Categorical_Plot_Practice.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# prerequisite package imports\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sb\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "from solutions_biv import categorical_solution_1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll continue to make use of the fuel economy dataset in this workspace."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
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       "      <th>id</th>\n",
       "      <th>make</th>\n",
       "      <th>model</th>\n",
       "      <th>year</th>\n",
       "      <th>VClass</th>\n",
       "      <th>drive</th>\n",
       "      <th>trans</th>\n",
       "      <th>fuelType</th>\n",
       "      <th>cylinders</th>\n",
       "      <th>displ</th>\n",
       "      <th>pv2</th>\n",
       "      <th>pv4</th>\n",
       "      <th>city</th>\n",
       "      <th>UCity</th>\n",
       "      <th>highway</th>\n",
       "      <th>UHighway</th>\n",
       "      <th>comb</th>\n",
       "      <th>co2</th>\n",
       "      <th>feScore</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>32204</td>\n",
       "      <td>Nissan</td>\n",
       "      <td>GT-R</td>\n",
       "      <td>2013</td>\n",
       "      <td>Subcompact Cars</td>\n",
       "      <td>All-Wheel Drive</td>\n",
       "      <td>Automatic (AM6)</td>\n",
       "      <td>Premium Gasoline</td>\n",
       "      <td>6</td>\n",
       "      <td>3.8</td>\n",
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       "      <td>Volkswagen</td>\n",
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       "      <td>Front-Wheel Drive</td>\n",
       "      <td>Automatic (AM-S6)</td>\n",
       "      <td>Premium Gasoline</td>\n",
       "      <td>4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>94</td>\n",
       "      <td>0</td>\n",
       "      <td>21.8706</td>\n",
       "      <td>26.9770</td>\n",
       "      <td>31.0367</td>\n",
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       "      <td>3.6</td>\n",
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       "      <td>21.2000</td>\n",
       "      <td>26.5716</td>\n",
       "      <td>35.1000</td>\n",
       "      <td>20.6716</td>\n",
       "      <td>429</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>32207</td>\n",
       "      <td>Volkswagen</td>\n",
       "      <td>CC 4motion</td>\n",
       "      <td>2013</td>\n",
       "      <td>Compact Cars</td>\n",
       "      <td>All-Wheel Drive</td>\n",
       "      <td>Automatic (S6)</td>\n",
       "      <td>Premium Gasoline</td>\n",
       "      <td>6</td>\n",
       "      <td>3.6</td>\n",
       "      <td>94</td>\n",
       "      <td>0</td>\n",
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       "      <td>20.5000</td>\n",
       "      <td>25.2190</td>\n",
       "      <td>33.5000</td>\n",
       "      <td>19.8774</td>\n",
       "      <td>446</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
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       "      <th>4</th>\n",
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       "      <td>Chevrolet</td>\n",
       "      <td>Malibu eAssist</td>\n",
       "      <td>2013</td>\n",
       "      <td>Midsize Cars</td>\n",
       "      <td>Front-Wheel Drive</td>\n",
       "      <td>Automatic (S6)</td>\n",
       "      <td>Regular Gasoline</td>\n",
       "      <td>4</td>\n",
       "      <td>2.4</td>\n",
       "      <td>0</td>\n",
       "      <td>95</td>\n",
       "      <td>24.7726</td>\n",
       "      <td>31.9796</td>\n",
       "      <td>35.5340</td>\n",
       "      <td>51.8816</td>\n",
       "      <td>28.6813</td>\n",
       "      <td>310</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "      id        make           model  year           VClass  \\\n",
       "0  32204      Nissan            GT-R  2013  Subcompact Cars   \n",
       "1  32205  Volkswagen              CC  2013     Compact Cars   \n",
       "2  32206  Volkswagen              CC  2013     Compact Cars   \n",
       "3  32207  Volkswagen      CC 4motion  2013     Compact Cars   \n",
       "4  32208   Chevrolet  Malibu eAssist  2013     Midsize Cars   \n",
       "\n",
       "               drive              trans          fuelType  cylinders  displ  \\\n",
       "0    All-Wheel Drive    Automatic (AM6)  Premium Gasoline          6    3.8   \n",
       "1  Front-Wheel Drive  Automatic (AM-S6)  Premium Gasoline          4    2.0   \n",
       "2  Front-Wheel Drive     Automatic (S6)  Premium Gasoline          6    3.6   \n",
       "3    All-Wheel Drive     Automatic (S6)  Premium Gasoline          6    3.6   \n",
       "4  Front-Wheel Drive     Automatic (S6)  Regular Gasoline          4    2.4   \n",
       "\n",
       "   pv2  pv4     city    UCity  highway  UHighway     comb  co2  feScore  \\\n",
       "0   79    0  16.4596  20.2988  22.5568   30.1798  18.7389  471        4   \n",
