2Gz2Qy3220uW7VqlWG3243q6mrDMAxj7ty5Ro8ePfxeN27cOMPpdJrPBw4caLhcLvO5z+cz4uPjjaysrIB693g8hiTD4/EEVI9L27dvnzF06FBj3759VrcCALjBXc7v9xUdaerevbtKSkr0b//2bzp8+LCOHz+uhx56SHv37lXPnj2vOMB5PB5JUnR0tN/y7OxstW7dWj179tS8efN08uRJc11hYaF69eqlmJgYc5nT6ZTX61VZWZlZk5yc7LdNp9OpwsJCSVJNTY2Kior8amw2m5KTk82a81VXV8vr9fo9AADAjeuK52mKj4/XL37xi6vWSG1trWbNmqUhQ4b4Ba8HHnhA7du3V3x8vEpKSvT4449r3759+vOf/yxJcrvdfoFJkvnc7XZ/a43X69WpU6d09OhR+Xy+i9bs3bv3ov1mZWUpMzPz++00AAC4bgQcmkpKStSzZ0/ZbDaVlJR8a21iYuJlN+JyufTRRx/pnXfe8Vs+ZcoU8++9evVSXFychg8frgMHDqhz586X/T5Xy7x585SWlmY+93q9ateunWX9AACAayvg0NSnTx+53W61adNGffr0UUhIiAzDuKAuJCTksq6gk6Tp06dr06ZNKigoUNu2bb+1dtCgQZKkzz77TJ07d1ZsbOwFV7lVVVVJkmJjY80/65adW2O329WkSROFhoYqNDT0ojV12zhfeHi4wsPDA99JAABwXQt4TFN5ebluuukm8++ff/65ysvLL3h8/vnnAb+5YRiaPn26Xn/9de3YsUMdO3b8ztfUzQMVFxcnSXI4HCotLfW7yi0nJ0d2u13du3c3a3Jzc/22k5OTI4fDIUkKCwtT//79/Wpqa2uVm5tr1gAAgIYt4CNN7du3lySdOXNGmZmZevLJJwMKOd/G5XJpw4YNevPNN9WiRQtzDFJkZKSaNGmiAwcOaMOGDRo1apRatWqlkpISzZ49W0lJSeYpwBEjRqh79+568MEHtXjxYrndbqWnp8vlcplHgqZOnarly5dr7ty5mjRpknbs2KHXXntNmzdvNntJS0vT+PHjNWDAAA0cOFBLly7ViRMnNHHixO+1jwCubz6fTyUlJTpy5Iiio6OVmJio0NBQq9sCYIUruTzPbrcbn3/++ZW81I+kiz5eeuklwzAMo6KiwkhKSjKio6ON8PBwo0uXLsacOXMuuCzwiy++MEaOHGk0adLEaN26tfHYY48ZZ86c8av529/+ZvTp08cICwszOnXqZL7HuX7zm98YCQkJRlhYmDFw4EDjvffeC3hfmHLg6mHKAQSL/Px8Y9y4ccbQoUPNx7hx4/ymRQFwfbuc3+8Qw7jIwKTvMH78ePXp00ezZ8++mvntuub1ehUZGSmPxyO73W51O9e1/fv3a8qUKVqzZo26du1qdTtooAoKCpSRkSGHw6HU1FR17NhR5eXlys7OVmFhoTIzM5WUlGR1mwC+p8v5/b6iKQduvvlmLViwQO+++6769++vZs2a+a3/j//4jyvZLAAEBZ/Pp5UrV8rhcGjhwoWy2b4Z/tmjRw8tXLhQ6enpWrVqlYYMGcKpOqABuaLQ9F//9V+KiopSUVGRioqK/NaFhIQQmgBc10pKSuR2u/Xkk0+agamOzWZTamqqXC6XSkpK1LdvX4u6BFDfrig0lZeXm3+vO7sXEhJydToCAIsdOXJEki55sUvd8ro6AA3DFd1GRfrmaFPPnj0VERGhiIgI9ezZU2vXrr2avQGAJepu5XTufyCeq275+bd8AnBju6LQNH/+fM2cOVN33323Nm7cqI0bN+ruu+/W7NmzNX/+/KvdIwDUq8TERMXGxio7O1u1tbV+62pra5Wdna24uLgruvsBgOvXFZ2eW7VqlV588UXdf//95rIf//jHSkxM1IwZM7RgwYKr1iAA1LfQ0FA9+uijysjIUHp6+iWvnmMQONCwXFFoOnPmjAYMGHDB8v79++vs2bPfuykAsFpSUpIyMzO1cuVKuVwuc3lcXBzTDQAN1BWFpgcffFCrVq3S888/77d8zZo1Sk1NvSqNAYDVkpKSNGTIEGYEByDpCkOT9M1A8O3bt2vw4MGSpPfff18VFRV66KGHlJaWZtadH6wA4HoSGhrKtAIAJF1haProo4/Ur18/SdKBAwckSa1bt1br1q310UcfmXVMQwAAAG4UVxSa/va3v13tPgAAAILaFc/TBAAA0JAQmgAAAAJwxQPBcW1UVVXJ4/FY3YalDh486PdnQxYZGamYmBir2wAASAox6m4eh+/F6/UqMjJSHo9Hdrv9irZRVVWl//fgQzpTU32Vu8P1qnFYuF7+/e8ITgBwjVzO7zdHmoKIx+PRmZpqneo0VLURkVa3A4vZTnukz/Pl8XgITQAQBAhNQag2IlK1zVpb3QYAADgHA8EBAAACQGgCAAAIAKEJAAAgAIQmAACAABCaAAAAAkBoAgAACAChCQAAIACEJgAAgAAQmgAAAAJAaAIAAAgAoQkAACAAhCYAAIAAEJoAAAACQGgCAAAIgKWhKSsrSz/4wQ/UokULtWnTRqNHj9a+ffv8ak6fPi2Xy6VWrVqpefPmGjt2rKqqqvxqKioqlJKSoqZNm6pNmzaaM2eOzp4961eTl5enfv36KTw8XF26dNG6desu6GfFihXq0KGDIiIiNGjQIO3ateuq7zMAALg+WRqa8vPz5XK59N577yknJ0dnzpzRiBEjdOLECbNm9uzZeuutt7Rx40bl5+fryy+/1JgxY8z1Pp9PKSkpqqmp0c6dO7V+/XqtW7dO8+fPN2vKy8uVkpKiO+64Q8XFxZo1a5YmT56sbdu2mTWvvvqq0tLSlJGRoT179qh3795yOp06fPhw/XwYAAAgqIUYhmFY3USdr776Sm3atFF+fr6SkpLk8Xh00003acOGDfrpT38qSdq7d6+6deumwsJCDR48WH/5y1/0ox/9SF9++aViYmIkSatXr9bjjz+ur776SmFhYXr88ce1efNmffTRR+Z73XfffTp27Ji2bt0qSRo0aJB+8IMfaPny5ZKk2tpatWvXTjNmzNB//ud/fmfvXq9XkZGR8ng8stvtV7T/+/fv15QpU3Si+49V26z1FW0DNw7biX+o2cf/rTVr1qhr165WtwMAN6TL+f0OqjFNHo9HkhQdHS1JKioq0pkzZ5ScnGzW3HrrrUpISFBhYaEkqbCwUL169TIDkyQ5nU55vV6VlZWZNeduo66mbhs1NTUqKiryq7HZbEpOTjZrzlddXS2v1+v3AAAAN66gCU21tbWaNWuWhgwZop49e0qS3G63wsLCFBUV5VcbExMjt9tt1pwbmOrW1637thqv16tTp07pH//4h3w+30Vr6rZxvqysLEVGRpqPdu3aXdmOAwCA60LQhCaXy6WPPvpIr7zyitWtBGTevHnyeDzm49ChQ1a3BAAArqFGVjcgSdOnT9emTZtUUFCgtm3bmstjY2NVU1OjY8eO+R1tqqqqUmxsrFlz/lVudVfXnVtz/hV3VVVVstvtatKkiUJDQxUaGnrRmrptnC88PFzh4eFXtsMAAOC6Y+mRJsMwNH36dL3++uvasWOHOnbs6Le+f//+aty4sXJzc81l+/btU0VFhRwOhyTJ4XCotLTU7yq3nJwc2e12de/e3aw5dxt1NXXbCAsLU//+/f1qamtrlZuba9YAAICGzdIjTS6XSxs2bNCbb76pFi1amOOHIiMj1aRJE0VGRurhhx9WWlqaoqOjZbfbNWPGDDkcDg0ePFiSNGLECHXv3l0PPvigFi9eLLfbrfT0dLlcLvNI0NSpU7V8+XLNnTtXkyZN0o4dO/Taa69p8+bNZi9paWkaP368BgwYoIEDB2rp0qU6ceKEJk6cWP8fDICg4fP5VFJSoiNHjig6OlqJiYkKDQ21ui0AFrA0NK1atUqSNGzYML/lL730kiZMmCBJWrJkiWw2m8aOHavq6mo5nU6tXLnSrA0NDdWmTZs0bdo0ORwONWvWTOPHj9eCBQvMmo4dO2rz5s2aPXu2li1bprZt22rt2rVyOp1mzbhx4/TVV19p/vz5crvd6tOnj7Zu3XrB4HAADUdBQYFWrlzpd0FIbGysHn30USUlJVnYGQArBNU8Tdcz5mnC1cY8TdYqKChQRkaGBg8erIEDByo8PFzV1dXatWuX3nvvPWVmZhKcgBvA5fx+B8VAcAAIJj6fTytXrlTXrl1VXl7uN19bbGysunbtqlWrVmnIkCGcqgMakKCZcgAAgkVJSYncbrf279+vTp06acWKFdqyZYtWrFihTp06af/+/aqsrFRJSYnVrQKoR4QmADjPP/7xD0nSwIEDtXDhQvXo0UNNmzZVjx49tHDhQg0cONCvDkDDQGgCgPMcO3ZMkvTDH/5QhmHoww8/VG5urj788EMZhqHbb7/drw5Aw8CYJgA4T91kum+++aZefvnlC66ea9GihV8dgIaBI00AcJ7Wrb+5evXTTz9VdXW1fvazn+lPf/qTfvazn6m6ulqffvqpXx2AhoEjTQBwnh49eig0NFQRERFq3LixfvWrX5nrYmJi1KxZM50+fVo9evSwsEsA9Y3QBADnKSsrk8/n08mTJ9WrVy/98Ic/VHV1tcLDw/W///u/ev/992UYhsrKytS3b1+r2wVQTwhNAHCeI0eOSJLGjBmjN954Q++99565LjQ0VGPGjNGf/vQnsw5Aw0BoAoDzREdHS5L+9Kc/yeFwXDAj+J/+9Ce/OqA+cT9E6xCaAOA8dWOa7Ha7nn76aTVq9H//VN59992699575fV6GdOEesf9EK3F1XMAcJ66MU3Hjh3T/PnzVVZWppMnT6qsrEzz58/XsWPH5PP5VFZWZnWraEDq7od4sVnqMzIyVFBQYHWLNzxCEwCcp26s0s9//nN9/vnncrlcGjVqlFwul8rLy/Xzn//crw641uruh+hwOC46S73D4dCqVavk8/msbvWGxuk5ADhP3Vil+Ph4ZWdnXzB+ZO/evX51wLVWdz/EJ598Ujab//EOm82m1NRUuVwulZSUcEXnNcSRJgA4T2JiomJjY5Wdna2QkBD17dtXw4cPV9++fRUSEqLs7GzFxcUpMTHR6lbRQNQd1ezYseNF19ct5+jntUVoAoDzhIaG6tFHH1VhYaHS09P9xjSlp6ersLBQ06ZN44ol1Ju6o5rl5eUXXV+3nKOf1xahCQAuIikpSZmZmRcd05SZmcmVSqhX5x79rK2t9VtXW1vL0c96wpgmALiEpKQkDRkyhDlxYLm6o58ZGRlKT09XamqqOnbsqPLycmVnZ6uwsFCZmZl8N68xQlMQsp06ZnULCAJ8D4JDaGgoA2sRFOqOfq5cuVIul8tcHhcXx9HPekJoCkJNyplrAwBwIY5+WovQFIROdUxSbZMoq9uAxWynjhGgAVyAo5/WITQFodomUapt1trqNgAAwDm4eg4AACAAhCYAAIAAEJoAAAACwJgmAPgWPp+PK5UASCI0AcAlFRQUaOXKlXK73eay2NhYPfroo8yJAzRAnJ4DgIsoKChQRkaGOnXqpBUrVmjLli1asWKFOnXqpIyMDBUUMB0E0NAQmgDgPD6fTytXrpTD4dDChQvVo0cPNW3aVD169NDChQvlcDi0atUq+Xw+q1sFUI8ITQBwnpKSErndbqWmpspm8/9n0mazKTU1VZWVlSopKbGoQwBWIDQBwHmOHDkiSerYseNF19ctr6sD0DBYOhC8oKBAv/zlL1VUVKTKykq9/vrrGj16tLl+woQJWr9+vd9rnE6ntm7daj4/cuSIZsyYobfeeks2m01jx47VsmXL1Lx5c7OmpKRELpdLu3fv1k033aQZM2Zo7ty5ftvduHGjnnzySX3xxRe6+eab9eyzz2rUqFHXZscBBOz06dOqqKio1/c8efKkpG/+jerUqdMF6w8cOGDW7d+/v157S0hIUERERL2+J4BvWBqaTpw4od69e2vSpEkaM2bMRWvuuusuvfTSS+bz8PBwv/V1h8lzcnJ05swZTZw4UVOmTNGGDRskSV6vVyNGjFBycrJWr16t0tJSTZo0SVFRUZoyZYokaefOnbr//vuVlZWlH/3oR9qwYYNGjx6tPXv2qGfPntdo7wEEoqKiwvz/an175plnvnX9c889V0+d/J81a9aoa9eu9f6+ACwOTSNHjtTIkSO/tSY8PFyxsbEXXffJJ59o69at2r17twYMGCBJ+s1vfqNRo0bpV7/6leLj45Wdna2amhr99re/VVhYmHr06KHi4mI9//zz5j/Ey5Yt01133aU5c+ZIkp5++mnl5ORo+fLlWr169UXfu7q6WtXV1eZzr9d72fsP4LslJCRozZo19f6+e/bs0QsvvKBevXqpb9++Wr9+vcaPH68PP/xQpaWl+vd//3f169ev3vtKSEio9/cE8I2gn6cpLy9Pbdq0UcuWLXXnnXdq4cKFatWqlSSpsLBQUVFRZmCSpOTkZNlsNr3//vv6yU9+osLCQiUlJSksLMyscTqdevbZZ3X06FG1bNlShYWFSktL83tfp9OpN95445J9ZWVlKTMz8+ruLIALREREWHJkpWvXroqPj9fKlSvNYQLr169XXFycMjMzmacJaICCeiD4XXfdpd/97nfKzc3Vs88+q/z8fI0cOdK8zNftdqtNmzZ+r2nUqJGio6PNyejcbrdiYmL8auqef1fNuRPanW/evHnyeDzm49ChQ99vZwEEnaSkJGVnZ+uxxx6TJD322GN6+eWXCUxAAxXUR5ruu+8+8++9evVSYmKiOnfurLy8PA0fPtzCzr45bXj++CoAN57Q0FDdcsstkqRbbrmFW6gADVhQH2k6X6dOndS6dWt99tlnkr65ncHhw4f9as6ePasjR46Y46BiY2NVVVXlV1P3/LtqLjWWCgAANDzXVWj6n//5H/3zn/9UXFycJMnhcOjYsWMqKioya3bs2KHa2loNGjTIrCkoKNCZM2fMmpycHN1yyy1q2bKlWZObm+v3Xjk5OXI4HNd6lwAAwHXC0tD09ddfq7i4WMXFxZKk8vJyFRcXq6KiQl9//bXmzJmj9957T1988YVyc3N1zz33qEuXLnI6nZKkbt266a677tIjjzyiXbt26d1339X06dN13333KT4+XpL0wAMPKCwsTA8//LDKysr06quvatmyZX4Dv2fOnKmtW7fqueee0969e/XUU0/pgw8+0PTp0+v9MwEAAMHJ0tD0wQcfqG/fvurbt68kKS0tTX379tX8+fMVGhqqkpIS/fjHP1bXrl318MMPq3///nr77bf9xhJlZ2fr1ltv1fDhwzVq1CjdfvvtfpcnR0ZGavv27SovL1f//v312GOPaf78+X7zvtx2223asGGD1qxZo969e+uPf/yj3njjDeZoAgAAJksHgg8bNkyGYVxy/bZt275zG9HR0eZElpeSmJiot99++1tr7r33Xt17773f+X4AAKBhuq7GNAEAAFiF0AQAABAAQhMAAEAACE0AAAABCOoZwRsq22mP1S0gCPA9AIDgQmgKIpGRkWocFi59nm91KwgSjcPCFRkZaXUbAAARmoJKTEyMXv797+TxNOwjDAcPHtSiRYv0xBNPqH379la3Y6nIyMgLbiYNALAGoSnIxMTE8CP5/2vfvr26du1qdRsAAEhiIDgAAEBACE0AAAABIDQBAAAEgNAEAAAQAEITAABAAAhNAAAAASA0AQAABIDQBAAAEABCEwAAQAAITQAAAAEgNAEAAASAe88BAHAZTp8+rYqKCqvbCCoJCQmKiIiwuo1rjtAEAMBlqKio0JQpU6xuI6isWbOmQdxgndAEAMBlSEhI0Jo1ayzt4eDBg1q0aJGeeOIJtW/f3tJepG8+k4aA0AQAuK5UVVXJ4/FY3QbOYfXpysjISMXExFzz9yE0AQCuG1VVVfp/Dz6kMzXVVrcSFBYtWmR1C0GhcVi4Xv797655cCI0AQCuGx6PR2dqqnWq01DVRkRa3Q6CgO20R/o8Xx6Ph9AEAMD5aiMiVdustdVtoIFhniYAAIAAEJoAAAACQGgCAAAIAKEJAAAgAAwEB/CtmBPnm4kEz/2zIauv+XCAYGRpaCooKNAvf/lLFRUVqbKyUq+//rpGjx5trjcMQxkZGXrxxRd17NgxDRkyRKtWrdLNN99s1hw5ckQzZszQW2+9JZvNprFjx2rZsmVq3ry5WVNSUiKXy6Xdu3frpptu0owZMzR37ly/XjZu3Kgnn3xSX3zxhW6++WY9++yzGjVq1DX/DIBgxpw4/pgTp/7mwwGCkaWh6cSJE+rdu7cmTZqkMWPGXLB+8eLF+vWvf63169erY8eOevLJJ+V0OvXxxx+bNwZMTU1VZWWlcnJydObMGU2cOFFTpkzRhg0bJEler1cjRoxQcnKyVq9erdLSUk2aNElRUVHmvYN27typ+++/X1lZWfrRj36kDRs2aPTo0dqzZ4969uxZfx8IEGSYEwfnqs/5cIBgZGloGjlypEaOHHnRdYZhaOnSpUpPT9c999wjSfrd7775r5s33nhD9913nz755BNt3bpVu3fv1oABAyRJv/nNbzRq1Cj96le/Unx8vLKzs1VTU6Pf/va3CgsLU48ePVRcXKznn3/eDE3Lli3TXXfdpTlz5kiSnn76aeXk5Gj58uVavXr1Rfurrq5WdfX//de31+u9ap8LEGyYEwcAgnggeHl5udxut5KTk81lkZGRGjRokAoLCyVJhYWFioqKMgOTJCUnJ8tms+n99983a5KSkhQWFmbWOJ1O7du3T0ePHjVrzn2fupq697mYrKwsRUZGmo927dp9/50GAABBK2hDk9vtlqQLDgHHxMSY69xut9q0aeO3vlGjRoqOjvarudg2zn2PS9XUrb+YefPmyePxmI9Dhw5d7i4CAIDrCFfPXaHw8HCFh4db3QYAAKgnQXukKTY2VtI3V++cq6qqylwXGxurw4cP+60/e/asjhw54ldzsW2c+x6XqqlbDwAAELShqWPHjoqNjVVubq65zOv16v3335fD4ZAkORwOHTt2TEVFRWbNjh07VFtbq0GDBpk1BQUFOnPmjFmTk5OjW265RS1btjRrzn2fupq69wEAALA0NH399dcqLi5WcXGxpG8GfxcXF6uiokIhISGaNWuWFi5cqP/+7/9WaWmpHnroIcXHx5tzOXXr1k133XWXHnnkEe3atUvvvvuupk+frvvuu0/x8fGSpAceeEBhYWF6+OGHVVZWpldffVXLli1TWlqa2cfMmTO1detWPffcc9q7d6+eeuopffDBB5o+fXp9fyQAACBIWTqm6YMPPtAdd9xhPq8LMuPHj9e6des0d+5cnThxQlOmTNGxY8d0++23a+vWreYcTZKUnZ2t6dOna/jw4ebklr/+9a/N9ZGRkdq+fbtcLpf69++v1q1ba/78+eZ0A5J02223acOGDUpPT9fPf/5z3XzzzXrjjTeYowkAAJgsDU3Dhg2TYRiXXB8SEqIFCxZowYIFl6yJjo42J7K8lMTERL399tvfWnPvvffq3nvv/faGAQBBwXbqmNUtIEjU53eBq+cAANedJuUFVreABojQBAC47pzqmKTaJlFWt4EgYDt1rN5CNKEJAHDdqW0Sxa19UO+CdsoBAACAYEJoAgAACAChCQAAIACMaQLwnbi8GxLfA4DQBOA7cXk3ABCaAASAy7sh1e+l3d/FdtpjdQsIEvX5XSA0AfhOXN6NYBEZGanGYeHS5/lWt4Ig0jgsXJGRkdf8fQhNAIDrRkxMjF7+/e/k8TTsI00HDx7UokWL9MQTT6h9+/ZWt2O5yMhIxcTEXPP3ITQBAK4rMTEx9fIDeSmnT59WRUWFZe8fjBISEhQREWF1G9ccoQkAgMtQUVGhKVOmWN2GJGnRokVWtyBJWrNmjbp27Wp1G9ccoQkAgMuQkJCgNWvWWN1GUElISLC6hXpBaAIA4DJEREQ0iKMquBAzggMAAASA0AQAABAATs8B+E5MJAiJ7wFAaIKfYLiU9uDBg35/WqmhXEZ7KUwkiPPV1ySCQDAKMQzDsLqJG4HX61VkZKQ8Ho/sdrvV7Vyx/fv3B82ltMGgoVxG+22qqqqYSJCJBE31NYkgUF8u5/ebI03ww6W0/hrKZbTfxuqJBINJ+/btG3yIBhoyQhP8cCktAAAXx9VzAAAAASA0AQAABIDQBAAAEABCEwAAQAAITQAAAAEgNAEAAASA0AQAABAAQhMAAEAAgjo0PfXUUwoJCfF73Hrrreb606dPy+VyqVWrVmrevLnGjh2rqqoqv21UVFQoJSVFTZs2VZs2bTRnzhydPXvWryYvL0/9+vVTeHi4unTponXr1tXH7gEAgOtIUIcmSerRo4cqKyvNxzvvvGOumz17tt566y1t3LhR+fn5+vLLLzVmzBhzvc/nU0pKimpqarRz506tX79e69at0/z5882a8vJypaSk6I477lBxcbFmzZqlyZMna9u2bfW6nwAAILgF/W1UGjVqpNjY2AuWezwe/dd//Zc2bNigO++8U5L00ksvqVu3bnrvvfc0ePBgbd++XR9//LH++te/KiYmRn369NHTTz+txx9/XE899ZTCwsK0evVqdezYUc8995wkqVu3bnrnnXe0ZMkSOZ3Oet1XABc6ffq0KioqLO3h4MGDfn9aKSEhQREREVa3ATRIQR+aPv30U8XHxysiIkIOh0NZWVlKSEhQUVGRzpw5o+TkZLP21ltvVUJCggoLCzV48GAVFhaqV69efjcbdTqdmjZtmsrKytS3b18VFhb6baOuZtasWd/aV3V1taqrq83nXq/36uwwAD8VFRWaMmWK1W1IkhYtWmR1C1qzZg33hwQsEtShadCgQVq3bp1uueUWVVZWKjMzUz/84Q/10Ucfye12KywsTFFRUX6viYmJkdvtliS53e4L7s5e9/y7arxer06dOqUmTZpctLesrCxlZmZejd0E8C0SEhK0Zs0aq9sIGgkJCVa3ADRYQR2aRo4caf49MTFRgwYNUvv27fXaa69dMszUl3nz5iktLc187vV61a5dOws7Am5MERERHFkBEBSCfiD4uaKiotS1a1d99tlnio2NVU1NjY4dO+ZXU1VVZY6Bio2NveBqurrn31Vjt9u/NZiFh4fLbrf7PQAAwI3rugpNX3/9tQ4cOKC4uDj1799fjRs3Vm5urrl+3759qqiokMPhkCQ5HA6Vlpbq8OHDZk1OTo7sdru6d+9u1py7jbqaum0AAABIQR6afvaznyk/P19ffPGFdu7cqZ/85CcKDQ3V/fffr8jISD388MNKS0vT3/72NxUVFWnixIlyOBwaPHiwJGnEiBHq3r27HnzwQf3973/Xtm3blJ6eLpfLpfDwcEnS1KlT9fnnn2vu3Lnau3evVq5cqddee02zZ8+2ctcBAECQCeoxTf/zP/+j+++/X//85z9100036fbbb9d7772nm266SZK0ZMkS2Ww2jR07VtXV1XI6nVq5cqX5+tDQUG3atEnTpk2Tw+FQs2bNNH78eC1YsMCs6dixozZv3qzZs2dr2bJlatu2rdauXct0AwAAwE+IYRiG1U3cCLxeryIjI+XxeBjfBADAdeJyfr+D+vQcAABAsCA0AQAABIDQBAAAEABCEwAAQAAITQAAAAEgNAEAAASA0AQAABAAQhMAAEAAgnpG8OtJ3RyhXq/X4k4AAECg6n63A5nrm9B0lRw/flyS1K5dO4s7AQAAl+v48eOKjIz81hpuo3KV1NbW6ssvv1SLFi0UEhJidTvXNa/Xq3bt2unQoUPckgZBge8kgg3fyavHMAwdP35c8fHxstm+fdQSR5quEpvNprZt21rdxg3FbrfzjwGCCt9JBBu+k1fHdx1hqsNAcAAAgAAQmgAAAAJAaELQCQ8PV0ZGhsLDw61uBZDEdxLBh++kNRgIDgAAEACONAEAAASA0AQAABAAQhMAAEAACE24bk2YMEGjR4+2ug1cxwzD0JQpUxQdHa2QkBAVFxd/r+3xnUQw4/v5/RGaADRYW7du1bp167Rp0yZVVlaqZ8+eVrcESJKeeeYZhYSEaNasWVa3gnMwIzgsUVNTo7CwMKvbQAN34MABxcXF6bbbbrO6FcC0e/duvfDCC0pMTLS6FZyHI02oF8OGDdP06dM1a9YstW7dWk6nUz/60Y/M9UuXLlVISIi2bt1qLuvSpYvWrl0rSfL5fEpLS1NUVJRatWqluXPnBnRHauBSJkyYoBkzZqiiokIhISFq3bo130lY7uuvv1ZqaqpefPFFtWzZ0lz+s5/9jO9nECA0od6sX79eYWFhevfddzV69Gi988478vl8kqT8/Hy1bt1aeXl5kqT//d//1YEDBzRs2DBJ0nPPPad169bpt7/9rd555x0dOXJEr7/+ukV7ghvBsmXLtGDBArVt21aVlZVasmQJ30lYzuVyKSUlRcnJyX7Lhw4dyvczGBhAPRg6dKjRt29f8/nRo0cNm81m7N6926itrTWio6ONrKwsY9CgQYZhGMbLL79s/Mu//ItZHxcXZyxevNh8fubMGaNt27bGPffcU2/7gBvPkiVLjPbt2xuGwXcS1vvDH/5g9OzZ0zh16pRhGN/8uzlz5kzDMPh+BguONKHe9O/f3/x7VFSUevfurby8PJWWliosLExTpkzRhx9+qK+//lr5+fkaOnSoJMnj8aiyslKDBg0yX9+oUSMNGDCg3vcBNy6+k7DSoUOHNHPmTGVnZysiIuKC9Xw/gwMDwVFvmjVr5vd82LBhysvLU3h4uIYOHaro6Gh169ZN77zzjvLz8/XYY49Z1CkaKr6TsEpRUZEOHz6sfv36mct8Pp8KCgq0fPlyVVdX8/0MAhxpgmXqztHn5uaa5+GHDRumP/zhD9q/f7+5LDIyUnFxcXr//ffN1549e1ZFRUUWdI0bGd9JWGX48OEqLS1VcXGx+RgwYIBSU1NVXFys0NBQvp9BgCNNsExSUpKOHz+uTZs26ZlnnpH0zT8AP/3pTxUXF6euXbuatTNnztQzzzyjm2++Wbfeequef/55HTt2zKLOcaPiOwmrtGjR4oJ5wpo1a6ZWrVqZy/l+Wo/QBMu0bNlSvXr1UlVVlW699VZJ3/yjUFtba56br/PYY4+psrJS48ePl81m06RJk/STn/xEHo/HitZxg+I7iWDG99N6IYbBxA0AAADfhTFNAAAAASA0AQAABIDQBAAAEABCEwAAQAAITQAAAAEgNAEAAASA0AQAABAAQhMAAEAACE0Ags6wYcM0a9asS67/4osvFBISouLi4nrr6bvk5eUpJCTkmt+q4qmnnlKfPn2u6XsAuDhuowLgutOuXTtVVlaqdevWVrcCoAEhNAG4rtTU1CgsLEyxsbFWtwKggeH0HABLnThxQg899JCaN2+uuLg4Pffcc37rO3TooKeffloPPfSQ7Ha7pkyZ4nd6rra2Vm3bttWqVav8Xvfhhx/KZrPp4MGDkqRjx45p8uTJuummm2S323XnnXfq73//+yX7+uijj2Sz2fTVV19Jko4cOSKbzab77rvPrFm4cKFuv/12v9cVFRVpwIABatq0qW677Tbt27fPb/2bb76pfv36KSIiQp06dVJmZqbOnj1rrr/cPvPy8jRw4EA1a9ZMUVFRGjJkiLnPAK4uQhMAS82ZM0f5+fl68803tX37duXl5WnPnj1+Nb/61a/Uu3dvffjhh3ryySf91tlsNt1///3asGGD3/Ls7GwNGTJE7du3lyTde++9Onz4sP7yl7+oqKhI/fr10/Dhw3XkyJGL9tWjRw+1atVK+fn5kqS3337b77kk5efna9iwYX6ve+KJJ/Tcc8/pgw8+UKNGjTRp0iRz3dtvv62HHnpIM2fO1Mcff6wXXnhB69at06JFi8yay+nz7NmzGj16tIYOHaqSkhIVFhZqypQpCgkJudTHDeD7MADAIsePHzfCwsKM1157zVz2z3/+02jSpIkxc+ZMwzAMo3379sbo0aP9XldeXm5IMj788EPDMAzjww8/NEJCQoyDBw8ahmEYPp/P+Jd/+Rdj1apVhmEYxttvv23Y7Xbj9OnTftvp3Lmz8cILL1yyvzFjxhgul8swDMOYNWuWMWfOHKNly5bGJ598YtTU1BhNmzY1tm/fbhiGYfztb38zJBl//etfzddv3rzZkGScOnXKMAzDGD58uPGLX/zC7z1+//vfG3FxcQH3mZGRYfTu3dv8rCQZeXl5l9wHAFcPR5oAWObAgQOqqanRoEGDzGXR0dG65ZZb/OoGDBjwrdvp06ePunXrZh5tys/P1+HDh3XvvfdKkv7+97/r66+/VqtWrdS8eXPzUV5ergMHDqiiosJv+S9+8QtJ0tChQ5WXl2du884771RSUpLy8vK0e/dunTlzRkOGDPHrJTEx0fx7XFycJOnw4cNmHwsWLPB7r0ceeUSVlZU6efLkd/Z5vujoaE2YMEFOp1N33323li1bpsrKyu/83AFcGQaCAwh6zZo1+86a1NRUbdiwQf/5n/+pDRs26K677lKrVq0kSV9//bXi4uLMAHSuqKgoRUVF+U1fEB0dLen/pj749NNP9fHHH+v222/X3r17lZeXp6NHj5pjl87VuHFj8+91p8lqa2vNPjIzMzVmzJgL+oiIiPjOPi/mpZde0n/8x39o69atevXVV5Wenq6cnBwNHjz4kp8VgCtDaAJgmc6dO6tx48Z6//33lZCQIEk6evSo9u/fr6FDh17Wth544AGlp6erqKhIf/zjH7V69WpzXb9+/eR2u9WoUSN16NDhoq/v0qXLBct69eqlli1bauHCherTp4+aN2+uYcOG6dlnn9XRo0cvGM/0Xfr166d9+/Zd9L0C7fNi+vbtq759+2revHlyOBzasGEDoQm4Bjg9B8AyzZs318MPP6w5c+Zox44d+uijjzRhwgTZbJf/T1OHDh1022236eGHH5bP59OPf/xjc11ycrIcDodGjx6t7du364svvtDOnTv1xBNP6IMPPrjkNkNCQpSUlKTs7GwzICUmJqq6ulq5ubmXHezmz5+v3/3ud8rMzFRZWZk++eQTvfLKK0pPT7+iPsvLyzVv3jwVFhbq4MGD2r59uz799FN169btsvoCEBhCEwBL/fKXv9QPf/hD3X333UpOTtbtt9+u/v37X9G2UlNT9fe//10/+clP1KRJE3N5SEiItmzZoqSkJE2cOFFdu3bVfffdp4MHDyomJuZbtzl06FD5fD4zNNlsNiUlJSkkJOSC8Uzfxel0atOmTdq+fbt+8IMfaPDgwVqyZIl5hd/l9tm0aVPt3btXY8eOVdeuXTVlyhS5XC79+7//+2X1BSAwIYZhGFY3AQAAEOw40gQAABAAQhMAAEAACE0AAAABIDQBAAAEgNAEAAAQAEITAABAAAhNAAAAASA0AQAABIDQBAAAEABCEwAAQAAITQAAAAH4/wDcCOYY9ml/SAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# drive-wheels\n",
