day35.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('data_science_job.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>enrollee_id</th>\n",
       "      <th>city</th>\n",
       "      <th>city_development_index</th>\n",
       "      <th>gender</th>\n",
       "      <th>relevent_experience</th>\n",
       "      <th>enrolled_university</th>\n",
       "      <th>education_level</th>\n",
       "      <th>major_discipline</th>\n",
       "      <th>experience</th>\n",
       "      <th>company_size</th>\n",
       "      <th>company_type</th>\n",
       "      <th>training_hours</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8949</td>\n",
       "      <td>city_103</td>\n",
       "      <td>0.920</td>\n",
       "      <td>Male</td>\n",
       "      <td>Has relevent experience</td>\n",
       "      <td>no_enrollment</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>STEM</td>\n",
       "      <td>20.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>29725</td>\n",
       "      <td>city_40</td>\n",
       "      <td>0.776</td>\n",
       "      <td>Male</td>\n",
       "      <td>No relevent experience</td>\n",
       "      <td>no_enrollment</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>STEM</td>\n",
       "      <td>15.0</td>\n",
       "      <td>50-99</td>\n",
       "      <td>Pvt Ltd</td>\n",
       "      <td>47.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11561</td>\n",
       "      <td>city_21</td>\n",
       "      <td>0.624</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No relevent experience</td>\n",
       "      <td>Full time course</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>STEM</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>83.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33241</td>\n",
       "      <td>city_115</td>\n",
       "      <td>0.789</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No relevent experience</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Business Degree</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pvt Ltd</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>666</td>\n",
       "      <td>city_162</td>\n",
       "      <td>0.767</td>\n",
       "      <td>Male</td>\n",
       "      <td>Has relevent experience</td>\n",
       "      <td>no_enrollment</td>\n",
       "      <td>Masters</td>\n",
       "      <td>STEM</td>\n",
       "      <td>20.0</td>\n",
       "      <td>50-99</td>\n",
       "      <td>Funded Startup</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   enrollee_id      city  city_development_index gender  \\\n",
       "0         8949  city_103                   0.920   Male   \n",
       "1        29725   city_40                   0.776   Male   \n",
       "2        11561   city_21                   0.624    NaN   \n",
