IntroToPandas.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7aa17923-3f98-4b2c-8c12-b7a8154027b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09788b96-624d-4a20-8e2c-98241a32c515",
   "metadata": {},
   "source": [
    "# 2 main datatypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "23ade241-59a8-4374-87dc-a1a4faa0b07c",
   "metadata": {},
   "outputs": [],
   "source": [
    "series = pd.Series([\"BMW\", \"Toyota\", \"Honda\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "df7008e2-58ff-492b-8c2c-3c4a84e850b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       BMW\n",
       "1    Toyota\n",
       "2     Honda\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99cbc1c5-8024-4c61-9cc0-6002900e1545",
   "metadata": {},
   "source": [
    "# series = 1-dimensional"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3c172301-ea1d-44bc-b1a6-ca9cea6268c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "colors = pd.Series(['Red','Blue','White'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "beffd767-7cbd-4c99-be5f-ca794ff74543",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      Red\n",
       "1     Blue\n",
       "2    White\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "colors"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b1b5852-0da3-417b-8122-bdbd81031391",
   "metadata": {},
   "source": [
    "# Dataframe = 2-dimensional"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "94aafbbc-90e0-466f-986f-cc0b73569693",
   "metadata": {},
   "outputs": [],
   "source": [
    "car_data = pd.DataFrame({\"Car make\": series, \"Color\": colors})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3a1bb629-eba2-4f42-9b38-64b9ae34f955",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
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       "    .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>Car make</th>\n",
       "      <th>Color</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BMW</td>\n",
       "      <td>Red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Honda</td>\n",
       "      <td>White</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Car make  Color\n",
       "0      BMW    Red\n",
       "1   Toyota   Blue\n",
       "2    Honda  White"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "059185d1-1e02-4475-9a1e-d2b57b828e48",
   "metadata": {},
   "source": [
    "# Import data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "123ba23f-11ea-4b78-a4a2-add663efebaf",
   "metadata": {},
   "outputs": [],
   "source": [
    "car_sales = pd.read_csv(\"car-sales.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1ee2d4ff-fba2-4d56-a0b0-11cd4e3049ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe thead th {\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 (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": [
    "car_sales"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "677a08a1-6743-410e-9691-87e60a3ec2ae",
   "metadata": {},
   "source": [
    "# Exporting a dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a05a846e-1191-4b8d-980f-421cdab058bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "car_sales.to_csv(\"exported_car_sales.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e941b8d1-53c0-43a9-a437-47615aea4045",
   "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>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": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exported_car_sales = pd.read_csv(\"exported_car_sales.csv\")\n",
    "exported_car_sales"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c7bfcb4-adbb-451f-927f-726f466575ad",
   "metadata": {},
   "source": [
    "## Describe data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b913b19d-4a2d-45eb-bd41-eeb1c12c4ed6",
   "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": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Attribute\n",
    "car_sales.dtypes\n",
    "\n",
    "# Function\n",
    "# car_sales.to_csv()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "587edff4-fb88-4cd9-b51f-6026492354ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Make', 'Colour', 'Odometer (KM)', 'Doors', 'Price'], dtype='object')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_columns = car_sales.columns\n",
    "car_columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e8ac4536-f483-430f-a3e3-dd364551a6c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=10, step=1)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "5b68150f-6295-4ead-a764-28d45558f8c0",
   "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>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": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "477b587f-3e64-457c-a293-74aaf08fc5e6",
   "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": [
    "car_sales.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "fd3fd2b3-e5dd-4911-8915-cdb8c9fd2696",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "38583.333333333336"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_prices = pd.Series([3000, 1500, 111250])\n",
    "car_prices.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "47717e80-4efd-42df-bf8d-0f2f25c9246d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Make             ToyotaHondaToyotaBMWNissanToyotaHondaHondaToyo...\n",
       "Colour               WhiteRedBlueBlackWhiteGreenBlueBlueWhiteWhite\n",
       "Odometer (KM)                                               786014\n",
       "Doors                                                           40\n",
       "Price            $4,000.00$5,000.00$7,000.00$22,000.00$3,500.00...\n",
       "dtype: object"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "b497bfea-a619-4e5c-8df8-9c63f3b5daae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "78601.4"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales[\"Odometer (KM)\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a73da3d3-ec4d-44ba-8637-0f6cf0bd88e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(car_sales)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf96483c-b5e6-4643-9741-2437adcb9b8f",