    "sns.boxplot(x=\"drive-wheels\", y=\"price\", data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Here we see that the distribution of price between the different drive-wheels categories differs. As such, drive-wheels could potentially be a predictor of price."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Descriptive Statistical Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symboling</th>\n",
       "      <th>normalized-losses</th>\n",
       "      <th>make</th>\n",
       "      <th>aspiration</th>\n",
       "      <th>num-of-doors</th>\n",
       "      <th>body-style</th>\n",
       "      <th>drive-wheels</th>\n",
       "      <th>engine-location</th>\n",
       "      <th>wheel-base</th>\n",
       "      <th>length</th>\n",
       "      <th>...</th>\n",
       "      <th>compression-ratio</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>peak-rpm</th>\n",
       "      <th>city-mpg</th>\n",
       "      <th>highway-mpg</th>\n",
       "      <th>price</th>\n",
       "      <th>city-L/100km</th>\n",
       "      <th>horsepower-binned</th>\n",
       "      <th>diesel</th>\n",
       "      <th>gas</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>201.000000</td>\n",
       "      <td>201.00000</td>\n",
       "      <td>201</td>\n",
       "      <td>201</td>\n",
       "      <td>201</td>\n",
       "      <td>201</td>\n",
       "      <td>201</td>\n",
       "      <td>201</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>200</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>201.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>toyota</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Low</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32</td>\n",
       "      <td>165</td>\n",
       "      <td>115</td>\n",
       "      <td>94</td>\n",
       "      <td>118</td>\n",
       "      <td>198</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>115</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.840796</td>\n",
       "      <td>122.00000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>98.797015</td>\n",
       "      <td>0.837102</td>\n",
       "      <td>...</td>\n",
       "      <td>10.164279</td>\n",
       "      <td>103.405534</td>\n",
       "      <td>5117.665368</td>\n",
       "      <td>25.179104</td>\n",
       "      <td>30.686567</td>\n",
       "      <td>13207.129353</td>\n",
       "      <td>9.944145</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.099502</td>\n",
       "      <td>0.900498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.254802</td>\n",
       "      <td>31.99625</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.066366</td>\n",
       "      <td>0.059213</td>\n",
       "      <td>...</td>\n",
       "      <td>4.004965</td>\n",
       "      <td>37.365700</td>\n",
       "      <td>478.113805</td>\n",
       "      <td>6.423220</td>\n",
       "      <td>6.815150</td>\n",
       "      <td>7947.066342</td>\n",
       "      <td>2.534599</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.300083</td>\n",
       "      <td>0.300083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-2.000000</td>\n",
       "      <td>65.00000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>86.600000</td>\n",
       "      <td>0.678039</td>\n",
       "      <td>...</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>4150.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>5118.000000</td>\n",
       "      <td>4.795918</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>101.00000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>94.500000</td>\n",
       "      <td>0.801538</td>\n",
       "      <td>...</td>\n",
       "      <td>8.600000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>4800.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>7775.000000</td>\n",
       "      <td>7.833333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>122.00000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>0.832292</td>\n",
       "      <td>...</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>5125.369458</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>10295.000000</td>\n",
       "      <td>9.791667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>137.00000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>102.400000</td>\n",
       "      <td>0.881788</td>\n",
       "      <td>...</td>\n",
       "      <td>9.400000</td>\n",
       "      <td>116.000000</td>\n",
       "      <td>5500.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>16500.000000</td>\n",
       "      <td>12.368421</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>256.00000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>120.900000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>262.000000</td>\n",
       "      <td>6600.000000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>45400.000000</td>\n",
       "      <td>18.076923</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         symboling  normalized-losses    make aspiration num-of-doors  \\\n",
       "count   201.000000          201.00000     201        201          201   \n",
       "unique         NaN                NaN      22          2            2   \n",
       "top            NaN                NaN  toyota        std         four   \n",
       "freq           NaN                NaN      32        165          115   \n",
       "mean      0.840796          122.00000     NaN        NaN          NaN   \n",
       "std       1.254802           31.99625     NaN        NaN          NaN   \n",
       "min      -2.000000           65.00000     NaN        NaN          NaN   \n",
       "25%       0.000000          101.00000     NaN        NaN          NaN   \n",
       "50%       1.000000          122.00000     NaN        NaN          NaN   \n",
       "75%       2.000000          137.00000     NaN        NaN          NaN   \n",
       "max       3.000000          256.00000     NaN        NaN          NaN   \n",
       "\n",
       "       body-style drive-wheels engine-location  wheel-base      length  ...  \\\n",
       "count         201          201             201  201.000000  201.000000  ...   \n",
       "unique          5            3               2         NaN         NaN  ...   \n",
       "top         sedan          fwd           front         NaN         NaN  ...   \n",
       "freq           94          118             198         NaN         NaN  ...   \n",
       "mean          NaN          NaN             NaN   98.797015    0.837102  ...   \n",
       "std           NaN          NaN             NaN    6.066366    0.059213  ...   \n",
       "min           NaN          NaN             NaN   86.600000    0.678039  ...   \n",
       "25%           NaN          NaN             NaN   94.500000    0.801538  ...   \n",
       "50%           NaN          NaN             NaN   97.000000    0.832292  ...   \n",
       "75%           NaN          NaN             NaN  102.400000    0.881788  ...   \n",
