pandas-exercises.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pandas Practice\n",
    "\n",
    "This notebook is dedicated to practicing different tasks with pandas. The solutions are available in a solutions notebook, however, you should always try to figure them out yourself first.\n",
    "\n",
    "It should be noted there may be more than one different way to answer a question or complete an exercise.\n",
    "\n",
    "Exercises are based off (and directly taken from) the quick introduction to pandas notebook.\n",
    "\n",
    "Different tasks will be detailed by comments or text.\n",
    "\n",
    "For further reference and resources, it's advised to check out the [pandas documentation](https://pandas.pydata.org/pandas-docs/stable/)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import pandas\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a series of three different colours\n",
    "colors = pd.Series(['red', 'yellow', 'blue'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       red\n",
       "1    yellow\n",
       "2      blue\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View the series of different colours\n",
    "colors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Tesla\n",
       "1     Fiat\n",
       "2     MINI\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a series of three different car types and view it\n",
    "car_types = pd.Series(['Tesla', 'Fiat', 'MINI'])\n",
    "car_types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>car_type</th>\n",
       "      <th>color</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Tesla</td>\n",
       "      <td>red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Fiat</td>\n",
       "      <td>yellow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MINI</td>\n",
       "      <td>blue</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  car_type   color\n",
       "0    Tesla     red\n",
       "1     Fiat  yellow\n",
       "2     MINI    blue"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Combine the Series of cars and colours into a DataFrame\n",
    "car = pd.DataFrame({'car_type': car_types, 'color': colors})\n",
    "car"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Make</th>\n",
       "      <th>Colour</th>\n",
       "      <th>Odometer (KM)</th>\n",
       "      <th>Doors</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>150043</td>\n",
       "      <td>4</td>\n",
       "      <td>$4,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Red</td>\n",
       "      <td>87899</td>\n",
       "      <td>4</td>\n",
       "      <td>$5,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Blue</td>\n",
       "      <td>32549</td>\n",
       "      <td>3</td>\n",
       "      <td>$7,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BMW</td>\n",
       "      <td>Black</td>\n",
       "      <td>11179</td>\n",
       "      <td>5</td>\n",
       "      <td>$22,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>213095</td>\n",
       "      <td>4</td>\n",
       "      <td>$3,500.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Green</td>\n",
       "      <td>99213</td>\n",
       "      <td>4</td>\n",
       "      <td>$4,500.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>45698</td>\n",
       "      <td>4</td>\n",
       "      <td>$7,500.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>54738</td>\n",
       "      <td>4</td>\n",
       "      <td>$7,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>60000</td>\n",
       "      <td>4</td>\n",
       "      <td>$6,250.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>31600</td>\n",
       "      <td>4</td>\n",
       "      <td>$9,700.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make Colour  Odometer (KM)  Doors       Price\n",
       "0  Toyota  White         150043      4   $4,000.00\n",
       "1   Honda    Red          87899      4   $5,000.00\n",
       "2  Toyota   Blue          32549      3   $7,000.00\n",
       "3     BMW  Black          11179      5  $22,000.00\n",
       "4  Nissan  White         213095      4   $3,500.00\n",
       "5  Toyota  Green          99213      4   $4,500.00\n",
       "6   Honda   Blue          45698      4   $7,500.00\n",
       "7   Honda   Blue          54738      4   $7,000.00\n",
       "8  Toyota  White          60000      4   $6,250.00\n",
       "9  Nissan  White          31600      4   $9,700.00"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import \"../data/car-sales.csv\" and turn it into a DataFrame\n",
    "car_sales = pd.read_csv('car-sales.csv')\n",
    "car_sales"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note:** Since you've imported `../data/car-sales.csv` as a DataFrame, we'll now refer to this DataFrame as 'the car sales DataFrame'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Export the DataFrame you created to a .csv file\n",
    "car_sales.to_csv('car-sales-exported.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Make             object\n",
       "Colour           object\n",
       "Odometer (KM)     int64\n",
       "Doors             int64\n",
       "Price            object\n",
       "dtype: object"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the different datatypes of the car data DataFrame\n",
    "car_sales.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Odometer (KM)</th>\n",
       "      <th>Doors</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>78601.400000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>61983.471735</td>\n",
       "      <td>0.471405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>11179.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>35836.250000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>57369.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>96384.500000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>213095.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Odometer (KM)      Doors\n",
       "count      10.000000  10.000000\n",
       "mean    78601.400000   4.000000\n",
       "std     61983.471735   0.471405\n",
       "min     11179.000000   3.000000\n",
       "25%     35836.250000   4.000000\n",
       "50%     57369.000000   4.000000\n",
       "75%     96384.500000   4.000000\n",
       "max    213095.000000   5.000000"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Describe your current car sales DataFrame using describe()\n",
    "car_sales.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10 entries, 0 to 9\n",
      "Data columns (total 5 columns):\n",
      " #   Column         Non-Null Count  Dtype \n",
      "---  ------         --------------  ----- \n",
      " 0   Make           10 non-null     object\n",
      " 1   Colour         10 non-null     object\n",
      " 2   Odometer (KM)  10 non-null     int64 \n",
      " 3   Doors          10 non-null     int64 \n",
      " 4   Price          10 non-null     object\n",
      "dtypes: int64(2), object(3)\n",
      "memory usage: 528.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "# Get information about your DataFrame using info()\n",
    "car_sales.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What does it show you? <br>\n",
    "It shows the columns, how many have nulls (NaN), and the data types of each of the columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10.5"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a Series of different numbers and find the mean of them\n",
    "rando = pd.Series([4, 7, 9, 12, 6, 22, 15, 9])\n",
    "rando.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "84"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a Series of different numbers and find the sum of them\n",
    "rando.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Make', 'Colour', 'Odometer (KM)', 'Doors', 'Price'], dtype='object')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# List out all the column names of the car sales DataFrame\n",
    "car_sales.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the length of the car sales DataFrame\n",
    "len(car_sales)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Make</th>\n",
       "      <th>Colour</th>\n",
       "      <th>Odometer (KM)</th>\n",
       "      <th>Doors</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>150043</td>\n",
       "      <td>4</td>\n",
       "      <td>$4,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Red</td>\n",
