Project_workforce_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2344605c",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Import liabiries\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns \n",
    "import pandas as pd \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c5e800ad",
   "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>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>educational-num</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>gender</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>native-country</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>25</td>\n",
       "      <td>Private</td>\n",
       "      <td>226802</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>89814</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Farming-fishing</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age workclass  fnlwgt education  educational-num      marital-status  \\\n",
       "0   25   Private  226802      11th                7       Never-married   \n",
       "1   38   Private   89814   HS-grad                9  Married-civ-spouse   \n",
       "\n",
       "          occupation relationship   race gender  capital-gain  capital-loss  \\\n",
       "0  Machine-op-inspct    Own-child  Black   Male             0             0   \n",
       "1    Farming-fishing      Husband  White   Male             0             0   \n",
       "\n",
       "   hours-per-week native-country income  \n",
       "0              40  United-States  <=50K  \n",
       "1              50  United-States  <=50K  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1=pd.read_csv(\"D:/python/PANDAS/Adult/adult.csv/adult.csv\")\n",
    "df1.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a5e9f0e2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of rows- 48842\n"
     ]
    }
   ],
   "source": [
    "## Check shape of dataset\n",
    "rows=df1.shape[0]\n",
    "print('Number of rows-',rows)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3f748b70",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of rows- 15\n"
     ]
    }
   ],
   "source": [
    "columns=df1.shape[1]\n",
    "print('Number of rows-',columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "40afeacc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 48842 entries, 0 to 48841\n",
      "Data columns (total 15 columns):\n",
      " #   Column           Non-Null Count  Dtype \n",
      "---  ------           --------------  ----- \n",
      " 0   age              48842 non-null  int64 \n",
      " 1   workclass        48842 non-null  object\n",
      " 2   fnlwgt           48842 non-null  int64 \n",
      " 3   education        48842 non-null  object\n",
      " 4   educational-num  48842 non-null  int64 \n",
      " 5   marital-status   48842 non-null  object\n",
      " 6   occupation       48842 non-null  object\n",
      " 7   relationship     48842 non-null  object\n",
      " 8   race             48842 non-null  object\n",
      " 9   gender           48842 non-null  object\n",
      " 10  capital-gain     48842 non-null  int64 \n",
      " 11  capital-loss     48842 non-null  int64 \n",
      " 12  hours-per-week   48842 non-null  int64 \n",
      " 13  native-country   48842 non-null  object\n",
      " 14  income           48842 non-null  object\n",
      "dtypes: int64(6), object(9)\n",
      "memory usage: 5.6+ MB\n"
     ]
    }
   ],
   "source": [
    "## Information of dataset\n",
    "df1.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "70e827fe",
   "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>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>educational-num</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>gender</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>native-country</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12393</th>\n",
       "      <td>37</td>\n",
       "      <td>Private</td>\n",
       "      <td>110331</td>\n",
       "      <td>Prof-school</td>\n",
       "      <td>15</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Other-service</td>\n",
       "      <td>Wife</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48701</th>\n",
       "      <td>23</td>\n",
       "      <td>Private</td>\n",
       "      <td>45834</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17918</th>\n",
       "      <td>28</td>\n",
       "      <td>Private</td>\n",
       "      <td>89718</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Sales</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>2202</td>\n",
       "      <td>0</td>\n",
       "      <td>48</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11352</th>\n",
       "      <td>30</td>\n",
       "      <td>Private</td>\n",
       "      <td>351770</td>\n",
       "      <td>9th</td>\n",
       "      <td>5</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Other-service</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36198</th>\n",
       "      <td>31</td>\n",
       "      <td>Private</td>\n",
       "      <td>164190</td>\n",
       "      <td>10th</td>\n",
       "      <td>6</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Transport-moving</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48573</th>\n",
       "      <td>41</td>\n",
       "      <td>Private</td>\n",
       "      <td>318046</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Transport-moving</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>48</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47252</th>\n",
       "      <td>41</td>\n",
       "      <td>Local-gov</td>\n",
       "      <td>33658</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Protective-serv</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33142</th>\n",
       "      <td>69</td>\n",
       "      <td>Private</td>\n",
       "      <td>312653</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Sales</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2965</th>\n",
       "      <td>21</td>\n",
       "      <td>?</td>\n",
       "      <td>334593</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>?</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32089</th>\n",
       "      <td>34</td>\n",
       "      <td>Private</td>\n",
       "      <td>186269</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>24421 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age  workclass  fnlwgt     education  educational-num  \\\n",
       "12393   37    Private  110331   Prof-school               15   \n",
       "48701   23    Private   45834     Bachelors               13   \n",
       "17918   28    Private   89718       HS-grad                9   \n",
       "11352   30    Private  351770           9th                5   \n",
       "36198   31    Private  164190          10th                6   \n",
       "...    ...        ...     ...           ...              ...   \n",
       "48573   41    Private  318046  Some-college               10   \n",
       "47252   41  Local-gov   33658  Some-college               10   \n",
       "33142   69    Private  312653  Some-college               10   \n",
       "2965    21          ?  334593  Some-college               10   \n",
       "32089   34    Private  186269       HS-grad                9   \n",
       "\n",
       "           marital-status        occupation   relationship   race  gender  \\\n",
       "12393  Married-civ-spouse     Other-service           Wife  White  Female   \n",
       "48701       Never-married   Exec-managerial  Not-in-family  White  Female   \n",
       "17918       Never-married             Sales  Not-in-family  White  Female   \n",
       "11352            Divorced     Other-service      Unmarried  White  Female   \n",
       "36198  Married-civ-spouse  Transport-moving        Husband  White    Male   \n",
       "...                   ...               ...            ...    ...     ...   \n",
       "48573  Married-civ-spouse  Transport-moving        Husband  White    Male   \n",
       "47252  Married-civ-spouse   Protective-serv        Husband  White    Male   \n",
       "33142  Married-civ-spouse             Sales        Husband  White    Male   \n",
       "2965        Never-married                 ?  Not-in-family  White    Male   \n",
       "32089            Divorced      Adm-clerical      Own-child  White    Male   \n",
       "\n",
       "       capital-gain  capital-loss  hours-per-week native-country income  \n",
       "12393             0             0              60  United-States   >50K  \n",
       "48701             0             0              50  United-States  <=50K  \n",
       "17918          2202             0              48  United-States  <=50K  \n",
       "11352             0             0              38  United-States  <=50K  \n",
       "36198             0             0              40  United-States  <=50K  \n",
       "...             ...           ...             ...            ...    ...  \n",
       "48573             0             0              48  United-States   >50K  \n",
       "47252             0             0              45  United-States   >50K  \n",
       "33142             0             0              25  United-States  <=50K  \n",
       "2965              0             0              40  United-States  <=50K  \n",
       "32089             0             0              40  United-States  <=50K  \n",
       "\n",
       "[24421 rows x 15 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Fetch random dataset around 50%\n",
    "df1.sample(frac=0.50,random_state=100)\n",
    "##random_state=100) use to freeze random data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "24d7aa20",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age                0\n",
       "workclass          0\n",
       "fnlwgt             0\n",
       "education          0\n",
       "educational-num    0\n",
       "marital-status     0\n",
       "occupation         0\n",
       "relationship       0\n",
       "race               0\n",
       "gender             0\n",
       "capital-gain       0\n",
       "capital-loss       0\n",