       "3        33241  city_115                   0.789    NaN   \n",
       "4          666  city_162                   0.767   Male   \n",
       "\n",
       "       relevent_experience enrolled_university education_level  \\\n",
       "0  Has relevent experience       no_enrollment        Graduate   \n",
       "1   No relevent experience       no_enrollment        Graduate   \n",
       "2   No relevent experience    Full time course        Graduate   \n",
       "3   No relevent experience                 NaN        Graduate   \n",
       "4  Has relevent experience       no_enrollment         Masters   \n",
       "\n",
       "  major_discipline  experience company_size    company_type  training_hours  \\\n",
       "0             STEM        20.0          NaN             NaN            36.0   \n",
       "1             STEM        15.0        50-99         Pvt Ltd            47.0   \n",
       "2             STEM         5.0          NaN             NaN            83.0   \n",
       "3  Business Degree         0.0          NaN         Pvt Ltd            52.0   \n",
       "4             STEM        20.0        50-99  Funded Startup             8.0   \n",
       "\n",
       "   target  \n",
       "0     1.0  \n",
       "1     0.0  \n",
       "2     0.0  \n",
       "3     1.0  \n",
       "4     0.0  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "enrollee_id                0.000000\n",
       "city                       0.000000\n",
       "city_development_index     2.500261\n",
       "gender                    23.530640\n",
       "relevent_experience        0.000000\n",
       "enrolled_university        2.014824\n",
       "education_level            2.401086\n",
       "major_discipline          14.683161\n",
       "experience                 0.339284\n",
       "company_size              30.994885\n",
       "company_type              32.049274\n",
       "training_hours             3.998330\n",
       "target                     0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().mean()*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(19158, 13)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['city_development_index',\n",
       " 'enrolled_university',\n",
       " 'education_level',\n",
       " 'experience',\n",
       " 'training_hours']"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols = [var for var in df.columns if df[var].isnull().mean() < 0.05 and df[var].isnull().mean() > 0]\n",
    "cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>city_development_index</th>\n",
       "      <th>enrolled_university</th>\n",
       "      <th>education_level</th>\n",
       "      <th>experience</th>\n",
       "      <th>training_hours</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15056</th>\n",
       "      <td>0.939</td>\n",
