Deworking-2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0aed118d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "id": "d4952903",
   "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>children</th>\n",
       "      <th>days_employed</th>\n",
       "      <th>dob_years</th>\n",
       "      <th>education</th>\n",
       "      <th>education_id</th>\n",
       "      <th>family_status</th>\n",
       "      <th>family_status_id</th>\n",
       "      <th>gender</th>\n",
       "      <th>income_type</th>\n",
       "      <th>debt</th>\n",
       "      <th>total_income</th>\n",
       "      <th>purpose</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-8437.673028</td>\n",
       "      <td>42</td>\n",
       "      <td>высшее</td>\n",
       "      <td>0</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>253875.639453</td>\n",
       "      <td>покупка жилья</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-4024.803754</td>\n",
       "      <td>36</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>112080.014102</td>\n",
       "      <td>приобретение автомобиля</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>-5623.422610</td>\n",
       "      <td>33</td>\n",
       "      <td>Среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>145885.952297</td>\n",
       "      <td>покупка жилья</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>-4124.747207</td>\n",
       "      <td>32</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>267628.550329</td>\n",
       "      <td>дополнительное образование</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>340266.072047</td>\n",
       "      <td>53</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>гражданский брак</td>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>пенсионер</td>\n",
       "      <td>0</td>\n",
       "      <td>158616.077870</td>\n",
       "      <td>сыграть свадьбу</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   children  days_employed  dob_years education  education_id  \\\n",
       "0         1   -8437.673028         42    высшее             0   \n",
       "1         1   -4024.803754         36   среднее             1   \n",
       "2         0   -5623.422610         33   Среднее             1   \n",
       "3         3   -4124.747207         32   среднее             1   \n",
       "4         0  340266.072047         53   среднее             1   \n",
       "\n",
       "      family_status  family_status_id gender income_type  debt   total_income  \\\n",
       "0   женат / замужем                 0      F   сотрудник     0  253875.639453   \n",
       "1   женат / замужем                 0      F   сотрудник     0  112080.014102   \n",
       "2   женат / замужем                 0      M   сотрудник     0  145885.952297   \n",
       "3   женат / замужем                 0      M   сотрудник     0  267628.550329   \n",
       "4  гражданский брак                 1      F   пенсионер     0  158616.077870   \n",
       "\n",
       "                      purpose  \n",
       "0               покупка жилья  \n",
       "1     приобретение автомобиля  \n",
       "2               покупка жилья  \n",
       "3  дополнительное образование  \n",
       "4             сыграть свадьбу  "
      ]
     },
     "execution_count": 294,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('/Users/georgezagorsky/Downloads/data.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "id": "6c463350",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 21525 entries, 0 to 21524\n",
      "Data columns (total 12 columns):\n",
      "children            21525 non-null int64\n",
      "days_employed       19351 non-null float64\n",
      "dob_years           21525 non-null int64\n",
      "education           21525 non-null object\n",
      "education_id        21525 non-null int64\n",
      "family_status       21525 non-null object\n",
      "family_status_id    21525 non-null int64\n",
      "gender              21525 non-null object\n",
      "income_type         21525 non-null object\n",
      "debt                21525 non-null int64\n",
      "total_income        19351 non-null float64\n",
      "purpose             21525 non-null object\n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 2.0+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42cf494f",
   "metadata": {},
   "source": [
    "# Изменение типов данных - предобработка данных"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "id": "641426e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "children            0.000000\n",
       "days_employed       0.100999\n",
       "dob_years           0.000000\n",
       "education           0.000000\n",
       "education_id        0.000000\n",
       "family_status       0.000000\n",
       "family_status_id    0.000000\n",
       "gender              0.000000\n",
       "income_type         0.000000\n",
       "debt                0.000000\n",
       "total_income        0.100999\n",
       "purpose             0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 296,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "32a7507b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "id": "5346c600",
   "metadata": {},
   "outputs": [],
   "source": [
    "t_d = df[(df['days_employed'].isnull() & (df['total_income'].isnull()))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "id": "84332a1a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "43.0"
      ]
     },
     "execution_count": 298,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_d['dob_years'].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "id": "c00981a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "42.0"
      ]
     },
     "execution_count": 299,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['dob_years'].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "id": "2d2533ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd1917aa250>"
      ]
     },
     "execution_count": 300,
     "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['dob_years'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "id": "1141f6a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd1f51fd0d0>"
      ]
     },
     "execution_count": 301,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD4CAYAAAAXUaZHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+17YcXAAAWJ0lEQVR4nO3df5DcdX3H8edLQEw5m4DQnTTJ9OiQ6iBXI9kBHJ3OHoxtAMfQGWthMppoOmdncAbHtCXYmaq1TOO0kepomZ4Gia31pCglE6EWQ64OnQHMYeQSIjXKWXODSdEQPUTs0Xf/2E/ocuxl9/bn9z59PWZ27vv9fL/f3ddm71753ve+u19FBGZmlpeX9TuAmZl1nsvdzCxDLnczswy53M3MMuRyNzPL0On9DgBw7rnnxuDgYEvbPvPMM5x11lmdDdQFztk5iyEjOGenLYacvc44MTHxVEScV3dhRPT9tnbt2mjV3r17W962l5yzcxZDxgjn7LTFkLPXGYF9MU+v+rCMmVmGXO5mZhlyuZuZZcjlbmaWIZe7mVmGXO5mZhlyuZuZZcjlbmaWIZe7mVmGmv74AUmnAfuA6Yh4i6TzgTHgVcAE8I6I+IWkM4HPAWuBHwG/HxFTHU9u/68Mbv3KC9NbhmbZVDPfbVPbru7ZY5l1ykL23G8ADtXMfxS4JSIuAI4Dm9P4ZuB4Gr8lrWdmZj3UVLlLWglcDXwmzQu4HLgzrbITuCZNr0/zpOVXpPXNzKxHFE1cQ1XSncBfAq8E/gjYBDyY9s6RtAq4NyIuknQAWBcRR9Ky7wKXRsRTc+5zBBgBKJVKa8fGxlp6AjMzMwwMDLS0bS85Z3smp0+8MF1aAkef7d1jD61Y2tJ2Rf23nMs5O6fXGYeHhyciolxvWcNj7pLeAhyLiAlJlU6FiohRYBSgXC5HpdLaXY+Pj9Pqtr3knO3ZNOeY+/bJHn5a9eQzLW22Zeh5tj/Q2ra9NDdnUf/GUNTvzVpFytjMT8gbgbdKugp4BfDLwMeBZZJOj4hZYCUwndafBlYBRySdDiyl+odVMzPrkYbH3CPipohYGRGDwLXA/RGxAdgLvC2tthG4O03vSvOk5fdHM8d+zMysY9o5z/1G4P2SDlM9HXJHGt8BvCqNvx/Y2l5EMzNbqAUduIyIcWA8TX8PuKTOOj8Hfq8D2czMrEV+h6qZWYZc7mZmGXK5m5llyOVuZpYhl7uZWYZc7mZmGXK5m5llyOVuZpYhl7uZWYZc7mZmGXK5m5llyOVuZpahHl7xwMwWg8EeXnx8rqJeKGQx8p67mVmGXO5mZhlyuZuZZcjlbmaWoYblLukVkh6W9C1JByV9OI3fLukJSfvTbU0al6RPSDos6VFJF3f7SZiZ2Ys1c7bMc8DlETEj6QzgAUn3pmV/HBF3zln/SmB1ul0K3Jq+mplZjzTcc4+qmTR7RrrFKTZZD3wubfcgsEzS8vajmplZsxRxqp5OK0mnARPABcCnIuJGSbcDb6C6Z78H2BoRz0naDWyLiAfStnuAGyNi35z7HAFGAEql0tqxsbGWnsDMzAwDAwMtbdtLztmeyekTL0yXlsDRZ/sYpknOuXBDK5bOu6yo35u1ep1xeHh4IiLK9ZY19SamiHgeWCNpGXCXpIuAm4AfAi8HRoEbgT9vNlREjKbtKJfLUalUmt30RcbHx2l1215yzvZsqnljzZahWbZPFv/9d865cFMbKvMuK+r3Zq0iZVzQ2TIR8TSwF1gXEU+mQy/PAZ8FLkmrTQOrajZbmcbMzKxHmjlb5ry0x46kJcCbgW+fPI4uScA1wIG0yS7gnemsmcuAExHxZFfSm5lZXc38LrYc2JmOu78MuCMidku6X9J5gID9wB+m9e8BrgIOAz8D3tX52GZmdioNyz0iHgVeX2f88nnWD+D69qOZmVmr/A5VM7MMudzNzDLkcjczy5DL3cwsQy53M7MMudzNzDLkcjczy5DL3cwsQy53M7MMudzNzDLkcjczy5DL3cwsQy53M7MMudzNzDLkcjczy5DL3cwsQ81cZu8Vkh6W9C1JByV9OI2fL+khSYclfVHSy9P4mWn+cFo+2N2nYGZmczWz5/4ccHlEvA5YA6xL10b9KHBLRFwAHAc2p/U3A8fT+C1pPTMz66GG5R5VM2n2jHQL4HLgzjS+k+pFsgHWp3nS8ivSRbTNzKxHVL3kaYOVqhfHngAuAD4F/BXwYNo7R9Iq4N6IuEjSAWBdRBxJy74LXBoRT825zxFgBKBUKq0dGxtr6QnMzMwwMDDQ0ra95JztmZw+8cJ0aQkcfbaPYZrknAs3tGLpvMuK+r1Zq9cZh4eHJyKiXG9ZwwtkA0TE88AaScuAu4DXtBsqIkaBUYByuRyVSqWl+xkfH6fVbXvJOduzaetXXpjeMjTL9smmvnX7yjkXbmpDZd5lRf3erFWkjAs6WyYingb2Am8Alkk6+R2xEphO09PAKoC0fCnwo46kNTOzpjRztsx5aY8dSUuANwOHqJb829JqG4G70/SuNE9afn80c+zHzMw6ppnfxZYDO9Nx95cBd0TEbkmPAWOS/gL4JrAjrb8D+HtJh4EfA9d2IbeZmZ1Cw3KPiEeB19cZ/x5wSZ3xnwO/15F0ZmbWEr9D1cwsQy53M7MMudzNzDLkcjczy5DL3cwsQy53M7MMudzNzDJUjA+UsEVjsOYzXsysuLznbmaWIZe7mVmGXO5mZhlyuZuZZcjlbmaWIZe7mVmGXO5mZhlyuZuZZaiZy+ytkrRX0mOSDkq6IY1/SNK0pP3pdlXNNjdJOizpcUm/080nYGZmL9XMO1RngS0R8YikVwITku5Ly26JiL+uXVnShVQvrfda4FeBr0n6jYh4vpPBzcxsfg333CPiyYh4JE3/lOrFsVecYpP1wFhEPBcRTwCHqXM5PjMz654FHXOXNEj1eqoPpaH3SnpU0m2Szk5jK4Af1Gx2hFP/Z2BmZh2miGhuRWkA+Dfg5oj4sqQS8BQQwEeA5RHxbkmfBB6MiH9I2+0A7o2IO+fc3wgwAlAqldaOjY219ARmZmYYGBhoadteyiXn5PSJHqapr7QEjj7b7xSNOefCDa1YOu+yxfAz1OuMw8PDExFRrresqU+FlHQG8CXg8xHxZYCIOFqz/NPA7jQ7Dayq2XxlGnuRiBgFRgHK5XJUKpVmorzE+Pg4rW7bS7nk3FSAT4XcMjTL9snif6Cpcy7c1IbKvMsWw89QkTI2c7aMgB3AoYj4WM348prVfhc4kKZ3AddKOlPS+cBq4OHORTYzs0aa+e/6jcA7gElJ+9PYB4DrJK2helhmCngPQEQclHQH8BjVM22u95kyZma91bDcI+IBQHUW3XOKbW4Gbm4jl5mZtcHvUDUzy5DL3cwsQ8X4E7mZGae+Ru+Wodmuna01te3qrtxvP3nP3cwsQy53M7MMudzNzDLkcjczy5DL3cwsQy53M7MMudzNzDLkcjczy5DL3cwsQy53M7MMudzNzDLkcjczy5DL3cwsQy53M7MMNXMN1VWS9kp6TNJBSTek8XMk3SfpO+nr2Wlckj4h6bCkRyVd3O0nYWZmL9bMnvsssCUiLgQuA66XdCGwFdgTEauBPWke4EqqF8VeDYwAt3Y8tZmZnVLDco+IJyPikTT9U+AQsAJYD+xMq+0ErknT64HPRdWDwDJJyzue3MzM5qWIaH5laRD4OnAR8J8RsSyNCzgeEcsk7Qa2pQtrI2kPcGNE7JtzXyNU9+wplUprx8bGWnoCMzMzDAwMtLRtL+WSc3L6RA/T1FdaAkef7XeKxpyzs7qZc2jF0o7cT69/zoeHhyciolxvWdOX2ZM0AHwJeF9E/KTa51UREZKa/1+ius0oMApQLpejUqksZPMXjI+P0+q2vZRLzm5d5mwhtgzNsn2y+FeIdM7O6mbOqQ2VjtxPkX7OmzpbRtIZVIv98xHx5TR89OThlvT1WBqfBlbVbL4yjZmZWY80c7aMgB3AoYj4WM2iXcDGNL0RuLtm/J3prJnLgBMR8WQHM5uZWQPN/I7zRuAdwKSk/WnsA8A24A5Jm4HvA29Py+4BrgIOAz8D3tXRxGZm1lDDck9/GNU8i6+os34A17eZy8zM2uB3qJqZZcjlbmaWIZe7mVmGXO5mZhlyuZuZZcjlbmaWIZe7mVmGXO5mZhlyuZuZZcjlbmaWIZe7mVmGXO5mZhlyuZuZZcjlbmaWIZe7mVmGXO5mZhlq5jJ7t0k6JulAzdiHJE1L2p9uV9Usu0nSYUmPS/qdbgU3M7P5NbPnfjuwrs74LRGxJt3uAZB0IXAt8Nq0zd9KOq1TYc3MrDkNyz0ivg78uMn7Ww+MRcRzEfEE1euoXtJGPjMza4GqlzxtsJI0COyOiIvS/IeATcBPgH3Alog4LumTwIMR8Q9pvR3AvRFxZ537HAFGAEql0tqxsbGWnsDMzAwDAwMtbdtLueScnD7RwzT1lZbA0Wf7naIx5+ysbuYcWrG0I/fT65/z4eHhiYgo11vW8ALZ87gV+AgQ6et24N0LuYOIGAVGAcrlclQqlZaCjI+P0+q2vZRLzk1bv9K7MPPYMjTL9slWv3V7xzk7q5s5pzZUOnI/Rfo5b+lsmYg4GhHPR8T/AJ/m/w69TAOralZdmcbMzKyHWip3SctrZn8XOHkmzS7gWklnSjofWA083F5EMzNbqIa/40j6AlABzpV0BPggUJG0huphmSngPQARcVDSHcBjwCxwfUQ8353oZmY2n4blHhHX1RnecYr1bwZubieUmZm1x+9QNTPLkMvdzCxDLnczswy53M3MMuRyNzPLkMvdzCxDLnczswy53M3MMuRyNzPLkMvdzCxDLnczswy53M3MMuRyNzPLkMvdzCxDLnczswy53M3MMuRyNzPLUMNyl3SbpGOSDtSMnSPpPknfSV/PTuOS9AlJhyU9KuniboY3M7P6mtlzvx1YN2dsK7AnIlYDe9I8wJVUL4q9GhgBbu1MTDMzW4iG5R4RXwd+PGd4PbAzTe8ErqkZ/1xUPQgsk7S8U2HNzKw5iojGK0mDwO6IuCjNPx0Ry9K0gOMRsUzSbmBbRDyQlu0BboyIfXXuc4Tq3j2lUmnt2NhYS09gZmaGgYGBlrbtpVxyTk6f6GGa+kpL4Oiz/U7RmHN2VjdzDq1Y2pH76fXP+fDw8ERElOstO73dO4+IkNT4f4iXbjcKjAKUy+WoVCotPf74+DitbttLueTctPUrvQszjy1Ds2yfbPtbt+ucs7O6mXNqQ6Uj91Okn/NWz5Y5evJwS/p6LI1PA6tq1luZxszMrIdaLfddwMY0vRG4u2b8nemsmcuAExHxZJsZzcxsgRr+jiPpC0AFOFfSEeCDwDbgDkmbge8Db0+r3wNcBRwGfga8qwuZzcysgYblHhHXzbPoijrrBnB9u6HMzKw9foeqmVmGXO5mZhlyuZuZZcjlbmaWIZe7mVmGXO5mZhlyuZuZZcjlbmaWIZe7mVmGXO5mZhlyuZuZZcjlbmaWIZe7mVmGXO5mZhkq/rW1zMy6bLBDl4/cMjS74EtRTm27uiOPPZf33M3MMtTWnrukKeCnwPPAbESUJZ0DfBEYBKaAt0fE8fZimpnZQnRiz304ItZERDnNbwX2RMRqYE+aNzOzHurGYZn1wM40vRO4pguPYWZmp6DqZU9b3Fh6AjgOBPB3ETEq6emIWJaWCzh+cn7OtiPACECpVFo7NjbWUoaZmRkGBgZafQo9k0vOyekTPUxTX2kJHH223ykac87OWgw5W8k4tGJpy483PDw8UXPU5EXaPVvmTRExLelXgPskfbt2YUSEpLr/e0TEKDAKUC6Xo1KptBRgfHycVrftpVxyLvRMgG7YMjTL9snin+jlnJ21GHK2knFqQ6UrWdo6LBMR0+nrMeAu4BLgqKTlAOnrsXZDmpnZwrRc7pLOkvTKk9PAbwMHgF3AxrTaRuDudkOamdnCtPM7Tgm4q3pYndOBf4yIf5H0DeAOSZuB7wNvbz+mmZktRMvlHhHfA15XZ/xHwBXthDIzs/b4HapmZhlyuZuZZcjlbmaWIZe7mVmGXO5mZhlyuZuZZcjlbmaWIZe7mVmGiv0pPFZXpy4JVk8rlwkzs+LxnruZWYZc7mZmGXK5m5llyOVuZpYhl7uZWYZc7mZmGVr0p0JOTp/o26l7U9uu7svjmpk14j13M7MMda3cJa2T9Likw5K2dutxzMzspbpS7pJOAz4FXAlcCFwn6cJuPJaZmb1Ut/bcLwEOR8T3IuIXwBiwvkuPZWZmcygiOn+n0tuAdRHxB2n+HcClEfHemnVGgJE0+2rg8RYf7lzgqTbi9opzds5iyAjO2WmLIWevM/5aRJxXb0HfzpaJiFFgtN37kbQvIsodiNRVztk5iyEjOGenLYacRcrYrcMy08CqmvmVaczMzHqgW+X+DWC1pPMlvRy4FtjVpccyM7M5unJYJiJmJb0X+CpwGnBbRBzsxmPRgUM7PeKcnbMYMoJzdtpiyFmYjF35g6qZmfWX36FqZpYhl7uZWYYWdbkX9SMOJN0m6ZikAzVj50i6T9J30tez+5xxlaS9kh6TdFDSDQXN+QpJD0v6Vsr54TR+vqSH0mv/xfSH+76SdJqkb0raXeCMU5ImJe2XtC+NFeo1T5mWSbpT0rclHZL0hqLllPTq9O948vYTSe8rSs5FW+4F/4iD24F1c8a2AnsiYjWwJ8330yywJSIuBC4Drk//fkXL+RxweUS8DlgDrJN0GfBR4JaIuAA4DmzuY8aTbgAO1cwXMSPAcESsqTkfu2ivOcDHgX+JiNcAr6P671qonBHxePp3XAOsBX4G3EVRckbEorwBbwC+WjN/E3BTv3PV5BkEDtTMPw4sT9PLgcf7nXFO3ruBNxc5J/BLwCPApVTfBXh6ve+FPmVbSfUH+XJgN6CiZUw5poBz54wV6jUHlgJPkE74KGrOOdl+G/j3IuVctHvuwArgBzXzR9JYUZUi4sk0/UOg1M8wtSQNAq8HHqKAOdPhjv3AMeA+4LvA0xExm1Ypwmv/N8CfAP+T5l9F8TICBPCvkibSR4BA8V7z84H/Aj6bDnN9RtJZFC9nrWuBL6TpQuRczOW+aEX1v/RCnIMqaQD4EvC+iPhJ7bKi5IyI56P6q+9Kqh9K95o+R3oRSW8BjkXERL+zNOFNEXEx1cOZ10v6rdqFBXnNTwcuBm6NiNcDzzDn0EZBcgKQ/pbyVuCf5i7rZ87FXO6L7SMOjkpaDpC+HutzHiSdQbXYPx8RX07Dhct5UkQ8DeyleohjmaSTb8Lr92v/RuCtkqaofgLq5VSPGRcpIwARMZ2+HqN6fPgSiveaHwGORMRDaf5OqmVftJwnXQk8EhFH03whci7mcl9sH3GwC9iYpjdSPcbdN5IE7AAORcTHahYVLed5kpal6SVU/y5wiGrJvy2t1tecEXFTRKyMiEGq34f3R8QGCpQRQNJZkl55cprqceIDFOw1j4gfAj+Q9Oo0dAXwGAXLWeM6/u+QDBQlZ7//ENHmHzGuAv6D6jHYP+13nppcXwCeBP6b6l7IZqrHYPcA3wG+BpzT54xvovrr4qPA/nS7qoA5fxP4Zsp5APizNP7rwMPAYaq/Dp/Z79c95aoAu4uYMeX5VrodPPkzU7TXPGVaA+xLr/s/A2cXNOdZwI+ApTVjhcjpjx8wM8vQYj4sY2Zm83C5m5llyOVuZpYhl7uZWYZc7mZmGXK5m5llyOVuZpah/wW/sYj/e2kasgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "t_d['dob_years'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "id": "fcb6b820",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd1d6e4bd90>"
      ]
     },
     "execution_count": 302,
     "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.plot(kind = 'scatter', x = 'days_employed', y = 'total_income')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "id": "5985196f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd1f5243dd0>"
      ]
     },
     "execution_count": 303,
     "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(df['days_employed'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "id": "9ce9c527",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd204296650>"
      ]
     },
     "execution_count": 304,
     "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(df['total_income'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "id": "1f031345",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'whiskers': [<matplotlib.lines.Line2D at 0x7fd19112b910>,\n",
       "  <matplotlib.lines.Line2D at 0x7fd19112b850>],\n",
       " 'caps': [<matplotlib.lines.Line2D at 0x7fd19112bcd0>,\n",
       "  <matplotlib.lines.Line2D at 0x7fd191146310>],\n",
       " 'boxes': [<matplotlib.lines.Line2D at 0x7fd1c758cb90>],\n",
       " 'medians': [<matplotlib.lines.Line2D at 0x7fd191146f10>],\n",
       " 'fliers': [<matplotlib.lines.Line2D at 0x7fd1c758cf50>],\n",
       " 'means': []}"
      ]
     },
     "execution_count": 305,
     "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": [
    "plt.boxplot(df['dob_years'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "id": "6cc81533",
   "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>children</th>\n",
       "      <th>days_employed</th>\n",
       "      <th>dob_years</th>\n",
       "      <th>education</th>\n",
       "      <th>education_id</th>\n",
       "      <th>family_status</th>\n",
       "      <th>family_status_id</th>\n",
       "      <th>gender</th>\n",
       "      <th>income_type</th>\n",
       "      <th>debt</th>\n",
       "      <th>total_income</th>\n",
       "      <th>purpose</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>99</td>\n",
       "      <td>0</td>\n",
       "      <td>346541.618895</td>\n",
       "      <td>0</td>\n",
       "      <td>Среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>пенсионер</td>\n",
       "      <td>0</td>\n",
       "      <td>71291.522491</td>\n",
       "      <td>автомобиль</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>149</td>\n",
       "      <td>0</td>\n",
       "      <td>-2664.273168</td>\n",
       "      <td>0</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>в разводе</td>\n",
       "      <td>3</td>\n",
       "      <td>F</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>70176.435951</td>\n",
       "      <td>операции с жильем</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>270</td>\n",
       "      <td>3</td>\n",
       "      <td>-1872.663186</td>\n",
       "      <td>0</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>102166.458894</td>\n",
       "      <td>ремонт жилью</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>578</td>\n",
       "      <td>0</td>\n",
       "      <td>397856.565013</td>\n",
       "      <td>0</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>пенсионер</td>\n",
       "      <td>0</td>\n",
       "      <td>97620.687042</td>\n",
       "      <td>строительство собственной недвижимости</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1040</td>\n",
       "      <td>0</td>\n",
       "      <td>-1158.029561</td>\n",
       "      <td>0</td>\n",
       "      <td>высшее</td>\n",
       "      <td>0</td>\n",
       "      <td>в разводе</td>\n",
       "      <td>3</td>\n",
       "      <td>F</td>\n",
       "      <td>компаньон</td>\n",
       "      <td>0</td>\n",
       "      <td>303994.134987</td>\n",
       "      <td>свой автомобиль</td>\n",
       "    </tr>\n",
       "    <tr>\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",
       "      <td>19829</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>жилье</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20462</td>\n",
       "      <td>0</td>\n",
       "      <td>338734.868540</td>\n",
       "      <td>0</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>пенсионер</td>\n",
       "      <td>0</td>\n",
       "      <td>259193.920299</td>\n",
       "      <td>покупка своего жилья</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20577</td>\n",
       "      <td>0</td>\n",
       "      <td>331741.271455</td>\n",
       "      <td>0</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>Не женат / не замужем</td>\n",
       "      <td>4</td>\n",
       "      <td>F</td>\n",
       "      <td>пенсионер</td>\n",
       "      <td>0</td>\n",
       "      <td>129788.762899</td>\n",
       "      <td>недвижимость</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21179</td>\n",
       "      <td>2</td>\n",
       "      <td>-108.967042</td>\n",
       "      <td>0</td>\n",
       "      <td>высшее</td>\n",
       "      <td>0</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>компаньон</td>\n",
       "      <td>0</td>\n",
       "      <td>240702.007382</td>\n",
       "      <td>строительство жилой недвижимости</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21313</td>\n",
       "      <td>0</td>\n",
       "      <td>-1268.487728</td>\n",
       "      <td>0</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>Не женат / не замужем</td>\n",
       "      <td>4</td>\n",
       "      <td>M</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>152419.184851</td>\n",
       "      <td>операции с жильем</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>101 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       children  days_employed  dob_years education  education_id  \\\n",
       "99            0  346541.618895          0   Среднее             1   \n",
       "149           0   -2664.273168          0   среднее             1   \n",
       "270           3   -1872.663186          0   среднее             1   \n",
       "578           0  397856.565013          0   среднее             1   \n",
       "1040          0   -1158.029561          0    высшее             0   \n",
       "...         ...            ...        ...       ...           ...   \n",
       "19829         0            NaN          0   среднее             1   \n",
       "20462         0  338734.868540          0   среднее             1   \n",
       "20577         0  331741.271455          0   среднее             1   \n",
       "21179         2    -108.967042          0    высшее             0   \n",
       "21313         0   -1268.487728          0   среднее             1   \n",
       "\n",
       "               family_status  family_status_id gender income_type  debt  \\\n",
       "99           женат / замужем                 0      F   пенсионер     0   \n",
       "149                в разводе                 3      F   сотрудник     0   \n",
       "270          женат / замужем                 0      F   сотрудник     0   \n",
       "578          женат / замужем                 0      F   пенсионер     0   \n",
       "1040               в разводе                 3      F   компаньон     0   \n",
       "...                      ...               ...    ...         ...   ...   \n",
       "19829        женат / замужем                 0      F   сотрудник     0   \n",
       "20462        женат / замужем                 0      F   пенсионер     0   \n",
       "20577  Не женат / не замужем                 4      F   пенсионер     0   \n",
       "21179        женат / замужем                 0      M   компаньон     0   \n",
       "21313  Не женат / не замужем                 4      M   сотрудник     0   \n",
       "\n",
       "        total_income                                 purpose  \n",
       "99      71291.522491                              автомобиль  \n",
       "149     70176.435951                       операции с жильем  \n",
       "270    102166.458894                            ремонт жилью  \n",
       "578     97620.687042  строительство собственной недвижимости  \n",
       "1040   303994.134987                         свой автомобиль  \n",
       "...              ...                                     ...  \n",
       "19829            NaN                                   жилье  \n",
       "20462  259193.920299                    покупка своего жилья  \n",
       "20577  129788.762899                            недвижимость  \n",
       "21179  240702.007382        строительство жилой недвижимости  \n",
       "21313  152419.184851                       операции с жильем  \n",
       "\n",
       "[101 rows x 12 columns]"
      ]
     },
     "execution_count": 306,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['dob_years'] < 5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "id": "21333bb4",
   "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>children</th>\n",
       "      <th>days_employed</th>\n",
       "      <th>dob_years</th>\n",
       "      <th>education</th>\n",
       "      <th>education_id</th>\n",
       "      <th>family_status</th>\n",
       "      <th>family_status_id</th>\n",
       "      <th>gender</th>\n",
       "      <th>income_type</th>\n",
       "      <th>debt</th>\n",
       "      <th>total_income</th>\n",
       "      <th>purpose</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-8437.673028</td>\n",
       "      <td>42</td>\n",
       "      <td>высшее</td>\n",
       "      <td>0</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>253875.639453</td>\n",
       "      <td>покупка жилья</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-4024.803754</td>\n",
       "      <td>36</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>112080.014102</td>\n",