       "hours-per-week     0\n",
       "native-country     0\n",
       "income             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Find out Null values\n",
    "## Column wise identification \n",
    "df1.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e2242ca5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        0\n",
       "1        0\n",
       "2        0\n",
       "3        0\n",
       "4        0\n",
       "        ..\n",
       "48837    0\n",
       "48838    0\n",
       "48839    0\n",
       "48840    0\n",
       "48841    0\n",
       "Length: 48842, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Column wise identification \n",
    "df1.isnull().sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "807e76fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Using heatmap to present Null values \n",
    "sns.heatmap(df1.isnull())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0f75636c",
   "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>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>educational-num</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>gender</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>native-country</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48837</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48838</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48839</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48840</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48841</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>48842 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         age  workclass  fnlwgt  education  educational-num  marital-status  \\\n",
       "0      False      False   False      False            False           False   \n",
       "1      False      False   False      False            False           False   \n",
       "2      False      False   False      False            False           False   \n",
       "3      False      False   False      False            False           False   \n",
       "4      False       True   False      False            False           False   \n",
       "...      ...        ...     ...        ...              ...             ...   \n",
       "48837  False      False   False      False            False           False   \n",
       "48838  False      False   False      False            False           False   \n",
       "48839  False      False   False      False            False           False   \n",
       "48840  False      False   False      False            False           False   \n",
       "48841  False      False   False      False            False           False   \n",
       "\n",
       "       occupation  relationship   race  gender  capital-gain  capital-loss  \\\n",
       "0           False         False  False   False         False         False   \n",
       "1           False         False  False   False         False         False   \n",
       "2           False         False  False   False         False         False   \n",
       "3           False         False  False   False         False         False   \n",
       "4            True         False  False   False         False         False   \n",
       "...           ...           ...    ...     ...           ...           ...   \n",
       "48837       False         False  False   False         False         False   \n",
       "48838       False         False  False   False         False         False   \n",
       "48839       False         False  False   False         False         False   \n",
       "48840       False         False  False   False         False         False   \n",
       "48841       False         False  False   False         False         False   \n",
       "\n",
       "       hours-per-week  native-country  income  \n",
       "0               False           False   False  \n",
       "1               False           False   False  \n",
       "2               False           False   False  \n",
       "3               False           False   False  \n",
       "4               False           False   False  \n",
       "...               ...             ...     ...  \n",
       "48837           False           False   False  \n",
       "48838           False           False   False  \n",
       "48839           False           False   False  \n",
       "48840           False           False   False  \n",
       "48841           False           False   False  \n",
       "\n",
       "[48842 rows x 15 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Replace ? by NaN \n",
    "df1.isin(['?'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "366b2ef3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age                   0\n",
       "workclass          2799\n",
       "fnlwgt                0\n",
       "education             0\n",
       "educational-num       0\n",
       "marital-status        0\n",
       "occupation         2809\n",
       "relationship          0\n",
       "race                  0\n",
       "gender                0\n",
       "capital-gain          0\n",
       "capital-loss          0\n",
       "hours-per-week        0\n",
       "native-country      857\n",
       "income                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.isin(['?']).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c8e06b35",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "11752f72",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Change '? ' to Nan in columns of workclass , occupation  , native-country \n",
    "\n",
    "df1['workclass']=df1['workclass'].replace('?',np.nan)\n",
    "df1['occupation']=df1['occupation'].replace('?',np.nan)\n",
    "df1['native-country']=df1['native-country'].replace('?',np.nan)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "51dd7abc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age                   0\n",
       "workclass          2799\n",
       "fnlwgt                0\n",
       "education             0\n",
       "educational-num       0\n",
       "marital-status        0\n",
       "occupation         2809\n",
       "relationship          0\n",
       "race                  0\n",
       "gender                0\n",
       "capital-gain          0\n",
       "capital-loss          0\n",
       "hours-per-week        0\n",
       "native-country      857\n",
       "income                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Findings Null values\n",
    "df1.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7deeceba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Heatmap for Nan values \n",
    "sns.heatmap(df1.isnull())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "80fa897a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age                0.000000\n",
       "workclass          5.730724\n",
       "fnlwgt             0.000000\n",
       "education          0.000000\n",
       "educational-num    0.000000\n",
       "marital-status     0.000000\n",
       "occupation         5.751198\n",
       "relationship       0.000000\n",
       "race               0.000000\n",
       "gender             0.000000\n",
       "capital-gain       0.000000\n",
       "capital-loss       0.000000\n",
       "hours-per-week     0.000000\n",
       "native-country     1.754637\n",
       "income             0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Calacuation of missing values percentage\n",
    "\n",
    "missing_values_per =df1.isnull().sum()*100/len(df1)\n",
    "missing_values_per"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e4695123",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(45222, 15)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Drop Nan values by dropna\n",
    "df1.dropna(how='any',inplace=True)\n",
    "df1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1080309f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Is there duplicate values present - True\n"
     ]
    }
   ],
   "source": [
    "## Finding out duplicate values \n",
    "duplicate=df1.duplicated().any()\n",
    "print('Is there duplicate values present -', duplicate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "01c246ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Copy dataset \n",
    "df2=df1.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "8801aab4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(45175, 15)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Drop duplicate values\n",
    "\n",
    "df2 = df2.drop_duplicates()\n",
    "df2.shape\n",
    "## Number of rows reduce "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c576e2fd",
   "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>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>educational-num</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>gender</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>native-country</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>45175.000000</td>\n",
       "      <td>45175</td>\n",
       "      <td>4.517500e+04</td>\n",
       "      <td>45175</td>\n",
       "      <td>45175.000000</td>\n",
       "      <td>45175</td>\n",
       "      <td>45175</td>\n",
       "      <td>45175</td>\n",
       "      <td>45175</td>\n",
       "      <td>45175</td>\n",
       "      <td>45175.000000</td>\n",
       "      <td>45175.000000</td>\n",
       "      <td>45175.000000</td>\n",
       "      <td>45175</td>\n",
       "      <td>45175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>14</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Private</td>\n",
       "      <td>NaN</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Craft-repair</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>33262</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14770</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21042</td>\n",
       "      <td>6010</td>\n",
       "      <td>18653</td>\n",
       "      <td>38859</td>\n",
       "      <td>30495</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41256</td>\n",
