1.Phase_other_train_and_personal_train.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import scipy.stats as stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# other_train\n",
    "df = pd.read_csv('./other_train.csv', sep=',', index_col=0) # First column is Unnamed\n",
    "for column_name in df:\n",
    "    if df[column_name].dtypes == 'object':\n",
    "        df[column_name] = df[column_name].str.strip()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 3983 entries, 0 to 3982\n",
      "Data columns (total 22 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   name              3983 non-null   object \n",
      " 1   address           3983 non-null   object \n",
      " 2   race              3971 non-null   object \n",
      " 3   marital-status    3709 non-null   object \n",
      " 4   occupation        3968 non-null   object \n",
      " 5   pregnant          3967 non-null   object \n",
      " 6   education-num     3971 non-null   float64\n",
      " 7   relationship      3972 non-null   object \n",
      " 8   skewness_glucose  3968 non-null   float64\n",
      " 9   mean_glucose      3974 non-null   float64\n",
      " 10  capital-gain      3964 non-null   float64\n",
      " 11  kurtosis_glucose  3970 non-null   float64\n",
      " 12  education         3969 non-null   object \n",
      " 13  fnlwgt            3965 non-null   float64\n",
      " 14  class             3972 non-null   float64\n",
      " 15  std_glucose       3966 non-null   float64\n",
      " 16  income            3971 non-null   object \n",
      " 17  medical_info      3967 non-null   object \n",
      " 18  native-country    3967 non-null   object \n",
      " 19  hours-per-week    3971 non-null   float64\n",
      " 20  capital-loss      3971 non-null   float64\n",
      " 21  workclass         3968 non-null   object \n",
      "dtypes: float64(10), object(12)\n",
      "memory usage: 715.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()\n",
    "# There are 22 columns. 10 atributes are float type and 12 are object.\n",
    "# All columns expect name and adress have null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>education-num</th>\n",
       "      <th>skewness_glucose</th>\n",
       "      <th>mean_glucose</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>kurtosis_glucose</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>class</th>\n",
       "      <th>std_glucose</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>capital-loss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3971.000000</td>\n",
       "      <td>3968.000000</td>\n",
       "      <td>3974.000000</td>\n",
       "      <td>3964.000000</td>\n",
       "      <td>3970.000000</td>\n",
       "      <td>3.965000e+03</td>\n",
       "      <td>3972.000000</td>\n",
       "      <td>3966.000000</td>\n",
       "      <td>3971.000000</td>\n",
       "      <td>3971.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>10.065223</td>\n",
       "      <td>4.233223</td>\n",
       "      <td>69.173141</td>\n",
       "      <td>1228.128406</td>\n",
       "      <td>0.952248</td>\n",
       "      <td>1.911784e+05</td>\n",
       "      <td>0.256294</td>\n",
       "      <td>45.172793</td>\n",
       "      <td>40.505162</td>\n",
       "      <td>73.254092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.671998</td>\n",
       "      <td>9.715162</td>\n",
       "      <td>3348.917032</td>\n",
       "      <td>8623.958204</td>\n",
       "      <td>1.614087</td>\n",
       "      <td>1.072410e+05</td>\n",
       "      <td>0.436641</td>\n",
       "      <td>7.621144</td>\n",
       "      <td>12.279149</td>\n",
       "      <td>369.551911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.755332</td>\n",
       "      <td>-18973.437500</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.641515</td>\n",
       "      <td>1.882700e+04</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>25.695250</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>-0.120081</td>\n",
       "      <td>83.955078</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.070311</td>\n",
       "      <td>1.179830e+05</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>40.151488</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.376026</td>\n",
       "      <td>110.613281</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.311092</td>\n",
       "      <td>1.795090e+05</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>45.878359</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>13.000000</td>\n",
       "      <td>2.090854</td>\n",
       "      <td>125.382812</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.776492</td>\n",
       "      <td>2.415230e+05</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>50.350939</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>16.000000</td>\n",
       "      <td>68.101622</td>\n",
       "      <td>15887.500000</td>\n",
       "      <td>99999.000000</td>\n",
       "      <td>8.069522</td>\n",
       "      <td>1.366120e+06</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>91.206475</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>2824.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       education-num  skewness_glucose  mean_glucose  capital-gain  \\\n",
       "count    3971.000000       3968.000000   3974.000000   3964.000000   \n",
       "mean       10.065223          4.233223     69.173141   1228.128406   \n",
       "std         2.671998          9.715162   3348.917032   8623.958204   \n",
       "min         1.000000         -1.755332 -18973.437500      0.000000   \n",
       "25%         9.000000         -0.120081     83.955078      0.000000   \n",
       "50%        10.000000          0.376026    110.613281      0.000000   \n",
       "75%        13.000000          2.090854    125.382812      0.000000   \n",
       "max        16.000000         68.101622  15887.500000  99999.000000   \n",
       "\n",
       "       kurtosis_glucose        fnlwgt        class  std_glucose  \\\n",
       "count       3970.000000  3.965000e+03  3972.000000  3966.000000   \n",
       "mean           0.952248  1.911784e+05     0.256294    45.172793   \n",
       "std            1.614087  1.072410e+05     0.436641     7.621144   \n",
       "min           -1.641515  1.882700e+04     0.000000    25.695250   \n",
       "25%            0.070311  1.179830e+05     0.000000    40.151488   \n",
       "50%            0.311092  1.795090e+05     0.000000    45.878359   \n",
       "75%            0.776492  2.415230e+05     1.000000    50.350939   \n",
       "max            8.069522  1.366120e+06     1.000000    91.206475   \n",
       "\n",
       "       hours-per-week  capital-loss  \n",
       "count     3971.000000   3971.000000  \n",
       "mean        40.505162     73.254092  \n",
       "std         12.279149    369.551911  \n",
       "min          1.000000      0.000000  \n",
       "25%         40.000000      0.000000  \n",
       "50%         40.000000      0.000000  \n",
       "75%         45.000000      0.000000  \n",
       "max         99.000000   2824.000000  "
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe() # Fundamental descriptive stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>address</th>\n",
       "      <th>race</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>pregnant</th>\n",
       "      <th>education-num</th>\n",
       "      <th>relationship</th>\n",
       "      <th>skewness_glucose</th>\n",
       "      <th>mean_glucose</th>\n",
       "      <th>...</th>\n",
       "      <th>education</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>class</th>\n",
       "      <th>std_glucose</th>\n",
       "      <th>income</th>\n",
       "      <th>medical_info</th>\n",
       "      <th>native-country</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>workclass</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>David Hayes</td>\n",
       "      <td>235 Jennifer Throughway Suite 569\\r\\nNew Antho...</td>\n",
       "      <td>Black</td>\n",
       "      <td>??</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>f</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>-0.197692</td>\n",
       "      <td>141.359375</td>\n",
       "      <td>...</td>\n",
       "      <td>9th</td>\n",
       "      <td>175431.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>49.842053</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>{'mean_oxygen':'4.04264214','std_oxygen':'19.4...</td>\n",
       "      <td>United-States</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Private</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Stanley Benny</td>\n",
       "      <td>08954 Johnson Course\\r\\nNew Lauraville, NV 22019</td>\n",
       "      <td>White</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>f</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Husband</td>\n",
       "      <td>16.019670</td>\n",
       "      <td>53.968750</td>\n",
       "      <td>...</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>66473.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>31.900220</td>\n",
       "      <td>&gt;50K</td>\n",
       "      <td>{'mean_oxygen':'9.294314381','std_oxygen':'35....</td>\n",
       "      <td>United-States</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Private</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Domingo Labriola</td>\n",
       "      <td>36319 Wiley Drive Suite 094\\r\\nSouth Michael, ...</td>\n",
       "      <td>Asian-Pac-Islander</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Farming-fishing</td>\n",
       "      <td>f</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Husband</td>\n",
       "      <td>21.803837</td>\n",
       "      <td>2257.812500</td>\n",
       "      <td>...</td>\n",
       "      <td>5th-6th</td>\n",
       "      <td>93206.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>35.793158</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>{'mean_oxygen':'138.7876254','std_oxygen':'79....</td>\n",
       "      <td>Philippines</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Private</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ronnie Moore</td>\n",
       "      <td>0755 Soto Oval Apt. 443\\r\\nWest Markside, TX 3...</td>\n",
       "      <td>White</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Other-service</td>\n",
       "      <td>FALSE</td>\n",
       "      <td>10.0</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>-0.112191</td>\n",
       "      <td>110.046875</td>\n",
       "      <td>...</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>137604.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>53.876575</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>{'mean_oxygen':'3.100334448','std_oxygen':'17....</td>\n",
       "      <td>United-States</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Private</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Victor Mcjunkin</td>\n",
       "      <td>95738 Keller Overpass\\r\\nKennedyland, MO 71071</td>\n",
       "      <td>White</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>f</td>\n",
       "      <td>10.0</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>-0.309642</td>\n",
       "      <td>137.960938</td>\n",
       "      <td>...</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>174592.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>51.885451</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>{'mean_oxygen':'4.203177258','std_oxygen':'23....</td>\n",
       "      <td>United-States</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Private</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               name                                            address  \\\n",
       "0       David Hayes  235 Jennifer Throughway Suite 569\\r\\nNew Antho...   \n",
       "1     Stanley Benny   08954 Johnson Course\\r\\nNew Lauraville, NV 22019   \n",
       "2  Domingo Labriola  36319 Wiley Drive Suite 094\\r\\nSouth Michael, ...   \n",
       "3      Ronnie Moore  0755 Soto Oval Apt. 443\\r\\nWest Markside, TX 3...   \n",
       "4   Victor Mcjunkin     95738 Keller Overpass\\r\\nKennedyland, MO 71071   \n",
       "\n",
       "                 race      marital-status       occupation pregnant  \\\n",
       "0               Black                  ??   Prof-specialty        f   \n",
       "1               White  Married-civ-spouse  Exec-managerial        f   \n",
       "2  Asian-Pac-Islander  Married-civ-spouse  Farming-fishing        f   \n",
       "3               White       Never-married    Other-service    FALSE   \n",
       "4               White                 NaN     Adm-clerical        f   \n",
       "\n",
       "   education-num   relationship  skewness_glucose  mean_glucose  ...  \\\n",
       "0            5.0  Not-in-family         -0.197692    141.359375  ...   \n",
       "1           13.0        Husband         16.019670     53.968750  ...   \n",
       "2            3.0        Husband         21.803837   2257.812500  ...   \n",
       "3           10.0  Not-in-family         -0.112191    110.046875  ...   \n",
       "4           10.0  Not-in-family         -0.309642    137.960938  ...   \n",
       "\n",
       "      education    fnlwgt class  std_glucose  income  \\\n",
       "0           9th  175431.0   0.0    49.842053   <=50K   \n",
       "1     Bachelors   66473.0   1.0    31.900220    >50K   \n",
       "2       5th-6th   93206.0   1.0    35.793158   <=50K   \n",
       "3  Some-college  137604.0   0.0    53.876575   <=50K   \n",
       "4  Some-college  174592.0   0.0    51.885451   <=50K   \n",
       "\n",
       "                                        medical_info native-country  \\\n",
       "0  {'mean_oxygen':'4.04264214','std_oxygen':'19.4...  United-States   \n",
       "1  {'mean_oxygen':'9.294314381','std_oxygen':'35....  United-States   \n",
       "2  {'mean_oxygen':'138.7876254','std_oxygen':'79....    Philippines   \n",
       "3  {'mean_oxygen':'3.100334448','std_oxygen':'17....  United-States   \n",
       "4  {'mean_oxygen':'4.203177258','std_oxygen':'23....  United-States   \n",
       "\n",
       "  hours-per-week capital-loss  workclass  \n",
       "0           40.0          0.0    Private  \n",