   "metadata": {},
   "source": [
    "## Viewing and selecting data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "56face5a-4a69-452b-aea6-4b4ee3d7ca80",
   "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>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": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "a250e998-b366-4bd1-bdd5-bb8db0ace421",
   "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": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "50069446-90d7-4147-9871-5c7389c46687",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      cat\n",
       "3      dog\n",
       "9     bird\n",
       "8    panda\n",
       "3    snake\n",
       "dtype: object"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# .loc & .iloc\n",
    "animals = pd.Series(['cat', 'dog', 'bird', 'panda', 'snake'], index=[0,3,9,8,3])\n",
    "animals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "e213d1d2-3537-421e-b333-663ac513bb7e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3      dog\n",
       "3    snake\n",
       "dtype: object"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "animals.loc[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "42a8042e-8666-4ffc-afea-13ef14e6d21f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'bird'"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "animals.loc[9]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "9de333e3-16cf-4385-b0c7-816b2b29b92c",
   "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": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# .loc refers to index\n",
    "car_sales.loc[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "120c9723-d273-4366-ac98-3ce1173ec262",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'panda'"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# .iloc refers to position\n",
    "animals.iloc[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "fd786407-166c-47c7-964c-87c458c3adb7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     cat\n",
       "3     dog\n",
       "9    bird\n",
       "dtype: object"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "animals.iloc[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "702dc582-c6c7-4b2a-89c4-612edd0d2bd3",
   "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": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales[\"Make\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "3573d14f-38d8-4f73-8a96-e633bc57b3d5",
   "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": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales.Make"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "3c1bce3f-7630-4186-a07e-9fbb709eceae",
   "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>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>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>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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make Colour  Odometer (KM)  Doors      Price\n",
       "0  Toyota  White         150043      4  $4,000.00\n",
       "2  Toyota   Blue          32549      3  $7,000.00\n",
       "5  Toyota  Green          99213      4  $4,500.00\n",
       "8  Toyota  White          60000      4  $6,250.00"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales[car_sales[\"Make\"] == \"Toyota\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "2a2ae282-4f5e-495f-a979-8ed66cf71f48",
   "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",
       "        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": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales[car_sales[\"Odometer (KM)\"] > 100000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "07e29f3d-be25-4eaa-b9f3-ca51fb030934",
   "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": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(car_sales['Make'], car_sales['Doors'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "b762a7c0-cdd4-4021-815e-038e681a3dae",
   "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": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales.groupby(['Make']).mean(numeric_only=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "c1373f59-b44a-4825-9095-03f009a16d50",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 48,
     "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": [
    "car_sales[\"Odometer (KM)\"].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "c5f08e0d-3e6e-4548-9052-74f72ac40f22",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 49,
     "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": [
    "car_sales[\"Odometer (KM)\"].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "c9e473ec-08a9-4887-ac76-4e8cec7e57ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('O')"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales[\"Price\"].dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "50d80e0c-cdd1-4056-9a99-4c6c7f3fca3c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Google: \"How to convert object to int in pandas\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "416f916f-32cd-4686-8c7a-f704759466c0",
   "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>6200</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   6200\n",
       "9  Nissan  White          31600      4   9700"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales['Price'] = car_sales['Price'].replace('[\\$\\,\\.]', '', regex=True).astype(int)\n",
    "car_sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "4e4d2134-0c25-4ca8-bd5a-4932df776cab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 63,
     "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": [
    "car_sales['Price'].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbf231d3-6744-404b-b423-0b3d0693db68",
   "metadata": {},
   "source": [
    "## Manipulating Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "e86b08e8-8777-454a-83d5-2eb0a36dd950",
