pandas_intro.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Introduction to pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2 main data types in pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "series=pd.Series([\"BMW\",\"Toyota\",\"Honda\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# series store data in one dimensonal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "col=pd.Series([\"Red\",\"Blue\",\"Green\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      Red\n",
       "1     Blue\n",
       "2    Green\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data farme is 2d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_frame=pd.DataFrame({\"model\":series,\"Color\":col})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</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>Green</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    model  Color\n",
       "0     BMW    Red\n",
       "1  Toyota   Blue\n",
       "2   Honda  Green"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import data\n",
    "car_sales=pd.read_csv(\"car-sales.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "car_sales.to_csv(\"car_export.csv\",index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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": 14,
   "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>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",
       "  </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",
       "4  Nissan  White         213095      4  $3,500.00\n",
       "5  Toyota  Green          99213      4  $4,500.00"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales[car_sales[\"Odometer (KM)\"]>60000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exported_cars=pd.read_csv(\"car_export.csv\")\n",
    "exported_cars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "patient=pd.read_csv(\"heart-disease.csv\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=303, step=1)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patient.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 303 entries, 0 to 302\n",
      "Data columns (total 14 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   age       303 non-null    int64  \n",
      " 1   sex       303 non-null    int64  \n",
      " 2   cp        303 non-null    int64  \n",
      " 3   trestbps  303 non-null    int64  \n",
      " 4   chol      303 non-null    int64  \n",
      " 5   fbs       303 non-null    int64  \n",
      " 6   restecg   303 non-null    int64  \n",
      " 7   thalach   303 non-null    int64  \n",
      " 8   exang     303 non-null    int64  \n",
      " 9   oldpeak   303 non-null    float64\n",
      " 10  slope     303 non-null    int64  \n",
      " 11  ca        303 non-null    int64  \n",
      " 12  thal      303 non-null    int64  \n",
      " 13  target    303 non-null    int64  \n",
      "dtypes: float64(1), int64(13)\n",
      "memory usage: 33.3 KB\n"
     ]
    }
   ],
   "source": [
    "patient.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "29"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patient[\"age\"].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'sex', 'cp', 'trestbps', 'chol', 'fbs', 'restecg', 'thalach',\n",
       "       'exang', 'oldpeak', 'slope', 'ca', 'thal', 'target'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patient.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age          59.0\n",
       "sex           1.0\n",
       "cp            0.0\n",
       "trestbps    135.0\n",
       "chol        234.0\n",
       "fbs           0.0\n",
       "restecg       1.0\n",
       "thalach     161.0\n",
       "exang         0.0\n",
       "oldpeak       0.5\n",
       "slope         1.0\n",
       "ca            0.0\n",
       "thal          3.0\n",
       "target        1.0\n",
       "Name: 20, dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patient.loc[20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>145</td>\n",
       "      <td>233</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>130</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>204</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
       "0   63    1   3       145   233    1        0      150      0      2.3      0   \n",
       "1   37    1   2       130   250    0        1      187      0      3.5      0   \n",
       "2   41    0   1       130   204    0        0      172      0      1.4      2   \n",
       "\n",
       "   ca  thal  target  \n",
       "0   0     1       1  \n",
       "1   0     2       1  \n",
       "2   0     2       1  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patient.iloc[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "colours=pd.Series([\"Blue\",\"Green\",\"Black\",\"Orange\"],index=[1,3,3,2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1      Blue\n",
       "3     Green\n",
       "3     Black\n",
       "2    Orange\n",
       "dtype: object"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "colours.loc[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "colour_code=pd.Series([\"#4637\",\"#5646\",\"#6546\",\"#56347\"],index=[1,3,3,2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "color_data_frame=pd.DataFrame({\"colorCode\":colour_code,\"colours\":colours})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>colorCode</th>\n",
       "      <th>colours</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>#4637</td>\n",
       "      <td>Blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>#5646</td>\n",
       "      <td>Green</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>#6546</td>\n",
       "      <td>Black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>#56347</td>\n",
       "      <td>Orange</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  colorCode colours\n",
       "1     #4637    Blue\n",
       "3     #5646   Green\n",
       "3     #6546   Black\n",
       "2    #56347  Orange"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "color_data_frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>colorCode</th>\n",
       "      <th>colours</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>#4637</td>\n",
       "      <td>Blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>#5646</td>\n",
       "      <td>Green</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>#6546</td>\n",
       "      <td>Black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>#56347</td>\n",