       "      <td>87899</td>\n",
       "      <td>4</td>\n",
       "      <td>$5,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Blue</td>\n",
       "      <td>32549</td>\n",
       "      <td>3</td>\n",
       "      <td>$7,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BMW</td>\n",
       "      <td>Black</td>\n",
       "      <td>11179</td>\n",
       "      <td>5</td>\n",
       "      <td>$22,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>213095</td>\n",
       "      <td>4</td>\n",
       "      <td>$3,500.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make Colour  Odometer (KM)  Doors       Price\n",
       "0  Toyota  White         150043      4   $4,000.00\n",
       "1   Honda    Red          87899      4   $5,000.00\n",
       "2  Toyota   Blue          32549      3   $7,000.00\n",
       "3     BMW  Black          11179      5  $22,000.00\n",
       "4  Nissan  White         213095      4   $3,500.00"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show the first 5 rows of the car sales DataFrame\n",
    "car_sales.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Make</th>\n",
       "      <th>Colour</th>\n",
       "      <th>Odometer (KM)</th>\n",
       "      <th>Doors</th>\n",
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       "      <th>0</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>150043</td>\n",
       "      <td>4</td>\n",
       "      <td>$4,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Red</td>\n",
       "      <td>87899</td>\n",
       "      <td>4</td>\n",
       "      <td>$5,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Blue</td>\n",
       "      <td>32549</td>\n",
       "      <td>3</td>\n",
       "      <td>$7,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BMW</td>\n",
       "      <td>Black</td>\n",
       "      <td>11179</td>\n",
       "      <td>5</td>\n",
       "      <td>$22,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>213095</td>\n",
       "      <td>4</td>\n",
       "      <td>$3,500.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Green</td>\n",
       "      <td>99213</td>\n",
       "      <td>4</td>\n",
       "      <td>$4,500.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>45698</td>\n",
       "      <td>4</td>\n",
       "      <td>$7,500.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make Colour  Odometer (KM)  Doors       Price\n",
       "0  Toyota  White         150043      4   $4,000.00\n",
       "1   Honda    Red          87899      4   $5,000.00\n",
       "2  Toyota   Blue          32549      3   $7,000.00\n",
       "3     BMW  Black          11179      5  $22,000.00\n",
       "4  Nissan  White         213095      4   $3,500.00\n",
       "5  Toyota  Green          99213      4   $4,500.00\n",
       "6   Honda   Blue          45698      4   $7,500.00"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show the first 7 rows of the car sales DataFrame\n",
    "car_sales.head(7)"
   ]
  },
  {
   "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>Make</th>\n",
       "      <th>Colour</th>\n",
       "      <th>Odometer (KM)</th>\n",
       "      <th>Doors</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Green</td>\n",
       "      <td>99213</td>\n",
       "      <td>4</td>\n",
       "      <td>$4,500.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>45698</td>\n",
       "      <td>4</td>\n",
       "      <td>$7,500.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>54738</td>\n",
       "      <td>4</td>\n",
       "      <td>$7,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>60000</td>\n",
       "      <td>4</td>\n",
       "      <td>$6,250.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>31600</td>\n",
       "      <td>4</td>\n",
       "      <td>$9,700.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make Colour  Odometer (KM)  Doors      Price\n",
       "5  Toyota  Green          99213      4  $4,500.00\n",
       "6   Honda   Blue          45698      4  $7,500.00\n",
       "7   Honda   Blue          54738      4  $7,000.00\n",
       "8  Toyota  White          60000      4  $6,250.00\n",
       "9  Nissan  White          31600      4  $9,700.00"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show the bottom 5 rows of the car sales DataFrame\n",
    "car_sales.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Make                    BMW\n",
       "Colour                Black\n",
       "Odometer (KM)         11179\n",
       "Doors                     5\n",
       "Price            $22,000.00\n",
       "Name: 3, dtype: object"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use .loc to select the row at index 3 of the car sales DataFrame\n",
    "car_sales.loc[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Make                    BMW\n",
       "Colour                Black\n",
       "Odometer (KM)         11179\n",
       "Doors                     5\n",
       "Price            $22,000.00\n",
       "Name: 3, dtype: object"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use .iloc to select the row at position 3 of the car sales DataFrame\n",
    "car_sales.iloc[3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice how they're the same? Why do you think this is? <br>\n",
    "The index and the position are the same\n",
    "\n",
    "Check the pandas documentation for [.loc](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.loc.html) and [.iloc](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.iloc.html). Think about a different situation each could be used for and try them out."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    150043\n",
       "1     87899\n",
       "2     32549\n",
       "3     11179\n",
       "4    213095\n",
       "5     99213\n",
       "6     45698\n",
       "7     54738\n",
       "8     60000\n",
       "9     31600\n",
       "Name: Odometer (KM), dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Select the \"Odometer (KM)\" column from the car sales DataFrame\n",
    "car_sales['Odometer (KM)']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "78601.4"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the mean of the \"Odometer (KM)\" column in the car sales DataFrame\n",
    "car_sales['Odometer (KM)'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>Make</th>\n",
       "      <th>Colour</th>\n",
       "      <th>Odometer (KM)</th>\n",
       "      <th>Doors</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>150043</td>\n",
       "      <td>4</td>\n",
       "      <td>$4,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>213095</td>\n",
       "      <td>4</td>\n",
       "      <td>$3,500.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make Colour  Odometer (KM)  Doors      Price\n",
       "0  Toyota  White         150043      4  $4,000.00\n",
       "4  Nissan  White         213095      4  $3,500.00"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Select the rows with over 100,000 kilometers on the Odometer\n",
    "car_sales[car_sales['Odometer (KM)'] > 100000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>Doors</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Make</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BMW</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Honda</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nissan</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Toyota</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Doors   3  4  5\n",
       "Make           \n",
       "BMW     0  0  1\n",
       "Honda   0  3  0\n",
       "Nissan  0  2  0\n",
       "Toyota  1  3  0"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a crosstab of the Make and Doors columns\n",
    "pd.crosstab(car_sales['Make'], car_sales['Doors'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>Odometer (KM)</th>\n",
       "      <th>Doors</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Make</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BMW</th>\n",
       "      <td>11179.000000</td>\n",
       "      <td>5.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Honda</th>\n",
       "      <td>62778.333333</td>\n",
       "      <td>4.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nissan</th>\n",
       "      <td>122347.500000</td>\n",
       "      <td>4.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Toyota</th>\n",
