Introduction_of_pandas.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8e1732a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "10b20c3c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib as mat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2eb49bab",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c39ae7e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2 main datatype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6c41bea6",
   "metadata": {},
   "outputs": [],
   "source": [
    "series=pd.Series([\"Bmw\",\"TOyota\",\"HOnda\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "76691de2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       Bmw\n",
       "1    TOyota\n",
       "2     HOnda\n",
       "dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b89d5e2f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# series = 1-dimensional"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6c2ef6ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "colours=pd.Series([\"Red\",\"Blue\",\"white\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "daee3ec9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      Red\n",
       "1     Blue\n",
       "2    white\n",
       "dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "colours"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e8b53eaa",
   "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>car make</th>\n",
       "      <th>colour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bmw</td>\n",
       "      <td>Red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>TOyota</td>\n",
       "      <td>Blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HOnda</td>\n",
       "      <td>white</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  car make colour\n",
       "0      Bmw    Red\n",
       "1   TOyota   Blue\n",
       "2    HOnda  white"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#dataFrame=2-dimensional\n",
    "car_data=pd.DataFrame({\"car make\":series,\"colour\":colours})\n",
    "car_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ff2cb90b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import data\n",
    "#sructure data \n",
    "car_sales=pd.read_csv(\"car-sales.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a0249463",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Make</th>\n",
       "      <th>Colour</th>\n",
       "      <th>Odometer (KM)</th>\n",
       "      <th>Doors</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>150043</td>\n",
       "      <td>4</td>\n",
       "      <td>$4,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Red</td>\n",
       "      <td>87899</td>\n",
       "      <td>4</td>\n",
       "      <td>$5,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Blue</td>\n",
       "      <td>32549</td>\n",
       "      <td>3</td>\n",
       "      <td>$7,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BMW</td>\n",
       "      <td>Black</td>\n",
       "      <td>11179</td>\n",
       "      <td>5</td>\n",
       "      <td>$22,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>213095</td>\n",
       "      <td>4</td>\n",
       "      <td>$3,500.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Green</td>\n",
       "      <td>99213</td>\n",
       "      <td>4</td>\n",
       "      <td>$4,500.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>45698</td>\n",
       "      <td>4</td>\n",
       "      <td>$7,500.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>54738</td>\n",
       "      <td>4</td>\n",
       "      <td>$7,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>60000</td>\n",
       "      <td>4</td>\n",
       "      <td>$6,250.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>31600</td>\n",
       "      <td>4</td>\n",
       "      <td>$9,700.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make Colour  Odometer (KM)  Doors       Price\n",
       "0  Toyota  White         150043      4   $4,000.00\n",
       "1   Honda    Red          87899      4   $5,000.00\n",
       "2  Toyota   Blue          32549      3   $7,000.00\n",
       "3     BMW  Black          11179      5  $22,000.00\n",
       "4  Nissan  White         213095      4   $3,500.00\n",
       "5  Toyota  Green          99213      4   $4,500.00\n",
       "6   Honda   Blue          45698      4   $7,500.00\n",
       "7   Honda   Blue          54738      4   $7,000.00\n",
       "8  Toyota  White          60000      4   $6,250.00\n",
       "9  Nissan  White          31600      4   $9,700.00"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5ec74f22",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Exporting a data frame \n",
    "car_sales.to_csv(\"exported-car-sales.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4b15c789",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Make</th>\n",
       "      <th>Colour</th>\n",
       "      <th>Odometer (KM)</th>\n",
       "      <th>Doors</th>\n",
       "      <th>Price</th>\n",
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       "  </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",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Green</td>\n",
       "      <td>99213</td>\n",
       "      <td>4</td>\n",
       "      <td>$4,500.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>45698</td>\n",
       "      <td>4</td>\n",
       "      <td>$7,500.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>54738</td>\n",
       "      <td>4</td>\n",
       "      <td>$7,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>60000</td>\n",
       "      <td>4</td>\n",
       "      <td>$6,250.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>31600</td>\n",
       "      <td>4</td>\n",
       "      <td>$9,700.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make Colour  Odometer (KM)  Doors       Price\n",
       "0  Toyota  White         150043      4   $4,000.00\n",
       "1   Honda    Red          87899      4   $5,000.00\n",
       "2  Toyota   Blue          32549      3   $7,000.00\n",
       "3     BMW  Black          11179      5  $22,000.00\n",
       "4  Nissan  White         213095      4   $3,500.00\n",
       "5  Toyota  Green          99213      4   $4,500.00\n",
       "6   Honda   Blue          45698      4   $7,500.00\n",
       "7   Honda   Blue          54738      4   $7,000.00\n",
       "8  Toyota  White          60000      4   $6,250.00\n",
       "9  Nissan  White          31600      4   $9,700.00"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exported_car_sales=pd.read_csv(\"exported-car-sales.csv\")\n",
    "exported_car_sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "61891c55",
   "metadata": {},
   "outputs": [
