1_EDA.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np \n",
    "\n",
    "import matplotlib.pyplot as plt \n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_columns', None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('dataset.csv',index_col=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Проанализирую датасет и целевое событие "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>cmlt_daily_game_currency</th>\n",
       "      <th>cmlt_seconds_with_us</th>\n",
       "      <th>cmlt_max_sessions_duration</th>\n",
       "      <th>cmlt_sum_sessions_duration</th>\n",
       "      <th>cmlt_count_sessions</th>\n",
       "      <th>cmlt_sum_quantum_duration</th>\n",
       "      <th>cmlt_count_quant</th>\n",
       "      <th>cmlt_max_quantum_duration</th>\n",
       "      <th>cmlt_max_quant</th>\n",
       "      <th>cmlt_final_level</th>\n",
       "      <th>cmlt_count_grind</th>\n",
       "      <th>cmlt_max_grind</th>\n",
       "      <th>cmlt_count_won_grind</th>\n",
       "      <th>cmlt_hard_buy</th>\n",
       "      <th>cmlt_hard_earn</th>\n",
       "      <th>cmlt_hard_gift</th>\n",
       "      <th>cmlt_hard_spent</th>\n",
       "      <th>cmlt_hard_max_spent</th>\n",
       "      <th>country</th>\n",
       "      <th>country_top_tier</th>\n",
       "      <th>network_name</th>\n",
       "      <th>date_install</th>\n",
       "      <th>first_command_time</th>\n",
       "      <th>cmlt_time_4grind</th>\n",
       "      <th>cmlt_time_with_us_4grind</th>\n",
       "      <th>cmlt_avg_time_for_level</th>\n",
       "      <th>cmlt_avg_duration</th>\n",
       "      <th>cmlt_avg_grind_duration</th>\n",
       "      <th>cmlt_winrate</th>\n",
       "      <th>cmlt_spent_hc_per_grind</th>\n",
       "      <th>cmlt_spent_hc_div_active_time</th>\n",
       "      <th>cmlt_seconds_div_active_time</th>\n",
       "      <th>cmlt_hard_med_spent</th>\n",
       "      <th>birthday</th>\n",
       "      <th>sex</th>\n",
       "      <th>is_cheater</th>\n",
       "      <th>has_email</th>\n",
       "      <th>time_confirm_email</th>\n",
       "      <th>target_game_currency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>117</td>\n",
       "      <td>234</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>RU</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021-03-13</td>\n",
       "      <td>2021-02-07 10:41:50.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>234.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>125</td>\n",
       "      <td>668</td>\n",
       "      <td>668</td>\n",
       "      <td>1</td>\n",
       "      <td>1448</td>\n",
       "      <td>4</td>\n",
       "      <td>271</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>MD</td>\n",
       "      <td>0</td>\n",
       "      <td>Facebook Installs</td>\n",
       "      <td>2021-04-22</td>\n",
       "      <td>2021-03-19 23:02:10.0</td>\n",
       "      <td>0.002994</td>\n",
       "      <td>0.016000</td>\n",
       "      <td>668.000000</td>\n",
       "      <td>668.000000</td>\n",
       "      <td>334.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>62.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>14070</td>\n",
       "      <td>4137</td>\n",
       "      <td>15551</td>\n",
       "      <td>12</td>\n",
       "      <td>41220</td>\n",
       "      <td>119</td>\n",
       "      <td>796</td>\n",
       "      <td>275</td>\n",
       "      <td>6</td>\n",
       "      <td>67</td>\n",
       "      <td>69</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "      <td>20</td>\n",
       "      <td>16</td>\n",
       "      <td>US</td>\n",
       "      <td>1</td>\n",
       "      <td>Organic</td>\n",
       "      <td>2021-03-24</td>\n",
       "      <td>2021-02-18 19:55:22.0</td>\n",
       "      <td>0.004308</td>\n",
       "      <td>0.004762</td>\n",
       "      <td>2591.833333</td>\n",
       "      <td>1295.916667</td>\n",
       "      <td>232.104478</td>\n",
       "      <td>0.358209</td>\n",
       "      <td>0.298507</td>\n",
       "      <td>0.298507</td>\n",
       "      <td>210.000000</td>\n",
       "      <td>2.857143</td>\n",
       "      <td>1935-10-22</td>\n",
       "      <td>female</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>290</td>\n",
       "      <td>290</td>\n",
       "      <td>1</td>\n",
       "      <td>176</td>\n",
       "      <td>2</td>\n",
       "      <td>44</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>BR</td>\n",
       "      <td>0</td>\n",
       "      <td>Organic</td>\n",
       "      <td>2021-03-25</td>\n",
       "      <td>2021-02-19 16:09:34.0</td>\n",
       "      <td>0.003448</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>290.000000</td>\n",
       "      <td>290.000000</td>\n",
       "      <td>290.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1987-08-08</td>\n",
       "      <td>female</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>887</td>\n",
       "      <td>1002</td>\n",
       "      <td>1002</td>\n",
       "      <td>1</td>\n",
       "      <td>2448</td>\n",
       "      <td>12</td>\n",
       "      <td>123</td>\n",
       "      <td>121</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>RU</td>\n",
       "      <td>0</td>\n",
       "      <td>Vungle</td>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>2021-02-26 07:45:15.0</td>\n",
       "      <td>0.006986</td>\n",
       "      <td>0.007892</td>\n",
       "      <td>250.500000</td>\n",
       "      <td>1002.000000</td>\n",
       "      <td>143.142857</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>126.714286</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   cmlt_daily_game_currency  cmlt_seconds_with_us  cmlt_max_sessions_duration  \\\n",
       "0                       0.0                     0                         117   \n",
       "1                       0.0                   125                         668   \n",
       "2                       0.0                 14070                        4137   \n",
       "3                       0.0                     0                         290   \n",
       "4                       0.0                   887                        1002   \n",
       "\n",
       "   cmlt_sum_sessions_duration  cmlt_count_sessions  cmlt_sum_quantum_duration  \\\n",
       "0                         234                    2                          0   \n",
       "1                         668                    1                       1448   \n",