       "1           40.0          0.0    Private  \n",
       "2           40.0          0.0    Private  \n",
       "3           40.0          0.0    Private  \n",
       "4           40.0          0.0    Private  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()# medical_info complex object. Can be separated into multiple columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "337"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape[0] - df.dropna().shape[0] # there are 337 nan values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       235 Jennifer Throughway Suite 569\\r\\nNew Antho...\n",
       "1        08954 Johnson Course\\r\\nNew Lauraville, NV 22019\n",
       "2       36319 Wiley Drive Suite 094\\r\\nSouth Michael, ...\n",
       "3       0755 Soto Oval Apt. 443\\r\\nWest Markside, TX 3...\n",
       "4          95738 Keller Overpass\\r\\nKennedyland, MO 71071\n",
       "                              ...                        \n",
       "3978    7401 Donaldson Lake Apt. 338\\r\\nPort Wayneview...\n",
       "3979    7060 Tammy Greens Apt. 475\\r\\nEast Michael, KS...\n",
       "3980           75021 Bishop Pass\\r\\nLake Rachel, ID 91709\n",
       "3981    81727 Rubio Rapids Apt. 057\\r\\nLawsonburgh, NM...\n",
       "3982          953 Caldwell Mount\\r\\nPort Willie, MS 14959\n",
       "Name: address, Length: 3983, dtype: object"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['address'] # We can separate address into three different columns(street, city, zip_code)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Black', 'White', 'Asian-Pac-Islander', 'Other', nan,\n",
       "       'Amer-Indian-Eskimo'], dtype=object)"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['race'].unique() # Can we change Other to nan or vice-versa?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Married-civ-spouse       1680\n",
       "Never-married            1180\n",
       "Divorced                  439\n",
       "??                        139\n",
       "Widowed                   111\n",
       "Separated                 109\n",
       "Married-spouse-absent      49\n",
       "Married-AF-spouse           2\n",
       "Name: marital-status, dtype: int64"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['marital-status'].value_counts() # We can change '??' values to nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Married-civ-spouse       1680\n",
       "Never-married            1180\n",
       "Divorced                  439\n",
       "Widowed                   111\n",
       "Separated                 109\n",
       "Married-spouse-absent      49\n",
       "Married-AF-spouse           2\n",
       "Name: marital-status, dtype: int64"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['marital-status'] = df['marital-status'].replace('??', np.nan)\n",
    "df['marital-status'].value_counts() # We have succesfully replaced '??' values with NaN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Craft-repair         512\n",
       "Prof-specialty       499\n",
       "Exec-managerial      492\n",
       "Adm-clerical         472\n",
       "Other-service        424\n",
       "Sales                423\n",
       "Machine-op-inspct    228\n",
       "?                    224\n",
       "Transport-moving     192\n",
       "Handlers-cleaners    177\n",
       "Farming-fishing      119\n",
       "Tech-support         115\n",
       "Protective-serv       72\n",
       "Priv-house-serv       19\n",
       "Name: occupation, dtype: int64"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['occupation'].value_counts() # Again we can replace '?' values with NaN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Craft-repair         512\n",
       "Prof-specialty       499\n",
       "Exec-managerial      492\n",
       "Adm-clerical         472\n",
       "Other-service        424\n",
       "Sales                423\n",
       "Machine-op-inspct    228\n",
       "Transport-moving     192\n",
       "Handlers-cleaners    177\n",
       "Farming-fishing      119\n",
       "Tech-support         115\n",
       "Protective-serv       72\n",
       "Priv-house-serv       19\n",
       "Name: occupation, dtype: int64"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['occupation'] = df['occupation'].replace('?', np.nan)\n",
    "df['occupation'].value_counts() # We have succesfully replaced '?' values with NaN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "f        3503\n",
       "F         194\n",
       "FALSE     192\n",
       "t          69\n",
       "TRUE        5\n",
       "T           4\n",
       "Name: pregnant, dtype: int64"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Replace 'f', 'F' and \"FALSE\" with one stable value \"False\", apply same method for \"True\"\n",
    "df['pregnant'].value_counts() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    3889\n",
       "True       78\n",
       "Name: pregnant, dtype: int64"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['pregnant'] = df['pregnant'].replace(['f', 'F', 'FALSE'], 'False')\n",
    "df['pregnant'] = df['pregnant'].replace(['t', 'T', 'TRUE'], 'True')\n",
    "df['pregnant'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9.0     1249\n",
       "10.0     870\n",
       "13.0     663\n",
       "14.0     220\n",
       "11.0     188\n",
       "7.0      138\n",
       "6.0      109\n",
       "12.0     103\n",
       "4.0       97\n",
       "15.0      82\n",
       "5.0       74\n",
       "16.0      54\n",
       "3.0       52\n",
       "8.0       44\n",
       "2.0       17\n",
       "1.0       11\n",
       "Name: education-num, dtype: int64"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['education-num'].value_counts() # This column is OK I guess"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Not-in-family', 'Husband', 'Own-child', 'Not_in_family',\n",
       "       'Unmarried', 'Own_child', 'Wife', 'Other-relative', nan,\n",
       "       'Other_relative'], dtype=object)"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['relationship'].value_counts() # There are multiple same values, but they are written differently, so we can fix it\n",
    "df['relationship'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Husband           1630\n",
       "Not-in-family      998\n",
       "Own-child          588\n",
       "Unmarried          427\n",
       "Wife               205\n",
       "Other-relative     124\n",
       "Name: relationship, dtype: int64"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['relationship'] = df['relationship'].replace('Not_in_family', 'Not-in-family')\n",
    "df['relationship'] = df['relationship'].replace('Own_child', 'Own-child')\n",
    "df['relationship'] = df['relationship'].replace('Other_relative', 'Other-relative')\n",
    "df['relationship'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.458587     2\n",
       " 0.396490     2\n",
       " 0.165603     2\n",
       " 3.673929     2\n",
       " 36.104846    2\n",
       "             ..\n",
       " 0.228760     1\n",
       "-0.119966     1\n",
       "-1.469010     1\n",
       " 24.270634    1\n",
       " 39.078306    1\n",
       "Name: skewness_glucose, Length: 3932, dtype: int64"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['skewness_glucose'].value_counts() # Maybe we can round this numbers? # 24.270634 and 39.078306 ??"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 123.031250     4\n",
       " 124.546875     4\n",
       " 108.968750     4\n",
       " 128.218750     4\n",
       " 122.968750     3\n",
       "               ..\n",
       " 88.179688      1\n",
       "-8657.812500    1\n",
       " 84.210938      1\n",
       " 68.234375      1\n",
       " 124.000000     1\n",
       "Name: mean_glucose, Length: 3449, dtype: int64"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['mean_glucose'].value_counts() # Maybe we can round this numbers -8657.812500 ??"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "HS-grad         1246\n",
       "Some-college     869\n",
       "Bachelors        662\n",
       "Masters          220\n",
       "Assoc-voc        188\n",
       "11th             137\n",
       "10th             109\n",
       "Assoc-acdm       103\n",
       "7th-8th           99\n",
       "Prof-school       82\n",
       "9th               75\n",
       "Doctorate         54\n",
       "5th-6th           52\n",
       "12th              45\n",
       "1st-4th           17\n",
       "Preschool         11\n",
       "Name: education, dtype: int64"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['education'].value_counts() # OK?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "113364.0    4\n",
       "144064.0    4\n",
       "34180.0     4\n",
       "161141.0    4\n",
       "123983.0    4\n",
       "           ..\n",
       "179594.0    1\n",
       "33619.0     1\n",
       "285335.0    1\n",
       "222539.0    1\n",
       "28160.0     1\n",
       "Name: fnlwgt, Length: 3671, dtype: int64"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['fnlwgt'].value_counts() # change name of the column to final_weight, how did we get value of this atribute ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    2954\n",
       "1.0    1018\n",
       "Name: class, dtype: int64"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['class'].value_counts() # This can be integer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "56.678746    2\n",
       "46.336825    2\n",
       "30.322605    2\n",
       "42.872164    2\n",
       "49.762971    2\n",
       "            ..\n",
       "52.244316    1\n",
       "58.893506    1\n",
       "52.811404    1\n",
       "51.129297    1\n",
       "36.876648    1\n",
       "Name: std_glucose, Length: 3929, dtype: int64"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['std_glucose'].value_counts() # Mayber round"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<=50K    2982\n",
       ">50K      989\n",
       "Name: income, dtype: int64"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['income'].value_counts() # OK"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'mean_oxygen':'3.120401338','std_oxygen':'23.53089235','kurtosis_oxygen':'8.331097683','skewness_oxygen':'71.68512789'}           2\n",
       "{'mean_oxygen':'3.091137124','std_oxygen':'15.97387777','kurtosis_oxygen':'7.50088876','skewness_oxygen':'76.03836498'}            2\n",
       "{'mean_oxygen':'79.75585284','std_oxygen':'71.3531698','kurtosis_oxygen':'0.850185387','skewness_oxygen':'-0.437195641'}           2\n",
       "{'mean_oxygen':'1.785117057','std_oxygen':'17.9354047','kurtosis_oxygen':'10.76379769','skewness_oxygen':'121.1406569'}            2\n",
       "{'mean_oxygen':'36.98160535','std_oxygen':'68.9542381','kurtosis_oxygen':'1.508511427','skewness_oxygen':'0.623755762'}            2\n",
       "                                                                                                                                  ..\n",
       "{'mean_oxygen':'36.16304348','std_oxygen':'67.88042313','kurtosis_oxygen':'1.514739181','skewness_oxygen':'0.631103897'}           1\n",
       "{'mean_oxygen':'8.35367893','std_oxygen':'35.96427754','kurtosis_oxygen':'4.4263579','skewness_oxygen':'19.06763528'}              1\n",
       "{'mean_oxygen':'31.38628763','std_oxygen':'51.3579928','kurtosis_oxygen':'2.2790806580000003','skewness_oxygen':'5.406194859'}     1\n",
       "{'mean_oxygen':'2.4565217390000003','std_oxygen':'16.61890216','kurtosis_oxygen':'9.019758569','skewness_oxygen':'95.72392251'}    1\n",
       "{'mean_oxygen':'25.48578595','std_oxygen':'64.60579433','kurtosis_oxygen':'2.162228241','skewness_oxygen':'2.745630212'}           1\n",
       "Name: medical_info, Length: 3932, dtype: int64"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['medical_info'].value_counts() # separate this into different columns?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "United-States         3554\n",
       "Mexico                  86\n",
       "?                       58\n",
       "Philippines             25\n",
       "Germany                 17\n",
       "India                   15\n",
       "Canada                  14\n",
       "Guatemala               13\n",
       "Japan                   11\n",
       "Puerto-Rico             11\n",
       "Jamaica                 11\n",
       "Cuba                    11\n",
       "Dominican-Republic      10\n",
       "Italy                   10\n",
       "Taiwan                  10\n",
       "China                   10\n",
       "England                 10\n",
       "South                    9\n",
       "El-Salvador              9\n",
       "Poland                   8\n",
       "Vietnam                  7\n",
       "Nicaragua                6\n",
       "Scotland                 5\n",
       "Haiti                    5\n",
       "Trinadad&Tobago          5\n",
       "Laos                     5\n",
       "Columbia                 4\n",
       "Peru                     4\n",
       "Ecuador                  4\n",
       "Iran                     3\n",
       "Portugal                 3\n",
       "Hong                     3\n",
       "France                   2\n",
       "Greece                   2\n",
       "Cambodia                 2\n",
       "Honduras                 2\n",
       "Hungary                  2\n",
       "Thailand                 1\n",
       "Name: native-country, dtype: int64"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['native-country'].value_counts() # replace '?' with NaN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "United-States         3554\n",
       "Mexico                  86\n",
       "Philippines             25\n",
       "Germany                 17\n",
       "India                   15\n",
       "Canada                  14\n",
       "Guatemala               13\n",
       "Japan                   11\n",
       "Cuba                    11\n",
       "Puerto-Rico             11\n",
       "Jamaica                 11\n",
       "China                   10\n",
       "Dominican-Republic      10\n",
       "England                 10\n",
       "Italy                   10\n",
       "Taiwan                  10\n",
       "South                    9\n",