       "      <td>85451.250000</td>\n",
       "      <td>3.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Odometer (KM)  Doors\n",
       "Make                        \n",
       "BMW      11179.000000   5.00\n",
       "Honda    62778.333333   4.00\n",
       "Nissan  122347.500000   4.00\n",
       "Toyota   85451.250000   3.75"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group columns of the car sales DataFrame by the Make column and find the average\n",
    "car_sales.groupby(['Make']).mean(numeric_only=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 37,
     "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": [
    "# Import Matplotlib and create a plot of the Odometer column\n",
    "# Don't forget to use %matplotlib inline\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "car_sales['Odometer (KM)'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 38,
     "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": [
    "# Create a histogram of the Odometer column using hist()\n",
    "car_sales['Odometer (KM)'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "no numeric data to plot",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[39], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m# Try to plot the Price column using plot()\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m \u001b[43mcar_sales\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mPrice\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32m~\\PycharmProjects\\completeMLandDS\\sample_project_1\\env\\lib\\site-packages\\pandas\\plotting\\_core.py:1031\u001b[0m, in \u001b[0;36mPlotAccessor.__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1028\u001b[0m             label_name \u001b[38;5;241m=\u001b[39m label_kw \u001b[38;5;129;01mor\u001b[39;00m data\u001b[38;5;241m.\u001b[39mcolumns\n\u001b[0;32m   1029\u001b[0m             data\u001b[38;5;241m.\u001b[39mcolumns \u001b[38;5;241m=\u001b[39m label_name\n\u001b[1;32m-> 1031\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m plot_backend\u001b[38;5;241m.\u001b[39mplot(data, kind\u001b[38;5;241m=\u001b[39mkind, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
      "File \u001b[1;32m~\\PycharmProjects\\completeMLandDS\\sample_project_1\\env\\lib\\site-packages\\pandas\\plotting\\_matplotlib\\__init__.py:71\u001b[0m, in \u001b[0;36mplot\u001b[1;34m(data, kind, **kwargs)\u001b[0m\n\u001b[0;32m     69\u001b[0m         kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124max\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(ax, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mleft_ax\u001b[39m\u001b[38;5;124m\"\u001b[39m, ax)\n\u001b[0;32m     70\u001b[0m plot_obj \u001b[38;5;241m=\u001b[39m PLOT_CLASSES[kind](data, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m---> 71\u001b[0m \u001b[43mplot_obj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgenerate\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     72\u001b[0m plot_obj\u001b[38;5;241m.\u001b[39mdraw()\n\u001b[0;32m     73\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m plot_obj\u001b[38;5;241m.\u001b[39mresult\n",
      "File \u001b[1;32m~\\PycharmProjects\\completeMLandDS\\sample_project_1\\env\\lib\\site-packages\\pandas\\plotting\\_matplotlib\\core.py:451\u001b[0m, in \u001b[0;36mMPLPlot.generate\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    449\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mgenerate\u001b[39m(\u001b[38;5;28mself\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m    450\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_args_adjust()\n\u001b[1;32m--> 451\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_compute_plot_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    452\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_setup_subplots()\n\u001b[0;32m    453\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_make_plot()\n",
      "File \u001b[1;32m~\\PycharmProjects\\completeMLandDS\\sample_project_1\\env\\lib\\site-packages\\pandas\\plotting\\_matplotlib\\core.py:636\u001b[0m, in \u001b[0;36mMPLPlot._compute_plot_data\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    634\u001b[0m \u001b[38;5;66;03m# no non-numeric frames or series allowed\u001b[39;00m\n\u001b[0;32m    635\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_empty:\n\u001b[1;32m--> 636\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mno numeric data to plot\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m    638\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdata \u001b[38;5;241m=\u001b[39m numeric_data\u001b[38;5;241m.\u001b[39mapply(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_convert_to_ndarray)\n",
      "\u001b[1;31mTypeError\u001b[0m: no numeric data to plot"
     ]
    }
   ],
   "source": [
    "# Try to plot the Price column using plot()\n",
    "car_sales['Price'].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Why didn't it work? Can you think of a solution?<br>\n",
    "It is not a number. It is a string. Need to change it to a string\n",
    "\n",
    "You might want to search for \"how to convert a pandas string column to numbers\".\n",
    "\n",
    "And if you're still stuck, check out this [Stack Overflow question and answer on turning a price column into integers](https://stackoverflow.com/questions/44469313/price-column-object-to-int-in-pandas).\n",
    "\n",
    "See how you can provide the example code there to the problem here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>Make</th>\n",
       "      <th>Colour</th>\n",
       "      <th>Odometer (KM)</th>\n",
       "      <th>Doors</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>150043</td>\n",
       "      <td>4</td>\n",
       "      <td>4000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Red</td>\n",
       "      <td>87899</td>\n",
       "      <td>4</td>\n",
       "      <td>5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Blue</td>\n",
       "      <td>32549</td>\n",
       "      <td>3</td>\n",
       "      <td>7000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BMW</td>\n",
       "      <td>Black</td>\n",
       "      <td>11179</td>\n",
       "      <td>5</td>\n",
       "      <td>22000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>213095</td>\n",
       "      <td>4</td>\n",
       "      <td>3500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Green</td>\n",
       "      <td>99213</td>\n",
       "      <td>4</td>\n",
       "      <td>4500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>45698</td>\n",
       "      <td>4</td>\n",
       "      <td>7500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>54738</td>\n",
       "      <td>4</td>\n",
       "      <td>7000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>60000</td>\n",
       "      <td>4</td>\n",
       "      <td>6250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>31600</td>\n",
       "      <td>4</td>\n",
       "      <td>9700</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make Colour  Odometer (KM)  Doors  Price\n",
       "0  Toyota  White         150043      4   4000\n",
       "1   Honda    Red          87899      4   5000\n",
       "2  Toyota   Blue          32549      3   7000\n",
       "3     BMW  Black          11179      5  22000\n",
       "4  Nissan  White         213095      4   3500\n",
       "5  Toyota  Green          99213      4   4500\n",
       "6   Honda   Blue          45698      4   7500\n",
       "7   Honda   Blue          54738      4   7000\n",
       "8  Toyota  White          60000      4   6250\n",
       "9  Nissan  White          31600      4   9700"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove the punctuation from price column\n",
    "car_sales['Price'] = car_sales['Price'].replace('[\\$\\,\\.*]', '', regex=True).astype(int)//100\n",
    "car_sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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",
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       "\n",
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       "        text-align: right;\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>Make</th>\n",
       "      <th>Colour</th>\n",
       "      <th>Odometer (KM)</th>\n",
       "      <th>Doors</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>150043</td>\n",
       "      <td>4</td>\n",