       "1   94    0  21.8706  26.9770  31.0367   42.4936  25.2227  349        6   \n",
       "2   94    0  17.4935  21.2000  26.5716   35.1000  20.6716  429        5   \n",
       "3   94    0  16.9415  20.5000  25.2190   33.5000  19.8774  446        5   \n",
       "4    0   95  24.7726  31.9796  35.5340   51.8816  28.6813  310        8   \n",
       "\n",
       "   ghgScore  \n",
       "0         4  \n",
       "1         6  \n",
       "2         5  \n",
       "3         5  \n",
       "4         8  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fuel_econ = pd.read_csv('../data/fuel_econ.csv')\n",
    "fuel_econ.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  **TO DO**\n",
    "Use a plot to explore whether or not there are differences in recommended fuel type depending on the vehicle class. Only investigate the difference between the two main fuel types found in the 'fuelType' variable: Regular Gasoline and Premium Gasoline. (The other fuel types represented in the dataset are of much lower frequency compared to the main two, that they'll be more distracting than informative.) \n",
    "\n",
    "\n",
    "**Note**: The dataset as provided does not retain any of the sorting of the 'VClass' variable, so you will also need to copy over any code you used previously to sort the category levels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "VClass            fuelType         \n",
       "Compact Cars      Diesel                36\n",
       "                  Natural Gas            3\n",
       "                  Premium Gasoline     593\n",
       "                  Regular Gasoline     490\n",
       "Large Cars        Diesel                 5\n",
       "                  Midgrade Gasoline     16\n",
       "                  Premium Gasoline     348\n",
       "                  Regular Gasoline     256\n",
       "Midsize Cars      Diesel                32\n",
       "                  Midgrade Gasoline      7\n",
       "                  Premium Gasoline     509\n",
       "                  Regular Gasoline     640\n",
       "Minicompact Cars  Premium Gasoline     318\n",
       "                  Regular Gasoline       3\n",
       "Subcompact Cars   Diesel                 7\n",
       "                  Premium Gasoline     534\n",
       "                  Regular Gasoline     132\n",
       "dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ct_counts = fuel_econ.groupby(['VClass', 'fuelType'], observed=False).size()\n",
    "ct_counts"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
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   "source": []
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   "execution_count": 22,
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       "      <td>2013</td>\n",
       "      <td>Compact Cars</td>\n",
       "      <td>Front-Wheel Drive</td>\n",
       "      <td>Automatic (AM-S6)</td>\n",
       "      <td>Premium Gasoline</td>\n",
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       "      <td>429</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>True</td>\n",
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       "      <th>3</th>\n",
       "      <td>32207</td>\n",
       "      <td>Volkswagen</td>\n",
       "      <td>CC 4motion</td>\n",
       "      <td>2013</td>\n",
       "      <td>Compact Cars</td>\n",
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       "      <td>Automatic (S6)</td>\n",
       "      <td>Premium Gasoline</td>\n",
       "      <td>6</td>\n",
       "      <td>3.6</td>\n",
       "      <td>...</td>\n",
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       "      <td>32208</td>\n",
       "      <td>Chevrolet</td>\n",
       "      <td>Malibu eAssist</td>\n",
       "      <td>2013</td>\n",
       "      <td>Midsize Cars</td>\n",
       "      <td>Front-Wheel Drive</td>\n",
       "      <td>Automatic (S6)</td>\n",
       "      <td>Regular Gasoline</td>\n",
       "      <td>4</td>\n",
       "      <td>2.4</td>\n",
       "      <td>...</td>\n",
       "      <td>95</td>\n",
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       "<p>5 rows × 21 columns</p>\n",
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      "text/plain": [
       "      id        make           model  year           VClass  \\\n",
       "0  32204      Nissan            GT-R  2013  Subcompact Cars   \n",
       "1  32205  Volkswagen              CC  2013     Compact Cars   \n",
       "2  32206  Volkswagen              CC  2013     Compact Cars   \n",
       "3  32207  Volkswagen      CC 4motion  2013     Compact Cars   \n",
       "4  32208   Chevrolet  Malibu eAssist  2013     Midsize Cars   \n",
       "\n",
       "               drive              trans          fuelType  cylinders  displ  \\\n",
       "0    All-Wheel Drive    Automatic (AM6)  Premium Gasoline          6    3.8   \n",
       "1  Front-Wheel Drive  Automatic (AM-S6)  Premium Gasoline          4    2.0   \n",
       "2  Front-Wheel Drive     Automatic (S6)  Premium Gasoline          6    3.6   \n",