       "      <td>Orange</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  colorCode colours\n",
       "1     #4637    Blue\n",
       "3     #5646   Green\n",
       "3     #6546   Black\n",
       "2    #56347  Orange"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "color_data_frame.iloc[:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1      Blue\n",
       "3     Green\n",
       "3     Black\n",
       "2    Orange\n",
       "Name: colours, dtype: object"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "color_data_frame.colours"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(car_sales[\"Make\"],car_sales[\"Doors\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "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>4000.0</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>5000.0</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>7000.0</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>22000.0</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>3500.0</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>4500.0</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>7500.0</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>7000.0</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>6250.0</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>9700.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make Colour  Odometer (KM)  Doors    Price\n",
       "0  toyota  White         150043      4   4000.0\n",
       "1   honda    Red          87899      4   5000.0\n",
       "2  toyota   Blue          32549      3   7000.0\n",
       "3     bmw  Black          11179      5  22000.0\n",
       "4  nissan  White         213095      4   3500.0\n",
       "5  toyota  Green          99213      4   4500.0\n",
       "6   honda   Blue          45698      4   7500.0\n",
       "7   honda   Blue          54738      4   7000.0\n",
       "8  toyota  White          60000      4   6250.0\n",
       "9  nissan  White          31600      4   9700.0"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "car_sales[\"Price\"].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "car_sales[\"Odometer (KM)\"].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD4CAYAAAD8Zh1EAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAATAklEQVR4nO3dcayddX3H8fdX2k3GxYKWXZuCFrduiaOJ0htgcZp7l+lKZSPb+ANCQNhMg4NEt5JQNUH3xxJ0wUyCsXaROBbm1QXdCNQ4Q7giW5i0rLQUZHasyUq7EjErXml01e/+OE/Xw+k59zzn3uece/zt/UpO7jnP8zvP+dzfefj03Oc85xCZiSTpZ99rljuAJKkZFrokFcJCl6RCWOiSVAgLXZIKsWK5Hnj16tW5bt265Xr40/zwhz/krLPOWu4YpzFXfeOYCcw1iHHMBOOVa/fu3d/LzPO6rszMZbls3Lgxx8kjjzyy3BG6Mld945gp01yDGMdMmeOVC9iVPXrVQy6SVAgLXZIKYaFLUiEsdEkqhIUuSYWw0CWpEH0LPSJeGxHfjoinImJ/RPxZlzEREXdFxIGI2BsRFw8nriSplzofLPoR8JuZOR8RK4HHIuJrmfl425jLgfXV5VLgs9VPSdKI9H2FXp3LPl/dXFldOr9E/Urg3mrs48A5EbGm2aiSpIVE1vgfXETEGcBu4JeBz2TmbR3rHwTuyMzHqtsPA7dl5q6OcVuALQCTk5MbZ2dnG/klmjA/P8/ExETfcfteODaCNKdMnglHj8OGtatG+rj91J2vURrHTGCuQYxjJhivXDMzM7szc6rbulrf5ZKZPwHeFhHnAF+NiIsy8+m2IdHtbl22swPYATA1NZXT09N1Hn4k5ubmqJPnhm0PDT9Mm60bTnDnvhUcvHZ6pI/bT935GqVxzATmGsQ4ZoLxzdVpoLNcMvO/gTlgU8eqQ8AFbbfPBw4vJZgkaTB1znI5r3plTkScCfwW8J2OYQ8A11dnu1wGHMvMI02HlST1VueQyxrgr6vj6K8BvpyZD0bETQCZuR3YCWwGDgCvADcOKa8kqYe+hZ6Ze4G3d1m+ve16Ajc3G02SNAg/KSpJhbDQJakQFrokFcJCl6RCWOiSVAgLXZIKYaFLUiEsdEkqhIUuSYWw0CWpEBa6JBXCQpekQljoklQIC12SCmGhS1IhLHRJKoSFLkmFsNAlqRAWuiQVwkKXpEJY6JJUCAtdkgphoUtSISx0SSqEhS5Jhehb6BFxQUQ8EhHPRsT+iPhglzHTEXEsIvZUl9uHE1eS1MuKGmNOAFsz88mIOBvYHRHfyMxnOsZ9KzOvaD6iJKmOvq/QM/NIZj5ZXf8B8CywdtjBJEmDicysPzhiHfAocFFmvty2fBq4HzgEHAZuzcz9Xe6/BdgCMDk5uXF2dnYJ0Zs1Pz/PxMRE33H7Xjg2gjSnTJ4JR4/DhrWrRvq4/dSdr1Eax0xgrkGMYyYYr1wzMzO7M3Oq27rahR4RE8A3gT/PzK90rHsd8NPMnI+IzcCnM3P9QtubmprKXbt21XrsUZibm2N6errvuHXbHhp+mDZbN5zgzn0rOHjHe0f6uP3Una9RGsdMYK5BjGMmGK9cEdGz0Gud5RIRK2m9Ar+vs8wBMvPlzJyvru8EVkbE6iVkliQNqM5ZLgF8Hng2Mz/VY8wbq3FExCXVdl9qMqgkaWF1znJ5B3AdsC8i9lTLPgK8CSAztwNXAR+IiBPAceDqHOTgvCRpyfoWemY+BkSfMXcDdzcVSpI0OD8pKkmFsNAlqRAWuiQVwkKXpEJY6JJUCAtdkgphoUtSISx0SSqEhS5JhbDQJakQFrokFcJCl6RCWOiSVAgLXZIKYaFLUiEsdEkqhIUuSYWw0CWpEBa6JBXCQpekQljoklQIC12SCmGhS1IhLHRJKoSFLkmFsNAlqRB9Cz0iLoiIRyLi2YjYHxEf7DImIuKuiDgQEXsj4uLhxJUk9bKixpgTwNbMfDIizgZ2R8Q3MvOZtjGXA+ury6XAZ6ufkqQR6fsKPTOPZOaT1fUfAM8CazuGXQncmy2PA+dExJrG00qSeorMrD84Yh3wKHBRZr7ctvxB4I7MfKy6/TBwW2bu6rj/FmALwOTk5MbZ2dkl/wJNmZ+fZ2Jiou+4fS8cG0GaUybPhKPHR/qQp9mwdtVpy+rO1yiNYyYw1yDGMROMV66ZmZndmTnVbV2dQy4ARMQEcD/wofYyP7m6y11O+5ciM3cAOwCmpqZyenq67sMP3dzcHHXy3LDtoeGHabN1wwnu3Ff7aRqKg9dOn7as7nyN0jhmAnMNYhwzwfjm6lTrLJeIWEmrzO/LzK90GXIIuKDt9vnA4aXHkyTVVecslwA+DzybmZ/qMewB4PrqbJfLgGOZeaTBnJKkPur8Lf8O4DpgX0TsqZZ9BHgTQGZuB3YCm4EDwCvAjY0nlSQtqG+hV290djtG3j4mgZubCiVJGpyfFJWkQljoklQIC12SCmGhS1IhLHRJKoSFLkmFsNAlqRAWuiQVwkKXpEJY6JJUCAtdkgphoUtSISx0SSqEhS5JhbDQJakQFrokFcJCl6RCWOiSVAgLXZIKYaFLUiEsdEkqhIUuSYWw0CWpEBa6JBXCQpekQvQt9Ii4JyJejIine6yfjohjEbGnutzefExJUj8raoz5AnA3cO8CY76VmVc0kkiStCh9X6Fn5qPA90eQRZK0BJGZ/QdFrAMezMyLuqybBu4HDgGHgVszc3+P7WwBtgBMTk5unJ2dXWzuxs3PzzMxMdF33L4Xjo0gzSmTZ8LR4yN9yNNsWLvqtGV152uUxjETmGsQ45gJxivXzMzM7syc6rauiUJ/HfDTzJyPiM3ApzNzfb9tTk1N5a5du/o+9qjMzc0xPT3dd9y6bQ8NP0ybrRtOcOe+OkfGhufgHe89bVnd+RqlccwE5hrEOGaC8coVET0LfclnuWTmy5k5X13fCayMiNVL3a4kaTBLLvSIeGNERHX9kmqbLy11u5KkwfT9Wz4ivghMA6sj4hDwMWAlQGZuB64CPhARJ4DjwNVZ5ziOJKlRfQs9M6/ps/5uWqc1SpKWkZ8UlaRCWOiSVAgLXZIKYaFLUiEsdEkqhIUuSYWw0CWpEBa6JBXCQpekQljoklQIC12SCmGhS1IhLHRJKoSFLkmFsNAlqRAWuiQVwkKXpEJY6JJUCAtdkgphoUtSISx0SSqEhS5JhbDQJakQFrokFcJCl6RCWOiSVIi+hR4R90TEixHxdI/1ERF3RcSBiNgbERc3H1OS1E+dV+hfADYtsP5yYH112QJ8dumxJEmD6lvomfko8P0FhlwJ3JstjwPnRMSapgJKkuqJzOw/KGId8GBmXtRl3YPAHZn5WHX7YeC2zNzVZewWWq/imZyc3Dg7O7uo0PteOLao+y1k8kw4erzxzS6ZueprMtOGtaua2RAwPz/PxMRErbHD2Ld7aZ+vJn/fpRhkrpZqkLluen9fynzPzMzszsypbutWLHqrp0SXZV3/lcjMHcAOgKmpqZyenl7UA96w7aFF3W8hWzec4M59TUxHs8xVX5OZDl473ch2AObm5qi7rw9j3+6lfb6a/H2XYpC5WqpB5rrp/X1Y893EWS6HgAvabp8PHG5gu5KkATRR6A8A11dnu1wGHMvMIw1sV5I0gL5/Q0TEF4FpYHVEHAI+BqwEyMztwE5gM3AAeAW4cVhhJUm99S30zLymz/oEbm4skSRpUfykqCQVwkKXpEJY6JJUCAtdkgphoUtSISx0SSqEhS5JhbDQJakQFrokFcJCl6RCWOiSVAgLXZIKYaFLUiEsdEkqhIUuSYWw0CWpEBa6JBXCQpekQljoklQIC12SCmGhS1IhLHRJKoSFLkmFsNAlqRAWuiQVolahR8SmiHguIg5ExLYu66cj4lhE7KkutzcfVZK0kBX9BkTEGcBngHcDh4AnIuKBzHymY+i3MvOKIWSUJNVQ5xX6JcCBzHw+M38MzAJXDjeWJGlQkZkLD4i4CtiUme+vbl8HXJqZt7SNmQbup/UK/jBwa2bu77KtLcAWgMnJyY2zs7OLCr3vhWOLut9CJs+Eo8cb3+ySmau+JjNtWLuqmQ0B8/PzTExM1Bo7jH27l/b5avL3XYpB5mqpBpnrpvf3pcz3zMzM7syc6rau7yEXILos6/xX4EngzZk5HxGbgb8H1p92p8wdwA6AqampnJ6ervHwp7th20OLut9Ctm44wZ376kzHaJmrviYzHbx2upHtAMzNzVF3Xx/Gvt1L+3w1+fsuxSBztVSDzHXT+/uw5rvOIZdDwAVtt8+n9Sr8/2Tmy5k5X13fCayMiNWNpZQk9VWn0J8A1kfEhRHxc8DVwAPtAyLijRER1fVLqu2+1HRYSVJvff+GyMwTEXEL8HXgDOCezNwfETdV67cDVwEfiIgTwHHg6ux3cF6S1KhaB4Wqwyg7O5Ztb7t+N3B3s9EkSYPwk6KSVAgLXZIKYaFLUiEsdEkqhIUuSYWw0CWpEBa6JBXCQpekQljoklQIC12SCmGhS1IhLHRJKoSFLkmFsNAlqRAWuiQVwkKXpEJY6JJUCAtdkgphoUtSISx0SSqEhS5JhbDQJakQFrokFcJCl6RCWOiSVAgLXZIKUavQI2JTRDwXEQciYluX9RERd1Xr90bExc1HlSQtpG+hR8QZwGeAy4G3AtdExFs7hl0OrK8uW4DPNpxTktRHnVfolwAHMvP5zPwxMAtc2THmSuDebHkcOCci1jScVZK0gMjMhQdEXAVsysz3V7evAy7NzFvaxjwI3JGZj1W3HwZuy8xdHdvaQusVPMCvAs819Ys0YDXwveUO0YW56hvHTGCuQYxjJhivXG/OzPO6rVhR487RZVnnvwJ1xpCZO4AdNR5z5CJiV2ZOLXeOTuaqbxwzgbkGMY6ZYHxzdapzyOUQcEHb7fOBw4sYI0kaojqF/gSwPiIujIifA64GHugY8wBwfXW2y2XAscw80nBWSdIC+h5yycwTEXEL8HXgDOCezNwfETdV67cDO4HNwAHgFeDG4UUemrE8FIS5BjGOmcBcgxjHTDC+uV6l75uikqSfDX5SVJIKYaFLUikys5gLrTNtHgGeBfYDH6yWfxx4AdhTXTa33efDtI79Pwf8dtvyjcC+at1dnDo89fPAl6rl/wKsq5ntYLW9PcCuatnrgW8A361+njuqXLQ+B7Cn7fIy8KHlmCvgHuBF4Om2ZSOZG+B91WN8F3hfjVx/AXwH2At8FTinWr4OON42b9tHnGskz1uvXD0yfaktz0FgzzLMVa9OWPb9aygdOOwHGOUFWANcXF0/G/g3Wl9X8HHg1i7j3wo8VT0hFwL/DpxRrfs28Ou0zrH/GnB5tfyPT+6AtM74+VLNbAeB1R3LPglsq65vAz4x6lzV+DOA/wLevBxzBbwLuJhXl8HQ54bWf9TPVz/Pra6f2yfXe4AV1fVPtOVa1z6u4/cbRa6hP28L5eqWqSPHncDtyzBXvTph2fevoXTgMDe+3BfgH4B3L7Czfxj4cNvtr1dP2BrgO23LrwE+1z6mur6C1qfHokaWg5xe6M8Ba9p2vOdGnasa/x7gn6rryzJXdPxHPoq5aR9TrfsccM1CuTrW/R5w30LjRpVrFM9bv1wLzEEA/wmsX4656tEJY7F/NX0p9hh6RKwD3k7rTyCAW6pvgrwnIs6tlq2ltaOddKhatra63rn8VffJzBPAMeANNSIl8I8Rsbv6CgSAyazO169+/uIy5ILWq4ovtt1e7rmC0cxNr23V9Ye0XqmddGFE/GtEfDMi3tn22KPKNeznbbG53gkczczvti0b+Vx1dMLPwv41sCILPSImgPuBD2Xmy7S+/fGXgLcBR2j9+Qe9v7Jgoa8yqPU1B128IzMvpvXNlDdHxLsWGDuyXNWHxX4X+Ltq0TjM1UKazLHofBHxUeAEcF+16Ajwpsx8O/CnwN9GxOtGmGsUz9ti5+saXv2CYeRz1aUTehmH+Vq04go9IlbSeuLuy8yvAGTm0cz8SWb+FPgrWt8gCb2/suBQdb1z+avuExErgFXA9/vlyszD1c8Xab2Zdglw9OS3UlY/Xxx1Llr/wDyZmUerfMs+V5VRzM2ivrIiIt4HXAFcm9Xf0pn5o8x8qbq+m9ax118ZVa4RPW8D56ru//u03jQ8mXWkc9WtExjj/WtJhnk8Z9QXWv8i3gv8ZcfyNW3X/wSYra7/Gq9+A+R5Tr0B8gRwGafeANlcLb+ZV78B8uUauc4Czm67/s/AJlpnTLS/MfPJUeaqxs4CNy73XHH6MeGhzw2tN6v+g9YbVudW11/fJ9cm4BngvI5x57XleAutM05eP8JcQ3/e+uXqzNQ2X99crrmidyeMxf7VeAcOc+OjvgC/QetPmr20nb4F/A2t04320vremfad/6O0XiE8R/WudbV8Cni6Wnc3p05Rei2twxMHaL3r/ZYaud5S7SRP0Tp16qPV8jcAD9M6penhjh1xFLl+AXgJWNW2bORzRevP8SPA/9B6VfNHo5obWsfBD1SXG2vkOkDruOjJ/evkf8h/UD23TwFPAr8z4lwjed565eqWqVr+BeCmjvyjnKtenbDs+9cwLn70X5IKUdwxdEn6/8pCl6RCWOiSVAgLXZIKYaFLUiEsdEkqhIUuSYX4X0awxPCjY0uaAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "car_sales[\"Odometer (KM)\"].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'car_sales' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-0b82018841ae>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcar_sales\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'car_sales' is not defined"
     ]
    }
   ],
   "source": [
    "car_sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "car_sales[\"Price\"]=car_sales[\"Price\"].str.replace('[$/,]','').astype(float)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Manupulating Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "car_sales[\"Make\"]=car_sales[\"Make\"].str.lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "car_sales[\"Price\"].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "missing_data=pd.read_csv('missing-data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>Honda</td>\n",
       "      <td>White</td>\n",
       "      <td>35431.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>15323.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BMW</td>\n",
       "      <td>Blue</td>\n",
       "      <td>192714.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>19943.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Honda</td>\n",
       "      <td>White</td>\n",
       "      <td>84714.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>28343.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>154365.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>13434.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>Blue</td>\n",
       "      <td>181577.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>14043.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Red</td>\n",
       "      <td>42652.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>23883.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Blue</td>\n",
       "      <td>163453.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8473.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Honda</td>\n",
       "      <td>White</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>20306.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>NaN</td>\n",
       "      <td>White</td>\n",
       "      <td>130538.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9374.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>51029.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>26683.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make Colour  Odometer (KM)  Doors    Price\n",
       "0   Honda  White        35431.0    4.0  15323.0\n",
       "1     BMW   Blue       192714.0    5.0  19943.0\n",
       "2   Honda  White        84714.0    4.0  28343.0\n",
       "3  Toyota  White       154365.0    4.0  13434.0\n",
       "4  Nissan   Blue       181577.0    3.0  14043.0\n",
       "5   Honda    Red        42652.0    4.0  23883.0\n",
       "6  Toyota   Blue       163453.0    4.0   8473.0\n",
       "7   Honda  White            NaN    4.0  20306.0\n",
       "8     NaN  White       130538.0    4.0   9374.0\n",
       "9   Honda   Blue        51029.0    4.0  26683.0"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "131253.23789473684"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_data[\"Odometer (KM)\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     35431.000000\n",
       "1    192714.000000\n",
       "2     84714.000000\n",
       "3    154365.000000\n",
       "4    181577.000000\n",
       "5     42652.000000\n",
       "6    163453.000000\n",
       "7    131253.237895\n",
       "8    130538.000000\n",
       "9     51029.000000\n",
       "Name: Odometer (KM), dtype: float64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_data[\"Odometer (KM)\"].fillna(missing_data[\"Odometer (KM)\"].mean()).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     35431.0\n",
       "1    192714.0\n",
       "2     84714.0\n",
       "3    154365.0\n",
       "4    181577.0\n",
       "5     42652.0\n",
       "6    163453.0\n",
       "7         NaN\n",
       "8    130538.0\n",
       "9     51029.0\n",
       "Name: Odometer (KM), dtype: float64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_data[\"Odometer (KM)\"].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "missing_data[\"Odometer (KM)\"].fillna(missing_data[\"Odometer (KM)\"].mean(),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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>Honda</td>\n",