       "      <td>no_enrollment</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11474</th>\n",
       "      <td>0.887</td>\n",
       "      <td>no_enrollment</td>\n",
       "      <td>Masters</td>\n",
       "      <td>5.0</td>\n",
       "      <td>107.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7940</th>\n",
       "      <td>0.920</td>\n",
       "      <td>no_enrollment</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>7.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11338</th>\n",
       "      <td>0.689</td>\n",
       "      <td>NaN</td>\n",
       "      <td>High School</td>\n",
       "      <td>3.0</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>552</th>\n",
       "      <td>0.698</td>\n",
       "      <td>no_enrollment</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>14.0</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       city_development_index enrolled_university education_level  experience  \\\n",
       "15056                   0.939       no_enrollment        Graduate         7.0   \n",
       "11474                   0.887       no_enrollment         Masters         5.0   \n",
       "7940                    0.920       no_enrollment        Graduate         7.0   \n",
       "11338                   0.689                 NaN     High School         3.0   \n",
       "552                     0.698       no_enrollment        Graduate        14.0   \n",
       "\n",
       "       training_hours  \n",
       "15056             7.0  \n",
       "11474           107.0  \n",
       "7940             11.0  \n",
       "11338            99.0  \n",
       "552              24.0  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[cols].sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Graduate          11598\n",
       "Masters            4361\n",
       "High School        2017\n",
       "Phd                 414\n",
       "Primary School      308\n",
       "Name: education_level, dtype: int64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['education_level'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8968577095730244"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df[cols].dropna()) / len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((19158, 13), (17182, 5))"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df = df[cols].dropna()\n",
    "df.shape, new_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "new_df.hist(bins=50, density=True, figsize=(12, 