       "      <td>4000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Red</td>\n",
       "      <td>87899</td>\n",
       "      <td>4</td>\n",
       "      <td>5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Blue</td>\n",
       "      <td>32549</td>\n",
       "      <td>3</td>\n",
       "      <td>7000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BMW</td>\n",
       "      <td>Black</td>\n",
       "      <td>11179</td>\n",
       "      <td>5</td>\n",
       "      <td>22000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>213095</td>\n",
       "      <td>4</td>\n",
       "      <td>3500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Green</td>\n",
       "      <td>99213</td>\n",
       "      <td>4</td>\n",
       "      <td>4500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>45698</td>\n",
       "      <td>4</td>\n",
       "      <td>7500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>54738</td>\n",
       "      <td>4</td>\n",
       "      <td>7000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>60000</td>\n",
       "      <td>4</td>\n",
       "      <td>6250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>31600</td>\n",
       "      <td>4</td>\n",
       "      <td>9700</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make Colour  Odometer (KM)  Doors  Price\n",
       "0  Toyota  White         150043      4   4000\n",
       "1   Honda    Red          87899      4   5000\n",
       "2  Toyota   Blue          32549      3   7000\n",
       "3     BMW  Black          11179      5  22000\n",
       "4  Nissan  White         213095      4   3500\n",
       "5  Toyota  Green          99213      4   4500\n",
       "6   Honda   Blue          45698      4   7500\n",
       "7   Honda   Blue          54738      4   7000\n",
       "8  Toyota  White          60000      4   6250\n",
       "9  Nissan  White          31600      4   9700"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the changes to the price column\n",
    "car_sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove the two extra zeros at the end of the price column\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Check the changes to the Price column\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Change the datatype of the Price column to integers\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    toyota\n",
       "1     honda\n",
       "2    toyota\n",
       "3       bmw\n",
       "4    nissan\n",
       "5    toyota\n",
       "6     honda\n",
       "7     honda\n",
       "8    toyota\n",
       "9    nissan\n",
       "Name: Make, dtype: object"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Lower the strings of the Make column\n",
    "car_sales['Make'].str.lower()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you check the car sales DataFrame, you'll notice the Make column hasn't been lowered.\n",
    "\n",
    "How could you make these changes permanent?<br>\n",
    "assign car_sales['Make'] the value\n",
    "\n",
    "Try it out."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make lowering the case of the Make column permanent\n",
    "car_sales['Make'] = car_sales['Make'].str.lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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>Make</th>\n",
       "      <th>Colour</th>\n",
       "      <th>Odometer (KM)</th>\n",
       "      <th>Doors</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>150043</td>\n",
       "      <td>4</td>\n",
       "      <td>4000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>honda</td>\n",
       "      <td>Red</td>\n",
       "      <td>87899</td>\n",
       "      <td>4</td>\n",
       "      <td>5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>toyota</td>\n",
       "      <td>Blue</td>\n",
       "      <td>32549</td>\n",
       "      <td>3</td>\n",
       "      <td>7000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bmw</td>\n",
       "      <td>Black</td>\n",
       "      <td>11179</td>\n",
       "      <td>5</td>\n",
       "      <td>22000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>213095</td>\n",
       "      <td>4</td>\n",
       "      <td>3500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>toyota</td>\n",
       "      <td>Green</td>\n",
       "      <td>99213</td>\n",
       "      <td>4</td>\n",
       "      <td>4500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>45698</td>\n",
       "      <td>4</td>\n",
       "      <td>7500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>54738</td>\n",
       "      <td>4</td>\n",
       "      <td>7000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>60000</td>\n",
       "      <td>4</td>\n",
       "      <td>6250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>31600</td>\n",
       "      <td>4</td>\n",
       "      <td>9700</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make Colour  Odometer (KM)  Doors  Price\n",
       "0  toyota  White         150043      4   4000\n",
       "1   honda    Red          87899      4   5000\n",
       "2  toyota   Blue          32549      3   7000\n",
       "3     bmw  Black          11179      5  22000\n",
       "4  nissan  White         213095      4   3500\n",
       "5  toyota  Green          99213      4   4500\n",
       "6   honda   Blue          45698      4   7500\n",
       "7   honda   Blue          54738      4   7000\n",
       "8  toyota  White          60000      4   6250\n",
       "9  nissan  White          31600      4   9700"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the car sales DataFrame\n",
    "car_sales"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice how the Make column stays lowered after reassigning.\n",
    "\n",
    "Now let's deal with missing data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Make</th>\n",
       "      <th>Colour</th>\n",
       "      <th>Odometer</th>\n",
       "      <th>Doors</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>150043.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$4,000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Red</td>\n",
       "      <td>87899.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$5,000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Blue</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>$7,000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BMW</td>\n",
       "      <td>Black</td>\n",
       "      <td>11179.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>$22,000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>213095.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$3,500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Green</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$4,500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Honda</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$7,500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>60000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>NaN</td>\n",
       "      <td>White</td>\n",
       "      <td>31600.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$9,700</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make Colour  Odometer  Doors    Price\n",
       "0  Toyota  White  150043.0    4.0   $4,000\n",
       "1   Honda    Red   87899.0    4.0   $5,000\n",
       "2  Toyota   Blue       NaN    3.0   $7,000\n",
       "3     BMW  Black   11179.0    5.0  $22,000\n",
       "4  Nissan  White  213095.0    4.0   $3,500\n",
       "5  Toyota  Green       NaN    4.0   $4,500\n",
       "6   Honda    NaN       NaN    4.0   $7,500\n",
       "7   Honda   Blue       NaN    4.0      NaN\n",
       "8  Toyota  White   60000.0    NaN      NaN\n",
       "9     NaN  White   31600.0    4.0   $9,700"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import the car sales DataFrame with missing data (\"../data/car-sales-missing-data.csv\")\n",
    "car_sales_missing = pd.read_csv('car-sales-missing-data.csv')\n",
    "\n",
    "# Check out the new DataFrame\n",
    "car_sales_missing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice the missing values are represented as `NaN` in pandas DataFrames.\n",
    "\n",
    "Let's try fill them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fill the Odometer column missing values with the mean of the column inplace\n",