       "max           NaN          NaN             NaN  120.900000    1.000000  ...   \n",
       "\n",
       "        compression-ratio  horsepower     peak-rpm    city-mpg highway-mpg  \\\n",
       "count          201.000000  201.000000   201.000000  201.000000  201.000000   \n",
       "unique                NaN         NaN          NaN         NaN         NaN   \n",
       "top                   NaN         NaN          NaN         NaN         NaN   \n",
       "freq                  NaN         NaN          NaN         NaN         NaN   \n",
       "mean            10.164279  103.405534  5117.665368   25.179104   30.686567   \n",
       "std              4.004965   37.365700   478.113805    6.423220    6.815150   \n",
       "min              7.000000   48.000000  4150.000000   13.000000   16.000000   \n",
       "25%              8.600000   70.000000  4800.000000   19.000000   25.000000   \n",
       "50%              9.000000   95.000000  5125.369458   24.000000   30.000000   \n",
       "75%              9.400000  116.000000  5500.000000   30.000000   34.000000   \n",
       "max             23.000000  262.000000  6600.000000   49.000000   54.000000   \n",
       "\n",
       "               price city-L/100km  horsepower-binned      diesel         gas  \n",
       "count     201.000000   201.000000                200  201.000000  201.000000  \n",
       "unique           NaN          NaN                  3         NaN         NaN  \n",
       "top              NaN          NaN                Low         NaN         NaN  \n",
       "freq             NaN          NaN                115         NaN         NaN  \n",
       "mean    13207.129353     9.944145                NaN    0.099502    0.900498  \n",
       "std      7947.066342     2.534599                NaN    0.300083    0.300083  \n",
       "min      5118.000000     4.795918                NaN    0.000000    0.000000  \n",
       "25%      7775.000000     7.833333                NaN    0.000000    1.000000  \n",
       "50%     10295.000000     9.791667                NaN    0.000000    1.000000  \n",
       "75%     16500.000000    12.368421                NaN    0.000000    1.000000  \n",
       "max     45400.000000    18.076923                NaN    1.000000    1.000000  \n",
       "\n",
       "[11 rows x 29 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(include = 'all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "make\n",
       "toyota           32\n",
       "nissan           18\n",
       "mazda            17\n",
       "mitsubishi       13\n",
       "honda            13\n",
       "subaru           12\n",
       "volkswagen       12\n",
       "volvo            11\n",
       "peugot           11\n",
       "dodge             9\n",
       "mercedes-benz     8\n",
       "bmw               8\n",
       "plymouth          7\n",
       "audi              6\n",
       "saab              6\n",
       "porsche           4\n",
       "chevrolet         3\n",
       "alfa-romero       3\n",
       "jaguar            3\n",
       "isuzu             2\n",
       "renault           2\n",
       "mercury           1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"make\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>value_counts</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>fwd</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>rwd</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4wd</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  value_counts  count\n",
       "0          fwd    118\n",
       "1          rwd     75\n",
       "2          4wd      8"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drive_wheels_counts = df['drive-wheels'].value_counts().to_frame()\n",
    "drive_wheels_counts.reset_index(inplace=True)\n",
    "drive_wheels_counts=drive_wheels_counts.rename(columns={'drive-wheels': 'value_counts'})\n",
    "drive_wheels_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>value_counts</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>drive-wheels</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>fwd</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>rwd</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4wd</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             value_counts  count\n",
       "drive-wheels                    \n",
       "0                     fwd    118\n",
       "1                     rwd     75\n",
       "2                     4wd      8"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drive_wheels_counts.index.name = 'drive-wheels'\n",
    "drive_wheels_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>engine-location</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>front</th>\n",
       "      <td>198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rear</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 count\n",
       "engine-location       \n",
       "front              198\n",
       "rear                 3"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# engine-location as variable\n",
    "engine_loc_counts = df['engine-location'].value_counts().to_frame()\n",
    "engine_loc_counts.rename(columns={'engine-location': 'value_counts'}, inplace=True)\n",
    "engine_loc_counts.index.name = 'engine-location'\n",
    "engine_loc_counts.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### After examining the value counts of the engine location, we see that engine location would not be a good predictor variable for the price. This is because we only have three cars with a rear engine and 198 with an engine in the front, so this result is skewed. Thus, we are not able to draw any conclusions about the engine location."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Grouping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['rwd', 'fwd', '4wd'], dtype=object)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The \"groupby\" method groups data by different categories. The data is grouped based on one or several variables, and analysis is performed on the individual groups\n",
    "\n",
    "# For example, let's group by the variable \"drive-wheels\". We see that there are 3 different categories of drive wheels.\n",
    "\n",
    "df['drive-wheels'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>drive-wheels</th>\n",
       "      <th>body-style</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>rwd</td>\n",
       "      <td>convertible</td>\n",
       "      <td>13495.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>rwd</td>\n",
       "      <td>convertible</td>\n",
       "      <td>16500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>rwd</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>16500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>fwd</td>\n",
       "      <td>sedan</td>\n",
       "      <td>13950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4wd</td>\n",
       "      <td>sedan</td>\n",
       "      <td>17450.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>rwd</td>\n",
       "      <td>sedan</td>\n",
       "      <td>16845.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>rwd</td>\n",
       "      <td>sedan</td>\n",
       "      <td>19045.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>rwd</td>\n",
       "      <td>sedan</td>\n",
       "      <td>21485.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>rwd</td>\n",