       "      <td>приобретение автомобиля</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>-5623.422610</td>\n",
       "      <td>33</td>\n",
       "      <td>Среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>145885.952297</td>\n",
       "      <td>покупка жилья</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>-4124.747207</td>\n",
       "      <td>32</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>267628.550329</td>\n",
       "      <td>дополнительное образование</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>340266.072047</td>\n",
       "      <td>53</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>гражданский брак</td>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>пенсионер</td>\n",
       "      <td>0</td>\n",
       "      <td>158616.077870</td>\n",
       "      <td>сыграть свадьбу</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   children  days_employed  dob_years education  education_id  \\\n",
       "0         1   -8437.673028         42    высшее             0   \n",
       "1         1   -4024.803754         36   среднее             1   \n",
       "2         0   -5623.422610         33   Среднее             1   \n",
       "3         3   -4124.747207         32   среднее             1   \n",
       "4         0  340266.072047         53   среднее             1   \n",
       "\n",
       "      family_status  family_status_id gender income_type  debt   total_income  \\\n",
       "0   женат / замужем                 0      F   сотрудник     0  253875.639453   \n",
       "1   женат / замужем                 0      F   сотрудник     0  112080.014102   \n",
       "2   женат / замужем                 0      M   сотрудник     0  145885.952297   \n",
       "3   женат / замужем                 0      M   сотрудник     0  267628.550329   \n",
       "4  гражданский брак                 1      F   пенсионер     0  158616.077870   \n",
       "\n",
       "                      purpose  \n",
       "0               покупка жилья  \n",
       "1     приобретение автомобиля  \n",
       "2               покупка жилья  \n",
       "3  дополнительное образование  \n",
       "4             сыграть свадьбу  "
      ]
     },
     "execution_count": 307,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df[df['dob_years'] != 0]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07d6a688",
   "metadata": {},
   "source": [
    "# 1) children"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "id": "17c1af9d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd1d64aa590>"
      ]
     },
     "execution_count": 308,
     "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['children'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "id": "120bf1b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd1d6658210>"
      ]
     },
     "execution_count": 309,
     "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(df['children']) # Выброс на 20 детей и отрицательное количество детей"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "id": "b2fbf2f2",
   "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>children</th>\n",
       "      <th>days_employed</th>\n",
       "      <th>dob_years</th>\n",
       "      <th>education</th>\n",
       "      <th>education_id</th>\n",
       "      <th>family_status</th>\n",
       "      <th>family_status_id</th>\n",
       "      <th>gender</th>\n",
       "      <th>income_type</th>\n",
       "      <th>debt</th>\n",
       "      <th>total_income</th>\n",
       "      <th>purpose</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-8437.673028</td>\n",
       "      <td>42</td>\n",
       "      <td>высшее</td>\n",
       "      <td>0</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>253875.639453</td>\n",
       "      <td>покупка жилья</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-4024.803754</td>\n",
       "      <td>36</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>112080.014102</td>\n",
       "      <td>приобретение автомобиля</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>-5623.422610</td>\n",
       "      <td>33</td>\n",
       "      <td>Среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>145885.952297</td>\n",
       "      <td>покупка жилья</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>-4124.747207</td>\n",
       "      <td>32</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>267628.550329</td>\n",
       "      <td>дополнительное образование</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>340266.072047</td>\n",
       "      <td>53</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>гражданский брак</td>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>пенсионер</td>\n",
       "      <td>0</td>\n",
       "      <td>158616.077870</td>\n",
       "      <td>сыграть свадьбу</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>-926.185831</td>\n",
       "      <td>27</td>\n",
       "      <td>высшее</td>\n",
       "      <td>0</td>\n",
       "      <td>гражданский брак</td>\n",
       "      <td>1</td>\n",
       "      <td>M</td>\n",
       "      <td>компаньон</td>\n",
       "      <td>0</td>\n",
       "      <td>255763.565419</td>\n",
       "      <td>покупка жилья</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>-2879.202052</td>\n",
       "      <td>43</td>\n",
       "      <td>высшее</td>\n",
       "      <td>0</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>компаньон</td>\n",
       "      <td>0</td>\n",
       "      <td>240525.971920</td>\n",
       "      <td>операции с жильем</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>-152.779569</td>\n",
       "      <td>50</td>\n",
       "      <td>СРЕДНЕЕ</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>135823.934197</td>\n",
       "      <td>образование</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>-6929.865299</td>\n",
       "      <td>35</td>\n",
       "      <td>ВЫСШЕЕ</td>\n",
       "      <td>0</td>\n",
       "      <td>гражданский брак</td>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>95856.832424</td>\n",
       "      <td>на проведение свадьбы</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>-2188.756445</td>\n",
       "      <td>41</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>144425.938277</td>\n",
       "      <td>покупка жилья для семьи</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>-4171.483647</td>\n",
       "      <td>36</td>\n",
       "      <td>высшее</td>\n",
       "      <td>0</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>компаньон</td>\n",
       "      <td>0</td>\n",
       "      <td>113943.491460</td>\n",
       "      <td>покупка недвижимости</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>-792.701887</td>\n",
       "      <td>40</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>77069.234271</td>\n",
       "      <td>покупка коммерческой недвижимости</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>65</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>гражданский брак</td>\n",
       "      <td>1</td>\n",
       "      <td>M</td>\n",
       "      <td>пенсионер</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>сыграть свадьбу</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>-1846.641941</td>\n",
       "      <td>54</td>\n",
       "      <td>неоконченное высшее</td>\n",
       "      <td>2</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>130458.228857</td>\n",
       "      <td>приобретение автомобиля</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>-1844.956182</td>\n",
       "      <td>56</td>\n",
       "      <td>высшее</td>\n",
       "      <td>0</td>\n",
       "      <td>гражданский брак</td>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>компаньон</td>\n",
       "      <td>1</td>\n",
       "      <td>165127.911772</td>\n",
       "      <td>покупка жилой недвижимости</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>-972.364419</td>\n",
       "      <td>26</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>116820.904450</td>\n",
       "      <td>строительство собственной недвижимости</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>-1719.934226</td>\n",
       "      <td>35</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>289202.704229</td>\n",
       "      <td>недвижимость</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>-2369.999720</td>\n",
       "      <td>33</td>\n",
       "      <td>высшее</td>\n",
       "      <td>0</td>\n",
       "      <td>гражданский брак</td>\n",
       "      <td>1</td>\n",
       "      <td>M</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>90410.586745</td>\n",
       "      <td>строительство недвижимости</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>400281.136913</td>\n",
       "      <td>53</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>вдовец / вдова</td>\n",
       "      <td>2</td>\n",
       "      <td>F</td>\n",
       "      <td>пенсионер</td>\n",
       "      <td>0</td>\n",
       "      <td>56823.777243</td>\n",
       "      <td>на покупку подержанного автомобиля</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>-10038.818549</td>\n",
       "      <td>48</td>\n",
       "      <td>СРЕДНЕЕ</td>\n",
       "      <td>1</td>\n",
       "      <td>в разводе</td>\n",
       "      <td>3</td>\n",
       "      <td>F</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>242831.107982</td>\n",
       "      <td>на покупку своего автомобиля</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>-1311.604166</td>\n",
       "      <td>36</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>209552.644578</td>\n",
       "      <td>недвижимость</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>-253.685166</td>\n",
       "      <td>33</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>гражданский брак</td>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>131812.203909</td>\n",
       "      <td>приобретение автомобиля</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>-1766.644138</td>\n",
       "      <td>24</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>гражданский брак</td>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>149681.140731</td>\n",
       "      <td>на покупку подержанного автомобиля</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>-272.981385</td>\n",
       "      <td>21</td>\n",
       "      <td>высшее</td>\n",
       "      <td>0</td>\n",
       "      <td>гражданский брак</td>\n",
       "      <td>1</td>\n",
       "      <td>M</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>128265.720871</td>\n",
       "      <td>сыграть свадьбу</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>338551.952911</td>\n",
       "      <td>57</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>Не женат / не замужем</td>\n",
       "      <td>4</td>\n",
       "      <td>F</td>\n",
       "      <td>пенсионер</td>\n",
       "      <td>0</td>\n",
       "      <td>290547.235997</td>\n",
       "      <td>операции с коммерческой недвижимостью</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>363548.489348</td>\n",
       "      <td>67</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>пенсионер</td>\n",
       "      <td>0</td>\n",
       "      <td>55112.757732</td>\n",
       "      <td>покупка недвижимости</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>госслужащий</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>образование</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>-529.191635</td>\n",
       "      <td>28</td>\n",
       "      <td>высшее</td>\n",
       "      <td>0</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>308848.983691</td>\n",
       "      <td>строительство собственной недвижимости</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>-717.274324</td>\n",
       "      <td>26</td>\n",
       "      <td>высшее</td>\n",
       "      <td>0</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>187863.237306</td>\n",
       "      <td>строительство собственной недвижимости</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>63</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>Не женат / не замужем</td>\n",
       "      <td>4</td>\n",
       "      <td>F</td>\n",
       "      <td>пенсионер</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>строительство жилой недвижимости</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>335581.668515</td>\n",
       "      <td>62</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>пенсионер</td>\n",
       "      <td>0</td>\n",
       "      <td>171456.067993</td>\n",
       "      <td>операции с коммерческой недвижимостью</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>-1682.083438</td>\n",
       "      <td>47</td>\n",
       "      <td>начальное</td>\n",
       "      <td>3</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>275485.684538</td>\n",
       "      <td>жилье</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>-4649.910832</td>\n",
       "      <td>34</td>\n",
       "      <td>СРЕДНЕЕ</td>\n",
       "      <td>1</td>\n",
       "      <td>гражданский брак</td>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>1</td>\n",
       "      <td>139057.464207</td>\n",
       "      <td>на проведение свадьбы</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>-1548.637544</td>\n",
       "      <td>48</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>компаньон</td>\n",
       "      <td>0</td>\n",
       "      <td>157245.786233</td>\n",
       "      <td>покупка жилья</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>-4488.067031</td>\n",
       "      <td>35</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>104660.449203</td>\n",
       "      <td>операции со своей недвижимостью</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>394021.072184</td>\n",
       "      <td>68</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>гражданский брак</td>\n",
       "      <td>1</td>\n",
       "      <td>M</td>\n",
       "      <td>пенсионер</td>\n",
       "      <td>0</td>\n",
       "      <td>77805.677436</td>\n",
       "      <td>на проведение свадьбы</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>-176.216688</td>\n",
       "      <td>33</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>Не женат / не замужем</td>\n",
       "      <td>4</td>\n",
       "      <td>M</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>138830.650734</td>\n",
       "      <td>автомобили</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>-6448.810860</td>\n",
       "      <td>43</td>\n",
       "      <td>ВЫСШЕЕ</td>\n",
       "      <td>0</td>\n",
       "      <td>гражданский брак</td>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>компаньон</td>\n",
       "      <td>0</td>\n",
       "      <td>154128.878672</td>\n",
       "      <td>приобретение автомобиля</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>-597.881827</td>\n",
       "      <td>25</td>\n",
       "      <td>высшее</td>\n",
       "      <td>0</td>\n",
       "      <td>Не женат / не замужем</td>\n",
       "      <td>4</td>\n",
       "      <td>M</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>1</td>\n",
       "      <td>192247.298639</td>\n",
       "      <td>образование</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>-650.587796</td>\n",
       "      <td>31</td>\n",
       "      <td>высшее</td>\n",
       "      <td>0</td>\n",
       "      <td>гражданский брак</td>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>компаньон</td>\n",
       "      <td>0</td>\n",
       "      <td>754240.797248</td>\n",
       "      <td>заняться образованием</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    children  days_employed  dob_years            education  education_id  \\\n",
       "0          1   -8437.673028         42               высшее             0   \n",
       "1          1   -4024.803754         36              среднее             1   \n",
       "2          0   -5623.422610         33              Среднее             1   \n",
       "3          3   -4124.747207         32              среднее             1   \n",
       "4          0  340266.072047         53              среднее             1   \n",
       "5          0    -926.185831         27               высшее             0   \n",
       "6          0   -2879.202052         43               высшее             0   \n",
       "7          0    -152.779569         50              СРЕДНЕЕ             1   \n",
       "8          2   -6929.865299         35               ВЫСШЕЕ             0   \n",
       "9          0   -2188.756445         41              среднее             1   \n",
       "10         2   -4171.483647         36               высшее             0   \n",
       "11         0    -792.701887         40              среднее             1   \n",
       "12         0            NaN         65              среднее             1   \n",
       "13         0   -1846.641941         54  неоконченное высшее             2   \n",
       "14         0   -1844.956182         56               высшее             0   \n",
       "15         1    -972.364419         26              среднее             1   \n",
       "16         0   -1719.934226         35              среднее             1   \n",
       "17         0   -2369.999720         33               высшее             0   \n",
       "18         0  400281.136913         53              среднее             1   \n",
       "19         0  -10038.818549         48              СРЕДНЕЕ             1   \n",
       "20         1   -1311.604166         36              среднее             1   \n",
       "21         1    -253.685166         33              среднее             1   \n",
       "22         1   -1766.644138         24              среднее             1   \n",
       "23         0    -272.981385         21               высшее             0   \n",
       "24         1  338551.952911         57              среднее             1   \n",
       "25         0  363548.489348         67              среднее             1   \n",
       "26         0            NaN         41              среднее             1   \n",
       "27         0    -529.191635         28               высшее             0   \n",
       "28         1    -717.274324         26               высшее             0   \n",
       "29         0            NaN         63              среднее             1   \n",
       "30         1  335581.668515         62              среднее             1   \n",
       "31         0   -1682.083438         47            начальное             3   \n",
       "32         0   -4649.910832         34              СРЕДНЕЕ             1   \n",
       "33         0   -1548.637544         48              среднее             1   \n",
       "34         0   -4488.067031         35              среднее             1   \n",
       "35         0  394021.072184         68              среднее             1   \n",
       "36         0    -176.216688         33              среднее             1   \n",
       "37         0   -6448.810860         43               ВЫСШЕЕ             0   \n",
       "38         0    -597.881827         25               высшее             0   \n",
       "39         0    -650.587796         31               высшее             0   \n",
       "\n",
       "            family_status  family_status_id gender  income_type  debt  \\\n",
       "0         женат / замужем                 0      F    сотрудник     0   \n",
       "1         женат / замужем                 0      F    сотрудник     0   \n",
       "2         женат / замужем                 0      M    сотрудник     0   \n",
       "3         женат / замужем                 0      M    сотрудник     0   \n",
       "4        гражданский брак                 1      F    пенсионер     0   \n",
       "5        гражданский брак                 1      M    компаньон     0   \n",
       "6         женат / замужем                 0      F    компаньон     0   \n",
       "7         женат / замужем                 0      M    сотрудник     0   \n",
       "8        гражданский брак                 1      F    сотрудник     0   \n",
       "9         женат / замужем                 0      M    сотрудник     0   \n",
       "10        женат / замужем                 0      M    компаньон     0   \n",
       "11        женат / замужем                 0      F    сотрудник     0   \n",
       "12       гражданский брак                 1      M    пенсионер     0   \n",
       "13        женат / замужем                 0      F    сотрудник     0   \n",
       "14       гражданский брак                 1      F    компаньон     1   \n",
       "15        женат / замужем                 0      F    сотрудник     0   \n",
       "16        женат / замужем                 0      F    сотрудник     0   \n",
       "17       гражданский брак                 1      M    сотрудник     0   \n",
       "18         вдовец / вдова                 2      F    пенсионер     0   \n",
       "19              в разводе                 3      F    сотрудник     0   \n",
       "20        женат / замужем                 0      M    сотрудник     0   \n",
       "21       гражданский брак                 1      F    сотрудник     0   \n",
       "22       гражданский брак                 1      F    сотрудник     0   \n",
       "23       гражданский брак                 1      M    сотрудник     0   \n",
       "24  Не женат / не замужем                 4      F    пенсионер     0   \n",
       "25        женат / замужем                 0      M    пенсионер     0   \n",
       "26        женат / замужем                 0      M  госслужащий     0   \n",
       "27        женат / замужем                 0      M    сотрудник     0   \n",
       "28        женат / замужем                 0      F    сотрудник     0   \n",
       "29  Не женат / не замужем                 4      F    пенсионер     0   \n",
       "30        женат / замужем                 0      F    пенсионер     0   \n",
       "31        женат / замужем                 0      F    сотрудник     0   \n",
       "32       гражданский брак                 1      F    сотрудник     1   \n",
       "33        женат / замужем                 0      F    компаньон     0   \n",
       "34        женат / замужем                 0      F    сотрудник     0   \n",
       "35       гражданский брак                 1      M    пенсионер     0   \n",
       "36  Не женат / не замужем                 4      M    сотрудник     0   \n",
       "37       гражданский брак                 1      F    компаньон     0   \n",
       "38  Не женат / не замужем                 4      M    сотрудник     1   \n",
       "39       гражданский брак                 1      F    компаньон     0   \n",
       "\n",
       "     total_income                                 purpose  \n",
       "0   253875.639453                           покупка жилья  \n",
       "1   112080.014102                 приобретение автомобиля  \n",
       "2   145885.952297                           покупка жилья  \n",
       "3   267628.550329              дополнительное образование  \n",
       "4   158616.077870                         сыграть свадьбу  \n",
       "5   255763.565419                           покупка жилья  \n",
       "6   240525.971920                       операции с жильем  \n",
       "7   135823.934197                             образование  \n",
       "8    95856.832424                   на проведение свадьбы  \n",
       "9   144425.938277                 покупка жилья для семьи  \n",
       "10  113943.491460                    покупка недвижимости  \n",
       "11   77069.234271       покупка коммерческой недвижимости  \n",
       "12            NaN                         сыграть свадьбу  \n",
       "13  130458.228857                 приобретение автомобиля  \n",