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       "      <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",
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       "      <td>0</td>\n",
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       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "      <th>3</th>\n",
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       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "      <th>4</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
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       "      <td>354</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>163</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "      <th>298</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>140</td>\n",
       "      <td>241</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>123</td>\n",
       "      <td>1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
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       "      <td>3</td>\n",
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       "      <td>264</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>132</td>\n",
       "      <td>0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
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       "      <td>68</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>144</td>\n",
       "      <td>193</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>141</td>\n",
       "      <td>0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>57</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>130</td>\n",
       "      <td>131</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>115</td>\n",
       "      <td>1</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>174</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>303 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
       "0     63    1   3       145   233    1        0      150      0      2.3   \n",
       "1     37    1   2       130   250    0        1      187      0      3.5   \n",
       "2     41    0   1       130   204    0        0      172      0      1.4   \n",
       "3     56    1   1       120   236    0        1      178      0      0.8   \n",
       "4     57    0   0       120   354    0        1      163      1      0.6   \n",
       "..   ...  ...  ..       ...   ...  ...      ...      ...    ...      ...   \n",
       "298   57    0   0       140   241    0        1      123      1      0.2   \n",
       "299   45    1   3       110   264    0        1      132      0      1.2   \n",
       "300   68    1   0       144   193    1        1      141      0      3.4   \n",
       "301   57    1   0       130   131    0        1      115      1      1.2   \n",
       "302   57    0   1       130   236    0        0      174      0      0.0   \n",
       "\n",
       "     slope  ca  thal  target  \n",
       "0        0   0     1       1  \n",
       "1        0   0     2       1  \n",
       "2        2   0     2       1  \n",
       "3        2   0     2       1  \n",
       "4        2   0     2       1  \n",
       "..     ...  ..   ...     ...  \n",
       "298      1   0     3       0  \n",
       "299      1   0     3       0  \n",
       "300      1   2     3       0  \n",
       "301      1   1     3       0  \n",
       "302      1   1     2       0  \n",
       "\n",
       "[303 rows x 14 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#import data for heart disease\n",
    "heart_disease=pd.read_csv(\"heart-disease.csv\")\n",
    "heart_disease"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "237f49bf",
   "metadata": {},
   "source": [
    "## viewing and selecting data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "3412aea7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Make</th>\n",
       "      <th>Colour</th>\n",
       "      <th>Odometer (KM)</th>\n",
       "      <th>Doors</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>150043</td>\n",
       "      <td>4</td>\n",
       "      <td>$4,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Red</td>\n",
       "      <td>87899</td>\n",
       "      <td>4</td>\n",
       "      <td>$5,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Blue</td>\n",
       "      <td>32549</td>\n",
       "      <td>3</td>\n",
       "      <td>$7,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BMW</td>\n",
       "      <td>Black</td>\n",
       "      <td>11179</td>\n",
       "      <td>5</td>\n",
       "      <td>$22,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>213095</td>\n",
       "      <td>4</td>\n",
       "      <td>$3,500.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make Colour  Odometer (KM)  Doors       Price\n",
       "0  Toyota  White         150043      4   $4,000.00\n",
       "1   Honda    Red          87899      4   $5,000.00\n",
       "2  Toyota   Blue          32549      3   $7,000.00\n",
       "3     BMW  Black          11179      5  $22,000.00\n",
       "4  Nissan  White         213095      4   $3,500.00"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "1240458a",
   "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",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>178</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>354</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>163</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>142</td>\n",
       "      <td>226</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>140</td>\n",
       "      <td>394</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>157</td>\n",
       "      <td>0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>52</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>108</td>\n",
       "      <td>233</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>147</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>43</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>130</td>\n",
       "      <td>315</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>162</td>\n",
       "      <td>0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>130</td>\n",
       "      <td>246</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>173</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
       "0    63    1   3       145   233    1        0      150      0      2.3   \n",
       "1    37    1   2       130   250    0        1      187      0      3.5   \n",
       "2    41    0   1       130   204    0        0      172      0      1.4   \n",
       "3    56    1   1       120   236    0        1      178      0      0.8   \n",
       "4    57    0   0       120   354    0        1      163      1      0.6   \n",