       "      <td>White</td>\n",
       "      <td>35431.000000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>15323.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BMW</td>\n",
       "      <td>Blue</td>\n",
       "      <td>192714.000000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>19943.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Honda</td>\n",
       "      <td>White</td>\n",
       "      <td>84714.000000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>28343.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>154365.000000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>13434.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>Blue</td>\n",
       "      <td>181577.000000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>14043.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Red</td>\n",
       "      <td>42652.000000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>23883.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Blue</td>\n",
       "      <td>163453.000000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8473.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Honda</td>\n",
       "      <td>White</td>\n",
       "      <td>131253.237895</td>\n",
       "      <td>4.0</td>\n",
       "      <td>20306.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>NaN</td>\n",
       "      <td>White</td>\n",
       "      <td>130538.000000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9374.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>51029.000000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>26683.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make Colour  Odometer (KM)  Doors    Price\n",
       "0   Honda  White   35431.000000    4.0  15323.0\n",
       "1     BMW   Blue  192714.000000    5.0  19943.0\n",
       "2   Honda  White   84714.000000    4.0  28343.0\n",
       "3  Toyota  White  154365.000000    4.0  13434.0\n",
       "4  Nissan   Blue  181577.000000    3.0  14043.0\n",
       "5   Honda    Red   42652.000000    4.0  23883.0\n",
       "6  Toyota   Blue  163453.000000    4.0   8473.0\n",
       "7   Honda  White  131253.237895    4.0  20306.0\n",
       "8     NaN  White  130538.000000    4.0   9374.0\n",
       "9   Honda   Blue   51029.000000    4.0  26683.0"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Make</th>\n",
       "      <th>Colour</th>\n",
       "      <th>Odometer (KM)</th>\n",
       "      <th>Doors</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Honda</td>\n",
       "      <td>White</td>\n",
       "      <td>35431.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>15323.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BMW</td>\n",
       "      <td>Blue</td>\n",
       "      <td>192714.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>19943.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Honda</td>\n",
       "      <td>White</td>\n",
       "      <td>84714.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>28343.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>154365.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>13434.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>Blue</td>\n",
       "      <td>181577.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>14043.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Black</td>\n",
       "      <td>35820.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>32042.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>NaN</td>\n",
       "      <td>White</td>\n",
       "      <td>155144.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5716.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>Blue</td>\n",
       "      <td>66604.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>31570.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>Honda</td>\n",
       "      <td>White</td>\n",
       "      <td>215883.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4001.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Blue</td>\n",
       "      <td>248360.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>12732.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Make Colour  Odometer (KM)  Doors    Price\n",
       "0     Honda  White        35431.0    4.0  15323.0\n",
       "1       BMW   Blue       192714.0    5.0  19943.0\n",
       "2     Honda  White        84714.0    4.0  28343.0\n",
       "3    Toyota  White       154365.0    4.0  13434.0\n",
       "4    Nissan   Blue       181577.0    3.0  14043.0\n",
       "..      ...    ...            ...    ...      ...\n",
       "995  Toyota  Black        35820.0    4.0  32042.0\n",
       "996     NaN  White       155144.0    3.0   5716.0\n",
       "997  Nissan   Blue        66604.0    4.0  31570.0\n",
       "998   Honda  White       215883.0    4.0   4001.0\n",
       "999  Toyota   Blue       248360.0    4.0  12732.0\n",
       "\n",
       "[1000 rows x 5 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_data_frame=pd.read_csv(\"missing-data.csv\")\n",
    "missing_data_frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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>Honda</td>\n",
       "      <td>White</td>\n",
       "      <td>35431.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>15323.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BMW</td>\n",
       "      <td>Blue</td>\n",
       "      <td>192714.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>19943.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Honda</td>\n",
       "      <td>White</td>\n",
       "      <td>84714.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>28343.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>154365.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>13434.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>Blue</td>\n",
       "      <td>181577.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>14043.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>994</th>\n",
       "      <td>BMW</td>\n",
       "      <td>Blue</td>\n",
       "      <td>163322.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>31666.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Black</td>\n",
       "      <td>35820.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>32042.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>Blue</td>\n",