       "2                       15551                   12                      41220   \n",
       "3                         290                    1                        176   \n",
       "4                        1002                    1                       2448   \n",
       "\n",
       "   cmlt_count_quant  cmlt_max_quantum_duration  cmlt_max_quant  \\\n",
       "0                 0                          0               0   \n",
       "1                 4                        271              28   \n",
       "2               119                        796             275   \n",
       "3                 2                         44               3   \n",
       "4                12                        123             121   \n",
       "\n",
       "   cmlt_final_level  cmlt_count_grind  cmlt_max_grind  cmlt_count_won_grind  \\\n",
       "0                 1                 0               0                     0   \n",
       "1                 1                 2               2                     2   \n",
       "2                 6                67              69                    24   \n",
       "3                 1                 1               1                     1   \n",
       "4                 4                 7               7                     7   \n",
       "\n",
       "   cmlt_hard_buy  cmlt_hard_earn  cmlt_hard_gift  cmlt_hard_spent  \\\n",
       "0              0               0              15                0   \n",
       "1              0               0              15                0   \n",
       "2              0               5              15               20   \n",
       "3              0               0              30                0   \n",
       "4              0               2              15                0   \n",
       "\n",
       "   cmlt_hard_max_spent country  country_top_tier       network_name  \\\n",
       "0                    0      RU                 0                NaN   \n",
       "1                    0      MD                 0  Facebook Installs   \n",
       "2                   16      US                 1            Organic   \n",
       "3                    0      BR                 0            Organic   \n",
       "4                    0      RU                 0             Vungle   \n",
       "\n",
       "  date_install     first_command_time  cmlt_time_4grind  \\\n",
       "0   2021-03-13  2021-02-07 10:41:50.0          0.000000   \n",
       "1   2021-04-22  2021-03-19 23:02:10.0          0.002994   \n",
       "2   2021-03-24  2021-02-18 19:55:22.0          0.004308   \n",
       "3   2021-03-25  2021-02-19 16:09:34.0          0.003448   \n",
       "4   2021-04-01  2021-02-26 07:45:15.0          0.006986   \n",
       "\n",
       "   cmlt_time_with_us_4grind  cmlt_avg_time_for_level  cmlt_avg_duration  \\\n",
       "0                  0.000000               234.000000         117.000000   \n",
       "1                  0.016000               668.000000         668.000000   \n",
       "2                  0.004762              2591.833333        1295.916667   \n",
       "3                  0.000000               290.000000         290.000000   \n",
       "4                  0.007892               250.500000        1002.000000   \n",
       "\n",
       "   cmlt_avg_grind_duration  cmlt_winrate  cmlt_spent_hc_per_grind  \\\n",
       "0                 0.000000      0.000000                 0.000000   \n",
       "1               334.000000      1.000000                 0.000000   \n",
       "2               232.104478      0.358209                 0.298507   \n",
       "3               290.000000      1.000000                 0.000000   \n",
       "4               143.142857      1.000000                 0.000000   \n",
       "\n",
       "   cmlt_spent_hc_div_active_time  cmlt_seconds_div_active_time  \\\n",
       "0                       0.000000                      0.000000   \n",
       "1                       0.000000                     62.500000   \n",
       "2                       0.298507                    210.000000   \n",
       "3                       0.000000                      0.000000   \n",
       "4                       0.000000                    126.714286   \n",
       "\n",
       "   cmlt_hard_med_spent    birthday     sex is_cheater has_email  \\\n",
       "0             0.000000         NaN     NaN        NaN       NaN   \n",
       "1             0.000000         NaN     NaN      False     False   \n",
       "2             2.857143  1935-10-22  female      False     False   \n",
       "3             0.000000  1987-08-08  female      False      True   \n",
       "4             0.000000         NaN     NaN      False     False   \n",
       "\n",
       "  time_confirm_email  target_game_currency  \n",
       "0                NaN                   0.0  \n",
       "1                NaN                   0.0  \n",
       "2                NaN                   0.0  \n",
       "3                NaN                   0.0  \n",
       "4                NaN                   0.0  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Размерность датасета: (250006, 39)\n"
     ]
    }
   ],
   "source": [
    "print(f\"Размерность датасета: {df.shape}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 250006 entries, 0 to 250005\n",
      "Data columns (total 39 columns):\n",
      " #   Column                         Non-Null Count   Dtype  \n",
      "---  ------                         --------------   -----  \n",
      " 0   cmlt_daily_game_currency       250006 non-null  float64\n",
      " 1   cmlt_seconds_with_us           250006 non-null  int64  \n",
      " 2   cmlt_max_sessions_duration     250006 non-null  int64  \n",
      " 3   cmlt_sum_sessions_duration     250006 non-null  int64  \n",
      " 4   cmlt_count_sessions            250006 non-null  int64  \n",
      " 5   cmlt_sum_quantum_duration      250006 non-null  int64  \n",
      " 6   cmlt_count_quant               250006 non-null  int64  \n",
      " 7   cmlt_max_quantum_duration      250006 non-null  int64  \n",
      " 8   cmlt_max_quant                 250006 non-null  int64  \n",
      " 9   cmlt_final_level               250006 non-null  int64  \n",
      " 10  cmlt_count_grind               250006 non-null  int64  \n",
      " 11  cmlt_max_grind                 250006 non-null  int64  \n",
      " 12  cmlt_count_won_grind           250006 non-null  int64  \n",
      " 13  cmlt_hard_buy                  250006 non-null  int64  \n",
      " 14  cmlt_hard_earn                 250006 non-null  int64  \n",