   "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": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales['Make'].str.lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "69290ed2-ca30-4a18-8ca3-f91902972c41",
   "metadata": {},
   "outputs": [],
   "source": [
    "car_sales['Make'] = car_sales['Make'].str.lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "0e9a5874-71be-44a6-926c-c63c1e2f4ae3",
   "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>6200</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   6200\n",
       "9  nissan  White          31600      4   9700"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "78e39033-e95a-40a4-8dda-f5a111c6e3b9",
   "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>6200</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   6200\n",
       "9  Nissan  White          31600      4   9700"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales['Make'] = car_sales['Make'].str.capitalize()\n",
    "car_sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "3e1a9fdb-1fae-4c72-87a7-d5f04753517e",
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
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       "      <td>Red</td>\n",
       "      <td>87899.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BMW</td>\n",
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       "      <td>5.0</td>\n",
       "      <td>$22,000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nissan</td>\n",
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       "      <td>213095.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$3,500</td>\n",
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       "    <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",
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       "    <tr>\n",
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       "      <td>Honda</td>\n",
       "      <td>Blue</td>\n",
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       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
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       "    <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",
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      ],
      "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": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales_missing = pd.read_csv('car-sales-missing-data.csv')\n",
    "car_sales_missing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "3cccc28a-97bd-41b7-bfab-dd8a68dc5535",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "92302.66666666667"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales_missing['Odometer'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "b81c2b2b-59a8-4359-a6b2-4031b8bb5c64",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    150043.000000\n",
       "1     87899.000000\n",
       "2     92302.666667\n",
       "3     11179.000000\n",
       "4    213095.000000\n",
       "5     92302.666667\n",
       "6     92302.666667\n",
       "7     92302.666667\n",
       "8     60000.000000\n",
       "9     31600.000000\n",
       "Name: Odometer, dtype: float64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales_missing['Odometer'].fillna(car_sales_missing['Odometer'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "8be2c4e5-d73a-4644-9e17-301446d88546",
   "metadata": {},
   "outputs": [
    {
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      "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": 71,
     "metadata": {},
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   ],
   "source": [
    "car_sales_missing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "8c147d37-1502-4cf8-84a3-118b42e4e85f",
   "metadata": {},
   "outputs": [
    {
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       "      <td>4.0</td>\n",
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       "      <td>Toyota</td>\n",
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       "      <td>$9,700</td>\n",
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      ],
      "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": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales_missing['Odometer'].fillna(car_sales_missing['Odometer'].mean(), inplace=True)\n",
    "car_sales_missing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "916aa40d-8006-4e1a-af35-d1df4cfadfa0",
   "metadata": {},
   "outputs": [
    {
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       "      <td>5.0</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",
       "      <td>NaN</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",
       "      <td>NaN</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",
       "      <td>NaN</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>6200</td>\n",
       "      <td>NaN</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make Colour  Odometer (KM)  Doors  Price  Seats\n",
       "0  Toyota  White         150043      4   4000    5.0\n",
       "1   Honda    Red          87899      4   5000    5.0\n",
       "2  Toyota   Blue          32549      3   7000    5.0\n",
       "3     Bmw  Black          11179      5  22000    5.0\n",
       "4  Nissan  White         213095      4   3500    5.0\n",
       "5  Toyota  Green          99213      4   4500    NaN\n",
       "6   Honda   Blue          45698      4   7500    NaN\n",
       "7   Honda   Blue          54738      4   7000    NaN\n",
       "8  Toyota  White          60000      4   6200    NaN\n",
       "9  Nissan  White          31600      4   9700    NaN"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Column from series\n",
    "seats_column = pd.Series([5, 5, 5, 5, 5])\n",
    "\n",
    "#New column called seats\n",
    "car_sales['Seats'] = seats_column\n",
    "car_sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "7ada4f60-4583-4605-9f1f-225346eb2124",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Odometer (KM)</th>\n",