12))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2dfc2344dc0>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAD4CAYAAAD2FnFTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAWL0lEQVR4nO3df4wc533f8fe3tOm0YhpapX0lRLqkE6YwIRgueSWFsk2OcZyQRBCmgBOIQU1VpXtSKhZtAaKiISA//ijAOmqLCBFEX0siVmuYdhG7oQQCsiB4YcgAax5TSRYjU76qrkRTteraVnsmbJXNt3/sSFoun92dvdvbH+T7BSxuZ+Z55r4zd7wPn5nZmchMJElq9xdGXYAkaTwZEJKkIgNCklRkQEiSigwISVLRO0ZdQD/WrVuXmzZt6rvfD3/4Q2655ZbBF7TCJrHuSawZJrNuax6eSay7tebz589/NzPf0/dKMnNiXtu3b8+l+PKXv7ykfqM2iXVPYs2Zk1m3NQ/PJNbdWjMwn0v4m+shJklSkQEhSSoyICRJRQaEJKnIgJAkFRkQkqQiA0KSVGRASJKKDAhJUtFE3WpjJUzfE8X585/yQUqSbm6OICRJRTf9CKKjKI8s8BGtkm4SBkQH07Pl+fPDLUOSRsZDTJKkIgNCklRkQEiSigwISVKRASFJKjIgJElFBoQkqciAkCQVGRCSpCIDQpJUZEBIkooMCElSUa2AiIg9EXExIhYi4mhheUTEQ9Xy5yJiW8uykxHxWkQ839bn9yPiG1X7L0bE2uVvjiRpUHoGRESsAh4G9gJbgQMRsbWt2V5gS/WaBR5pWfZHwJ7Cqp8Ebs/MDwIvAp/ot3hJ0sqpM4LYASxk5kuZ+QZwCtjf1mY/8Gg2nQXWRsR6gMz8CvC99pVm5pcy82o1eRbYsNSNkCQNXmSPB+BExEeBPZn58Wr6Y8DOzDzc0uZx4FhmPl1NPwXcn5nz1fQm4PHMvL3D93gM+Fxm/ofCslmaoxKmpqa2nzp1qt9tZHFxkTVr1hSXvfDy+b7W9YH3be/7+y9Vt7rH1STWDJNZtzUPzyTW3Vrz7t27z2fmdL/rqPPAoNKj1dpTpU6b8sojHgCuAp8pLc/MOWAOYHp6OmdmZuqs9hqNRoNO/Y7cs7uvdc0fHN4T5brVPa4msWaYzLqteXgmse5B1FwnIC4BG1umNwCXl9DmOhFxF/ArwIez11BGkjRUdQLiHLAlIjYD3wbuBH6zrc1p4HBEnAJ2Aq9n5qvdVhoRe4D7gZ/PzCt9Vz6GpufKI7j5WR9UKmny9AyIzLwaEYeBJ4BVwMnMvBAR91bLjwNngH3AAnAFuPvN/hHxWWAGWBcRl4DfycwTwB8C7wKejAiAs5l57wC3bfjO93c+Q5LGWZ0RBJl5hmYItM473vI+gfs69D3QYf7P1C9TkjRsfpJaklRkQEiSigwISVKRASFJKjIgJElFBoQkqciAkCQVGRCSpCIDQpJUZEBIkooMCElSkQEhSSoyICRJRbXu5qq3Td9TenieJN14HEFIkooMCElSkQEhSSoyICRJRQaEJKnIgJAkFRkQkqQiA0KSVFQrICJiT0RcjIiFiDhaWB4R8VC1/LmI2Nay7GREvBYRz7f1uTUinoyIb1Zf3738zZEkDUrPgIiIVcDDwF5gK3AgIra2NdsLbKles8AjLcv+CNhTWPVR4KnM3AI8VU1LksZEnRHEDmAhM1/KzDeAU8D+tjb7gUez6SywNiLWA2TmV4DvFda7H/h09f7TwK8tZQMkSSsjMrN7g4iPAnsy8+PV9MeAnZl5uKXN48CxzHy6mn4KuD8z56vpTcDjmXl7S58fZObalunvZ+Z1h5kiYpbmqISpqantp06d6nsjFxcXWbNmTXHZCy+f73t9/frA+7YvqV+3usfVJNYMk1m3NQ/PJNbdWvPu3bvPZ+Z0v+uoc7O+0t3p2lOlTpslycw5YA5geno6Z2Zm+l5Ho9GgU78j9+xeRnX1zB9c2q7oVve4msSaYTLrtubhmcS6B1FznUNMl4CNLdMbgMtLaNPuO28ehqq+vlajFknSkNQJiHPAlojYHBGrgTuB021tTgMHq6uZ7gBez8xXe6z3NHBX9f4u4E/6qFuStMJ6HmLKzKsRcRh4AlgFnMzMCxFxb7X8OHAG2AcsAFeAu9/sHxGfBWaAdRFxCfidzDwBHAM+HxGHgJeBXx/khk2y6bnmocJDtx7iyNyRt+bPz86PqiRJN6FaDwzKzDM0Q6B13vGW9wnc16HvgQ7z/xfw4dqV3kzOVyfOdx14+70kDZmfpJYkFRkQkqQiA0KSVGRASJKKDIhRiii/JGkM1LqKSStjenbUFUhSZ44gJElFjiCG4M0PvknSJDEghsEPu0maQB5ikiQVGRCSpCIDQpJUZEBIkooMCElSkQEhSSoyICRJRQaEJKnIgJAkFRkQkqQiA0KSVGRASJKKDAhJUlGtgIiIPRFxMSIWIuJoYXlExEPV8uciYluvvhHxoYg4GxHPRMR8ROwYzCZJkgahZ0BExCrgYWAvsBU4EBFb25rtBbZUr1ngkRp9Pwn8XmZ+CPjtalqSNCbqjCB2AAuZ+VJmvgGcAva3tdkPPJpNZ4G1EbG+R98E/nL1/qeAy8vcFknSAEVmdm8Q8VFgT2Z+vJr+GLAzMw+3tHkcOJaZT1fTTwH3A5s69Y2IDwBPAEEzqP5WZv73wvefpTkqYWpqavupU6f63sjFxUVe+dEr5YVXrvS9vmFZd8sGvvvDS29Nf+B920dYTT2Li4usWbNm1GX0bRLrtubhmcS6W2vevXv3+czs+9GWdZ4oF4V57anSqU23vr8F/LPM/OOI+A3gBPCL1zXOnAPmAKanp3NmZqZGyddqNBqceOwf9d1v1A7tepATXz3y1vT8we5hPg4ajQZL+RmN2iTWbc3DM4l1D6LmOoeYLgEbW6Y3cP3hoE5tuvW9C/hC9f4/0jwcJUkaE3UC4hywJSI2R8Rq4E7gdFub08DB6mqmO4DXM/PVHn0vAz9fvf8F4JvL3BZJ0gD1PMSUmVcj4jDN8wWrgJOZeSEi7q2WHwfOAPuABeAKcHe3vtWq/yHwBxHxDuBHVOcZJEnjoc45CDLzDM0QaJ13vOV9AvfV7VvNfxoY/7OuknST8pPUkqQiA0KSVFTrEJPGRJSuGgZ6fJZFkpbCgJgg0x1O488PtwxJNwkPMUmSigwISVKRASFJKjIgJElFBoQkqciAkCQVGRCSpCIDQpJUZEBIkooMCElSkQEhSSoyICRJRQaEJKnIgJAkFRkQkqQiA0KSVGRASJKKDAhJUpEBIUkqqhUQEbEnIi5GxEJEHC0sj4h4qFr+XERsq9M3Iv5xtexCRHxy+ZsjSRqUd/RqEBGrgIeBjwCXgHMRcToz/6yl2V5gS/XaCTwC7OzWNyJ2A/uBD2bmjyPivYPcMEnS8tQZQewAFjLzpcx8AzhF8w97q/3Ao9l0FlgbEet79P0t4Fhm/hggM18bwPZIkgak5wgCuA14pWX6Es1RQq82t/Xo+7PA34mIfwH8CDiSmefav3lEzAKzAFNTUzQajRolX2txcZFDux7su9+orbtlQ626l7JPVsri4uJY1VPXJNZtzcMziXUPouY6ARGFeVmzTbe+7wDeDdwB/E3g8xHx/sy8Zt2ZOQfMAUxPT+fMzEyNkq/VaDQ48diRvvuN2qFdD3Liq73rnj/Y/uMYnUajwVJ+RqM2iXVb8/BMYt2DqLlOQFwCNrZMbwAu12yzukvfS8AXqkD4WkT8ObAO+J+1q5ckrZg65yDOAVsiYnNErAbuBE63tTkNHKyuZroDeD0zX+3R9z8BvwAQET9LM0y+u+wtkiQNRM8RRGZejYjDwBPAKuBkZl6IiHur5ceBM8A+YAG4AtzdrW+16pPAyYh4HngDuKv98JIkaXTqHGIiM8/QDIHWecdb3idwX92+1fw3gL/XT7GSpOHxk9SSpCIDQpJUZEBIkooMCElSUa2T1JpQUfqcIuDFYpJqMCBuYNOz5fnzwy1D0oTyEJMkqcgRxE1o+p7yoaf5T3noSdLbHEFIkooMCElSkQEhSSoyICRJRQaEJKnIgJAkFXmZ6w2g02WrkrQcjiAkSUWOIPQ2790kqYUBobd47yZJrTzEJEkqcgShwet0qAo8XCVNEANCA9fpUBV4uEqaJB5ikiQV1QqIiNgTERcjYiEijhaWR0Q8VC1/LiK29dH3SERkRKxb3qZIkgapZ0BExCrgYWAvsBU4EBFb25rtBbZUr1ngkTp9I2Ij8BHg5WVviSRpoOqMIHYAC5n5Uma+AZwC9re12Q88mk1ngbURsb5G338D/HPAM5eSNGbqnKS+DXilZfoSsLNGm9u69Y2IXwW+nZnPRperXiJiluaohKmpKRqNRo2Sr7W4uMihXQ/23W/U1t2yYSzq7mef99rXS/n5DcPi4uLY1taJNQ/PJNY9iJrrBETpr3f7//g7tSnOj4i/BDwA/FKvb56Zc8AcwPT0dM7MzPTqcp1Go8GJx4703W/UDu16kBNfHX3d8wfrD/B67et+1jVMjUaDpfxujZI1D88k1j2ImuscYroEbGyZ3gBcrtmm0/yfBjYDz0bEt6r5fxoRf7Wf4iVJK6dOQJwDtkTE5ohYDdwJnG5rcxo4WF3NdAfwema+2qlvZn49M9+bmZsycxPNINmWmf9jUBumIYi4/nX+/KirkjQgPQ8xZebViDgMPAGsAk5m5oWIuLdafhw4A+wDFoArwN3d+q7IlmjoSh+IO/Qe4MWhlyJpBdT6JHVmnqEZAq3zjre8T+C+un0LbTbVqUOj4fMmpJuTn6SWJBUZEJKkIgNCklRkQEiSigwISVKRASFJKjIgJElFPlFOQzU9N12cPz/rs+akcWNAaLi8FYc0MTzEJEkqMiAkSUUGhCSpyICQJBUZEJKkIgNCklRkQEiSigwISVKRASFJKjIgJElFBoQkqciAkCQVGRCSpKJaAREReyLiYkQsRMTRwvKIiIeq5c9FxLZefSPi9yPiG1X7L0bE2sFskiRpEHoGRESsAh4G9gJbgQMRsbWt2V5gS/WaBR6p0fdJ4PbM/CDwIvCJZW+NJGlg6owgdgALmflSZr4BnAL2t7XZDzyaTWeBtRGxvlvfzPxSZl6t+p8FNgxgeyRJA1LngUG3Aa+0TF8CdtZoc1vNvgD/APhc6ZtHxCzNUQlTU1M0Go0aJV9rcXGRQ7se7LvfqK27ZcPE1b3Umpfycx2kxcXFkdfQL2senkmsexA11wmIKMzLmm169o2IB4CrwGdK3zwz54A5gOnp6ZyZmelR7vUajQYnHjvSd79RO7TrQU58dbLqXmrN8wfbf6WGq9FosJTfrVGy5uGZxLoHUXOdgLgEbGyZ3gBcrtlmdbe+EXEX8CvAhzNztH8hJEnXqHMO4hywJSI2R8Rq4E7gdFub08DB6mqmO4DXM/PVbn0jYg9wP/CrmXllQNsjSRqQniOIzLwaEYeBJ4BVwMnMvBAR91bLjwNngH3AAnAFuLtb32rVfwi8C3gyIgDOZua9g9w4Tb7pe0pHKWH+U6MZcI5bPdJKqnOIicw8QzMEWucdb3mfwH11+1bzf6avSqXliPIf9ulPbQfg0K2HODL39rmT+dn5oZQljbNaASFNuunZDgvOn29+3XXg7feSAANCE6rToZ6BrX9uekXXL00CA0IqcTQhebM+SVKZASFJKvIQk8bCSp9TkNQ/RxCSpCIDQpJUZEBIkooMCElSkQEhSSoyICRJRQaEJKnIgJAkFflBOWlEfLaExp0BId1kDCbVZUBIK6nDg4oA6PSMig7ab0H+1kOOOtx51j/4Wi4DQhqETk+s6zMEoI/7UvV4yJH3t9JyGRDSACwlCKRx51VMkqQiRxCSgNGdvO52KOxmO48ybvvCgJA0UJ2e5z0/O7/y33uFQ25Q65+U80MGhKTBGuDzvFd8VFPz4oJDux7kyD27O65mUv7g96tWQETEHuAPgFXAv8vMY23Lo1q+D7