      " 15  cmlt_hard_gift                 250006 non-null  int64  \n",
      " 16  cmlt_hard_spent                250006 non-null  int64  \n",
      " 17  cmlt_hard_max_spent            250006 non-null  int64  \n",
      " 18  country                        249950 non-null  object \n",
      " 19  country_top_tier               250006 non-null  int64  \n",
      " 20  network_name                   224850 non-null  object \n",
      " 21  date_install                   250006 non-null  object \n",
      " 22  first_command_time             250006 non-null  object \n",
      " 23  cmlt_time_4grind               250006 non-null  float64\n",
      " 24  cmlt_time_with_us_4grind       250006 non-null  float64\n",
      " 25  cmlt_avg_time_for_level        250006 non-null  float64\n",
      " 26  cmlt_avg_duration              250006 non-null  float64\n",
      " 27  cmlt_avg_grind_duration        250006 non-null  float64\n",
      " 28  cmlt_winrate                   250006 non-null  float64\n",
      " 29  cmlt_spent_hc_per_grind        250006 non-null  float64\n",
      " 30  cmlt_spent_hc_div_active_time  250006 non-null  float64\n",
      " 31  cmlt_seconds_div_active_time   250006 non-null  float64\n",
      " 32  cmlt_hard_med_spent            250006 non-null  float64\n",
      " 33  birthday                       64243 non-null   object \n",
      " 34  sex                            63041 non-null   object \n",
      " 35  is_cheater                     244347 non-null  object \n",
      " 36  has_email                      244347 non-null  object \n",
      " 37  time_confirm_email             4234 non-null    object \n",
      " 38  target_game_currency           250006 non-null  float64\n",
      "dtypes: float64(12), int64(18), object(9)\n",
      "memory usage: 76.3+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Посмотрим на пропущенные значения"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "nan_fraction = pd.DataFrame(df.isna().mean(), columns=['fraction']).reset_index().rename(columns={'index':'columns'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "В основном категориальные данные имеют пропуски. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\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>columns</th>\n",
       "      <th>fraction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>country</td>\n",
       "      <td>0.000224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>network_name</td>\n",
       "      <td>0.100622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>birthday</td>\n",
       "      <td>0.743034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>sex</td>\n",
       "      <td>0.747842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>is_cheater</td>\n",
       "      <td>0.022635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>has_email</td>\n",
       "      <td>0.022635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>time_confirm_email</td>\n",
       "      <td>0.983064</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               columns  fraction\n",
       "18             country  0.000224\n",
       "20        network_name  0.100622\n",
       "33            birthday  0.743034\n",
       "34                 sex  0.747842\n",
       "35          is_cheater  0.022635\n",
       "36           has_email  0.022635\n",
       "37  time_confirm_email  0.983064"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nan_fraction.query('fraction > 0')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Целевое событие принимает 2713 уникальных значений. Решаем задачу регрессии."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2713"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['target_game_currency'].nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "На гистограмме большая часть значений целевого собятия принимает значение 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(df['target_game_currency'])\n",
    "plt.title('Распределенеие target_game_currency')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Проанализируем целевое событие по перцентилеям"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>target_game_currency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2.500060e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.783516e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.782120e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.044268e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       target_game_currency\n",
       "count          2.500060e+05\n",
       "mean           4.783516e+02\n",
       "std            8.782120e+03\n",
       "min            0.000000e+00\n",
       "25%            0.000000e+00\n",
       "50%            0.000000e+00\n",
       "75%            0.000000e+00\n",
       "max            1.044268e+06"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['target_game_currency']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = df['target_game_currency']\n",
    "percentiles = range(1, 101)\n",
    "percentile_values = np.percentile(data, percentiles)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "percentile_df = pd.DataFrame({'Percentile': percentiles, 'Value': percentile_values})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "На 97 перцентиле находятся все наши наблюдения целевого события"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>Percentile</th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>91</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>92</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>93</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>94</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>95</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>96</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>97</td>\n",
       "      <td>94.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>98</td>\n",
       "      <td>850.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>99</td>\n",
       "      <td>5697.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>100</td>\n",
       "      <td>1044267.55</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Percentile       Value\n",
       "90          91        0.00\n",
       "91          92        0.00\n",
       "92          93        0.00\n",
       "93          94        0.00\n",
       "94          95        0.00\n",
       "95          96        0.00\n",
       "96          97       94.05\n",
       "97          98      850.25\n",
       "98          99     5697.15\n",
