Practices/BK2_Ch3.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {
    "collapsed": true,
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sn\n",
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "outputs": [
    {
     "data": {
      "text/plain": "       age          job  marital            education  default housing loan  \\\n0       56    housemaid  married             basic.4y       no      no   no   \n1       57     services  married          high.school  unknown      no   no   \n2       41  blue-collar  married              unknown  unknown      no   no   \n3       25     services   single          high.school       no     yes   no   \n4       29  blue-collar   single          high.school       no      no  yes   \n...    ...          ...      ...                  ...      ...     ...  ...   \n26869   36       admin.  married    university.degree       no      no   no   \n26870   37       admin.  married    university.degree       no     yes   no   \n26871   29   unemployed   single             basic.4y       no     yes   no   \n26872   73      retired  married  professional.course       no     yes   no   \n26873   46  blue-collar  married  professional.course       no      no   no   \n\n         contact month day_of_week  ...  campaign  days_since_previous  \\\n0      telephone   may         mon  ...         1                  999   \n1      telephone   may         mon  ...         1                  999   \n2      telephone   may         mon  ...         1                  999   \n3      telephone   may         mon  ...         1                  999   \n4      telephone   may         mon  ...         1                  999   \n...          ...   ...         ...  ...       ...                  ...   \n26869   cellular   nov         fri  ...         2                  999   \n26870   cellular   nov         fri  ...         1                  999   \n26871   cellular   nov         fri  ...         1                    9   \n26872   cellular   nov         fri  ...         1                  999   \n26873   cellular   nov         fri  ...         1                  999   \n\n       previous  previous_outcome emp.var.rate  cons.price.idx  cons.conf.idx  \\\n0             0       nonexistent          1.1          93.994          -36.4   \n1             0       nonexistent          1.1          93.994          -36.4   \n2             0       nonexistent          1.1          93.994          -36.4   \n3             0       nonexistent          1.1          93.994          -36.4   \n4             0       nonexistent          1.1          93.994          -36.4   \n...         ...               ...          ...             ...            ...   \n26869         0       nonexistent         -1.1          94.767          -50.8   \n26870         0       nonexistent         -1.1          94.767          -50.8   \n26871         1           success         -1.1          94.767          -50.8   \n26872         0       nonexistent         -1.1          94.767          -50.8   \n26873         0       nonexistent         -1.1          94.767          -50.8   \n\n       euribor3m  nr.employed  response  \n0          4.857         5191        no  \n1          4.857         5191        no  \n2          4.857         5191        no  \n3          4.857         5191        no  \n4          4.857         5191        no  \n...          ...          ...       ...  \n26869      1.028         4963        no  \n26870      1.028         4963       yes  \n26871      1.028         4963        no  \n26872      1.028         4963       yes  \n26873      1.028         4963        no  \n\n[26874 rows x 21 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>age</th>\n      <th>job</th>\n      <th>marital</th>\n      <th>education</th>\n      <th>default</th>\n      <th>housing</th>\n      <th>loan</th>\n      <th>contact</th>\n      <th>month</th>\n      <th>day_of_week</th>\n      <th>...</th>\n      <th>campaign</th>\n      <th>days_since_previous</th>\n      <th>previous</th>\n      <th>previous_outcome</th>\n      <th>emp.var.rate</th>\n      <th>cons.price.idx</th>\n      <th>cons.conf.idx</th>\n      <th>euribor3m</th>\n      <th>nr.employed</th>\n      <th>response</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>56</td>\n      <td>housemaid</td>\n      <td>married</td>\n      <td>basic.4y</td>\n      <td>no</td>\n      <td>no</td>\n      <td>no</td>\n      <td>telephone</td>\n      <td>may</td>\n      <td>mon</td>\n      <td>...</td>\n      <td>1</td>\n      <td>999</td>\n      <td>0</td>\n      <td>nonexistent</td>\n      <td>1.1</td>\n      <td>93.994</td>\n      <td>-36.4</td>\n      <td>4.857</td>\n      <td>5191</td>\n      <td>no</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>57</td>\n      <td>services</td>\n      <td>married</td>\n      <td>high.school</td>\n      <td>unknown</td>\n      <td>no</td>\n      <td>no</td>\n      <td>telephone</td>\n      <td>may</td>\n      <td>mon</td>\n      <td>...</td>\n      <td>1</td>\n      <td>999</td>\n      <td>0</td>\n      <td>nonexistent</td>\n      <td>1.1</td>\n      <td>93.994</td>\n      <td>-36.4</td>\n      <td>4.857</td>\n      <td>5191</td>\n      <td>no</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>41</td>\n      <td>blue-collar</td>\n      <td>married</td>\n      <td>unknown</td>\n      <td>unknown</td>\n      <td>no</td>\n      <td>no</td>\n      <td>telephone</td>\n      <td>may</td>\n      <td>mon</td>\n      <td>...