       "      <td>33973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>38.556170</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.897388e+05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.119314</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1102.576270</td>\n",
       "      <td>88.687593</td>\n",
       "      <td>40.942512</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>13.215349</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.056524e+05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.551740</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7510.249876</td>\n",
       "      <td>405.156611</td>\n",
       "      <td>12.007730</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.349200e+04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>28.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.173925e+05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>37.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.783120e+05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>47.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.379030e+05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>90.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.490400e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>99999.000000</td>\n",
       "      <td>4356.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 age workclass        fnlwgt education  educational-num  \\\n",
       "count   45175.000000     45175  4.517500e+04     45175     45175.000000   \n",
       "unique           NaN         7           NaN        16              NaN   \n",
       "top              NaN   Private           NaN   HS-grad              NaN   \n",
       "freq             NaN     33262           NaN     14770              NaN   \n",
       "mean       38.556170       NaN  1.897388e+05       NaN        10.119314   \n",
       "std        13.215349       NaN  1.056524e+05       NaN         2.551740   \n",
       "min        17.000000       NaN  1.349200e+04       NaN         1.000000   \n",
       "25%        28.000000       NaN  1.173925e+05       NaN         9.000000   \n",
       "50%        37.000000       NaN  1.783120e+05       NaN        10.000000   \n",
       "75%        47.000000       NaN  2.379030e+05       NaN        13.000000   \n",
       "max        90.000000       NaN  1.490400e+06       NaN        16.000000   \n",
       "\n",
       "            marital-status    occupation relationship   race gender  \\\n",
       "count                45175         45175        45175  45175  45175   \n",
       "unique                   7            14            6      5      2   \n",
       "top     Married-civ-spouse  Craft-repair      Husband  White   Male   \n",
       "freq                 21042          6010        18653  38859  30495   \n",
       "mean                   NaN           NaN          NaN    NaN    NaN   \n",
       "std                    NaN           NaN          NaN    NaN    NaN   \n",
       "min                    NaN           NaN          NaN    NaN    NaN   \n",
       "25%                    NaN           NaN          NaN    NaN    NaN   \n",
       "50%                    NaN           NaN          NaN    NaN    NaN   \n",
       "75%                    NaN           NaN          NaN    NaN    NaN   \n",
       "max                    NaN           NaN          NaN    NaN    NaN   \n",
       "\n",
       "        capital-gain  capital-loss  hours-per-week native-country income  \n",
       "count   45175.000000  45175.000000    45175.000000          45175  45175  \n",
       "unique           NaN           NaN             NaN             41      2  \n",
       "top              NaN           NaN             NaN  United-States  <=50K  \n",
       "freq             NaN           NaN             NaN          41256  33973  \n",
       "mean     1102.576270     88.687593       40.942512            NaN    NaN  \n",
       "std      7510.249876    405.156611       12.007730            NaN    NaN  \n",
       "min         0.000000      0.000000        1.000000            NaN    NaN  \n",
       "25%         0.000000      0.000000       40.000000            NaN    NaN  \n",
       "50%         0.000000      0.000000       40.000000            NaN    NaN  \n",
       "75%         0.000000      0.000000       45.000000            NaN    NaN  \n",
       "max     99999.000000   4356.000000       99.000000            NaN    NaN  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Get statistics \n",
    "df2.describe(include='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "7eca310c",
   "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>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>educational-num</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>gender</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>native-country</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>25</td>\n",
       "      <td>Private</td>\n",
       "      <td>226802</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>89814</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Farming-fishing</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28</td>\n",
       "      <td>Local-gov</td>\n",
       "      <td>336951</td>\n",
       "      <td>Assoc-acdm</td>\n",
       "      <td>12</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Protective-serv</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>44</td>\n",
       "      <td>Private</td>\n",
       "      <td>160323</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>7688</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>34</td>\n",
       "      <td>Private</td>\n",
       "      <td>198693</td>\n",
       "      <td>10th</td>\n",
       "      <td>6</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Other-service</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  workclass  fnlwgt     education  educational-num      marital-status  \\\n",
       "0   25    Private  226802          11th                7       Never-married   \n",
       "1   38    Private   89814       HS-grad                9  Married-civ-spouse   \n",
       "2   28  Local-gov  336951    Assoc-acdm               12  Married-civ-spouse   \n",
       "3   44    Private  160323  Some-college               10  Married-civ-spouse   \n",
       "5   34    Private  198693          10th                6       Never-married   \n",
       "\n",
       "          occupation   relationship   race gender  capital-gain  capital-loss  \\\n",
       "0  Machine-op-inspct      Own-child  Black   Male             0             0   \n",
       "1    Farming-fishing        Husband  White   Male             0             0   \n",
       "2    Protective-serv        Husband  White   Male             0             0   \n",
       "3  Machine-op-inspct        Husband  Black   Male          7688             0   \n",
       "5      Other-service  Not-in-family  White   Male             0             0   \n",
       "\n",
       "   hours-per-week native-country income  \n",
       "0              40  United-States  <=50K  \n",
       "1              50  United-States  <=50K  \n",
       "2              40  United-States   >50K  \n",
       "3              40  United-States   >50K  \n",
       "5              30  United-States  <=50K  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## education and educational-num both represnt same information \n",
    "df2.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "00aff18c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['11th', 'HS-grad', 'Assoc-acdm', 'Some-college', '10th',\n",
       "       'Prof-school', '7th-8th', 'Bachelors', 'Masters', '5th-6th',\n",
       "       'Assoc-voc', '9th', 'Doctorate', '12th', '1st-4th', 'Preschool'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['education'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "0f52cbbe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 7,  9, 12, 10,  6, 15,  4, 13, 14,  3, 11,  5, 16,  8,  2,  1],\n",
       "      dtype=int64)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['educational-num'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "3dd3af7b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'workclass', 'fnlwgt', 'education', 'educational-num',\n",
       "       'marital-status', 'occupation', 'relationship', 'race', 'gender',\n",
       "       'capital-gain', 'capital-loss', 'hours-per-week', 'native-country',\n",
       "       'income'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "55a9f408",
   "metadata": {},
   "outputs": [],
   "source": [
    "## capital gain and loss both have 75% 0 value, so drop those columns \n",
    "df2=df2.drop(['capital-loss','capital-gain','educational-num'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "629438ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>gender</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>native-country</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>25</td>\n",
       "      <td>Private</td>\n",
       "      <td>226802</td>\n",
       "      <td>11th</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>89814</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Farming-fishing</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
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       "      <td>50</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28</td>\n",
       "      <td>Local-gov</td>\n",
       "      <td>336951</td>\n",
       "      <td>Assoc-acdm</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Protective-serv</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>44</td>\n",
       "      <td>Private</td>\n",
       "      <td>160323</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>34</td>\n",
       "      <td>Private</td>\n",
       "      <td>198693</td>\n",
       "      <td>10th</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Other-service</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>30</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48837</th>\n",
       "      <td>27</td>\n",
       "      <td>Private</td>\n",
       "      <td>257302</td>\n",