       "      <td>66604.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>31570.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>Honda</td>\n",
       "      <td>White</td>\n",
       "      <td>215883.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4001.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Blue</td>\n",
       "      <td>248360.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>12732.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>773 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Make Colour  Odometer (KM)  Doors    Price\n",
       "0     Honda  White        35431.0    4.0  15323.0\n",
       "1       BMW   Blue       192714.0    5.0  19943.0\n",
       "2     Honda  White        84714.0    4.0  28343.0\n",
       "3    Toyota  White       154365.0    4.0  13434.0\n",
       "4    Nissan   Blue       181577.0    3.0  14043.0\n",
       "..      ...    ...            ...    ...      ...\n",
       "994     BMW   Blue       163322.0    3.0  31666.0\n",
       "995  Toyota  Black        35820.0    4.0  32042.0\n",
       "997  Nissan   Blue        66604.0    4.0  31570.0\n",
       "998   Honda  White       215883.0    4.0   4001.0\n",
       "999  Toyota   Blue       248360.0    4.0  12732.0\n",
       "\n",
       "[773 rows x 5 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_data_dropped=missing_data_frame.dropna()\n",
    "missing_data_dropped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "missing_data_dropped.to_csv(\"car-sales-missing-dropped.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# column from series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "seats=pd.Series([5,5,5,5,5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    5\n",
       "1    5\n",
       "2    5\n",
       "3    5\n",
       "4    5\n",
       "dtype: int64"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "car_sales[\"Seats\"]=seats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "car_sales[\"Seats\"].fillna(5.0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "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",
       "    </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>4000.0</td>\n",
       "      <td>5.0</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>5000.0</td>\n",
       "      <td>5.0</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>7000.0</td>\n",
       "      <td>5.0</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>22000.0</td>\n",
       "      <td>5.0</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>3500.0</td>\n",
       "      <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>4500.0</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>7500.0</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>7000.0</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>6250.0</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>9700.0</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   4000.0    5.0\n",
       "1   honda    Red          87899      4   5000.0    5.0\n",
       "2  toyota   Blue          32549      3   7000.0    5.0\n",
       "3     bmw  Black          11179      5  22000.0    5.0\n",
       "4  nissan  White         213095      4   3500.0    5.0\n",
       "5  toyota  Green          99213      4   4500.0    5.0\n",
       "6   honda   Blue          45698      4   7500.0    5.0\n",
       "7   honda   Blue          54738      4   7000.0    5.0\n",
       "8  toyota  White          60000      4   6250.0    5.0\n",
       "9  nissan  White          31600      4   9700.0    5.0"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "    #colum from series\n",
    "    seats_colum=pd.Series([5,5,5,5])\n",
    "    car_sales['Seats']=seats_colum\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Make</th>\n",
       "      <th>Colour</th>\n",
       "      <th>Odometer (KM)</th>\n",
       "      <th>Doors</th>\n",
       "      <th>Price</th>\n",
       "      <th>Seats</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>4000.0</td>\n",
       "      <td>5.0</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>5000.0</td>\n",
       "      <td>5.0</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>7000.0</td>\n",
       "      <td>5.0</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>22000.0</td>\n",
       "      <td>5.0</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>3500.0</td>\n",
       "      <td>NaN</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>4500.0</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>7500.0</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>7000.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>60000</td>\n",
       "      <td>4</td>\n",
       "      <td>6250.0</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>9700.0</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   4000.0    5.0\n",
       "1   Honda    Red          87899      4   5000.0    5.0\n",
       "2  Toyota   Blue          32549      3   7000.0    5.0\n",
       "3     BMW  Black          11179      5  22000.0    5.0\n",
       "4  Nissan  White         213095      4   3500.0    NaN\n",
       "5  Toyota  Green          99213      4   4500.0    NaN\n",
       "6   Honda   Blue          45698      4   7500.0    NaN\n",
       "7   Honda   Blue          54738      4   7000.0    NaN\n",
       "8  Toyota  White          60000      4   6250.0    NaN\n",
       "9  Nissan  White          31600      4   9700.0    NaN"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "car_sales[\"Seats\"].fillna(5,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\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",
       "    </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>4000.0</td>\n",
       "      <td>5.0</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>5000.0</td>\n",
       "      <td>5.0</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>7000.0</td>\n",
       "      <td>5.0</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>22000.0</td>\n",
       "      <td>5.0</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>3500.0</td>\n",
       "      <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>4500.0</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>7500.0</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>7000.0</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>6250.0</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>9700.0</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   4000.0    5.0\n",
       "1   Honda    Red          87899      4   5000.0    5.0\n",