       "      <td>sedan</td>\n",
       "      <td>22470.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>rwd</td>\n",
       "      <td>sedan</td>\n",
       "      <td>22625.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>201 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    drive-wheels   body-style    price\n",
       "0            rwd  convertible  13495.0\n",
       "1            rwd  convertible  16500.0\n",
       "2            rwd    hatchback  16500.0\n",
       "3            fwd        sedan  13950.0\n",
       "4            4wd        sedan  17450.0\n",
       "..           ...          ...      ...\n",
       "196          rwd        sedan  16845.0\n",
       "197          rwd        sedan  19045.0\n",
       "198          rwd        sedan  21485.0\n",
       "199          rwd        sedan  22470.0\n",
       "200          rwd        sedan  22625.0\n",
       "\n",
       "[201 rows x 3 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_group_one = df[[\"drive-wheels\", \"body-style\", \"price\"]]\n",
    "df_group_one"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>drive-wheels</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4wd</td>\n",
       "      <td>10241.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>fwd</td>\n",
       "      <td>9244.779661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>rwd</td>\n",
       "      <td>19757.613333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  drive-wheels         price\n",
       "0          4wd  10241.000000\n",
       "1          fwd   9244.779661\n",
       "2          rwd  19757.613333"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculate the average price for each of the different categories of data\n",
    "\n",
    "df_grouped = df_group_one.groupby([\"drive-wheels\"], as_index=False).agg({\"price\":\"mean\"})\n",
    "df_grouped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>body-style</th>\n",
       "      <th>drive-wheels</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>convertible</td>\n",
       "      <td>fwd</td>\n",
       "      <td>11595.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "      <td>23949.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>hardtop</td>\n",
       "      <td>fwd</td>\n",
       "      <td>8249.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>hardtop</td>\n",
       "      <td>rwd</td>\n",
       "      <td>24202.714286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>hatchback</td>\n",
       "      <td>4wd</td>\n",
       "      <td>7603.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>hatchback</td>\n",
       "      <td>fwd</td>\n",
       "      <td>8396.387755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>hatchback</td>\n",
       "      <td>rwd</td>\n",
       "      <td>14337.777778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>sedan</td>\n",
       "      <td>4wd</td>\n",
       "      <td>12647.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>9811.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>21711.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>wagon</td>\n",
       "      <td>4wd</td>\n",
       "      <td>9095.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>wagon</td>\n",
       "      <td>fwd</td>\n",
       "      <td>9997.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>wagon</td>\n",
       "      <td>rwd</td>\n",
       "      <td>16994.222222</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     body-style drive-wheels         price\n",
       "0   convertible          fwd  11595.000000\n",
       "1   convertible          rwd  23949.600000\n",
       "2       hardtop          fwd   8249.000000\n",
       "3       hardtop          rwd  24202.714286\n",
       "4     hatchback          4wd   7603.000000\n",
       "5     hatchback          fwd   8396.387755\n",
       "6     hatchback          rwd  14337.777778\n",
       "7         sedan          4wd  12647.333333\n",
       "8         sedan          fwd   9811.800000\n",
       "9         sedan          rwd  21711.833333\n",
       "10        wagon          4wd   9095.750000\n",
       "11        wagon          fwd   9997.333333\n",
       "12        wagon          rwd  16994.222222"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# group by multiple variables\n",
    "\n",
    "group_test1 = df_group_one.groupby([\"body-style\", \"drive-wheels\"], as_index=False).mean()\n",
    "group_test1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "\n",
       "    .dataframe thead tr th {\n",
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       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"5\" halign=\"left\">price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>body-style</th>\n",
       "      <th>convertible</th>\n",
       "      <th>hardtop</th>\n",
       "      <th>hatchback</th>\n",
       "      <th>sedan</th>\n",
       "      <th>wagon</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>drive-wheels</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4wd</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7603.000000</td>\n",
       "      <td>12647.333333</td>\n",
       "      <td>9095.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fwd</th>\n",
       "      <td>11595.0</td>\n",
       "      <td>8249.000000</td>\n",
       "      <td>8396.387755</td>\n",
       "      <td>9811.800000</td>\n",
       "      <td>9997.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rwd</th>\n",
       "      <td>23949.6</td>\n",
       "      <td>24202.714286</td>\n",
       "      <td>14337.777778</td>\n",
       "      <td>21711.833333</td>\n",
       "      <td>16994.222222</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   price                                            \\\n",
       "body-style   convertible       hardtop     hatchback         sedan   \n",
       "drive-wheels                                                         \n",
       "4wd                  NaN           NaN   7603.000000  12647.333333   \n",
       "fwd              11595.0   8249.000000   8396.387755   9811.800000   \n",
       "rwd              23949.6  24202.714286  14337.777778  21711.833333   \n",
       "\n",
       "                            \n",
       "body-style           wagon  \n",
       "drive-wheels                \n",
       "4wd            9095.750000  \n",
       "fwd            9997.333333  \n",
       "rwd           16994.222222  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We can convert the dataframe to a pivot table using the method \"pivot\" to create a pivot table from the groups\n",
    "\n",
    "grouped_pivot = group_test1.pivot(index=\"drive-wheels\", columns=\"body-style\")\n",
    "grouped_pivot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"5\" halign=\"left\">price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>body-style</th>\n",
       "      <th>convertible</th>\n",
       "      <th>hardtop</th>\n",
       "      <th>hatchback</th>\n",
       "      <th>sedan</th>\n",
       "      <th>wagon</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>drive-wheels</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4wd</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7603.000000</td>\n",
       "      <td>12647.333333</td>\n",
       "      <td>9095.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fwd</th>\n",
       "      <td>11595.0</td>\n",
       "      <td>8249.000000</td>\n",