       "      <td>Assoc-acdm</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Tech-support</td>\n",
       "      <td>Wife</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>38</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48838</th>\n",
       "      <td>40</td>\n",
       "      <td>Private</td>\n",
       "      <td>154374</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48839</th>\n",
       "      <td>58</td>\n",
       "      <td>Private</td>\n",
       "      <td>151910</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>Widowed</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48840</th>\n",
       "      <td>22</td>\n",
       "      <td>Private</td>\n",
       "      <td>201490</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>20</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48841</th>\n",
       "      <td>52</td>\n",
       "      <td>Self-emp-inc</td>\n",
       "      <td>287927</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Wife</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>45175 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age     workclass  fnlwgt     education      marital-status  \\\n",
       "0       25       Private  226802          11th       Never-married   \n",
       "1       38       Private   89814       HS-grad  Married-civ-spouse   \n",
       "2       28     Local-gov  336951    Assoc-acdm  Married-civ-spouse   \n",
       "3       44       Private  160323  Some-college  Married-civ-spouse   \n",
       "5       34       Private  198693          10th       Never-married   \n",
       "...    ...           ...     ...           ...                 ...   \n",
       "48837   27       Private  257302    Assoc-acdm  Married-civ-spouse   \n",
       "48838   40       Private  154374       HS-grad  Married-civ-spouse   \n",
       "48839   58       Private  151910       HS-grad             Widowed   \n",
       "48840   22       Private  201490       HS-grad       Never-married   \n",
       "48841   52  Self-emp-inc  287927       HS-grad  Married-civ-spouse   \n",
       "\n",
       "              occupation   relationship   race  gender  hours-per-week  \\\n",
       "0      Machine-op-inspct      Own-child  Black    Male              40   \n",
       "1        Farming-fishing        Husband  White    Male              50   \n",
       "2        Protective-serv        Husband  White    Male              40   \n",
       "3      Machine-op-inspct        Husband  Black    Male              40   \n",
       "5          Other-service  Not-in-family  White    Male              30   \n",
       "...                  ...            ...    ...     ...             ...   \n",
       "48837       Tech-support           Wife  White  Female              38   \n",
       "48838  Machine-op-inspct        Husband  White    Male              40   \n",
       "48839       Adm-clerical      Unmarried  White  Female              40   \n",
       "48840       Adm-clerical      Own-child  White    Male              20   \n",
       "48841    Exec-managerial           Wife  White  Female              40   \n",
       "\n",
       "      native-country income  \n",
       "0      United-States  <=50K  \n",
       "1      United-States  <=50K  \n",
       "2      United-States   >50K  \n",
       "3      United-States   >50K  \n",
       "5      United-States  <=50K  \n",
       "...              ...    ...  \n",
       "48837  United-States  <=50K  \n",
       "48838  United-States   >50K  \n",
       "48839  United-States  <=50K  \n",
       "48840  United-States  <=50K  \n",
       "48841  United-States   >50K  \n",
       "\n",
       "[45175 rows x 12 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c3ce23b",
   "metadata": {},
   "source": [
    "# Univariate Analysis "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6e00a0e",
   "metadata": {},
   "source": [
    "# 1. what is distribution of age column ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "efd425ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    45175.000000\n",
       "mean        38.556170\n",
       "std         13.215349\n",
       "min         17.000000\n",
       "25%         28.000000\n",
       "50%         37.000000\n",
       "75%         47.000000\n",
       "max         90.000000\n",
       "Name: age, dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['age'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "f8b5d7e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df2['age'].hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e16a7de4",
   "metadata": {},
   "source": [
    "# 2. Find out total number of person having age between 17 to 48 in clussive using between function "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "447d0320",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "34858"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df2[(df2['age']>=17) & (df2['age']<=48)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "c8ba9499",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sum of persons age between 17 to 48 - 34858\n"
     ]
    }
   ],
   "source": [
    "N_p=len(df2[df2['age'].between(17,48)])\n",
    "print('Sum of persons age between 17 to 48 -',N_p)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6691d133",
   "metadata": {},
   "source": [
    "# 3. Distribution of workclass "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "1aafb15c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'workclass', 'fnlwgt', 'education', 'marital-status',\n",
       "       'occupation', 'relationship', 'race', 'gender', 'hours-per-week',\n",
       "       'native-country', 'income'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "82275518",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count       45175\n",
       "unique          7\n",
       "top       Private\n",
       "freq        33262\n",
       "Name: workclass, dtype: object"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['workclass'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "361e3011",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Histogram \n",
    "plt.figure(figsize=(8,8))\n",
    "df2['workclass'].hist()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3622bf8",
   "metadata": {},
   "source": [
    "# 4. how many people have bachelors and Masters degree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "735bd0d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'workclass', 'fnlwgt', 'education', 'marital-status',\n",
       "       'occupation', 'relationship', 'race', 'gender', 'hours-per-week',\n",
       "       'native-country', 'income'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "7f7f5bd1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "HS-grad         14770\n",
       "Some-college     9887\n",
       "Bachelors        7559\n",
       "Masters          2513\n",
       "Assoc-voc        1958\n",
       "11th             1619\n",
       "Assoc-acdm       1507\n",
       "10th             1223\n",
       "7th-8th           822\n",
       "Prof-school       785\n",
       "9th               676\n",
       "12th              575\n",
       "Doctorate         544\n",
       "5th-6th           447\n",
       "1st-4th           220\n",
       "Preschool          70\n",
       "Name: education, dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['education'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "f1af4893",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7559"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_bac = len(df2[(df2['education']=='Bachelors')]) \n",
    "num_bac"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "224c56e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2513"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_mas = len(df2[(df2['education']=='Masters')]) \n",
    "num_mas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "8753eedb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of Bachelors & Masters -  10072\n"
     ]
    }
   ],
   "source": [
    "total=num_bac + num_mas\n",
    "print('Total number of Bachelors & Masters - ', total)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "194862dd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of Bachelors & Masters -  10072\n"
     ]
    }
   ],
   "source": [
    "## Another method \n",
    "\n",
    "total_1 = sum(df2['education'].isin(['Bachelors', 'Masters']))\n",
    "\n",
    "print('Total number of Bachelors & Masters - ', total_1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "734a3680",
   "metadata": {},
   "source": [
    "# Bivariate Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "99add8c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'workclass', 'fnlwgt', 'education', 'marital-status',\n",
       "       'occupation', 'relationship', 'race', 'gender', 'hours-per-week',\n",
       "       'native-country', 'income'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "5b1b36f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='income', ylabel='age'>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Realtionship between age and 'income'\n",
    "sns.boxplot(x = 'income', y = 'age', data = df2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "b85a38ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Most of Younger people having salary less than 50K\n",
    "## Most of seniour people having salary more than 50K\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd6e3e26",
   "metadata": {},
   "source": [
    "# 5.Replace salary value [<=50K and >=50K] with 0 and 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "ada1d3c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Finding unique values of salary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "f98c57ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['<=50K', '>50K'], dtype=object)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['income'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "e4bb5da5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<=50K    33973\n",
       ">50K     11202\n",