       "3    All-Wheel Drive     Automatic (S6)  Premium Gasoline          6    3.6   \n",
       "4  Front-Wheel Drive     Automatic (S6)  Regular Gasoline          4    2.4   \n",
       "\n",
       "   ...  pv4     city    UCity  highway  UHighway     comb  co2  feScore  \\\n",
       "0  ...    0  16.4596  20.2988  22.5568   30.1798  18.7389  471        4   \n",
       "1  ...    0  21.8706  26.9770  31.0367   42.4936  25.2227  349        6   \n",
       "2  ...    0  17.4935  21.2000  26.5716   35.1000  20.6716  429        5   \n",
       "3  ...    0  16.9415  20.5000  25.2190   33.5000  19.8774  446        5   \n",
       "4  ...   95  24.7726  31.9796  35.5340   51.8816  28.6813  310        8   \n",
       "\n",
       "   ghgScore  main_fuelType  \n",
       "0         4           True  \n",
       "1         6           True  \n",
       "2         5           True  \n",
       "3         5           True  \n",
       "4         8           True  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The Series.apply() method invokes the `lambda` function on each value of `trans` column.\n",
    "# In python, a `lambda` function is an anonymous function that can have only one expression\n",
    "\n",
    "# Create new column with True for the main_fuelType and False otherwise\n",
    "main_fuelType = ['Regular Gasoline', 'Premium Gasoline']\n",
    "fuel_econ['main_fuelType'] = fuel_econ['fuelType'].apply(lambda x:x in main_fuelType)\n",
    "fuel_econ.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "VClass            main_fuelType\n",
       "Compact Cars      False              39\n",
       "                  True             1083\n",
       "Large Cars        False              21\n",
       "                  True              604\n",
       "Midsize Cars      False              39\n",
       "                  True             1149\n",
       "Minicompact Cars  True              321\n",
       "Subcompact Cars   False               7\n",
       "                  True              666\n",
       "dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# YOUR CODE HERE\n",
    "# Use group_by() and size() to get the number of cars and each combination of the two variable levels as a pandas Series\n",
    "ct_counts = fuel_econ.groupby(['VClass', 'main_fuelType'], observed=False).size()\n",
    "ct_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
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       "      <th>fuelType</th>\n",
       "      <th>cylinders</th>\n",
       "      <th>displ</th>\n",
       "      <th>...</th>\n",
       "      <th>pv4</th>\n",
       "      <th>city</th>\n",
       "      <th>UCity</th>\n",
       "      <th>highway</th>\n",
       "      <th>UHighway</th>\n",
       "      <th>comb</th>\n",
       "      <th>co2</th>\n",
       "      <th>feScore</th>\n",
       "      <th>ghgScore</th>\n",
       "      <th>main_fuelType</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>32204</td>\n",
       "      <td>Nissan</td>\n",
       "      <td>GT-R</td>\n",
       "      <td>2013</td>\n",
       "      <td>Subcompact Cars</td>\n",
       "      <td>All-Wheel Drive</td>\n",
       "      <td>Automatic (AM6)</td>\n",
       "      <td>Premium Gasoline</td>\n",
       "      <td>6</td>\n",
       "      <td>3.8</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>16.4596</td>\n",
       "      <td>20.2988</td>\n",
       "      <td>22.5568</td>\n",
       "      <td>30.1798</td>\n",
       "      <td>18.7389</td>\n",
       "      <td>471</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>32205</td>\n",
       "      <td>Volkswagen</td>\n",
       "      <td>CC</td>\n",
       "      <td>2013</td>\n",
       "      <td>Compact Cars</td>\n",
       "      <td>Front-Wheel Drive</td>\n",
       "      <td>Automatic (AM-S6)</td>\n",
       "      <td>Premium Gasoline</td>\n",
       "      <td>4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>21.8706</td>\n",
       "      <td>26.9770</td>\n",
       "      <td>31.0367</td>\n",
       "      <td>42.4936</td>\n",
       "      <td>25.2227</td>\n",
       "      <td>349</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>32206</td>\n",
       "      <td>Volkswagen</td>\n",
       "      <td>CC</td>\n",
       "      <td>2013</td>\n",
       "      <td>Compact Cars</td>\n",
       "      <td>Front-Wheel Drive</td>\n",
       "      <td>Automatic (S6)</td>\n",
       "      <td>Premium Gasoline</td>\n",
       "      <td>6</td>\n",
       "      <td>3.6</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>17.4935</td>\n",
       "      <td>21.2000</td>\n",
       "      <td>26.5716</td>\n",
       "      <td>35.1000</td>\n",
       "      <td>20.6716</td>\n",
       "      <td>429</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>32207</td>\n",
       "      <td>Volkswagen</td>\n",
       "      <td>CC 4motion</td>\n",
       "      <td>2013</td>\n",
       "      <td>Compact Cars</td>\n",
       "      <td>All-Wheel Drive</td>\n",