       "El-Salvador              9\n",
       "Poland                   8\n",
       "Vietnam                  7\n",
       "Nicaragua                6\n",
       "Haiti                    5\n",
       "Scotland                 5\n",
       "Laos                     5\n",
       "Trinadad&Tobago          5\n",
       "Peru                     4\n",
       "Ecuador                  4\n",
       "Columbia                 4\n",
       "Hong                     3\n",
       "Iran                     3\n",
       "Portugal                 3\n",
       "Hungary                  2\n",
       "Cambodia                 2\n",
       "France                   2\n",
       "Honduras                 2\n",
       "Greece                   2\n",
       "Thailand                 1\n",
       "Name: native-country, dtype: int64"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['native-country'] = df['native-country'].replace('?', np.nan)\n",
    "df['native-country'].value_counts() # # We have succesfully replaced '?' values with NaN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "40.0    1860\n",
       "50.0     359\n",
       "45.0     208\n",
       "60.0     173\n",
       "35.0     160\n",
       "        ... \n",
       "29.0       1\n",
       "81.0       1\n",
       "67.0       1\n",
       "68.0       1\n",
       "77.0       1\n",
       "Name: hours-per-week, Length: 76, dtype: int64"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['hours-per-week'].value_counts() # OK"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0       3815\n",
       "1887.0      23\n",
       "1977.0      22\n",
       "1902.0      15\n",
       "2415.0      10\n",
       "1590.0       7\n",
       "1485.0       7\n",
       "1876.0       5\n",
       "1848.0       5\n",
       "1741.0       3\n",
       "2258.0       3\n",
       "2042.0       3\n",
       "1740.0       3\n",
       "1980.0       3\n",
       "1092.0       3\n",
       "1672.0       3\n",
       "1628.0       2\n",
       "1721.0       2\n",
       "1504.0       2\n",
       "1579.0       2\n",
       "1594.0       2\n",
       "2339.0       2\n",
       "323.0        2\n",
       "2377.0       2\n",
       "1719.0       2\n",
       "2174.0       2\n",
       "2444.0       2\n",
       "2179.0       1\n",
       "1408.0       1\n",
       "1258.0       1\n",
       "1380.0       1\n",
       "625.0        1\n",
       "2057.0       1\n",
       "2051.0       1\n",
       "880.0        1\n",
       "2002.0       1\n",
       "1573.0       1\n",
       "1762.0       1\n",
       "1602.0       1\n",
       "1669.0       1\n",
       "2603.0       1\n",
       "2352.0       1\n",
       "1617.0       1\n",
       "2559.0       1\n",
       "2824.0       1\n",
       "2392.0       1\n",
       "Name: capital-loss, dtype: int64"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['capital-loss'].value_counts() # OK"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Private             2770\n",
       "Self-emp-not-inc     293\n",
       "Local-gov            260\n",
       "?                    227\n",
       "State-gov            150\n",
       "Self-emp-inc         142\n",
       "Federal-gov          124\n",
       "Without-pay            2\n",
       "Name: workclass, dtype: int64"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['workclass'].value_counts() # Again we can replace '?' with NaN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Private             2770\n",
       "Self-emp-not-inc     293\n",
       "Local-gov            260\n",
       "State-gov            150\n",
       "Self-emp-inc         142\n",
       "Federal-gov          124\n",
       "Without-pay            2\n",
       "Name: workclass, dtype: int64"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['workclass'] = df['workclass'].replace('?', np.nan)\n",
    "df['workclass'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22c22a40190>"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['education-num'], bins=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22c22ac1d00>"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['fnlwgt'], bins=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22c22b6f940>"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['hours-per-week'], bins=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\vanco\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:369: UserWarning: Default bandwidth for data is 0; skipping density estimation.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22c22be7ee0>"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['capital-loss'], bins=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22c22cb3310>"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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mASun+vsjImLydqR75xDgY5JGf84/2f6UpC8DyyWdDTwInA5ge42k5cBa4DHgHNvbdqj6iIiYlCmHvu2vA8/r0v4IcNI4+ywDlk31d0ZExI7JFbkRERXZ0dE7sRuZe/4n2y4BgAcufmnbJURUK0f6EREVSehHRFQkoR8RUZGEfkRERRL6EREVSehHRFQkoR8RUZGEfkRERRL6EREVSehHRFQkoR8RUZGEfkRERRL6EREVSehHRFQkUytHlTLNdNQqR/oRERVJ6EdEVCShHxFRkYR+RERFBh76khZLulfSOknnD/r3R0TUbKCjdyRNA/4e+E1gA/BlSStsrx1kHRGxXUYy1WXQQzYXAetsfx1A0rXAqUBCPyJaV8M/gIPu3pkNrO9Y31DaIiJiAGR7cL9MOh042fYflvVXA4tsv37MdkuBpWX154F7B1ZkdwcB/95yDcMin8V2+Sy2y2ex3bB8Fs+yPXNs46C7dzYAh3aszwE2jt3I9uXA5YMqqhdJI7YXtl3HMMhnsV0+i+3yWWw37J/FoLt3vgzMk3S4pL2BJcCKAdcQEVGtgR7p235M0uuATwPTgCttrxlkDRERNRv4hGu2rweuH/Tv3UFD09U0BPJZbJfPYrt8FtsN9Wcx0BO5ERHRrkzDEBFRkYR+RERFEvoRERVJ6Ef0QdIekr7Wdh3DonweL2i7jpi8hP441Ph9Sf+zrB8maVHbdbVB0pvHrE+T9MG26mmD7ceBr0g6rO1ahkH5PN7Rdh3DQtIpkt5eHi9vu56JJPTHdynwK8AZZX0LzQyhNTpM0gUAkvYBPgbc125JrZgFrJF0s6QVo4+2i2rRjZJeKUltF9ImSW8FzqOZOHItcG5pG0oZsjkOSXfaPkbSXbaPLm1fsf28tmsbtPJH/UFgNXACcIPtd7Vb1eBJ+vVu7bZvGXQtw0DSFmBfYBvwQ0CAbc9otbABk/RVYEH59jM6hfxdtp/bbmXdDfzirN3IT8p/PANImgk83m5JgyXpmI7VdwP/APwbcIukY2zf2U5l7bB9i6RnAfNsf0bSz9BcWV4l2/u3XcMQOQD4dll+epuF9JLQH98lNN0YB0taBvwO8D/aLWngxvbZfgeYX9oNnDjwilok6Y9oZn99BnAEzbTg7wVOarOutpRvgK8CDrf9FkmHArNsr2y5tEF7K3CXpM/RfNt5IXBBuyWNL907E5D0CzR/0AJutn13yyVFiyStorkR0O0dXX6rbf9Su5W1Q9JlNN9+T7T9i5IOBG60/cstlzZwkmYBv0yTFbfb/lbLJY0rJ3LHIekDtu+x/fe2/8723ZI+0HZdbZD015IO6Fg/UNJftVlTS7ba/vHoiqQ9Kd1/lXq+7XOAHwHY/g6wd7sltWZ03vppwAsk/XabxUwkoT++IztXSv/+sS3V0rYX2350dKX8cb+kxXracoukNwLTJf0m8GHg4y3X1Kbqz3sBSLoSuBJ4JfDy8nhZq0VNIH36Y5ShiaN/2N+j+boG8GOGfPa8XWiapH1sbwWQNB3Yp+Wa2nA+cDbNKKbX0MwW+4+tVtSubue9/nu7JbXiONvz2y6iX+nTH4ekt9oe2pMxgyTpL4FTgPfTHNX9AbDC9ttaLSxal/NeIOkK4B2217ZdSz8S+mNI+gXb94wZrvhTtQ1THCXpxWz/477R9qdbLmlgJK1mgr77YR2PPQile+cQOnoNbD/YXkWDJ+mFNN183wK2sv16haH8/yKhP4aky20vLcOvxrLtqoYpBpSx+QDnlOfRE/qvAn5g+81P3uupT9LrgQuBh2gu0BrqsNtVJK0D/oym2++n5zRsf6O1oiaQ0I+eJB0HvAf4RZrRGdOA71d45eW/2T6+V1stStg93/YjbdfSJkmf3Z0OBnMidwJlFsG5PPGr6zWtFdSev6O5if2HgYXAmcCzW62oHftK+lXbX4Cf/v+xb8s1tWk98N22ixgC90j6J5ounq2jjbY/2l5J40voj6OMyT8CWEXz1RWaft0aQx/b6yRNs70NeL+kL7ZdUwvOBq6UNHqZ/aM0J7WrIunPyuLXgc9L+iRPDLt3tlJYe6bTvP/f6mgzkNDfzSwE5jv9XwA/kLQ3sErS24BNVHiEa/sO4HmSZtB0jdZ6lDs6586D5bE32y/Kqu7vxfZ/abuGyUif/jgkfRg41/amtmtpWzmR+TCwF/CnNBNKXWp7XauFDViZVvqVPLnLr9YTuafb/nCvtqcqSX9p+22S3kOXf+xsn9tCWT3lSH8MSR+n+Q+4P7BW0kqe+NX1lLZqa0vHKIQfAm9qs5aWXUfTh30HHf9PVOwCmvM8vdqeqkavSRhptYpJSug/2WdpPpe7gJ+0XEurMj79SebYXtx2EW0r12y8BJgt6ZKOl2YAj7VT1eDZHp2C4xbbD3S+JmloJ51L6D/ZbOAFNFMxfAX4Is0c8rfZ/vZEOz4FDe38IS35oqRfsr267UJatpHm6PZ04P/SHBhsoxmv/6ct1tWWj0g6xfY34ac32/k7YChnX02f/jjKicuFNP8A/Ep5PLo7zbGxK0g6CHikxhPcktbSDFW9n93gystdRdJewDLgD4EHaD6HQ2mm6Xij7aq+IZej+ktpJlo7Bvhr4OW217da2DhypD++6TRfV59eHhtprrirRrko62KaOwK9heZK1IOAPSSdaftTbdbXghe3XcCQeBuwH/As21sAyoimt5fHeS3WNnC2vyzpXOBGmmmmf9P25pbLGleO9MeQdDnNtMpbgNuBLwFfKtMJV0XSCE0319NpZhh9se0vlUm2PjR6I5HaSDoYeNroeoVzzdwHPGfst70yD889tue1U9lgdQz6GDWfZjjzd2B4B33kSP/JDqOZNvg+4JvABpqLcGq0p+0bASS92faXAMqEdO1W1gJJp9DcKvKZNENYn0UzguPIifZ7CnK37j3b2yTVdBT59rYLmIqE/hi2F5d7fx5J05//58BRkr5NczL3wlYLHKzOG2L8cMxrNf1xj3oLcBzwGdtHSzoBOKPlmtqwtnTvPeHqdEm/D9zTUk0DZ/sWAEn7Aj+0/bik5wC/ANzQanETSPfOBCTNAY6nCf+XAf/J9gET7/XUIWkb8H2aE3XTgR+MvgQ8zfZebdXWBkkjthdK+gpwdPkjX2l7Udu1DZKk2TRTDPyQ5poF09wfdjrwitFRLLWQdAfwa8CBNN3BIzSzr76q1cLGkSP9McoJmRfQhP1PKMM1aW6HVtWJXNvT2q5hyDwqaT/gVuCDkh6monHpo0qoP1/SiTTfiAXcYPvmditrjWz/QNLZwHvKVbqr2i5qPDnSH0PSOylj8zMFQ3QqX+N/RBNyr6I5wf3B2qcWrp2ku4A/Ad4FnG17jaTVtjNOPyLiqabcOesvaA4U/0bSzwFvGNa5dxL6ET1I2kLTby2eeAJ79OKsqm4mE71J2tP2UHb97dF2ARHDzvb+tmd0PM/oXG+7vmiHpC90LH9gzMsrB1xO3xL6EX2SdESZXhlJL5J0rqRqRnPFk3TeU+KoMa8N7YUsCf2I/n0E2Cbp2cAVwOHAP7VbUrTI4yx3Wx8aGbIZ0b/HbT8m6RXA39p+Txm5EXU6oPy/sEdZ/u3SLpqRXUMpoR/Rv59IOgM4i2ZGRWjuJhZ1ugU4pWP55R2v3Tr4cvqT0TsRfZI0H/hjmuk4PiTpcOB3bV/ccmkRfUvoR0TsJJI+YXuobz6U7p2IHnLbyJiE2W0X0EtCP6K3oT5yi6Ey9Cf2070TMQWSXmb7E23XETFZCf2IKZB0p+1j2q4j2ifpeOAimpvq7Mn26Tl+rs26xpPunYipGdorLmPgrgD+lObeAttarqWnhH7E1Lym7QJiaHzX9tDeKWusdO9E9EnSOTTz5z9a1g8EzrB9abuVRZskXQxMo7mb2NbRdtt3tlbUBBL6EX2StMr2gjFtd9k+uq2aon2SPtel2bZPHHgxfUj3TkT/9pAklyMlSdOAvVuuKVpm+4S2a5iMhH5E/z4NLJf0XpqLtf4Y+FS7JcUwkPRSmvsFP220zfab26tofOneieiTpD1oTuCeRDN650bgH20P/YiN2HXKQcDPACcA/wj8DrDS9tmtFjaOhH5ExA6Q9FXbz+143g/4qO3faru2btK9E9EnSfOAtwLzeeLX+KG8CCcG5ofl+QeSngk8QnODnaGUO2dF9O/9wGXAYzRf5a8Bxt4bNerziXLbzP8F3Ak8AFzbakUTSPdORJ8k3WH7WEmrbf9SaftX27/Wdm0xHMo9lJ9m+7tt1zKedO9E9O9H5WTufZJeB3wTOLjlmqIlkk60/dmO2yR2vobtj7ZRVy8J/Yj+vYFmlMa5wFtounjOarWiaNOvA5/libdJHGWaK3SHTrp3InqQdDDwRuDZwGrgrba/125VEVOT0I/oQdKnaGZQvJXmhir72/7PrRYVrZP0ZxO9bvudg6plMtK9E9Hbz9r+b2X505KGciKtGLj9y/PPA78MrCjrL6c5QBhKCf2I3lRm1BydQ39a57rtb7dWWbTG9psAJN0IHGN7S1m/CPhwi6VNKKEf0dvTabp3Om+cMnq0byAXZ9XtMODHHes/Bua2U0pvCf2IHmzPbbuGGGofAFZK+hjNQcAraC7cG0o5kRsxBZIusn1R23XEcJB0LPCrZfVW23e1Wc9EEvoRU5Abo0encm+FQ+joPbH9YHsVjS/dOxFTkxujBwCSXg9cCDxEc2N00XTzPLfNusaTI/2IKZC0h+3H264j2idpHfB824+0XUs/cqQf0SdJM4E/ohmZsafUHOzb/oMWy4r2rQeGdoK1sRL6Ef27DvhX4DM0X+MjAL4OfF7SJ4Gto425Ijdi9/cztv9r20XE0HmwPPYuj6GWPv2IPkn6K+CLtq9vu5aIqUroR/RJ0hZgX5qv8D+hjNKwPaPVwqIVkj5OM0qnK9unDLCcvqV7J6JPtvfvvVVU5O1tFzAVOdKPmIQy0do8nnhj9KGdUTEGS9Ixtod6FtaEfkSfJP0hcB4wB1gFHAfcZvvEVguLobE7XKm9R9sFROxGzqOZN/0btk8AjgY2t1tSDJmhv1I7oR/Rvx/Z/hGApH1s30NzA42olKRpkv53R9ObWiumTzmRG9G/DZIOAP4FuEnSd4CNLdcULbK9TdJMSXvb/rHtf2m7pl7Spx8xBZJ+nebmKp+y/eNe28dTl6R/AI6huV3i90fbc0VuxG5K0gzb35P0jI7m1eV5PyC3S6zbxvLYg+33zR1aOdKP6EHSJ2y/TNL9NBfjdJ6ss+3cLjGQtK/t7/fesl0J/YiIHSDpV4ArgP1sHybpecBrbP9Jy6V1ldE7EX2SdLykfcvy70t6p6TD2q4rWve3wMnAIwC2vwK8sNWKJpDQj+jfZcAPypHcXwLfoLkpdlTO9voxTUM79XZCP6J/j7npDz0VeLftd7MbnLiLXW69pBcAlrS3pL8A7m67qPEk9CP6t0XSBcCrgU+Wm2FnBFz8MXAOMBvYACwo60MpJ3Ij+iTpZ4HfA1ba/oKkFwLvt31Ey6VF9C1HKRF9sv0tSZ8Ffq9cen8/zUm8qJikw4HXU+6dPNqe+fQjdlOSngMsAc6gGaHxzzTfkk9otbAYFv9CM2Tz48DjLdfSU7p3InqQ9DjNDdHPtr2utH09F2UFgKTbbT+/7Tr6lSP9iN5eSXOk/zlJnwKuZTeYQjcG5t2SLgRupLmVJgDDejOVHOlH9KlcmHUaTTfPicDVwMds39hqYdEqSW+lGdH1/9jeveNhvblOQj9iCsrka6cDvzusf9wxGJLuAZ67u8y2mtCPiNgBkv4ZeL3th9uupR/p04+I2DGHAPdI+jLb+/Rt+9QWaxpXjvQjInZAuaHOT1eBXwXOsH1kSyVNKNMwRETsANu3AN8FXgpcBZwEvLfNmiaS7p2IiCnYXS/aS/dORMQU7K4X7aV7JyJial4JfIvmor33STqJ3eCivRzpR0TsgN3tor2EfkTETrI7XLSX0I+IqEj69CMiKpLQj4ioSEI/IqIiCf2IiIr8f1QJCvJ0XpaQAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['race'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22c22ce3850>"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['marital-status'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22c22d78040>"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['occupation'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22c20628910>"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['pregnant'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22c1f1e7b50>"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['relationship'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22c20a9e310>"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['education'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22c20b2b850>"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Zmu07gbMpC+5dRo8W3bN9oe1XAY+mDLn7XeDmbqsayoLB7jVJ2wK96W6z/Tbbe1P+hh8JXCjp8x2XNSu2/y+wJ3AS8AfAdfWT1x6jrKPV0L+jrvIJQO1i6MvY1XOBlZIOlvSbwMeAczquadbqEc6pwBrgRcCHgJ07LWpINShfCPwxsD9wWrcVDeWjwAWSjpb0R8D59Kv+CTcDPwJ+Qn8O2HAZI/+j+nUXsD3wCUnvHFUNTY7Tr//AtwEvA14DvAq42vafd1rYLEjaCnglcDAg4DzgQ7Y3dFrYLEk6g3KEf7btO7quZ1iS/pWyMuw5wErgS7Z/1W1Vw5F0KAOvH9vndlzSrEk6Bvg9YCGli/ZfbV/dbVWzI+m1wFGURdY+BHzG9p31b/o62yM54m819LcCjgaeTXnhn2v7g91W1Y7aD7t/vXqp7d50j0g6BDi/L2+y9zWS3gGcYfuKrmsZlqS3AyfZ/v4Utz3e9jUjqaPR0H+d7ffM1DaOJB0EvBV4FGUZDVE+NT66y7pmqy6v/ffAlyi1/x/gT21/osu6ZkvS/YBjgKfVpguBD9TzFGNP0u8Af0vpEhEbXz/bdVrYECTtS3ndAHzZ9re6rGdYknYE7j75b/sHI/35jYb+5bafOKntm7af0FVNsyXpO8AbKCdA7z7atP2TzooagqRvAc+aOLqXtBD4vO19u61sdupIo/uxsR/8SGDDxB4R407SGuC3R3VUubnVLpLlwKdq0wuAE22/r7uqZkfSbwPvppyAvply4HZNPTE9Mr0aIz1fko6gDBPcXdLgks4PppwQ6oOf2T676yLmYatJ3Tk/oV8DCvaf9Ab1hfpG1hc39TXwq5cDB9j+OYCkvwUuBsY+9IG/Ag6kHOQ8QdIzgCNGXURToQ98DVhH2bHmXQPt64Fvd1LR8L4o6e8oRzp3nwi1fXl3JQ3lHEnnUkYdQTkpN06rrs5kg6Q9bP8ngKRHM/CJqwdW1ZPRn+Ger59PbfohY0Xc8997Q23rgztt/0TSVpK2sv3F+qY1Uk2Ffj2B8n3gyV3XMg8TewoP7rhj4Dc7qGVotv9U0gsp8yRE+Wj+6Y7LGsafUt54v0up/1HAH3Zb0lC2A/6bMohhgtnYXTLuTgG+LmniNXM4Zdx7H9wm6UHAl4HTJd1MGbY5Uq326R9I+Tj4eMrEpgXAz/t0Miu6Uyc3PY4S+t/p49DTPpP0RMqsaAEX2f5mxyXNSl264xeUul8KPAQ4fdTn41oN/VWUrRo/TjlifhnwmHEepy/p921/VNKfTHW77XePuqa5kLSee0+E+xmwCnij7e+OvqrZq6NfJvsZsHqch55KepPtd0p6H1NMRLT92g7KGpqkh03RvL5Ho6ceASyl/B98w/aPRl1DU907g2yvkbSgjrc+RdLXuq5pBhMLfPViYbhpvBu4EfgXyhHPMsqaJNcCJwNP76yy2Tma0j34BUr9TwcuAR4r6e22P9JhbdOZOHm7qtMq5u9yYBfgVsq//0OBdbWr5BW2L+uyuOlIejnwF2x87byvvmZOHmkdjR7pXwQ8kzIr7keUk7t/0Jdhg30m6eu2D5jUdontAyV9a9z/DyT9G/By2zfV6zsB76eMKrnI9j5d1jcMSY/o4khzPiR9APj0xCxiSc8GDqHMjn7P5NfWOJF0LfCUie4cSQ8Hvmb7caOso09D5TanIym/+6uBn1OOHF7YaUVzIKkvI3YG/UrS706MYJD0uwO39eEIZLeJwK9uBh5r+6dAL7oYBvRp1NSEJYPLRtg+D3ia7UsY/4Xj1lJGCk5YD9ww6iJa7d55InCW7duBt3VdzDz0ZajaoJcC7wFOqNcvBn6/LmL26s6qmr0vS/p3yvkgKAcLF9WTdLdt+mFjqY+vn59K+jPK+k1QhvzeWlebHfc1kH5IGXn0WcoBzmHApRPn6UZ1Xq7V7p1TKEMcL6K8eM61PfKhU/Ml6a/qcq0xInVZ699h4+iRrwCfdA//kCS9yvYJM99zfEjaAXgLG//9vwy8nXIyfVfbazosb1qS3jLd7bZHcgDaZOjD3WuoHEo5UngqZRGtsZ9KL+lvbf/ZTG19MNVyGH0i6Xm2/73rOoY1MOTRwFd7NLHvHiTtbHtd13XMRZfnU1rt0+/zRh7PmqLt0JFXsXn0sXth0Nu7LmBYkv6Csm7Qwykz00+R1NdPi5/ruoB56Ox8SpN9+nV53GXAMyirPX6IsgPS2KrriL8KeLSkiSUjBDwI+Gpnhc1Pn/9ooZ9vWkcAT7D9C7h7qeLLKevC9E0f//0ndFZ7k6FP2arsDOCVPZpN+S+UTyZ/Axw30L6+jhzpnfvA+YhXdl3AHFxPWdb3F/X6NsB/dlbN/PR5D4zOam+2T7/P+rie+CZm4kLP1nOX9GvAGyknDV8haU/gcX3p25f0GcoGNudT/j+eRTkZfTOM78zcTczEvVsfDnwkfcT2kTO1bWlNHelL+ortp04RQL0JninWE/+opLFfT9x232cSTziFcg5oYtG+tZThm70IfeDT9WvClzqqY1iXUf5mB7tFJq6bslH9uLvHuvl1mOmTRl1EjvR7pvbnP3lgPfEHAhfb/t/dVjacrncPmitJq2wvGdx0pw8ziaM7ko4H3gxsS1nhFMqb1S8pq8weP8p6mhu9U2eBXtl1HfPQ5/XEkfR8SdcB36NsNXg95VxFX/yyTiQzgKQ9GFiXftxJep6kb0r6qaTbJa2XdHvXdQ1D0vaSlkp62sRX1zVNx/bf1E+6f2d7u/r1YNsPH3XgQ2PdOwC2fyXpW5J27cvR5SR9Xk8c4C8Zg92D5uGtwDnALpJOp+wL0Kf19P+RMrlsdU8nlL0ceB2wGLiC8lq6mH7sJ3H2VG9Qti8aZRFNdu9I+gLlZNallLV3ALD9/M6KGoKkJ7FxE5LerCcO9+ge+RZl6OCvJF1qe2nXtc1WXSjrQMq//yW2f9xxSbMm6YvAwbbHfcmCKUlaTfnbvcT2fpL+F/A227/XcWkzqov1TXgAZYnly2yP9A2ruSP9qs/r7UA5wllH/f/r2aeWid2DLqLD3YPmStIFtg9mYI7BQFsfvAk4S9KF3HO7xF7sxwD8wvYvJCFpG9vfkTTSVSrnyvZvD16XtAvwzlHX0VToS3oA8MfAY4DVwEl9W3NH0msoa4/cxMb+fAN9OZF7GPA/wBvYuHvQ2M9sra+dXwN2kLQ9G8+jbAc8srPChrcC+C/Kkeb9O65lLtZKeihlj9/zJd1K2Z+hj9YCI1+Ku6nuHZUNoe+kLNJ0KPB926/rtqrhSFoDHDDqLdY2F0m7A+sGZoRuC+xk+/pOC5uBpNcBr6cE/A/ZGPq3Ax+0/f+6qm0YE91rXdexOUj6DcpBwzm2f9l1PTPRPXct2wrYD7je9u+PtI7GQn+17V+vl7cGLu3bgl+1T/ZZffuEMkFlq8qnTPyRSro/ZdGv/butbHYkvWbc50RMpy678IW6Dn2vSNoK+HafNqoZJOmogat3UQJ/5EuoNNW9w8AmF7bvKqvk9s53gS9J+hz97JPdevCozPYva/D3gu33SdoH2It7zjP4cHdVDeVY4E2SfkkZJ96biYl9H3ln+7T6Wn9sbbq2izpaC/19B8YkC9i2Xu/NCx/4Qf26P/3sk71F0vNtnwkg6TCgT6Nf3kLZF3cvykqJh1KWMehF6N8HZkbvDFwlqXcj7yQ9nbLC6fWUzNlF0lEZshmz1uWa3HNVJzOdDiyqTTcAR9ruxaJfdcjgvsA3be+rskfuhyaPzBhXdROYlwK72/7LOoJkZ9uXdlzarNR+/HuxfeGoaxmWpMuAl9i+tl5/LPAx2yNdiqG1I/37mrMoWz/2Rg33A+uwTdleP9Njxsz/1G6GuyRtR1morA/rvkw4gbKt4G9SJsr9F/BPlLHvY8/2hZIeBexp+/N1AbwFXdc1S/ebCHwA2/+hspnTSCX0+613JyUkPYQy5PRp9fqFwNtt/6zTwmZvVR0y+EHKImD/RZnk1xcH2H6ipG8C2L61T+dUJL2CsuDgw4A9KJ8YPwD0YZ7EKkknAR+p119KeQ2NVEK/3/q4nvjJwJVs3LTmSMrSEr/TWUVDsP2qevEDks4BtrP97ekeM2burKs7TqwdtJDx31B80LGUmaxfB7B9XV28rw+OodT/Wupsesonr5FK6PeQBvY4lfTEnu1xuoftFw5cf5ukKzqrZkiDs28n5hb0bEbueylLK+8oaQXwIqBPm9ncUUd8AXcPve7Ficm6YdO761dnEvo9o7LH6YvZuJ7+KZI+brsv2939j6Sn2v4KgKSDKDN0x9p9ZUau7dPrCcWDKb/D4bav6bisYVwo6c2UkXfPomwh+m8zPGYs1Nf6W4FHMZC9tkd6Tiijd3pG0jXcc4/TbYHLbT++28pmR2XXrw9TZlIC3AocNe5dJFPMyJ2wnh7NyB0kabntE7uuYxh1gtbRwLMpb1rnUkZPjX2QSfoOZfmRyxhYHn3Us+sT+j0j6WzgCNu31esPBT5q+3ndVjacOvIF27dLer3tf+y6pulI2p+yVsqL6gSto4AXUsZcv9U92K5vMkmX921Gep9J+rrtAzqvI6HfL+rpHqfTkfQD27t2Xcd0JF0OPNP2T+ua6GcAr6Gsn/J42y/qtMAZ1BUp75jUdvfuX+Ouzo/YZFh5jHeOq+fgoAxeWEDpmh2cTT/Sc3Lp0++fvu5xOp0+DD1dMHA0/3uUbe4+CXyyJyeiLwaeqHtuxN2LCWXVxCfZY+v3wWGP/33vu4+Vd026PrjgnRnxBjAJ/Z6xfVrXNWwBffi4uUDS1nWhu4MpY8Un9OHv6P61S+opku4eHitpKYDtT23ykWPA9vehnAy1fdDATcdJ+ipjvDy37WcASHq07e8O3iZp5BP7+vBijQGSnkeZSTkxAqAX6wZJWs/U4S7KhtHj7mOUkSM/pow2+jKApMcAfZhY9seUo+KHcu8jfLNxNNi4e+Ck0V9PAR7YcU2z9QnuPYP+40CWYYhp9XKP074v9GV7haQLKAt+nTfwb78VpW9/rNWQ/EpdT79PeypPdjRwcp3Zbcob7h91W9L0VLZ03Bt4yOCnLMpw3wdM/agtWE+PciPo/x6n0Y1JYXMv4969M1kd/aU+LN9RV5I9HHg+cObATeuBM2x/baT1JPT7pQ4d/Eugr3ucRgcknTLNzbY91kfLE+qqpn8NPNL2oZL2Ap7ch08vkp5s++LO60jo94uk8yiLfK1mYM0U233f7D1iRnWeyinAn9elrbemLHP96x2XtkmS3mT7nbrndol3G/Uw6/Tp98/DbD+76yKin/p8pFztYHulpOPh7h3wNsz0oI5tUz+hf4uNu5V1Zqsuf3jMyeclJfRjrk6lLF0wsV7Qf1CWl+iLn0t6OBtXCT2Q8R899RDgPcDfUU46P46yW9y/dTEEO907PVOHPj6QcsTQqz1Oo3uSvmF7/8HZuJKusL1f17XNRp3d+j5gH8oS3QspS2OM9dpNAHXfgiXAU4An16/bbO81yjrSvdMzfR/6GJ3r45Eyqpuh2768bpn4OMoBz7W27+y4vNnaljJM8yH160bKubmRypF+z/R9j9PoVl+PlAcXh5P0yUl7Mow1SSdSxumvp2z+cglwie1bu6gnffr9cwLlY+FL6vWJPU4jNknS/pIeURf3+g3gzZQhv+dRVg8dd4MnP/u0JzHArsA2wI8oy3KvBW7rqpiEfv8cYPtY4BdQ9jgFerPHaXTmnynngKD0Kf855WDhVqAPa+p7E5fHnu1DKCvj/n1teiPwDUnnSRr5UOv06fdP3/c4jW70fZXQfSXdTl2rqV6GngxkqMt2XCnpNso5lJ9RVg5dCrxllLUk9Pun73ucRjd6vUqo7QVd1zBXkl5L+XR1EHAn8FXKUtcn08GJ3LH/z457ug/scRrd6PsqoX22G2WFzTfYXtdxLRm902d93OM0ulOHZ06sEvrz2vZY4EGj3r0pupPQ77HscRoRw8ronZ6QtM1UzSMvJCJ6LaHfHxcDSPrIQFuf9jiNiDGQE7n90es9TiNiPCT0++O+ssdpRHQoJ3J7RtLRPVr7PCLGTEK/J+5re5xGRDfSvdMf0520TfdORMxKjvQjIhqSIZs9I2knSSfVDaKRtJeko7uuK2tcL/kAAACtSURBVCL6IaHfP6fS7z1OI6JDCf3+2cH2SupyynXVxA3dlhQRfZHQ759e7nEaEeMho3f650+AM4E9JH2VusdptyVFRF/kSL8n7gN7nEbEGEjo90ff9ziNiDGQ7p3+6PsepxExBnKk3x8LJE28SR8MfGHgtrx5R8SsJCz6I3ucRsS8ZRmGHskepxExXwn9iIiGpE8/IqIhCf2IiIYk9CMiGpLQj4hoSEI/IqIh/x+XEQbIvLNJoQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['workclass'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "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>name</th>\n",
       "      <th>address</th>\n",
       "      <th>race</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>pregnant</th>\n",
       "      <th>education-num</th>\n",
       "      <th>relationship</th>\n",
       "      <th>skewness_glucose</th>\n",
       "      <th>mean_glucose</th>\n",
       "      <th>...</th>\n",
       "      <th>education</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>class</th>\n",
       "      <th>std_glucose</th>\n",
       "      <th>income</th>\n",
       "      <th>medical_info</th>\n",
       "      <th>native-country</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>workclass</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>David Hayes</td>\n",
       "      <td>235 Jennifer Throughway Suite 569\\r\\nNew Antho...</td>\n",
       "      <td>Black</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>False</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>-0.197692</td>\n",
       "      <td>141.359375</td>\n",
       "      <td>...</td>\n",
       "      <td>9th</td>\n",
       "      <td>175431.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>49.842053</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>{'mean_oxygen':'4.04264214','std_oxygen':'19.4...</td>\n",
       "      <td>United-States</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Private</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Stanley Benny</td>\n",
       "      <td>08954 Johnson Course\\r\\nNew Lauraville, NV 22019</td>\n",
       "      <td>White</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>False</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Husband</td>\n",
       "      <td>16.019670</td>\n",
       "      <td>53.968750</td>\n",
       "      <td>...</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>66473.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>31.900220</td>\n",
       "      <td>&gt;50K</td>\n",
       "      <td>{'mean_oxygen':'9.294314381','std_oxygen':'35....</td>\n",
       "      <td>United-States</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Private</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Domingo Labriola</td>\n",
       "      <td>36319 Wiley Drive Suite 094\\r\\nSouth Michael, ...</td>\n",
       "      <td>Asian-Pac-Islander</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Farming-fishing</td>\n",
       "      <td>False</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Husband</td>\n",
       "      <td>21.803837</td>\n",
       "      <td>2257.812500</td>\n",
       "      <td>...</td>\n",
       "      <td>5th-6th</td>\n",
       "      <td>93206.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>35.793158</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>{'mean_oxygen':'138.7876254','std_oxygen':'79....</td>\n",
       "      <td>Philippines</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Private</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ronnie Moore</td>\n",
       "      <td>0755 Soto Oval Apt. 443\\r\\nWest Markside, TX 3...</td>\n",
       "      <td>White</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Other-service</td>\n",
       "      <td>False</td>\n",
       "      <td>10.0</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>-0.112191</td>\n",
       "      <td>110.046875</td>\n",
       "      <td>...</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>137604.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>53.876575</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>{'mean_oxygen':'3.100334448','std_oxygen':'17....</td>\n",
       "      <td>United-States</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Private</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Victor Mcjunkin</td>\n",
       "      <td>95738 Keller Overpass\\r\\nKennedyland, MO 71071</td>\n",
       "      <td>White</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>False</td>\n",
       "      <td>10.0</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>-0.309642</td>\n",
       "      <td>137.960938</td>\n",
       "      <td>...</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>174592.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>51.885451</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>{'mean_oxygen':'4.203177258','std_oxygen':'23....</td>\n",
       "      <td>United-States</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Private</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               name                                            address  \\\n",
       "0       David Hayes  235 Jennifer Throughway Suite 569\\r\\nNew Antho...   \n",
       "1     Stanley Benny   08954 Johnson Course\\r\\nNew Lauraville, NV 22019   \n",
       "2  Domingo Labriola  36319 Wiley Drive Suite 094\\r\\nSouth Michael, ...   \n",
       "3      Ronnie Moore  0755 Soto Oval Apt. 443\\r\\nWest Markside, TX 3...   \n",
       "4   Victor Mcjunkin     95738 Keller Overpass\\r\\nKennedyland, MO 71071   \n",
       "\n",
       "                 race      marital-status       occupation pregnant  \\\n",
       "0               Black                 NaN   Prof-specialty    False   \n",
       "1               White  Married-civ-spouse  Exec-managerial    False   \n",
       "2  Asian-Pac-Islander  Married-civ-spouse  Farming-fishing    False   \n",
       "3               White       Never-married    Other-service    False   \n",
       "4               White                 NaN     Adm-clerical    False   \n",
       "\n",
       "   education-num   relationship  skewness_glucose  mean_glucose  ...  \\\n",
       "0            5.0  Not-in-family         -0.197692    141.359375  ...   \n",
       "1           13.0        Husband         16.019670     53.968750  ...   \n",
       "2            3.0        Husband         21.803837   2257.812500  ...   \n",
       "3           10.0  Not-in-family         -0.112191    110.046875  ...   \n",
       "4           10.0  Not-in-family         -0.309642    137.960938  ...   \n",
       "\n",
       "      education    fnlwgt class  std_glucose  income  \\\n",
       "0           9th  175431.0   0.0    49.842053   <=50K   \n",
       "1     Bachelors   66473.0   1.0    31.900220    >50K   \n",
       "2       5th-6th   93206.0   1.0    35.793158   <=50K   \n",
       "3  Some-college  137604.0   0.0    53.876575   <=50K   \n",
       "4  Some-college  174592.0   0.0    51.885451   <=50K   \n",
       "\n",
       "                                        medical_info native-country  \\\n",
       "0  {'mean_oxygen':'4.04264214','std_oxygen':'19.4...  United-States   \n",
       "1  {'mean_oxygen':'9.294314381','std_oxygen':'35....  United-States   \n",
       "2  {'mean_oxygen':'138.7876254','std_oxygen':'79....    Philippines   \n",
       "3  {'mean_oxygen':'3.100334448','std_oxygen':'17....  United-States   \n",
       "4  {'mean_oxygen':'4.203177258','std_oxygen':'23....  United-States   \n",
       "\n",
       "  hours-per-week capital-loss  workclass  \n",
       "0           40.0          0.0    Private  \n",
       "1           40.0          0.0    Private  \n",
       "2           40.0          0.0    Private  \n",
       "3           40.0          0.0    Private  \n",
       "4           40.0          0.0    Private  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22c20b98790>"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x='fnlwgt', y='mean_glucose', hue='race', data=df) # moze byt mean pri tomto minusovy ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22c20c46910>"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x='fnlwgt', y='std_glucose', hue='occupation', data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22c20ce5e20>"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x='fnlwgt', y='hours-per-week', hue='income', data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22c20d5af10>"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x='income', y='hours-per-week', data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'iris' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-124-570eefdd79f0>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0msns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mscatterplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0miris\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'petal_length'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'petal_width'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0msns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mregplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"petal_length\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"petal_width\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0miris\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;31m# This is used for parova analyza\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mage\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msex\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'iris' is not defined"
     ]
    }
   ],
   "source": [
    "sns.scatterplot(data=iris, x='petal_length', y='petal_width')\n",
    "sns.regplot(x=\"petal_length\", y=\"petal_width\", data=iris)\n",
    "\n",
    "# This is used for parova analyza\n",
    "plt.scatter(df.age, df.sex)\n",
    "sns.pairplot(df, hue=\"age\") # This isn't working\n",
    "sns.regplot(x=\"petal_length\", y=\"petal_width\", data=iris)\n",
    "\n",
    "# Corr\n",
    "print(\"Pearson correlation: %.3f\" % iris.petal_length.corr(iris.petal_width))\n",
    "iris.corr()\n",
    "fig, ax = plt.subplots(figsize=(10,8))\n",
    "sns.heatmap(iris.corr(), ax=ax, annot=True, fmt=\".3f\")\n",
    "sns.boxplot('species', 'petal_length', data=iris)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "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>education-num</th>\n",
       "      <th>skewness_glucose</th>\n",
       "      <th>mean_glucose</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>kurtosis_glucose</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>class</th>\n",
       "      <th>std_glucose</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>capital-loss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>education-num</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.011979</td>\n",
       "      <td>0.012340</td>\n",
       "      <td>0.121256</td>\n",
       "      <td>-0.010947</td>\n",
       "      <td>-0.051732</td>\n",
       "      <td>-0.021412</td>\n",
       "      <td>-0.004888</td>\n",