       "2  Toyota   Blue          32549      3   7000.0    5.0\n",
       "3     BMW  Black          11179      5  22000.0    5.0\n",
       "4  Nissan  White         213095      4   3500.0    5.0\n",
       "5  Toyota  Green          99213      4   4500.0    5.0\n",
       "6   Honda   Blue          45698      4   7500.0    5.0\n",
       "7   Honda   Blue          54738      4   7000.0    5.0\n",
       "8  Toyota  White          60000      4   6250.0    5.0\n",
       "9  Nissan  White          31600      4   9700.0    5.0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "#colum from python list\n",
    "fuel_economy=[7.5,9.2,5.0,8.7,4.7,7.6,8.7,3.0,4.5,5.7]\n",
    "car_sales[\"Fuel per 100Km\"]=fuel_economy\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "car_sales[\"Total fuel used\"]=car_sales[\"Odometer (KM)\"]/100 * car_sales[\"Fuel per 100Km\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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",
       "    </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>4000.0</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>5000.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.2</td>\n",
       "      <td>8086.708</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>7000.0</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>22000.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.7</td>\n",
       "      <td>972.573</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>3500.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.7</td>\n",
       "      <td>10015.465</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>4500.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.6</td>\n",
       "      <td>7540.188</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>7500.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.7</td>\n",
       "      <td>3975.726</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>7000.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1642.140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>60000</td>\n",
       "      <td>4</td>\n",
       "      <td>6250.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>2700.000</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>9700.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.7</td>\n",
       "      <td>1801.200</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   4000.0    5.0             7.5   \n",
       "1   Honda    Red          87899      4   5000.0    5.0             9.2   \n",
       "2  Toyota   Blue          32549      3   7000.0    5.0             5.0   \n",
       "3     BMW  Black          11179      5  22000.0    5.0             8.7   \n",
       "4  Nissan  White         213095      4   3500.0    5.0             4.7   \n",
       "5  Toyota  Green          99213      4   4500.0    5.0             7.6   \n",
       "6   Honda   Blue          45698      4   7500.0    5.0             8.7   \n",
       "7   Honda   Blue          54738      4   7000.0    5.0             3.0   \n",
       "8  Toyota  White          60000      4   6250.0    5.0             4.5   \n",
       "9  Nissan  White          31600      4   9700.0    5.0             5.7   \n",
       "\n",
       "   Total fuel used  \n",
       "0        11253.225  \n",
       "1         8086.708  \n",
       "2         1627.450  \n",
       "3          972.573  \n",
       "4        10015.465  \n",
       "5         7540.188  \n",
       "6         3975.726  \n",
       "7         1642.140  \n",
       "8         2700.000  \n",
       "9         1801.200  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "car_sales_shuffled=car_sales.sample(frac=1)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>9</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>31600</td>\n",
       "      <td>4</td>\n",
       "      <td>9700.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.7</td>\n",
       "      <td>1801.200</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>3500.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.7</td>\n",
       "      <td>10015.465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>60000</td>\n",
       "      <td>4</td>\n",
       "      <td>6250.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>2700.000</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>7000.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1627.450</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>7500.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.7</td>\n",
       "      <td>3975.726</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>5000.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.2</td>\n",
       "      <td>8086.708</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>4500.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.6</td>\n",
       "      <td>7540.188</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>7000.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1642.140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>150043</td>\n",
       "      <td>4</td>\n",
       "      <td>4000.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>11253.225</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>22000.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.7</td>\n",
       "      <td>972.573</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make Colour  Odometer (KM)  Doors    Price  Seats  Fuel per 100Km  \\\n",
       "9  Nissan  White          31600      4   9700.0    5.0             5.7   \n",
       "4  Nissan  White         213095      4   3500.0    5.0             4.7   \n",
       "8  Toyota  White          60000      4   6250.0    5.0             4.5   \n",
       "2  Toyota   Blue          32549      3   7000.0    5.0             5.0   \n",
       "6   Honda   Blue          45698      4   7500.0    5.0             8.7   \n",
       "1   Honda    Red          87899      4   5000.0    5.0             9.2   \n",
       "5  Toyota  Green          99213      4   4500.0    5.0             7.6   \n",
       "7   Honda   Blue          54738      4   7000.0    5.0             3.0   \n",
       "0  Toyota  White         150043      4   4000.0    5.0             7.5   \n",
       "3     BMW  Black          11179      5  22000.0    5.0             8.7   \n",
       "\n",
       "   Total fuel used  \n",
       "9         1801.200  \n",
       "4        10015.465  \n",
       "8         2700.000  \n",
       "2         1627.450  \n",
       "6         3975.726  \n",
       "1         8086.708  \n",