       "      <td>8396.387755</td>\n",
       "      <td>9811.800000</td>\n",
       "      <td>9997.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rwd</th>\n",
       "      <td>23949.6</td>\n",
       "      <td>24202.714286</td>\n",
       "      <td>14337.777778</td>\n",
       "      <td>21711.833333</td>\n",
       "      <td>16994.222222</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   price                                            \\\n",
       "body-style   convertible       hardtop     hatchback         sedan   \n",
       "drive-wheels                                                         \n",
       "4wd                  0.0      0.000000   7603.000000  12647.333333   \n",
       "fwd              11595.0   8249.000000   8396.387755   9811.800000   \n",
       "rwd              23949.6  24202.714286  14337.777778  21711.833333   \n",
       "\n",
       "                            \n",
       "body-style           wagon  \n",
       "drive-wheels                \n",
       "4wd            9095.750000  \n",
       "fwd            9997.333333  \n",
       "rwd           16994.222222  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#fill missing values with 0\n",
    "\n",
    "grouped_pivot = grouped_pivot.fillna(0)\n",
    "grouped_pivot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>body-style</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>convertible</td>\n",
       "      <td>21890.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>hardtop</td>\n",
       "      <td>22208.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>hatchback</td>\n",
       "      <td>9957.441176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>sedan</td>\n",
       "      <td>14459.755319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>wagon</td>\n",
       "      <td>12371.960000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    body-style         price\n",
       "0  convertible  21890.500000\n",
       "1      hardtop  22208.500000\n",
       "2    hatchback   9957.441176\n",
       "3        sedan  14459.755319\n",
       "4        wagon  12371.960000"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use the \"groupby\" function to find the average \"price\" of each car based on \"body-style\"\n",
    "\n",
    "group_two = df[[\"price\", \"body-style\"]]\n",
    "group_test2 = group_two.groupby([\"body-style\"], as_index=False).mean()\n",
    "group_test2\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Q5XBYfux6pD+2nXIMAACwBZkQAABMcDocclrMZFg9P9wRhAAAYEKUpCiLMURUSEYSvijHAAAAW5AJAQDABKfTYXl1C6tjAABA0FgdY11Q5ZjS0lLddNNN6t+/vxISEpSfn6+DBw+e97z169crPT1dsbGxGjVqlDZv3mx6wAAA4OIQVBCyfft2FRUVaefOnaqsrNSpU6c0adIktba2dnnOjh07NGPGDBUWFmrPnj3Kz89Xfn6+9u3bZ3nwAADYpXN1jNUWyYIqx1RUVAS8LisrU0JCgurq6nT77bef9Zxly5Zp8uTJeuaZZyRJr7zyiiorK7V8+XKtWrXK5LABALCX02F9dUyETwmxtjqmublZkhQfH99ln5qaGuXk5AQcy83NVU1NTZfntLW1yev1BjQAAHqTzompVlskMz0x1efzac6cObrllls0cuTILvt5PB4lJiYGHEtMTJTH4+nynNLSUr300ktnHP/63+fL5XKZHXLk2vWB3SMIX38ut3sEYeuLZ9LtHkJYM5zH7R5C2PKeaNHzdg8C3WI6E1JUVKR9+/Zp7dq1oRyPJGnevHlqbm72t8OHD4f8HgAAWMGcEOtMZUKKi4u1ceNGVVdXa/Dgwefsm5SUpKampoBjTU1NSkpK6vKcmJgYxcTEmBkaAAAXRFQI5oRYPT/cBZUJMQxDxcXF2rBhg7Zt26a0tLTznuN2u7V169aAY5WVlXK73cGNFAAAXFSCyoQUFRVpzZo1+uCDD9S/f3//vI64uDj17dtXklRQUKCrrrpKpaWlkqTZs2dr/PjxWrJkiaZOnaq1a9eqtrZWq1evDvFHAQDgwmEDO+uCyoSsXLlSzc3NmjBhggYNGuRv69at8/dpbGzUkSNH/K/HjRunNWvWaPXq1crMzNT777+v8vLyc05mBQCgt4tyOkLSIllQmRDDMM7bp6qq6oxj999/v+6///5gbgUAAC5y7B0DAIAJlGOsIwgBAMAEVsdYZ+mJqQAAAGaRCQEAwARHCMoxDsoxAAAgWKFY3cLqGAAAEDSnrO+CG+lzIiL98wMAAJsQhAAAYEKUwxGSFozq6mpNmzZNycnJcjgcKi8vD3j/wQcflMPhCGiTJ08O6PP9999r5syZcrlcGjBggAoLC9XS0hLQ5/PPP9dtt92m2NhYpaSkaNGiRWeMZf369UpPT1dsbKxGjRqlzZs3B/VZJIIQAABMsWMX3dbWVmVmZmrFihVd9pk8ebKOHDnib//2b/8W8P7MmTO1f/9+VVZW+jejfeyxx/zve71eTZo0Sampqaqrq9PixYu1cOHCgO1WduzYoRkzZqiwsFB79uxRfn6+8vPztW/fvqA+D3NCAACwmdfrDXjd1W7yeXl5ysvLO+e1YmJiutyp/s9//rMqKiq0e/dujRkzRpL0T//0T5oyZYp+/etfKzk5We+++67a29v19ttvKzo6Wtdff73q6+v1m9/8xh+sLFu2TJMnT9YzzzwjSXrllVdUWVmp5cuXa9WqVd3+3GRCAAAwIcoZmiZJKSkpiouL87fOTWDNqKqqUkJCgoYNG6YnnnhC3333nf+9mpoaDRgwwB+ASFJOTo6cTqc+/fRTf5/bb79d0dHR/j65ubk6ePCgfvjhB3+fnJycgPvm5uaqpqYmqLGSCQEAwASnw/pj1ztX1xw+fFgul8t//GxZkO6YPHmy7r33XqWlpenLL7/UCy+8oLy8PNXU1CgqKkoej0cJCQkB51xyySWKj4+Xx+ORJHk8HqWlpQX0SUxM9L93+eWXy+Px+I/9vE/nNbqLIAQAAJu5XK6AIMSsBx54wP/nUaNGKSMjQ3/3d3+nqqoq3XnnnZavH2qUYwAAMMEZgpUxPb2B3TXXXKMrr7xShw4dkiQlJSXp6NGjAX1++uknff/99/55JElJSWpqagro0/n6fH26movSFYIQAABMsGN1TLC+/vprfffddxo0aJAkye126/jx46qrq/P32bZtm3w+n7Kzs/19qqurderUKX+fyspKDRs2TJdffrm/z9atWwPuVVlZKbfbHdT4CEIAADAhlBNTu6ulpUX19fWqr6+XJDU0NKi+vl6NjY1qaWnRM888o507d+ovf/mLtm7dqnvuuUdDhw5Vbm6uJGn48OGaPHmyHn30Ue3atUuffPKJiouL9cADDyg5OVmS9Itf/ELR0dEqLCzU/v37tW7dOi1btkwlJSX+ccyePVsVFRVasmSJDhw4oIULF6q2tlbFxcVBfR6CEAAAwkRtba1uuOEG3XDDDZKkkpIS3XDDDZo/f76ioqL0+eef6+6779Z1112nwsJCjR49Wv/5n/8ZMNH13XffVXp6uu68805NmTJFt956a8AzQOLi4vTRRx+poaFBo0eP1lNPPaX58+cHPEtk3LhxWrNmjVavXq3MzEy9//77Ki8v18iRI4P6PA7DMAyL30mP83q9iouL09HDDSGZuBNxdn1g9wjClzPK7hGErT6p6XYPIawZTtYNmOU90aL4zPFqbm7ukd+Mzt+kTw40ql9/a9dvOeHVLelDemysvR3/lgMAYILDcbpZvUYkoxwDAABsQSYEAAATnHLIKYsPK7N4frgjCAEAwATKMdZRjgEAALYgEwIAgAmn946xfo1IRhACAIAJlGOsoxwDAABsQSYEAAATWB1jHUEIAABmhKAcE+ExCEEIAABmMDHVOuaEAAAAW5AJAQDABIesV1MiPBFCEAIAgBlOh0NOi5NCrJ4f7ijHAAAAW5AJAQDABIdC8LCykIwkfBGEAABgglPWywmRXo6I9M8PAABsQiYEAAATHA6HHBbrMVbPD3cEIQAAmMDDyqyjHAMAAGxBJgQAABMcIdg7JsKrMQQhAACYweoY6whCAAAwgYmp1kV6EAYAAGxCJgQAABNYHWMdQQgAACZFeAxhGeUYAABgCzIhAACYQDnGOoIQAABMYHWMdZRjAACALciEAABgAuUY6whCAAAwwSHrq2MiPAYJvhxTXV2tadOmKTk5WQ6HQ+Xl5efsX1VV5a+b/bx5PB6zYwYAABeBoDMhra2tyszM1MMPP6x777232+cdPHhQLpfL/zohISHYWwMA0Gs4HQ45LU4stXp+uAs6CMnLy1NeXl7QN0pISNCAAQO61betrU1tbW3+116vN+j7AQDQk9hF17oLtjomKytLgwYN0l133aVPPvnknH1LS0sVFxfnbykpKRdolAAAdI/DMELSIlmPByGDBg3SqlWr9Pvf/16///3vlZKSogkTJuizzz7r8px58+apubnZ3w4fPtzTwwQAABdYj6+OGTZsmIYNG+Z/PW7cOH355ZdaunSp/uVf/uWs58TExCgmJqanhwYAgHmG73Szeo0IZsvDym6++WYdOnTIjlsDABASDsMXkhbJbAlC6uvrNWjQIDtuDQAAeomgyzEtLS0BWYyGhgbV19crPj5eQ4YM0bx58/TXv/5V//zP/yxJeu2115SWlqbrr79eJ0+e1FtvvaVt27bpo48+Ct2nAADgQqMcY1nQQUhtba0mTpzof11SUiJJmjVrlsrKynTkyBE1Njb6329vb9dTTz2lv/71r7r00kuVkZGhP/3pTwHXAAAg7BjG6Wb1GhHMYRi9/xvwer2Ki4vT0cMNAQ88Qzft+sDuEYQvZ5TdIwhbfVLT7R5CWDOc7KphlvdEi+Izx6u5ublHfjP8v0lff2X5+l6vVwmDU3tsrL0d/5YDAGAG5RjLCEIAADDh9MPGrAURPKwMAADABmRCAAAwg3KMZQQhAACYQRBiGUEIAABmEIRYxpwQAABgCzIhAACYYfgkH5kQKwhCAAAwIRQb0LGBHQAAgA3IhAAAYAYTUy0jCAEAwAw2sLOMcgwAALAFmRAAAMygHGMZQQgAACawgZ11lGMAAIAtyIQAAGAG5RjLCEIAADCDIMQyghAAAMwgCLGMOSEAAMAWZEIAADCBvWOsIxMCAIAZPl9oWhCqq6s1bdo0JScny+FwqLy8POB9wzA0f/58DRo0SH379lVOTo6++OKLgD7ff/+9Zs6cKZfLpQEDBqiwsFAtLS0BfT7//HPddtttio2NVUpKihYtWnTGWNavX6/09HTFxsZq1KhR2rx5c1CfRSIIAQAgbLS2tiozM1MrVqw46/uLFi3S66+/rlWrVunTTz/VZZddptzcXJ08edLfZ+bMmdq/f78qKyu1ceNGVVdX67HHHvO/7/V6NWnSJKWmpqqurk6LFy/WwoULtXr1an+fHTt2aMaMGSosLNSePXuUn5+v/Px87du3L6jP4zCM3v+kFK/Xq7i4OB093CCXy2X3cMLPrg/sHkH4ckbZPYKw1Sc13e4hhDXDSbXcLO+JFsVnjldzc3OP/GZ0/iZ9//l/ytW/n7VrnWhRfMZtpsbqcDi0YcMG5efnSzqdBUlOTtZTTz2lp59+WpLU3NysxMRElZWV6YEHHtCf//xnjRgxQrt379aYMWMkSRUVFZoyZYq+/vprJScna+XKlfrHf/xHeTweRUdHS5Kef/55lZeX68CBA5Kk6dOnq7W1VRs3bvSPZ+zYscrKytKqVau6/RnIhAAAYEbn6hirTacDm5+3tra2oIfT0NAgj8ejnJwc/7G4uDhlZ2erpqZGklRTU6MBAwb4AxBJysnJkdPp1Keffurvc/vtt/sDEEnKzc3VwYMH9cMPP/j7/Pw+nX0679NdBCEAANgsJSVFcXFx/lZaWhr0NTwejyQpMTEx4HhiYqL/PY/Ho4SEhID3L7nkEsXHxwf0Ods1fn6Prvp0vt9d5PsAADAhlKtjDh8+HFCOiYmJsXTdcEEQAgCAGSF8WJnL5bI8fyUpKUmS1NTUpEGDBvmPNzU1KSsry9/n6NGjAef99NNP+v777/3nJyUlqampKaBP5+vz9el8v7soxwAAcBFIS0tTUlKStm7d6j/m9Xr16aefyu12S5LcbreOHz+uuro6f59t27bJ5/MpOzvb36e6ulqnTp3y96msrNSwYcN0