    "car_sales_missing['Odometer'].fillna(car_sales_missing['Odometer'].mean(), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Make</th>\n",
       "      <th>Colour</th>\n",
       "      <th>Odometer</th>\n",
       "      <th>Doors</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>150043.000000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$4,000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Red</td>\n",
       "      <td>87899.000000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$5,000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Blue</td>\n",
       "      <td>92302.666667</td>\n",
       "      <td>3.0</td>\n",
       "      <td>$7,000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BMW</td>\n",
       "      <td>Black</td>\n",
       "      <td>11179.000000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>$22,000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>213095.000000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$3,500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Green</td>\n",
       "      <td>92302.666667</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$4,500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Honda</td>\n",
       "      <td>NaN</td>\n",
       "      <td>92302.666667</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$7,500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>92302.666667</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>NaN</td>\n",
       "      <td>White</td>\n",
       "      <td>31600.000000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$9,700</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make Colour       Odometer  Doors    Price\n",
       "0  Toyota  White  150043.000000    4.0   $4,000\n",
       "1   Honda    Red   87899.000000    4.0   $5,000\n",
       "2  Toyota   Blue   92302.666667    3.0   $7,000\n",
       "3     BMW  Black   11179.000000    5.0  $22,000\n",
       "4  Nissan  White  213095.000000    4.0   $3,500\n",
       "5  Toyota  Green   92302.666667    4.0   $4,500\n",
       "6   Honda    NaN   92302.666667    4.0   $7,500\n",
       "7   Honda   Blue   92302.666667    4.0      NaN\n",
       "8  Toyota  White   60000.000000    NaN      NaN\n",
       "9     NaN  White   31600.000000    4.0   $9,700"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View the car sales missing DataFrame and verify the changes\n",
    "car_sales_missing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove the rest of the missing data inplace\n",
    "car_sales_missing['Make'].fillna('Fiat', inplace=True)\n",
    "car_sales_missing['Colour'].fillna('Pink', inplace=True)\n",
    "car_sales_missing['Doors'].fillna(2, inplace=True)\n",
    "car_sales_missing['Price'].fillna('$7,777', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Make</th>\n",
       "      <th>Colour</th>\n",
       "      <th>Odometer</th>\n",
       "      <th>Doors</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>150043.000000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$4,000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Red</td>\n",
       "      <td>87899.000000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$5,000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Blue</td>\n",
       "      <td>92302.666667</td>\n",
       "      <td>3.0</td>\n",
       "      <td>$7,000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BMW</td>\n",
       "      <td>Black</td>\n",
       "      <td>11179.000000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>$22,000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>213095.000000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$3,500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Green</td>\n",
       "      <td>92302.666667</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$4,500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Pink</td>\n",
       "      <td>92302.666667</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$7,500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>92302.666667</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$7,777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>$7,777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Fiat</td>\n",
       "      <td>White</td>\n",
       "      <td>31600.000000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$9,700</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make Colour       Odometer  Doors    Price\n",
       "0  Toyota  White  150043.000000    4.0   $4,000\n",
       "1   Honda    Red   87899.000000    4.0   $5,000\n",
       "2  Toyota   Blue   92302.666667    3.0   $7,000\n",
       "3     BMW  Black   11179.000000    5.0  $22,000\n",
       "4  Nissan  White  213095.000000    4.0   $3,500\n",
       "5  Toyota  Green   92302.666667    4.0   $4,500\n",
       "6   Honda   Pink   92302.666667    4.0   $7,500\n",
       "7   Honda   Blue   92302.666667    4.0   $7,777\n",
       "8  Toyota  White   60000.000000    2.0   $7,777\n",
       "9    Fiat  White   31600.000000    4.0   $9,700"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Verify the missing values are removed by viewing the DataFrame\n",
    "car_sales_missing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll now start to add columns to our DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Make</th>\n",
       "      <th>Colour</th>\n",
       "      <th>Odometer</th>\n",
       "      <th>Doors</th>\n",
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       "      <th>0</th>\n",
       "      <td>Toyota</td>\n",
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       "      <td>4.0</td>\n",
       "      <td>$4,000</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Red</td>\n",
       "      <td>87899.000000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$5,000</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Blue</td>\n",
       "      <td>92302.666667</td>\n",
       "      <td>3.0</td>\n",
       "      <td>$7,000</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BMW</td>\n",
       "      <td>Black</td>\n",
       "      <td>11179.000000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>$22,000</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>213095.000000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$3,500</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Green</td>\n",
       "      <td>92302.666667</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$4,500</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Pink</td>\n",
       "      <td>92302.666667</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$7,500</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>92302.666667</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$7,777</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>$7,777</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Fiat</td>\n",
       "      <td>White</td>\n",
       "      <td>31600.000000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$9,700</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make Colour       Odometer  Doors    Price  Seats\n",
       "0  Toyota  White  150043.000000    4.0   $4,000      5\n",
       "1   Honda    Red   87899.000000    4.0   $5,000      5\n",
       "2  Toyota   Blue   92302.666667    3.0   $7,000      5\n",
       "3     BMW  Black   11179.000000    5.0  $22,000      5\n",
       "4  Nissan  White  213095.000000    4.0   $3,500      5\n",
       "5  Toyota  Green   92302.666667    4.0   $4,500      5\n",
       "6   Honda   Pink   92302.666667    4.0   $7,500      5\n",
       "7   Honda   Blue   92302.666667    4.0   $7,777      5\n",
       "8  Toyota  White   60000.000000    2.0   $7,777      5\n",
       "9    Fiat  White   31600.000000    4.0   $9,700      5"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a \"Seats\" column where every row has a value of 5\n",
    "car_sales_missing['Seats'] = 5\n",
    "car_sales_missing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Make</th>\n",
       "      <th>Colour</th>\n",
       "      <th>Odometer</th>\n",
       "      <th>Doors</th>\n",
       "      <th>Price</th>\n",
       "      <th>Seats</th>\n",
       "      <th>Engine Size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>150043.000000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$4,000</td>\n",
       "      <td>5</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Red</td>\n",