       "5         7540.188  \n",
       "7         1642.140  \n",
       "0        11253.225  \n",
       "3          972.573  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales_shuffled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Red</td>\n",
       "      <td>87899</td>\n",
       "      <td>4</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.2</td>\n",
       "      <td>8086.708</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>22000.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.7</td>\n",
       "      <td>972.573</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>9700.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.7</td>\n",
       "      <td>1801.200</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>7000.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1627.450</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>4500.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.6</td>\n",
       "      <td>7540.188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>150043</td>\n",
       "      <td>4</td>\n",
       "      <td>4000.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>11253.225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>60000</td>\n",
       "      <td>4</td>\n",
       "      <td>6250.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>2700.000</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>7000.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1642.140</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>3500.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.7</td>\n",
       "      <td>10015.465</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>7500.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.7</td>\n",
       "      <td>3975.726</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make Colour  Odometer (KM)  Doors    Price  Seats  Fuel per 100Km  \\\n",
       "1   Honda    Red          87899      4   5000.0    5.0             9.2   \n",
       "3     BMW  Black          11179      5  22000.0    5.0             8.7   \n",
       "9  Nissan  White          31600      4   9700.0    5.0             5.7   \n",
       "2  Toyota   Blue          32549      3   7000.0    5.0             5.0   \n",
       "5  Toyota  Green          99213      4   4500.0    5.0             7.6   \n",
       "0  Toyota  White         150043      4   4000.0    5.0             7.5   \n",
       "8  Toyota  White          60000      4   6250.0    5.0             4.5   \n",
       "7   Honda   Blue          54738      4   7000.0    5.0             3.0   \n",
       "4  Nissan  White         213095      4   3500.0    5.0             4.7   \n",
       "6   Honda   Blue          45698      4   7500.0    5.0             8.7   \n",
       "\n",
       "   Total fuel used  \n",
       "1         8086.708  \n",
       "3          972.573  \n",
       "9         1801.200  \n",
       "2         1627.450  \n",
       "5         7540.188  \n",
       "0        11253.225  \n",
       "8         2700.000  \n",
       "7         1642.140  \n",
       "4        10015.465  \n",
       "6         3975.726  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#only select 20% of data\n",
    "car_sales_shuffled.sample(frac=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "car_sales_shuffled.reset_index(drop=True,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>31600</td>\n",
       "      <td>4</td>\n",
       "      <td>9700.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.7</td>\n",
       "      <td>1801.200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>213095</td>\n",
       "      <td>4</td>\n",
       "      <td>3500.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.7</td>\n",
       "      <td>10015.465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>60000</td>\n",
       "      <td>4</td>\n",
       "      <td>6250.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>2700.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Blue</td>\n",
       "      <td>32549</td>\n",
       "      <td>3</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1627.450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>45698</td>\n",
       "      <td>4</td>\n",
       "      <td>7500.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.7</td>\n",
       "      <td>3975.726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Red</td>\n",
       "      <td>87899</td>\n",
       "      <td>4</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.2</td>\n",
       "      <td>8086.708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Green</td>\n",
       "      <td>99213</td>\n",
       "      <td>4</td>\n",
       "      <td>4500.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.6</td>\n",
       "      <td>7540.188</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>7000.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1642.140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>150043</td>\n",
       "      <td>4</td>\n",
       "      <td>4000.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>11253.225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>BMW</td>\n",
       "      <td>Black</td>\n",
       "      <td>11179</td>\n",
       "      <td>5</td>\n",
       "      <td>22000.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.7</td>\n",
       "      <td>972.573</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make Colour  Odometer (KM)  Doors    Price  Seats  Fuel per 100Km  \\\n",
       "0  Nissan  White          31600      4   9700.0    5.0             5.7   \n",
       "1  Nissan  White         213095      4   3500.0    5.0             4.7   \n",
       "2  Toyota  White          60000      4   6250.0    5.0             4.5   \n",
       "3  Toyota   Blue          32549      3   7000.0    5.0             5.0   \n",
       "4   Honda   Blue          45698      4   7500.0    5.0             8.7   \n",
       "5   Honda    Red          87899      4   5000.0    5.0             9.2   \n",
       "6  Toyota  Green          99213      4   4500.0    5.0             7.6   \n",
       "7   Honda   Blue          54738      4   7000.0    5.0             3.0   \n",
       "8  Toyota  White         150043      4   4000.0    5.0             7.5   \n",
       "9     BMW  Black          11179      5  22000.0    5.0             8.7   \n",
       "\n",
       "   Total fuel used  \n",
       "0         1801.200  \n",
       "1        10015.465  \n",
       "2         2700.000  \n",
       "3         1627.450  \n",