</td>\n      <td>1</td>\n      <td>999</td>\n      <td>0</td>\n      <td>nonexistent</td>\n      <td>1.1</td>\n      <td>93.994</td>\n      <td>-36.4</td>\n      <td>4.857</td>\n      <td>5191</td>\n      <td>no</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>25</td>\n      <td>services</td>\n      <td>single</td>\n      <td>high.school</td>\n      <td>no</td>\n      <td>yes</td>\n      <td>no</td>\n      <td>telephone</td>\n      <td>may</td>\n      <td>mon</td>\n      <td>...</td>\n      <td>1</td>\n      <td>999</td>\n      <td>0</td>\n      <td>nonexistent</td>\n      <td>1.1</td>\n      <td>93.994</td>\n      <td>-36.4</td>\n      <td>4.857</td>\n      <td>5191</td>\n      <td>no</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>29</td>\n      <td>blue-collar</td>\n      <td>single</td>\n      <td>high.school</td>\n      <td>no</td>\n      <td>no</td>\n      <td>yes</td>\n      <td>telephone</td>\n      <td>may</td>\n      <td>mon</td>\n      <td>...</td>\n      <td>1</td>\n      <td>999</td>\n      <td>0</td>\n      <td>nonexistent</td>\n      <td>1.1</td>\n      <td>93.994</td>\n      <td>-36.4</td>\n      <td>4.857</td>\n      <td>5191</td>\n      <td>no</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>26869</th>\n      <td>36</td>\n      <td>admin.</td>\n      <td>married</td>\n      <td>university.degree</td>\n      <td>no</td>\n      <td>no</td>\n      <td>no</td>\n      <td>cellular</td>\n      <td>nov</td>\n      <td>fri</td>\n      <td>...</td>\n      <td>2</td>\n      <td>999</td>\n      <td>0</td>\n      <td>nonexistent</td>\n      <td>-1.1</td>\n      <td>94.767</td>\n      <td>-50.8</td>\n      <td>1.028</td>\n      <td>4963</td>\n      <td>no</td>\n    </tr>\n    <tr>\n      <th>26870</th>\n      <td>37</td>\n      <td>admin.</td>\n      <td>married</td>\n      <td>university.degree</td>\n      <td>no</td>\n      <td>yes</td>\n      <td>no</td>\n      <td>cellular</td>\n      <td>nov</td>\n      <td>fri</td>\n      <td>...</td>\n      <td>1</td>\n      <td>999</td>\n      <td>0</td>\n      <td>nonexistent</td>\n      <td>-1.1</td>\n      <td>94.767</td>\n      <td>-50.8</td>\n      <td>1.028</td>\n      <td>4963</td>\n      <td>yes</td>\n    </tr>\n    <tr>\n      <th>26871</th>\n      <td>29</td>\n      <td>unemployed</td>\n      <td>single</td>\n      <td>basic.4y</td>\n      <td>no</td>\n      <td>yes</td>\n      <td>no</td>\n      <td>cellular</td>\n      <td>nov</td>\n      <td>fri</td>\n      <td>...</td>\n      <td>1</td>\n      <td>9</td>\n      <td>1</td>\n      <td>success</td>\n      <td>-1.1</td>\n      <td>94.767</td>\n      <td>-50.8</td>\n      <td>1.028</td>\n      <td>4963</td>\n      <td>no</td>\n    </tr>\n    <tr>\n      <th>26872</th>\n      <td>73</td>\n      <td>retired</td>\n      <td>married</td>\n      <td>professional.course</td>\n      <td>no</td>\n      <td>yes</td>\n      <td>no</td>\n      <td>cellular</td>\n      <td>nov</td>\n      <td>fri</td>\n      <td>...</td>\n      <td>1</td>\n      <td>999</td>\n      <td>0</td>\n      <td>nonexistent</td>\n      <td>-1.1</td>\n      <td>94.767</td>\n      <td>-50.8</td>\n      <td>1.028</td>\n      <td>4963</td>\n      <td>yes</td>\n    </tr>\n    <tr>\n      <th>26873</th>\n      <td>46</td>\n      <td>blue-collar</td>\n      <td>married</td>\n      <td>professional.course</td>\n      <td>no</td>\n      <td>no</td>\n      <td>no</td>\n      <td>cellular</td>\n      <td>nov</td>\n      <td>fri</td>\n      <td>...</td>\n      <td>1</td>\n      <td>999</td>\n      <td>0</td>\n      <td>nonexistent</td>\n      <td>-1.1</td>\n      <td>94.767</td>\n      <td>-50.8</td>\n      <td>1.028</td>\n      <td>4963</td>\n      <td>no</td>\n    </tr>\n  </tbody>\n</table>\n<p>26874 rows × 21 columns</p>\n</div>"
     },
     "metadata": {},
     "output_type": "execute_result",
     "execution_count": 212
    }
   ],
   "source": [
    "BT= pd.read_csv('bank_marketing_training')\n",
    "BT"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "outputs": [
    {
     "data": {
      "text/plain": "(26874, 21)"
     },
     "metadata": {},
     "output_type": "execute_result",
     "execution_count": 213
    }
   ],
   "source": [
    "BT.shape"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "outputs": [
    {
     "data": {
      "text/plain": "<AxesSubplot:title={'center':'days_since_previous histogram'}, ylabel='Frequency'>"
     },
     "metadata": {},
     "output_type": "execute_result",
     "execution_count": 214
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "BT['days_since_previous'].plot(kind='hist',title='days_since_previous histogram')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "outputs": [
    {
     "data": {
      "text/plain": "<AxesSubplot:title={'center':'days_since_previous histogram'}, ylabel='Frequency'>"
     },
     "metadata": {},
     "output_type": "execute_result",
     "execution_count": 215
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "BT['days_since_previous']=BT['days_since_previous'].replace({999:np.NaN})\n",
    "BT['days_since_previous'].plot(kind='hist',title='days_since_previous histogram')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "outputs": [
    {
     "data": {
      "text/plain": "age                             56\njob                      housemaid\nmarital                    married\neducation                 basic.4y\ndefault                         no\nhousing                         no\nloan                            no\ncontact                  telephone\nmonth                          may\nday_of_week                    mon\nduration                       261\ncampaign                         1\ndays_since_previous            NaN\nprevious                         0\nprevious_outcome       nonexistent\nemp.var.rate                   1.1\ncons.price.idx              93.994\ncons.conf.idx                -36.4\neuribor3m                    4.857\nnr.employed                   5191\nresponse                        no\nName: 0, dtype: object"