       "Name: income, dtype: int64"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Counting of ['<=50K', '>50K']\n",
    "count = df2['income'].value_counts()\n",
    "count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "2b703c40",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='income', ylabel='count'>"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Ploting countplot of income \n",
    "sns.countplot(x='income', data=df2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "4532fb9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "## replace salary value [<=50K and >=50K] with 0 and 1\n",
    "\n",
    "df5=df2.replace(to_replace = ['<=50K', '>50K'], value=[0,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "9e16cece",
   "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>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>gender</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>native-country</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>25</td>\n",
       "      <td>Private</td>\n",
       "      <td>226802</td>\n",
       "      <td>11th</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>89814</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Farming-fishing</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>50</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age workclass  fnlwgt education      marital-status         occupation  \\\n",
       "0   25   Private  226802      11th       Never-married  Machine-op-inspct   \n",
       "1   38   Private   89814   HS-grad  Married-civ-spouse    Farming-fishing   \n",
       "\n",
       "  relationship   race gender  hours-per-week native-country  income  \n",
       "0    Own-child  Black   Male              40  United-States       0  \n",
       "1      Husband  White   Male              50  United-States       0  "
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae010412",
   "metadata": {},
   "source": [
    "# 6. Which workclass getting highest salary "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "20c1ff36",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'workclass', 'fnlwgt', 'education', 'marital-status',\n",
       "       'occupation', 'relationship', 'race', 'gender', 'hours-per-week',\n",
       "       'native-country', 'income'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "ca596355",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method Series.unique of 0             Private\n",
       "1             Private\n",
       "2           Local-gov\n",
       "3             Private\n",
       "5             Private\n",
       "             ...     \n",
       "48837         Private\n",
       "48838         Private\n",
       "48839         Private\n",
       "48840         Private\n",
       "48841    Self-emp-inc\n",
       "Name: workclass, Length: 45175, dtype: object>"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['workclass'].unique"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "419adb6a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "workclass\n",
       "Self-emp-inc        0.554407\n",
       "Federal-gov         0.390469\n",
       "Local-gov           0.295161\n",
       "Self-emp-not-inc    0.279051\n",
       "State-gov           0.267215\n",
       "Private             0.217816\n",
       "Without-pay         0.095238\n",
       "Name: income, dtype: float64"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hg_salary =df5.groupby('workclass')['income'].mean().sort_values(ascending=False)\n",
    "hg_salary"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8fd5c76f",
   "metadata": {},
   "source": [
    "# Highest salary by gender and workclass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "8ddfcaed",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "Could not convert <=50K>50K<=50K<=50K<=50K>50K<=50K>50K>50K<=50K<=50K<=50K<=50K<=50K>50K>50K<=50K<=50K<=50K>50K<=50K<=50K>50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K>50K<=50K>50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K>50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K>50K>50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K>50K>50K<=50K<=50K<=50K<=50K>50K<=50K>50K<=50K<=50K<=50K<=50K<=50K>50K<=50K<=50K<=50K<=50K>50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K>50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K>50K<=50K<=50K<=50K<=50K>50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K>50K<=50K<=50K<=50K<=50K>50K<=50K>50K<=50K>50K<=50K<=50K<=50K>50K>50K>50K<=50K>50K>50K<=50K<=50K<=50K<=50K<=50K>50K<=50K<=50K<=50K>50K<=50K<=50K<=50K>50K<=50K<=50K<=50K<=50K>50K<=50K<=50K<=50K<=50K<=50K>50K<=50K>50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K>50K<=50K<=50K<=50K<=50K<=50K<=50K>50K<=50K<=50K<=50K<=50K<=50K<=50K>50K>50K>50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K>50K<=50K<=50K>50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K>50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K>50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K>50K<=50K>50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K>50K<=50K>50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K>50K<=50K<=50K<=50K<=50K>50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K>50K<=50K<=50K<=50K>50K>50K<=50K>50K<=50K<=50K<=50K<=50K>50K<=50K<=50K<=50K>50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K>50K>50K<=50K<=50K<=50K<=50K<=50K<=50K>50K<=50K<=50K<=50K<=50K<=50K>50K<=50K>50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K>50K<=50K>50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K>50K>50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K>50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K>50K<=50K<=50K<=50K>50K<=50K>50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K>50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K>50K>50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K>50K<=50K<=50K<=50K<=50K<=50K>50K<=50K>50K<=50K<=50K<=50K<=50K<=50K>50K<=50K<=50K<=50K<=50K<=50K>50K<=50K>50K<=50K<=50K>50K>50K<=50K<=50K to numeric",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNotImplementedError\u001b[0m                       Traceback (most recent call last)",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\groupby\\groupby.py:1791\u001b[0m, in \u001b[0;36mGroupBy._cython_agg_general.<locals>.array_func\u001b[1;34m(values)\u001b[0m\n\u001b[0;32m   1790\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 1791\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgrouper\u001b[38;5;241m.\u001b[39m_cython_operation(\n\u001b[0;32m   1792\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124maggregate\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m   1793\u001b[0m         values,\n\u001b[0;32m   1794\u001b[0m         how,\n\u001b[0;32m   1795\u001b[0m         axis\u001b[38;5;241m=\u001b[39mdata\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m-\u001b[39m \u001b[38;5;241m1\u001b[39m,\n\u001b[0;32m   1796\u001b[0m         min_count\u001b[38;5;241m=\u001b[39mmin_count,\n\u001b[0;32m   1797\u001b[0m         \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[0;32m   1798\u001b[0m     )\n\u001b[0;32m   1799\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mNotImplementedError\u001b[39;00m:\n\u001b[0;32m   1800\u001b[0m     \u001b[38;5;66;03m# generally if we have numeric_only=False\u001b[39;00m\n\u001b[0;32m   1801\u001b[0m     \u001b[38;5;66;03m# and non-applicable functions\u001b[39;00m\n\u001b[0;32m   1802\u001b[0m     \u001b[38;5;66;03m# try to python agg\u001b[39;00m\n\u001b[0;32m   1803\u001b[0m     \u001b[38;5;66;03m# TODO: shouldn't min_count matter?\u001b[39;00m\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\groupby\\ops.py:1039\u001b[0m, in \u001b[0;36mBaseGrouper._cython_operation\u001b[1;34m(self, kind, values, how, axis, min_count, **kwargs)\u001b[0m\n\u001b[0;32m   1038\u001b[0m ngroups \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mngroups\n\u001b[1;32m-> 1039\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m cy_op\u001b[38;5;241m.\u001b[39mcython_operation(\n\u001b[0;32m   1040\u001b[0m     values\u001b[38;5;241m=\u001b[39mvalues,\n\u001b[0;32m   1041\u001b[0m     axis\u001b[38;5;241m=\u001b[39maxis,\n\u001b[0;32m   1042\u001b[0m     min_count\u001b[38;5;241m=\u001b[39mmin_count,\n\u001b[0;32m   1043\u001b[0m     comp_ids\u001b[38;5;241m=\u001b[39mids,\n\u001b[0;32m   1044\u001b[0m     ngroups\u001b[38;5;241m=\u001b[39mngroups,\n\u001b[0;32m   1045\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[0;32m   1046\u001b[0m )\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\groupby\\ops.py:708\u001b[0m, in \u001b[0;36mWrappedCythonOp.cython_operation\u001b[1;34m(self, values, axis, min_count, comp_ids, ngroups, **kwargs)\u001b[0m\n\u001b[0;32m    700\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_ea_wrap_cython_operation(\n\u001b[0;32m    701\u001b[0m         values,\n\u001b[0;32m    702\u001b[0m         min_count\u001b[38;5;241m=\u001b[39mmin_count,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    705\u001b[0m         \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[0;32m    706\u001b[0m     )\n\u001b[1;32m--> 708\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_cython_op_ndim_compat(\n\u001b[0;32m    709\u001b[0m     values,\n\u001b[0;32m    710\u001b[0m     min_count\u001b[38;5;241m=\u001b[39mmin_count,\n\u001b[0;32m    711\u001b[0m     ngroups\u001b[38;5;241m=\u001b[39mngroups,\n\u001b[0;32m    712\u001b[0m     comp_ids\u001b[38;5;241m=\u001b[39mcomp_ids,\n\u001b[0;32m    713\u001b[0m     mask\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[0;32m    714\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[0;32m    715\u001b[0m )\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\groupby\\ops.py:512\u001b[0m, in \u001b[0;36mWrappedCythonOp._cython_op_ndim_compat\u001b[1;34m(self, values, min_count, ngroups, comp_ids, mask, result_mask, **kwargs)\u001b[0m\n\u001b[0;32m    511\u001b[0m     result_mask \u001b[38;5;241m=\u001b[39m result_mask[\u001b[38;5;28;01mNone\u001b[39;00m, :]\n\u001b[1;32m--> 512\u001b[0m res \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_call_cython_op(\n\u001b[0;32m    513\u001b[0m     values2d,\n\u001b[0;32m    514\u001b[0m     min_count\u001b[38;5;241m=\u001b[39mmin_count,\n\u001b[0;32m    515\u001b[0m     ngroups\u001b[38;5;241m=\u001b[39mngroups,\n\u001b[0;32m    516\u001b[0m     comp_ids\u001b[38;5;241m=\u001b[39mcomp_ids,\n\u001b[0;32m    517\u001b[0m     mask\u001b[38;5;241m=\u001b[39mmask,\n\u001b[0;32m    518\u001b[0m     result_mask\u001b[38;5;241m=\u001b[39mresult_mask,\n\u001b[0;32m    519\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[0;32m    520\u001b[0m )\n\u001b[0;32m    521\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m res\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m:\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\groupby\\ops.py:571\u001b[0m, in \u001b[0;36mWrappedCythonOp._call_cython_op\u001b[1;34m(self, values, min_count, ngroups, comp_ids, mask, result_mask, **kwargs)\u001b[0m\n\u001b[0;32m    570\u001b[0m out_shape \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_output_shape(ngroups, values)\n\u001b[1;32m--> 571\u001b[0m func \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_cython_function(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mkind, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhow, values\u001b[38;5;241m.