       "      <td>0.143040</td>\n",
       "      <td>0.090149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>skewness_glucose</th>\n",
       "      <td>-0.011979</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.009434</td>\n",
       "      <td>0.000182</td>\n",
       "      <td>0.961722</td>\n",
       "      <td>-0.024980</td>\n",
       "      <td>0.680597</td>\n",
       "      <td>-0.666135</td>\n",
       "      <td>-0.012818</td>\n",
       "      <td>0.002315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean_glucose</th>\n",
       "      <td>0.012340</td>\n",
       "      <td>-0.009434</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.024359</td>\n",
       "      <td>-0.005865</td>\n",
       "      <td>-0.022246</td>\n",
       "      <td>-0.015211</td>\n",
       "      <td>0.022384</td>\n",
       "      <td>0.024153</td>\n",
       "      <td>-0.024901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>capital-gain</th>\n",
       "      <td>0.121256</td>\n",
       "      <td>0.000182</td>\n",
       "      <td>0.024359</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.007008</td>\n",
       "      <td>0.010546</td>\n",
       "      <td>-0.014670</td>\n",
       "      <td>0.008979</td>\n",
       "      <td>0.096427</td>\n",
       "      <td>-0.028317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kurtosis_glucose</th>\n",
       "      <td>-0.010947</td>\n",
       "      <td>0.961722</td>\n",
       "      <td>-0.005865</td>\n",
       "      <td>-0.007008</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.024385</td>\n",
       "      <td>0.788871</td>\n",
       "      <td>-0.653934</td>\n",
       "      <td>-0.008163</td>\n",
       "      <td>0.003581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fnlwgt</th>\n",
       "      <td>-0.051732</td>\n",
       "      <td>-0.024980</td>\n",
       "      <td>-0.022246</td>\n",
       "      <td>0.010546</td>\n",
       "      <td>-0.024385</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.009104</td>\n",
       "      <td>0.026743</td>\n",
       "      <td>-0.021165</td>\n",
       "      <td>-0.034717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>class</th>\n",
       "      <td>-0.021412</td>\n",
       "      <td>0.680597</td>\n",
       "      <td>-0.015211</td>\n",
       "      <td>-0.014670</td>\n",
       "      <td>0.788871</td>\n",
       "      <td>-0.009104</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.500898</td>\n",
       "      <td>0.003044</td>\n",
       "      <td>0.003158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std_glucose</th>\n",
       "      <td>-0.004888</td>\n",
       "      <td>-0.666135</td>\n",
       "      <td>0.022384</td>\n",
       "      <td>0.008979</td>\n",
       "      <td>-0.653934</td>\n",
       "      <td>0.026743</td>\n",
       "      <td>-0.500898</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.018442</td>\n",
       "      <td>-0.007243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hours-per-week</th>\n",
       "      <td>0.143040</td>\n",
       "      <td>-0.012818</td>\n",
       "      <td>0.024153</td>\n",
       "      <td>0.096427</td>\n",
       "      <td>-0.008163</td>\n",
       "      <td>-0.021165</td>\n",
       "      <td>0.003044</td>\n",
       "      <td>0.018442</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.070361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>capital-loss</th>\n",
       "      <td>0.090149</td>\n",
       "      <td>0.002315</td>\n",
       "      <td>-0.024901</td>\n",
       "      <td>-0.028317</td>\n",
       "      <td>0.003581</td>\n",
       "      <td>-0.034717</td>\n",
       "      <td>0.003158</td>\n",
       "      <td>-0.007243</td>\n",
       "      <td>0.070361</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  education-num  skewness_glucose  mean_glucose  capital-gain  \\\n",
       "education-num          1.000000         -0.011979      0.012340      0.121256   \n",
       "skewness_glucose      -0.011979          1.000000     -0.009434      0.000182   \n",
       "mean_glucose           0.012340         -0.009434      1.000000      0.024359   \n",
       "capital-gain           0.121256          0.000182      0.024359      1.000000   \n",
       "kurtosis_glucose      -0.010947          0.961722     -0.005865     -0.007008   \n",
       "fnlwgt                -0.051732         -0.024980     -0.022246      0.010546   \n",
       "class                 -0.021412          0.680597     -0.015211     -0.014670   \n",
       "std_glucose           -0.004888         -0.666135      0.022384      0.008979   \n",
       "hours-per-week         0.143040         -0.012818      0.024153      0.096427   \n",
       "capital-loss           0.090149          0.002315     -0.024901     -0.028317   \n",
       "\n",
       "                  kurtosis_glucose    fnlwgt     class  std_glucose  \\\n",
       "education-num            -0.010947 -0.051732 -0.021412    -0.004888   \n",
       "skewness_glucose          0.961722 -0.024980  0.680597    -0.666135   \n",
       "mean_glucose             -0.005865 -0.022246 -0.015211     0.022384   \n",
       "capital-gain             -0.007008  0.010546 -0.014670     0.008979   \n",
       "kurtosis_glucose          1.000000 -0.024385  0.788871    -0.653934   \n",
       "fnlwgt                   -0.024385  1.000000 -0.009104     0.026743   \n",
       "class                     0.788871 -0.009104  1.000000    -0.500898   \n",
       "std_glucose              -0.653934  0.026743 -0.500898     1.000000   \n",
       "hours-per-week           -0.008163 -0.021165  0.003044     0.018442   \n",
       "capital-loss              0.003581 -0.034717  0.003158    -0.007243   \n",
       "\n",
       "                  hours-per-week  capital-loss  \n",
       "education-num           0.143040      0.090149  \n",
       "skewness_glucose       -0.012818      0.002315  \n",
       "mean_glucose            0.024153     -0.024901  \n",
       "capital-gain            0.096427     -0.028317  \n",
       "kurtosis_glucose       -0.008163      0.003581  \n",
       "fnlwgt                 -0.021165     -0.034717  \n",
       "class                   0.003044      0.003158  \n",
       "std_glucose             0.018442     -0.007243  \n",
       "hours-per-week          1.000000      0.070361  \n",
       "capital-loss            0.070361      1.000000  "
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22c216dbdf0>"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='education-num', y='income', data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# personal_traindata:image/png;base64,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\n",
    "df = pd.read_csv(\"./personal_train.csv\", index_col=0, sep=',') # First column was Unnamend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3540.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>51.816949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>11.914637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>44.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>52.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>60.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>113.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               age\n",
       "count  3540.000000\n",
       "mean     51.816949\n",
       "std      11.914637\n",
       "min      -1.000000\n",
       "25%      44.000000\n",
       "50%      52.000000\n",
       "75%      60.000000\n",
       "max     113.000000"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe() # Fundamental descriptive stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 3933 entries, 0 to 3932\n",
      "Data columns (total 5 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   name           3933 non-null   object \n",
      " 1   address        3933 non-null   object \n",
      " 2   age            3540 non-null   float64\n",
      " 3   sex            3933 non-null   object \n",
      " 4   date_of_birth  3933 non-null   object \n",
      "dtypes: float64(1), object(4)\n",
      "memory usage: 184.4+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info() # number of records: 3933, number of atributes: 5\n",
    "# name = object\n",
    "# address = object\n",
    "# age = float\n",
    "# sex = object\n",
    "# date_of_birth = object\n",
    "# there are 393 cells in age column with null value\n",
    "# other columns don't have null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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>name</th>\n",
       "      <th>address</th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>date_of_birth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Roscoe Bohannon</td>\n",
       "      <td>7183 Osborne Ways Apt. 651\\r\\nEast Andrew, OH ...</td>\n",
       "      <td>63.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>1956-10-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ernest Kline</td>\n",
       "      <td>391 Ball Road Suite 961\\r\\nFlowersborough, IN ...</td>\n",
       "      <td>58.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>1961-05-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Harold Hendriks</td>\n",
       "      <td>8702 Vincent Square\\r\\nNew Jerryfurt, CO 30614</td>\n",
       "      <td>47.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>1972-06-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Randy Baptiste</td>\n",
       "      <td>2751 Harris Crossroad\\r\\nWest Ashley, CA 30311</td>\n",
       "      <td>60.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>1959-02-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Anthony Colucci</td>\n",
       "      <td>904 Robert Cliffs Suite 186\\r\\nWest Kyle, CO 7...</td>\n",
       "      <td>31.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>1988-08-04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              name                                            address   age  \\\n",
       "0  Roscoe Bohannon  7183 Osborne Ways Apt. 651\\r\\nEast Andrew, OH ...  63.0   \n",
       "1     Ernest Kline  391 Ball Road Suite 961\\r\\nFlowersborough, IN ...  58.0   \n",
       "2  Harold Hendriks     8702 Vincent Square\\r\\nNew Jerryfurt, CO 30614  47.0   \n",
       "3   Randy Baptiste     2751 Harris Crossroad\\r\\nWest Ashley, CA 30311  60.0   \n",
       "4  Anthony Colucci  904 Robert Cliffs Suite 186\\r\\nWest Kyle, CO 7...  31.0   \n",
       "\n",
       "       sex date_of_birth  \n",
       "0   Female    1956-10-02  \n",
       "1   Female    1961-05-14  \n",
       "2     Male    1972-06-19  \n",
       "3   Female    1959-02-19  \n",
       "4   Female    1988-08-04  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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>name</th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>date_of_birth</th>\n",
       "      <th>street</th>\n",
       "      <th>city</th>\n",
       "      <th>zip_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Roscoe Bohannon</td>\n",
       "      <td>63.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>1956-10-02</td>\n",
       "      <td>7183 Osborne Ways Apt. 651</td>\n",
       "      <td>East Andrew</td>\n",
       "      <td>OH 53211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ernest Kline</td>\n",
       "      <td>58.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>1961-05-14</td>\n",
       "      <td>391 Ball Road Suite 961</td>\n",
       "      <td>Flowersborough</td>\n",
       "      <td>IN 57550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Harold Hendriks</td>\n",
       "      <td>47.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>1972-06-19</td>\n",
       "      <td>8702 Vincent Square</td>\n",
       "      <td>New Jerryfurt</td>\n",
       "      <td>CO 30614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Randy Baptiste</td>\n",
       "      <td>60.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>1959-02-19</td>\n",
       "      <td>2751 Harris Crossroad</td>\n",
       "      <td>West Ashley</td>\n",
       "      <td>CA 30311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Anthony Colucci</td>\n",
       "      <td>31.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>1988-08-04</td>\n",
       "      <td>904 Robert Cliffs Suite 186</td>\n",
       "      <td>West Kyle</td>\n",
       "      <td>CO 77139</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              name   age      sex date_of_birth                       street  \\\n",
       "0  Roscoe Bohannon  63.0   Female    1956-10-02   7183 Osborne Ways Apt. 651   \n",
       "1     Ernest Kline  58.0   Female    1961-05-14      391 Ball Road Suite 961   \n",
       "2  Harold Hendriks  47.0     Male    1972-06-19          8702 Vincent Square   \n",
       "3   Randy Baptiste  60.0   Female    1959-02-19        2751 Harris Crossroad   \n",
       "4  Anthony Colucci  31.0   Female    1988-08-04  904 Robert Cliffs Suite 186   \n",
       "\n",
       "              city   zip_code  \n",
       "0      East Andrew   OH 53211  \n",
       "1   Flowersborough   IN 57550  \n",
       "2    New Jerryfurt   CO 30614  \n",
       "3      West Ashley   CA 30311  \n",
       "4        West Kyle   CO 77139  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_addresses = df['address'].replace('\\r', ',', regex=True)\n",
    "df_addresses = df_addresses.replace('\\n', ' ', regex=True)\n",
    "df_addresses = df_addresses.str.split(',')\n",
    "df_addresses.head()\n",
    "\n",
    "cities = []\n",
    "streets = []\n",
    "zip_codes = []\n",
    "\n",
    "for address in df_addresses:\n",
    "    if len(address) < 3: # these records have cities in address\n",
    "        streets.append(address[0])\n",
    "        cities.append(None)\n",
    "        zip_codes.append(address[1])\n",
    "    else: # these records don't have cities in address\n",
    "        streets.append(address[0])\n",
    "        cities.append(address[1])\n",
    "        zip_codes.append(address[2])\n",
    "   \n",
    "df['street'] = streets\n",
    "df['city'] = cities\n",
    "df['zip_code'] = zip_codes\n",
    "\n",
    "df = df.drop('address', axis=1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 3933 entries, 0 to 3932\n",