       "      <td>87899.000000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$5,000</td>\n",
       "      <td>5</td>\n",
       "      <td>4.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Blue</td>\n",
       "      <td>92302.666667</td>\n",
       "      <td>3.0</td>\n",
       "      <td>$7,000</td>\n",
       "      <td>5</td>\n",
       "      <td>2.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BMW</td>\n",
       "      <td>Black</td>\n",
       "      <td>11179.000000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>$22,000</td>\n",
       "      <td>5</td>\n",
       "      <td>3.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>213095.000000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$3,500</td>\n",
       "      <td>5</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Green</td>\n",
       "      <td>92302.666667</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$4,500</td>\n",
       "      <td>5</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Pink</td>\n",
       "      <td>92302.666667</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$7,500</td>\n",
       "      <td>5</td>\n",
       "      <td>2.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>92302.666667</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$7,777</td>\n",
       "      <td>5</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>$7,777</td>\n",
       "      <td>5</td>\n",
       "      <td>4.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Fiat</td>\n",
       "      <td>White</td>\n",
       "      <td>31600.000000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$9,700</td>\n",
       "      <td>5</td>\n",
       "      <td>1.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make Colour       Odometer  Doors    Price  Seats  Engine Size\n",
       "0  Toyota  White  150043.000000    4.0   $4,000      5          1.3\n",
       "1   Honda    Red   87899.000000    4.0   $5,000      5          4.3\n",
       "2  Toyota   Blue   92302.666667    3.0   $7,000      5          2.9\n",
       "3     BMW  Black   11179.000000    5.0  $22,000      5          3.1\n",
       "4  Nissan  White  213095.000000    4.0   $3,500      5          3.6\n",
       "5  Toyota  Green   92302.666667    4.0   $4,500      5          4.5\n",
       "6   Honda   Pink   92302.666667    4.0   $7,500      5          2.4\n",
       "7   Honda   Blue   92302.666667    4.0   $7,777      5          1.8\n",
       "8  Toyota  White   60000.000000    2.0   $7,777      5          4.1\n",
       "9    Fiat  White   31600.000000    4.0   $9,700      5          1.7"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a column called \"Engine Size\" with random values between 1.3 and 4.5\n",
    "# Remember: If you're doing it from a Python list, the list has to be the same length\n",
    "# as the DataFrame\n",
    "car_sales_missing['Engine Size'] = [1.3, 4.3, 2.9, 3.1, 3.6, 4.5, 2.4, 1.8, 4.1, 1.7]\n",
    "car_sales_missing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>Make</th>\n",
       "      <th>Colour</th>\n",
       "      <th>Odometer</th>\n",
       "      <th>Doors</th>\n",
       "      <th>Price</th>\n",
       "      <th>Seats</th>\n",
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       "      <th>Price per KM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>150043.000000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$4,000</td>\n",
       "      <td>5</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.026659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Red</td>\n",
       "      <td>87899.000000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$5,000</td>\n",
       "      <td>5</td>\n",
       "      <td>4.3</td>\n",
       "      <td>0.056883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Blue</td>\n",
       "      <td>92302.666667</td>\n",
       "      <td>3.0</td>\n",
       "      <td>$7,000</td>\n",
       "      <td>5</td>\n",
       "      <td>2.9</td>\n",
       "      <td>0.075837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BMW</td>\n",
       "      <td>Black</td>\n",
       "      <td>11179.000000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>$22,000</td>\n",
       "      <td>5</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.967976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>213095.000000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$3,500</td>\n",
       "      <td>5</td>\n",
       "      <td>3.6</td>\n",
       "      <td>0.016425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Green</td>\n",
       "      <td>92302.666667</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$4,500</td>\n",
       "      <td>5</td>\n",
       "      <td>4.5</td>\n",
       "      <td>0.048753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Pink</td>\n",
       "      <td>92302.666667</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$7,500</td>\n",
       "      <td>5</td>\n",
       "      <td>2.4</td>\n",
       "      <td>0.081254</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>92302.666667</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$7,777</td>\n",
       "      <td>5</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.084255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>$7,777</td>\n",
       "      <td>5</td>\n",
       "      <td>4.1</td>\n",
       "      <td>0.129617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Fiat</td>\n",
       "      <td>White</td>\n",
       "      <td>31600.000000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$9,700</td>\n",
       "      <td>5</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.306962</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make Colour       Odometer  Doors    Price  Seats  Engine Size  \\\n",
       "0  Toyota  White  150043.000000    4.0   $4,000      5          1.3   \n",
       "1   Honda    Red   87899.000000    4.0   $5,000      5          4.3   \n",
       "2  Toyota   Blue   92302.666667    3.0   $7,000      5          2.9   \n",
       "3     BMW  Black   11179.000000    5.0  $22,000      5          3.1   \n",
       "4  Nissan  White  213095.000000    4.0   $3,500      5          3.6   \n",
       "5  Toyota  Green   92302.666667    4.0   $4,500      5          4.5   \n",
       "6   Honda   Pink   92302.666667    4.0   $7,500      5          2.4   \n",
       "7   Honda   Blue   92302.666667    4.0   $7,777      5          1.8   \n",
       "8  Toyota  White   60000.000000    2.0   $7,777      5          4.1   \n",
       "9    Fiat  White   31600.000000    4.0   $9,700      5          1.7   \n",
       "\n",
       "   Price per KM  \n",
       "0      0.026659  \n",
       "1      0.056883  \n",
       "2      0.075837  \n",
       "3      1.967976  \n",
       "4      0.016425  \n",
       "5      0.048753  \n",
       "6      0.081254  \n",
       "7      0.084255  \n",
       "8      0.129617  \n",
       "9      0.306962  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a column which represents the price of a car per kilometer\n",
    "# Then view the DataFrame\n",
    "car_sales_missing['Price per KM'] = car_sales_missing['Price'].replace('[\\$\\,]', \"\", regex=True).astype(int)/car_sales_missing['Odometer']\n",
    "car_sales_missing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>1.3</td>\n",
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       "      <th>1</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Red</td>\n",
       "      <td>87899.000000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$5,000</td>\n",
       "      <td>5</td>\n",
       "      <td>4.3</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Blue</td>\n",
       "      <td>92302.666667</td>\n",
       "      <td>3.0</td>\n",
       "      <td>$7,000</td>\n",
       "      <td>5</td>\n",
       "      <td>2.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BMW</td>\n",
       "      <td>Black</td>\n",
       "      <td>11179.000000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>$22,000</td>\n",
       "      <td>5</td>\n",
       "      <td>3.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>213095.000000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$3,500</td>\n",
       "      <td>5</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Green</td>\n",
       "      <td>92302.666667</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$4,500</td>\n",