       "      <th>Doors</th>\n",
       "      <th>Price</th>\n",
       "      <th>Seats</th>\n",
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       "      <th>1</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Red</td>\n",
       "      <td>87899</td>\n",
       "      <td>4</td>\n",
       "      <td>5000</td>\n",
       "      <td>5.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Toyota</td>\n",
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       "      <td>32549</td>\n",
       "      <td>3</td>\n",
       "      <td>7000</td>\n",
       "      <td>5.0</td>\n",
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       "    <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",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nissan</td>\n",
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       "      <td>5.0</td>\n",
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       "    <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",
       "      <td>5.0</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",
       "      <td>5.0</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",
       "      <td>5.0</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>6200</td>\n",
       "      <td>5.0</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",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make Colour  Odometer (KM)  Doors  Price  Seats\n",
       "0  Toyota  White         150043      4   4000    5.0\n",
       "1   Honda    Red          87899      4   5000    5.0\n",
       "2  Toyota   Blue          32549      3   7000    5.0\n",
       "3     Bmw  Black          11179      5  22000    5.0\n",
       "4  Nissan  White         213095      4   3500    5.0\n",
       "5  Toyota  Green          99213      4   4500    5.0\n",
       "6   Honda   Blue          45698      4   7500    5.0\n",
       "7   Honda   Blue          54738      4   7000    5.0\n",
       "8  Toyota  White          60000      4   6200    5.0\n",
       "9  Nissan  White          31600      4   9700    5.0"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales['Seats'].fillna(5, inplace=True)\n",
    "car_sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "ca4ac11d-1999-4ab9-ba71-8da807641a53",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Column for Python list\n",
    "fuel_economy = [7.5, 9.2, 5.0, 9.6, 8.7, 5.3, 7.8, 9.3, 4.7, 8.8]\n",
    "car_sales['Fuel per 100KM'] = fuel_economy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "d8807a4d-2af8-4ab6-9e64-136908de116d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>4</td>\n",
       "      <td>4000</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Honda</td>\n",
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       "      <td>Toyota</td>\n",
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       "      <td>5.0</td>\n",
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       "    <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",
       "      <td>5.0</td>\n",
       "      <td>9.6</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nissan</td>\n",
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       "      <td>213095</td>\n",
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       "      <td>3500</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.7</td>\n",
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       "    <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",
       "      <td>5.0</td>\n",
       "      <td>5.3</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",
       "      <td>5.0</td>\n",
       "      <td>7.8</td>\n",
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       "    <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",
       "      <td>5.0</td>\n",
       "      <td>9.3</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>6200</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.7</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",
       "      <td>5.0</td>\n",
       "      <td>8.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make Colour  Odometer (KM)  Doors  Price  Seats  Fuel per 100KM\n",
       "0  Toyota  White         150043      4   4000    5.0             7.5\n",
       "1   Honda    Red          87899      4   5000    5.0             9.2\n",
       "2  Toyota   Blue          32549      3   7000    5.0             5.0\n",
       "3     Bmw  Black          11179      5  22000    5.0             9.6\n",
       "4  Nissan  White         213095      4   3500    5.0             8.7\n",
       "5  Toyota  Green          99213      4   4500    5.0             5.3\n",
       "6   Honda   Blue          45698      4   7500    5.0             7.8\n",
       "7   Honda   Blue          54738      4   7000    5.0             9.3\n",
       "8  Toyota  White          60000      4   6200    5.0             4.7\n",
       "9  Nissan  White          31600      4   9700    5.0             8.8"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "bf9cff93-23b9-4b70-8ce0-e6d24f66c0ea",
   "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 (KM)</th>\n",
       "      <th>Doors</th>\n",
       "      <th>Price</th>\n",
       "      <th>Seats</th>\n",
       "      <th>Fuel per 100KM</th>\n",
       "      <th>Total fuel used</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",
       "      <td>5.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>11253.225</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",
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       "      <th>2</th>\n",
       "      <td>Toyota</td>\n",
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       "      <td>3</td>\n",
       "      <td>7000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1627.450</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",
       "      <td>5.0</td>\n",
       "      <td>9.6</td>\n",
       "      <td>1073.184</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",
       "      <td>5.0</td>\n",