+eWX+/v8/D6dfTrv010EIQAAmGEYIZiYGtwC1ZaWFtXX16u+vl7S6cmo9fX1amxslMPh0Jw5c/SrX/1K//7v/669e/eqoKBAycnJ/hU0w4cP1+TJk/Xoo49q165d+uSTT1RcXKwHHnhAycnJkqRf/OIXio6OVmFhofbv369169Zp2bJlKikp8Y9j9uzZqqio0JIlS3TgwAEtXLhQtbW1Ki4uDurzUI4BAMAMo0PydVi/RhBqa2s1ceJE/+vOwGDWrFkqKyvTs88+q9bWVj322GM6fvy4br31VlVUVCg2NtZ/zrvvvqvi4mLdeeedcjqduu+++/T666/734+Li9NHH32koqIijR49WldeeaXmz58f8CyRcePGac2aNXrxxRf1wgsv6Nprr1V5eblGjhwZ1OfhOSGRgOeEmMdzQkzjOSHW8JwQ8y7Yc0J2fyhXv8usXaulVfE35fXYWHs7yjEAAMAWhNoAAJjhC0E5xur5YY4gBAAAMwhCLKMcAwAAbEEmBAAAE4yODhkd1jIZVs8PdwQhAACY4fOdblavEcEoxwAAAFuQCQEAwAyfLwQTUyM7E0IQAgCACYavQ4bFIMTq+eGOcgwAALAFmRAAAMwwQjAx1aAcAwAAgkQ5xjqCEAAAzOCJqZYxJwQAANiCTAgAAGbwsDLLCEIAADCBx7ZbRzkGAADYgkwIAABm8MRUy4LOhFRXV2vatGlKTk6Ww+FQeXn5ec+pqqrSjTfeqJiYGA0dOlRlZWUmhgoAQC/SuTrGaotgQQchra2tyszM1IoVK7rVv6GhQVOnTtXEiRNVX1+vOXPm6JFHHtGWLVuCHiwAALh4BF2OycvLU15eXrf7r1q1SmlpaVqyZIkkafjw4fr444+1dOlS5ebmBnt7AAB6BcPnk2GxnGL1/HDX43NCampqlJOTE3AsNzdXc+bM6fKctrY2tbW1+V97vd6eGh4AAObwsDLLenx1jMfjUWJiYsCxxMREeb1e/fjjj2c9p7S0VHFxcf6WkpLS08MEAAAXWK9cHTNv3jyVlJT4X3u9XqWkpOi5lNsVzapiXEC/eb/Y7iGErR9v/ge7hxDW+n1RbfcQwpbR0nqBbhSCTIgR2ZmQHg9CkpKS1NTUFHCsqalJLpdLffv2Pes5MTExiomJ6emhAQBgGnNCrOvxIMTtdmvz5s0BxyorK+V2u3v61gAA9ByeE2JZ0LWNlpYW1dfXq76+XtLpJbj19fVqbGyUdLqUUlBQ4O//+OOP67//+7/17LPP6sCBA3rjjTf03nvvae7cuaH5BAAAICwFnQmpra3VxIkT/a87527MmjVLZWVlOnLkiD8gkaS0tDRt2rRJc+fO1bJlyzR48GC99dZbLM8FAIQ3VsdYFnQQMmHCBBmG0eX7Z3sa6oQJE7Rnz55gbwUAQK/FBnbWsdQEAADYolcu0QUAoNfz+axPLI3wiakEIQAAmMGcEMsoxwAAAFuQCQEAwATD1yHDYibD6vnhjiAEAAATeGKqdZRjAACALciEAABgguEzZHRYzYR0/dytSEAQAgCACUaHz3oQYvH8cEcQAgCACcwJsY45IQAAwBZkQgAAMIFyjHUEIQAAmEAQYh3lGAAAYAsyIQAAmGB0dMjXYfGJqRbPD3cEIQAAmGAYIVgdY1COAQAAuODIhAAAYAITU60jCAEAwASCEOsoxwAAAFuQCQEAwATDZ4Tgse1sYAcAAILk6/DJZ7GcYvX8cEcQAgCACcwJsY45IQAAwBZkQgAAMIFMiHUEIQAAmMATU62jHAMAAGxBJgQAABMox1hHEAIAgAkEIdZRjgEAALYgEwIAgAk+n08+ixNTrZ4f7ghCAAAwgXKMdZRjAACALciEAABgwulMSIfla0QyghAAAEwwfCF4WBlzQgAAQLAMXwjmhER4EMKcEAAAwsTChQvlcDgCWnp6uv/9kydPqqioSFdccYX69eun++67T01NTQHXaGxs1NSpU3XppZcqISFBzzzzjH766aeAPlVVVbrxxhsVExOjoUOHqqysrEc+D0EIAABm/M/qGCtNJjIp119/vY4cOeJvH3/8sf+9uXPn6o9//KPWr1+v7du365tvvtG99977/4fc0aGpU6eqvb1dO3bs0O9+9zuVlZVp/vz5/j4NDQ2aOnWqJk6cqPr6es2ZM0ePPPKItmzZYu37OgvKMQAAmODr8MlnsRxj5vxLLrlESUlJZxxvbm7Wb3/7W61Zs0Z33HGHJOmdd97R8OHDtXPnTo0dO1YfffSR/uu//kt/+tOflJiYqKysLL3yyit67rnntHDhQkVHR2vVqlVKS0vTkiVLJEnDhw/Xxx9/rKVLlyo3N9fS5/1bZEIAALCZ1+sNaG1tbV32/eKLL5ScnKxrrrlGM2fOVGNjoySprq5Op06dUk5Ojr9venq6hgwZopqaGklSTU2NRo0apcTERH+f3Nxceb1e7d+/39/n59fo7NN5jVAiCAEAwITO1TFWmySlpKQoLi7O30pLS896z+zsbJWVlamiokIrV65UQ0ODbrvtNp04cUIej0fR0dEaMGBAwDmJiYnyeDySJI/HExCAdL7f+d65+ni9Xv3444+Wv7efoxwDAIAJoXxi6uHDh+VyufzHY2Jizto/Ly/P/+eMjAxlZ2crNTVV7733nvr27WtpLHYgEwIAgM1cLldA6yoI+VsDBgzQddddp0OHDikpKUnt7e06fvx4QJ+mpib/HJKkpKQzVst0vj5fH5fLFfJAhyAEAAATjA4jJM2KlpYWffnllxo0aJBGjx6tPn36aOvWrf73Dx48qMbGRrndbkmS2+3W3r17dfToUX+fyspKuVwujRgxwt/n59fo7NN5jVCiHAMAgAk+XwhWxwT5sLKnn35a06ZNU2pqqr755hstWLBAUVFRmjFjhuLi4lRYWKiSkhLFx8fL5XLpySeflNvt1tixYyVJkyZN0ogRI/TLX/5SixYtksfj0YsvvqiioiJ/9uXxxx/X8uXL9eyzz+rhhx/Wtm3b9N5772nTpk2WPuvZEIQAABAmvv76a82YMUPfffedBg4cqFtvvVU7d+7UwIEDJUlLly6V0+nUfffdp7a2NuXm5uqNN97wnx8VFaWNGzfqiSeekNvt1mWXXaZZs2bp5Zdf9vdJS0vTpk2bNHfuXC1btkyDBw/WW2+9FfLluRJBCAAAphg+Q4bPWjkl2PPXrl17zvdjY2O1YsUKrVixoss+qamp2rx58zmvM2HCBO3ZsyeosZlBEAIAgAm+DsnntBaE+Kxtwhv2CEIAADDB6PDJcIZmiW6kYnUMAACwBZkQAABMMDoMGRbLMVaX6IY7U5mQFStW6Oqrr1ZsbKyys7O1a9euLvuWlZWdse1wbGys6QEDANAb+DqMkLRIFnQQsm7dOpWUlGjBggX67LPPlJmZqdzc3IAHn/wtl8sVsO3wV199ZWnQAAAg/AUdhPzmN7/Ro48+qoceekgjRozQqlWrdOmll+rtt9/u8hyHw6GkpCR/+9uNcQAACDede8dYbZEsqCCkvb1ddXV1AVv8Op1O5eTknHOL35aWFqWmpiolJUX33HOPf7vgrrS1tZ2xrTEAAL2JzzDk81lsBuWYbvv222/V0dFx1i1+O7cA/lvDhg3T22+/rQ8++ED/+q//Kp/Pp3Hjxunrr7/u8j6lpaUBWxqnpKQEM0wAABAGenyJrtvtVkFBgbKysjR+/Hj94Q9/0MCBA/Xmm292ec68efPU3Nzsb4cPH+7pYQIAEJxQbF4X4RNTg1qie+WVVyoqKuqsW/x2bgF8Pn369NENN9ygQ4cOddknJiam29sYAwBgB1+HTz6HxQ3smBPSfdHR0Ro9enTAFr8+n09bt27t9ha/HR0d2rt3rwYNGhTcSAEAwEUl6IeVlZSUaNasWRozZoxuvvlmvfbaa2ptbdVDDz0kSSooKNBVV12l0tJSSdLLL7+ssWPHaujQoTp+/LgWL16sr776So888khoPwkAABeQ0WHIcPCwMiuCDkKmT5+uY8eOaf78+fJ4PMrKylJFRYV/smpjY6Oczv+fYPnhhx/06KOPyuPx6PLLL9fo0aO1Y8cOjRgxInSfAgCAC4wgxDpTj20vLi5WcXHxWd+rqqoKeL106VItXbrUzG0AAOi1mBNiHRvYAQAAW7CBHQAAJhiGIcNnsRwT4Q8rIwgBAMAEX4chn6wFEWxgBwAAYAMyIQAAmGB0GDJkbWIpq2MAAEDQTgchLNG1gnIMAACwBZkQAABMYGKqdQQhAACYYPh8MhwOy9eIZJRjAACALciEAABgAuUY6whCAAAwwfCFYHWMxSeuhjuCEAAAzOjwyTCszQkRc0IAAAAuPDIhAACY4Osw5LO4AZ2PcgwAAAiW0WFY3gU30ueEUI4BAAC2IBMCAIAJPiME5RiL54c7ghAAAEzoMAx1WAwirJ4f7ijHAAAAW5AJAQDAhA7jdLN6jUhGEAIAgAmUY6yjHAMAAGxBJgQAABMox1hHEAIAgAm+EJRjWKILAACC1qEQZEJCMpLwxZwQAABgCzIhAACY0GEY6hCrY6wgCAEAwIQOw3o5JdInplKOAQAAtiATAgCACWRCrCMIAQDABOaEWEc5BgAA2IJMCAAAJvhCUI7xRXYihCAEAAAzKMdYRzkGAADYgkwIAAAmsDrGOoIQAABMOB2EWC3HhGgwYYogBAAAE8iEWMecEAAAYAsyIQAAmMDqGOsIQgAAMMGQ5AvBNSIZ5RgAAGALMiEAAJhAOcY6ghAAAExgdYx1lGMAAIAtyIQAAGAC5RjrCEIAADCBcox1lGMAAIAtyIQAAGAC5RjrCEIAADDBF4JyjC+yYxBz5ZgVK1bo6quvVmxsrLKzs7Vr165z9l+/fr3S09MVGxurUaNGafPmzaYGCwBAb9FhGCFpwQr2N7g3CzoIWbdunUpKSrRgwQJ99tlnyszMVG5uro4ePXrW/jt27NCMGTNUWFioPXv2KD8/X/n5+dq3b5/lwQMAEEmC/Q3u7RyGEVwYlp2drZtuuknLly+XJPl8PqWkpOjJJ5/U888/f0b/6dOnq7W1VRs3bvQfGzt2rLKysrRq1aqz3qOtrU1tbW3+183NzRoyZIhm6ipFM5cWF9D/+efH7B5C2Gq/8xG7hxDW+n35id1DCFve1v+rtLwCHT9+XHFxcaG/vteruLi4kPwmtcund/VXHT58WC6Xy388JiZGMTExZ/QP9je41zOC0NbWZkRFRRkbNmwIOF5QUGDcfffdZz0nJSXFWLp0acCx+fPnGxkZGV3eZ8GCBYZO7+tDo9FoNJqp9uWXXwbzE9dtP/74o5GUlBSycfbr1++MYwsWLDjjvmZ+g3u7oCamfvvtt+ro6FBiYmLA8cTERB04cOCs53g8nrP293g8Xd5n3rx5Kikp8b8+fvy4UlNT1djY2CNR7cXM6/UqJSXljCgb58d3Zw3fn3l8d9Z0Zs/j4+N75PqxsbFqaGhQe3t7SK5nGIYcDkfAsbNlQcz8Bvd2vXJ1TFdpqLi4OP6DNMnlcvHdmcR3Zw3fn3l8d9Y4nT1Xvo+NjVVsbGyPXT9SBPVP6Morr1RUVJSampoCjjc1NSkpKems5yQlJQXVHwAAnMnMb3BvF1QQEh0drdGjR2vr1q3+Yz6fT1u3bpXb7T7rOW63O6C/JFVWVnbZHwAAnMnMb3BvF3Q5pqSkRLNmzdKYMWN0880367XXXlNra6seeughSVJBQYGuuuoqlZaWSpJmz56t8ePHa8mSJZo6darWrl2r2tparV69utv3jImJ0YIFC85aosG58d2Zx3dnDd+feXx31lzM39/5foPDTdBLdCVp+fLlWrx4sTwej7KysvT6668rOztbkjRhwgRdffXVKisr8/dfv369XnzxRf3lL3/Rtddeq0WLFmnKlCkh+xAAAESKc/0GhxtTQQgAAIBVPPkLAADYgiAEAADYgiAEAADYgiAEAADYotcHIRfTlsUXUnV1taZNm6bk5GQ5HA6Vl5fbPaSwUVpaqptuukn9+/dXQkKC8vPzdfDgQbuHFRZWrlypjIwM/5M+3W63PvzwQ7uHFZZeffVVORwOzZkzx+6hhIWFCxfK4XAEtPT0dLuHhfPo1UHIxbZl8YXU2tqqzMxMrVixwu6hhJ3t27erqKhIO3fuVGVlpU6dOqVJkyaptbXV7qH1eoMHD9arr76quro61dbW6o477tA999yj/fv32z20sLJ79269+eabysjIsHsoYeX666/XkSNH/O3jjz+2e0g4j169RPei27LYJg6HQxs2bFB+fr7dQwlLx44dU0JCgrZv367bb7/d7uGEnfj4eC1evFiFhYV2DyUstLS06MYbb9Qbb7yhX/3qV8rKytJrr71m97B6vYULF6q8vFz19fV2DwVB6LWZkPb2dtXV1SknJ8d/zOl0KicnRzU1NTaODJGmublZknpsR86LVUdHh9auXavW1tawfaS0HYqKijR16tSA//ehe7744gslJyfrmmuu0cyZM9XY2Gj3kHAevXIXXeni3LIY4cfn82nOnDm65ZZbNHLkSLuHExb27t0rt9utkydPql+/ftqwYYNGjBhh97DCwtq1a/XZZ59p9+7ddg8l7GRnZ6usrEzDhg3TkSNH9NJLL+m2227Tvn371L9/f7uHhy702iAE6A2Kioq0b98+astBGDZsmOrr69Xc3Kz3339fs2bN0vbt2wlEzuPw4cOaPXu2Kisr2SLehLy8PP+fMzIylJ2drdTUVL333nuUAnuxXhuEXIxbFiO8FBcXa+PGjaqurtbgwYPtHk7YiI6O1tChQyVJo0eP1u7du7Vs2TK9+eabNo+sd6urq9PRo0d14403+o91dHSourpay5cvV1tbm6KiomwcYXgZMGCArrvuOh06dMjuoeAceu2ckItxy2KEB8MwVFxcrA0bNmjbtm1KS0uze0hhzefzqa2tze5h9Hp33nmn9u7dq/r6en8bM2aMZs6cqfr6egKQILW0tOjLL7/UoEGD7B4KzqHXZkKki2/L4guppaUl4G8ADQ0Nqq+vV3x8vIYMGWLjyHq/oqIirVmzRh988IH69+8vj8cjSYqLi1Pfvn1tHl3vNm/ePOXl5WnIkCE6ceKE1qxZo6qqKm3ZssXuofV6/fv3P2Pe0WWXXaYrrriC+Ujd8PTTT2vatGlKTU3VN998owULFigqKkozZsywe2g4h14dhEyfPl3Hjh3T/Pnz/VsWV1RUnDFZFWeqra3VxIkT/a9LSkokSbNmzVJZWZlNowoPK1eulCRNmDAh4Pg777yjBx988MIPKIwcPXpUBQUFOnLkiOLi4pSRkaEtW7borrvusntouMh9/fXXmjFjhr777jsNHDhQt956q3bu3KmBAwfaPTScQ69+TggAALh49do5IQAA4OJGEAIAAGxBEAIAAGxBEAIAAGxBEAIAAGxBEAIAAGxBEAIAAGxBEAIAAGxBEAIAAGxBEAIAAGxBEAIAAGzx/wCSB6D5nVCDUAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Drive Wheels and Body Style vs. Price\n",