       "99         100  1044267.55"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "percentile_df.tail(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['cat_col_target']  = df['target_game_currency'].apply(lambda x: 1 if x>0.0 else 0 )\n",
    "df['log_col_target']  = df['target_game_currency'].apply(lambda x: np.log(x) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cat_col_target\n",
       "0    0.963657\n",
       "1    0.036343\n",
       "Name: proportion, dtype: float64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['cat_col_target'].value_counts(normalize = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(df['log_col_target'])\n",
    "plt.title('Распределение target_game_currency в логарифмической шкале')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['birthday'] = pd.to_datetime(df['birthday'])\n",
    "df['date_install'] = pd.to_datetime(df['date_install'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>date_install</th>\n",
       "      <th>birthday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-03-13</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-04-22</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-03-24</td>\n",
       "      <td>1935-10-22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-03-25</td>\n",
       "      <td>1987-08-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  date_install   birthday\n",
       "0   2021-03-13        NaT\n",
       "1   2021-04-22        NaT\n",
       "2   2021-03-24 1935-10-22\n",
       "3   2021-03-25 1987-08-08\n",
       "4   2021-04-01        NaT"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.select_dtypes('datetime').head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['age'] = ((df['date_install'] - df['birthday']) / pd.Timedelta(days=365.25))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(df['age'])\n",
    "plt.title('Возраст игроков')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age\n",
       "0.093087     64\n",
       "0.087611     57\n",
       "0.090349     56\n",
       "0.082136     37\n",
       "0.079398     31\n",
       "             ..\n",
       "19.457906     1\n",
       "42.685832     1\n",
       "25.503080     1\n",
       "20.774812     1\n",
       "83.904175     1\n",
       "Name: count, Length: 22351, dtype: int64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['age'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Средний возраст 50 лет, етсь наблюдения где -1. Как  признак не надежный и в признаках по дате рождения 70% данных отсутствует"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    64243.000000\n",
       "mean        50.354267\n",
       "std         19.486147\n",
       "min         -1.806982\n",
       "25%         39.649555\n",
       "50%         53.804244\n",
       "75%         63.950719\n",
       "max        125.245722\n",
       "Name: age, dtype: float64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['age'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>mean</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>sum</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>62.0</td>\n",
       "      <td>2666.402846</td>\n",
       "      <td>0.0</td>\n",
       "      <td>998700.80</td>\n",
       "      <td>4263578.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>63.0</td>\n",
       "      <td>2740.267193</td>\n",
       "      <td>0.0</td>\n",
       "      <td>429296.45</td>\n",
       "      <td>4167946.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>57.0</td>\n",
       "      <td>2390.497583</td>\n",
       "      <td>0.0</td>\n",
       "      <td>717893.15</td>\n",
       "      <td>3808062.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>55.0</td>\n",
       "      <td>2139.973439</td>\n",
       "      <td>0.0</td>\n",
       "      <td>575563.20</td>\n",
       "      <td>3428237.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>66.0</td>\n",
       "      <td>2454.840190</td>\n",
       "      <td>0.0</td>\n",
       "      <td>535532.10</td>\n",
       "      <td>3365585.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>65.0</td>\n",
       "      <td>2237.913310</td>\n",
       "      <td>0.0</td>\n",
       "      <td>254068.95</td>\n",
       "      <td>3144268.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>59.0</td>\n",
       "      <td>1880.228125</td>\n",
       "      <td>0.0</td>\n",
       "      <td>389517.10</td>\n",
       "      <td>3068532.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>72.0</td>\n",
       "      <td>3051.666135</td>\n",
       "      <td>0.0</td>\n",
       "      <td>847061.80</td>\n",
       "      <td>2581709.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>67.0</td>\n",
       "      <td>1985.635927</td>\n",
       "      <td>0.0</td>\n",
       "      <td>308692.05</td>\n",
       "      <td>2539628.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>69.0</td>\n",
       "      <td>2212.545815</td>\n",
       "      <td>0.0</td>\n",
       "      <td>643376.10</td>\n",
       "      <td>2511239.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     age         mean  min        max         sum\n",
       "64  62.0  2666.402846  0.0  998700.80  4263578.15\n",
       "65  63.0  2740.267193  0.0  429296.45  4167946.40\n",
       "59  57.0  2390.497583  0.0  717893.15  3808062.65\n",
       "57  55.0  2139.973439  0.0  575563.20  3428237.45\n",
       "68  66.0  2454.840190  0.0  535532.10  3365585.90\n",
       "67  65.0  2237.913310  0.0  254068.95  3144268.20\n",
       "61  59.0  1880.228125  0.0  389517.10  3068532.30\n",
       "74  72.0  3051.666135  0.0  847061.80  2581709.55\n",
       "69  67.0  1985.635927  0.0  308692.05  2539628.35\n",
       "71  69.0  2212.545815  0.0  643376.10  2511239.50"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df\n",
    " .groupby('age')\n",
    " ['target_game_currency']\n",
    " .agg(\n",
    "     {'sum','mean','min','max'}\n",
    "     )\n",
    " .reset_index(drop=False)\n",
    " .sort_values('sum',ascending=False)\n",
    " ).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Женщины приносят много денег на 66.7%. Но признак пол тоже на 70% не полный и как признак тоже не подходит "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "      <th>sum</th>\n",
       "      <th>min</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>female</th>\n",
       "      <td>998700.80</td>\n",
       "      <td>1622.804312</td>\n",
       "      <td>61376081.90</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>717893.15</td>\n",
       "      <td>937.798210</td>\n",
