5.cardiogoodsfitness_dataframe_csv_example.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4a159780",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0ab4fe1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(\"CardioGoodFitness-1.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9ba6d6f7",
   "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>Product</th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Education</th>\n",
       "      <th>MaritalStatus</th>\n",
       "      <th>Usage</th>\n",
       "      <th>Fitness</th>\n",
       "      <th>Income</th>\n",
       "      <th>Miles</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TM195</td>\n",
       "      <td>18</td>\n",
       "      <td>Male</td>\n",
       "      <td>14</td>\n",
       "      <td>Single</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>29562</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>TM195</td>\n",
       "      <td>19</td>\n",
       "      <td>Male</td>\n",
       "      <td>15</td>\n",
       "      <td>Single</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>31836</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>TM195</td>\n",
       "      <td>19</td>\n",
       "      <td>Female</td>\n",
       "      <td>14</td>\n",
       "      <td>Partnered</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>30699</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>TM195</td>\n",
       "      <td>19</td>\n",
       "      <td>Male</td>\n",
       "      <td>12</td>\n",
       "      <td>Single</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>32973</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>TM195</td>\n",
       "      <td>20</td>\n",
       "      <td>Male</td>\n",
       "      <td>13</td>\n",
       "      <td>Partnered</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>35247</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Product  Age  Gender  Education MaritalStatus  Usage  Fitness  Income  Miles\n",
       "0   TM195   18    Male         14        Single      3        4   29562    112\n",
       "1   TM195   19    Male         15        Single      2        3   31836     75\n",
       "2   TM195   19  Female         14     Partnered      4        3   30699     66\n",
       "3   TM195   19    Male         12        Single      3        3   32973     85\n",
       "4   TM195   20    Male         13     Partnered      4        2   35247     47"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f13b49d7",
   "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>Product</th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Education</th>\n",
       "      <th>MaritalStatus</th>\n",
       "      <th>Usage</th>\n",
       "      <th>Fitness</th>\n",
       "      <th>Income</th>\n",
       "      <th>Miles</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>TM798</td>\n",
       "      <td>40</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>Single</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>83416</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>TM798</td>\n",
       "      <td>42</td>\n",
       "      <td>Male</td>\n",
       "      <td>18</td>\n",
       "      <td>Single</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>89641</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>TM798</td>\n",
       "      <td>45</td>\n",
       "      <td>Male</td>\n",
       "      <td>16</td>\n",
       "      <td>Single</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>90886</td>\n",
       "      <td>160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>TM798</td>\n",
       "      <td>47</td>\n",
       "      <td>Male</td>\n",
       "      <td>18</td>\n",
       "      <td>Partnered</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>104581</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>TM798</td>\n",
       "      <td>48</td>\n",
       "      <td>Male</td>\n",
       "      <td>18</td>\n",
       "      <td>Partnered</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>95508</td>\n",
       "      <td>180</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Product  Age Gender  Education MaritalStatus  Usage  Fitness  Income  \\\n",
       "175   TM798   40   Male         21        Single      6        5   83416   \n",
       "176   TM798   42   Male         18        Single      5        4   89641   \n",
       "177   TM798   45   Male         16        Single      5        5   90886   \n",
       "178   TM798   47   Male         18     Partnered      4        5  104581   \n",
       "179   TM798   48   Male         18     Partnered      4        5   95508   \n",
       "\n",
       "     Miles  \n",
       "175    200  \n",
       "176    200  \n",
       "177    160  \n",
       "178    120  \n",
       "179    180  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e40d92b0",
   "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>Product</th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Education</th>\n",
       "      <th>MaritalStatus</th>\n",
       "      <th>Usage</th>\n",
       "      <th>Fitness</th>\n",
       "      <th>Income</th>\n",
       "      <th>Miles</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>TM195</td>\n",
       "      <td>38</td>\n",
       "      <td>Male</td>\n",
       "      <td>14</td>\n",
       "      <td>Single</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>52302</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Product  Age Gender  Education MaritalStatus  Usage  Fitness  Income  Miles\n",