       "..  ...  ...  ..       ...   ...  ...      ...      ...    ...      ...   \n",
       "95   53    1   0       142   226    0        0      111      1      0.0   \n",
       "96   62    0   0       140   394    0        0      157      0      1.2   \n",
       "97   52    1   0       108   233    1        1      147      0      0.1   \n",
       "98   43    1   2       130   315    0        1      162      0      1.9   \n",
       "99   53    1   2       130   246    1        0      173      0      0.0   \n",
       "\n",
       "    slope  ca  thal  target  \n",
       "0       0   0     1       1  \n",
       "1       0   0     2       1  \n",
       "2       2   0     2       1  \n",
       "3       2   0     2       1  \n",
       "4       2   0     2       1  \n",
       "..    ...  ..   ...     ...  \n",
       "95      2   0     3       1  \n",
       "96      1   0     2       1  \n",
       "97      2   3     3       1  \n",
       "98      2   1     2       1  \n",
       "99      2   3     2       1  \n",
       "\n",
       "[100 rows x 14 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heart_disease.head(100)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "e72f85b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     car\n",
       "3     dog\n",
       "9     cow\n",
       "8     fox\n",
       "3    lion\n",
       "dtype: object"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#.loc & iloc\n",
    "array_animals=[\"car\",\"dog\",\"cow\",\"fox\",\"lion\"]\n",
    "animals=pd.Series(array_animals,index=['0','3','9','8','3'])\n",
    "animals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "93914b85",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3     dog\n",
       "3    lion\n",
       "dtype: object"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#loc of Index\n",
    "animals.loc['3']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "f2f88182",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'cow'"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "animals.loc['9']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "1a0a0a7b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Make                    BMW\n",
       "Colour                Black\n",
       "Odometer (KM)         11179\n",
       "Doors                     5\n",
       "Price            $22,000.00\n",
       "Name: 3, dtype: object"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales.loc[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "b173ddbf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'fox'"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#iloc refer to position\n",
    "animals.iloc[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "741baa7c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     car\n",
       "3     dog\n",
       "9     cow\n",
       "8     fox\n",
       "3    lion\n",
       "dtype: object"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "animals\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "b9f53a2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    car\n",
       "3    dog\n",
       "9    cow\n",
       "dtype: object"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "animals.iloc[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "c3071b2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     car\n",
       "3     dog\n",
       "9     cow\n",
       "8     fox\n",
       "3    lion\n",
       "dtype: object"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "animals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "cc254137",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Toyota\n",
       "1     Honda\n",
       "2    Toyota\n",
       "3       BMW\n",
       "4    Nissan\n",
       "5    Toyota\n",
       "6     Honda\n",
       "7     Honda\n",
       "8    Toyota\n",
       "9    Nissan\n",
       "Name: Make, dtype: object"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales[\"Make\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "334e530e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    White\n",
       "1      Red\n",
       "2     Blue\n",
       "3    Black\n",
       "4    White\n",
       "5    Green\n",
       "6     Blue\n",
       "7     Blue\n",
       "8    White\n",
       "9    White\n",
       "Name: Colour, dtype: object"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales[\"Colour\"]\n",
    "car_sales.Colour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "8ccb6746",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    White\n",
       "1      Red\n",
       "2     Blue\n",
       "3    Black\n",
       "4    White\n",
       "5    Green\n",
       "6     Blue\n",
       "7     Blue\n",
       "8    White\n",
       "9    White\n",
       "Name: Colour, dtype: object"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales.Colour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "821abbcb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    150043\n",
       "1     87899\n",
       "2     32549\n",
       "3     11179\n",
       "4    213095\n",
       "5     99213\n",
       "6     45698\n",
       "7     54738\n",
       "8     60000\n",
       "9     31600\n",
       "Name: Odometer (KM), dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales[\"Odometer (KM)\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "1dba101a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Make</th>\n",
       "      <th>Colour</th>\n",
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       "      <td>Toyota</td>\n",
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       "      <td>150043</td>\n",
       "      <td>4</td>\n",
       "      <td>$4,000.00</td>\n",
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       "    <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",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BMW</td>\n",
       "      <td>Black</td>\n",
       "      <td>11179</td>\n",
       "      <td>5</td>\n",
       "      <td>$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": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "b1c5d597",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Make</th>\n",
       "      <th>Colour</th>\n",
       "      <th>Odometer (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",
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       "      <td>4</td>\n",
       "      <td>$4,000.00</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Blue</td>\n",
       "      <td>32549</td>\n",
       "      <td>3</td>\n",
       "      <td>$7,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Green</td>\n",
       "      <td>99213</td>\n",
       "      <td>4</td>\n",