       "      <td>Automatic (S6)</td>\n",
       "      <td>Premium Gasoline</td>\n",
       "      <td>6</td>\n",
       "      <td>3.6</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>16.9415</td>\n",
       "      <td>20.5000</td>\n",
       "      <td>25.2190</td>\n",
       "      <td>33.5000</td>\n",
       "      <td>19.8774</td>\n",
       "      <td>446</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32208</td>\n",
       "      <td>Chevrolet</td>\n",
       "      <td>Malibu eAssist</td>\n",
       "      <td>2013</td>\n",
       "      <td>Midsize Cars</td>\n",
       "      <td>Front-Wheel Drive</td>\n",
       "      <td>Automatic (S6)</td>\n",
       "      <td>Regular Gasoline</td>\n",
       "      <td>4</td>\n",
       "      <td>2.4</td>\n",
       "      <td>...</td>\n",
       "      <td>95</td>\n",
       "      <td>24.7726</td>\n",
       "      <td>31.9796</td>\n",
       "      <td>35.5340</td>\n",
       "      <td>51.8816</td>\n",
       "      <td>28.6813</td>\n",
       "      <td>310</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      id        make           model  year           VClass  \\\n",
       "0  32204      Nissan            GT-R  2013  Subcompact Cars   \n",
       "1  32205  Volkswagen              CC  2013     Compact Cars   \n",
       "2  32206  Volkswagen              CC  2013     Compact Cars   \n",
       "3  32207  Volkswagen      CC 4motion  2013     Compact Cars   \n",
       "4  32208   Chevrolet  Malibu eAssist  2013     Midsize Cars   \n",
       "\n",
       "               drive              trans          fuelType  cylinders  displ  \\\n",
       "0    All-Wheel Drive    Automatic (AM6)  Premium Gasoline          6    3.8   \n",
       "1  Front-Wheel Drive  Automatic (AM-S6)  Premium Gasoline          4    2.0   \n",
       "2  Front-Wheel Drive     Automatic (S6)  Premium Gasoline          6    3.6   \n",
       "3    All-Wheel Drive     Automatic (S6)  Premium Gasoline          6    3.6   \n",
       "4  Front-Wheel Drive     Automatic (S6)  Regular Gasoline          4    2.4   \n",
       "\n",
       "   ...  pv4     city    UCity  highway  UHighway     comb  co2  feScore  \\\n",
       "0  ...    0  16.4596  20.2988  22.5568   30.1798  18.7389  471        4   \n",
       "1  ...    0  21.8706  26.9770  31.0367   42.4936  25.2227  349        6   \n",
       "2  ...    0  17.4935  21.2000  26.5716   35.1000  20.6716  429        5   \n",
       "3  ...    0  16.9415  20.5000  25.2190   33.5000  19.8774  446        5   \n",
       "4  ...   95  24.7726  31.9796  35.5340   51.8816  28.6813  310        8   \n",
       "\n",
       "   ghgScore  main_fuelType  \n",
       "0         4           True  \n",
       "1         6           True  \n",
       "2         5           True  \n",
       "3         5           True  \n",
       "4         8           True  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop rows that are not amongs the main fuelType: Premium/Regular\n",
    "fuel_econ = fuel_econ[fuel_econ['main_fuelType'] != False]\n",
    "fuel_econ.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "VClass            fuelType        \n",
       "Compact Cars      Premium Gasoline    593\n",
       "                  Regular Gasoline    490\n",
       "Large Cars        Premium Gasoline    348\n",
       "                  Regular Gasoline    256\n",
       "Midsize Cars      Premium Gasoline    509\n",
       "                  Regular Gasoline    640\n",
       "Minicompact Cars  Premium Gasoline    318\n",
       "                  Regular Gasoline      3\n",
       "Subcompact Cars   Premium Gasoline    534\n",
       "                  Regular Gasoline    132\n",
       "dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use group_by() and size() to get the number of cars and each combination of the two variable levels as a pandas Series\n",
    "ct_counts = fuel_econ.groupby(['VClass', 'fuelType'], observed=False).size()\n",
    "ct_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='VClass', ylabel='count'>"
      ]
     },
     "execution_count": 30,
     "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": [
    "# Plot the bar chart\n",
    "sb.countplot(data = fuel_econ, x = 'VClass', hue = 'fuelType')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Expected Output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I chose a clustered bar chart instead of a heat map in this case since there weren't a lot of numbers to plot. If you chose a heat map, did you remember to add a color bar and include annotations? From this plot, you can see that more cars use premium gas over regular gas, and that the smaller cars are biased towards the premium gas grade. It is only in midsize sedans where regular gasoline was used in more cars than premium gasoline.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# run this cell to check your work against ours\n",
    "categorical_solution_1()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}