       "      <td>5</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Pink</td>\n",
       "      <td>92302.666667</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$7,500</td>\n",
       "      <td>5</td>\n",
       "      <td>2.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>92302.666667</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$7,777</td>\n",
       "      <td>5</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>$7,777</td>\n",
       "      <td>5</td>\n",
       "      <td>4.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Fiat</td>\n",
       "      <td>White</td>\n",
       "      <td>31600.000000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$9,700</td>\n",
       "      <td>5</td>\n",
       "      <td>1.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make Colour       Odometer  Doors    Price  Seats  Engine Size\n",
       "0  Toyota  White  150043.000000    4.0   $4,000      5          1.3\n",
       "1   Honda    Red   87899.000000    4.0   $5,000      5          4.3\n",
       "2  Toyota   Blue   92302.666667    3.0   $7,000      5          2.9\n",
       "3     BMW  Black   11179.000000    5.0  $22,000      5          3.1\n",
       "4  Nissan  White  213095.000000    4.0   $3,500      5          3.6\n",
       "5  Toyota  Green   92302.666667    4.0   $4,500      5          4.5\n",
       "6   Honda   Pink   92302.666667    4.0   $7,500      5          2.4\n",
       "7   Honda   Blue   92302.666667    4.0   $7,777      5          1.8\n",
       "8  Toyota  White   60000.000000    2.0   $7,777      5          4.1\n",
       "9    Fiat  White   31600.000000    4.0   $9,700      5          1.7"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove the last column you added using .drop()\n",
    "car_sales_missing.drop('Price per KM', axis=1, inplace=True)\n",
    "car_sales_missing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>Green</td>\n",
       "      <td>92302.666667</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$4,500</td>\n",
       "      <td>5</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>213095.000000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$3,500</td>\n",
       "      <td>5</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>150043.000000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$4,000</td>\n",
       "      <td>5</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Red</td>\n",
       "      <td>87899.000000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$5,000</td>\n",
       "      <td>5</td>\n",
       "      <td>4.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>92302.666667</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$7,777</td>\n",
       "      <td>5</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Fiat</td>\n",
       "      <td>White</td>\n",
       "      <td>31600.000000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$9,700</td>\n",
       "      <td>5</td>\n",
       "      <td>1.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Pink</td>\n",
       "      <td>92302.666667</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$7,500</td>\n",
       "      <td>5</td>\n",
       "      <td>2.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>$7,777</td>\n",
       "      <td>5</td>\n",
       "      <td>4.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Blue</td>\n",
       "      <td>92302.666667</td>\n",
       "      <td>3.0</td>\n",
       "      <td>$7,000</td>\n",
       "      <td>5</td>\n",
       "      <td>2.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BMW</td>\n",
       "      <td>Black</td>\n",
       "      <td>11179.000000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>$22,000</td>\n",
       "      <td>5</td>\n",
       "      <td>3.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make Colour       Odometer  Doors    Price  Seats  Engine Size\n",
       "5  Toyota  Green   92302.666667    4.0   $4,500      5          4.5\n",
       "4  Nissan  White  213095.000000    4.0   $3,500      5          3.6\n",
       "0  Toyota  White  150043.000000    4.0   $4,000      5          1.3\n",
       "1   Honda    Red   87899.000000    4.0   $5,000      5          4.3\n",
       "7   Honda   Blue   92302.666667    4.0   $7,777      5          1.8\n",
       "9    Fiat  White   31600.000000    4.0   $9,700      5          1.7\n",
       "6   Honda   Pink   92302.666667    4.0   $7,500      5          2.4\n",
       "8  Toyota  White   60000.000000    2.0   $7,777      5          4.1\n",
       "2  Toyota   Blue   92302.666667    3.0   $7,000      5          2.9\n",
       "3     BMW  Black   11179.000000    5.0  $22,000      5          3.1"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Shuffle the DataFrame using sample() with the frac parameter set to 1\n",
    "# Save the the shuffled DataFrame to a new variable\n",
    "car_sales_shuffled = car_sales_missing.sample(frac=1)\n",
    "car_sales_shuffled"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice how the index numbers get moved around. The [`sample()`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.sample.html) function is a great way to get random samples from your DataFrame. It's also another great way to shuffle the rows by setting `frac=1`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>Make</th>\n",
       "      <th>Colour</th>\n",
       "      <th>Odometer</th>\n",
       "      <th>Doors</th>\n",
       "      <th>Price</th>\n",
       "      <th>Seats</th>\n",
       "      <th>Engine Size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Green</td>\n",
       "      <td>92302.666667</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$4,500</td>\n",
       "      <td>5</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>213095.000000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$3,500</td>\n",
       "      <td>5</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>150043.000000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$4,000</td>\n",
       "      <td>5</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Red</td>\n",
       "      <td>87899.000000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$5,000</td>\n",
       "      <td>5</td>\n",
       "      <td>4.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>92302.666667</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$7,777</td>\n",
       "      <td>5</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Fiat</td>\n",
       "      <td>White</td>\n",
       "      <td>31600.000000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$9,700</td>\n",
       "      <td>5</td>\n",
       "      <td>1.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Pink</td>\n",
       "      <td>92302.666667</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$7,500</td>\n",
       "      <td>5</td>\n",
       "      <td>2.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>$7,777</td>\n",
       "      <td>5</td>\n",
       "      <td>4.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Blue</td>\n",
       "      <td>92302.666667</td>\n",
       "      <td>3.0</td>\n",
       "      <td>$7,000</td>\n",
       "      <td>5</td>\n",
       "      <td>2.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>BMW</td>\n",
       "      <td>Black</td>\n",
       "      <td>11179.000000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>$22,000</td>\n",
       "      <td>5</td>\n",
       "      <td>3.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make Colour       Odometer  Doors    Price  Seats  Engine Size\n",
       "0  Toyota  Green   92302.666667    4.0   $4,500      5          4.5\n",
       "1  Nissan  White  213095.000000    4.0   $3,500      5          3.6\n",
       "2  Toyota  White  150043.000000    4.0   $4,000      5          1.3\n",
       "3   Honda    Red   87899.000000    4.0   $5,000      5          4.3\n",
       "4   Honda   Blue   92302.666667    4.0   $7,777      5          1.8\n",
       "5    Fiat  White   31600.000000    4.0   $9,700      5          1.7\n",
       "6   Honda   Pink   92302.666667    4.0   $7,500      5          2.4\n",
       "7  Toyota  White   60000.000000    2.0   $7,777      5          4.1\n",
       "8  Toyota   Blue   92302.666667    3.0   $7,000      5          2.9\n",
       "9     BMW  Black   11179.000000    5.0  $22,000      5          3.1"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reset the indexes of the shuffled DataFrame\n",