       "      <td>$4,500.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
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       "      <td>$6,250.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make Colour  Odometer (KM)  Doors      Price\n",
       "0  Toyota  White         150043      4  $4,000.00\n",
       "2  Toyota   Blue          32549      3  $7,000.00\n",
       "5  Toyota  Green          99213      4  $4,500.00\n",
       "8  Toyota  White          60000      4  $6,250.00"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales[car_sales[\"Make\"]==\"Toyota\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "3e1c90ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Make             object\n",
       "Colour           object\n",
       "Odometer (KM)     int64\n",
       "Doors             int64\n",
       "Price            object\n",
       "dtype: object"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "284807b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "  <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>293</th>\n",
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       "      <td>120</td>\n",
       "      <td>169</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>144</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>295</th>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>140</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>144</td>\n",
       "      <td>1</td>\n",
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       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>63</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>124</td>\n",
       "      <td>197</td>\n",
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       "      <td>136</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>59</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>164</td>\n",
       "      <td>176</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>90</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>140</td>\n",
       "      <td>241</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>123</td>\n",
       "      <td>1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>110</td>\n",
       "      <td>264</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>132</td>\n",
       "      <td>0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>68</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>144</td>\n",
       "      <td>193</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>141</td>\n",
       "      <td>0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>57</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>130</td>\n",
       "      <td>131</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>115</td>\n",
       "      <td>1</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>174</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
       "293   67    1   2       152   212    0        0      150      0      0.8   \n",
       "294   44    1   0       120   169    0        1      144      1      2.8   \n",
       "295   63    1   0       140   187    0        0      144      1      4.0   \n",
       "296   63    0   0       124   197    0        1      136      1      0.0   \n",
       "297   59    1   0       164   176    1        0       90      0      1.0   \n",
       "298   57    0   0       140   241    0        1      123      1      0.2   \n",
       "299   45    1   3       110   264    0        1      132      0      1.2   \n",
       "300   68    1   0       144   193    1        1      141      0      3.4   \n",
       "301   57    1   0       130   131    0        1      115      1      1.2   \n",
       "302   57    0   1       130   236    0        0      174      0      0.0   \n",
       "\n",
       "     slope  ca  thal  target  \n",
       "293      1   0     3       0  \n",
       "294      0   0     1       0  \n",
       "295      2   2     3       0  \n",
       "296      1   0     2       0  \n",
       "297      1   2     1       0  \n",
       "298      1   0     3       0  \n",
       "299      1   0     3       0  \n",
       "300      1   2     3       0  \n",
       "301      1   1     3       0  \n",
       "302      1   1     2       0  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heart_disease.tail(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "f100a7a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=10, step=1)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "07842474",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Make', 'Colour', 'Odometer (KM)', 'Doors', 'Price'], dtype='object')"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "a6d18799",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['Toyota', 'White', 150043, 4, '$4,000.00'],\n",
       "       ['Honda', 'Red', 87899, 4, '$5,000.00'],\n",
       "       ['Toyota', 'Blue', 32549, 3, '$7,000.00'],\n",
       "       ['BMW', 'Black', 11179, 5, '$22,000.00'],\n",
       "       ['Nissan', 'White', 213095, 4, '$3,500.00'],\n",
       "       ['Toyota', 'Green', 99213, 4, '$4,500.00'],\n",
       "       ['Honda', 'Blue', 45698, 4, '$7,500.00'],\n",
       "       ['Honda', 'Blue', 54738, 4, '$7,000.00'],\n",
       "       ['Toyota', 'White', 60000, 4, '$6,250.00'],\n",
       "       ['Nissan', 'White', 31600, 4, '$9,700.00']], dtype=object)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "eb3477bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Odometer (KM)</th>\n",
       "      <th>Doors</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10.000000</td>\n",
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       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>78601.400000</td>\n",
       "      <td>4.000000</td>\n",
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       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>61983.471735</td>\n",
       "      <td>0.471405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>11179.000000</td>\n",
       "      <td>3.000000</td>\n",
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       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>35836.250000</td>\n",
       "      <td>4.000000</td>\n",
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       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>57369.000000</td>\n",
       "      <td>4.000000</td>\n",
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       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>96384.500000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>213095.000000</td>\n",
       "      <td>5.000000</td>\n",
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      "text/plain": [
       "       Odometer (KM)      Doors\n",