       "      <td>20401800.05</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>other</th>\n",
       "      <td>643376.10</td>\n",
       "      <td>1657.449509</td>\n",
       "      <td>5743062.55</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              max         mean          sum  min\n",
       "sex                                             \n",
       "female  998700.80  1622.804312  61376081.90  0.0\n",
       "male    717893.15   937.798210  20401800.05  0.0\n",
       "other   643376.10  1657.449509   5743062.55  0.0"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('sex')['target_game_currency'].agg({'sum','mean','min','max'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "      <th>sum</th>\n",
       "      <th>min</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>31177</th>\n",
       "      <td>other</td>\n",
       "      <td>69.325120</td>\n",
       "      <td>643376.10</td>\n",
       "      <td>643376.100000</td>\n",
       "      <td>643376.10</td>\n",
       "      <td>643376.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15642</th>\n",
       "      <td>female</td>\n",
       "      <td>77.697467</td>\n",
       "      <td>436376.80</td>\n",
       "      <td>436376.800000</td>\n",
       "      <td>436376.80</td>\n",
       "      <td>436376.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14502</th>\n",
       "      <td>female</td>\n",
       "      <td>72.514716</td>\n",
       "      <td>847061.80</td>\n",
       "      <td>423530.900000</td>\n",
       "      <td>847061.80</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10481</th>\n",
       "      <td>female</td>\n",
       "      <td>59.865845</td>\n",
       "      <td>389517.10</td>\n",
       "      <td>389517.100000</td>\n",
       "      <td>389517.10</td>\n",
       "      <td>389517.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15456</th>\n",
       "      <td>female</td>\n",
       "      <td>76.676249</td>\n",
       "      <td>719246.90</td>\n",
       "      <td>359623.450000</td>\n",
       "      <td>719246.90</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24207</th>\n",
       "      <td>male</td>\n",
       "      <td>57.943874</td>\n",
       "      <td>717893.15</td>\n",
       "      <td>358946.575000</td>\n",
       "      <td>717893.15</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24189</th>\n",
       "      <td>male</td>\n",
       "      <td>57.880903</td>\n",
       "      <td>357723.45</td>\n",
       "      <td>357723.450000</td>\n",
       "      <td>357723.45</td>\n",
       "      <td>357723.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12114</th>\n",
       "      <td>female</td>\n",
       "      <td>64.698152</td>\n",
       "      <td>323921.50</td>\n",
       "      <td>323921.500000</td>\n",
       "      <td>323921.50</td>\n",
       "      <td>323921.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14353</th>\n",
       "      <td>female</td>\n",
       "      <td>71.989049</td>\n",
       "      <td>618271.40</td>\n",
       "      <td>309182.725000</td>\n",
       "      <td>618365.45</td>\n",
       "      <td>94.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7812</th>\n",
       "      <td>female</td>\n",
       "      <td>51.780972</td>\n",
       "      <td>590988.35</td>\n",
       "      <td>295494.175000</td>\n",
       "      <td>590988.35</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23490</th>\n",
       "      <td>male</td>\n",
       "      <td>55.236140</td>\n",
       "      <td>575563.20</td>\n",
       "      <td>287781.600000</td>\n",
       "      <td>575563.20</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13335</th>\n",
       "      <td>female</td>\n",
       "      <td>68.553046</td>\n",
       "      <td>261221.50</td>\n",
       "      <td>261221.500000</td>\n",
       "      <td>261221.50</td>\n",
       "      <td>261221.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15937</th>\n",
       "      <td>female</td>\n",
       "      <td>79.556468</td>\n",
       "      <td>518731.35</td>\n",
       "      <td>259365.675000</td>\n",
       "      <td>518731.35</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24564</th>\n",
       "      <td>male</td>\n",
       "      <td>59.301848</td>\n",
       "      <td>252811.15</td>\n",
       "      <td>252811.150000</td>\n",
       "      <td>252811.15</td>\n",
       "      <td>252811.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14678</th>\n",
       "      <td>female</td>\n",
       "      <td>73.141684</td>\n",
       "      <td>239642.25</td>\n",
       "      <td>239642.250000</td>\n",
       "      <td>239642.25</td>\n",
       "      <td>239642.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15479</th>\n",
       "      <td>female</td>\n",
       "      <td>76.780287</td>\n",
       "      <td>469047.30</td>\n",
       "      <td>234570.675000</td>\n",
       "      <td>469141.35</td>\n",
       "      <td>94.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30504</th>\n",
       "      <td>other</td>\n",
       "      <td>54.280630</td>\n",
       "      <td>225408.40</td>\n",
       "      <td>225408.400000</td>\n",
       "      <td>225408.40</td>\n",
       "      <td>225408.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11379</th>\n",
       "      <td>female</td>\n",
       "      <td>62.499658</td>\n",
       "      <td>998700.80</td>\n",
       "      <td>216468.710000</td>\n",
       "      <td>1082343.55</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11872</th>\n",
       "      <td>female</td>\n",
       "      <td>63.967146</td>\n",
       "      <td>429296.45</td>\n",
       "      <td>214648.225000</td>\n",
       "      <td>429296.45</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9928</th>\n",
       "      <td>female</td>\n",
       "      <td>58.225873</td>\n",
       "      <td>625850.50</td>\n",
       "      <td>208616.833333</td>\n",
       "      <td>625850.50</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          sex        age        max           mean         sum        min\n",
       "31177   other  69.325120  643376.10  643376.100000   643376.10  643376.10\n",
       "15642  female  77.697467  436376.80  436376.800000   436376.80  436376.80\n",
       "14502  female  72.514716  847061.80  423530.900000   847061.80       0.00\n",
       "10481  female  59.865845  389517.10  389517.100000   389517.10  389517.10\n",
       "15456  female  76.676249  719246.90  359623.450000   719246.90       0.00\n",
       "24207    male  57.943874  717893.15  358946.575000   717893.15       0.00\n",