       "70   TM195   38   Male         14        Single      2        3   52302     56"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8adffb2c",
   "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>Product</th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Education</th>\n",
       "      <th>MaritalStatus</th>\n",
       "      <th>Usage</th>\n",
       "      <th>Fitness</th>\n",
       "      <th>Income</th>\n",
       "      <th>Miles</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>TM195</td>\n",
       "      <td>40</td>\n",
       "      <td>Male</td>\n",
       "      <td>16</td>\n",
       "      <td>Partnered</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>61398</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>TM498</td>\n",
       "      <td>34</td>\n",
       "      <td>Male</td>\n",
       "      <td>16</td>\n",
       "      <td>Partnered</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>59124</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>TM798</td>\n",
       "      <td>23</td>\n",
       "      <td>Male</td>\n",
       "      <td>16</td>\n",
       "      <td>Single</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>48556</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>TM498</td>\n",
       "      <td>35</td>\n",
       "      <td>Female</td>\n",
       "      <td>16</td>\n",
       "      <td>Single</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>50028</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>TM195</td>\n",
       "      <td>36</td>\n",
       "      <td>Male</td>\n",
       "      <td>12</td>\n",
       "      <td>Single</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>44343</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Product  Age  Gender  Education MaritalStatus  Usage  Fitness  Income  \\\n",
       "73    TM195   40    Male         16     Partnered      3        3   61398   \n",
       "126   TM498   34    Male         16     Partnered      3        4   59124   \n",
       "145   TM798   23    Male         16        Single      4        5   48556   \n",
       "130   TM498   35  Female         16        Single      3        2   50028   \n",
       "66    TM195   36    Male         12        Single      4        3   44343   \n",
       "\n",
       "     Miles  \n",
       "73      66  \n",
       "126     85  \n",
       "145    100  \n",
       "130     64  \n",
       "66      94  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a2b73922",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Product          object\n",
       "Age               int64\n",
       "Gender           object\n",
       "Education         int64\n",
       "MaritalStatus    object\n",
       "Usage             int64\n",
       "Fitness           int64\n",
       "Income            int64\n",
       "Miles             int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "cabf764e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 180 entries, 0 to 179\n",
      "Data columns (total 9 columns):\n",
      " #   Column         Non-Null Count  Dtype \n",
      "---  ------         --------------  ----- \n",
      " 0   Product        180 non-null    object\n",
      " 1   Age            180 non-null    int64 \n",
      " 2   Gender         180 non-null    object\n",
      " 3   Education      180 non-null    int64 \n",
      " 4   MaritalStatus  180 non-null    object\n",
      " 5   Usage          180 non-null    int64 \n",
      " 6   Fitness        180 non-null    int64 \n",
      " 7   Income         180 non-null    int64 \n",
      " 8   Miles          180 non-null    int64 \n",
      "dtypes: int64(6), object(3)\n",
      "memory usage: 12.8+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "973c6a94",
   "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>Education</th>\n",
       "      <th>Usage</th>\n",
       "      <th>Fitness</th>\n",
       "      <th>Income</th>\n",
       "      <th>Miles</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>180.000000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>180.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>28.788889</td>\n",
       "      <td>15.572222</td>\n",
       "      <td>3.455556</td>\n",
       "      <td>3.311111</td>\n",
       "      <td>53719.577778</td>\n",
       "      <td>103.194444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.943498</td>\n",
       "      <td>1.617055</td>\n",
       "      <td>1.084797</td>\n",
       "      <td>0.958869</td>\n",
       "      <td>16506.684226</td>\n",
       "      <td>51.863605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>29562.000000</td>\n",
       "      <td>21.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>24.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>44058.750000</td>\n",
       "      <td>66.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>26.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>50596.500000</td>\n",
       "      <td>94.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>33.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>58668.000000</td>\n",
       "      <td>114.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>50.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>104581.000000</td>\n",
       "      <td>360.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Age   Education       Usage     Fitness         Income  \\\n",