       "count      10.000000  10.000000\n",
       "mean    78601.400000   4.000000\n",
       "std     61983.471735   0.471405\n",
       "min     11179.000000   3.000000\n",
       "25%     35836.250000   4.000000\n",
       "50%     57369.000000   4.000000\n",
       "75%     96384.500000   4.000000\n",
       "max    213095.000000   5.000000"
      ]
     },
     "execution_count": 56,
     "metadata": {},
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    "car_sales.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "18701c5d",
   "metadata": {},
   "outputs": [
    {
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       "      <td>Toyota</td>\n",
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       "      <td>$4,000.00</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Honda</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Honda</td>\n",
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       "      <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",
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       "      <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",
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      ],
      "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": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "car_sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "b26b0e06",
   "metadata": {},
   "outputs": [
    {
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       "      <td>BMW</td>\n",
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       "      <td>11179</td>\n",
       "      <td>213095</td>\n",
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       "      <th>Doors</th>\n",
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       "      <td>4</td>\n",
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       "      <th>Price</th>\n",
       "      <td>$4,000.00</td>\n",
       "      <td>$5,000.00</td>\n",
       "      <td>$7,000.00</td>\n",
       "      <td>$22,000.00</td>\n",
       "      <td>$3,500.00</td>\n",
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       "      <td>$7,000.00</td>\n",
       "      <td>$6,250.00</td>\n",
       "      <td>$9,700.00</td>\n",
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      ],
      "text/plain": [
       "                       0          1          2           3          4  \\\n",
       "Make              Toyota      Honda     Toyota         BMW     Nissan   \n",
       "Colour             White        Red       Blue       Black      White   \n",
       "Odometer (KM)     150043      87899      32549       11179     213095   \n",
       "Doors                  4          4          3           5          4   \n",
       "Price          $4,000.00  $5,000.00  $7,000.00  $22,000.00  $3,500.00   \n",
       "\n",
       "                       5          6          7          8          9  \n",
       "Make              Toyota      Honda      Honda     Toyota     Nissan  \n",
       "Colour             Green       Blue       Blue      White      White  \n",
       "Odometer (KM)      99213      45698      54738      60000      31600  \n",
       "Doors                  4          4          4          4          4  \n",
       "Price          $4,500.00  $7,500.00  $7,000.00  $6,250.00  $9,700.00  "
      ]
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     "execution_count": 58,
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   "source": [
    "car_sales.T"
   ]
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  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "72a55a83",
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   "outputs": [
    {
     "data": {
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       "      <td>$7,000.00</td>\n",
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       "      <td>Honda</td>\n",
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       "      <td>$7,000.00</td>\n",
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       "      <th>6</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Blue</td>\n",
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       "      <td>4</td>\n",
       "      <td>$7,500.00</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",
       "3     BMW  Black          11179      5  $22,000.00\n",
       "4  Nissan  White         213095      4   $3,500.00\n",
       "0  Toyota  White         150043      4   $4,000.00\n",
       "5  Toyota  Green          99213      4   $4,500.00\n",
       "1   Honda    Red          87899      4   $5,000.00\n",
       "8  Toyota  White          60000      4   $6,250.00\n",
       "2  Toyota   Blue          32549      3   $7,000.00\n",
       "7   Honda   Blue          54738      4   $7,000.00\n",
       "6   Honda   Blue          45698      4   $7,500.00\n",
       "9  Nissan  White          31600      4   $9,700.00"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales.sort_values(by=\"Price\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "2e5f6ce4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Colour</th>\n",
       "      <th>Odometer (KM)</th>\n",
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       "      <th>1</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Red</td>\n",
       "      <td>87899</td>\n",
       "      <td>4</td>\n",
       "      <td>$5,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Toyota</td>\n",
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      "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"
      ]
     },
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     "metadata": {},
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    }
   ],
   "source": [
    "car_sales[0:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "61f88c0b",
   "metadata": {},
   "outputs": [
    {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Make</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>$4,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Honda</td>\n",
       "      <td>$5,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>$7,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BMW</td>\n",
       "      <td>$22,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>$3,500.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>$4,500.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Honda</td>\n",
       "      <td>$7,500.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Honda</td>\n",
       "      <td>$7,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>$6,250.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>$9,700.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make       Price\n",
       "0  Toyota   $4,000.00\n",