      "Data columns (total 7 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   name           3933 non-null   object \n",
      " 1   age            3540 non-null   float64\n",
      " 2   sex            3933 non-null   object \n",
      " 3   date_of_birth  3933 non-null   object \n",
      " 4   street         3933 non-null   object \n",
      " 5   city           3657 non-null   object \n",
      " 6   zip_code       3933 non-null   object \n",
      "dtypes: float64(1), object(6)\n",
      "memory usage: 245.8+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info() # 276 number of cells in city column have null value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "643"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape[0] - df.dropna().shape[0] # there are 643 nan values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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>name</th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>date_of_birth</th>\n",
       "      <th>street</th>\n",
       "      <th>city</th>\n",
       "      <th>zip_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Harold Miller</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Female</td>\n",
       "      <td>66-06-24</td>\n",
       "      <td>8514 Elizabeth Crescent</td>\n",
       "      <td>West Joseland</td>\n",
       "      <td>GA 49359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Charles Czachorowski</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Male</td>\n",
       "      <td>1957-06-06</td>\n",
       "      <td>6798 Wagner Locks Suite 377</td>\n",
       "      <td>Lake Brenda</td>\n",
       "      <td>DC 53038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Robert Grantham</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Male</td>\n",
       "      <td>1971-08-08</td>\n",
       "      <td>822 Adams Plains</td>\n",
       "      <td>New Karen</td>\n",
       "      <td>AK 92114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Jeffrey Taylor</td>\n",
       "      <td>42.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>27/04/1977</td>\n",
       "      <td>USNV Kim</td>\n",
       "      <td>None</td>\n",
       "      <td>FPO AA 96043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Thaddeus Mackerl</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Female</td>\n",
       "      <td>1969-06-15</td>\n",
       "      <td>8122 Jessica Knolls Apt. 880</td>\n",
       "      <td>Dakotafurt</td>\n",
       "      <td>WV 62098</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3915</th>\n",
       "      <td>Drew Garst</td>\n",
       "      <td>50.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>69-01-03</td>\n",
       "      <td>USNS Duarte</td>\n",
       "      <td>None</td>\n",
       "      <td>FPO AA 22046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3920</th>\n",
       "      <td>David Lackey</td>\n",
       "      <td>44.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>1975-02-15</td>\n",
       "      <td>USNV Patterson</td>\n",
       "      <td>None</td>\n",
       "      <td>FPO AA 81791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3926</th>\n",
       "      <td>Joseph Leroux</td>\n",
       "      <td>59.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>1960/01/04</td>\n",
       "      <td>Unit 7574 Box 5626</td>\n",
       "      <td>None</td>\n",
       "      <td>DPO AE 77296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3930</th>\n",
       "      <td>Jared Kinsey</td>\n",
       "      <td>48.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>1971-05-10</td>\n",
       "      <td>Unit 8416 Box 3801</td>\n",
       "      <td>None</td>\n",
       "      <td>DPO AE 31166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3932</th>\n",
       "      <td>Adalberto Daughters</td>\n",
       "      <td>33.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>1986-10-03</td>\n",
       "      <td>Unit 7931 Box 0601</td>\n",
       "      <td>None</td>\n",
       "      <td>DPO AE 50505</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>643 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      name   age      sex date_of_birth  \\\n",
       "8            Harold Miller   NaN   Female      66-06-24   \n",
       "9     Charles Czachorowski   NaN     Male    1957-06-06   \n",
       "10         Robert Grantham   NaN     Male    1971-08-08   \n",
       "21          Jeffrey Taylor  42.0     Male    27/04/1977   \n",
       "24        Thaddeus Mackerl   NaN   Female    1969-06-15   \n",
       "...                    ...   ...      ...           ...   \n",
       "3915            Drew Garst  50.0     Male      69-01-03   \n",
       "3920          David Lackey  44.0     Male    1975-02-15   \n",
       "3926         Joseph Leroux  59.0     Male    1960/01/04   \n",
       "3930          Jared Kinsey  48.0     Male    1971-05-10   \n",
       "3932   Adalberto Daughters  33.0     Male    1986-10-03   \n",
       "\n",
       "                            street            city       zip_code  \n",
       "8          8514 Elizabeth Crescent   West Joseland       GA 49359  \n",
       "9      6798 Wagner Locks Suite 377     Lake Brenda       DC 53038  \n",
       "10                822 Adams Plains       New Karen       AK 92114  \n",
       "21                        USNV Kim            None   FPO AA 96043  \n",
       "24    8122 Jessica Knolls Apt. 880      Dakotafurt       WV 62098  \n",
       "...                            ...             ...            ...  \n",
       "3915                   USNS Duarte            None   FPO AA 22046  \n",
       "3920                USNV Patterson            None   FPO AA 81791  \n",
       "3926            Unit 7574 Box 5626            None   DPO AE 77296  \n",
       "3930            Unit 8416 Box 3801            None   DPO AE 31166  \n",
       "3932            Unit 7931 Box 0601            None   DPO AE 50505  \n",
       "\n",
       "[643 rows x 7 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.isnull().any(axis=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22c20562cd0>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.sex.value_counts().plot(kind='bar') # there are about 1600 more records about men than about women "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22c208837f0>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(figsize=(20,10))\n",
    "df.age.value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22c1f35d250>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df.age, bins=30) # Distribution of age values, Positive kurtosis and normal skewness"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22c21122be0>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df.age, bins=30) # Distribution of age values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "personal = df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "# other_train\n",
    "df = pd.read_csv('./other_train.csv', sep=',', index_col=0) # First column is Unnamed\n",
    "for column_name in df:\n",
    "    if df[column_name].dtypes == 'object':\n",
    "        df[column_name] = df[column_name].str.strip()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>address</th>\n",
       "      <th>race</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>pregnant</th>\n",
       "      <th>education-num</th>\n",
       "      <th>relationship</th>\n",
       "      <th>skewness_glucose</th>\n",
       "      <th>mean_glucose</th>\n",
       "      <th>...</th>\n",
       "      <th>education</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>class</th>\n",
       "      <th>std_glucose</th>\n",
       "      <th>income</th>\n",
       "      <th>medical_info</th>\n",
       "      <th>native-country</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>workclass</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>David Hayes</td>\n",
       "      <td>235 Jennifer Throughway Suite 569\\r\\nNew Antho...</td>\n",
       "      <td>Black</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>False</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>-0.197692</td>\n",
       "      <td>141.359375</td>\n",
       "      <td>...</td>\n",
       "      <td>9th</td>\n",
       "      <td>175431.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>49.842053</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>{'mean_oxygen':'4.04264214','std_oxygen':'19.4...</td>\n",
       "      <td>United-States</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Private</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Stanley Benny</td>\n",
       "      <td>08954 Johnson Course\\r\\nNew Lauraville, NV 22019</td>\n",
       "      <td>White</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>False</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Husband</td>\n",
       "      <td>16.019670</td>\n",
       "      <td>53.968750</td>\n",
       "      <td>...</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>66473.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>31.900220</td>\n",
       "      <td>&gt;50K</td>\n",
       "      <td>{'mean_oxygen':'9.294314381','std_oxygen':'35....</td>\n",
       "      <td>United-States</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Private</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Domingo Labriola</td>\n",
       "      <td>36319 Wiley Drive Suite 094\\r\\nSouth Michael, ...</td>\n",
       "      <td>Asian-Pac-Islander</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Farming-fishing</td>\n",
       "      <td>False</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Husband</td>\n",
       "      <td>21.803837</td>\n",
       "      <td>2257.812500</td>\n",
       "      <td>...</td>\n",
       "      <td>5th-6th</td>\n",
       "      <td>93206.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>35.793158</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>{'mean_oxygen':'138.7876254','std_oxygen':'79....</td>\n",
       "      <td>Philippines</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Private</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ronnie Moore</td>\n",
       "      <td>0755 Soto Oval Apt. 443\\r\\nWest Markside, TX 3...</td>\n",
       "      <td>White</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Other-service</td>\n",
       "      <td>False</td>\n",
       "      <td>10.0</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>-0.112191</td>\n",
       "      <td>110.046875</td>\n",
       "      <td>...</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>137604.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>53.876575</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>{'mean_oxygen':'3.100334448','std_oxygen':'17....</td>\n",
       "      <td>United-States</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Private</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Victor Mcjunkin</td>\n",
       "      <td>95738 Keller Overpass\\r\\nKennedyland, MO 71071</td>\n",
       "      <td>White</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>False</td>\n",
       "      <td>10.0</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>-0.309642</td>\n",
       "      <td>137.960938</td>\n",
       "      <td>...</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>174592.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>51.885451</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>{'mean_oxygen':'4.203177258','std_oxygen':'23....</td>\n",
       "      <td>United-States</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Private</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               name                                            address  \\\n",
       "0       David Hayes  235 Jennifer Throughway Suite 569\\r\\nNew Antho...   \n",
       "1     Stanley Benny   08954 Johnson Course\\r\\nNew Lauraville, NV 22019   \n",
       "2  Domingo Labriola  36319 Wiley Drive Suite 094\\r\\nSouth Michael, ...   \n",
       "3      Ronnie Moore  0755 Soto Oval Apt. 443\\r\\nWest Markside, TX 3...   \n",
       "4   Victor Mcjunkin     95738 Keller Overpass\\r\\nKennedyland, MO 71071   \n",
       "\n",
       "                 race      marital-status       occupation pregnant  \\\n",
       "0               Black                 NaN   Prof-specialty    False   \n",
       "1               White  Married-civ-spouse  Exec-managerial    False   \n",
       "2  Asian-Pac-Islander  Married-civ-spouse  Farming-fishing    False   \n",
       "3               White       Never-married    Other-service    False   \n",
       "4               White                 NaN     Adm-clerical    False   \n",
       "\n",
       "   education-num   relationship  skewness_glucose  mean_glucose  ...  \\\n",
       "0            5.0  Not-in-family         -0.197692    141.359375  ...   \n",
       "1           13.0        Husband         16.019670     53.968750  ...   \n",
       "2            3.0        Husband         21.803837   2257.812500  ...   \n",
       "3           10.0  Not-in-family         -0.112191    110.046875  ...   \n",
       "4           10.0  Not-in-family         -0.309642    137.960938  ...   \n",
       "\n",
       "      education    fnlwgt class  std_glucose  income  \\\n",
       "0           9th  175431.0   0.0    49.842053   <=50K   \n",
       "1     Bachelors   66473.0   1.0    31.900220    >50K   \n",
       "2       5th-6th   93206.0   1.0    35.793158   <=50K   \n",
       "3  Some-college  137604.0   0.0    53.876575   <=50K   \n",
       "4  Some-college  174592.0   0.0    51.885451   <=50K   \n",
       "\n",
       "                                        medical_info native-country  \\\n",
       "0  {'mean_oxygen':'4.04264214','std_oxygen':'19.4...  United-States   \n",
       "1  {'mean_oxygen':'9.294314381','std_oxygen':'35....  United-States   \n",
       "2  {'mean_oxygen':'138.7876254','std_oxygen':'79....    Philippines   \n",
       "3  {'mean_oxygen':'3.100334448','std_oxygen':'17....  United-States   \n",
       "4  {'mean_oxygen':'4.203177258','std_oxygen':'23....  United-States   \n",
       "\n",
       "  hours-per-week capital-loss  workclass  \n",
       "0           40.0          0.0    Private  \n",
       "1           40.0          0.0    Private  \n",