       "24189    male  57.880903  357723.45  357723.450000   357723.45  357723.45\n",
       "12114  female  64.698152  323921.50  323921.500000   323921.50  323921.50\n",
       "14353  female  71.989049  618271.40  309182.725000   618365.45      94.05\n",
       "7812   female  51.780972  590988.35  295494.175000   590988.35       0.00\n",
       "23490    male  55.236140  575563.20  287781.600000   575563.20       0.00\n",
       "13335  female  68.553046  261221.50  261221.500000   261221.50  261221.50\n",
       "15937  female  79.556468  518731.35  259365.675000   518731.35       0.00\n",
       "24564    male  59.301848  252811.15  252811.150000   252811.15  252811.15\n",
       "14678  female  73.141684  239642.25  239642.250000   239642.25  239642.25\n",
       "15479  female  76.780287  469047.30  234570.675000   469141.35      94.05\n",
       "30504   other  54.280630  225408.40  225408.400000   225408.40  225408.40\n",
       "11379  female  62.499658  998700.80  216468.710000  1082343.55       0.00\n",
       "11872  female  63.967146  429296.45  214648.225000   429296.45       0.00\n",
       "9928   female  58.225873  625850.50  208616.833333   625850.50       0.00"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df\n",
    " .groupby(['sex','age'])\n",
    " ['target_game_currency']\n",
    " .agg(\n",
    "     {'sum','mean','min','max'}\n",
    "     )\n",
    " .reset_index(drop=False)\n",
    " .sort_values('mean',ascending=False)\n",
    " ).head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "group_df_country = (df\n",
    " .groupby('country')\n",
    " ['target_game_currency']\n",
    " .agg(\n",
    "     {'sum','mean','min','max'}\n",
    "     )\n",
    " .reset_index(drop=False)\n",
    " .sort_values('sum',ascending=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='country', ylabel='sum'>"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(group_df_country.head(20), x='country', y = 'sum')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "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>country</th>\n",
       "      <th>mean</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>sum</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>US</td>\n",
       "      <td>1330.026654</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1044267.55</td>\n",
       "      <td>67029353.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>DE</td>\n",
       "      <td>476.178634</td>\n",
       "      <td>0.0</td>\n",
       "      <td>998700.80</td>\n",
       "      <td>13393000.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>CA</td>\n",
       "      <td>880.109029</td>\n",
       "      <td>0.0</td>\n",
       "      <td>847061.80</td>\n",
       "      <td>7027670.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>GB</td>\n",
       "      <td>480.949607</td>\n",
       "      <td>0.0</td>\n",
       "      <td>618271.40</td>\n",
       "      <td>6359115.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>JP</td>\n",
       "      <td>658.239975</td>\n",
       "      <td>0.0</td>\n",
       "      <td>216741.55</td>\n",
       "      <td>3404417.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>AU</td>\n",
       "      <td>862.352048</td>\n",
       "      <td>0.0</td>\n",
       "      <td>268544.10</td>\n",
       "      <td>2821615.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>FR</td>\n",
       "      <td>212.004129</td>\n",
       "      <td>0.0</td>\n",
       "      <td>291935.95</td>\n",
       "      <td>2690544.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>CH</td>\n",
       "      <td>1514.912278</td>\n",
       "      <td>0.0</td>\n",
       "      <td>389517.10</td>\n",
       "      <td>2301151.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>IT</td>\n",
       "      <td>157.430167</td>\n",
       "      <td>0.0</td>\n",
       "      <td>237859.10</td>\n",
       "      <td>1459377.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>KR</td>\n",
       "      <td>493.320491</td>\n",
       "      <td>0.0</td>\n",
       "      <td>285390.45</td>\n",
       "      <td>1214555.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>BE</td>\n",
       "      <td>231.981578</td>\n",
       "      <td>0.0</td>\n",
       "      <td>168714.30</td>\n",
       "      <td>989169.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>AT</td>\n",
       "      <td>492.601271</td>\n",
       "      <td>0.0</td>\n",
       "      <td>140657.95</td>\n",
       "      <td>988650.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>NL</td>\n",
       "      <td>196.754194</td>\n",
       "      <td>0.0</td>\n",
       "      <td>240848.75</td>\n",
       "      <td>914907.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>DK</td>\n",
       "      <td>580.885023</td>\n",
       "      <td>0.0</td>\n",
       "      <td>183250.25</td>\n",
       "      <td>876555.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>RU</td>\n",
       "      <td>53.612287</td>\n",
       "      <td>0.0</td>\n",
       "      <td>252811.15</td>\n",
       "      <td>872647.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>ES</td>\n",
       "      <td>154.574578</td>\n",
       "      <td>0.0</td>\n",
       "      <td>161160.85</td>\n",
       "      <td>861598.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>MX</td>\n",
       "      <td>293.000180</td>\n",
       "      <td>0.0</td>\n",
       "      <td>348608.20</td>\n",
       "      <td>571057.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>BR</td>\n",
       "      <td>82.891342</td>\n",
       "      <td>0.0</td>\n",
       "      <td>356114.15</td>\n",
       "      <td>502155.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>NO</td>\n",
       "      <td>975.835919</td>\n",
       "      <td>0.0</td>\n",
       "      <td>149397.95</td>\n",
       "      <td>408875.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>PL</td>\n",
       "      <td>100.970822</td>\n",
       "      <td>0.0</td>\n",
       "      <td>211446.25</td>\n",
       "      <td>406609.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    country         mean  min         max          sum\n",
       "200      US  1330.026654  0.0  1044267.55  67029353.30\n",
       "50       DE   476.178634  0.0   998700.80  13393000.25\n",
       "34       CA   880.109029  0.0   847061.80   7027670.60\n",
       "67       GB   480.949607  0.0   618271.40   6359115.70\n",
       "100      JP   658.239975  0.0   216741.55   3404417.15\n",