       "1   Honda   $5,000.00\n",
       "2  Toyota   $7,000.00\n",
       "3     BMW  $22,000.00\n",
       "4  Nissan   $3,500.00\n",
       "5  Toyota   $4,500.00\n",
       "6   Honda   $7,500.00\n",
       "7   Honda   $7,000.00\n",
       "8  Toyota   $6,250.00\n",
       "9  Nissan   $9,700.00"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales.loc[:,[\"Make\",\"Price\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "de9f5b4c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Make        Nissan\n",
       "Price    $9,700.00\n",
       "Name: 9, dtype: object"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales.loc[9,[\"Make\",\"Price\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "3a3855a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    White\n",
       "1      Red\n",
       "2     Blue\n",
       "3    Black\n",
       "4    White\n",
       "5    Green\n",
       "6     Blue\n",
       "7     Blue\n",
       "8    White\n",
       "9    White\n",
       "Name: Colour, dtype: object"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales[\"Colour\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "cdf75f9c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Toyota\n",
       "1     Honda\n",
       "2    Toyota\n",
       "3       BMW\n",
       "4    Nissan\n",
       "5    Toyota\n",
       "6     Honda\n",
       "7     Honda\n",
       "8    Toyota\n",
       "9    Nissan\n",
       "Name: Make, dtype: object"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales[\"Make\"]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "e7bffe88",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Toyota\n",
       "1     Honda\n",
       "2    Toyota\n",
       "3       BMW\n",
       "4    Nissan\n",
       "5    Toyota\n",
       "6     Honda\n",
       "7     Honda\n",
       "8    Toyota\n",
       "9    Nissan\n",
       "Name: Make, dtype: object"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales.Make"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "ad2682bf",
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'DataFrame' object has no attribute 'Odometer'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_3383/240837061.py\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[0mOdometer\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mKM\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/Desktop/sample_project/env/lib/python3.9/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m   5581\u001b[0m         ):\n\u001b[1;32m   5582\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5583\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5584\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5585\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'Odometer'"
     ]
    }
   ],
   "source": [
    "car_sales.Odometer (KM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "de6fbc19",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    150043\n",
       "1     87899\n",
       "2     32549\n",
       "3     11179\n",
       "4    213095\n",
       "5     99213\n",
       "6     45698\n",
       "7     54738\n",
       "8     60000\n",
       "9     31600\n",
       "Name: Odometer (KM), dtype: int64"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales[\"Odometer (KM)\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "044eec08",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Make</th>\n",
       "      <th>Colour</th>\n",
       "      <th>Odometer (KM)</th>\n",
       "      <th>Doors</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>150043</td>\n",
       "      <td>4</td>\n",
       "      <td>$4,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Blue</td>\n",
       "      <td>32549</td>\n",
       "      <td>3</td>\n",
       "      <td>$7,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Green</td>\n",
       "      <td>99213</td>\n",
       "      <td>4</td>\n",
       "      <td>$4,500.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>60000</td>\n",
       "      <td>4</td>\n",
       "      <td>$6,250.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make Colour  Odometer (KM)  Doors      Price\n",
       "0  Toyota  White         150043      4  $4,000.00\n",
       "2  Toyota   Blue          32549      3  $7,000.00\n",
       "5  Toyota  Green          99213      4  $4,500.00\n",
       "8  Toyota  White          60000      4  $6,250.00"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales[car_sales[\"Make\"]==\"Toyota\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e54b5100",
   "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>400000</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>350000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make Colour  Odometer (KM)  Doors   Price\n",
       "0  Toyota  White         150043      4  400000\n",
       "4  Nissan  White         213095      4  350000"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales[car_sales[\"Odometer (KM)\"]>100000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "f08a6b9a",
   "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": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " pd.crosstab(car_sales[\"Make\"],car_sales[\"Doors\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "52e758c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Odometer (KM)</th>\n",
       "      <th>Doors</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Make</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BMW</th>\n",
       "      <td>11179.000000</td>\n",
       "      <td>5.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Honda</th>\n",
       "      <td>62778.333333</td>\n",
       "      <td>4.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nissan</th>\n",
       "      <td>122347.500000</td>\n",
       "      <td>4.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Toyota</th>\n",
       "      <td>85451.250000</td>\n",
       "      <td>3.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Odometer (KM)  Doors\n",
       "Make                        \n",
       "BMW      11179.000000   5.00\n",
       "Honda    62778.333333   4.00\n",
       "Nissan  122347.500000   4.00\n",
       "Toyota   85451.250000   3.75"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Groupby\n",
    "car_sales.groupby([\"Make\"]).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "7e60e6ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline \n",
    "import matplotlib.pyplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "12febe9f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 98,
     "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": 100,
   "id": "0cf99a3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 100,