       "2           40.0          0.0    Private  \n",
       "3           40.0          0.0    Private  \n",
       "4           40.0          0.0    Private  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 3983 entries, 0 to 3982\n",
      "Data columns (total 22 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   name              3983 non-null   object \n",
      " 1   address           3983 non-null   object \n",
      " 2   race              3971 non-null   object \n",
      " 3   marital-status    3570 non-null   object \n",
      " 4   occupation        3744 non-null   object \n",
      " 5   pregnant          3967 non-null   object \n",
      " 6   education-num     3971 non-null   float64\n",
      " 7   relationship      3972 non-null   object \n",
      " 8   skewness_glucose  3968 non-null   float64\n",
      " 9   mean_glucose      3974 non-null   float64\n",
      " 10  capital-gain      3964 non-null   float64\n",
      " 11  kurtosis_glucose  3970 non-null   float64\n",
      " 12  education         3969 non-null   object \n",
      " 13  fnlwgt            3965 non-null   float64\n",
      " 14  class             3972 non-null   float64\n",
      " 15  std_glucose       3966 non-null   float64\n",
      " 16  income            3971 non-null   object \n",
      " 17  medical_info      3967 non-null   object \n",
      " 18  native-country    3909 non-null   object \n",
      " 19  hours-per-week    3971 non-null   float64\n",
      " 20  capital-loss      3971 non-null   float64\n",
      " 21  workclass         3741 non-null   object \n",
      "dtypes: float64(10), object(12)\n",
      "memory usage: 875.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = pd.DataFrame() \n",
    "\n",
    "# Iterate through every row and take values from medical_info column\n",
    "for _,column_data in df['medical_info'].iteritems(): \n",
    "    if isinstance(column_data, str): # If value isn't nan\n",
    "        column_data = column_data[1:-1] # Delect curly brackets from start and end of string\n",
    "        column_data_split = column_data.split(',') # Split string by comma\n",
    "\n",
    "        row = {} \n",
    "        for name_value in column_data_split: # Iterate through split string\n",
    "            name_value_split = name_value.split(':') # Split into name of column and value of columns\n",
    "            # Store value into dictionary with name of the column as a key\n",
    "            row[name_value_split[0][1:-1]] = [np.float(name_value_split[1][1:-1])] # Get rid of '' by [1:-1]\n",
    "\n",
    "        row_data_frame = pd.DataFrame(row) # Convert dictionary into DataFrame\n",
    "        df2 = df2.append(row_data_frame, ignore_index=True) # Append row_data_frame into df2\n",
    "    else: # Value is nan, append nan row into df2\n",
    "        df2 = df2.append(pd.Series(np.nan), ignore_index=True)\n",
    "        \n",
    "df2 = df2.drop(columns=[0]) # Drop last column\n",
    "\n",
    "# Add values from df2 to main df\n",
    "for column_name in df2.columns:\n",
    "    df[column_name] = df2[column_name]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "del df['medical_info'] # medical info is already divided into another columns, so we can erase it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "joined = df.join(personal.set_index('name'), on='name') # join other train and personal train on name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "del joined['address'] # we can also erase disgusting address column, because we have it in detail from personal train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>race</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>pregnant</th>\n",
       "      <th>education-num</th>\n",
       "      <th>relationship</th>\n",
       "      <th>skewness_glucose</th>\n",
       "      <th>mean_glucose</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>...</th>\n",
       "      <th>mean_oxygen</th>\n",
       "      <th>std_oxygen</th>\n",
       "      <th>kurtosis_oxygen</th>\n",
       "      <th>skewness_oxygen</th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>date_of_birth</th>\n",
       "      <th>street</th>\n",
       "      <th>city</th>\n",
       "      <th>zip_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>David Hayes</td>\n",
       "      <td>Black</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>False</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>-0.197692</td>\n",
       "      <td>141.359375</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.042642</td>\n",
       "      <td>19.456469</td>\n",
       "      <td>6.410765</td>\n",
       "      <td>49.813954</td>\n",
       "      <td>45.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>1974-06-18 00:00:00</td>\n",
       "      <td>235 Jennifer Throughway Suite 569</td>\n",
       "      <td>New Anthonyland</td>\n",
       "      <td>PA 19524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Stanley Benny</td>\n",
       "      <td>White</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>False</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Husband</td>\n",
       "      <td>16.019670</td>\n",
       "      <td>53.968750</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>9.294314</td>\n",
       "      <td>35.958174</td>\n",
       "      <td>4.424007</td>\n",
       "      <td>19.868969</td>\n",
       "      <td>42.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>1977-03-12</td>\n",
       "      <td>08954 Johnson Course</td>\n",
       "      <td>New Lauraville</td>\n",
       "      <td>NV 22019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Domingo Labriola</td>\n",
       "      <td>Asian-Pac-Islander</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Farming-fishing</td>\n",
       "      <td>False</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Husband</td>\n",
       "      <td>21.803837</td>\n",
       "      <td>2257.812500</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>138.787625</td>\n",
       "      <td>79.152788</td>\n",
       "      <td>-0.445270</td>\n",
       "      <td>-0.909086</td>\n",
       "      <td>46.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>1973-02-08</td>\n",
       "      <td>36319 Wiley Drive Suite 094</td>\n",
       "      <td>South Michael</td>\n",
       "      <td>WI 41806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ronnie Moore</td>\n",
       "      <td>White</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Other-service</td>\n",
       "      <td>False</td>\n",
       "      <td>10.0</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>-0.112191</td>\n",
       "      <td>110.046875</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.100334</td>\n",
       "      <td>17.819373</td>\n",
       "      <td>8.434320</td>\n",
       "      <td>85.665564</td>\n",
       "      <td>53.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>1966/05/03</td>\n",
       "      <td>0755 Soto Oval Apt. 443</td>\n",
       "      <td>West Markside</td>\n",
       "      <td>TX 32737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Victor Mcjunkin</td>\n",
       "      <td>White</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>False</td>\n",
       "      <td>10.0</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>-0.309642</td>\n",
       "      <td>137.960938</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.203177</td>\n",
       "      <td>23.949006</td>\n",
       "      <td>6.531796</td>\n",
       "      <td>45.866688</td>\n",
       "      <td>52.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>20/05/1967</td>\n",
       "      <td>95738 Keller Overpass</td>\n",
       "      <td>Kennedyland</td>\n",
       "      <td>MO 71071</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               name                race      marital-status       occupation  \\\n",
       "0       David Hayes               Black                 NaN   Prof-specialty   \n",
       "1     Stanley Benny               White  Married-civ-spouse  Exec-managerial   \n",
       "2  Domingo Labriola  Asian-Pac-Islander  Married-civ-spouse  Farming-fishing   \n",
       "3      Ronnie Moore               White       Never-married    Other-service   \n",
       "4   Victor Mcjunkin               White                 NaN     Adm-clerical   \n",
       "\n",
       "  pregnant  education-num   relationship  skewness_glucose  mean_glucose  \\\n",
       "0    False            5.0  Not-in-family         -0.197692    141.359375   \n",
       "1    False           13.0        Husband         16.019670     53.968750   \n",
       "2    False            3.0        Husband         21.803837   2257.812500   \n",
       "3    False           10.0  Not-in-family         -0.112191    110.046875   \n",
       "4    False           10.0  Not-in-family         -0.309642    137.960938   \n",
       "\n",
       "   capital-gain  ...  mean_oxygen std_oxygen  kurtosis_oxygen  \\\n",
       "0           0.0  ...     4.042642  19.456469         6.410765   \n",
       "1           0.0  ...     9.294314  35.958174         4.424007   \n",
       "2           0.0  ...   138.787625  79.152788        -0.445270   \n",
       "3           0.0  ...     3.100334  17.819373         8.434320   \n",
       "4           0.0  ...     4.203177  23.949006         6.531796   \n",
       "\n",
       "   skewness_oxygen   age      sex        date_of_birth  \\\n",
       "0        49.813954  45.0   Female  1974-06-18 00:00:00   \n",
       "1        19.868969  42.0     Male           1977-03-12   \n",
       "2        -0.909086  46.0     Male           1973-02-08   \n",
       "3        85.665564  53.0     Male           1966/05/03   \n",
       "4        45.866688  52.0   Female           20/05/1967   \n",
       "\n",
       "                              street              city   zip_code  \n",
       "0  235 Jennifer Throughway Suite 569   New Anthonyland   PA 19524  \n",
       "1               08954 Johnson Course    New Lauraville   NV 22019  \n",
       "2        36319 Wiley Drive Suite 094     South Michael   WI 41806  \n",
       "3            0755 Soto Oval Apt. 443     West Markside   TX 32737  \n",
       "4              95738 Keller Overpass       Kennedyland   MO 71071  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joined.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hypothesis 1: Prof-specialty has bigger education number than Craft-repair"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22c240ba7c0>"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot('education-num', 'occupation', data=joined)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "prof = joined.loc[joined.occupation == \"Prof-specialty\", 'education-num']\n",
    "craft = joined.loc[joined.occupation == \"Craft-repair\", 'education-num']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Deleting NaN values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [],
   "source": [
    "nan_value = float(\"NaN\")\n",
    "prof.dropna()\n",
    "craft = craft.apply (pd.to_numeric, errors='coerce')\n",
    "prof = prof.apply (pd.to_numeric, errors='coerce')\n",
    "prof.replace(\"\", nan_value, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [],
   "source": [
    "craft.dropna(how='any', inplace=True)\n",
    "craft = craft.reset_index(drop=True)\n",
    "prof.dropna(how='any', inplace=True)\n",
    "prof = prof.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22c25071ca0>"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(prof)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22c25071b20>"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(craft)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "import statsmodels.stats.api as sms\n",
    "_ = sm.ProbPlot(prof, fit=True).qqplot(line='45')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = sm.ProbPlot(craft, fit=True).qqplot(line='45')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.shapiro(prof)\n",
    "stats.shapiro(prof).pvalue < 0.05"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.shapiro(craft)\n",
    "stats.shapiro(craft).pvalue < 0.05"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LeveneResult(statistic=0.022422528548436682, pvalue=0.8809984890742923)"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.levene(prof, craft)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MannwhitneyuResult(statistic=20024.5, pvalue=9.521682387651138e-123)"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.mannwhitneyu(prof, craft) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Rozdiel medzi education-num pre prof-specialty a craft-repair je signifikantny "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22c262c4700>"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x='occupation', y='education-num', data=joined[(joined.occupation == 'Prof-specialty') | (joined.occupation == 'Craft-repair')], \n",
    "            capsize=0.1, errwidth=2, palette=sns.color_palette(\"Blues\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hypothesis 2: More educated people have higher income"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22c944828b0>"
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot('education-num', 'income', data=joined)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [],
   "source": [
    "poor = joined.loc[joined.income == \"<=50K\", 'education-num']\n",
    "rich = joined.loc[joined.income == \">50K\", 'education-num']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [],
   "source": [
    "nan_value = float(\"NaN\")\n",
    "poor.dropna()\n",
    "rich = rich.apply (pd.to_numeric, errors='coerce')\n",
    "poor = poor.apply (pd.to_numeric, errors='coerce')\n",
    "poor.replace(\"\", nan_value, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [],
   "source": [
    "rich.dropna(how='any', inplace=True)\n",
    "rich = rich.reset_index(drop=True)\n",
    "poor.dropna(how='any', inplace=True)\n",
    "poor = poor.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22c95ae7610>"
      ]
     },
     "execution_count": 243,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(poor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22c95ff63d0>"
      ]
     },