     },
     "metadata": {},
     "output_type": "execute_result",
     "execution_count": 216
    }
   ],
   "source": [
    "BT.iloc[0]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "outputs": [
    {
     "data": {
      "text/plain": "                      age   job  marital  education  default  housing  loan  \\\neducation_numeric                                                             \nbasic.4y             2688  2688     2688       2688     2688     2688  2688   \nbasic.6y             1498  1498     1498       1498     1498     1498  1498   \nbasic.9y             4050  4050     4050       4050     4050     4050  4050   \nhigh.school          6130  6130     6130       6130     6130     6130  6130   \nilliterate             12    12       12         12       12       12    12   \nprofessional.course  3423  3423     3423       3423     3423     3423  3423   \nuniversity.degree    7946  7946     7946       7946     7946     7946  7946   \nunknown              1127  1127     1127       1127     1127     1127  1127   \n\n                     contact  month  day_of_week  ...  campaign  \\\neducation_numeric                                 ...             \nbasic.4y                2688   2688         2688  ...      2688   \nbasic.6y                1498   1498         1498  ...      1498   \nbasic.9y                4050   4050         4050  ...      4050   \nhigh.school             6130   6130         6130  ...      6130   \nilliterate                12     12           12  ...        12   \nprofessional.course     3423   3423         3423  ...      3423   \nuniversity.degree       7946   7946         7946  ...      7946   \nunknown                 1127   1127         1127  ...      1127   \n\n                     days_since_previous  previous  previous_outcome  \\\neducation_numeric                                                      \nbasic.4y                              91      2688              2688   \nbasic.6y                              30      1498              1498   \nbasic.9y                              76      4050              4050   \nhigh.school                          214      6130              6130   \nilliterate                             0        12                12   \nprofessional.course                  131      3423              3423   \nuniversity.degree                    372      7946              7946   \nunknown                               68      1127              1127   \n\n                     emp.var.rate  cons.price.idx  cons.conf.idx  euribor3m  \\\neducation_numeric                                                             \nbasic.4y                     2688            2688           2688       2688   \nbasic.6y                     1498            1498           1498       1498   \nbasic.9y                     4050            4050           4050       4050   \nhigh.school                  6130            6130           6130       6130   \nilliterate                     12              12             12         12   \nprofessional.course          3423            3423           3423       3423   \nuniversity.degree            7946            7946           7946       7946   \nunknown                      1127            1127           1127       1127   \n\n                     nr.employed  response  \neducation_numeric                           \nbasic.4y                    2688      2688  \nbasic.6y                    1498      1498  \nbasic.9y                    4050      4050  \nhigh.school                 6130      6130  \nilliterate                    12        12  \nprofessional.course         3423      3423  \nuniversity.degree           7946      7946  \nunknown                     1127      1127  \n\n[8 rows x 21 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>age</th>\n      <th>job</th>\n      <th>marital</th>\n      <th>education</th>\n      <th>default</th>\n      <th>housing</th>\n      <th>loan</th>\n      <th>contact</th>\n      <th>month</th>\n      <th>day_of_week</th>\n      <th>...</th>\n      <th>campaign</th>\n      <th>days_since_previous</th>\n      <th>previous</th>\n      <th>previous_outcome</th>\n      <th>emp.var.rate</th>\n      <th>cons.price.idx</th>\n      <th>cons.conf.idx</th>\n      <th>euribor3m</th>\n      <th>nr.employed</th>\n      <th>response</th>\n    </tr>\n    <tr>\n      <th>education_numeric</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>basic.4y</th>\n      <td>2688</td>\n      <td>2688</td>\n      <td>2688</td>\n      <td>2688</td>\n      <td>2688</td>\n      <td>2688</td>\n      <td>2688</td>\n      <td>2688</td>\n      <td>2688</td>\n      <td>2688</td>\n      <td>...</td>\n      <td>2688</td>\n      <td>91</td>\n      <td>2688</td>\n      <td>2688</td>\n      <td>2688</td>\n      <td>2688</td>\n      <td>2688</td>\n      <td>2688</td>\n      <td>2688</td>\n      <td>2688</td>\n    </tr>\n    <tr>\n      <th>basic.6y</th>\n      <td>1498</td>\n      <td>1498</td>\n      <td>1498</td>\n      <td>1498</td>\n      <td>1498</td>\n      <td>1498</td>\n      <td>1498</td>\n      <td>1498</td>\n      <td>1498</td>\n      <td>1498</td>\n      <td>...</td>\n      <td>1498</td>\n      <td>30</td>\n      <td>1498</td>\n      <td>1498</td>\n      <td>1498</td>\n      <td>1498</td>\n      <td>1498</td>\n      <td>1498</td>\n      <td>1498</td>\n      <td>1498</td>\n    </tr>\n    <tr>\n      <th>basic.9y</th>\n      <td>4050</td>\n      <td>4050</td>\n      <td>4050</td>\n      <td>4050</td>\n      <td>4050</td>\n      <td>4050</td>\n      <td>4050</td>\n      <td>4050</td>\n      <td>4050</td>\n      <td>4050</td>\n      <td>...