       "      <td>8.7</td>\n",
       "      <td>18539.265</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",
       "      <td>5.0</td>\n",
       "      <td>5.3</td>\n",
       "      <td>5258.289</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",
       "      <td>5.0</td>\n",
       "      <td>7.8</td>\n",
       "      <td>3564.444</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",
       "      <td>5.0</td>\n",
       "      <td>9.3</td>\n",
       "      <td>5090.634</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>6200</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.7</td>\n",
       "      <td>2820.000</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",
       "      <td>5.0</td>\n",
       "      <td>8.8</td>\n",
       "      <td>2780.800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make Colour  Odometer (KM)  Doors  Price  Seats  Fuel per 100KM  \\\n",
       "0  Toyota  White         150043      4   4000    5.0             7.5   \n",
       "1   Honda    Red          87899      4   5000    5.0             9.2   \n",
       "2  Toyota   Blue          32549      3   7000    5.0             5.0   \n",
       "3     Bmw  Black          11179      5  22000    5.0             9.6   \n",
       "4  Nissan  White         213095      4   3500    5.0             8.7   \n",
       "5  Toyota  Green          99213      4   4500    5.0             5.3   \n",
       "6   Honda   Blue          45698      4   7500    5.0             7.8   \n",
       "7   Honda   Blue          54738      4   7000    5.0             9.3   \n",
       "8  Toyota  White          60000      4   6200    5.0             4.7   \n",
       "9  Nissan  White          31600      4   9700    5.0             8.8   \n",
       "\n",
       "   Total fuel used  \n",
       "0        11253.225  \n",
       "1         8086.708  \n",
       "2         1627.450  \n",
       "3         1073.184  \n",
       "4        18539.265  \n",
       "5         5258.289  \n",
       "6         3564.444  \n",
       "7         5090.634  \n",
       "8         2820.000  \n",
       "9         2780.800  "
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales['Total fuel used'] = car_sales['Odometer (KM)']/100 * car_sales['Fuel per 100KM']\n",
    "car_sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "f2465321-2491-418e-bbbe-d534496cebec",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>6</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>45698</td>\n",
       "      <td>4</td>\n",
       "      <td>7500</td>\n",
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      "text/plain": [
       "     Make Colour  Odometer (KM)  Doors  Price  Seats  Fuel per 100KM  \\\n",
       "0  Toyota  White         150043      4   4000    5.0             7.5   \n",
       "1   Honda    Red          87899      4   5000    5.0             9.2   \n",
       "2  Toyota   Blue          32549      3   7000    5.0             5.0   \n",
       "3     Bmw  Black          11179      5  22000    5.0             9.6   \n",
       "4  Nissan  White         213095      4   3500    5.0             8.7   \n",
       "5  Toyota  Green          99213      4   4500    5.0             5.3   \n",
       "6   Honda   Blue          45698      4   7500    5.0             7.8   \n",
       "7   Honda   Blue          54738      4   7000    5.0             9.3   \n",
       "8  Toyota  White          60000      4   6200    5.0             4.7   \n",
       "9  Nissan  White          31600      4   9700    5.0             8.8   \n",
       "\n",
       "   Total fuel used  Passed Road Test  Number of wheels  \n",
       "0        11253.225              True                 4  \n",
       "1         8086.708              True                 4  \n",
       "2         1627.450              True                 4  \n",
       "3         1073.184              True                 4  \n",
       "4        18539.265              True                 4  \n",
       "5         5258.289              True                 4  \n",
       "6         3564.444              True                 4  \n",
       "7         5090.634              True                 4  \n",
       "8         2820.000              True                 4  \n",
       "9         2780.800              True                 4  "
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a column from a single value\n",
    "car_sales['Number of wheels'] = 4\n",
    "car_sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "f59b7d25-9838-4b70-898c-d14d957a9378",
   "metadata": {},
   "outputs": [],
   "source": [
    "car_sales['Passed Road Test'] = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "36154467-e479-4e62-ace8-1079d7699caf",
   "metadata": {},
   "outputs": [
    {
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       "      <td>4</td>\n",
       "      <td>7500</td>\n",
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       "      <td>9700</td>\n",
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      "text/plain": [
       "     Make Colour  Odometer (KM)  Doors  Price  Seats  Fuel per 100KM  \\\n",
       "0  Toyota  White         150043      4   4000    5.0             7.5   \n",
       "1   Honda    Red          87899      4   5000    5.0             9.2   \n",
       "2  Toyota   Blue          32549      3   7000    5.0             5.0   \n",
       "3     Bmw  Black          11179      5  22000    5.0             9.6   \n",
       "4  Nissan  White         213095      4   3500    5.0             8.7   \n",
       "5  Toyota  Green          99213      4   4500    5.0             5.3   \n",
       "6   Honda   Blue          45698      4   7500    5.0             7.8   \n",
       "7   Honda   Blue          54738      4   7000    5.0             9.3   \n",
       "8  Toyota  White          60000      4   6200    5.0             4.7   \n",
       "9  Nissan  White          31600      4   9700    5.0             8.8   \n",
       "\n",