gC/P3M/NNufSPiVuBzwCbgW8BvZOb3l79Jkoah3z+K0/dEzz+0K6VjrV5c0FXPgIiIVcDDwEeAS8C5iDidmX/W0mwvsKV67QQeAXb26HsUeCozj0XE0Wr6/sFtmqRBGMf/HY9jTTeiOlcx7QAWMvOlzHwDOAXsb2uzH3g0m84CayNifY+++4FPV+8/DfzaMrdFkjRAkdn9WF5EfBTYk5kfr6Y/BuzMzMMtbR4HjmXm09X0UzRHA5s69Y2IH2Tm2pZ1fD8z3134/rO8PRD868DFJWznOuC7S+g3apNY9yTWDJNZtzUPzyTW3VrzX8vM9/S7gjrnIEpjufZU6dSmTt+uMnMOmOunT7uImM/M8qUVY2wS657EmmEy67bm4ZnEugdRc51DTJeAjS3TG4DLNdt06/ud6jAU1dfX6pctSVppdQLiHLAlIjZHxGrgTuB0W5vTwMFougN4PTNf7dH3NHBX9f4u4E+WuS2SpAHqeYgpM69GxGHgCZqXqp7MzAsRcW+1/DhwhuYlrgs0L3O9u1vfatXHgM9HxCHgZeDXB7pl11rWIaoRmsS6J7FmmMy6rXl4JrHuZdfc8yS1JOnm5M36JElFBoQkqeiGD4iI2BMRFyNiofrE9liKiG9FxNcj4pmImK/m3RoRT0bEN6uv131OZAR1noyI1yLi+ZZ5HeuMiE9U+/5iRPzyGNX8uxHx7Wp/PxMR+8as5o0R8eWIeCEiLkTEP6nmj/u+7lT32O7viPiJiPhaRDxb1fx71fyx3dddah7sfs7MG/ZF88T4fwXeD6wGngW2jrquDrV+C1jXNu+TwNHq/VHgX45BnT8HbAOe71UnsLXa5+8CNlc/i1VjUvPvAkcKbcel5vXAtur9TwIvVrWN+77uVPfY7m+an9daU71/J/CfgTvGeV93qXmg+/lGH0HUuU3IOBu725Fk5leA77XN7lTnfuBUZv44M/8bzavcdgyl0BYdau5kXGp+NasbXmbm/wFeAG5j/Pd1p7o7GXnd2bRYTb6zeiVjvK+71NzJkmq+0QPiNuCVlulLdP9lHaUEvhQR56vbiwBMZfPzJFRf3zuy6rrrVOe47//DEfFcdQjqzcMHY1dzRGwC/gbN/yVOzL5uqxvGeH9HxKqIeIbmB3afzMyx39cdaoYB7ucbPSCWfauPIdqVmdto3hn3voj4uVEXNADjvP8fAX4a+BDwKvCvqvljVXNErAH+GPinmfm/uzUtzBunusd6f2fm/8vMD9G828OOiLi9S/Nxrnmg+/lGD4g6twkZC5l5ufr6GvBFmsO/SbkdSac6x3b/Z+Z3qn9gfw78W94ebo9NzRHxTpp/ZD+TmV+oZo/9vi7VPQn7GyAzfwA0gD1MwL6Ga2se9H6+0QOizm1CRi4ibomIn3zzPfBLwPNMzu1IOtV5GrgzIt4VEZtpPi/kayOo7zpv/sOv/F2a+xvGpOaICOAE8EJm/uuWRWO9rzvVPc77OyLeExFrq/d/EfhF4BuM8b7uVPPA9/Mwz7yP4kXzFiAv0jxr/8Co6+lQ4/tpXmHwLHDhzTqBvwI8BXyz+nrrGNT6WZpD1/9L838lh7rVCTxQ7fuLwN4xqvnfA18Hnqv+8awfs5r/Ns1DAM8Bz1SvfROwrzvVPbb7G/gg8F+q2p4HfruaP7b7ukvNA93P3mpDklR0ox9ikiQtkQEhSSoyICRJRQaEJKnIgJAkFRkQkqQiA0KSVPT/AbkckoiQnVV5AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "# original data\n",
    "df['training_hours'].hist(bins=50, ax=ax, density=True, color='red')\n",
    "\n",
    "# data after cca, the argument alpha makes the color transparent, so we can\n",
    "# see the overlay of the 2 distributions\n",
    "new_df['training_hours'].hist(bins=50, ax=ax, color='green', density=True, alpha=0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2dfc24f13d0>"
      ]
     },
     "execution_count": 44,
     "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": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "# original data\n",
    "df['training_hours'].plot.density(color='red')\n",
    "\n",
    "# data after cca\n",