</td>\n      <td>4050</td>\n      <td>76</td>\n      <td>4050</td>\n      <td>4050</td>\n      <td>4050</td>\n      <td>4050</td>\n      <td>4050</td>\n      <td>4050</td>\n      <td>4050</td>\n      <td>4050</td>\n    </tr>\n    <tr>\n      <th>high.school</th>\n      <td>6130</td>\n      <td>6130</td>\n      <td>6130</td>\n      <td>6130</td>\n      <td>6130</td>\n      <td>6130</td>\n      <td>6130</td>\n      <td>6130</td>\n      <td>6130</td>\n      <td>6130</td>\n      <td>...</td>\n      <td>6130</td>\n      <td>214</td>\n      <td>6130</td>\n      <td>6130</td>\n      <td>6130</td>\n      <td>6130</td>\n      <td>6130</td>\n      <td>6130</td>\n      <td>6130</td>\n      <td>6130</td>\n    </tr>\n    <tr>\n      <th>illiterate</th>\n      <td>12</td>\n      <td>12</td>\n      <td>12</td>\n      <td>12</td>\n      <td>12</td>\n      <td>12</td>\n      <td>12</td>\n      <td>12</td>\n      <td>12</td>\n      <td>12</td>\n      <td>...</td>\n      <td>12</td>\n      <td>0</td>\n      <td>12</td>\n      <td>12</td>\n      <td>12</td>\n      <td>12</td>\n      <td>12</td>\n      <td>12</td>\n      <td>12</td>\n      <td>12</td>\n    </tr>\n    <tr>\n      <th>professional.course</th>\n      <td>3423</td>\n      <td>3423</td>\n      <td>3423</td>\n      <td>3423</td>\n      <td>3423</td>\n      <td>3423</td>\n      <td>3423</td>\n      <td>3423</td>\n      <td>3423</td>\n      <td>3423</td>\n      <td>...</td>\n      <td>3423</td>\n      <td>131</td>\n      <td>3423</td>\n      <td>3423</td>\n      <td>3423</td>\n      <td>3423</td>\n      <td>3423</td>\n      <td>3423</td>\n      <td>3423</td>\n      <td>3423</td>\n    </tr>\n    <tr>\n      <th>university.degree</th>\n      <td>7946</td>\n      <td>7946</td>\n      <td>7946</td>\n      <td>7946</td>\n      <td>7946</td>\n      <td>7946</td>\n      <td>7946</td>\n      <td>7946</td>\n      <td>7946</td>\n      <td>7946</td>\n      <td>...</td>\n      <td>7946</td>\n      <td>372</td>\n      <td>7946</td>\n      <td>7946</td>\n      <td>7946</td>\n      <td>7946</td>\n      <td>7946</td>\n      <td>7946</td>\n      <td>7946</td>\n      <td>7946</td>\n    </tr>\n    <tr>\n      <th>unknown</th>\n      <td>1127</td>\n      <td>1127</td>\n      <td>1127</td>\n      <td>1127</td>\n      <td>1127</td>\n      <td>1127</td>\n      <td>1127</td>\n      <td>1127</td>\n      <td>1127</td>\n      <td>1127</td>\n      <td>...</td>\n      <td>1127</td>\n      <td>68</td>\n      <td>1127</td>\n      <td>1127</td>\n      <td>1127</td>\n      <td>1127</td>\n      <td>1127</td>\n      <td>1127</td>\n      <td>1127</td>\n      <td>1127</td>\n    </tr>\n  </tbody>\n</table>\n<p>8 rows × 21 columns</p>\n</div>"
     },
     "metadata": {},
     "output_type": "execute_result",
     "execution_count": 217
    }
   ],
   "source": [
    "BT['education_numeric']=BT['education']\n",
    "BT.groupby('education_numeric').count()\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "outputs": [
    {
     "data": {
      "text/plain": "       age          job  marital            education  default housing loan  \\\n0       56    housemaid  married             basic.4y       no      no   no   \n1       57     services  married          high.school  unknown      no   no   \n2       41  blue-collar  married              unknown  unknown      no   no   \n3       25     services   single          high.school       no     yes   no   \n4       29  blue-collar   single          high.school       no      no  yes   \n...    ...          ...      ...                  ...      ...     ...  ...   \n26869   36       admin.  married    university.degree       no      no   no   \n26870   37       admin.  married    university.degree       no     yes   no   \n26871   29   unemployed   single             basic.4y       no     yes   no   \n26872   73      retired  married  professional.course       no     yes   no   \n26873   46  blue-collar  married  professional.course       no      no   no   \n\n         contact month day_of_week  ...  days_since_previous  previous  \\\n0      telephone   may         mon  ...                  NaN         0   \n1      telephone   may         mon  ...                  NaN         0   \n2      telephone   may         mon  ...                  NaN         0   \n3      telephone   may         mon  ...                  NaN         0   \n4      telephone   may         mon  ...                  NaN         0   \n...          ...   ...         ...  ...                  ...       ...   \n26869   cellular   nov         fri  ...                  NaN         0   \n26870   cellular   nov         fri  ...                  NaN         0   \n26871   cellular   nov         fri  ...                  9.0         1   \n26872   cellular   nov         fri  ...                  NaN         0   \n26873   cellular   nov         fri  ...                  NaN         0   \n\n       previous_outcome  emp.var.rate cons.price.idx  cons.conf.idx  \\\n0           nonexistent           1.1         93.994          -36.4   \n1           nonexistent           1.1         93.994          -36.4   \n2           nonexistent           1.1         93.994          -36.4   \n3           nonexistent           1.1         93.994          -36.4   \n4           nonexistent           1.1         93.994          -36.4   \n...                 ...           ...            ...            ...   \n26869       nonexistent          -1.1         94.767          -50.8   \n26870       nonexistent          -1.1         94.767          -50.8   \n26871           success          -1.1         94.767          -50.8   \n26872       nonexistent          -1.1         94.767          -50.8   \n26873       nonexistent          -1.1         94.767          -50.8   \n\n       euribor3m  nr.employed  response  education_numeric  \n0          4.857         5191        no                4.0  \n1          4.857         5191        no               12.0  \n2          4.857         5191        no                NaN  \n3          4.857         5191        no               12.0  \n4          4.857         5191        no               12.0  \n...          ...          ...       ...                ...  \n26869      1.028         4963        no               16.0  \n26870      1.028         4963       yes               16.0  \n26871      1.028         4963        no                4.0  \n26872      1.028         4963       yes               12.0  \n26873      1.028         4963        no               12.0  \n\n[26874 rows x 22 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>age</th>\n      <th>job</th>\n      <th>marital</th>\n      <th>education</th>\n      <th>default</th>\n      <th>housing</th>\n      <th>loan</th>\n      <th>contact</th>\n      <th>month</th>\n      <th>day_of_week</th>\n      <th>...</th>\n      <th>days_since_previous</th>\n      <th>previous</th>\n      <th>previous_outcome</th>\n      <th>emp.var.rate</th>\n      <th>cons.price.idx</th>\n      <th>cons.conf.idx</th>\n      <th>euribor3m</th>\n      <th>nr.employed</th>\n      <th>response</th>\n      <th>education_numeric</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>56</td>\n      <td>housemaid</td>\n      <td>married</td>\n      <td>basic.4y</td>\n      <td>no</td>\n      <td>no</td>\n      <td>no</td>\n      <td>telephone</td>\n      <td>may</td>\n      <td>mon</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>nonexistent</td>\n      <td>1.1</td>\n      <td>93.994</td>\n      <td>-36.4</td>\n      <td>4.857</td>\n      <td>5191</td>\n      <td>no</td>\n      <td>4.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>57</td>\n      <td>services</td>\n      <td>married</td>\n      <td>high.school</td>\n      <td>unknown</td>\n      <td>no</td>\n      <td>no</td>\n      <td>telephone</td>\n      <td>may</td>\n      <td>mon</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>nonexistent</td>\n      <td>1.1</td>\n      <td>93.994</td>\n      <td>-36.4</td>\n      <td>4.857</td>\n      <td>5191</td>\n      <td>no</td>\n      <td>12.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>41</td>\n      <td>blue-collar</td>\n      <td>married</td>\n      <td>unknown</td>\n      <td>unknown</td>\n      <td>no</td>\n      <td>no</td>\n      <td>telephone</td>\n      <td>may</td>\n      <td>mon</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>nonexistent</td>\n      <td>1.1</td>\n      <td>93.994</td>\n      <td>-36.4</td>\n      <td>4.857</td>\n      <td>5191</td>\n      <td>no</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>25</td>\n      <td>services</td>\n      <td>single</td>\n      <td>high.school</td>\n      <td>no</td>\n      <td>yes</td>\n      <td>no</td>\n      <td>telephone</td>\n      <td>may</td>\n      <td>mon</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>nonexistent</td>\n      <td>1.1</td>\n      <td>93.994</td>\n      <td>-36.4</td>\n      <td>4.857</td>\n      <td>5191</td>\n      <td>no</td>\n      <td>12.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>29</td>\n      <td>blue-collar</td>\n      <td>single</td>\n      <td>high.school</td>\n      <td>no</td>\n      <td>no</td>\n      <td>yes</td>\n      <td>telephone</td>\n      <td>may</td>\n      <td>mon</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>nonexistent</td>\n      <td>1.1</td>\n      <td>93.994</td>\n      <td>-36.4</td>\n      <td>4.857</td>\n      <td>5191</td>\n      <td>no</td>\n      <td>12.0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>26869</th>\n      <td>36</td>\n      <td>admin.</td>\n      <td>married</td>\n      <td>university.degree</td>\n      <td>no</td>\n      <td>no</td>\n      <td>no</td>\n      <td>cellular</td>\n      <td>nov</td>\n      <td>fri</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>nonexistent</td>\n      <td>-1.1</td>\n      <td>94.767</td>\n      <td>-50.8</td>\n      <td>1.028</td>\n      <td>4963</td>\n      <td>no</td>\n      <td>16.0</td>\n    </tr>\n    <tr>\n      <th>26870</th>\n      <td>37</td>\n      <td>admin.</td>\n      <td>married</td>\n      <td>university.degree</td>\n      <td>no</td>\n      <td>yes</td>\n      <td>no</td>\n      <td>cellular</td>\n      <td>nov</td>\n      <td>fri</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>nonexistent</td>\n      <td>-1.1</td>\n      <td>94.767</td>\n      <td>-50.8</td>\n      <td>1.028</td>\n      <td>4963</td>\n      <td>yes</td>\n      <td>16.0</td>\n    </tr>\n    <tr>\n      <th>26871</th>\n      <td>29</td>\n      <td>unemployed</td>\n      <td>single</td>\n      <td>basic.4y</td>\n      <td>no</td>\n      <td>yes</td>\n      <td>no</td>\n      <td>cellular</td>\n      <td>nov</td>\n      <td>fri</td>\n      <td>...</td>\n      <td>9.0</td>\n      <td>1</td>\n      <td>success</td>\n      <td>-1.1</td>\n      <td>94.767</td>\n      <td>-50.8</td>\n      <td>1.028</td>\n      <td>4963</td>\n      <td>no</td>\n      <td>4.0</td>\n    </tr>\n    <tr>\n      <th>26872</th>\n      <td>73</td>\n      <td>retired</td>\n      <td>married</td>\n      <td>professional.course</td>\n      <td>no</td>\n      <td>yes</td>\n      <td>no</td>\n      <td>cellular</td>\n      <td>nov</td>\n      <td>fri</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>nonexistent</td>\n      <td>-1.1</td>\n      <td>94.767</td>\n      <td>-50.8</td>\n      <td>1.028</td>\n      <td>4963</td>\n      <td>yes</td>\n      <td>12.0</td>\n    </tr>\n    <tr>\n      <th>26873</th>\n      <td>46</td>\n      <td>blue-collar</td>\n      <td>married</td>\n      <td>professional.course</td>\n      <td>no</td>\n      <td>no</td>\n      <td>no</td>\n      <td>cellular</td>\n      <td>nov</td>\n      <td>fri</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>nonexistent</td>\n      <td>-1.1</td>\n      <td>94.767</td>\n      <td>-50.8</td>\n      <td>1.028</td>\n      <td>4963</td>\n      <td>no</td>\n      <td>12.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>26874 rows × 22 columns</p>\n</div>"