       "   Total fuel used  Numbner of wheels  Passed Road Test  \n",
       "0        11253.225                  4              True  \n",
       "1         8086.708                  4              True  \n",
       "2         1627.450                  4              True  \n",
       "3         1073.184                  4              True  \n",
       "4        18539.265                  4              True  \n",
       "5         5258.289                  4              True  \n",
       "6         3564.444                  4              True  \n",
       "7         5090.634                  4              True  \n",
       "8         2820.000                  4              True  \n",
       "9         2780.800                  4              True  "
      ]
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     "metadata": {},
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   ],
   "source": [
    "car_sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "626be2b3-03c3-460a-9796-d4482e10b05d",
   "metadata": {},
   "outputs": [],
   "source": [
    "car_sales.drop('Numbner of wheels', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "9ee01473-bcd6-42c4-ba38-866d58229db7",
   "metadata": {},
   "outputs": [
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       "      <td>7000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.3</td>\n",
       "      <td>5090.634</td>\n",
       "      <td>True</td>\n",
       "      <td>4</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>6200</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.7</td>\n",
       "      <td>2820.000</td>\n",
       "      <td>True</td>\n",
       "      <td>4</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",
       "      <td>5.0</td>\n",
       "      <td>8.8</td>\n",
       "      <td>2780.800</td>\n",
       "      <td>True</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make Colour  Odometer (KM)  Doors  Price  Seats  Fuel per 100KM  \\\n",
       "0  Toyota  White         150043      4   4000    5.0             7.5   \n",
       "1   Honda    Red          87899      4   5000    5.0             9.2   \n",
       "2  Toyota   Blue          32549      3   7000    5.0             5.0   \n",
       "3     Bmw  Black          11179      5  22000    5.0             9.6   \n",
       "4  Nissan  White         213095      4   3500    5.0             8.7   \n",
       "5  Toyota  Green          99213      4   4500    5.0             5.3   \n",
       "6   Honda   Blue          45698      4   7500    5.0             7.8   \n",
       "7   Honda   Blue          54738      4   7000    5.0             9.3   \n",
       "8  Toyota  White          60000      4   6200    5.0             4.7   \n",
       "9  Nissan  White          31600      4   9700    5.0             8.8   \n",
       "\n",
       "   Total fuel used  Passed Road Test  Number of wheels  \n",
       "0        11253.225              True                 4  \n",
       "1         8086.708              True                 4  \n",
       "2         1627.450              True                 4  \n",
       "3         1073.184              True                 4  \n",
       "4        18539.265              True                 4  \n",
       "5         5258.289              True                 4  \n",
       "6         3564.444              True                 4  \n",
       "7         5090.634              True                 4  \n",
       "8         2820.000              True                 4  \n",
       "9         2780.800              True                 4  "
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "14d2c122-1bb9-47ed-b03a-add599e7d7ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Make</th>\n",
       "      <th>Colour</th>\n",
       "      <th>Odometer (KM)</th>\n",
       "      <th>Doors</th>\n",
       "      <th>Price</th>\n",
       "      <th>Seats</th>\n",
       "      <th>Fuel per 100KM</th>\n",
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       "      <td>4</td>\n",
       "      <td>4000</td>\n",
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       "      <th>2</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Blue</td>\n",
       "      <td>32549</td>\n",
       "      <td>3</td>\n",
       "      <td>7000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1627.450</td>\n",
       "      <td>True</td>\n",
       "      <td>4</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",
       "      <td>5.0</td>\n",
       "      <td>8.7</td>\n",
       "      <td>18539.265</td>\n",
       "      <td>True</td>\n",
       "      <td>4</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",
       "      <td>5.0</td>\n",
       "      <td>9.2</td>\n",
       "      <td>8086.708</td>\n",
       "      <td>True</td>\n",
       "      <td>4</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>6200</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.7</td>\n",
       "      <td>2820.000</td>\n",
       "      <td>True</td>\n",
       "      <td>4</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",
       "      <td>5.0</td>\n",
       "      <td>9.6</td>\n",
       "      <td>1073.184</td>\n",
       "      <td>True</td>\n",
       "      <td>4</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",
       "      <td>5.0</td>\n",
       "      <td>9.3</td>\n",
       "      <td>5090.634</td>\n",
       "      <td>True</td>\n",
       "      <td>4</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",
       "      <td>5.0</td>\n",
       "      <td>7.8</td>\n",
       "      <td>3564.444</td>\n",
       "      <td>True</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make Colour  Odometer (KM)  Doors  Price  Seats  Fuel per 100KM  \\\n",
       "5  Toyota  Green          99213      4   4500    5.0             5.3   \n",
       "9  Nissan  White          31600      4   9700    5.0             8.8   \n",
       "0  Toyota  White         150043      4   4000    5.0             7.5   \n",