    "new_df['training_hours'].plot.density(color='green')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2dfc218dca0>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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PmJKkyfJ0spJUKANckgplgK8SEfsj4tsR8Z2IuHHI/b2I+O+IOFZd/ngSdTZpVM/VNr2q3wcj4itt19i0Gs/zH616jo9HxLMRceEkam1KjZ5/OiL+LiLuq57nd06izqbU6PeCiLgzIu6PiK9HxGWTqHPLMtPLyt8BzgH+HfgF4IXAfcAr12zTAw5PutaWe97FytG1e6rbL5103ePuec32bwL+YdJ1t/A8fxD4k+r6xcDjwAsnXfsY+/1T4EPV9V8Bjk667s1cnIE/7yenBsjM/wVOnxpgJ6vT8+8Ad2TmCYDMPNlyjU3b6PN8LfDZViobnzo9J/CSiAhghpUAf6bdMhtTp99XAkcBMvNbwN6I6LRb5tYZ4M97GfCfq24/Uo2t9WvV28wvRMSvtlPa2NTp+ZeBCyKiHxGLEfH21qobj7rPMxHxU8B+4HMt1DVOdXr+M2AfKwfjPQC8LzOfa6e8xtXp9z7gtwEi4grgFawcjFgUzwf+vDqnBvgmK+ckWI6Iq4C/BS4Ze2XjU6fnc4FZ4ErgxcBXI+KfM/Pfxl3cmNQ6BUTlTcA/ZebjY6ynDXV6/i3gGPB64BeBIxHxj5n51LiLG4M6/d4CfCIijrHyC+tfKPAdhzPw5408NUBmPpWZy9X1zwPnRcRF7ZXYuDqnQ3gE+GJmPp2Zp4B7gFe1VN84bOQUEG+j/OUTqNfzO1lZKsvM/A7wPVbWhktU9//yOzPzcuDtrKz7f6+9EpthgD9v5KkBIuJnqjXC02+7XgD8sPVKm1PndAh3Ab8eEedWSwqvAZZarrNJtU4BERE/DfwGK/2Xrk7PJ1h5l0W1Fnwp8N1Wq2xOnf/Lu6r7AN4N3FPiuw2XUCq5zqkBIuJ3q/v/HHgL8HsR8QzwP8Dbsvozdonq9JyZSxHxReB+4DlWPnnp+OSq3pqazzPANcDfZ+bTEyq1MTV7vhn4TEQ8wMoSxAeqd1zFqdnvPuAvI+JZVvayumFiBW+Bh9JLUqFcQpGkQhngklQoA1ySCmWAS1KhDHBJKpQBLkmFMsAlqVD/BxwObbba0SkqAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "# original data\n",
    "df['city_development_index'].hist(bins=50, ax=ax, density=True, color='red')\n",
    "\n",
    "# data after cca, the argument alpha makes the color transparent, so we can\n",
    "# see the overlay of the 2 distributions\n",
    "new_df['city_development_index'].hist(bins=50, ax=ax, color='green', density=True, alpha=0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2dfc218dd90>"
      ]
     },
     "execution_count": 46,
     "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": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "# original data\n",
    "df['city_development_index'].plot.density(color='red')\n",
    "\n",
    "# data after cca\n",
    "new_df['city_development_index'].plot.density(color='green')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2dfc23c38e0>"
      ]
     },
     "execution_count": 47,
     "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": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "# original data\n",
    "df['experience'].hist(bins=50, ax=ax, density=True, color='red')\n",
    "\n",
    "# data after cca, the argument alpha makes the color transparent, so we can\n",
    "# see the overlay of the 2 distributions\n",
    "new_df['experience'].hist(bins=50, ax=ax, color='green', density=True, alpha=0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2dfc29474c0>"
      ]
     },
     "execution_count": 48,
     "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": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "# original data\n",
    "df['experience'].plot.density(color='red')\n",
    "\n",
    "# data after cca\n",
    "new_df['experience'].plot.density(color='green')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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>original</th>\n",
       "      <th>cca</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>no_enrollment</th>\n",