       "4         3975.726  \n",
       "5         8086.708  \n",
       "6         7540.188  \n",
       "7         1642.140  \n",
       "8        11253.225  \n",
       "9          972.573  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales_shuffled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Make</th>\n",
       "      <th>Colour</th>\n",
       "      <th>Odometer (KM)</th>\n",
       "      <th>Doors</th>\n",
       "      <th>Price</th>\n",
       "      <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>4000.0</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>5000.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.2</td>\n",
       "      <td>8086.708</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>7000.0</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>22000.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.7</td>\n",
       "      <td>972.573</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>3500.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.7</td>\n",
       "      <td>10015.465</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>4500.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.6</td>\n",
       "      <td>7540.188</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>7500.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.7</td>\n",
       "      <td>3975.726</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>7000.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1642.140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>60000</td>\n",
       "      <td>4</td>\n",
       "      <td>6250.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>2700.000</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>9700.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.7</td>\n",
       "      <td>1801.200</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   4000.0    5.0             7.5   \n",
       "1   Honda    Red          87899      4   5000.0    5.0             9.2   \n",
       "2  Toyota   Blue          32549      3   7000.0    5.0             5.0   \n",
       "3     BMW  Black          11179      5  22000.0    5.0             8.7   \n",
       "4  Nissan  White         213095      4   3500.0    5.0             4.7   \n",
       "5  Toyota  Green          99213      4   4500.0    5.0             7.6   \n",
       "6   Honda   Blue          45698      4   7500.0    5.0             8.7   \n",
       "7   Honda   Blue          54738      4   7000.0    5.0             3.0   \n",
       "8  Toyota  White          60000      4   6250.0    5.0             4.5   \n",
       "9  Nissan  White          31600      4   9700.0    5.0             5.7   \n",
       "\n",
       "   Total fuel used  \n",
       "0        11253.225  \n",
       "1         8086.708  \n",
       "2         1627.450  \n",
       "3          972.573  \n",
       "4        10015.465  \n",
       "5         7540.188  \n",
       "6         3975.726  \n",
       "7         1642.140  \n",
       "8         2700.000  \n",
       "9         1801.200  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " car_sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "car_sales[\"Odometer (KM)\"]=car_sales[\"Odometer (KM)\"].apply(lambda x:x/1.6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>93776.875</td>\n",
       "      <td>4</td>\n",
       "      <td>4000.0</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>54936.875</td>\n",
       "      <td>4</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.2</td>\n",
       "      <td>8086.708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Blue</td>\n",
       "      <td>20343.125</td>\n",
       "      <td>3</td>\n",
       "      <td>7000.0</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>6986.875</td>\n",
       "      <td>5</td>\n",
       "      <td>22000.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.7</td>\n",
       "      <td>972.573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>133184.375</td>\n",
       "      <td>4</td>\n",
       "      <td>3500.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.7</td>\n",
       "      <td>10015.465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Green</td>\n",
       "      <td>62008.125</td>\n",
       "      <td>4</td>\n",
       "      <td>4500.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.6</td>\n",
       "      <td>7540.188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>28561.250</td>\n",
       "      <td>4</td>\n",
       "      <td>7500.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.7</td>\n",
       "      <td>3975.726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>34211.250</td>\n",
       "      <td>4</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1642.140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>37500.000</td>\n",
       "      <td>4</td>\n",
       "      <td>6250.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>2700.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>19750.000</td>\n",
       "      <td>4</td>\n",
       "      <td>9700.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.7</td>\n",
       "      <td>1801.200</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      93776.875      4   4000.0    5.0             7.5   \n",
       "1   Honda    Red      54936.875      4   5000.0    5.0             9.2   \n",
       "2  Toyota   Blue      20343.125      3   7000.0    5.0             5.0   \n",
       "3     BMW  Black       6986.875      5  22000.0    5.0             8.7   \n",
       "4  Nissan  White     133184.375      4   3500.0    5.0             4.7   \n",
       "5  Toyota  Green      62008.125      4   4500.0    5.0             7.6   \n",
       "6   Honda   Blue      28561.250      4   7500.0    5.0             8.7   \n",
       "7   Honda   Blue      34211.250      4   7000.0    5.0             3.0   \n",
       "8  Toyota  White      37500.000      4   6250.0    5.0             4.5   \n",
       "9  Nissan  White      19750.000      4   9700.0    5.0             5.7   \n",
       "\n",
       "   Total fuel used  \n",
       "0        11253.225  \n",
       "1         8086.708  \n",
       "2         1627.450  \n",
       "3          972.573  \n",
       "4        10015.465  \n",
       "5         7540.188  \n",
       "6         3975.726  \n",
       "7         1642.140  \n",
       "8         2700.000  \n",
       "9         1801.200  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    " "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}