       "2  Toyota   Blue          32549      3   7000    5.0             5.0   \n",
       "4  Nissan  White         213095      4   3500    5.0             8.7   \n",
       "1   Honda    Red          87899      4   5000    5.0             9.2   \n",
       "8  Toyota  White          60000      4   6200    5.0             4.7   \n",
       "3     Bmw  Black          11179      5  22000    5.0             9.6   \n",
       "7   Honda   Blue          54738      4   7000    5.0             9.3   \n",
       "6   Honda   Blue          45698      4   7500    5.0             7.8   \n",
       "\n",
       "   Total fuel used  Passed Road Test  Number of wheels  \n",
       "5         5258.289              True                 4  \n",
       "9         2780.800              True                 4  \n",
       "0        11253.225              True                 4  \n",
       "2         1627.450              True                 4  \n",
       "4        18539.265              True                 4  \n",
       "1         8086.708              True                 4  \n",
       "8         2820.000              True                 4  \n",
       "3         1073.184              True                 4  \n",
       "7         5090.634              True                 4  \n",
       "6         3564.444              True                 4  "
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales_shuffled = car_sales.sample(frac=1)\n",
    "car_sales_shuffled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "4dc4d1ea-ddb0-4984-a9d4-3adfe0687a08",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\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",
       "      <th>Seats</th>\n",
       "      <th>Fuel per 100KM</th>\n",
       "      <th>Total fuel used</th>\n",
       "      <th>Passed Road Test</th>\n",
       "      <th>Number of wheels</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>4500</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.3</td>\n",
       "      <td>5258.289</td>\n",
       "      <td>True</td>\n",
       "      <td>4</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",
       "      <td>5.0</td>\n",
       "      <td>9.2</td>\n",
       "      <td>8086.708</td>\n",
       "      <td>True</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make Colour  Odometer (KM)  Doors  Price  Seats  Fuel per 100KM  \\\n",
       "5  Toyota  Green          99213      4   4500    5.0             5.3   \n",
       "1   Honda    Red          87899      4   5000    5.0             9.2   \n",
       "\n",
       "   Total fuel used  Passed Road Test  Number of wheels  \n",
       "5         5258.289              True                 4  \n",
       "1         8086.708              True                 4  "
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Only select 20% of data\n",
    "car_sales_shuffled.sample(frac=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "e6ab684e-b4b0-4137-8c13-a6b0d9c170bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>True</td>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Blue</td>\n",
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       "      <td>3</td>\n",
       "      <td>7000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1627.450</td>\n",
       "      <td>True</td>\n",
       "      <td>4</td>\n",
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       "    <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",
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       "      <td>18539.265</td>\n",
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       "      <th>5</th>\n",
       "      <td>Honda</td>\n",
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       "      <td>4</td>\n",
       "      <td>5000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.2</td>\n",
       "      <td>8086.708</td>\n",
       "      <td>True</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>60000</td>\n",
       "      <td>4</td>\n",
       "      <td>6200</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.7</td>\n",
       "      <td>2820.000</td>\n",
       "      <td>True</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Bmw</td>\n",
       "      <td>Black</td>\n",
       "      <td>11179</td>\n",
       "      <td>5</td>\n",
       "      <td>22000</td>\n",
       "      <td>5.0</td>\n",
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       "      <td>1073.184</td>\n",
       "      <td>True</td>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Honda</td>\n",
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       "      <td>54738</td>\n",
       "      <td>4</td>\n",
       "      <td>7000</td>\n",
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       "      <td>5090.634</td>\n",
       "      <td>True</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>45698</td>\n",
       "      <td>4</td>\n",
       "      <td>7500</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.8</td>\n",
       "      <td>3564.444</td>\n",
       "      <td>True</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make Colour  Odometer (KM)  Doors  Price  Seats  Fuel per 100KM  \\\n",
       "0  Toyota  Green          99213      4   4500    5.0             5.3   \n",
       "1  Nissan  White          31600      4   9700    5.0             8.8   \n",
       "2  Toyota  White         150043      4   4000    5.0             7.5   \n",
       "3  Toyota   Blue          32549      3   7000    5.0             5.0   \n",
       "4  Nissan  White         213095      4   3500    5.0             8.7   \n",
       "5   Honda    Red          87899      4   5000    5.0             9.2   \n",
       "6  Toyota  White          60000      4   6200    5.0             4.7   \n",
       "7     Bmw  Black          11179      5  22000    5.0             9.6   \n",
       "8   Honda   Blue          54738      4   7000    5.0             9.3   \n",
       "9   Honda   Blue          45698      4   7500    5.0             7.8   \n",
       "\n",