     "execution_count": 244,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(rich)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = sm.ProbPlot(poor, fit=True).qqplot(line='45')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = sm.ProbPlot(rich, fit=True).qqplot(line='45')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ShapiroResult(statistic=0.9127867817878723, pvalue=2.4707834652821607e-38)"
      ]
     },
     "execution_count": 253,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.shapiro(poor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 254,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.shapiro(poor).pvalue < 0.05"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ShapiroResult(statistic=0.9222409725189209, pvalue=3.6064562263267975e-22)"
      ]
     },
     "execution_count": 255,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.shapiro(rich)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 256,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.shapiro(rich).pvalue < 0.05"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LeveneResult(statistic=20.805011027024538, pvalue=5.238895248836376e-06)"
      ]
     },
     "execution_count": 258,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.levene(poor, rich)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MannwhitneyuResult(statistic=802523.5, pvalue=7.67084773516636e-107)"
      ]
     },
     "execution_count": 259,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.mannwhitneyu(poor, rich) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22c965c5040>"
      ]
     },
     "execution_count": 260,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x='income', y='education-num', data=joined[(joined.income == '<=50K') | (joined.income == '>50K')], \n",
    "            capsize=0.1, errwidth=2, palette=sns.color_palette(\"Blues\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "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>name</th>\n",
       "      <th>address</th>\n",
       "      <th>race</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>pregnant</th>\n",
       "      <th>education-num</th>\n",
       "      <th>relationship</th>\n",
       "      <th>skewness_glucose</th>\n",
       "      <th>mean_glucose</th>\n",
       "      <th>...</th>\n",
       "      <th>std_glucose</th>\n",
       "      <th>income</th>\n",
       "      <th>native-country</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>workclass</th>\n",
       "      <th>mean_oxygen</th>\n",
       "      <th>std_oxygen</th>\n",
       "      <th>kurtosis_oxygen</th>\n",
       "      <th>skewness_oxygen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>David Hayes</td>\n",
       "      <td>235 Jennifer Throughway Suite 569\\r\\nNew Antho...</td>\n",
       "      <td>Black</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>False</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>-0.197692</td>\n",
       "      <td>141.359375</td>\n",
       "      <td>...</td>\n",
       "      <td>49.842053</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>United-States</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Private</td>\n",
       "      <td>4.042642</td>\n",
       "      <td>19.456469</td>\n",
       "      <td>6.410765</td>\n",
       "      <td>49.813954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Stanley Benny</td>\n",
       "      <td>08954 Johnson Course\\r\\nNew Lauraville, NV 22019</td>\n",
       "      <td>White</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>False</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Husband</td>\n",
       "      <td>16.019670</td>\n",
       "      <td>53.968750</td>\n",
       "      <td>...</td>\n",
       "      <td>31.900220</td>\n",
       "      <td>&gt;50K</td>\n",
       "      <td>United-States</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Private</td>\n",
       "      <td>9.294314</td>\n",
       "      <td>35.958174</td>\n",
       "      <td>4.424007</td>\n",
       "      <td>19.868969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Domingo Labriola</td>\n",
       "      <td>36319 Wiley Drive Suite 094\\r\\nSouth Michael, ...</td>\n",
       "      <td>Asian-Pac-Islander</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Farming-fishing</td>\n",
       "      <td>False</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Husband</td>\n",
       "      <td>21.803837</td>\n",
       "      <td>2257.812500</td>\n",
       "      <td>...</td>\n",
       "      <td>35.793158</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>Philippines</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Private</td>\n",
       "      <td>138.787625</td>\n",
       "      <td>79.152788</td>\n",
       "      <td>-0.445270</td>\n",
       "      <td>-0.909086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ronnie Moore</td>\n",
       "      <td>0755 Soto Oval Apt. 443\\r\\nWest Markside, TX 3...</td>\n",
       "      <td>White</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Other-service</td>\n",
       "      <td>False</td>\n",
       "      <td>10.0</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>-0.112191</td>\n",
       "      <td>110.046875</td>\n",
       "      <td>...</td>\n",
       "      <td>53.876575</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>United-States</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Private</td>\n",
       "      <td>3.100334</td>\n",
       "      <td>17.819373</td>\n",
       "      <td>8.434320</td>\n",
       "      <td>85.665564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Victor Mcjunkin</td>\n",
       "      <td>95738 Keller Overpass\\r\\nKennedyland, MO 71071</td>\n",
       "      <td>White</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>False</td>\n",
       "      <td>10.0</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>-0.309642</td>\n",
       "      <td>137.960938</td>\n",
       "      <td>...</td>\n",
       "      <td>51.885451</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>United-States</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Private</td>\n",
       "      <td>4.203177</td>\n",
       "      <td>23.949006</td>\n",
       "      <td>6.531796</td>\n",
       "      <td>45.866688</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               name                                            address  \\\n",
       "0       David Hayes  235 Jennifer Throughway Suite 569\\r\\nNew Antho...   \n",
       "1     Stanley Benny   08954 Johnson Course\\r\\nNew Lauraville, NV 22019   \n",
       "2  Domingo Labriola  36319 Wiley Drive Suite 094\\r\\nSouth Michael, ...   \n",
       "3      Ronnie Moore  0755 Soto Oval Apt. 443\\r\\nWest Markside, TX 3...   \n",
       "4   Victor Mcjunkin     95738 Keller Overpass\\r\\nKennedyland, MO 71071   \n",
       "\n",
       "                 race      marital-status       occupation pregnant  \\\n",
       "0               Black                 NaN   Prof-specialty    False   \n",
       "1               White  Married-civ-spouse  Exec-managerial    False   \n",
       "2  Asian-Pac-Islander  Married-civ-spouse  Farming-fishing    False   \n",
       "3               White       Never-married    Other-service    False   \n",
       "4               White                 NaN     Adm-clerical    False   \n",
       "\n",
       "   education-num   relationship  skewness_glucose  mean_glucose  ...  \\\n",
       "0            5.0  Not-in-family         -0.197692    141.359375  ...   \n",
       "1           13.0        Husband         16.019670     53.968750  ...   \n",
       "2            3.0        Husband         21.803837   2257.812500  ...   \n",
       "3           10.0  Not-in-family         -0.112191    110.046875  ...   \n",
       "4           10.0  Not-in-family         -0.309642    137.960938  ...   \n",
       "\n",
       "   std_glucose  income native-country  hours-per-week  capital-loss  \\\n",
       "0    49.842053   <=50K  United-States            40.0           0.0   \n",
       "1    31.900220    >50K  United-States            40.0           0.0   \n",
       "2    35.793158   <=50K    Philippines            40.0           0.0   \n",
       "3    53.876575   <=50K  United-States            40.0           0.0   \n",
       "4    51.885451   <=50K  United-States            40.0           0.0   \n",
       "\n",
       "   workclass mean_oxygen std_oxygen  kurtosis_oxygen  skewness_oxygen  \n",
       "0    Private    4.042642  19.456469         6.410765        49.813954  \n",
       "1    Private    9.294314  35.958174         4.424007        19.868969  \n",
       "2    Private  138.787625  79.152788        -0.445270        -0.909086  \n",
       "3    Private    3.100334  17.819373         8.434320        85.665564  \n",
       "4    Private    4.203177  23.949006         6.531796        45.866688  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 248,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joined_dataframe = df\n",
    "\n",
    "for column_name in df2.columns:\n",
    "    joined_dataframe[column_name] = df2[column_name]\n",
    "    \n",
    "joined_dataframe.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'[0] not found in axis'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-249-df86e128908e>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mjoined_dataframe\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mjoined_dataframe\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36mdrop\u001b[1;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[0;32m   3988\u001b[0m                 \u001b[0mweight\u001b[0m  \u001b[1;36m1.0\u001b[0m     \u001b[1;36m0.8\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3989\u001b[0m         \"\"\"\n\u001b[1;32m-> 3990\u001b[1;33m         return super().drop(\n\u001b[0m\u001b[0;32m   3991\u001b[0m             \u001b[0mlabels\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3992\u001b[0m             \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36mdrop\u001b[1;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[0;32m   3934\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabels\u001b[0m \u001b[1;32min\u001b[0m \u001b[0maxes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3935\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mlabels\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3936\u001b[1;33m                 \u001b[0mobj\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_drop_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3937\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3938\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0minplace\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m_drop_axis\u001b[1;34m(self, labels, axis, level, errors)\u001b[0m\n\u001b[0;32m   3968\u001b[0m                 \u001b[0mnew_axis\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3969\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3970\u001b[1;33m                 \u001b[0mnew_axis\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3971\u001b[0m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreindex\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0maxis_name\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mnew_axis\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3972\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mdrop\u001b[1;34m(self, labels, errors)\u001b[0m\n\u001b[0;32m   5016\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mmask\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0many\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5017\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0merrors\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[1;34m\"ignore\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 5018\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mf\"{labels[mask]} not found in axis\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   5019\u001b[0m             \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m~\u001b[0m\u001b[0mmask\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5020\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdelete\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: '[0] not found in axis'"
     ]
    }
   ],
   "source": [
    "joined_dataframe = joined_dataframe.drop(columns=[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "joined_dataframe['mean_oxygen'] = pd.to_numeric(joined_dataframe['mean_oxygen'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "131\n",
      "232\n",
      "318\n",
      "680\n",
      "869\n",
      "1171\n",
      "1678\n",
      "1741\n",
      "1991\n",
      "2115\n",
      "2197\n",
      "2327\n",
      "3064\n",
      "3068\n",
      "3691\n",
      "3712\n"
     ]
    }
   ],
   "source": [
    "count = 0\n",
    "\n",
    "for _, column_data in df['medical_info'].iteritems():\n",
    "    if not isinstance(column_data, str):\n",
    "        print(count)\n",
    "    count = count + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    },
    {
     "ename": "AttributeError",
     "evalue": "'float' object has no attribute 'dtype'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-72-8cc8e57327cc>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0m_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumn_data\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mjoined_dataframe\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'mean_oxygen'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miteritems\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m     \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcount\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m     \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcolumn_data\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      5\u001b[0m     \u001b[0mcount\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcount\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'float' object has no attribute 'dtype'"
     ]
    }
   ],
   "source": [
    "count = 0\n",
    "for _, column_data in joined_dataframe['mean_oxygen'].iteritems():\n",
    "    print(count)\n",
    "    print(column_data.dtype)\n",
    "    count = count + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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