     },
     "metadata": {},
     "output_type": "execute_result",
     "execution_count": 218
    }
   ],
   "source": [
    "dict_edu= {'education_numeric':{\"illiterate\":0,\"basic.4y\":4,\n",
    "                                \"basic.6y\":6,\"basic.9y\":9,\"high.school\":12,\n",
    "          \"professional.course\":12,\"university.degree\":16,\"unknown\":np.NaN}}\n",
    "BT.replace(dict_edu,inplace=True)\n",
    "BT"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "outputs": [
    {
     "data": {
      "text/plain": "<seaborn.axisgrid.FacetGrid at 0x1fdbf01f978>"
     },
     "metadata": {},
     "output_type": "execute_result",
     "execution_count": 219
    },
    {
     "data": {
      "text/plain": "<Figure size 360x360 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sn.catplot(y='education', kind='count',data=BT)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "outputs": [
    {
     "data": {
      "text/plain": "   age          job  marital    education  default housing loan    contact  \\\n0   56    housemaid  married     basic.4y       no      no   no  telephone   \n1   57     services  married  high.school  unknown      no   no  telephone   \n2   41  blue-collar  married      unknown  unknown      no   no  telephone   \n3   25     services   single  high.school       no     yes   no  telephone   \n4   29  blue-collar   single  high.school       no      no  yes  telephone   \n\n  month day_of_week  ...  previous  previous_outcome  emp.var.rate  \\\n0   may         mon  ...         0       nonexistent           1.1   \n1   may         mon  ...         0       nonexistent           1.1   \n2   may         mon  ...         0       nonexistent           1.1   \n3   may         mon  ...         0       nonexistent           1.1   \n4   may         mon  ...         0       nonexistent           1.1   \n\n   cons.price.idx cons.conf.idx  euribor3m  nr.employed  response  \\\n0          93.994         -36.4      4.857         5191        no   \n1          93.994         -36.4      4.857         5191        no   \n2          93.994         -36.4      4.857         5191        no   \n3          93.994         -36.4      4.857         5191        no   \n4          93.994         -36.4      4.857         5191        no   \n\n   education_numeric     age_z  \n0                4.0  1.539625  \n1               12.0  1.635778  \n2                NaN  0.097330  \n3               12.0 -1.441118  \n4               12.0 -1.056506  \n\n[5 rows x 23 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>age</th>\n      <th>job</th>\n      <th>marital</th>\n      <th>education</th>\n      <th>default</th>\n      <th>housing</th>\n      <th>loan</th>\n      <th>contact</th>\n      <th>month</th>\n      <th>day_of_week</th>\n      <th>...</th>\n      <th>previous</th>\n      <th>previous_outcome</th>\n      <th>emp.var.rate</th>\n      <th>cons.price.idx</th>\n      <th>cons.conf.idx</th>\n      <th>euribor3m</th>\n      <th>nr.employed</th>\n      <th>response</th>\n      <th>education_numeric</th>\n      <th>age_z</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>56</td>\n      <td>housemaid</td>\n      <td>married</td>\n      <td>basic.4y</td>\n      <td>no</td>\n      <td>no</td>\n      <td>no</td>\n      <td>telephone</td>\n      <td>may</td>\n      <td>mon</td>\n      <td>...</td>\n      <td>0</td>\n      <td>nonexistent</td>\n      <td>1.1</td>\n      <td>93.994</td>\n      <td>-36.4</td>\n      <td>4.857</td>\n      <td>5191</td>\n      <td>no</td>\n      <td>4.0</td>\n      <td>1.539625</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>57</td>\n      <td>services</td>\n      <td>married</td>\n      <td>high.school</td>\n      <td>unknown</td>\n      <td>no</td>\n      <td>no</td>\n      <td>telephone</td>\n      <td>may</td>\n      <td>mon</td>\n      <td>...</td>\n      <td>0</td>\n      <td>nonexistent</td>\n      <td>1.1</td>\n      <td>93.994</td>\n      <td>-36.4</td>\n      <td>4.857</td>\n      <td>5191</td>\n      <td>no</td>\n      <td>12.0</td>\n      <td>1.635778</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>41</td>\n      <td>blue-collar</td>\n      <td>married</td>\n      <td>unknown</td>\n      <td>unknown</td>\n      <td>no</td>\n      <td>no</td>\n      <td>telephone</td>\n      <td>may</td>\n      <td>mon</td>\n      <td>...</td>\n      <td>0</td>\n      <td>nonexistent</td>\n      <td>1.1</td>\n      <td>93.994</td>\n      <td>-36.4</td>\n      <td>4.857</td>\n      <td>5191</td>\n      <td>no</td>\n      <td>NaN</td>\n      <td>0.097330</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>25</td>\n      <td>services</td>\n      <td>single</td>\n      <td>high.school</td>\n      <td>no</td>\n      <td>yes</td>\n      <td>no</td>\n      <td>telephone</td>\n      <td>may</td>\n      <td>mon</td>\n      <td>...</td>\n      <td>0</td>\n      <td>nonexistent</td>\n      <td>1.1</td>\n      <td>93.994</td>\n      <td>-36.4</td>\n      <td>4.857</td>\n      <td>5191</td>\n      <td>no</td>\n      <td>12.0</td>\n      <td>-1.441118</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>29</td>\n      <td>blue-collar</td>\n      <td>single</td>\n      <td>high.school</td>\n      <td>no</td>\n      <td>no</td>\n      <td>yes</td>\n      <td>telephone</td>\n      <td>may</td>\n      <td>mon</td>\n      <td>...</td>\n      <td>0</td>\n      <td>nonexistent</td>\n      <td>1.1</td>\n      <td>93.994</td>\n      <td>-36.4</td>\n      <td>4.857</td>\n      <td>5191</td>\n      <td>no</td>\n      <td>12.0</td>\n      <td>-1.056506</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 23 columns</p>\n</div>"