       "14  165127.911772              покупка жилой недвижимости  \n",
       "15  116820.904450  строительство собственной недвижимости  \n",
       "16  289202.704229                            недвижимость  \n",
       "17   90410.586745              строительство недвижимости  \n",
       "18   56823.777243      на покупку подержанного автомобиля  \n",
       "19  242831.107982            на покупку своего автомобиля  \n",
       "20  209552.644578                            недвижимость  \n",
       "21  131812.203909                 приобретение автомобиля  \n",
       "22  149681.140731      на покупку подержанного автомобиля  \n",
       "23  128265.720871                         сыграть свадьбу  \n",
       "24  290547.235997   операции с коммерческой недвижимостью  \n",
       "25   55112.757732                    покупка недвижимости  \n",
       "26            NaN                             образование  \n",
       "27  308848.983691  строительство собственной недвижимости  \n",
       "28  187863.237306  строительство собственной недвижимости  \n",
       "29            NaN        строительство жилой недвижимости  \n",
       "30  171456.067993   операции с коммерческой недвижимостью  \n",
       "31  275485.684538                                   жилье  \n",
       "32  139057.464207                   на проведение свадьбы  \n",
       "33  157245.786233                           покупка жилья  \n",
       "34  104660.449203         операции со своей недвижимостью  \n",
       "35   77805.677436                   на проведение свадьбы  \n",
       "36  138830.650734                              автомобили  \n",
       "37  154128.878672                 приобретение автомобиля  \n",
       "38  192247.298639                             образование  \n",
       "39  754240.797248                   заняться образованием  "
      ]
     },
     "execution_count": 310,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(40)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "360c6e89",
   "metadata": {},
   "source": [
    "0) Женат замежуем\n",
    "1) гражднаский брак\n",
    "2) вдовец/вдова\n",
    "3) в разводе\n",
    "4) не женат, не замужем"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "id": "06a628af",
   "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>children</th>\n",
       "      <th>days_employed</th>\n",
       "      <th>dob_years</th>\n",
       "      <th>education</th>\n",
       "      <th>education_id</th>\n",
       "      <th>family_status</th>\n",
       "      <th>family_status_id</th>\n",
       "      <th>gender</th>\n",
       "      <th>income_type</th>\n",
       "      <th>debt</th>\n",
       "      <th>total_income</th>\n",
       "      <th>purpose</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>291</td>\n",
       "      <td>-1</td>\n",
       "      <td>-4417.703588</td>\n",
       "      <td>46</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>гражданский брак</td>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>102816.346412</td>\n",
       "      <td>профильное образование</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>705</td>\n",
       "      <td>-1</td>\n",
       "      <td>-902.084528</td>\n",
       "      <td>50</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>госслужащий</td>\n",
       "      <td>0</td>\n",
       "      <td>137882.899271</td>\n",
       "      <td>приобретение автомобиля</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>742</td>\n",
       "      <td>-1</td>\n",
       "      <td>-3174.456205</td>\n",
       "      <td>57</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>64268.044444</td>\n",
       "      <td>дополнительное образование</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>800</td>\n",
       "      <td>-1</td>\n",
       "      <td>349987.852217</td>\n",
       "      <td>54</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>Не женат / не замужем</td>\n",
       "      <td>4</td>\n",
       "      <td>F</td>\n",
       "      <td>пенсионер</td>\n",
       "      <td>0</td>\n",
       "      <td>86293.724153</td>\n",
       "      <td>дополнительное образование</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>941</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>57</td>\n",
       "      <td>Среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>пенсионер</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>на покупку своего автомобиля</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1363</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1195.264956</td>\n",
       "      <td>55</td>\n",
       "      <td>СРЕДНЕЕ</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>компаньон</td>\n",
       "      <td>0</td>\n",
       "      <td>69550.699692</td>\n",
       "      <td>профильное образование</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1929</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1461.303336</td>\n",
       "      <td>38</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>Не женат / не замужем</td>\n",
       "      <td>4</td>\n",
       "      <td>M</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>109121.569013</td>\n",
       "      <td>покупка жилья</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2073</td>\n",
       "      <td>-1</td>\n",
       "      <td>-2539.761232</td>\n",
       "      <td>42</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>в разводе</td>\n",
       "      <td>3</td>\n",
       "      <td>F</td>\n",
       "      <td>компаньон</td>\n",
       "      <td>0</td>\n",
       "      <td>162638.609373</td>\n",
       "      <td>покупка жилья</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3814</td>\n",
       "      <td>-1</td>\n",
       "      <td>-3045.290443</td>\n",
       "      <td>26</td>\n",
       "      <td>Среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>гражданский брак</td>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>госслужащий</td>\n",
       "      <td>0</td>\n",
       "      <td>131892.785435</td>\n",
       "      <td>на проведение свадьбы</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4201</td>\n",
       "      <td>-1</td>\n",
       "      <td>-901.101738</td>\n",
       "      <td>41</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>госслужащий</td>\n",
       "      <td>0</td>\n",
       "      <td>226375.766751</td>\n",
       "      <td>операции со своей недвижимостью</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4402</td>\n",
       "      <td>-1</td>\n",
       "      <td>398001.302888</td>\n",
       "      <td>64</td>\n",
       "      <td>СРЕДНЕЕ</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>пенсионер</td>\n",
       "      <td>0</td>\n",
       "      <td>163264.062064</td>\n",
       "      <td>покупка недвижимости</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4542</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1811.899756</td>\n",
       "      <td>32</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>160544.718446</td>\n",
       "      <td>образование</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5272</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1143.485347</td>\n",
       "      <td>46</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>вдовец / вдова</td>\n",
       "      <td>2</td>\n",
       "      <td>F</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>278708.018820</td>\n",
       "      <td>покупка недвижимости</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6013</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1361.258696</td>\n",
       "      <td>46</td>\n",
       "      <td>высшее</td>\n",
       "      <td>0</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>143008.454914</td>\n",
       "      <td>строительство собственной недвижимости</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6385</td>\n",
       "      <td>-1</td>\n",
       "      <td>370215.476226</td>\n",
       "      <td>48</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>вдовец / вдова</td>\n",
       "      <td>2</td>\n",
       "      <td>F</td>\n",
       "      <td>пенсионер</td>\n",
       "      <td>0</td>\n",
       "      <td>36052.447435</td>\n",
       "      <td>образование</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7190</td>\n",
       "      <td>-1</td>\n",
       "      <td>-5928.202068</td>\n",
       "      <td>34</td>\n",
       "      <td>СРЕДНЕЕ</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>184315.121979</td>\n",
       "      <td>приобретение автомобиля</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7278</td>\n",
       "      <td>-1</td>\n",
       "      <td>-526.318451</td>\n",
       "      <td>51</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>146928.769439</td>\n",
       "      <td>операции с коммерческой недвижимостью</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7615</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>образование</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7685</td>\n",
       "      <td>-1</td>\n",
       "      <td>-3237.360455</td>\n",
       "      <td>53</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>вдовец / вдова</td>\n",
       "      <td>2</td>\n",
       "      <td>F</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>159676.174958</td>\n",
       "      <td>строительство жилой недвижимости</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8229</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1803.441074</td>\n",
       "      <td>54</td>\n",
       "      <td>высшее</td>\n",
       "      <td>0</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>компаньон</td>\n",
       "      <td>0</td>\n",
       "      <td>138809.082930</td>\n",
       "      <td>покупка недвижимости</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8251</td>\n",
       "      <td>-1</td>\n",
       "      <td>-3113.998449</td>\n",
       "      <td>31</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>гражданский брак</td>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>54412.056005</td>\n",
       "      <td>свой автомобиль</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8561</td>\n",
       "      <td>-1</td>\n",
       "      <td>-195.479496</td>\n",
       "      <td>31</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>145577.881522</td>\n",
       "      <td>ремонт жилью</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9565</td>\n",
       "      <td>-1</td>\n",
       "      <td>-2896.629686</td>\n",
       "      <td>34</td>\n",
       "      <td>высшее</td>\n",
       "      <td>0</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>126754.656529</td>\n",
       "      <td>операции с недвижимостью</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9574</td>\n",
       "      <td>-1</td>\n",
       "      <td>-2710.419901</td>\n",
       "      <td>28</td>\n",
       "      <td>высшее</td>\n",
       "      <td>0</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>госслужащий</td>\n",
       "      <td>0</td>\n",
       "      <td>303137.161001</td>\n",
       "      <td>получение дополнительного образования</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10177</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1743.604011</td>\n",
       "      <td>37</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>310367.509001</td>\n",
       "      <td>получение образования</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10365</td>\n",
       "      <td>-1</td>\n",
       "      <td>345774.125957</td>\n",
       "      <td>63</td>\n",
       "      <td>ВЫСШЕЕ</td>\n",
       "      <td>0</td>\n",
       "      <td>вдовец / вдова</td>\n",
       "      <td>2</td>\n",
       "      <td>F</td>\n",
       "      <td>пенсионер</td>\n",
       "      <td>0</td>\n",
       "      <td>170762.751325</td>\n",
       "      <td>строительство жилой недвижимости</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10918</td>\n",
       "      <td>-1</td>\n",
       "      <td>340499.039342</td>\n",
       "      <td>50</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>пенсионер</td>\n",
       "      <td>0</td>\n",
       "      <td>170762.773162</td>\n",
       "      <td>покупка своего жилья</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11109</td>\n",
       "      <td>-1</td>\n",
       "      <td>-3438.463024</td>\n",
       "      <td>59</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>219874.012345</td>\n",
       "      <td>операции с жильем</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11268</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1048.782203</td>\n",
       "      <td>30</td>\n",
       "      <td>высшее</td>\n",
       "      <td>0</td>\n",
       "      <td>гражданский брак</td>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>компаньон</td>\n",
       "      <td>0</td>\n",
       "      <td>321603.700207</td>\n",
       "      <td>на покупку автомобиля</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11617</td>\n",
       "      <td>-1</td>\n",
       "      <td>-370.827130</td>\n",
       "      <td>27</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>компаньон</td>\n",
       "      <td>0</td>\n",
       "      <td>164591.260338</td>\n",
       "      <td>покупка жилья для сдачи</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12304</td>\n",
       "      <td>-1</td>\n",
       "      <td>-8493.101252</td>\n",
       "      <td>61</td>\n",
       "      <td>неоконченное высшее</td>\n",
       "      <td>2</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>315006.182056</td>\n",
       "      <td>недвижимость</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13786</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>Не женат / не замужем</td>\n",
       "      <td>4</td>\n",
       "      <td>M</td>\n",
       "      <td>компаньон</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>автомобиль</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14357</td>\n",
       "      <td>-1</td>\n",
       "      <td>-268.337037</td>\n",
       "      <td>23</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>в разводе</td>\n",
       "      <td>3</td>\n",
       "      <td>F</td>\n",
       "      <td>компаньон</td>\n",
       "      <td>0</td>\n",
       "      <td>92257.579312</td>\n",
       "      <td>покупка жилой недвижимости</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15147</td>\n",
       "      <td>-1</td>\n",
       "      <td>-9851.184337</td>\n",
       "      <td>44</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>в разводе</td>\n",
       "      <td>3</td>\n",
       "      <td>F</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>110990.810581</td>\n",
       "      <td>свой автомобиль</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15435</td>\n",
       "      <td>-1</td>\n",
       "      <td>-3614.220232</td>\n",
       "      <td>40</td>\n",
       "      <td>высшее</td>\n",
       "      <td>0</td>\n",
       "      <td>гражданский брак</td>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>98127.537462</td>\n",
       "      <td>сыграть свадьбу</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16129</td>\n",
       "      <td>-1</td>\n",
       "      <td>-457.861760</td>\n",
       "      <td>33</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>1</td>\n",
       "      <td>149641.079451</td>\n",
       "      <td>автомобиль</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16265</td>\n",
       "      <td>-1</td>\n",
       "      <td>-2802.218127</td>\n",
       "      <td>40</td>\n",
       "      <td>высшее</td>\n",
       "      <td>0</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>111984.472021</td>\n",
       "      <td>покупка жилья</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17095</td>\n",
       "      <td>-1</td>\n",
       "      <td>-2809.693200</td>\n",
       "      <td>34</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>182543.890127</td>\n",
       "      <td>на покупку подержанного автомобиля</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17429</td>\n",
       "      <td>-1</td>\n",
       "      <td>-895.379738</td>\n",
       "      <td>37</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>компаньон</td>\n",
       "      <td>0</td>\n",
       "      <td>214814.018780</td>\n",
       "      <td>жилье</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17657</td>\n",
       "      <td>-1</td>\n",
       "      <td>-4571.957475</td>\n",
       "      <td>41</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>122105.415823</td>\n",
       "      <td>операции со своей недвижимостью</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18219</td>\n",
       "      <td>-1</td>\n",
       "      <td>-3575.215641</td>\n",
       "      <td>33</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>128362.023879</td>\n",
       "      <td>высшее образование</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19100</td>\n",
       "      <td>-1</td>\n",
       "      <td>-617.246968</td>\n",
       "      <td>38</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>Не женат / не замужем</td>\n",
       "      <td>4</td>\n",
       "      <td>M</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>122205.497527</td>\n",
       "      <td>строительство жилой недвижимости</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19366</td>\n",
       "      <td>-1</td>\n",
       "      <td>-372.034749</td>\n",
       "      <td>43</td>\n",
       "      <td>СРЕДНЕЕ</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>155588.766795</td>\n",
       "      <td>сделка с подержанным автомобилем</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19417</td>\n",
       "      <td>-1</td>\n",
       "      <td>350340.760224</td>\n",
       "      <td>28</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>в разводе</td>\n",
       "      <td>3</td>\n",
       "      <td>F</td>\n",
       "      <td>пенсионер</td>\n",
       "      <td>0</td>\n",
       "      <td>52872.993654</td>\n",
       "      <td>автомобили</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20393</td>\n",
       "      <td>-1</td>\n",
       "      <td>355157.107212</td>\n",
       "      <td>69</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>Не женат / не замужем</td>\n",
       "      <td>4</td>\n",
       "      <td>F</td>\n",
       "      <td>пенсионер</td>\n",
       "      <td>0</td>\n",
       "      <td>116521.045858</td>\n",
       "      <td>операции с недвижимостью</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20717</td>\n",
       "      <td>-1</td>\n",
       "      <td>-661.822321</td>\n",
       "      <td>32</td>\n",
       "      <td>высшее</td>\n",
       "      <td>0</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>137405.384917</td>\n",
       "      <td>покупка жилой недвижимости</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21140</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1422.668059</td>\n",
       "      <td>44</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>компаньон</td>\n",
       "      <td>0</td>\n",
       "      <td>169562.091999</td>\n",
       "      <td>операции со своей недвижимостью</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       children  days_employed  dob_years            education  education_id  \\\n",
       "291          -1   -4417.703588         46              среднее             1   \n",
       "705          -1    -902.084528         50              среднее             1   \n",
       "742          -1   -3174.456205         57              среднее             1   \n",
       "800          -1  349987.852217         54              среднее             1   \n",
       "941          -1            NaN         57              Среднее             1   \n",
       "1363         -1   -1195.264956         55              СРЕДНЕЕ             1   \n",
       "1929         -1   -1461.303336         38              среднее             1   \n",
       "2073         -1   -2539.761232         42              среднее             1   \n",
       "3814         -1   -3045.290443         26              Среднее             1   \n",
       "4201         -1    -901.101738         41              среднее             1   \n",
       "4402         -1  398001.302888         64              СРЕДНЕЕ             1   \n",
       "4542         -1   -1811.899756         32              среднее             1   \n",
       "5272         -1   -1143.485347         46              среднее             1   \n",
       "6013         -1   -1361.258696         46               высшее             0   \n",
       "6385         -1  370215.476226         48              среднее             1   \n",
       "7190         -1   -5928.202068         34              СРЕДНЕЕ             1   \n",
       "7278         -1    -526.318451         51              среднее             1   \n",
       "7615         -1            NaN         35              среднее             1   \n",
       "7685         -1   -3237.360455         53              среднее             1   \n",
       "8229         -1   -1803.441074         54               высшее             0   \n",
       "8251         -1   -3113.998449         31              среднее             1   \n",
       "8561         -1    -195.479496         31              среднее             1   \n",
       "9565         -1   -2896.629686         34               высшее             0   \n",
       "9574         -1   -2710.419901         28               высшее             0   \n",
       "10177        -1   -1743.604011         37              среднее             1   \n",
       "10365        -1  345774.125957         63               ВЫСШЕЕ             0   \n",
       "10918        -1  340499.039342         50              среднее             1   \n",
       "11109        -1   -3438.463024         59              среднее             1   \n",
       "11268        -1   -1048.782203         30               высшее             0   \n",
       "11617        -1    -370.827130         27              среднее             1   \n",
       "12304        -1   -8493.101252         61  неоконченное высшее             2   \n",
       "13786        -1            NaN         42              среднее             1   \n",
       "14357        -1    -268.337037         23              среднее             1   \n",
       "15147        -1   -9851.184337         44              среднее             1   \n",
       "15435        -1   -3614.220232         40               высшее             0   \n",
       "16129        -1    -457.861760         33              среднее             1   \n",
       "16265        -1   -2802.218127         40               высшее             0   \n",
       "17095        -1   -2809.693200         34              среднее             1   \n",
       "17429        -1    -895.379738         37              среднее             1   \n",
       "17657        -1   -4571.957475         41              среднее             1   \n",
       "18219        -1   -3575.215641         33              среднее             1   \n",
       "19100        -1    -617.246968         38              среднее             1   \n",
       "19366        -1    -372.034749         43              СРЕДНЕЕ             1   \n",
       "19417        -1  350340.760224         28              среднее             1   \n",
       "20393        -1  355157.107212         69              среднее             1   \n",
       "20717        -1    -661.822321         32               высшее             0   \n",
       "21140        -1   -1422.668059         44              среднее             1   \n",
       "\n",
       "               family_status  family_status_id gender  income_type  debt  \\\n",