    "car_sales_shuffled.reset_index(drop=True, inplace=True)\n",
    "car_sales_shuffled"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice the index numbers have been changed to have order (start from 0)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Make</th>\n",
       "      <th>Colour</th>\n",
       "      <th>Odometer</th>\n",
       "      <th>Doors</th>\n",
       "      <th>Price</th>\n",
       "      <th>Seats</th>\n",
       "      <th>Engine Size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Green</td>\n",
       "      <td>57689.166667</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$4,500</td>\n",
       "      <td>5</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>133184.375000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$3,500</td>\n",
       "      <td>5</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>93776.875000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$4,000</td>\n",
       "      <td>5</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Red</td>\n",
       "      <td>54936.875000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$5,000</td>\n",
       "      <td>5</td>\n",
       "      <td>4.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>57689.166667</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$7,777</td>\n",
       "      <td>5</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Fiat</td>\n",
       "      <td>White</td>\n",
       "      <td>19750.000000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$9,700</td>\n",
       "      <td>5</td>\n",
       "      <td>1.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Pink</td>\n",
       "      <td>57689.166667</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$7,500</td>\n",
       "      <td>5</td>\n",
       "      <td>2.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>37500.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>$7,777</td>\n",
       "      <td>5</td>\n",
       "      <td>4.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Blue</td>\n",
       "      <td>57689.166667</td>\n",
       "      <td>3.0</td>\n",
       "      <td>$7,000</td>\n",
       "      <td>5</td>\n",
       "      <td>2.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>BMW</td>\n",
       "      <td>Black</td>\n",
       "      <td>6986.875000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>$22,000</td>\n",
       "      <td>5</td>\n",
       "      <td>3.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make Colour       Odometer  Doors    Price  Seats  Engine Size\n",
       "0  Toyota  Green   57689.166667    4.0   $4,500      5          4.5\n",
       "1  Nissan  White  133184.375000    4.0   $3,500      5          3.6\n",
       "2  Toyota  White   93776.875000    4.0   $4,000      5          1.3\n",
       "3   Honda    Red   54936.875000    4.0   $5,000      5          4.3\n",
       "4   Honda   Blue   57689.166667    4.0   $7,777      5          1.8\n",
       "5    Fiat  White   19750.000000    4.0   $9,700      5          1.7\n",
       "6   Honda   Pink   57689.166667    4.0   $7,500      5          2.4\n",
       "7  Toyota  White   37500.000000    2.0   $7,777      5          4.1\n",
       "8  Toyota   Blue   57689.166667    3.0   $7,000      5          2.9\n",
       "9     BMW  Black    6986.875000    5.0  $22,000      5          3.1"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Change the Odometer values from kilometers to miles using a Lambda function\n",
    "# Then view the DataFrame\n",
    "car_sales_shuffled['Odometer'] = car_sales_shuffled['Odometer'].apply(lambda x: x/1.6)\n",
    "car_sales_shuffled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Make</th>\n",
       "      <th>Color</th>\n",
       "      <th>Odometer (Mi)</th>\n",
       "      <th>Doors</th>\n",
       "      <th>Price</th>\n",
       "      <th>Seats</th>\n",
       "      <th>Engine Size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Green</td>\n",
       "      <td>57689.166667</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$4,500</td>\n",
       "      <td>5</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>133184.375000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$3,500</td>\n",
       "      <td>5</td>\n",
       "      <td>3.6</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>93776.875000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$4,000</td>\n",
       "      <td>5</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Red</td>\n",
       "      <td>54936.875000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$5,000</td>\n",
       "      <td>5</td>\n",
       "      <td>4.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>57689.166667</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$7,777</td>\n",
       "      <td>5</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Fiat</td>\n",
       "      <td>White</td>\n",
       "      <td>19750.000000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$9,700</td>\n",
       "      <td>5</td>\n",
       "      <td>1.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Pink</td>\n",
       "      <td>57689.166667</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$7,500</td>\n",
       "      <td>5</td>\n",
       "      <td>2.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>37500.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>$7,777</td>\n",
       "      <td>5</td>\n",
       "      <td>4.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Blue</td>\n",
       "      <td>57689.166667</td>\n",
       "      <td>3.0</td>\n",
       "      <td>$7,000</td>\n",
       "      <td>5</td>\n",
       "      <td>2.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>BMW</td>\n",
       "      <td>Black</td>\n",
       "      <td>6986.875000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>$22,000</td>\n",
       "      <td>5</td>\n",
       "      <td>3.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make  Color  Odometer (Mi)  Doors    Price  Seats  Engine Size\n",
       "0  Toyota  Green   57689.166667    4.0   $4,500      5          4.5\n",
       "1  Nissan  White  133184.375000    4.0   $3,500      5          3.6\n",
       "2  Toyota  White   93776.875000    4.0   $4,000      5          1.3\n",
       "3   Honda    Red   54936.875000    4.0   $5,000      5          4.3\n",
       "4   Honda   Blue   57689.166667    4.0   $7,777      5          1.8\n",
       "5    Fiat  White   19750.000000    4.0   $9,700      5          1.7\n",
       "6   Honda   Pink   57689.166667    4.0   $7,500      5          2.4\n",
       "7  Toyota  White   37500.000000    2.0   $7,777      5          4.1\n",
       "8  Toyota   Blue   57689.166667    3.0   $7,000      5          2.9\n",
       "9     BMW  Black    6986.875000    5.0  $22,000      5          3.1"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Change the title of the Odometer (KM) to represent miles instead of kilometers\n",
    "# df.rename(columns={\"A\": \"a\", \"B\": \"c\"})\n",
    "car_sales_shuffled.rename(columns={'Colour': 'Color', 'Odometer': 'Odometer (Mi)'}, inplace=True)\n",
    "car_sales_shuffled"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extensions\n",
    "\n",
    "For more exercises, check out the pandas documentation, particularly the [10-minutes to pandas section](https://pandas.pydata.org/pandas-docs/stable/user_guide/10min.html). \n",
    "\n",
    "One great exercise would be to retype out the entire section into a Jupyter Notebook of your own.\n",
    "\n",
    "Get hands-on with the code and see what it does.\n",
    "\n",
    "The next place you should check out are the [top questions and answers on Stack Overflow for pandas](https://stackoverflow.com/questions/tagged/pandas?sort=MostVotes&edited=true). Often, these contain some of the most useful and common pandas functions. Be sure to play around with the different filters!\n",
    "\n",
    "Finally, always remember, the best way to learn something new to is try it. Make mistakes. Ask questions, get things wrong, take note of the things you do most often. And don't worry if you keep making the same mistake, pandas has many ways to do the same thing and is a big library. So it'll likely take a while before you get the hang of it."
   ]
  }
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