       "      <td>0.721213</td>\n",
       "      <td>0.735188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Full time course</th>\n",
       "      <td>0.196106</td>\n",
       "      <td>0.200733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Part time course</th>\n",
       "      <td>0.062533</td>\n",
       "      <td>0.064079</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  original       cca\n",
       "no_enrollment     0.721213  0.735188\n",
       "Full time course  0.196106  0.200733\n",
       "Part time course  0.062533  0.064079"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp = pd.concat([\n",
    "            # percentage of observations per category, original data\n",
    "            df['enrolled_university'].value_counts() / len(df),\n",
    "\n",
    "            # percentage of observations per category, cca data\n",
    "            new_df['enrolled_university'].value_counts() / len(new_df)\n",
    "        ],\n",
    "        axis=1)\n",
    "\n",
    "# add column names\n",
    "temp.columns = ['original', 'cca']\n",
    "\n",
    "temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>original</th>\n",
       "      <th>cca</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Graduate</th>\n",
       "      <td>0.605387</td>\n",
       "      <td>0.619835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Masters</th>\n",
       "      <td>0.227633</td>\n",
       "      <td>0.234082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>High School</th>\n",
       "      <td>0.105282</td>\n",
       "      <td>0.107380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Phd</th>\n",
       "      <td>0.021610</td>\n",
       "      <td>0.022116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Primary School</th>\n",
       "      <td>0.016077</td>\n",
       "      <td>0.016587</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                original       cca\n",
       "Graduate        0.605387  0.619835\n",
       "Masters         0.227633  0.234082\n",
       "High School     0.105282  0.107380\n",
       "Phd             0.021610  0.022116\n",
       "Primary School  0.016077  0.016587"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp = pd.concat([\n",
    "            # percentage of observations per category, original data\n",
    "            df['education_level'].value_counts() / len(df),\n",
    "\n",
    "            # percentage of observations per category, cca data\n",
    "            new_df['education_level'].value_counts() / len(new_df)\n",
    "        ],\n",
    "        axis=1)\n",
    "\n",
    "# add column names\n",
    "temp.columns = ['original', 'cca']\n",
    "\n",
    "temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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