       "291         гражданский брак                 1      F    сотрудник     0   \n",
       "705          женат / замужем                 0      F  госслужащий     0   \n",
       "742          женат / замужем                 0      F    сотрудник     0   \n",
       "800    Не женат / не замужем                 4      F    пенсионер     0   \n",
       "941          женат / замужем                 0      F    пенсионер     0   \n",
       "1363         женат / замужем                 0      F    компаньон     0   \n",
       "1929   Не женат / не замужем                 4      M    сотрудник     0   \n",
       "2073               в разводе                 3      F    компаньон     0   \n",
       "3814        гражданский брак                 1      F  госслужащий     0   \n",
       "4201         женат / замужем                 0      F  госслужащий     0   \n",
       "4402         женат / замужем                 0      F    пенсионер     0   \n",
       "4542         женат / замужем                 0      F    сотрудник     0   \n",
       "5272          вдовец / вдова                 2      F    сотрудник     0   \n",
       "6013         женат / замужем                 0      F    сотрудник     0   \n",
       "6385          вдовец / вдова                 2      F    пенсионер     0   \n",
       "7190         женат / замужем                 0      M    сотрудник     0   \n",
       "7278         женат / замужем                 0      F    сотрудник     0   \n",
       "7615         женат / замужем                 0      M    сотрудник     0   \n",
       "7685          вдовец / вдова                 2      F    сотрудник     0   \n",
       "8229         женат / замужем                 0      F    компаньон     0   \n",
       "8251        гражданский брак                 1      F    сотрудник     0   \n",
       "8561         женат / замужем                 0      F    сотрудник     0   \n",
       "9565         женат / замужем                 0      M    сотрудник     0   \n",
       "9574         женат / замужем                 0      M  госслужащий     0   \n",
       "10177        женат / замужем                 0      M    сотрудник     0   \n",
       "10365         вдовец / вдова                 2      F    пенсионер     0   \n",
       "10918        женат / замужем                 0      M    пенсионер     0   \n",
       "11109        женат / замужем                 0      F    сотрудник     0   \n",
       "11268       гражданский брак                 1      F    компаньон     0   \n",
       "11617        женат / замужем                 0      F    компаньон     0   \n",
       "12304        женат / замужем                 0      M    сотрудник     0   \n",
       "13786  Не женат / не замужем                 4      M    компаньон     0   \n",
       "14357              в разводе                 3      F    компаньон     0   \n",
       "15147              в разводе                 3      F    сотрудник     0   \n",
       "15435       гражданский брак                 1      F    сотрудник     0   \n",
       "16129        женат / замужем                 0      F    сотрудник     1   \n",
       "16265        женат / замужем                 0      M    сотрудник     0   \n",
       "17095        женат / замужем                 0      F    сотрудник     0   \n",
       "17429        женат / замужем                 0      F    компаньон     0   \n",
       "17657        женат / замужем                 0      F    сотрудник     0   \n",
       "18219        женат / замужем                 0      F    сотрудник     0   \n",
       "19100  Не женат / не замужем                 4      M    сотрудник     0   \n",
       "19366        женат / замужем                 0      M    сотрудник     0   \n",
       "19417              в разводе                 3      F    пенсионер     0   \n",
       "20393  Не женат / не замужем                 4      F    пенсионер     0   \n",
       "20717        женат / замужем                 0      F    сотрудник     0   \n",
       "21140        женат / замужем                 0      F    компаньон     0   \n",
       "\n",
       "        total_income                                 purpose  \n",
       "291    102816.346412                  профильное образование  \n",
       "705    137882.899271                 приобретение автомобиля  \n",
       "742     64268.044444              дополнительное образование  \n",
       "800     86293.724153              дополнительное образование  \n",
       "941              NaN            на покупку своего автомобиля  \n",
       "1363    69550.699692                  профильное образование  \n",
       "1929   109121.569013                           покупка жилья  \n",
       "2073   162638.609373                           покупка жилья  \n",
       "3814   131892.785435                   на проведение свадьбы  \n",
       "4201   226375.766751         операции со своей недвижимостью  \n",
       "4402   163264.062064                    покупка недвижимости  \n",
       "4542   160544.718446                             образование  \n",
       "5272   278708.018820                    покупка недвижимости  \n",
       "6013   143008.454914  строительство собственной недвижимости  \n",
       "6385    36052.447435                             образование  \n",
       "7190   184315.121979                 приобретение автомобиля  \n",
       "7278   146928.769439   операции с коммерческой недвижимостью  \n",
       "7615             NaN                             образование  \n",
       "7685   159676.174958        строительство жилой недвижимости  \n",
       "8229   138809.082930                    покупка недвижимости  \n",
       "8251    54412.056005                         свой автомобиль  \n",
       "8561   145577.881522                            ремонт жилью  \n",
       "9565   126754.656529                операции с недвижимостью  \n",
       "9574   303137.161001   получение дополнительного образования  \n",
       "10177  310367.509001                   получение образования  \n",
       "10365  170762.751325        строительство жилой недвижимости  \n",
       "10918  170762.773162                    покупка своего жилья  \n",
       "11109  219874.012345                       операции с жильем  \n",
       "11268  321603.700207                   на покупку автомобиля  \n",
       "11617  164591.260338                 покупка жилья для сдачи  \n",
       "12304  315006.182056                            недвижимость  \n",
       "13786            NaN                              автомобиль  \n",
       "14357   92257.579312              покупка жилой недвижимости  \n",
       "15147  110990.810581                         свой автомобиль  \n",
       "15435   98127.537462                         сыграть свадьбу  \n",
       "16129  149641.079451                              автомобиль  \n",
       "16265  111984.472021                           покупка жилья  \n",
       "17095  182543.890127      на покупку подержанного автомобиля  \n",
       "17429  214814.018780                                   жилье  \n",
       "17657  122105.415823         операции со своей недвижимостью  \n",
       "18219  128362.023879                      высшее образование  \n",
       "19100  122205.497527        строительство жилой недвижимости  \n",
       "19366  155588.766795        сделка с подержанным автомобилем  \n",
       "19417   52872.993654                              автомобили  \n",
       "20393  116521.045858                операции с недвижимостью  \n",
       "20717  137405.384917              покупка жилой недвижимости  \n",
       "21140  169562.091999         операции со своей недвижимостью  "
      ]
     },
     "execution_count": 311,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['children'] < 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "id": "b88cc3b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Скорее всего такая аномалия вызвана что парсер -1 перепутал с 1 - сделаем замену"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "id": "7441e96b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/georgezagorsky/opt/anaconda3/envs/praktikum/lib/python3.7/site-packages/pandas/core/indexing.py:494: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self.obj[item] = s\n"
     ]
    }
   ],
   "source": [
    "df.loc[df['children'] == -1, 'children'] = df.loc[df['children'] == -1, 'children'] * (-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "id": "77a42e9f",
   "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>children</th>\n",
       "      <th>days_employed</th>\n",
       "      <th>dob_years</th>\n",
       "      <th>education</th>\n",
       "      <th>education_id</th>\n",
       "      <th>family_status</th>\n",
       "      <th>family_status_id</th>\n",
       "      <th>gender</th>\n",
       "      <th>income_type</th>\n",
       "      <th>debt</th>\n",
       "      <th>total_income</th>\n",
       "      <th>purpose</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [children, days_employed, dob_years, education, education_id, family_status, family_status_id, gender, income_type, debt, total_income, purpose]\n",
       "Index: []"
      ]
     },
     "execution_count": 314,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['children'] < 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "id": "04330f50",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    21424.000000\n",
       "mean         0.542662\n",
       "std          1.375647\n",
       "min          0.000000\n",
       "25%          0.000000\n",
       "50%          0.000000\n",
       "75%          1.000000\n",
       "max         20.000000\n",
       "Name: children, dtype: float64"
      ]
     },
     "execution_count": 315,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['children'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "id": "d61bcd45",
   "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>children</th>\n",
       "      <th>days_employed</th>\n",
       "      <th>dob_years</th>\n",
       "      <th>education</th>\n",
       "      <th>education_id</th>\n",
       "      <th>family_status</th>\n",
       "      <th>family_status_id</th>\n",
       "      <th>gender</th>\n",
       "      <th>income_type</th>\n",
       "      <th>debt</th>\n",
       "      <th>total_income</th>\n",
       "      <th>purpose</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>606</td>\n",
       "      <td>20</td>\n",
       "      <td>-880.221113</td>\n",
       "      <td>21</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>компаньон</td>\n",
       "      <td>0</td>\n",
       "      <td>145334.865002</td>\n",
       "      <td>покупка жилья</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>720</td>\n",
       "      <td>20</td>\n",
       "      <td>-855.595512</td>\n",
       "      <td>44</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>компаньон</td>\n",
       "      <td>0</td>\n",
       "      <td>112998.738649</td>\n",
       "      <td>покупка недвижимости</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1074</td>\n",
       "      <td>20</td>\n",
       "      <td>-3310.411598</td>\n",
       "      <td>56</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>1</td>\n",
       "      <td>229518.537004</td>\n",
       "      <td>получение образования</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2510</td>\n",
       "      <td>20</td>\n",
       "      <td>-2714.161249</td>\n",
       "      <td>59</td>\n",
       "      <td>высшее</td>\n",
       "      <td>0</td>\n",
       "      <td>вдовец / вдова</td>\n",
       "      <td>2</td>\n",
       "      <td>F</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>264474.835577</td>\n",
       "      <td>операции с коммерческой недвижимостью</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3302</td>\n",
       "      <td>20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>Не женат / не замужем</td>\n",
       "      <td>4</td>\n",
       "      <td>F</td>\n",
       "      <td>госслужащий</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>профильное образование</td>\n",
       "    </tr>\n",
       "    <tr>\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",
       "      <td>21008</td>\n",
       "      <td>20</td>\n",
       "      <td>-1240.257910</td>\n",
       "      <td>40</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>1</td>\n",
       "      <td>133524.010303</td>\n",
       "      <td>свой автомобиль</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21325</td>\n",
       "      <td>20</td>\n",
       "      <td>-601.174883</td>\n",
       "      <td>37</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>компаньон</td>\n",
       "      <td>0</td>\n",
       "      <td>102986.065978</td>\n",
       "      <td>профильное образование</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21390</td>\n",
       "      <td>20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>53</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>компаньон</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>покупка жилой недвижимости</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21404</td>\n",
       "      <td>20</td>\n",
       "      <td>-494.788448</td>\n",
       "      <td>52</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>компаньон</td>\n",
       "      <td>0</td>\n",
       "      <td>156629.683642</td>\n",
       "      <td>операции со своей недвижимостью</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21491</td>\n",
       "      <td>20</td>\n",
       "      <td>-173.954460</td>\n",
       "      <td>27</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>155520.899688</td>\n",
       "      <td>на покупку подержанного автомобиля</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>75 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       children  days_employed  dob_years education  education_id  \\\n",
       "606          20    -880.221113         21   среднее             1   \n",
       "720          20    -855.595512         44   среднее             1   \n",
       "1074         20   -3310.411598         56   среднее             1   \n",
       "2510         20   -2714.161249         59    высшее             0   \n",
       "3302         20            NaN         35   среднее             1   \n",
       "...         ...            ...        ...       ...           ...   \n",
       "21008        20   -1240.257910         40   среднее             1   \n",
       "21325        20    -601.174883         37   среднее             1   \n",
       "21390        20            NaN         53   среднее             1   \n",
       "21404        20    -494.788448         52   среднее             1   \n",
       "21491        20    -173.954460         27   среднее             1   \n",
       "\n",
       "               family_status  family_status_id gender  income_type  debt  \\\n",
       "606          женат / замужем                 0      M    компаньон     0   \n",
       "720          женат / замужем                 0      F    компаньон     0   \n",
       "1074         женат / замужем                 0      F    сотрудник     1   \n",
       "2510          вдовец / вдова                 2      F    сотрудник     0   \n",
       "3302   Не женат / не замужем                 4      F  госслужащий     0   \n",
       "...                      ...               ...    ...          ...   ...   \n",
       "21008        женат / замужем                 0      F    сотрудник     1   \n",
       "21325        женат / замужем                 0      F    компаньон     0   \n",
       "21390        женат / замужем                 0      M    компаньон     0   \n",
       "21404        женат / замужем                 0      M    компаньон     0   \n",
       "21491        женат / замужем                 0      F    сотрудник     0   \n",
       "\n",
       "        total_income                                purpose  \n",
       "606    145334.865002                          покупка жилья  \n",
       "720    112998.738649                   покупка недвижимости  \n",
       "1074   229518.537004                  получение образования  \n",
       "2510   264474.835577  операции с коммерческой недвижимостью  \n",
       "3302             NaN                 профильное образование  \n",
       "...              ...                                    ...  \n",
       "21008  133524.010303                        свой автомобиль  \n",
       "21325  102986.065978                 профильное образование  \n",
       "21390            NaN             покупка жилой недвижимости  \n",
       "21404  156629.683642        операции со своей недвижимостью  \n",
       "21491  155520.899688     на покупку подержанного автомобиля  \n",
       "\n",
       "[75 rows x 12 columns]"
      ]
     },
     "execution_count": 316,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['children'] > 10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "id": "5b22f7de",
   "metadata": {},
   "outputs": [],
   "source": [
    "t_df = df[(df['children'] == 20) & (df['children'] + 18 > df['dob_years'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "id": "1338439d",
   "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>children</th>\n",
       "      <th>days_employed</th>\n",
       "      <th>dob_years</th>\n",
       "      <th>education</th>\n",
       "      <th>education_id</th>\n",
       "      <th>family_status</th>\n",
       "      <th>family_status_id</th>\n",
       "      <th>gender</th>\n",
       "      <th>income_type</th>\n",
       "      <th>debt</th>\n",
       "      <th>total_income</th>\n",
       "      <th>purpose</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>606</td>\n",
       "      <td>20</td>\n",
       "      <td>-880.221113</td>\n",
       "      <td>21</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>компаньон</td>\n",
       "      <td>0</td>\n",
       "      <td>145334.865002</td>\n",
       "      <td>покупка жилья</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3302</td>\n",
       "      <td>20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>Не женат / не замужем</td>\n",
       "      <td>4</td>\n",
       "      <td>F</td>\n",
       "      <td>госслужащий</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>профильное образование</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3671</td>\n",
       "      <td>20</td>\n",
       "      <td>-913.161503</td>\n",
       "      <td>23</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>Не женат / не замужем</td>\n",
       "      <td>4</td>\n",
       "      <td>F</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>101255.492076</td>\n",
       "      <td>на покупку подержанного автомобиля</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3735</td>\n",
       "      <td>20</td>\n",
       "      <td>-805.044438</td>\n",
       "      <td>26</td>\n",
       "      <td>высшее</td>\n",
       "      <td>0</td>\n",
       "      <td>Не женат / не замужем</td>\n",
       "      <td>4</td>\n",
       "      <td>M</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>137200.646181</td>\n",
       "      <td>ремонт жилью</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5020</td>\n",
       "      <td>20</td>\n",
       "      <td>-231.783475</td>\n",
       "      <td>37</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>гражданский брак</td>\n",
       "      <td>1</td>\n",
       "      <td>M</td>\n",
       "      <td>компаньон</td>\n",
       "      <td>0</td>\n",
       "      <td>260203.344629</td>\n",
       "      <td>сыграть свадьбу</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5315</td>\n",
       "      <td>20</td>\n",
       "      <td>-2047.754733</td>\n",
       "      <td>24</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>100415.236833</td>\n",
       "      <td>покупка коммерческой недвижимости</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6198</td>\n",
       "      <td>20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35</td>\n",
       "      <td>высшее</td>\n",
       "      <td>0</td>\n",
       "      <td>Не женат / не замужем</td>\n",
       "      <td>4</td>\n",
       "      <td>M</td>\n",
       "      <td>компаньон</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>жилье</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8883</td>\n",
       "      <td>20</td>\n",
       "      <td>-4749.817420</td>\n",
       "      <td>36</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>441721.334145</td>\n",
       "      <td>высшее образование</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8950</td>\n",
       "      <td>20</td>\n",
       "      <td>-4511.016324</td>\n",
       "      <td>32</td>\n",
       "      <td>Среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>300682.082113</td>\n",
       "      <td>на покупку подержанного автомобиля</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9889</td>\n",
       "      <td>20</td>\n",
       "      <td>-2232.776113</td>\n",
       "      <td>34</td>\n",
       "      <td>СРЕДНЕЕ</td>\n",
       "      <td>1</td>\n",
       "      <td>Не женат / не замужем</td>\n",
       "      <td>4</td>\n",
       "      <td>M</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>53971.576721</td>\n",
       "      <td>покупка жилья</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10736</td>\n",
       "      <td>20</td>\n",
       "      <td>-4378.723308</td>\n",
       "      <td>30</td>\n",
       "      <td>высшее</td>\n",
       "      <td>0</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>компаньон</td>\n",
       "      <td>0</td>\n",
       "      <td>154706.262149</td>\n",
       "      <td>высшее образование</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10782</td>\n",
       "      <td>20</td>\n",
       "      <td>-1044.201789</td>\n",
       "      <td>34</td>\n",
       "      <td>Среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>в разводе</td>\n",
       "      <td>3</td>\n",
       "      <td>M</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>1</td>\n",
       "      <td>144544.530170</td>\n",
       "      <td>строительство собственной недвижимости</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11449</td>\n",
       "      <td>20</td>\n",
       "      <td>-188.529435</td>\n",
       "      <td>30</td>\n",
       "      <td>высшее</td>\n",
       "      <td>0</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>компаньон</td>\n",
       "      <td>0</td>\n",
       "      <td>146675.568148</td>\n",
       "      <td>образование</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11489</td>\n",
       "      <td>20</td>\n",
       "      <td>-519.638567</td>\n",
       "      <td>34</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>389841.083378</td>\n",
       "      <td>покупка жилой недвижимости</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12390</td>\n",
       "      <td>20</td>\n",
       "      <td>-1005.701423</td>\n",
       "      <td>31</td>\n",
       "      <td>ВЫСШЕЕ</td>\n",
       "      <td>0</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>компаньон</td>\n",
       "      <td>0</td>\n",
       "      <td>181325.759333</td>\n",
       "      <td>жилье</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12909</td>\n",
       "      <td>20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>операции с жильем</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12910</td>\n",
       "      <td>20</td>\n",
       "      <td>-2312.623375</td>\n",
       "      <td>37</td>\n",
       "      <td>Среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>125952.873318</td>\n",
       "      <td>операции с коммерческой недвижимостью</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14474</td>\n",