     "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)\"].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ca98c247",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('O')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales[\"Price\"].dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a8e0220",
   "metadata": {},
   "source": [
    "# I have a column called amount with holds values that loo like this $ 3u33 when i do dataframe.dtype(). it returns this column as an object how can i convert this column to type int?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33e1ab78",
   "metadata": {},
   "source": [
    "code  car_sales[\"Price\"] = car_sales[\"Price\"].str.replace('[\\$\\,]', '').astype(int)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "579784a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Make</th>\n",
       "      <th>Colour</th>\n",
       "      <th>Odometer (KM)</th>\n",
       "      <th>Doors</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>150043</td>\n",
       "      <td>4</td>\n",
       "      <td>4000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Red</td>\n",
       "      <td>87899</td>\n",
       "      <td>4</td>\n",
       "      <td>5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Blue</td>\n",
       "      <td>32549</td>\n",
       "      <td>3</td>\n",
       "      <td>7000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BMW</td>\n",
       "      <td>Black</td>\n",
       "      <td>11179</td>\n",
       "      <td>5</td>\n",
       "      <td>22000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>213095</td>\n",
       "      <td>4</td>\n",
       "      <td>3500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Green</td>\n",
       "      <td>99213</td>\n",
       "      <td>4</td>\n",
       "      <td>4500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>45698</td>\n",
       "      <td>4</td>\n",
       "      <td>7500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>54738</td>\n",
       "      <td>4</td>\n",
       "      <td>7000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>60000</td>\n",
       "      <td>4</td>\n",
       "      <td>6250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>31600</td>\n",
       "      <td>4</td>\n",
       "      <td>9700</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make Colour  Odometer (KM)  Doors  Price\n",
       "0  Toyota  White         150043      4   4000\n",
       "1   Honda    Red          87899      4   5000\n",
       "2  Toyota   Blue          32549      3   7000\n",
       "3     BMW  Black          11179      5  22000\n",
       "4  Nissan  White         213095      4   3500\n",
       "5  Toyota  Green          99213      4   4500\n",
       "6   Honda   Blue          45698      4   7500\n",
       "7   Honda   Blue          54738      4   7000\n",
       "8  Toyota  White          60000      4   6250\n",
       "9  Nissan  White          31600      4   9700"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "80751f1b",
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "Can only use .str accessor with string values!",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_18125/2395595375.py\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\"Price\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0mcar_sales\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Price\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreplace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'[\\$\\,\\.]'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m''\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/Desktop/sample_project/env/lib/python3.9/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m   5581\u001b[0m         ):\n\u001b[1;32m   5582\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5583\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5584\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5585\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Desktop/sample_project/env/lib/python3.9/site-packages/pandas/core/accessor.py\u001b[0m in \u001b[0;36m__get__\u001b[0;34m(self, obj, cls)\u001b[0m\n\u001b[1;32m    180\u001b[0m             \u001b[0;31m# we're accessing the attribute of the class, i.e., Dataset.geo\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    181\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_accessor\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 182\u001b[0;31m         \u001b[0maccessor_obj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_accessor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    183\u001b[0m         \u001b[0;31m# Replace the property with the accessor object. Inspired by:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    184\u001b[0m         \u001b[0;31m# https://www.pydanny.com/cached-property.html\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Desktop/sample_project/env/lib/python3.9/site-packages/pandas/core/strings/accessor.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;32m    175\u001b[0m         \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marrays\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstring_\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mStringDtype\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    176\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 177\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_inferred_dtype\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    178\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_is_categorical\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mis_categorical_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    179\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_is_string\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mStringDtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Desktop/sample_project/env/lib/python3.9/site-packages/pandas/core/strings/accessor.py\u001b[0m in \u001b[0;36m_validate\u001b[0;34m(data)\u001b[0m\n\u001b[1;32m    229\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    230\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0minferred_dtype\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mallowed_types\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 231\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Can only use .str accessor with string values!\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    232\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0minferred_dtype\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    233\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: Can only use .str accessor with string values!"