\u001b[39mdtype, is_numeric)\n\u001b[0;32m    572\u001b[0m values \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_cython_vals(values)\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\groupby\\ops.py:192\u001b[0m, in \u001b[0;36mWrappedCythonOp._get_cython_function\u001b[1;34m(cls, kind, how, dtype, is_numeric)\u001b[0m\n\u001b[0;32m    190\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mobject\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m f\u001b[38;5;241m.\u001b[39m__signatures__:\n\u001b[0;32m    191\u001b[0m     \u001b[38;5;66;03m# raise NotImplementedError here rather than TypeError later\u001b[39;00m\n\u001b[1;32m--> 192\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mNotImplementedError\u001b[39;00m(\n\u001b[0;32m    193\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfunction is not implemented for this dtype: \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    194\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m[how->\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mhow\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m,dtype->\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mdtype_str\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m]\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    195\u001b[0m     )\n\u001b[0;32m    196\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m f\n",
      "\u001b[1;31mNotImplementedError\u001b[0m: function is not implemented for this dtype: [how->mean,dtype->object]",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\nanops.py:1630\u001b[0m, in \u001b[0;36m_ensure_numeric\u001b[1;34m(x)\u001b[0m\n\u001b[0;32m   1629\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 1630\u001b[0m     x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mfloat\u001b[39m(x)\n\u001b[0;32m   1631\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (\u001b[38;5;167;01mTypeError\u001b[39;00m, \u001b[38;5;167;01mValueError\u001b[39;00m):\n\u001b[0;32m   1632\u001b[0m     \u001b[38;5;66;03m# e.g. \"1+1j\" or \"foo\"\u001b[39;00m\n",
      "\u001b[1;31mValueError\u001b[0m: could not convert string to float: '<=50K>50K<=50K<=50K<=50K>50K<=50K>50K>50K<=50K<=50K<=50K<=50K<=50K>50K>50K<=50K<=50K<=50K>50K<=50K<=50K>50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K>50K<=50K>50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K>50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K>50K>50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K>50K>50K<=50K<=50K<=50K<=50K>50K<=50K>50K<=50K<=50K<=50K<=50K<=50K>50K<=50K<=50K<=50K<=50K>50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K>50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K>50K<=50K<=50K<=50K<=50K>50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K>50K<=50K<=50K<=50K<=50K>50K<=50K>50K<=50K>50K<=50K<=50K<=50K>50K>50K>50K<=50K>50K>50K<=50K<=50K<=50K<=50K<=50K>50K<=50K<=50K<=50K>50K<=50K<=50K<=50K>50K<=50K<=50K<=50K<=50K>50K<=50K<=50K<=50K<=50K<=50K>50K<=50K>50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K>50K<=50K<=50K<=50K<=50K<=50K<=50K>50K<=50K<=50K<=50K<=50K<=50K<=50K>50K>50K>50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K>50K<=50K<=50K>50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K>50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K>50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K>50K<=50K>50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K>50K<=50K>50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K>50K<=50K<=50K<=50K<=50K>50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K>50K<=50K<=50K<=50K>50K>50K<=50K>50K<=50K<=50K<=50K<=50K>50K<=50K<=50K<=50K>50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K>50K>50K<=50K<=50K<=50K<=50K<=50K<=50K>50K<=50K<=50K<=50K<=50K<=50K>50K<=50K>50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K>50K<=50K>50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K>50K>50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K>50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K>50K<=50K<=50K<=50K>50K<=50K>50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K>50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K>50K>50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K>50K<=50K<=50K<=50K<=50K<=50K>50K<=50K>50K<=50K<=50K<=50K<=50K<=50K>50K<=50K<=50K<=50K<=50K<=50K>50K<=50K>50K<=50K<=50K>50K>50K<=50K<=50K'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\nanops.py:1634\u001b[0m, in \u001b[0;36m_ensure_numeric\u001b[1;34m(x)\u001b[0m\n\u001b[0;32m   1633\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 1634\u001b[0m     x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mcomplex\u001b[39m(x)\n\u001b[0;32m   1635\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[0;32m   1636\u001b[0m     \u001b[38;5;66;03m# e.g. \"foo\"\u001b[39;00m\n",
      "\u001b[1;31mValueError\u001b[0m: complex() arg is a malformed string",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[164], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m hg_salary \u001b[38;5;241m=\u001b[39mdf2\u001b[38;5;241m.\u001b[39mgroupby([\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mgender\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mworkclass\u001b[39m\u001b[38;5;124m'\u001b[39m])[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mincome\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mmean()\u001b[38;5;241m.\u001b[39msort_values(ascending\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[0;32m      2\u001b[0m hg_salary\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\groupby\\groupby.py:2183\u001b[0m, in \u001b[0;36mGroupBy.mean\u001b[1;34m(self, numeric_only, engine, engine_kwargs)\u001b[0m\n\u001b[0;32m   2181\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_numba_agg_general(sliding_mean, engine_kwargs)\n\u001b[0;32m   2182\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 2183\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_cython_agg_general(\n\u001b[0;32m   2184\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmean\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m   2185\u001b[0m         alt\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mlambda\u001b[39;00m x: Series(x)\u001b[38;5;241m.\u001b[39mmean(numeric_only\u001b[38;5;241m=\u001b[39mnumeric_only_bool),\n\u001b[0;32m   2186\u001b[0m         numeric_only\u001b[38;5;241m=\u001b[39mnumeric_only,\n\u001b[0;32m   2187\u001b[0m     )\n\u001b[0;32m   2188\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m result\u001b[38;5;241m.\u001b[39m__finalize__(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj, method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgroupby\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\groupby\\groupby.py:1810\u001b[0m, in \u001b[0;36mGroupBy._cython_agg_general\u001b[1;34m(self, how, alt, numeric_only, min_count, ignore_failures, **kwargs)\u001b[0m\n\u001b[0;32m   1806\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m result\n\u001b[0;32m   1808\u001b[0m \u001b[38;5;66;03m# TypeError -> we may have an exception in trying to aggregate\u001b[39;00m\n\u001b[0;32m   1809\u001b[0m \u001b[38;5;66;03m#  continue and exclude the block\u001b[39;00m\n\u001b[1;32m-> 1810\u001b[0m new_mgr \u001b[38;5;241m=\u001b[39m data\u001b[38;5;241m.\u001b[39mgrouped_reduce(array_func, ignore_failures\u001b[38;5;241m=\u001b[39mignore_failures)\n\u001b[0;32m   1812\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m is_ser \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(new_mgr) \u001b[38;5;241m<\u001b[39m orig_len:\n\u001b[0;32m   1813\u001b[0m     warn_dropping_nuisance_columns_deprecated(\u001b[38;5;28mtype\u001b[39m(\u001b[38;5;28mself\u001b[39m), how, numeric_only)\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\internals\\base.py:199\u001b[0m, in \u001b[0;36mSingleDataManager.grouped_reduce\u001b[1;34m(self, func, ignore_failures)\u001b[0m\n\u001b[0;32m    193\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    194\u001b[0m \u001b[38;5;124;03mignore_failures : bool, default False\u001b[39;00m\n\u001b[0;32m    195\u001b[0m \u001b[38;5;124;03m    Not used; for compatibility with ArrayManager/BlockManager.\u001b[39;00m\n\u001b[0;32m    196\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    198\u001b[0m arr \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39marray\n\u001b[1;32m--> 199\u001b[0m res \u001b[38;5;241m=\u001b[39m func(arr)\n\u001b[0;32m    200\u001b[0m index \u001b[38;5;241m=\u001b[39m default_index(\u001b[38;5;28mlen\u001b[39m(res))\n\u001b[0;32m    202\u001b[0m mgr \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtype\u001b[39m(\u001b[38;5;28mself\u001b[39m)\u001b[38;5;241m.\u001b[39mfrom_array(res, index)\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\groupby\\groupby.py:1804\u001b[0m, in \u001b[0;36mGroupBy._cython_agg_general.<locals>.array_func\u001b[1;34m(values)\u001b[0m\n\u001b[0;32m   1791\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgrouper\u001b[38;5;241m.