     },
     "metadata": {},
     "output_type": "execute_result",
     "execution_count": 220
    }
   ],
   "source": [
    "BT['age_z']=stats.zscore(BT['age'])\n",
    "BT.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "outputs": [
    {
     "data": {
      "text/plain": "       age      job   marital            education  default housing loan  \\\n18166   73  retired   married    university.degree       no     yes   no   \n18179   88  retired  divorced             basic.4y       no     yes   no   \n18181   88  retired  divorced             basic.4y       no      no   no   \n18183   88  retired  divorced             basic.4y       no     yes  yes   \n18186   88  retired  divorced             basic.4y       no     yes   no   \n...    ...      ...       ...                  ...      ...     ...  ...   \n26735   84  retired  divorced             basic.4y  unknown     yes   no   \n26739   74  retired  divorced             basic.4y       no     yes   no   \n26740   74  retired  divorced             basic.4y       no     yes   no   \n26750   81  retired   married             basic.4y       no     yes   no   \n26872   73  retired   married  professional.course       no     yes   no   \n\n        contact month day_of_week  ...  previous  previous_outcome  \\\n18166  cellular   mar         tue  ...         1           failure   \n18179  cellular   mar         wed  ...         0       nonexistent   \n18181  cellular   mar         wed  ...         0       nonexistent   \n18183  cellular   mar         wed  ...         0       nonexistent   \n18186  cellular   mar         wed  ...         0       nonexistent   \n...         ...   ...         ...  ...       ...               ...   \n26735  cellular   oct         fri  ...         0       nonexistent   \n26739  cellular   oct         mon  ...         0       nonexistent   \n26740  cellular   oct         mon  ...         2           success   \n26750  cellular   oct         wed  ...         2           failure   \n26872  cellular   nov         fri  ...         0       nonexistent   \n\n       emp.var.rate  cons.price.idx cons.conf.idx  euribor3m  nr.employed  \\\n18166          -1.8          92.843         -50.0      1.687         5099   \n18179          -1.8          92.843         -50.0      1.663         5099   \n18181          -1.8          92.843         -50.0      1.663         5099   \n18183          -1.8          92.843         -50.0      1.663         5099   \n18186          -1.8          92.843         -50.0      1.663         5099   \n...             ...             ...           ...        ...          ...   \n26735          -1.1          94.601         -49.5      0.993         4963   \n26739          -1.1          94.601         -49.5      1.000         4963   \n26740          -1.1          94.601         -49.5      1.000         4963   \n26750          -1.1          94.601         -49.5      1.016         4963   \n26872          -1.1          94.767         -50.8      1.028         4963   \n\n       response  education_numeric     age_z  \n18166        no               16.0  3.174226  \n18179        no                4.0  4.616521  \n18181       yes                4.0  4.616521  \n18183       yes                4.0  4.616521  \n18186       yes                4.0  4.616521  \n...         ...                ...       ...  \n26735        no                4.0  4.231909  \n26739        no                4.0  3.270379  \n26740        no                4.0  3.270379  \n26750       yes                4.0  3.943450  \n26872       yes               12.0  3.174226  \n\n[228 rows x 23 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>age</th>\n      <th>job</th>\n      <th>marital</th>\n      <th>education</th>\n      <th>default</th>\n      <th>housing</th>\n      <th>loan</th>\n      <th>contact</th>\n      <th>month</th>\n      <th>day_of_week</th>\n      <th>...</th>\n      <th>previous</th>\n      <th>previous_outcome</th>\n      <th>emp.var.rate</th>\n      <th>cons.price.idx</th>\n      <th>cons.conf.idx</th>\n      <th>euribor3m</th>\n      <th>nr.employed</th>\n      <th>response</th>\n      <th>education_numeric</th>\n      <th>age_z</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>18166</th>\n      <td>73</td>\n      <td>retired</td>\n      <td>married</td>\n      <td>university.degree</td>\n      <td>no</td>\n      <td>yes</td>\n      <td>no</td>\n      <td>cellular</td>\n      <td>mar</td>\n      <td>tue</td>\n      <td>...</td>\n      <td>1</td>\n      <td>failure</td>\n      <td>-1.8</td>\n      <td>92.843</td>\n      <td>-50.0</td>\n      <td>1.687</td>\n      <td>5099</td>\n      <td>no</td>\n      <td>16.0</td>\n      <td>3.174226</td>\n    </tr>\n    <tr>\n      <th>18179</th>\n      <td>88</td>\n      <td>retired</td>\n      <td>divorced</td>\n      <td>basic.4y</td>\n      <td>no</td>\n      <td>yes</td>\n      <td>no</td>\n      <td>cellular</td>\n      <td>mar</td>\n      <td>wed</td>\n      <td>...