    "# use the grouped results\n",
    "plt.pcolor(grouped_pivot, cmap='RdBu')\n",
    "plt.colorbar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The heatmap plots the target variable (price) proportional to colour with respect to the variables 'drive-wheel' and 'body-style' on the vertical and horizontal axis, respectively. This allows us to visualize how the price is related to 'drive-wheel' and 'body-style'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "im = ax.pcolor(grouped_pivot, cmap='RdBu')\n",
    "\n",
    "#label names\n",
    "row_labels = grouped_pivot.columns.levels[1]\n",
    "col_labels = grouped_pivot.index\n",
    "\n",
    "#move ticks and labels to the center\n",
    "ax.set_xticks(np.arange(grouped_pivot.shape[1]) + 0.5, minor=False)\n",
    "ax.set_yticks(np.arange(grouped_pivot.shape[0]) + 0.5, minor=False)\n",
    "\n",
    "#insert labels\n",
    "ax.set_xticklabels(row_labels, minor=False)\n",
    "ax.set_yticklabels(col_labels, minor=False)\n",
    "\n",
    "#rotate label if too long\n",
    "plt.xticks(rotation=90)\n",
    "\n",
    "\n",
    "fig.colorbar(im)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Correlation and Causation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's calculate the Pearson Correlation Coefficient and P-value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Pearson Correlation Coefficient is 0.5846418222655081  with a P-value of P = 8.076488270732847e-20\n"
     ]
    }
   ],
   "source": [
    "# Wheel-Base vs. Price\n",
    "\n",
    "pearson_coef, p_value = stats.pearsonr(df['wheel-base'], df['price'])\n",
    "print(\"The Pearson Correlation Coefficient is\", pearson_coef, \" with a P-value of P =\", p_value)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the p-value is < 0.001, the correlation between wheel-base and price is statistically significant, although the linear relationship isn't extremely strong (~0.585)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Pearson Correlation Coefficient is 0.809574567003656  with a P-value of P =  6.369057428259638e-48\n"
     ]
    }
   ],
   "source": [
    "# Horsepower vs. Price\n",
    "\n",
    "pearson_coef, p_value = stats.pearsonr(df['horsepower'], df['price'])\n",
    "print(\"The Pearson Correlation Coefficient is\", pearson_coef, \" with a P-value of P = \", p_value) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the p-value is $<$ 0.001, the correlation between length and price is statistically significant, and the linear relationship is moderately strong (~0.691)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Pearson Correlation Coefficient is 0.7512653440522674  with a P-value of P = 9.200335510481227e-38\n"
     ]
    }
   ],
   "source": [
    "# Width vs. Price\n",
    "\n",
    "pearson_coef, p_value = stats.pearsonr(df['width'], df['price'])\n",
    "print(\"The Pearson Correlation Coefficient is\", pearson_coef, \" with a P-value of P =\", p_value ) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the p-value is < 0.001, the correlation between width and price is statistically significant, and the linear relationship is quite strong (~0.751)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Pearson Correlation Coefficient is 0.8344145257702845  with a P-value of P =  2.189577238893924e-53\n"
     ]
    }
   ],
   "source": [
    "# Curb-Weight vs. Price\n",
    "\n",
    "pearson_coef, p_value = stats.pearsonr(df['curb-weight'], df['price'])\n",
    "print( \"The Pearson Correlation Coefficient is\", pearson_coef, \" with a P-value of P = \", p_value)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the p-value is < 0.001, the correlation between curb-weight and price is statistically significant, and the linear relationship is quite strong (~0.834)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Pearson Correlation Coefficient is 0.8723351674455185  with a P-value of P = 9.26549162219869e-64\n"
     ]
    }
   ],
   "source": [
    "# Engine-Size vs. Price\n",
    "\n",
    "pearson_coef, p_value = stats.pearsonr(df['engine-size'], df['price'])\n",
    "print(\"The Pearson Correlation Coefficient is\", pearson_coef, \" with a P-value of P =\", p_value) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the p-value is < 0.001, the correlation between engine-size and price is statistically significant, and the linear relationship is very strong (~0.872)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Pearson Correlation Coefficient is 0.5431553832626603  with a P-value of P =   8.049189483935274e-17\n"
     ]
    }
   ],
   "source": [
    "# Bore vs. Price\n",
    "\n",
    "pearson_coef, p_value = stats.pearsonr(df['bore'], df['price'])\n",
    "print(\"The Pearson Correlation Coefficient is\", pearson_coef, \" with a P-value of P =  \", p_value )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the p-value is $<$ 0.001, the correlation between bore and price is statistically significant, but the linear relationship is only moderate (~0.521)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Pearson Correlation Coefficient is -0.6865710067844679  with a P-value of P =  2.3211320655675065e-29\n"
     ]
    }
   ],
   "source": [
    "# City-mpg vs. Price\n",
    "\n",
    "pearson_coef, p_value = stats.pearsonr(df['city-mpg'], df['price'])\n",
    "print(\"The Pearson Correlation Coefficient is\", pearson_coef, \" with a P-value of P = \", p_value)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the p-value is < 0.001, the correlation between city-mpg and price is statistically significant, and the coefficient of about -0.687 shows that the relationship is negative and moderately strong."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Pearson Correlation Coefficient is -0.7046922650589529  with a P-value of P =  1.7495471144476634e-31\n"
     ]
    }
   ],
   "source": [
    "# Highway-mpg vs. Price\n",
    "\n",
    "pearson_coef, p_value = stats.pearsonr(df['highway-mpg'], df['price'])\n",
    "print( \"The Pearson Correlation Coefficient is\", pearson_coef, \" with a P-value of P = \", p_value )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the p-value is < 0.001, the correlation between highway-mpg and price is statistically significant, and the coefficient of about -0.705 shows that the relationship is negative and moderately strong."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>We now have a better idea of what our data looks like and which variables are important to take into account when predicting the car price. We have narrowed it down to the following variables:</p>\n",
    "\n",
    "Continuous numerical variables:\n",
    "<ul>\n",
    "    <li>Length</li>\n",
    "    <li>Width</li>\n",
    "    <li>Curb-weight</li>\n",
    "    <li>Engine-size</li>\n",
    "    <li>Horsepower</li>\n",
    "    <li>City-mpg</li>\n",
    "    <li>Highway-mpg</li>\n",
    "    <li>Wheel-base</li>\n",
    "    <li>Bore</li>\n",
    "</ul>\n",
    "    \n",
    "Categorical variables:\n",
    "<ul>\n",
    "    <li>Drive-wheels</li>\n",
    "</ul>\n",
    "\n",
    "<p>As we now move into building machine learning models to automate our analysis, feeding the model with variables that meaningfully affect our target variable will improve our model's prediction performance.</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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