\u001b[39m_cython_operation(\n\u001b[0;32m   1792\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124maggregate\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m   1793\u001b[0m         values,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   1797\u001b[0m         \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[0;32m   1798\u001b[0m     )\n\u001b[0;32m   1799\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mNotImplementedError\u001b[39;00m:\n\u001b[0;32m   1800\u001b[0m     \u001b[38;5;66;03m# generally if we have numeric_only=False\u001b[39;00m\n\u001b[0;32m   1801\u001b[0m     \u001b[38;5;66;03m# and non-applicable functions\u001b[39;00m\n\u001b[0;32m   1802\u001b[0m     \u001b[38;5;66;03m# try to python agg\u001b[39;00m\n\u001b[0;32m   1803\u001b[0m     \u001b[38;5;66;03m# TODO: shouldn't min_count matter?\u001b[39;00m\n\u001b[1;32m-> 1804\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_agg_py_fallback(values, ndim\u001b[38;5;241m=\u001b[39mdata\u001b[38;5;241m.\u001b[39mndim, alt\u001b[38;5;241m=\u001b[39malt)\n\u001b[0;32m   1806\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\groupby\\groupby.py:1745\u001b[0m, in \u001b[0;36mGroupBy._agg_py_fallback\u001b[1;34m(self, values, ndim, alt)\u001b[0m\n\u001b[0;32m   1740\u001b[0m     ser \u001b[38;5;241m=\u001b[39m df\u001b[38;5;241m.\u001b[39miloc[:, \u001b[38;5;241m0\u001b[39m]\n\u001b[0;32m   1742\u001b[0m \u001b[38;5;66;03m# We do not get here with UDFs, so we know that our dtype\u001b[39;00m\n\u001b[0;32m   1743\u001b[0m \u001b[38;5;66;03m#  should always be preserved by the implemented aggregations\u001b[39;00m\n\u001b[0;32m   1744\u001b[0m \u001b[38;5;66;03m# TODO: Is this exactly right; see WrappedCythonOp get_result_dtype?\u001b[39;00m\n\u001b[1;32m-> 1745\u001b[0m res_values \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgrouper\u001b[38;5;241m.\u001b[39magg_series(ser, alt, preserve_dtype\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m   1747\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(values, Categorical):\n\u001b[0;32m   1748\u001b[0m     \u001b[38;5;66;03m# Because we only get here with known dtype-preserving\u001b[39;00m\n\u001b[0;32m   1749\u001b[0m     \u001b[38;5;66;03m#  reductions, we cast back to Categorical.\u001b[39;00m\n\u001b[0;32m   1750\u001b[0m     \u001b[38;5;66;03m# TODO: if we ever get \"rank\" working, exclude it here.\u001b[39;00m\n\u001b[0;32m   1751\u001b[0m     res_values \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtype\u001b[39m(values)\u001b[38;5;241m.\u001b[39m_from_sequence(res_values, dtype\u001b[38;5;241m=\u001b[39mvalues\u001b[38;5;241m.\u001b[39mdtype)\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\groupby\\ops.py:1081\u001b[0m, in \u001b[0;36mBaseGrouper.agg_series\u001b[1;34m(self, obj, func, preserve_dtype)\u001b[0m\n\u001b[0;32m   1078\u001b[0m     preserve_dtype \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[0;32m   1080\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1081\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_aggregate_series_pure_python(obj, func)\n\u001b[0;32m   1083\u001b[0m npvalues \u001b[38;5;241m=\u001b[39m lib\u001b[38;5;241m.\u001b[39mmaybe_convert_objects(result, try_float\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[0;32m   1084\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m preserve_dtype:\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\groupby\\ops.py:1104\u001b[0m, in \u001b[0;36mBaseGrouper._aggregate_series_pure_python\u001b[1;34m(self, obj, func)\u001b[0m\n\u001b[0;32m   1101\u001b[0m splitter \u001b[38;5;241m=\u001b[39m get_splitter(obj, ids, ngroups, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m)\n\u001b[0;32m   1103\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i, group \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(splitter):\n\u001b[1;32m-> 1104\u001b[0m     res \u001b[38;5;241m=\u001b[39m func(group)\n\u001b[0;32m   1105\u001b[0m     res \u001b[38;5;241m=\u001b[39m libreduction\u001b[38;5;241m.\u001b[39mextract_result(res)\n\u001b[0;32m   1107\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m initialized:\n\u001b[0;32m   1108\u001b[0m         \u001b[38;5;66;03m# We only do this validation on the first iteration\u001b[39;00m\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\groupby\\groupby.py:2185\u001b[0m, in \u001b[0;36mGroupBy.mean.<locals>.<lambda>\u001b[1;34m(x)\u001b[0m\n\u001b[0;32m   2181\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_numba_agg_general(sliding_mean, engine_kwargs)\n\u001b[0;32m   2182\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m   2183\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_cython_agg_general(\n\u001b[0;32m   2184\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmean\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m-> 2185\u001b[0m         alt\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mlambda\u001b[39;00m x: Series(x)\u001b[38;5;241m.\u001b[39mmean(numeric_only\u001b[38;5;241m=\u001b[39mnumeric_only_bool),\n\u001b[0;32m   2186\u001b[0m         numeric_only\u001b[38;5;241m=\u001b[39mnumeric_only,\n\u001b[0;32m   2187\u001b[0m     )\n\u001b[0;32m   2188\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m result\u001b[38;5;241m.\u001b[39m__finalize__(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj, method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgroupby\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\generic.py:11847\u001b[0m, in \u001b[0;36mNDFrame._add_numeric_operations.<locals>.mean\u001b[1;34m(self, axis, skipna, level, numeric_only, **kwargs)\u001b[0m\n\u001b[0;32m  11829\u001b[0m \u001b[38;5;129m@doc\u001b[39m(\n\u001b[0;32m  11830\u001b[0m     _num_doc,\n\u001b[0;32m  11831\u001b[0m     desc\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mReturn the mean of the values over the requested axis.\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m  11845\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[0;32m  11846\u001b[0m ):\n\u001b[1;32m> 11847\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m NDFrame\u001b[38;5;241m.\u001b[39mmean(\u001b[38;5;28mself\u001b[39m, axis, skipna, level, numeric_only, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\generic.py:11401\u001b[0m, in \u001b[0;36mNDFrame.mean\u001b[1;34m(self, axis, skipna, level, numeric_only, **kwargs)\u001b[0m\n\u001b[0;32m  11393\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mmean\u001b[39m(\n\u001b[0;32m  11394\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m  11395\u001b[0m     axis: Axis \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m|\u001b[39m lib\u001b[38;5;241m.\u001b[39mNoDefault \u001b[38;5;241m=\u001b[39m lib\u001b[38;5;241m.\u001b[39mno_default,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m  11399\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[0;32m  11400\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Series \u001b[38;5;241m|\u001b[39m \u001b[38;5;28mfloat\u001b[39m:\n\u001b[1;32m> 11401\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_stat_function(\n\u001b[0;32m  11402\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmean\u001b[39m\u001b[38;5;124m\"\u001b[39m, nanops\u001b[38;5;241m.\u001b[39mnanmean, axis, skipna, level, numeric_only, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs\n\u001b[0;32m  11403\u001b[0m     )\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\generic.py:11353\u001b[0m, in \u001b[0;36mNDFrame._stat_function\u001b[1;34m(self, name, func, axis, skipna, level, numeric_only, **kwargs)\u001b[0m\n\u001b[0;32m  11343\u001b[0m     warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[0;32m  11344\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUsing the level keyword in DataFrame and Series aggregations is \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m  11345\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdeprecated and will be removed in a future version. Use groupby \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m  11348\u001b[0m         stacklevel\u001b[38;5;241m=\u001b[39mfind_stack_level(),\n\u001b[0;32m  11349\u001b[0m     )\n\u001b[0;32m  11350\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_agg_by_level(\n\u001b[0;32m  11351\u001b[0m         name, axis\u001b[38;5;241m=\u001b[39maxis, level\u001b[38;5;241m=\u001b[39mlevel, skipna\u001b[38;5;241m=\u001b[39mskipna, numeric_only\u001b[38;5;241m=\u001b[39mnumeric_only\n\u001b[0;32m  11352\u001b[0m     )\n\u001b[1;32m> 11353\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reduce(\n\u001b[0;32m  11354\u001b[0m     func, name\u001b[38;5;241m=\u001b[39mname, axis\u001b[38;5;241m=\u001b[39maxis, skipna\u001b[38;5;241m=\u001b[39mskipna, numeric_only\u001b[38;5;241m=\u001b[39mnumeric_only\n\u001b[0;32m  11355\u001b[0m )\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\series.py:4816\u001b[0m, in \u001b[0;36mSeries._reduce\u001b[1;34m(self, op, name, axis, skipna, numeric_only, filter_type, **kwds)\u001b[0m\n\u001b[0;32m   4812\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mNotImplementedError\u001b[39;00m(\n\u001b[0;32m   4813\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSeries.\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mname\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m does not implement \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mkwd_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   4814\u001b[0m     )\n\u001b[0;32m   4815\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m np\u001b[38;5;241m.\u001b[39merrstate(\u001b[38;5;28mall\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mignore\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m-> 4816\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m op(delegate, skipna\u001b[38;5;241m=\u001b[39mskipna, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwds)\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\nanops.py:93\u001b[0m, in \u001b[0;36mdisallow.__call__.<locals>._f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     91\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m     92\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m np\u001b[38;5;241m.