       "count  180.000000  180.000000  180.000000  180.000000     180.000000   \n",
       "mean    28.788889   15.572222    3.455556    3.311111   53719.577778   \n",
       "std      6.943498    1.617055    1.084797    0.958869   16506.684226   \n",
       "min     18.000000   12.000000    2.000000    1.000000   29562.000000   \n",
       "25%     24.000000   14.000000    3.000000    3.000000   44058.750000   \n",
       "50%     26.000000   16.000000    3.000000    3.000000   50596.500000   \n",
       "75%     33.000000   16.000000    4.000000    4.000000   58668.000000   \n",
       "max     50.000000   21.000000    7.000000    5.000000  104581.000000   \n",
       "\n",
       "            Miles  \n",
       "count  180.000000  \n",
       "mean   103.194444  \n",
       "std     51.863605  \n",
       "min     21.000000  \n",
       "25%     66.000000  \n",
       "50%     94.000000  \n",
       "75%    114.750000  \n",
       "max    360.000000  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ea16203a",
   "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>Product</th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Education</th>\n",
       "      <th>MaritalStatus</th>\n",
       "      <th>Usage</th>\n",
       "      <th>Fitness</th>\n",
       "      <th>Income</th>\n",
       "      <th>Miles</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>180</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>180</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>180</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>180.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>TM195</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Partnered</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>80</td>\n",
       "      <td>NaN</td>\n",
       "      <td>104</td>\n",
       "      <td>NaN</td>\n",
       "      <td>107</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>28.788889</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.572222</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.455556</td>\n",
       "      <td>3.311111</td>\n",
       "      <td>53719.577778</td>\n",
       "      <td>103.194444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>6.943498</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.617055</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.084797</td>\n",
       "      <td>0.958869</td>\n",
       "      <td>16506.684226</td>\n",
       "      <td>51.863605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>29562.000000</td>\n",
       "      <td>21.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>44058.750000</td>\n",
       "      <td>66.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>50596.500000</td>\n",
       "      <td>94.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>58668.000000</td>\n",
       "      <td>114.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>104581.000000</td>\n",
       "      <td>360.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Product         Age Gender   Education MaritalStatus       Usage  \\\n",
       "count      180  180.000000    180  180.000000           180  180.000000   \n",
       "unique       3         NaN      2         NaN             2         NaN   \n",
       "top      TM195         NaN   Male         NaN     Partnered         NaN   \n",
       "freq        80         NaN    104         NaN           107         NaN   \n",
       "mean       NaN   28.788889    NaN   15.572222           NaN    3.455556   \n",
       "std        NaN    6.943498    NaN    1.617055           NaN    1.084797   \n",
       "min        NaN   18.000000    NaN   12.000000           NaN    2.000000   \n",
       "25%        NaN   24.000000    NaN   14.000000           NaN    3.000000   \n",
       "50%        NaN   26.000000    NaN   16.000000           NaN    3.000000   \n",
       "75%        NaN   33.000000    NaN   16.000000           NaN    4.000000   \n",
       "max        NaN   50.000000    NaN   21.000000           NaN    7.000000   \n",
       "\n",
       "           Fitness         Income       Miles  \n",
       "count   180.000000     180.000000  180.000000  \n",
       "unique         NaN            NaN         NaN  \n",
       "top            NaN            NaN         NaN  \n",
       "freq           NaN            NaN         NaN  \n",
       "mean      3.311111   53719.577778  103.194444  \n",
       "std       0.958869   16506.684226   51.863605  \n",
       "min       1.000000   29562.000000   21.000000  \n",
       "25%       3.000000   44058.750000   66.000000  \n",
       "50%       3.000000   50596.500000   94.000000  \n",
       "75%       4.000000   58668.000000  114.750000  \n",
       "max       5.000000  104581.000000  360.000000  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(include=\"all\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "fd20b9ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Product          0\n",
       "Age              0\n",
       "Gender           0\n",
       "Education        0\n",
       "MaritalStatus    0\n",
       "Usage            0\n",
       "Fitness          0\n",
       "Income           0\n",
       "Miles            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum() #it tells about missing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3d2b8004",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}