       "      <td>20</td>\n",
       "      <td>-2625.072839</td>\n",
       "      <td>27</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>244092.075999</td>\n",
       "      <td>высшее образование</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15674</td>\n",
       "      <td>20</td>\n",
       "      <td>-5419.859866</td>\n",
       "      <td>33</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>гражданский брак</td>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>155567.570284</td>\n",
       "      <td>свадьба</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16371</td>\n",
       "      <td>20</td>\n",
       "      <td>-419.894814</td>\n",
       "      <td>29</td>\n",
       "      <td>неоконченное высшее</td>\n",
       "      <td>2</td>\n",
       "      <td>Не женат / не замужем</td>\n",
       "      <td>4</td>\n",
       "      <td>M</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>122062.334283</td>\n",
       "      <td>операции с коммерческой недвижимостью</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16795</td>\n",
       "      <td>20</td>\n",
       "      <td>-2530.540521</td>\n",
       "      <td>29</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>компаньон</td>\n",
       "      <td>0</td>\n",
       "      <td>95454.865761</td>\n",
       "      <td>недвижимость</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16886</td>\n",
       "      <td>20</td>\n",
       "      <td>-957.251278</td>\n",
       "      <td>33</td>\n",
       "      <td>высшее</td>\n",
       "      <td>0</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>86656.369322</td>\n",
       "      <td>сделка с подержанным автомобилем</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17509</td>\n",
       "      <td>20</td>\n",
       "      <td>-461.939717</td>\n",
       "      <td>31</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>Не женат / не замужем</td>\n",
       "      <td>4</td>\n",
       "      <td>F</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>90937.893204</td>\n",
       "      <td>жилье</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18476</td>\n",
       "      <td>20</td>\n",
       "      <td>-4686.111677</td>\n",
       "      <td>36</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>госслужащий</td>\n",
       "      <td>0</td>\n",
       "      <td>258005.804002</td>\n",
       "      <td>на покупку подержанного автомобиля</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18890</td>\n",
       "      <td>20</td>\n",
       "      <td>-904.259293</td>\n",
       "      <td>37</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>компаньон</td>\n",
       "      <td>1</td>\n",
       "      <td>212811.568585</td>\n",
       "      <td>на покупку подержанного автомобиля</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19562</td>\n",
       "      <td>20</td>\n",
       "      <td>-721.344373</td>\n",
       "      <td>30</td>\n",
       "      <td>неоконченное высшее</td>\n",
       "      <td>2</td>\n",
       "      <td>гражданский брак</td>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>компаньон</td>\n",
       "      <td>0</td>\n",
       "      <td>133319.593450</td>\n",
       "      <td>сыграть свадьбу</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20038</td>\n",
       "      <td>20</td>\n",
       "      <td>-782.499396</td>\n",
       "      <td>32</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>Не женат / не замужем</td>\n",
       "      <td>4</td>\n",
       "      <td>M</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>1</td>\n",
       "      <td>117758.551168</td>\n",
       "      <td>покупка коммерческой недвижимости</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21325</td>\n",
       "      <td>20</td>\n",
       "      <td>-601.174883</td>\n",
       "      <td>37</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>компаньон</td>\n",
       "      <td>0</td>\n",
       "      <td>102986.065978</td>\n",
       "      <td>профильное образование</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21491</td>\n",
       "      <td>20</td>\n",
       "      <td>-173.954460</td>\n",
       "      <td>27</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>155520.899688</td>\n",
       "      <td>на покупку подержанного автомобиля</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       children  days_employed  dob_years            education  education_id  \\\n",
       "606          20    -880.221113         21              среднее             1   \n",
       "3302         20            NaN         35              среднее             1   \n",
       "3671         20    -913.161503         23              среднее             1   \n",
       "3735         20    -805.044438         26               высшее             0   \n",
       "5020         20    -231.783475         37              среднее             1   \n",
       "5315         20   -2047.754733         24              среднее             1   \n",
       "6198         20            NaN         35               высшее             0   \n",
       "8883         20   -4749.817420         36              среднее             1   \n",
       "8950         20   -4511.016324         32              Среднее             1   \n",
       "9889         20   -2232.776113         34              СРЕДНЕЕ             1   \n",
       "10736        20   -4378.723308         30               высшее             0   \n",
       "10782        20   -1044.201789         34              Среднее             1   \n",
       "11449        20    -188.529435         30               высшее             0   \n",
       "11489        20    -519.638567         34              среднее             1   \n",
       "12390        20   -1005.701423         31               ВЫСШЕЕ             0   \n",
       "12909        20            NaN         25              среднее             1   \n",
       "12910        20   -2312.623375         37              Среднее             1   \n",
       "14474        20   -2625.072839         27              среднее             1   \n",
       "15674        20   -5419.859866         33              среднее             1   \n",
       "16371        20    -419.894814         29  неоконченное высшее             2   \n",
       "16795        20   -2530.540521         29              среднее             1   \n",
       "16886        20    -957.251278         33               высшее             0   \n",
       "17509        20    -461.939717         31              среднее             1   \n",
       "18476        20   -4686.111677         36              среднее             1   \n",
       "18890        20    -904.259293         37              среднее             1   \n",
       "19562        20    -721.344373         30  неоконченное высшее             2   \n",
       "20038        20    -782.499396         32              среднее             1   \n",
       "21325        20    -601.174883         37              среднее             1   \n",
       "21491        20    -173.954460         27              среднее             1   \n",
       "\n",
       "               family_status  family_status_id gender  income_type  debt  \\\n",
       "606          женат / замужем                 0      M    компаньон     0   \n",
       "3302   Не женат / не замужем                 4      F  госслужащий     0   \n",
       "3671   Не женат / не замужем                 4      F    сотрудник     0   \n",
       "3735   Не женат / не замужем                 4      M    сотрудник     0   \n",
       "5020        гражданский брак                 1      M    компаньон     0   \n",
       "5315         женат / замужем                 0      F    сотрудник     0   \n",
       "6198   Не женат / не замужем                 4      M    компаньон     0   \n",
       "8883         женат / замужем                 0      M    сотрудник     0   \n",
       "8950         женат / замужем                 0      M    сотрудник     0   \n",
       "9889   Не женат / не замужем                 4      M    сотрудник     0   \n",
       "10736        женат / замужем                 0      F    компаньон     0   \n",
       "10782              в разводе                 3      M    сотрудник     1   \n",
       "11449        женат / замужем                 0      M    компаньон     0   \n",
       "11489        женат / замужем                 0      F    сотрудник     0   \n",
       "12390        женат / замужем                 0      F    компаньон     0   \n",
       "12909        женат / замужем                 0      M    сотрудник     0   \n",
       "12910        женат / замужем                 0      M    сотрудник     0   \n",
       "14474        женат / замужем                 0      F    сотрудник     0   \n",
       "15674       гражданский брак                 1      F    сотрудник     0   \n",
       "16371  Не женат / не замужем                 4      M    сотрудник     0   \n",
       "16795        женат / замужем                 0      M    компаньон     0   \n",
       "16886        женат / замужем                 0      F    сотрудник     0   \n",
       "17509  Не женат / не замужем                 4      F    сотрудник     0   \n",
       "18476        женат / замужем                 0      M  госслужащий     0   \n",
       "18890        женат / замужем                 0      F    компаньон     1   \n",
       "19562       гражданский брак                 1      F    компаньон     0   \n",
       "20038  Не женат / не замужем                 4      M    сотрудник     1   \n",
       "21325        женат / замужем                 0      F    компаньон     0   \n",
       "21491        женат / замужем                 0      F    сотрудник     0   \n",
       "\n",
       "        total_income                                 purpose  \n",
       "606    145334.865002                           покупка жилья  \n",
       "3302             NaN                  профильное образование  \n",
       "3671   101255.492076      на покупку подержанного автомобиля  \n",
       "3735   137200.646181                            ремонт жилью  \n",
       "5020   260203.344629                         сыграть свадьбу  \n",
       "5315   100415.236833       покупка коммерческой недвижимости  \n",
       "6198             NaN                                   жилье  \n",
       "8883   441721.334145                      высшее образование  \n",
       "8950   300682.082113      на покупку подержанного автомобиля  \n",
       "9889    53971.576721                           покупка жилья  \n",
       "10736  154706.262149                      высшее образование  \n",
       "10782  144544.530170  строительство собственной недвижимости  \n",
       "11449  146675.568148                             образование  \n",
       "11489  389841.083378              покупка жилой недвижимости  \n",
       "12390  181325.759333                                   жилье  \n",
       "12909            NaN                       операции с жильем  \n",
       "12910  125952.873318   операции с коммерческой недвижимостью  \n",
       "14474  244092.075999                      высшее образование  \n",
       "15674  155567.570284                                 свадьба  \n",
       "16371  122062.334283   операции с коммерческой недвижимостью  \n",
       "16795   95454.865761                            недвижимость  \n",
       "16886   86656.369322        сделка с подержанным автомобилем  \n",
       "17509   90937.893204                                   жилье  \n",
       "18476  258005.804002      на покупку подержанного автомобиля  \n",
       "18890  212811.568585      на покупку подержанного автомобиля  \n",
       "19562  133319.593450                         сыграть свадьбу  \n",
       "20038  117758.551168       покупка коммерческой недвижимости  \n",
       "21325  102986.065978                  профильное образование  \n",
       "21491  155520.899688      на покупку подержанного автомобиля  "
      ]
     },
     "execution_count": 318,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "id": "92797604",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[(df['children'] <= 20) & (df['children'] + 18 < df['dob_years'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "id": "26f568fa",
   "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>children</th>\n",
       "      <th>days_employed</th>\n",
       "      <th>dob_years</th>\n",
       "      <th>education</th>\n",
       "      <th>education_id</th>\n",
       "      <th>family_status</th>\n",
       "      <th>family_status_id</th>\n",
       "      <th>gender</th>\n",
       "      <th>income_type</th>\n",
       "      <th>debt</th>\n",
       "      <th>total_income</th>\n",
       "      <th>purpose</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-8437.673028</td>\n",
       "      <td>42</td>\n",
       "      <td>высшее</td>\n",
       "      <td>0</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>253875.639453</td>\n",
       "      <td>покупка жилья</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-4024.803754</td>\n",
       "      <td>36</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>112080.014102</td>\n",
       "      <td>приобретение автомобиля</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>-5623.422610</td>\n",
       "      <td>33</td>\n",
       "      <td>Среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>145885.952297</td>\n",
       "      <td>покупка жилья</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>-4124.747207</td>\n",
       "      <td>32</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>267628.550329</td>\n",
       "      <td>дополнительное образование</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>340266.072047</td>\n",
       "      <td>53</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>гражданский брак</td>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>пенсионер</td>\n",
       "      <td>0</td>\n",
       "      <td>158616.077870</td>\n",
       "      <td>сыграть свадьбу</td>\n",
       "    </tr>\n",
       "    <tr>\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",
       "      <td>21520</td>\n",
       "      <td>1</td>\n",
       "      <td>-4529.316663</td>\n",
       "      <td>43</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>гражданский брак</td>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>компаньон</td>\n",
       "      <td>0</td>\n",
       "      <td>224791.862382</td>\n",
       "      <td>операции с жильем</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21521</td>\n",
       "      <td>0</td>\n",
       "      <td>343937.404131</td>\n",
       "      <td>67</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>пенсионер</td>\n",
       "      <td>0</td>\n",
       "      <td>155999.806512</td>\n",
       "      <td>сделка с автомобилем</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21522</td>\n",
       "      <td>1</td>\n",
       "      <td>-2113.346888</td>\n",
       "      <td>38</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>гражданский брак</td>\n",
       "      <td>1</td>\n",
       "      <td>M</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>1</td>\n",
       "      <td>89672.561153</td>\n",
       "      <td>недвижимость</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21523</td>\n",
       "      <td>3</td>\n",
       "      <td>-3112.481705</td>\n",
       "      <td>38</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>1</td>\n",
       "      <td>244093.050500</td>\n",
       "      <td>на покупку своего автомобиля</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21524</td>\n",
       "      <td>2</td>\n",
       "      <td>-1984.507589</td>\n",
       "      <td>40</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>82047.418899</td>\n",
       "      <td>на покупку автомобиля</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>21393 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       children  days_employed  dob_years education  education_id  \\\n",
       "0             1   -8437.673028         42    высшее             0   \n",
       "1             1   -4024.803754         36   среднее             1   \n",
       "2             0   -5623.422610         33   Среднее             1   \n",
       "3             3   -4124.747207         32   среднее             1   \n",
       "4             0  340266.072047         53   среднее             1   \n",
       "...         ...            ...        ...       ...           ...   \n",
       "21520         1   -4529.316663         43   среднее             1   \n",
       "21521         0  343937.404131         67   среднее             1   \n",
       "21522         1   -2113.346888         38   среднее             1   \n",
       "21523         3   -3112.481705         38   среднее             1   \n",
       "21524         2   -1984.507589         40   среднее             1   \n",
       "\n",
       "          family_status  family_status_id gender income_type  debt  \\\n",
       "0       женат / замужем                 0      F   сотрудник     0   \n",
       "1       женат / замужем                 0      F   сотрудник     0   \n",
       "2       женат / замужем                 0      M   сотрудник     0   \n",
       "3       женат / замужем                 0      M   сотрудник     0   \n",
       "4      гражданский брак                 1      F   пенсионер     0   \n",
       "...                 ...               ...    ...         ...   ...   \n",
       "21520  гражданский брак                 1      F   компаньон     0   \n",
       "21521   женат / замужем                 0      F   пенсионер     0   \n",
       "21522  гражданский брак                 1      M   сотрудник     1   \n",
       "21523   женат / замужем                 0      M   сотрудник     1   \n",
       "21524   женат / замужем                 0      F   сотрудник     0   \n",
       "\n",
       "        total_income                       purpose  \n",
       "0      253875.639453                 покупка жилья  \n",
       "1      112080.014102       приобретение автомобиля  \n",
       "2      145885.952297                 покупка жилья  \n",
       "3      267628.550329    дополнительное образование  \n",
       "4      158616.077870               сыграть свадьбу  \n",
       "...              ...                           ...  \n",
       "21520  224791.862382             операции с жильем  \n",
       "21521  155999.806512          сделка с автомобилем  \n",
       "21522   89672.561153                  недвижимость  \n",
       "21523  244093.050500  на покупку своего автомобиля  \n",
       "21524   82047.418899         на покупку автомобиля  \n",
       "\n",
       "[21393 rows x 12 columns]"
      ]
     },
     "execution_count": 320,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "id": "8a6e6fd5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd1d6811210>"
      ]
     },
     "execution_count": 321,
     "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['children'].hist() # ideal"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20cd871a",
   "metadata": {},
   "source": [
    "#  days_employed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "id": "60e8fc7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     19232.000000\n",
       "mean      63127.270092\n",
       "std      140899.711689\n",
       "min      -18388.949901\n",
       "25%       -2748.825497\n",
       "50%       -1204.164714\n",
       "75%        -290.654442\n",
       "max      401755.400475\n",
       "Name: days_employed, dtype: float64"
      ]
     },
     "execution_count": 322,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['days_employed'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "id": "50c5fe1e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd1d6893e90>"
      ]
     },
     "execution_count": 323,
     "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['days_employed'].hist() # Отрицательные значения и значения за 300 тысяч дней"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 324,
   "id": "7fbfaeb6",
   "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>children</th>\n",
       "      <th>days_employed</th>\n",
       "      <th>dob_years</th>\n",
       "      <th>education</th>\n",
       "      <th>education_id</th>\n",
       "      <th>family_status</th>\n",
       "      <th>family_status_id</th>\n",
       "      <th>gender</th>\n",
       "      <th>income_type</th>\n",
       "      <th>debt</th>\n",
       "      <th>total_income</th>\n",
       "      <th>purpose</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-8437.673028</td>\n",
       "      <td>42</td>\n",
       "      <td>высшее</td>\n",
       "      <td>0</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>253875.639453</td>\n",
       "      <td>покупка жилья</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-4024.803754</td>\n",
       "      <td>36</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>112080.014102</td>\n",
       "      <td>приобретение автомобиля</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>-5623.422610</td>\n",
       "      <td>33</td>\n",
       "      <td>Среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>145885.952297</td>\n",
       "      <td>покупка жилья</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>-4124.747207</td>\n",
       "      <td>32</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>267628.550329</td>\n",
       "      <td>дополнительное образование</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>-926.185831</td>\n",
       "      <td>27</td>\n",
       "      <td>высшее</td>\n",
       "      <td>0</td>\n",
       "      <td>гражданский брак</td>\n",
       "      <td>1</td>\n",
       "      <td>M</td>\n",
       "      <td>компаньон</td>\n",
       "      <td>0</td>\n",
       "      <td>255763.565419</td>\n",
       "      <td>покупка жилья</td>\n",
       "    </tr>\n",
       "    <tr>\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",
       "      <td>21519</td>\n",
       "      <td>1</td>\n",
       "      <td>-2351.431934</td>\n",
       "      <td>37</td>\n",
       "      <td>ученая степень</td>\n",
       "      <td>4</td>\n",
       "      <td>в разводе</td>\n",
       "      <td>3</td>\n",
       "      <td>M</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>115949.039788</td>\n",
       "      <td>покупка коммерческой недвижимости</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21520</td>\n",
       "      <td>1</td>\n",
       "      <td>-4529.316663</td>\n",
       "      <td>43</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>гражданский брак</td>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>компаньон</td>\n",
       "      <td>0</td>\n",
       "      <td>224791.862382</td>\n",
       "      <td>операции с жильем</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21522</td>\n",
       "      <td>1</td>\n",
       "      <td>-2113.346888</td>\n",
       "      <td>38</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>гражданский брак</td>\n",
       "      <td>1</td>\n",
       "      <td>M</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>1</td>\n",
       "      <td>89672.561153</td>\n",
       "      <td>недвижимость</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21523</td>\n",
       "      <td>3</td>\n",
       "      <td>-3112.481705</td>\n",
       "      <td>38</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>1</td>\n",
       "      <td>244093.050500</td>\n",
       "      <td>на покупку своего автомобиля</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21524</td>\n",
       "      <td>2</td>\n",
       "      <td>-1984.507589</td>\n",
       "      <td>40</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>82047.418899</td>\n",