       "11       AU   862.352048  0.0   268544.10   2821615.90\n",
       "65       FR   212.004129  0.0   291935.95   2690544.40\n",
       "37       CH  1514.912278  0.0   389517.10   2301151.75\n",
       "96       IT   157.430167  0.0   237859.10   1459377.65\n",
       "106      KR   493.320491  0.0   285390.45   1214555.05\n",
       "18       BE   231.981578  0.0   168714.30    989169.45\n",
       "10       AT   492.601271  0.0   140657.95    988650.75\n",
       "146      NL   196.754194  0.0   240848.75    914907.00\n",
       "51       DK   580.885023  0.0   183250.25    876555.50\n",
       "168      RU    53.612287  0.0   252811.15    872647.20\n",
       "59       ES   154.574578  0.0   161160.85    861598.70\n",
       "139      MX   293.000180  0.0   348608.20    571057.35\n",
       "28       BR    82.891342  0.0   356114.15    502155.75\n",
       "147      NO   975.835919  0.0   149397.95    408875.25\n",
       "157      PL   100.970822  0.0   211446.25    406609.50"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df\n",
    " .groupby('country')\n",
    " ['target_game_currency']\n",
    " .agg(\n",
    "     {'sum','mean','min','max'}\n",
    "     )\n",
    " .reset_index(drop=False)\n",
    " .sort_values('sum',ascending=False)\n",
    " ).head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "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>country</th>\n",
       "      <th>mean</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>sum</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>GY</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>HN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>HT</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>IQ</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>KE</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>KH</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>KM</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>KN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>LA</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215</th>\n",
       "      <td>ZW</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    country  mean  min  max  sum\n",
       "82       GY   0.0  0.0  0.0  0.0\n",
       "84       HN   0.0  0.0  0.0  0.0\n",
       "86       HT   0.0  0.0  0.0  0.0\n",
       "93       IQ   0.0  0.0  0.0  0.0\n",
       "101      KE   0.0  0.0  0.0  0.0\n",
       "103      KH   0.0  0.0  0.0  0.0\n",
       "104      KM   0.0  0.0  0.0  0.0\n",
       "105      KN   0.0  0.0  0.0  0.0\n",
       "110      LA   0.0  0.0  0.0  0.0\n",
       "215      ZW   0.0  0.0  0.0  0.0"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df\n",
    " .groupby('country')\n",
    " ['target_game_currency']\n",
    " .agg(\n",
    "     {'sum','mean','min','max'}\n",
    "     )\n",
    " .reset_index(drop=False)\n",
    " .sort_values('sum',ascending=False)\n",
    " ).tail(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['network_name'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Organic                  170179\n",
       "Vungle                    21906\n",
       "Off-Facebook Installs     10842\n",
       "Chartboost                 8357\n",
       "Facebook Installs          5045\n",
       "Unity Ads                  2540\n",
       "Google Ads UAC             1827\n",
       "Untrusted Devices          1666\n",
       "G5 Games                   1341\n",
       "Google Ads ACI              963\n",
       "Google Organic Search       103\n",
       "Instagram Installs           54\n",
       "Unattributed                 27\n",
       "Name: network_name, dtype: int64"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['network_name'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "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>network_name</th>\n",
       "      <th>mean</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>sum</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Organic</td>\n",
       "      <td>439.181591</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1044267.55</td>\n",
       "      <td>74739483.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Vungle</td>\n",
       "      <td>398.531939</td>\n",
       "      <td>0.0</td>\n",
       "      <td>575563.20</td>\n",
       "      <td>8730240.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Chartboost</td>\n",
       "      <td>553.838860</td>\n",
       "      <td>0.0</td>\n",
       "      <td>618271.40</td>\n",
       "      <td>4628431.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Facebook Installs</td>\n",
       "      <td>766.002418</td>\n",
       "      <td>0.0</td>\n",
       "      <td>429296.45</td>\n",
       "      <td>3864482.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Off-Facebook Installs</td>\n",
       "      <td>231.009472</td>\n",
       "      <td>0.0</td>\n",
       "      <td>240128.65</td>\n",
       "      <td>2504604.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Google Ads ACI</td>\n",
       "      <td>2472.423832</td>\n",
       "      <td>0.0</td>\n",
       "      <td>847061.80</td>\n",
       "      <td>2380944.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Untrusted Devices</td>\n",
       "      <td>1289.072449</td>\n",
       "      <td>0.0</td>\n",
       "      <td>717893.15</td>\n",
       "      <td>2147594.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>G5 Games</td>\n",
       "      <td>1417.827890</td>\n",
       "      <td>0.0</td>\n",
       "      <td>625850.50</td>\n",
       "      <td>1901307.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Unity Ads</td>\n",
       "      <td>460.393937</td>\n",
       "      <td>0.0</td>\n",
       "      <td>436376.80</td>\n",
       "      <td>1169400.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Google Ads UAC</td>\n",
       "      <td>606.580980</td>\n",
       "      <td>0.0</td>\n",
       "      <td>125665.05</td>\n",
       "      <td>1108223.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Unattributed</td>\n",
       "      <td>16788.751852</td>\n",
       "      <td>0.0</td>\n",
       "      <td>268326.55</td>\n",
       "      <td>453296.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Instagram Installs</td>\n",
       "      <td>779.650926</td>\n",
       "      <td>0.0</td>\n",
       "      <td>36524.65</td>\n",
       "      <td>42101.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Google Organic Search</td>\n",
       "      <td>342.599515</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30555.80</td>\n",