\u001b[39merrstate(invalid\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mignore\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m---> 93\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m f(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m     94\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m     95\u001b[0m     \u001b[38;5;66;03m# we want to transform an object array\u001b[39;00m\n\u001b[0;32m     96\u001b[0m     \u001b[38;5;66;03m# ValueError message to the more typical TypeError\u001b[39;00m\n\u001b[0;32m     97\u001b[0m     \u001b[38;5;66;03m# e.g. this is normally a disallowed function on\u001b[39;00m\n\u001b[0;32m     98\u001b[0m     \u001b[38;5;66;03m# object arrays that contain strings\u001b[39;00m\n\u001b[0;32m     99\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m is_object_dtype(args[\u001b[38;5;241m0\u001b[39m]):\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\nanops.py:155\u001b[0m, in \u001b[0;36mbottleneck_switch.__call__.<locals>.f\u001b[1;34m(values, axis, skipna, **kwds)\u001b[0m\n\u001b[0;32m    153\u001b[0m         result \u001b[38;5;241m=\u001b[39m alt(values, axis\u001b[38;5;241m=\u001b[39maxis, skipna\u001b[38;5;241m=\u001b[39mskipna, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwds)\n\u001b[0;32m    154\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 155\u001b[0m     result \u001b[38;5;241m=\u001b[39m alt(values, axis\u001b[38;5;241m=\u001b[39maxis, skipna\u001b[38;5;241m=\u001b[39mskipna, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwds)\n\u001b[0;32m    157\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\nanops.py:418\u001b[0m, in \u001b[0;36m_datetimelike_compat.<locals>.new_func\u001b[1;34m(values, axis, skipna, mask, **kwargs)\u001b[0m\n\u001b[0;32m    415\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m datetimelike \u001b[38;5;129;01mand\u001b[39;00m mask \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m    416\u001b[0m     mask \u001b[38;5;241m=\u001b[39m isna(values)\n\u001b[1;32m--> 418\u001b[0m result \u001b[38;5;241m=\u001b[39m func(values, axis\u001b[38;5;241m=\u001b[39maxis, skipna\u001b[38;5;241m=\u001b[39mskipna, mask\u001b[38;5;241m=\u001b[39mmask, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m    420\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m datetimelike:\n\u001b[0;32m    421\u001b[0m     result \u001b[38;5;241m=\u001b[39m _wrap_results(result, orig_values\u001b[38;5;241m.\u001b[39mdtype, fill_value\u001b[38;5;241m=\u001b[39miNaT)\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\nanops.py:706\u001b[0m, in \u001b[0;36mnanmean\u001b[1;34m(values, axis, skipna, mask)\u001b[0m\n\u001b[0;32m    703\u001b[0m     dtype_count \u001b[38;5;241m=\u001b[39m dtype\n\u001b[0;32m    705\u001b[0m count \u001b[38;5;241m=\u001b[39m _get_counts(values\u001b[38;5;241m.\u001b[39mshape, mask, axis, dtype\u001b[38;5;241m=\u001b[39mdtype_count)\n\u001b[1;32m--> 706\u001b[0m the_sum \u001b[38;5;241m=\u001b[39m _ensure_numeric(values\u001b[38;5;241m.\u001b[39msum(axis, dtype\u001b[38;5;241m=\u001b[39mdtype_sum))\n\u001b[0;32m    708\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m axis \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(the_sum, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mndim\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mFalse\u001b[39;00m):\n\u001b[0;32m    709\u001b[0m     count \u001b[38;5;241m=\u001b[39m cast(np\u001b[38;5;241m.\u001b[39mndarray, count)\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\nanops.py:1637\u001b[0m, in \u001b[0;36m_ensure_numeric\u001b[1;34m(x)\u001b[0m\n\u001b[0;32m   1634\u001b[0m             x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mcomplex\u001b[39m(x)\n\u001b[0;32m   1635\u001b[0m         \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[0;32m   1636\u001b[0m             \u001b[38;5;66;03m# e.g. \"foo\"\u001b[39;00m\n\u001b[1;32m-> 1637\u001b[0m             \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCould not convert \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mx\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m to numeric\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[0;32m   1638\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m x\n",
      "\u001b[1;31mTypeError\u001b[0m: Could not convert <=50K>50K<=50K<=50K<=50K>50K<=50K>50K>50K<=50K<=50K<=50K<=50K<=50K>50K>50K<=50K<=50K<=50K>50K<=50K<=50K>50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K>50K<=50K>50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K>50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K>50K>50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K>50K>50K<=50K<=50K<=50K<=50K>50K<=50K>50K<=50K<=50K<=50K<=50K<=50K>50K<=50K<=50K<=50K<=50K>50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K>50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K>50K<=50K<=50K<=50K<=50K>50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K>50K<=50K<=50K<=50K<=50K>50K<=50K>50K<=50K>50K<=50K<=50K<=50K>50K>50K>50K<=50K>50K>50K<=50K<=50K<=50K<=50K<=50K>50K<=50K<=50K<=50K>50K<=50K<=50K<=50K>50K<=50K<=50K<=50K<=50K>50K<=50K<=50K<=50K<=50K<=50K>50K<=50K>50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K>50K<=50K<=50K<=50K<=50K<=50K<=50K>50K<=50K<=50K<=50K<=50K<=50K<=50K>50K>50K>50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K>50K<=50K<=50K>50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K>50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K>50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K>50K<=50K>50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K>50K<=50K>50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K>50K<=50K<=50K<=50K<=50K>50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K>50K<=50K<=50K<=50K>50K>50K<=50K>50K<=50K<=50K<=50K<=50K>50K<=50K<=50K<=50K>50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K>50K>50K<=50K<=50K<=50K<=50K<=50K<=50K>50K<=50K<=50K<=50K<=50K<=50K>50K<=50K>50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K>50K<=50K>50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K>50K>50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K>50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K>50K<=50K<=50K<=50K>50K<=50K>50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K>50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K>50K>50K<=50K<=50K<=50K<=50K<=50K<=50K<=50K>50K<=50K<=50K<=50K<=50K<=50K>50K<=50K>50K<=50K<=50K<=50K<=50K<=50K>50K<=50K<=50K<=50K<=50K<=50K>50K<=50K>50K<=50K<=50K>50K>50K<=50K<=50K to numeric"
     ]
    }
   ],
   "source": [
    "hg_salary =df2.groupby(['gender','workclass'])['income'].mean().sort_values(ascending=False)\n",
    "hg_salary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "79ebbe30",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='workclass', ylabel='count'>"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,8))\n",
    "sns.countplot(x='workclass', data= df5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5962747d",
   "metadata": {},
   "source": [
    "# 7. Which gende has potentiality for higher income"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "d3d73bad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gender\n",
       "Male      0.312609\n",
       "Female    0.113692\n",
       "Name: income, dtype: float64"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5.groupby('gender')['income'].mean().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e249ad2c",
   "metadata": {},
   "source": [
    "# 8. Change datatype of workforce to categorical datatype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "2cffc443",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 45175 entries, 0 to 48841\n",
      "Data columns (total 12 columns):\n",
      " #   Column          Non-Null Count  Dtype   \n",
      "---  ------          --------------  -----   \n",
      " 0   age             45175 non-null  int64   \n",
      " 1   workclass       45175 non-null  category\n",
      " 2   fnlwgt          45175 non-null  int64   \n",
      " 3   education       45175 non-null  object  \n",
      " 4   marital-status  45175 non-null  object  \n",
      " 5   occupation      45175 non-null  object  \n",
      " 6   relationship    45175 non-null  object  \n",
      " 7   race            45175 non-null  object  \n",
      " 8   gender          45175 non-null  object  \n",
      " 9   hours-per-week  45175 non-null  int64   \n",
      " 10  native-country  45175 non-null  object  \n",
      " 11  income          45175 non-null  int64   \n",
      "dtypes: category(1), int64(4), object(7)\n",
      "memory usage: 4.2+ MB\n"
     ]
    }
   ],
   "source": [
    "df5.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "3c86c35f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df5['workclass']=df5['workclass'].astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "a97218be",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 45175 entries, 0 to 48841\n",
      "Data columns (total 12 columns):\n",
      " #   Column          Non-Null Count  Dtype   \n",
      "---  ------          --------------  -----   \n",
      " 0   age             45175 non-null  int64   \n",
      " 1   workclass       45175 non-null  category\n",
      " 2   fnlwgt          45175 non-null  int64   \n",
      " 3   education       45175 non-null  object  \n",
      " 4   marital-status  45175 non-null  object  \n",
      " 5   occupation      45175 non-null  object  \n",
      " 6   relationship    45175 non-null  object  \n",
      " 7   race            45175 non-null  object  \n",
      " 8   gender          45175 non-null  object  \n",
      " 9   hours-per-week  45175 non-null  int64   \n",
      " 10  native-country  45175 non-null  object  \n",
      " 11  income          45175 non-null  int64   \n",
      "dtypes: category(1), int64(4), object(7)\n",
      "memory usage: 4.2+ MB\n"
     ]
    }
   ],
   "source": [
    "df5.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f8bd18a7",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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