       "      <td>на покупку автомобиля</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15804 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       children  days_employed  dob_years       education  education_id  \\\n",
       "0             1   -8437.673028         42          высшее             0   \n",
       "1             1   -4024.803754         36         среднее             1   \n",
       "2             0   -5623.422610         33         Среднее             1   \n",
       "3             3   -4124.747207         32         среднее             1   \n",
       "5             0    -926.185831         27          высшее             0   \n",
       "...         ...            ...        ...             ...           ...   \n",
       "21519         1   -2351.431934         37  ученая степень             4   \n",
       "21520         1   -4529.316663         43         среднее             1   \n",
       "21522         1   -2113.346888         38         среднее             1   \n",
       "21523         3   -3112.481705         38         среднее             1   \n",
       "21524         2   -1984.507589         40         среднее             1   \n",
       "\n",
       "          family_status  family_status_id gender income_type  debt  \\\n",
       "0       женат / замужем                 0      F   сотрудник     0   \n",
       "1       женат / замужем                 0      F   сотрудник     0   \n",
       "2       женат / замужем                 0      M   сотрудник     0   \n",
       "3       женат / замужем                 0      M   сотрудник     0   \n",
       "5      гражданский брак                 1      M   компаньон     0   \n",
       "...                 ...               ...    ...         ...   ...   \n",
       "21519         в разводе                 3      M   сотрудник     0   \n",
       "21520  гражданский брак                 1      F   компаньон     0   \n",
       "21522  гражданский брак                 1      M   сотрудник     1   \n",
       "21523   женат / замужем                 0      M   сотрудник     1   \n",
       "21524   женат / замужем                 0      F   сотрудник     0   \n",
       "\n",
       "        total_income                            purpose  \n",
       "0      253875.639453                      покупка жилья  \n",
       "1      112080.014102            приобретение автомобиля  \n",
       "2      145885.952297                      покупка жилья  \n",
       "3      267628.550329         дополнительное образование  \n",
       "5      255763.565419                      покупка жилья  \n",
       "...              ...                                ...  \n",
       "21519  115949.039788  покупка коммерческой недвижимости  \n",
       "21520  224791.862382                  операции с жильем  \n",
       "21522   89672.561153                       недвижимость  \n",
       "21523  244093.050500       на покупку своего автомобиля  \n",
       "21524   82047.418899              на покупку автомобиля  \n",
       "\n",
       "[15804 rows x 12 columns]"
      ]
     },
     "execution_count": 324,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['days_employed'] < 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "id": "d97e7918",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd1c7854bd0>"
      ]
     },
     "execution_count": 325,
     "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.plot(kind = 'scatter', x = 'days_employed', y = 'dob_years')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "id": "1ddb8e64",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 648x648 with 49 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.plotting.scatter_matrix(df, figsize = (9, 9));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "id": "dbb8e544",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[df['days_employed'] < 0, 'days_employed'] = df.loc[df['days_employed'] < 0, 'days_employed'] * (-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "id": "c461bf80",
   "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>children</th>\n",
       "      <th>days_employed</th>\n",
       "      <th>dob_years</th>\n",
       "      <th>education</th>\n",
       "      <th>education_id</th>\n",
       "      <th>family_status</th>\n",
       "      <th>family_status_id</th>\n",
       "      <th>gender</th>\n",
       "      <th>income_type</th>\n",
       "      <th>debt</th>\n",
       "      <th>total_income</th>\n",
       "      <th>purpose</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [children, days_employed, dob_years, education, education_id, family_status, family_status_id, gender, income_type, debt, total_income, purpose]\n",
       "Index: []"
      ]
     },
     "execution_count": 328,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['days_employed'] < 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "id": "7e7b857e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.10101435048847754"
      ]
     },
     "execution_count": 329,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['days_employed'].isna().mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "id": "0f14d432",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Выберем меру изменчивости"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "id": "d80d1e19",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd1c75dba90>"
      ]
     },
     "execution_count": 331,
     "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['days_employed'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "id": "afc6fc77",
   "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>children</th>\n",
       "      <th>days_employed</th>\n",
       "      <th>dob_years</th>\n",
       "      <th>education</th>\n",
       "      <th>education_id</th>\n",
       "      <th>family_status</th>\n",
       "      <th>family_status_id</th>\n",
       "      <th>gender</th>\n",
       "      <th>income_type</th>\n",
       "      <th>debt</th>\n",
       "      <th>total_income</th>\n",
       "      <th>purpose</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>340266.072047</td>\n",
       "      <td>53</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>гражданский брак</td>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>пенсионер</td>\n",
       "      <td>0</td>\n",
       "      <td>158616.077870</td>\n",
       "      <td>сыграть свадьбу</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>400281.136913</td>\n",
       "      <td>53</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>вдовец / вдова</td>\n",
       "      <td>2</td>\n",
       "      <td>F</td>\n",
       "      <td>пенсионер</td>\n",
       "      <td>0</td>\n",
       "      <td>56823.777243</td>\n",
       "      <td>на покупку подержанного автомобиля</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>338551.952911</td>\n",
       "      <td>57</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>Не женат / не замужем</td>\n",
       "      <td>4</td>\n",
       "      <td>F</td>\n",
       "      <td>пенсионер</td>\n",
       "      <td>0</td>\n",
       "      <td>290547.235997</td>\n",
       "      <td>операции с коммерческой недвижимостью</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>363548.489348</td>\n",
       "      <td>67</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>пенсионер</td>\n",
       "      <td>0</td>\n",
       "      <td>55112.757732</td>\n",
       "      <td>покупка недвижимости</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>335581.668515</td>\n",
       "      <td>62</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>пенсионер</td>\n",
       "      <td>0</td>\n",
       "      <td>171456.067993</td>\n",
       "      <td>операции с коммерческой недвижимостью</td>\n",
       "    </tr>\n",
       "    <tr>\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",
       "      <td>21505</td>\n",
       "      <td>0</td>\n",
       "      <td>338904.866406</td>\n",
       "      <td>53</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>гражданский брак</td>\n",
       "      <td>1</td>\n",
       "      <td>M</td>\n",
       "      <td>пенсионер</td>\n",
       "      <td>0</td>\n",
       "      <td>75439.993167</td>\n",
       "      <td>сыграть свадьбу</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21508</td>\n",
       "      <td>0</td>\n",
       "      <td>386497.714078</td>\n",
       "      <td>62</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>пенсионер</td>\n",
       "      <td>0</td>\n",
       "      <td>72638.590915</td>\n",
       "      <td>недвижимость</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21509</td>\n",
       "      <td>0</td>\n",
       "      <td>362161.054124</td>\n",
       "      <td>59</td>\n",
       "      <td>высшее</td>\n",
       "      <td>0</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>пенсионер</td>\n",
       "      <td>0</td>\n",
       "      <td>73029.059379</td>\n",
       "      <td>операции с недвижимостью</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21518</td>\n",
       "      <td>0</td>\n",
       "      <td>373995.710838</td>\n",
       "      <td>59</td>\n",
       "      <td>СРЕДНЕЕ</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>пенсионер</td>\n",
       "      <td>0</td>\n",
       "      <td>153864.650328</td>\n",
       "      <td>сделка с автомобилем</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21521</td>\n",
       "      <td>0</td>\n",
       "      <td>343937.404131</td>\n",
       "      <td>67</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>пенсионер</td>\n",
       "      <td>0</td>\n",
       "      <td>155999.806512</td>\n",
       "      <td>сделка с автомобилем</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3428 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       children  days_employed  dob_years education  education_id  \\\n",
       "4             0  340266.072047         53   среднее             1   \n",
       "18            0  400281.136913         53   среднее             1   \n",
       "24            1  338551.952911         57   среднее             1   \n",
       "25            0  363548.489348         67   среднее             1   \n",
       "30            1  335581.668515         62   среднее             1   \n",
       "...         ...            ...        ...       ...           ...   \n",
       "21505         0  338904.866406         53   среднее             1   \n",
       "21508         0  386497.714078         62   среднее             1   \n",
       "21509         0  362161.054124         59    высшее             0   \n",
       "21518         0  373995.710838         59   СРЕДНЕЕ             1   \n",
       "21521         0  343937.404131         67   среднее             1   \n",
       "\n",
       "               family_status  family_status_id gender income_type  debt  \\\n",
       "4           гражданский брак                 1      F   пенсионер     0   \n",
       "18            вдовец / вдова                 2      F   пенсионер     0   \n",
       "24     Не женат / не замужем                 4      F   пенсионер     0   \n",
       "25           женат / замужем                 0      M   пенсионер     0   \n",
       "30           женат / замужем                 0      F   пенсионер     0   \n",
       "...                      ...               ...    ...         ...   ...   \n",
       "21505       гражданский брак                 1      M   пенсионер     0   \n",
       "21508        женат / замужем                 0      M   пенсионер     0   \n",
       "21509        женат / замужем                 0      M   пенсионер     0   \n",
       "21518        женат / замужем                 0      F   пенсионер     0   \n",
       "21521        женат / замужем                 0      F   пенсионер     0   \n",
       "\n",
       "        total_income                                purpose  \n",
       "4      158616.077870                        сыграть свадьбу  \n",
       "18      56823.777243     на покупку подержанного автомобиля  \n",
       "24     290547.235997  операции с коммерческой недвижимостью  \n",
       "25      55112.757732                   покупка недвижимости  \n",
       "30     171456.067993  операции с коммерческой недвижимостью  \n",
       "...              ...                                    ...  \n",
       "21505   75439.993167                        сыграть свадьбу  \n",
       "21508   72638.590915                           недвижимость  \n",
       "21509   73029.059379               операции с недвижимостью  \n",
       "21518  153864.650328                   сделка с автомобилем  \n",
       "21521  155999.806512                   сделка с автомобилем  \n",
       "\n",
       "[3428 rows x 12 columns]"
      ]
     },
     "execution_count": 332,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['days_employed'] > 300 * 10 ** 3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "id": "980f2232",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14330.725172125"
      ]
     },
     "execution_count": 333,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "343937.404131 / 24"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "id": "f3dad338",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[df['days_employed'] > 300 * 10 ** 3, 'days_employed'] = df.loc[df['days_employed'] > 300 * 10 ** 3, 'days_employed'] / 24 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "id": "e03e1dcb",
   "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>children</th>\n",
       "      <th>days_employed</th>\n",
       "      <th>dob_years</th>\n",
       "      <th>education</th>\n",
       "      <th>education_id</th>\n",
       "      <th>family_status</th>\n",
       "      <th>family_status_id</th>\n",
       "      <th>gender</th>\n",
       "      <th>income_type</th>\n",
       "      <th>debt</th>\n",
       "      <th>total_income</th>\n",
       "      <th>purpose</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [children, days_employed, dob_years, education, education_id, family_status, family_status_id, gender, income_type, debt, total_income, purpose]\n",
       "Index: []"
      ]
     },
     "execution_count": 335,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['days_employed'] > 300 * 10 ** 3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "id": "0fba8390",
   "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>children</th>\n",
       "      <th>days_employed</th>\n",
       "      <th>dob_years</th>\n",
       "      <th>education</th>\n",
       "      <th>education_id</th>\n",
       "      <th>family_status</th>\n",
       "      <th>family_status_id</th>\n",
       "      <th>gender</th>\n",
       "      <th>income_type</th>\n",
       "      <th>debt</th>\n",
       "      <th>total_income</th>\n",
       "      <th>purpose</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [children, days_employed, dob_years, education, education_id, family_status, family_status_id, gender, income_type, debt, total_income, purpose]\n",
       "Index: []"
      ]
     },
     "execution_count": 336,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['days_employed'] < 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "id": "47e5a6a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.10101435048847754"
      ]
     },
     "execution_count": 337,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['days_employed'].isna().mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "id": "88714157",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd1f491ce50>"
      ]
     },
     "execution_count": 338,
     "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['days_employed'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 339,
   "id": "0e25db00",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[df['days_employed'].isnull(), 'days_employed'] = df['days_employed'].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "id": "bb1b3101",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 340,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['days_employed'].isna().mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "id": "71ebd763",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.days_employed = df.days_employed.astype('int64')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41ccc6c5",
   "metadata": {},
   "source": [
    "# dob_years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 342,
   "id": "658fa3d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    21393.000000\n",
       "mean        43.515496\n",
       "std         12.245218\n",
       "min         19.000000\n",
       "25%         33.000000\n",
       "50%         43.000000\n",
       "75%         53.000000\n",
       "max         75.000000\n",
       "Name: dob_years, dtype: float64"
      ]
     },
     "execution_count": 342,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['dob_years'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 343,
   "id": "77ba41ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd1d622c150>"
      ]
     },
     "execution_count": 343,
     "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['dob_years'].hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da0f8473",
   "metadata": {},
   "source": [
    "# education"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 344,
   "id": "a4229892",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count       21393\n",
       "unique         15\n",
       "top       среднее\n",
       "freq        13673\n",
       "Name: education, dtype: object"
      ]
     },
     "execution_count": 344,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['education'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 345,
   "id": "fb76dcd4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['высшее', 'среднее', 'Среднее', 'СРЕДНЕЕ', 'ВЫСШЕЕ',\n",
       "       'неоконченное высшее', 'начальное', 'Высшее',\n",
       "       'НЕОКОНЧЕННОЕ ВЫСШЕЕ', 'Неоконченное высшее', 'НАЧАЛЬНОЕ',\n",
       "       'Начальное', 'Ученая степень', 'УЧЕНАЯ СТЕПЕНЬ', 'ученая степень'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 345,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['education'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 346,
   "id": "44407db3",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['education'] = df['education'].str.lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 347,
   "id": "66b8b42f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['высшее', 'среднее', 'неоконченное высшее', 'начальное',\n",
       "       'ученая степень'], dtype=object)"
      ]
     },
     "execution_count": 347,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['education'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c6bb31c",
   "metadata": {},
   "source": [
    "# education_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 348,
   "id": "77aacc15",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    21393.000000\n",
       "mean         0.817838\n",
       "std          0.548299\n",
       "min          0.000000\n",
       "25%          1.000000\n",
       "50%          1.000000\n",
       "75%          1.000000\n",
       "max          4.000000\n",
       "Name: education_id, dtype: float64"
      ]
     },
     "execution_count": 348,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['education_id'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "id": "447bce21",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd1f4917850>"
      ]
     },
     "execution_count": 349,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYMAAAD4CAYAAAAO9oqkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+17YcXAAAW/klEQVR4nO3df4xdZ33n8fenMSEBQxwadhrF3nV2sViFuO3GoyQV2mpMuokDCEfaFAVliYNCLS2hTbdZkYQVaxaINmibZhsoIJdYJJBlkk3ZjZsfTa2QEUJqQjA/4vyAZggGbKVxi42pIYU1+90/7pMyO4w9c8+dO+PY75d0Nec853nO+Z4zc+cz59wz96aqkCQd235psQuQJC0+w0CSZBhIkgwDSRKGgSQJWLLYBXR1yimn1MqVKzuN/dGPfsTLX/7y+S1oHlhXf6yrP9bVn6O1ru3bt/9dVb36FxZU1YvysWbNmurqoYce6jx2mKyrP9bVH+vqz9FaF/DlmuF3qpeJJEmGgSTJMJAkYRhIkjAMJEkYBpIkDANJEoaBJAnDQJLEi/jtKNSfldfe23ns1asPcnnH8TtveFPn7UpaOJ4ZSJIMA0mSYSBJwjCQJGEYSJIwDCRJGAaSJAwDSRKGgSQJw0CShGEgSWIOYZBkS5I9SR6fYdnVSSrJKW0+SW5OMpnksSRnTem7IcnT7bFhSvuaJDvamJuTZL52TpI0N3M5M/gUsG56Y5IVwPnAd6c0Xwisao+NwMdb31cBm4BzgLOBTUlObmM+DvzOlHG/sC1J0nDNGgZV9QVg7wyLbgLeA9SUtvXAbdXzMLAsyanABcC2qtpbVfuAbcC6tuyVVfVwVRVwG3DRYLskSepXp7ewTrIe2F1VX592Vec04HtT5ne1tsO175qh/VDb3UjvjIORkREmJia6lM+BAwc6jx2mYdZ19eqDnceOnNh9/DCP87H4fRyEdfXnWKur7zBI8jLgvfQuES2oqtoMbAYYHR2tsbGxTuuZmJig69hhGmZdXT+PAHpBcOOObh99sfPSsc7bnc2x+H0chHX151irq8vdRP8COB34epKdwHLgK0l+BdgNrJjSd3lrO1z78hnaJUkLqO8wqKodVfVPqmplVa2kd2nnrKr6G2ArcFm7q+hcYH9VPQs8AJyf5OT2wvH5wANt2Q+TnNvuIroMuHue9k2SNEdzubX0s8BfAa9NsivJFYfpfh/wDDAJ/CnwLoCq2gt8EHi0PT7Q2mh9PtnGfAu4v9uuSJK6mvVCcFW9bZblK6dMF3DlIfptAbbM0P5l4MzZ6pAkDY//gSxJMgwkSYaBJAnDQJKEYSBJwjCQJGEYSJIwDCRJGAaSJAwDSRKGgSQJw0CShGEgScIwkCRhGEiSMAwkSRgGkiQMA0kShoEkiTmEQZItSfYkeXxK239L8o0kjyX5X0mWTVl2XZLJJN9McsGU9nWtbTLJtVPaT0/ySGu/I8nx87mDkqTZzeXM4FPAumlt24Azq+pXgb8GrgNIcgZwCfC6NuZjSY5LchzwJ8CFwBnA21pfgA8DN1XVa4B9wBUD7ZEkqW+zhkFVfQHYO63tL6vqYJt9GFjeptcD41X1k6r6NjAJnN0ek1X1TFX9FBgH1icJ8Abgrjb+VuCiAfdJktSnVNXsnZKVwD1VdeYMy/4cuKOqPpPko8DDVfWZtuwW4P7WdV1VvbO1vx04B3h/6/+a1r4CuH+m7bTlG4GNACMjI2vGx8fnvqdTHDhwgKVLl3YaO0zDrGvH7v2dx46cCM89323s6tNO6rzd2RyL38dBWFd/jta61q5du72qRqe3LxmkqCT/CTgI3D7IeuaqqjYDmwFGR0drbGys03omJiboOnaYhlnX5dfe23ns1asPcuOObj8qOy8d67zd2RyL38dBWFd/jrW6OodBksuBNwPn1c9PL3YDK6Z0W97aOET794FlSZa0y05T+0uSFkinW0uTrAPeA7ylqn48ZdFW4JIkL01yOrAK+BLwKLCq3Tl0PL0Xmbe2EHkIuLiN3wDc3W1XJEldzeXW0s8CfwW8NsmuJFcAHwVeAWxL8rUknwCoqieAO4Engb8Arqyqn7W/+t8NPAA8BdzZ+gJcA/xBkkngl4Fb5nUPJUmzmvUyUVW9bYbmQ/7CrqrrgetnaL8PuG+G9mfo3W0kSVok/geyJMkwkCQZBpIkDANJEoaBJAnDQJKEYSBJwjCQJGEYSJIwDCRJGAaSJAwDSRKGgSQJw0CShGEgScIwkCRhGEiSMAwkScztM5C3JNmT5PEpba9Ksi3J0+3rya09SW5OMpnksSRnTRmzofV/OsmGKe1rkuxoY25OkvneSUnS4c3lzOBTwLppbdcCD1bVKuDBNg9wIbCqPTYCH4deeACbgHPofd7xphcCpPX5nSnjpm9LkjRks4ZBVX0B2DuteT1wa5u+FbhoSvtt1fMwsCzJqcAFwLaq2ltV+4BtwLq27JVV9XBVFXDblHVJkhZIer+DZ+mUrATuqaoz2/wPqmpZmw6wr6qWJbkHuKGqvtiWPQhcA4wBJ1TVh1r7+4DngYnW/7da+78GrqmqNx+ijo30zjgYGRlZMz4+3mmnDxw4wNKlSzuNHaZh1rVj9/7OY0dOhOee7zZ29Wkndd7ubI7F7+MgrKs/R2tda9eu3V5Vo9PblwxUFVBVlWT2RJkHVbUZ2AwwOjpaY2NjndYzMTFB17HDNMy6Lr/23s5jr159kBt3dPtR2XnpWOftzuZY/D4Owrr6c6zV1fVuoufaJR7a1z2tfTewYkq/5a3tcO3LZ2iXJC2grmGwFXjhjqANwN1T2i9rdxWdC+yvqmeBB4Dzk5zcXjg+H3igLfthknPb5abLpqxLkrRAZj33T/JZetf8T0myi95dQTcAdya5AvgO8NbW/T7gjcAk8GPgHQBVtTfJB4FHW78PVNULL0q/i94dSycC97eHJGkBzRoGVfW2Qyw6b4a+BVx5iPVsAbbM0P5l4MzZ6pAkDY//gSxJMgwkSYaBJAnDQJKEYSBJwjCQJGEYSJIwDCRJGAaSJAwDSRKGgSQJw0CShGEgScIwkCRhGEiSMAwkSRgGkiQMA0kSA4ZBkv+Q5Ikkjyf5bJITkpye5JEkk0nuSHJ86/vSNj/Zlq+csp7rWvs3k1ww2C5JkvrVOQySnAb8HjBaVWcCxwGXAB8Gbqqq1wD7gCvakCuAfa39ptaPJGe0ca8D1gEfS3Jc17okSf0b9DLREuDEJEuAlwHPAm8A7mrLbwUuatPr2zxt+XlJ0trHq+onVfVtYBI4e8C6JEl9SFV1H5xcBVwPPA/8JXAV8HD7658kK4D7q+rMJI8D66pqV1v2LeAc4P1tzGda+y1tzF0zbG8jsBFgZGRkzfj4eKe6Dxw4wNKlSzuNHaZh1rVj9/7OY0dOhOee7zZ29Wkndd7ubI7F7+MgrKs/R2tda9eu3V5Vo9Pbl3RdYZKT6f1VfzrwA+B/0rvMMzRVtRnYDDA6OlpjY2Od1jMxMUHXscM0zLouv/bezmOvXn2QG3d0+1HZeelY5+3O5lj8Pg7CuvpzrNU1yGWi3wK+XVV/W1X/B/gc8HpgWbtsBLAc2N2mdwMrANryk4DvT22fYYwkaQEMEgbfBc5N8rJ27f884EngIeDi1mcDcHeb3trmacs/X71rVFuBS9rdRqcDq4AvDVCXJKlPnS8TVdUjSe4CvgIcBL5K7xLOvcB4kg+1tlvakFuATyeZBPbSu4OIqnoiyZ30guQgcGVV/axrXZKk/nUOA4Cq2gRsmtb8DDPcDVRV/wD89iHWcz29F6IlSYvA/0CWJBkGkiTDQJKEYSBJwjCQJGEYSJIwDCRJGAaSJAwDSRKGgSQJw0CShGEgScIwkCRhGEiSMAwkSRgGkiQMA0kShoEkCcNAksSAYZBkWZK7knwjyVNJfiPJq5JsS/J0+3py65skNyeZTPJYkrOmrGdD6/90kg2D7pQkqT+Dnhn8MfAXVfUvgV8DngKuBR6sqlXAg20e4EJgVXtsBD4OkORVwCbgHOBsYNMLASJJWhidwyDJScBvArcAVNVPq+oHwHrg1tbtVuCiNr0euK16HgaWJTkVuADYVlV7q2ofsA1Y17UuSVL/UlXdBia/DmwGnqR3VrAduArYXVXLWp8A+6pqWZJ7gBuq6ott2YPANcAYcEJVfai1vw94vqr+cIZtbqR3VsHIyMia8fHxTrUfOHCApUuXdho7TMOsa8fu/Z3HjpwIzz3fbezq007qvN3ZHIvfx0FYV3+O1rrWrl27vapGp7cvGaCmJcBZwO9W1SNJ/pifXxICoKoqSbe0mUFVbaYXQIyOjtbY2Fin9UxMTNB17DANs67Lr72389irVx/kxh3dflR2XjrWebuzORa/j4Owrv4ca3UN8prBLmBXVT3S5u+iFw7Ptcs/tK972vLdwIop45e3tkO1S5IWSOcwqKq/Ab6X5LWt6Tx6l4y2Ai/cEbQBuLtNbwUua3cVnQvsr6pngQeA85Oc3F44Pr+1SZIWyCCXiQB+F7g9yfHAM8A76AXMnUmuAL4DvLX1vQ94IzAJ/Lj1par2Jvkg8Gjr94Gq2jtgXZKkPgwUBlX1NeAXXoigd5YwvW8BVx5iPVuALYPUIknqzv9AliQZBpIkw0CShGEgSWLwu4lelHbs3j/QP2F1tfOGNy34NiVpLjwzkCQZBpIkw0CShGEgScIwkCRhGEiSMAwkSRgGkiQMA0kShoEkCcNAkoRhIEnCMJAkMQ9hkOS4JF9Nck+bPz3JI0kmk9zRPh+ZJC9t85Nt+cop67iutX8zyQWD1iRJ6s98nBlcBTw1Zf7DwE1V9RpgH3BFa78C2Nfab2r9SHIGcAnwOmAd8LEkx81DXZKkORooDJIsB94EfLLNB3gDcFfrcitwUZte3+Zpy89r/dcD41X1k6r6NjAJnD1IXZKk/qSqug9O7gL+K/AK4D8ClwMPt7/+SbICuL+qzkzyOLCuqna1Zd8CzgHe38Z8prXf0sbcNW1zJNkIbAQYGRlZMz4+3qnuPXv389zznYYOZPVpJx12+YEDB1i6dOlQtr1j9/7OY0dOpPPxmm2fBzHM4zUI6+qPdfVn0LrWrl27vapGp7d3/qSzJG8G9lTV9iRjnSvrQ1VtBjYDjI6O1thYt81+5Pa7uXHHwn/I285Lxw67fGJigq77NJtBPtnt6tUHOx+v2fZ5EMM8XoOwrv5YV3+GVdcgvxFfD7wlyRuBE4BXAn8MLEuypKoOAsuB3a3/bmAFsCvJEuAk4PtT2l8wdYwkaQF0fs2gqq6rquVVtZLeC8Cfr6pLgYeAi1u3DcDdbXprm6ct/3z1rlFtBS5pdxudDqwCvtS1LklS/4ZxreQaYDzJh4CvAre09luATyeZBPbSCxCq6okkdwJPAgeBK6vqZ0OoS5J0CPMSBlU1AUy06WeY4W6gqvoH4LcPMf564Pr5qEWS1D//A1mSZBhIkgwDSRKGgSQJw0CShGEgScIwkCRhGEiSMAwkSRgGkiQMA0kShoEkCcNAkoRhIEnCMJAkYRhIkjAMJEkYBpIkBgiDJCuSPJTkySRPJLmqtb8qybYkT7evJ7f2JLk5yWSSx5KcNWVdG1r/p5NsGHy3JEn9GOTM4CBwdVWdAZwLXJnkDOBa4MGqWgU82OYBLgRWtcdG4OPQCw9gE3AOvc9O3vRCgEiSFkbnMKiqZ6vqK23674GngNOA9cCtrdutwEVtej1wW/U8DCxLcipwAbCtqvZW1T5gG7Cua12SpP6lqgZfSbIS+AJwJvDdqlrW2gPsq6plSe4BbqiqL7ZlDwLXAGPACVX1odb+PuD5qvrDGbazkd5ZBSMjI2vGx8c71btn736ee77T0IGsPu2kwy4/cOAAS5cuHcq2d+ze33nsyIl0Pl6z7fMghnm8BmFd/bGu/gxa19q1a7dX1ej09iUDVQUkWQr8GfD7VfXD3u//nqqqJIOnzc/XtxnYDDA6OlpjY2Od1vOR2+/mxh0D73rfdl46dtjlExMTdN2n2Vx+7b2dx169+mDn4zXbPg9imMdrENbVH+vqz7DqGuhuoiQvoRcEt1fV51rzc+3yD+3rnta+G1gxZfjy1naodknSAhnkbqIAtwBPVdUfTVm0FXjhjqANwN1T2i9rdxWdC+yvqmeBB4Dzk5zcXjg+v7VJkhbIINdKXg+8HdiR5Gut7b3ADcCdSa4AvgO8tS27D3gjMAn8GHgHQFXtTfJB4NHW7wNVtXeAuiRJfeocBu2F4Bxi8Xkz9C/gykOsawuwpWstkqTB+B/IkiTDQJJkGEiSMAwkSRgGkiQMA0kShoEkCcNAkoRhIEnCMJAkYRhIkjAMJEnMw4fbSPr/7di9f6APExrEzhvetCjb1YufZwaSJMNAkmQYSJIwDCRJGAaSJLybSNI8WDnA3VNXrz7Y+e4r756aP0fMmUGSdUm+mWQyybWLXY8kHUuOiDBIchzwJ8CFwBnA25KcsbhVSdKx44gIA+BsYLKqnqmqnwLjwPpFrkmSjhmpqsWugSQXA+uq6p1t/u3AOVX17mn9NgIb2+xrgW923OQpwN91HDtM1tUf6+qPdfXnaK3rn1XVq6c3vqheQK6qzcDmQdeT5MtVNToPJc0r6+qPdfXHuvpzrNV1pFwm2g2smDK/vLVJkhbAkRIGjwKrkpye5HjgEmDrItckSceMI+IyUVUdTPJu4AHgOGBLVT0xxE0OfKlpSKyrP9bVH+vqzzFV1xHxArIkaXEdKZeJJEmLyDCQJB3dYTDbW1wkeWmSO9ryR5KsPELqujzJ3yb5Wnu8cwFq2pJkT5LHD7E8SW5uNT+W5Kxh1zTHusaS7J9yrP7zAtW1IslDSZ5M8kSSq2bos+DHbI51LfgxS3JCki8l+Xqr67/M0GfBn49zrGvBn49Ttn1ckq8muWeGZfN7vKrqqHzQeyH6W8A/B44Hvg6cMa3Pu4BPtOlLgDuOkLouBz66wMfrN4GzgMcPsfyNwP1AgHOBR46QusaAexbh5+tU4Kw2/Qrgr2f4Pi74MZtjXQt+zNoxWNqmXwI8Apw7rc9iPB/nUteCPx+nbPsPgP8x0/drvo/X0XxmMJe3uFgP3Nqm7wLOS5IjoK4FV1VfAPYepst64LbqeRhYluTUI6CuRVFVz1bVV9r03wNPAadN67bgx2yOdS24dgwOtNmXtMf0u1cW/Pk4x7oWRZLlwJuATx6iy7wer6M5DE4Dvjdlfhe/+KT4xz5VdRDYD/zyEVAXwL9tlxbuSrJihuULba51L4bfaKf59yd53UJvvJ2e/yt6f1VOtajH7DB1wSIcs3bJ42vAHmBbVR3yeC3g83EudcHiPB//O/Ae4P8eYvm8Hq+jOQxezP4cWFlVvwps4+fpr1/0FXrvtfJrwEeA/72QG0+yFPgz4Per6ocLue3DmaWuRTlmVfWzqvp1eu8wcHaSMxdiu7OZQ10L/nxM8mZgT1VtH/a2XnA0h8Fc3uLiH/skWQKcBHx/seuqqu9X1U/a7CeBNUOuaS6OyLcMqaofvnCaX1X3AS9JcspCbDvJS+j9wr29qj43Q5dFOWaz1bWYx6xt8wfAQ8C6aYsW4/k4a12L9Hx8PfCWJDvpXUp+Q5LPTOszr8fraA6DubzFxVZgQ5u+GPh8tVdjFrOuadeV30Lvuu9i2wpc1u6QORfYX1XPLnZRSX7lheukSc6m9zM99F8gbZu3AE9V1R8dotuCH7O51LUYxyzJq5Msa9MnAv8G+Ma0bgv+fJxLXYvxfKyq66pqeVWtpPc74vNV9e+mdZvX43VEvB3FMNQh3uIiyQeAL1fVVnpPmk8nmaT3IuUlR0hdv5fkLcDBVtflw64ryWfp3WVySpJdwCZ6L6ZRVZ8A7qN3d8wk8GPgHcOuaY51XQz8+yQHgeeBSxYg0KH3l9vbgR3tejPAe4F/OqW2xThmc6lrMY7ZqcCt6X2Q1S8Bd1bVPYv9fJxjXQv+fDyUYR4v345CknRUXyaSJM2RYSBJMgwkSYaBJAnDQJKEYSBJwjCQJAH/DwfkdtRIees7AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['education_id'].hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6fdf82e4",
   "metadata": {},
   "source": [
    "# family_status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "id": "c1bbc2d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['женат / замужем', 'гражданский брак', 'вдовец / вдова',\n",
       "       'в разводе', 'Не женат / не замужем'], dtype=object)"
      ]
     },
     "execution_count": 350,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['family_status'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 351,
   "id": "4527923c",
   "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>family_status</th>\n",
       "      <th>education_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Не женат / не замужем</td>\n",
       "      <td>2789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>в разводе</td>\n",
       "      <td>1184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>вдовец / вдова</td>\n",
       "      <td>955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>гражданский брак</td>\n",
       "      <td>4153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>12312</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           family_status  education_id\n",
       "0  Не женат / не замужем          2789\n",
       "1              в разводе          1184\n",
       "2         вдовец / вдова           955\n",
       "3       гражданский брак          4153\n",
       "4        женат / замужем         12312"
      ]
     },
     "execution_count": 351,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('family_status', as_index = False).agg({'education_id' : 'count'})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "354beec3",
   "metadata": {},
   "source": [
    "# family_status_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 352,
   "id": "f023f3b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    21393.000000\n",
       "mean         0.970925\n",
       "std          1.419195\n",
       "min          0.000000\n",
       "25%          0.000000\n",
       "50%          0.000000\n",
       "75%          1.000000\n",
       "max          4.000000\n",
       "Name: family_status_id, dtype: float64"
      ]
     },
     "execution_count": 352,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['family_status_id'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 353,
   "id": "04e57003",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 21393 entries, 0 to 21524\n",
      "Data columns (total 12 columns):\n",
      "children            21393 non-null int64\n",
      "days_employed       21393 non-null int64\n",
      "dob_years           21393 non-null int64\n",
      "education           21393 non-null object\n",
      "education_id        21393 non-null int64\n",
      "family_status       21393 non-null object\n",
      "family_status_id    21393 non-null int64\n",
      "gender              21393 non-null object\n",
      "income_type         21393 non-null object\n",
      "debt                21393 non-null int64\n",
      "total_income        19232 non-null float64\n",
      "purpose             21393 non-null object\n",
      "dtypes: float64(1), int64(6), object(5)\n",
      "memory usage: 2.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 354,
   "id": "e053d22c",
   "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>children</th>\n",
       "      <th>days_employed</th>\n",
       "      <th>dob_years</th>\n",
       "      <th>education</th>\n",
       "      <th>education_id</th>\n",
       "      <th>family_status</th>\n",
       "      <th>family_status_id</th>\n",
       "      <th>gender</th>\n",
       "      <th>income_type</th>\n",
       "      <th>debt</th>\n",
       "      <th>total_income</th>\n",
       "      <th>purpose</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8437</td>\n",
       "      <td>42</td>\n",
       "      <td>высшее</td>\n",
       "      <td>0</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>253875.639453</td>\n",
       "      <td>покупка жилья</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4024</td>\n",
       "      <td>36</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>112080.014102</td>\n",
       "      <td>приобретение автомобиля</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>5623</td>\n",
       "      <td>33</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>145885.952297</td>\n",
       "      <td>покупка жилья</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4124</td>\n",
       "      <td>32</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>267628.550329</td>\n",
       "      <td>дополнительное образование</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>14177</td>\n",
       "      <td>53</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>гражданский брак</td>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>пенсионер</td>\n",
       "      <td>0</td>\n",
       "      <td>158616.077870</td>\n",
       "      <td>сыграть свадьбу</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   children  days_employed  dob_years education  education_id  \\\n",
       "0         1           8437         42    высшее             0   \n",
       "1         1           4024         36   среднее             1   \n",
       "2         0           5623         33   среднее             1   \n",
       "3         3           4124         32   среднее             1   \n",
       "4         0          14177         53   среднее             1   \n",
       "\n",
       "      family_status  family_status_id gender income_type  debt   total_income  \\\n",
       "0   женат / замужем                 0      F   сотрудник     0  253875.639453   \n",
       "1   женат / замужем                 0      F   сотрудник     0  112080.014102   \n",
       "2   женат / замужем                 0      M   сотрудник     0  145885.952297   \n",
       "3   женат / замужем                 0      M   сотрудник     0  267628.550329   \n",
       "4  гражданский брак                 1      F   пенсионер     0  158616.077870   \n",
       "\n",
       "                      purpose  \n",
       "0               покупка жилья  \n",
       "1     приобретение автомобиля  \n",
       "2               покупка жилья  \n",
       "3  дополнительное образование  \n",
       "4             сыграть свадьбу  "
      ]
     },
     "execution_count": 354,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 355,
   "id": "42069ecd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd1c736db90>"
      ]
     },
     "execution_count": 355,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYMAAAD4CAYAAAAO9oqkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+17YcXAAAUvElEQVR4nO3df4xd5X3n8fenGALFrU1CNMva3jWruFkRaLswAqpI1TjsgoEIIy3NOmKDicha2pKWbiMl0FWXbhIkoi1NA2kTWbEVk3gzsDRduwRKvcAoqrQQ4oRifoQyJU5ji+IWE6cTaLJOv/vHfZxMJzOeuffO3DHm/ZJGc87zPOec7zlw5zP3OWeuU1VIkl7ffmqxC5AkLT7DQJJkGEiSDANJEoaBJAlYstgF9Or000+v1atX97Tt9773PU499dT5LWgeWFd3rKs71tWd47Wu3bt3/11VvfknOqrqNfl13nnnVa8efvjhnrddSNbVHevqjnV153itC/hqTfMz1WkiSZJhIEkyDCRJGAaSJAwDSRKGgSQJw0CShGEgScIwkCTxGv44in7s2X+Ia2/80sCPu/fWywd+TEmaC98ZSJJmD4MkW5McSPLkpLb/keQbSZ5I8sdJlk/quynJeJJnk1wyqX1daxtPcuOk9jOTPNra70py0nyeoCRpdnN5Z/BZYN2Utl3A2VX188BfAjcBJDkL2AC8rW3zh0lOSHIC8AfApcBZwLvbWICPAR+vqrcALwPX9XVGkqSuzRoGVfVl4OCUtj+rqsNt9RFgZVteD4xW1fer6pvAOHB++xqvquer6gfAKLA+SYB3APe07bcBV/Z5TpKkLqXziaazDEpWA/dW1dnT9P0JcFdVfT7JJ4FHqurzrW8LcH8buq6q3tfa3wNcAPxOG/+W1r4KuH+647T+TcAmgKGhofNGR0fnfqaTHDh4iBdf7WnTvpyzYtlR+ycmJli6dOmAqpk76+qOdXXHurrTb11r167dXVXDU9v7epooyX8FDgPb+9nPXFXVZmAzwPDwcI2MjPS0nzu27+C2PYN/kGrv1SNH7R8bG6PXc1pI1tUd6+qOdXVnoerq+SdikmuBdwIX1Y/fXuwHVk0atrK1MUP7S8DyJEvatNPk8ZKkAenp0dIk64APAldU1SuTunYCG5K8IcmZwBrgK8BjwJr25NBJdG4y72wh8jBwVdt+I7Cjt1ORJPVqLo+WfgH4v8Bbk+xLch3wSeBngF1JHk/yaYCqegq4G3ga+FPg+qr6Yfut//3AA8AzwN1tLMCHgN9MMg68Cdgyr2coSZrVrNNEVfXuaZpn/IFdVbcAt0zTfh9w3zTtz9N52kiStEj8C2RJkmEgSTIMJEkYBpIkDANJEoaBJAnDQJKEYSBJwjCQJGEYSJIwDCRJGAaSJAwDSRKGgSQJw0CShGEgScIwkCRhGEiSMAwkSRgGkiQMA0kShoEkCcNAkoRhIEliDmGQZGuSA0menNT2xiS7kjzXvp/W2pPk9iTjSZ5Icu6kbTa28c8l2Tip/bwke9o2tyfJfJ+kJOno5vLO4LPAuiltNwIPVtUa4MG2DnApsKZ9bQI+BZ3wAG4GLgDOB24+EiBtzH+atN3UY0mSFtisYVBVXwYOTmleD2xry9uAKye131kdjwDLk5wBXALsqqqDVfUysAtY1/p+tqoeqaoC7py0L0nSgPR6z2Coql5oy38DDLXlFcC3J43b19qO1r5vmnZJ0gAt6XcHVVVJaj6KmU2STXSmnxgaGmJsbKyn/QydAh845/A8VjY3s9U7MTHR8zktJOvqjnV1x7q6s1B19RoGLyY5o6peaFM9B1r7fmDVpHErW9t+YGRK+1hrXznN+GlV1WZgM8Dw8HCNjIzMNPSo7ti+g9v29J2DXdt79chR+8fGxuj1nBaSdXXHurpjXd1ZqLp6nSbaCRx5ImgjsGNS+zXtqaILgUNtOukB4OIkp7UbxxcDD7S+7ya5sD1FdM2kfUmSBmTWX4+TfIHOb/WnJ9lH56mgW4G7k1wHfAt4Vxt+H3AZMA68ArwXoKoOJvkI8Fgb9+GqOnJT+lfpPLF0CnB/+5IkDdCsYVBV756h66JpxhZw/Qz72Qpsnab9q8DZs9UhSVo4/gWyJMkwkCQZBpIkDANJEoaBJAnDQJKEYSBJwjCQJGEYSJIwDCRJGAaSJAwDSRKGgSQJw0CShGEgScIwkCRhGEiSMAwkSRgGkiQMA0kShoEkCcNAkoRhIEnCMJAkYRhIkugzDJL8lyRPJXkyyReSnJzkzCSPJhlPcleSk9rYN7T18da/etJ+bmrtzya5pL9TkiR1q+cwSLIC+HVguKrOBk4ANgAfAz5eVW8BXgaua5tcB7zc2j/expHkrLbd24B1wB8mOaHXuiRJ3et3mmgJcEqSJcBPAy8A7wDuaf3bgCvb8vq2Tuu/KEla+2hVfb+qvgmMA+f3WZckqQupqt43Tm4AbgFeBf4MuAF4pP32T5JVwP1VdXaSJ4F1VbWv9f0VcAHwO22bz7f2LW2be6Y53iZgE8DQ0NB5o6OjPdV94OAhXny1p037cs6KZUftn5iYYOnSpQOqZu6sqzvW1R3r6k6/da1du3Z3VQ1PbV/S6w6TnEbnt/ozge8A/4vONM+CqarNwGaA4eHhGhkZ6Wk/d2zfwW17ej71nu29euSo/WNjY/R6TgvJurpjXd2xru4sVF39TBP9W+CbVfW3VfX/gC8CbweWt2kjgJXA/ra8H1gF0PqXAS9Nbp9mG0nSAPQTBn8NXJjkp9vc/0XA08DDwFVtzEZgR1ve2dZp/Q9VZ45qJ7ChPW10JrAG+EofdUmSutTzXElVPZrkHuBrwGHg63SmcL4EjCb5aGvb0jbZAnwuyThwkM4TRFTVU0nuphMkh4Hrq+qHvdYlSepeXxPnVXUzcPOU5ueZ5mmgqvoH4Fdm2M8tdG5ES5IWgX+BLEkyDCRJhoEkCcNAkoRhIEnCMJAkYRhIkjAMJEkYBpIkDANJEoaBJAnDQJKEYSBJwjCQJGEYSJIwDCRJGAaSJAwDSRKGgSQJw0CShGEgScIwkCRhGEiSMAwkSRgGkiT6DIMky5Pck+QbSZ5J8ktJ3phkV5Ln2vfT2tgkuT3JeJInkpw7aT8b2/jnkmzs96QkSd3p953BJ4A/rap/DfwC8AxwI/BgVa0BHmzrAJcCa9rXJuBTAEneCNwMXACcD9x8JEAkSYPRcxgkWQb8MrAFoKp+UFXfAdYD29qwbcCVbXk9cGd1PAIsT3IGcAmwq6oOVtXLwC5gXa91SZK6l6rqbcPkF4HNwNN03hXsBm4A9lfV8jYmwMtVtTzJvcCtVfXnre9B4EPACHByVX20tf828GpV/e40x9xE510FQ0ND542OjvZU+4GDh3jx1Z427cs5K5YdtX9iYoKlS5cOqJq5s67uWFd3rKs7/da1du3a3VU1PLV9SR81LQHOBX6tqh5N8gl+PCUEQFVVkt7SZhpVtZlOADE8PFwjIyM97eeO7Tu4bU8/p96bvVePHLV/bGyMXs9pIVlXd6yrO9bVnYWqq597BvuAfVX1aFu/h044vNimf2jfD7T+/cCqSduvbG0ztUuSBqTnMKiqvwG+neStrekiOlNGO4EjTwRtBHa05Z3ANe2poguBQ1X1AvAAcHGS09qN44tbmyRpQPqdK/k1YHuSk4DngffSCZi7k1wHfAt4Vxt7H3AZMA680sZSVQeTfAR4rI37cFUd7LMuSVIX+gqDqnoc+IkbEXTeJUwdW8D1M+xnK7C1n1okSb3zL5AlSYaBJMkwkCRhGEiSMAwkSRgGkiQMA0kShoEkCcNAkoRhIEnCMJAkYRhIkjAMJEkYBpIkDANJEoaBJAnDQJKEYSBJwjCQJGEYSJIwDCRJGAaSJAwDSRKGgSSJeQiDJCck+XqSe9v6mUkeTTKe5K4kJ7X2N7T18da/etI+bmrtzya5pN+aJEndWTIP+7gBeAb42bb+MeDjVTWa5NPAdcCn2veXq+otSTa0cf8hyVnABuBtwD8H/k+Sn6uqH85DbWpW3/ilnrf9wDmHubbH7ffeennPx5U0OH29M0iyErgc+ExbD/AO4J42ZBtwZVte39Zp/Re18euB0ar6flV9ExgHzu+nLklSd/qdJvp94IPAP7b1NwHfqarDbX0fsKItrwC+DdD6D7XxP2qfZhtJ0gD0PE2U5J3AgaranWRk/ko66jE3AZsAhoaGGBsb62k/Q6d0pj4GbbZ6JyYmej6n2fRzvv1cr4U6H1jY69UP6+qOdXVnoerq557B24ErklwGnEznnsEngOVJlrTf/lcC+9v4/cAqYF+SJcAy4KVJ7UdM3uafqKrNwGaA4eHhGhkZ6anwO7bv4LY983G7pDt7rx45av/Y2Bi9ntNsep3zh04Q9Hq9Zjvnfizk9eqHdXXHurqzUHX1PE1UVTdV1cqqWk3nBvBDVXU18DBwVRu2EdjRlne2dVr/Q1VVrX1De9roTGAN8JVe65IkdW8hfj3+EDCa5KPA14EtrX0L8Lkk48BBOgFCVT2V5G7gaeAwcL1PEknSYM1LGFTVGDDWlp9nmqeBquofgF+ZYftbgFvmoxZJUvf8C2RJkmEgSTIMJEkYBpIkDANJEoaBJAnDQJKEYSBJwjCQJGEYSJIwDCRJGAaSJAwDSRKGgSQJw0CSxML84zaSdNxb3cc/JduPz647dUH26zsDSZJhIEkyDCRJGAaSJAwDSRKGgSQJw0CShGEgScIwkCTRRxgkWZXk4SRPJ3kqyQ2t/Y1JdiV5rn0/rbUnye1JxpM8keTcSfva2MY/l2Rj/6clSepGP+8MDgMfqKqzgAuB65OcBdwIPFhVa4AH2zrApcCa9rUJ+BR0wgO4GbgAOB+4+UiASJIGo+cwqKoXquprbfnvgWeAFcB6YFsbtg24si2vB+6sjkeA5UnOAC4BdlXVwap6GdgFrOu1LklS91JV/e8kWQ18GTgb+OuqWt7aA7xcVcuT3AvcWlV/3voeBD4EjAAnV9VHW/tvA69W1e9Oc5xNdN5VMDQ0dN7o6GhP9R44eIgXX+1p076cs2LZUfsnJiZYunTpghx7z/5DPW87dAo9X6/ZzrkfC3m9+mFd3Xmt1tXPa6ofZy47oa/rtXbt2t1VNTy1ve9PLU2yFPgj4Deq6rudn/8dVVVJ+k+bH+9vM7AZYHh4uEZGRnrazx3bd3DbnsF/YOveq0eO2j82Nkav5zSba/v4hMUPnHO45+s12zn3YyGvVz+sqzuv1br6eU3147PrTl2Q69XX00RJTqQTBNur6out+cU2/UP7fqC17wdWTdp8ZWubqV2SNCD9PE0UYAvwTFX93qSuncCRJ4I2AjsmtV/Tniq6EDhUVS8ADwAXJzmt3Ti+uLVJkgakn7mStwPvAfYkeby1/RZwK3B3kuuAbwHvan33AZcB48ArwHsBqupgko8Aj7VxH66qg33UJUnqUs9h0G4EZ4bui6YZX8D1M+xrK7C111okSf3xL5AlSYaBJMkwkCRhGEiSMAwkSRgGkiQMA0kShoEkiXn4oDpJWt3nByH2+qFve2+9vOfj6p/ynYEkyTCQJBkGkiS8ZyDNuz37Dy3aP3ziHLp65TsDSZJhIEkyDCRJGAaSJAwDSRKGgSQJw0CShGEgScIwkCRhGEiSMAwkSRgGkiSOoTBIsi7Js0nGk9y42PVI0uvJMREGSU4A/gC4FDgLeHeSsxa3Kkl6/TgmwgA4Hxivquer6gfAKLB+kWuSpNeNVNVi10CSq4B1VfW+tv4e4IKqev+UcZuATW31rcCzPR7ydODvetx2IVlXd6yrO9bVneO1rn9ZVW+e2via+sdtqmozsLnf/ST5alUNz0NJ88q6umNd3bGu7rze6jpWpon2A6smra9sbZKkAThWwuAxYE2SM5OcBGwAdi5yTZL0unFMTBNV1eEk7wceAE4AtlbVUwt4yL6nmhaIdXXHurpjXd15XdV1TNxAliQtrmNlmkiStIgMA0nS8R0Gs33ERZI3JLmr9T+aZPUxUte1Sf42yePt630DqGlrkgNJnpyhP0lubzU/keTcha5pjnWNJDk06Vr9twHVtSrJw0meTvJUkhumGTPwazbHugZ+zZKcnOQrSf6i1fXfpxkz8NfjHOsa+Otx0rFPSPL1JPdO0ze/16uqjssvOjei/wr4V8BJwF8AZ00Z86vAp9vyBuCuY6Sua4FPDvh6/TJwLvDkDP2XAfcDAS4EHj1G6hoB7l2E/7/OAM5tyz8D/OU0/x0Hfs3mWNfAr1m7Bkvb8onAo8CFU8YsxutxLnUN/PU46di/CfzP6f57zff1Op7fGczlIy7WA9va8j3ARUlyDNQ1cFX1ZeDgUYasB+6sjkeA5UnOOAbqWhRV9UJVfa0t/z3wDLBiyrCBX7M51jVw7RpMtNUT29fUp1cG/nqcY12LIslK4HLgMzMMmdfrdTyHwQrg25PW9/GTL4ofjamqw8Ah4E3HQF0A/75NLdyTZNU0/YM217oXwy+1t/n3J3nboA/e3p7/Gzq/VU62qNfsKHXBIlyzNuXxOHAA2FVVM16vAb4e51IXLM7r8feBDwL/OEP/vF6v4zkMXsv+BFhdVT8P7OLH6a+f9DU6n7XyC8AdwP8e5MGTLAX+CPiNqvruII99NLPUtSjXrKp+WFW/SOcTBs5PcvYgjjubOdQ18NdjkncCB6pq90If64jjOQzm8hEXPxqTZAmwDHhpseuqqpeq6vtt9TPAeQtc01wckx8ZUlXfPfI2v6ruA05Mcvogjp3kRDo/cLdX1RenGbIo12y2uhbzmrVjfgd4GFg3pWsxXo+z1rVIr8e3A1ck2UtnKvkdST4/Zcy8Xq/jOQzm8hEXO4GNbfkq4KFqd2MWs64p88pX0Jn3XWw7gWvaEzIXAoeq6oXFLirJPzsyT5rkfDr/Ty/4D5B2zC3AM1X1ezMMG/g1m0tdi3HNkrw5yfK2fArw74BvTBk28NfjXOpajNdjVd1UVSurajWdnxEPVdV/nDJsXq/XMfFxFAuhZviIiyQfBr5aVTvpvGg+l2Sczk3KDcdIXb+e5ArgcKvr2oWuK8kX6DxlcnqSfcDNdG6mUVWfBu6j83TMOPAK8N6FrmmOdV0F/Ockh4FXgQ0DCHTo/Ob2HmBPm28G+C3gX0yqbTGu2VzqWoxrdgawLZ1/yOqngLur6t7Ffj3Osa6Bvx5nspDXy4+jkCQd19NEkqQ5MgwkSYaBJMkwkCRhGEiSMAwkSRgGkiTg/wOb+IVajf9OsAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['family_status_id'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 356,
   "id": "41dc23d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4])"
      ]
     },
     "execution_count": 356,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['family_status_id'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d574f37",
   "metadata": {},
   "source": [
    "# gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 357,
   "id": "96c55644",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['F', 'M', 'XNA'], dtype=object)"
      ]
     },
     "execution_count": 357,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['gender'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 358,
   "id": "34ee4477",
   "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>gender</th>\n",
       "      <th>counter</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>14149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>M</td>\n",
       "      <td>7243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>XNA</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  gender  counter\n",
       "0      F    14149\n",
       "1      M     7243\n",
       "2    XNA        1"
      ]
     },
     "execution_count": 358,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('gender', as_index = False).agg({'family_status_id' : 'count'}).rename(columns = {'family_status_id' : 'counter'})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6bcc5577",
   "metadata": {},
   "source": [
    "# income_type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 359,
   "id": "6bb4aa85",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['сотрудник', 'пенсионер', 'компаньон', 'госслужащий',\n",
       "       'безработный', 'предприниматель', 'студент', 'в декрете'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 359,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['income_type'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 360,
   "id": "b2ce665a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['income_type'] = df['income_type'].str.replace(' ', '_')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 361,
   "id": "bda2755d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['сотрудник', 'пенсионер', 'компаньон', 'госслужащий',\n",
       "       'безработный', 'предприниматель', 'студент', 'в_декрете'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 361,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['income_type'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96412dd2",
   "metadata": {},
   "source": [
    "# total_income"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 362,
   "id": "78947e70",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1.923200e+04\n",
       "mean     1.674704e+05\n",
       "std      1.031013e+05\n",
       "min      2.066726e+04\n",
       "25%      1.031013e+05\n",
       "50%      1.450208e+05\n",
       "75%      2.033700e+05\n",
       "max      2.265604e+06\n",
       "Name: total_income, dtype: float64"
      ]
     },
     "execution_count": 362,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['total_income'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 363,
   "id": "c7bf0aed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.10101435048847754"
      ]
     },
     "execution_count": 363,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['total_income'].isna().mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 364,
   "id": "e8313621",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd1c73a0dd0>"
      ]
     },
     "execution_count": 364,
     "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['total_income'].hist() # Нужно использовать медиану для заполнения"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 365,
   "id": "e5cd9297",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[df['total_income'].isnull(), 'total_income'] = df['total_income'].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 366,
   "id": "115e8c4c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "165209.8058487514"
      ]
     },
     "execution_count": 366,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stack = []\n",
    "for i in range(0, len(df), 20):\n",
    "    new_df = df[i:i + 20]\n",
    "    stack.append(new_df['total_income'].mean())\n",
    "    \n",
    "res = sum(stack) / len(stack)\n",
    "res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 367,
   "id": "e83e4e7a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['total_income'] = df['total_income'].astype('int64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 368,
   "id": "56242b3a",
   "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>children</th>\n",
       "      <th>days_employed</th>\n",
       "      <th>dob_years</th>\n",
       "      <th>education</th>\n",
       "      <th>education_id</th>\n",
       "      <th>family_status</th>\n",
       "      <th>family_status_id</th>\n",
       "      <th>gender</th>\n",
       "      <th>income_type</th>\n",
       "      <th>debt</th>\n",
       "      <th>total_income</th>\n",
       "      <th>purpose</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8437</td>\n",
       "      <td>42</td>\n",
       "      <td>высшее</td>\n",
       "      <td>0</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>253875</td>\n",
       "      <td>покупка жилья</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4024</td>\n",
       "      <td>36</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>112080</td>\n",
       "      <td>приобретение автомобиля</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>5623</td>\n",
       "      <td>33</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>145885</td>\n",
       "      <td>покупка жилья</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4124</td>\n",
       "      <td>32</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>267628</td>\n",
       "      <td>дополнительное образование</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>14177</td>\n",
       "      <td>53</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>гражданский брак</td>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>пенсионер</td>\n",
       "      <td>0</td>\n",
       "      <td>158616</td>\n",