     ]
    }
   ],
   "source": [
    "car_sales[\"Price\"]= car_sales[\"Price\"].str.replace('[\\$\\,\\.]', '').astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "5c5a7634",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 23,
     "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": "markdown",
   "id": "09eea0c5",
   "metadata": {},
   "source": [
    "##Manipulating Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "6ee4fe77",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    toyota\n",
       "1     honda\n",
       "2    toyota\n",
       "3       bmw\n",
       "4    nissan\n",
       "5    toyota\n",
       "6     honda\n",
       "7     honda\n",
       "8    toyota\n",
       "9    nissan\n",
       "Name: Make, dtype: object"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales[\"Make\"].str.lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "eab0ee83",
   "metadata": {},
   "outputs": [
    {
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       "      <td>Honda</td>\n",
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       "      <td>Toyota</td>\n",
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       "     Make Colour  Odometer (KM)  Doors    Price\n",
       "0  Toyota  White         150043      4   400000\n",
       "1   Honda    Red          87899      4   500000\n",
       "2  Toyota   Blue          32549      3   700000\n",
       "3     BMW  Black          11179      5  2200000\n",
       "4  Nissan  White         213095      4   350000\n",
       "5  Toyota  Green          99213      4   450000\n",
       "6   Honda   Blue          45698      4   750000\n",
       "7   Honda   Blue          54738      4   700000\n",
       "8  Toyota  White          60000      4   625000\n",
       "9  Nissan  White          31600      4   970000"
      ]
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     "execution_count": 25,
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   "source": [
    "car_sales\n"
   ]
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  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "76c51729",
   "metadata": {},
   "outputs": [],
   "source": [
    "car_sales[\"Make\"]=car_sales[\"Make\"].str.lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "d5984f22",
   "metadata": {},
   "outputs": [
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       "     Make Colour  Odometer (KM)  Doors    Price\n",
       "0  toyota  White         150043      4   400000\n",
       "1   honda    Red          87899      4   500000\n",
       "2  toyota   Blue          32549      3   700000\n",
       "3     bmw  Black          11179      5  2200000\n",
       "4  nissan  White         213095      4   350000\n",
       "5  toyota  Green          99213      4   450000\n",
       "6   honda   Blue          45698      4   750000\n",
       "7   honda   Blue          54738      4   700000\n",
       "8  toyota  White          60000      4   625000\n",
       "9  nissan  White          31600      4   970000"
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   "cell_type": "code",
   "execution_count": 32,
   "id": "62a8b2e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "car_sales_missing=pd.read_csv(\"car-sales-missing.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "b4e2aeac",
   "metadata": {},
   "outputs": [
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       "     Make Colour  Odometer (KM)  Doors      Price\n",
       "0  Toyota  White       150043.0    4.0  $4,000.00\n",
       "1   Honda    Red        87899.0    4.0  $5,000.00\n",
       "2  Toyota   Blue            NaN    NaN  $7,000.00\n",
       "3     BMW    NaN        11179.0    5.0        NaN\n",
       "4  Nissan  White       213095.0    4.0  $3,500.00\n",
       "5  Toyota  Green            NaN    4.0  $4,500.00\n",
       "6   Honda    NaN        45698.0    4.0  $7,500.00\n",
       "7   Honda   Blue        54738.0    4.0  $7,000.00\n",
       "8  Toyota  White        60000.0    4.0        NaN\n",
       "9  Nissan  White        31600.0    4.0  $9,700.00"
      ]
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     "execution_count": 33,
     "metadata": {},
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   "source": [
    "car_sales_missing"
   ]
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  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "7f079897",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "81781.5"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales_missing[\"Odometer (KM)\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "b79b3c89",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    150043.0\n",
       "1     87899.0\n",
       "2     81781.5\n",
       "3     11179.0\n",
       "4    213095.0\n",
       "5     81781.5\n",
       "6     45698.0\n",
       "7     54738.0\n",
       "8     60000.0\n",
       "9     31600.0\n",
       "Name: Odometer (KM), dtype: float64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales_missing[\"Odometer (KM)\"].fillna(car_sales_missing[\"Odometer (KM)\"].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "6c2874f0",
   "metadata": {},
   "outputs": [
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      ],
      "text/plain": [
       "     Make Colour  Odometer (KM)  Doors      Price\n",
       "0  Toyota  White       150043.0    4.0  $4,000.00\n",
       "1   Honda    Red        87899.0    4.0  $5,000.00\n",
       "2  Toyota   Blue            NaN    NaN  $7,000.00\n",
       "3     BMW    NaN        11179.0    5.0        NaN\n",
       "4  Nissan  White       213095.0    4.0  $3,500.00\n",
       "5  Toyota  Green            NaN    4.0  $4,500.00\n",
       "6   Honda    NaN        45698.0    4.0  $7,500.00\n",
       "7   Honda   Blue        54738.0    4.0  $7,000.00\n",
       "8  Toyota  White        60000.0    4.0        NaN\n",
       "9  Nissan  White        31600.0    4.0  $9,700.00"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales_missing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "e298058d",
   "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>150043.0</td>\n",
       "      <td>4.0</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>87899.0</td>\n",
       "      <td>4.0</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$7,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BMW</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11179.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>213095.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$3,500.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make Colour  Odometer (KM)  Doors      Price\n",
       "0  Toyota  White       150043.0    4.0  $4,000.00\n",
       "1   Honda    Red        87899.0    4.0  $5,000.00\n",
       "2  Toyota   Blue            NaN    NaN  $7,000.00\n",
       "3     BMW    NaN        11179.0    5.0        NaN\n",
       "4  Nissan  White       213095.0    4.0  $3,500.00"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales_missing.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4d20bf95",
   "metadata": {},
   "outputs": [],
   "source": [
    "car_sales_missing[\"Odometer (KM)\"]=car_sales_missing[\"Odometer (KM)\"].fillna(car_sales_missing[\"Odometer (KM)\"].mean(),inplace=True)"
   ]
  }
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