</td>\n      <td>0</td>\n      <td>nonexistent</td>\n      <td>-1.8</td>\n      <td>92.843</td>\n      <td>-50.0</td>\n      <td>1.663</td>\n      <td>5099</td>\n      <td>no</td>\n      <td>4.0</td>\n      <td>4.616521</td>\n    </tr>\n    <tr>\n      <th>18181</th>\n      <td>88</td>\n      <td>retired</td>\n      <td>divorced</td>\n      <td>basic.4y</td>\n      <td>no</td>\n      <td>no</td>\n      <td>no</td>\n      <td>cellular</td>\n      <td>mar</td>\n      <td>wed</td>\n      <td>...</td>\n      <td>0</td>\n      <td>nonexistent</td>\n      <td>-1.8</td>\n      <td>92.843</td>\n      <td>-50.0</td>\n      <td>1.663</td>\n      <td>5099</td>\n      <td>yes</td>\n      <td>4.0</td>\n      <td>4.616521</td>\n    </tr>\n    <tr>\n      <th>18183</th>\n      <td>88</td>\n      <td>retired</td>\n      <td>divorced</td>\n      <td>basic.4y</td>\n      <td>no</td>\n      <td>yes</td>\n      <td>yes</td>\n      <td>cellular</td>\n      <td>mar</td>\n      <td>wed</td>\n      <td>...</td>\n      <td>0</td>\n      <td>nonexistent</td>\n      <td>-1.8</td>\n      <td>92.843</td>\n      <td>-50.0</td>\n      <td>1.663</td>\n      <td>5099</td>\n      <td>yes</td>\n      <td>4.0</td>\n      <td>4.616521</td>\n    </tr>\n    <tr>\n      <th>18186</th>\n      <td>88</td>\n      <td>retired</td>\n      <td>divorced</td>\n      <td>basic.4y</td>\n      <td>no</td>\n      <td>yes</td>\n      <td>no</td>\n      <td>cellular</td>\n      <td>mar</td>\n      <td>wed</td>\n      <td>...</td>\n      <td>0</td>\n      <td>nonexistent</td>\n      <td>-1.8</td>\n      <td>92.843</td>\n      <td>-50.0</td>\n      <td>1.663</td>\n      <td>5099</td>\n      <td>yes</td>\n      <td>4.0</td>\n      <td>4.616521</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>26735</th>\n      <td>84</td>\n      <td>retired</td>\n      <td>divorced</td>\n      <td>basic.4y</td>\n      <td>unknown</td>\n      <td>yes</td>\n      <td>no</td>\n      <td>cellular</td>\n      <td>oct</td>\n      <td>fri</td>\n      <td>...</td>\n      <td>0</td>\n      <td>nonexistent</td>\n      <td>-1.1</td>\n      <td>94.601</td>\n      <td>-49.5</td>\n      <td>0.993</td>\n      <td>4963</td>\n      <td>no</td>\n      <td>4.0</td>\n      <td>4.231909</td>\n    </tr>\n    <tr>\n      <th>26739</th>\n      <td>74</td>\n      <td>retired</td>\n      <td>divorced</td>\n      <td>basic.4y</td>\n      <td>no</td>\n      <td>yes</td>\n      <td>no</td>\n      <td>cellular</td>\n      <td>oct</td>\n      <td>mon</td>\n      <td>...</td>\n      <td>0</td>\n      <td>nonexistent</td>\n      <td>-1.1</td>\n      <td>94.601</td>\n      <td>-49.5</td>\n      <td>1.000</td>\n      <td>4963</td>\n      <td>no</td>\n      <td>4.0</td>\n      <td>3.270379</td>\n    </tr>\n    <tr>\n      <th>26740</th>\n      <td>74</td>\n      <td>retired</td>\n      <td>divorced</td>\n      <td>basic.4y</td>\n      <td>no</td>\n      <td>yes</td>\n      <td>no</td>\n      <td>cellular</td>\n      <td>oct</td>\n      <td>mon</td>\n      <td>...</td>\n      <td>2</td>\n      <td>success</td>\n      <td>-1.1</td>\n      <td>94.601</td>\n      <td>-49.5</td>\n      <td>1.000</td>\n      <td>4963</td>\n      <td>no</td>\n      <td>4.0</td>\n      <td>3.270379</td>\n    </tr>\n    <tr>\n      <th>26750</th>\n      <td>81</td>\n      <td>retired</td>\n      <td>married</td>\n      <td>basic.4y</td>\n      <td>no</td>\n      <td>yes</td>\n      <td>no</td>\n      <td>cellular</td>\n      <td>oct</td>\n      <td>wed</td>\n      <td>...</td>\n      <td>2</td>\n      <td>failure</td>\n      <td>-1.1</td>\n      <td>94.601</td>\n      <td>-49.5</td>\n      <td>1.016</td>\n      <td>4963</td>\n      <td>yes</td>\n      <td>4.0</td>\n      <td>3.943450</td>\n    </tr>\n    <tr>\n      <th>26872</th>\n      <td>73</td>\n      <td>retired</td>\n      <td>married</td>\n      <td>professional.course</td>\n      <td>no</td>\n      <td>yes</td>\n      <td>no</td>\n      <td>cellular</td>\n      <td>nov</td>\n      <td>fri</td>\n      <td>...</td>\n      <td>0</td>\n      <td>nonexistent</td>\n      <td>-1.1</td>\n      <td>94.767</td>\n      <td>-50.8</td>\n      <td>1.028</td>\n      <td>4963</td>\n      <td>yes</td>\n      <td>12.0</td>\n      <td>3.174226</td>\n    </tr>\n  </tbody>\n</table>\n<p>228 rows × 23 columns</p>\n</div>"
     },
     "metadata": {},
     "output_type": "execute_result",
     "execution_count": 221
    }
   ],
   "source": [
    "outliers_BT=BT.query('age_z > 3 | age_z < -3')\n",
    "outliers_BT"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "outputs": [
    {
     "data": {
      "text/plain": "       age   marital\n24840   91   married\n24833   91   married\n26520   89  divorced\n26015   89  divorced\n23628   88  divorced",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>age</th>\n      <th>marital</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>24840</th>\n      <td>91</td>\n      <td>married</td>\n    </tr>\n    <tr>\n      <th>24833</th>\n      <td>91</td>\n      <td>married</td>\n    </tr>\n    <tr>\n      <th>26520</th>\n      <td>89</td>\n      <td>divorced</td>\n    </tr>\n    <tr>\n      <th>26015</th>\n      <td>89</td>\n      <td>divorced</td>\n    </tr>\n    <tr>\n      <th>23628</th>\n      <td>88</td>\n      <td>divorced</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "output_type": "execute_result",
     "execution_count": 222
    }
   ],
   "source": [
    "outliers_BT_sorted=outliers_BT.sort_values(['age_z'], ascending=False)\n",
    "outliers_BT_sorted[['age' , 'marital']].head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  }
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