       "      <td>сыграть свадьбу</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   children  days_employed  dob_years education  education_id  \\\n",
       "0         1           8437         42    высшее             0   \n",
       "1         1           4024         36   среднее             1   \n",
       "2         0           5623         33   среднее             1   \n",
       "3         3           4124         32   среднее             1   \n",
       "4         0          14177         53   среднее             1   \n",
       "\n",
       "      family_status  family_status_id gender income_type  debt  total_income  \\\n",
       "0   женат / замужем                 0      F   сотрудник     0        253875   \n",
       "1   женат / замужем                 0      F   сотрудник     0        112080   \n",
       "2   женат / замужем                 0      M   сотрудник     0        145885   \n",
       "3   женат / замужем                 0      M   сотрудник     0        267628   \n",
       "4  гражданский брак                 1      F   пенсионер     0        158616   \n",
       "\n",
       "                      purpose  \n",
       "0               покупка жилья  \n",
       "1     приобретение автомобиля  \n",
       "2               покупка жилья  \n",
       "3  дополнительное образование  \n",
       "4             сыграть свадьбу  "
      ]
     },
     "execution_count": 368,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5b0ef9e",
   "metadata": {},
   "source": [
    "# purpose"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 369,
   "id": "4a49eee3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['покупка жилья', 'приобретение автомобиля',\n",
       "       'дополнительное образование', 'сыграть свадьбу',\n",
       "       'операции с жильем', 'образование', 'на проведение свадьбы',\n",
       "       'покупка жилья для семьи', 'покупка недвижимости',\n",
       "       'покупка коммерческой недвижимости', 'покупка жилой недвижимости',\n",
       "       'строительство собственной недвижимости', 'недвижимость',\n",
       "       'строительство недвижимости', 'на покупку подержанного автомобиля',\n",
       "       'на покупку своего автомобиля',\n",
       "       'операции с коммерческой недвижимостью',\n",
       "       'строительство жилой недвижимости', 'жилье',\n",
       "       'операции со своей недвижимостью', 'автомобили',\n",
       "       'заняться образованием', 'сделка с подержанным автомобилем',\n",
       "       'получение образования', 'автомобиль', 'свадьба',\n",
       "       'получение дополнительного образования', 'покупка своего жилья',\n",
       "       'операции с недвижимостью', 'получение высшего образования',\n",
       "       'свой автомобиль', 'сделка с автомобилем',\n",
       "       'профильное образование', 'высшее образование',\n",
       "       'покупка жилья для сдачи', 'на покупку автомобиля', 'ремонт жилью',\n",
       "       'заняться высшим образованием'], dtype=object)"
      ]
     },
     "execution_count": 369,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['purpose'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ea003808",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 370,
   "id": "be5d2152",
   "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>children</th>\n",
       "      <th>days_employed</th>\n",
       "      <th>dob_years</th>\n",
       "      <th>education</th>\n",
       "      <th>education_id</th>\n",
       "      <th>family_status</th>\n",
       "      <th>family_status_id</th>\n",
       "      <th>gender</th>\n",
       "      <th>income_type</th>\n",
       "      <th>debt</th>\n",
       "      <th>total_income</th>\n",
       "      <th>purpose</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8437</td>\n",
       "      <td>42</td>\n",
       "      <td>высшее</td>\n",
       "      <td>0</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>253875</td>\n",
       "      <td>покупка жилья</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   children  days_employed  dob_years education  education_id  \\\n",
       "0         1           8437         42    высшее             0   \n",
       "\n",
       "     family_status  family_status_id gender income_type  debt  total_income  \\\n",
       "0  женат / замужем                 0      F   сотрудник     0        253875   \n",
       "\n",
       "         purpose  \n",
       "0  покупка жилья  "
      ]
     },
     "execution_count": 370,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 371,
   "id": "5e5d0e62",
   "metadata": {},
   "outputs": [],
   "source": [
    "def categorize_income(row):\n",
    "    if 'жиль' in row:\n",
    "        return 'Покупка жилья'\n",
    "    elif 'автомоб' in row:\n",
    "        return 'Операция с автомобилем'\n",
    "    elif 'образован' in row:\n",
    "        return 'Образование'\n",
    "    elif 'свадьб' in row:\n",
    "        return 'Проведение свадьбы'\n",
    "    elif 'недвижимос' in row:\n",
    "        return 'Недвижимость'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 375,
   "id": "41e4b4e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0              Покупка жилья\n",
       "1     Операция с автомобилем\n",
       "2              Покупка жилья\n",
       "3                Образование\n",
       "4         Проведение свадьбы\n",
       "5              Покупка жилья\n",
       "6              Покупка жилья\n",
       "7                Образование\n",
       "8         Проведение свадьбы\n",
       "9              Покупка жилья\n",
       "10              Недвижимость\n",
       "11              Недвижимость\n",
       "12        Проведение свадьбы\n",
       "13    Операция с автомобилем\n",
       "14              Недвижимость\n",
       "15              Недвижимость\n",
       "16              Недвижимость\n",
       "17              Недвижимость\n",
       "18    Операция с автомобилем\n",
       "19    Операция с автомобилем\n",
       "20              Недвижимость\n",
       "21    Операция с автомобилем\n",
       "22    Операция с автомобилем\n",
       "23        Проведение свадьбы\n",
       "24              Недвижимость\n",
       "25              Недвижимость\n",
       "26               Образование\n",
       "27              Недвижимость\n",
       "28              Недвижимость\n",
       "29              Недвижимость\n",
       "Name: category_income, dtype: object"
      ]
     },
     "execution_count": 375,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['category_income'] = df['purpose'].apply(categorize_income)\n",
    "df['category_income'].head(30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 377,
   "id": "3706acda",
   "metadata": {},
   "outputs": [],
   "source": [
    "def categorize_total_income(digit):\n",
    "    if digit > 0 and digit < 40 * 10 ** 3:\n",
    "        return 'D'\n",
    "    elif digit > 40 * 10 ** 3 and digit < 100 * 10 ** 3:\n",
    "        return 'C'\n",
    "    elif digit > 100 * 10 ** 3 and digit < 200 * 10 ** 3:\n",
    "        return 'B'\n",
    "    elif digit > 200 * 10 ** 3:\n",
    "        return 'A'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 380,
   "id": "204bb628",
   "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>children</th>\n",
       "      <th>days_employed</th>\n",
       "      <th>dob_years</th>\n",
       "      <th>education</th>\n",
       "      <th>education_id</th>\n",
       "      <th>family_status</th>\n",
       "      <th>family_status_id</th>\n",
       "      <th>gender</th>\n",
       "      <th>income_type</th>\n",
       "      <th>debt</th>\n",
       "      <th>total_income</th>\n",
       "      <th>purpose</th>\n",
       "      <th>category_income</th>\n",
       "      <th>categorize_income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8437</td>\n",
       "      <td>42</td>\n",
       "      <td>высшее</td>\n",
       "      <td>0</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>253875</td>\n",
       "      <td>покупка жилья</td>\n",
       "      <td>Покупка жилья</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4024</td>\n",
       "      <td>36</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>112080</td>\n",
       "      <td>приобретение автомобиля</td>\n",
       "      <td>Операция с автомобилем</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>5623</td>\n",
       "      <td>33</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>145885</td>\n",
       "      <td>покупка жилья</td>\n",
       "      <td>Покупка жилья</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4124</td>\n",
       "      <td>32</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>267628</td>\n",
       "      <td>дополнительное образование</td>\n",
       "      <td>Образование</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>14177</td>\n",
       "      <td>53</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>гражданский брак</td>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>пенсионер</td>\n",
       "      <td>0</td>\n",
       "      <td>158616</td>\n",
       "      <td>сыграть свадьбу</td>\n",
       "      <td>Проведение свадьбы</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   children  days_employed  dob_years education  education_id  \\\n",
       "0         1           8437         42    высшее             0   \n",
       "1         1           4024         36   среднее             1   \n",
       "2         0           5623         33   среднее             1   \n",
       "3         3           4124         32   среднее             1   \n",
       "4         0          14177         53   среднее             1   \n",
       "\n",
       "      family_status  family_status_id gender income_type  debt  total_income  \\\n",
       "0   женат / замужем                 0      F   сотрудник     0        253875   \n",
       "1   женат / замужем                 0      F   сотрудник     0        112080   \n",
       "2   женат / замужем                 0      M   сотрудник     0        145885   \n",
       "3   женат / замужем                 0      M   сотрудник     0        267628   \n",
       "4  гражданский брак                 1      F   пенсионер     0        158616   \n",
       "\n",
       "                      purpose         category_income categorize_income  \n",
       "0               покупка жилья           Покупка жилья                 A  \n",
       "1     приобретение автомобиля  Операция с автомобилем                 B  \n",
       "2               покупка жилья           Покупка жилья                 B  \n",
       "3  дополнительное образование             Образование                 A  \n",
       "4             сыграть свадьбу      Проведение свадьбы                 B  "
      ]
     },
     "execution_count": 380,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['categorize_income'] = df['total_income'].apply(categorize_total_income)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 382,
   "id": "a2bd3d1e",
   "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>family_status_id</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>education</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>высшее</td>\n",
       "      <td>5219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>начальное</td>\n",
       "      <td>282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>неоконченное высшее</td>\n",
       "      <td>740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>среднее</td>\n",
       "      <td>15146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ученая степень</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     family_status_id\n",
       "education                            \n",
       "высшее                           5219\n",
       "начальное                         282\n",
       "неоконченное высшее               740\n",
       "среднее                         15146\n",
       "ученая степень                      6"
      ]
     },
     "execution_count": 382,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('education').agg({'family_status_id' : 'count'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 384,
   "id": "e1b17cdf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Влияет ли высшее образование на выплату по кредиту"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75ccb440",
   "metadata": {},
   "source": [
    "# Влияет ли высшее образование на выплаты по кредиту"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 388,
   "id": "bcf24455",
   "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</th>\n",
       "      <th>debt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>высшее</td>\n",
       "      <td>0.053075</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  education      debt\n",
       "0    высшее  0.053075"
      ]
     },
     "execution_count": 388,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['education'] == 'высшее'].groupby('education', as_index = False).agg({'debt' : 'mean'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 389,
   "id": "3037cfba",
   "metadata": {},
   "outputs": [],
   "source": [
    "res_df = df[df['education'] == 'высшее']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 391,
   "id": "cf64621e",
   "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>children</th>\n",
       "      <th>days_employed</th>\n",
       "      <th>dob_years</th>\n",
       "      <th>education</th>\n",
       "      <th>education_id</th>\n",
       "      <th>family_status</th>\n",
       "      <th>family_status_id</th>\n",
       "      <th>gender</th>\n",
       "      <th>income_type</th>\n",
       "      <th>debt</th>\n",
       "      <th>total_income</th>\n",
       "      <th>purpose</th>\n",
       "      <th>category_income</th>\n",
       "      <th>categorize_income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8437</td>\n",
       "      <td>42</td>\n",
       "      <td>высшее</td>\n",
       "      <td>0</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>253875</td>\n",
       "      <td>покупка жилья</td>\n",
       "      <td>Покупка жилья</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>926</td>\n",
       "      <td>27</td>\n",
       "      <td>высшее</td>\n",
       "      <td>0</td>\n",
       "      <td>гражданский брак</td>\n",
       "      <td>1</td>\n",
       "      <td>M</td>\n",
       "      <td>компаньон</td>\n",
       "      <td>0</td>\n",
       "      <td>255763</td>\n",
       "      <td>покупка жилья</td>\n",
       "      <td>Покупка жилья</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2879</td>\n",
       "      <td>43</td>\n",
       "      <td>высшее</td>\n",
       "      <td>0</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>компаньон</td>\n",
       "      <td>0</td>\n",
       "      <td>240525</td>\n",
       "      <td>операции с жильем</td>\n",
       "      <td>Покупка жилья</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>6929</td>\n",
       "      <td>35</td>\n",
       "      <td>высшее</td>\n",
       "      <td>0</td>\n",
       "      <td>гражданский брак</td>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>95856</td>\n",
       "      <td>на проведение свадьбы</td>\n",
       "      <td>Проведение свадьбы</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>4171</td>\n",
       "      <td>36</td>\n",
       "      <td>высшее</td>\n",
       "      <td>0</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>компаньон</td>\n",
       "      <td>0</td>\n",
       "      <td>113943</td>\n",
       "      <td>покупка недвижимости</td>\n",
       "      <td>Недвижимость</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    children  days_employed  dob_years education  education_id  \\\n",
       "0          1           8437         42    высшее             0   \n",
       "5          0            926         27    высшее             0   \n",
       "6          0           2879         43    высшее             0   \n",
       "8          2           6929         35    высшее             0   \n",
       "10         2           4171         36    высшее             0   \n",
       "\n",
       "       family_status  family_status_id gender income_type  debt  total_income  \\\n",
       "0    женат / замужем                 0      F   сотрудник     0        253875   \n",
       "5   гражданский брак                 1      M   компаньон     0        255763   \n",
       "6    женат / замужем                 0      F   компаньон     0        240525   \n",
       "8   гражданский брак                 1      F   сотрудник     0         95856   \n",
       "10   женат / замужем                 0      M   компаньон     0        113943   \n",
       "\n",
       "                  purpose     category_income categorize_income  \n",
       "0           покупка жилья       Покупка жилья                 A  \n",
       "5           покупка жилья       Покупка жилья                 A  \n",
       "6       операции с жильем       Покупка жилья                 A  \n",
       "8   на проведение свадьбы  Проведение свадьбы                 C  \n",
       "10   покупка недвижимости        Недвижимость                 B  "
      ]
     },
     "execution_count": 391,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 393,
   "id": "dcd706b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5219"
      ]
     },
     "execution_count": 393,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(res_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 395,
   "id": "bad087ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "summa = sum(res_df['debt'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 397,
   "id": "808bc4fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.053075301781950565"
      ]
     },
     "execution_count": 397,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summa / 5219"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86f4d712",
   "metadata": {},
   "source": [
    "# Влияте ли начальное образование на выплату по кредиту"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 400,
   "id": "56a0ad16",
   "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</th>\n",
       "      <th>res_conversion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>начальное</td>\n",
       "      <td>0.109929</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   education  res_conversion\n",
       "0  начальное        0.109929"
      ]
     },
     "execution_count": 400,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['education'] == 'начальное'].groupby('education', as_index = False).agg({'debt' : 'mean'}) \\\n",
    "    .rename(columns = {'debt' : 'res_conversion'})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8bc54b8",
   "metadata": {},
   "source": [
    "# Тестировка"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 402,
   "id": "f8acabbd",
   "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>children</th>\n",
       "      <th>days_employed</th>\n",
       "      <th>dob_years</th>\n",
       "      <th>education</th>\n",
       "      <th>education_id</th>\n",
       "      <th>family_status</th>\n",
       "      <th>family_status_id</th>\n",
       "      <th>gender</th>\n",
       "      <th>income_type</th>\n",
       "      <th>debt</th>\n",
       "      <th>total_income</th>\n",
       "      <th>purpose</th>\n",
       "      <th>category_income</th>\n",
       "      <th>categorize_income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8437</td>\n",
       "      <td>42</td>\n",
       "      <td>высшее</td>\n",
       "      <td>0</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>253875</td>\n",
       "      <td>покупка жилья</td>\n",
       "      <td>Покупка жилья</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4024</td>\n",
       "      <td>36</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>112080</td>\n",
       "      <td>приобретение автомобиля</td>\n",
       "      <td>Операция с автомобилем</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>5623</td>\n",
       "      <td>33</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>145885</td>\n",
       "      <td>покупка жилья</td>\n",
       "      <td>Покупка жилья</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4124</td>\n",
       "      <td>32</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>267628</td>\n",
       "      <td>дополнительное образование</td>\n",
       "      <td>Образование</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>14177</td>\n",
       "      <td>53</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>гражданский брак</td>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>пенсионер</td>\n",
       "      <td>0</td>\n",
       "      <td>158616</td>\n",
       "      <td>сыграть свадьбу</td>\n",
       "      <td>Проведение свадьбы</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   children  days_employed  dob_years education  education_id  \\\n",
       "0         1           8437         42    высшее             0   \n",
       "1         1           4024         36   среднее             1   \n",
       "2         0           5623         33   среднее             1   \n",
       "3         3           4124         32   среднее             1   \n",
       "4         0          14177         53   среднее             1   \n",
       "\n",
       "      family_status  family_status_id gender income_type  debt  total_income  \\\n",
       "0   женат / замужем                 0      F   сотрудник     0        253875   \n",
       "1   женат / замужем                 0      F   сотрудник     0        112080   \n",
       "2   женат / замужем                 0      M   сотрудник     0        145885   \n",
       "3   женат / замужем                 0      M   сотрудник     0        267628   \n",
       "4  гражданский брак                 1      F   пенсионер     0        158616   \n",
       "\n",
       "                      purpose         category_income categorize_income  \n",
       "0               покупка жилья           Покупка жилья                 A  \n",
       "1     приобретение автомобиля  Операция с автомобилем                 B  \n",
       "2               покупка жилья           Покупка жилья                 B  \n",
       "3  дополнительное образование             Образование                 A  \n",
       "4             сыграть свадьбу      Проведение свадьбы                 B  "
      ]
     },
     "execution_count": 402,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 404,
   "id": "9bc3dde3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Есть ли зависимость между семейным положением и количеством работы"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 408,
   "id": "23973045",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd1d6d4f650>"
      ]
     },
     "execution_count": 408,
     "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['days_employed'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 410,
   "id": "919fa388",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Есть ли такая закономерность что люди младше 30 гораздо больше разведенных - нет"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 413,
   "id": "f303c06b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "96"
      ]
     },
     "execution_count": 413,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df[(df['family_status_id'] == 3) & (df['dob_years'] < 30)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 415,
   "id": "2a34f605",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "200"
      ]
     },
     "execution_count": 415,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df[(df['family_status_id'] == 3) & (df['dob_years'] < 35)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "69b6f406",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.his"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}