       "   Total fuel used  Passed Road Test  Number of wheels  \n",
       "0         5258.289              True                 4  \n",
       "1         2780.800              True                 4  \n",
       "2        11253.225              True                 4  \n",
       "3         1627.450              True                 4  \n",
       "4        18539.265              True                 4  \n",
       "5         8086.708              True                 4  \n",
       "6         2820.000              True                 4  \n",
       "7         1073.184              True                 4  \n",
       "8         5090.634              True                 4  \n",
       "9         3564.444              True                 4  "
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Google: Pandas reset_index\n",
    "car_sales_shuffled.reset_index(drop=True, inplace=True)\n",
    "car_sales_shuffled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "5639d155-f4bb-4618-b581-e08dd2b373b7",
   "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",
       "      <th>Seats</th>\n",
       "      <th>Fuel per 100KM</th>\n",
       "      <th>Total fuel used</th>\n",
       "      <th>Passed Road Test</th>\n",
       "      <th>Number of wheels</th>\n",
       "      <th>Odometer (Mi)</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",
       "      <td>5.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>11253.225</td>\n",
       "      <td>True</td>\n",
       "      <td>4</td>\n",
       "      <td>93776.875</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",
       "      <td>5.0</td>\n",
       "      <td>9.2</td>\n",
       "      <td>8086.708</td>\n",
       "      <td>True</td>\n",
       "      <td>4</td>\n",
       "      <td>54936.875</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",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1627.450</td>\n",
       "      <td>True</td>\n",
       "      <td>4</td>\n",
       "      <td>20343.125</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",
       "      <td>5.0</td>\n",
       "      <td>9.6</td>\n",
       "      <td>1073.184</td>\n",
       "      <td>True</td>\n",
       "      <td>4</td>\n",
       "      <td>6986.875</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",
       "      <td>5.0</td>\n",
       "      <td>8.7</td>\n",
       "      <td>18539.265</td>\n",
       "      <td>True</td>\n",
       "      <td>4</td>\n",
       "      <td>133184.375</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",
       "      <td>5.0</td>\n",
       "      <td>5.3</td>\n",
       "      <td>5258.289</td>\n",
       "      <td>True</td>\n",
       "      <td>4</td>\n",
       "      <td>62008.125</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",
       "      <td>5.0</td>\n",
       "      <td>7.8</td>\n",
       "      <td>3564.444</td>\n",
       "      <td>True</td>\n",
       "      <td>4</td>\n",
       "      <td>28561.250</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",
       "      <td>5.0</td>\n",
       "      <td>9.3</td>\n",
       "      <td>5090.634</td>\n",
       "      <td>True</td>\n",
       "      <td>4</td>\n",
       "      <td>34211.250</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>6200</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.7</td>\n",
       "      <td>2820.000</td>\n",
       "      <td>True</td>\n",
       "      <td>4</td>\n",
       "      <td>37500.000</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",
       "      <td>5.0</td>\n",
       "      <td>8.8</td>\n",
       "      <td>2780.800</td>\n",
       "      <td>True</td>\n",
       "      <td>4</td>\n",
       "      <td>19750.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make Colour  Odometer (KM)  Doors  Price  Seats  Fuel per 100KM  \\\n",
       "0  Toyota  White         150043      4   4000    5.0             7.5   \n",
       "1   Honda    Red          87899      4   5000    5.0             9.2   \n",
       "2  Toyota   Blue          32549      3   7000    5.0             5.0   \n",
       "3     Bmw  Black          11179      5  22000    5.0             9.6   \n",
       "4  Nissan  White         213095      4   3500    5.0             8.7   \n",
       "5  Toyota  Green          99213      4   4500    5.0             5.3   \n",
       "6   Honda   Blue          45698      4   7500    5.0             7.8   \n",
       "7   Honda   Blue          54738      4   7000    5.0             9.3   \n",
       "8  Toyota  White          60000      4   6200    5.0             4.7   \n",
       "9  Nissan  White          31600      4   9700    5.0             8.8   \n",
       "\n",
       "   Total fuel used  Passed Road Test  Number of wheels  Odometer (Mi)  \n",
       "0        11253.225              True                 4      93776.875  \n",
       "1         8086.708              True                 4      54936.875  \n",
       "2         1627.450              True                 4      20343.125  \n",
       "3         1073.184              True                 4       6986.875  \n",
       "4        18539.265              True                 4     133184.375  \n",
       "5         5258.289              True                 4      62008.125  \n",
       "6         3564.444              True                 4      28561.250  \n",
       "7         5090.634              True                 4      34211.250  \n",
       "8         2820.000              True                 4      37500.000  \n",
       "9         2780.800              True                 4      19750.000  "
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Apply function to a column\n",
    "# Convert odometer to miles\n",
    "car_sales['Odometer (Mi)'] = car_sales['Odometer (KM)'].apply(lambda x: x/1.6)\n",
    "car_sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "658a7374-1fa8-497e-85ab-2b6b8a1d890f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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