       "      <td>35287.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             network_name          mean  min         max          sum\n",
       "8                 Organic    439.181591  0.0  1044267.55  74739483.95\n",
       "12                 Vungle    398.531939  0.0   575563.20   8730240.65\n",
       "0              Chartboost    553.838860  0.0   618271.40   4628431.35\n",
       "1       Facebook Installs    766.002418  0.0   429296.45   3864482.20\n",
       "7   Off-Facebook Installs    231.009472  0.0   240128.65   2504604.70\n",
       "3          Google Ads ACI   2472.423832  0.0   847061.80   2380944.15\n",
       "11      Untrusted Devices   1289.072449  0.0   717893.15   2147594.70\n",
       "2                G5 Games   1417.827890  0.0   625850.50   1901307.20\n",
       "10              Unity Ads    460.393937  0.0   436376.80   1169400.60\n",
       "4          Google Ads UAC    606.580980  0.0   125665.05   1108223.45\n",
       "9            Unattributed  16788.751852  0.0   268326.55    453296.30\n",
       "6      Instagram Installs    779.650926  0.0    36524.65     42101.15\n",
       "5   Google Organic Search    342.599515  0.0    30555.80     35287.75"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df\n",
    " .groupby('network_name')\n",
    " ['target_game_currency']\n",
    " .agg(\n",
    "     {'sum','mean','min','max'}\n",
    "     )\n",
    " .reset_index(drop=False)\n",
    " .sort_values('sum',ascending=False)\n",
    " )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "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>country</th>\n",
       "      <th>network_name</th>\n",
       "      <th>sex</th>\n",
       "      <th>mean</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>sum</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1421</th>\n",
       "      <td>US</td>\n",
       "      <td>Organic</td>\n",
       "      <td>female</td>\n",
       "      <td>3173.260898</td>\n",
       "      <td>0.0</td>\n",
       "      <td>719246.90</td>\n",
       "      <td>23964466.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1422</th>\n",
       "      <td>US</td>\n",
       "      <td>Organic</td>\n",
       "      <td>male</td>\n",
       "      <td>2079.454382</td>\n",
       "      <td>0.0</td>\n",
       "      <td>331961.35</td>\n",
       "      <td>4793142.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>363</th>\n",
       "      <td>DE</td>\n",
       "      <td>Organic</td>\n",
       "      <td>female</td>\n",
       "      <td>1146.734861</td>\n",
       "      <td>0.0</td>\n",
       "      <td>160230.80</td>\n",
       "      <td>2965456.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>543</th>\n",
       "      <td>GB</td>\n",
       "      <td>Organic</td>\n",
       "      <td>female</td>\n",
       "      <td>1799.167370</td>\n",
       "      <td>0.0</td>\n",
       "      <td>233811.15</td>\n",
       "      <td>2770717.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1423</th>\n",
       "      <td>US</td>\n",
       "      <td>Organic</td>\n",
       "      <td>other</td>\n",
       "      <td>5863.599543</td>\n",
       "      <td>0.0</td>\n",
       "      <td>643376.10</td>\n",
       "      <td>2568256.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>748</th>\n",
       "      <td>IT</td>\n",
       "      <td>Google Organic Search</td>\n",
       "      <td>other</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>749</th>\n",
       "      <td>IT</td>\n",
       "      <td>Instagram Installs</td>\n",
       "      <td>male</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AE</td>\n",
       "      <td>Google Ads UAC</td>\n",
       "      <td>female</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>752</th>\n",
       "      <td>IT</td>\n",
       "      <td>Off-Facebook Installs</td>\n",
       "      <td>other</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1501</th>\n",
       "      <td>ZW</td>\n",
       "      <td>Vungle</td>\n",
       "      <td>female</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1502 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     country           network_name     sex         mean  min        max  \\\n",
       "1421      US                Organic  female  3173.260898  0.0  719246.90   \n",
       "1422      US                Organic    male  2079.454382  0.0  331961.35   \n",
       "363       DE                Organic  female  1146.734861  0.0  160230.80   \n",
       "543       GB                Organic  female  1799.167370  0.0  233811.15   \n",
       "1423      US                Organic   other  5863.599543  0.0  643376.10   \n",
       "...      ...                    ...     ...          ...  ...        ...   \n",
       "748       IT  Google Organic Search   other     0.000000  0.0       0.00   \n",
       "749       IT     Instagram Installs    male     0.000000  0.0       0.00   \n",
       "1         AE         Google Ads UAC  female     0.000000  0.0       0.00   \n",
       "752       IT  Off-Facebook Installs   other     0.000000  0.0       0.00   \n",
       "1501      ZW                 Vungle  female     0.000000  0.0       0.00   \n",
       "\n",
       "              sum  \n",
       "1421  23964466.30  \n",
       "1422   4793142.35  \n",
       "363    2965456.35  \n",
       "543    2770717.75  \n",
       "1423   2568256.60  \n",
       "...           ...  \n",
       "748          0.00  \n",
       "749          0.00  \n",
       "1            0.00  \n",
       "752          0.00  \n",
       "1501         0.00  \n",
       "\n",
       "[1502 rows x 7 columns]"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df\n",
    " .groupby(['country','network_name','sex'])\n",
    " ['target_game_currency']\n",
    " .agg(\n",
    "     {'sum','mean','min','max'}\n",
    "     )\n",
    " .reset_index(drop=False)\n",
    " .sort_values('sum',ascending=False)\n",
    " )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>cmlt_daily_game_currency</th>\n",
       "      <th>cmlt_seconds_with_us</th>\n",
       "      <th>cmlt_max_sessions_duration</th>\n",
       "      <th>cmlt_sum_sessions_duration</th>\n",
       "      <th>cmlt_count_sessions</th>\n",
       "      <th>cmlt_sum_quantum_duration</th>\n",
       "      <th>cmlt_count_quant</th>\n",
       "      <th>cmlt_max_quantum_duration</th>\n",
       "      <th>cmlt_max_quant</th>\n",
       "      <th>cmlt_final_level</th>\n",
       "      <th>cmlt_count_grind</th>\n",
       "      <th>cmlt_max_grind</th>\n",
       "      <th>cmlt_count_won_grind</th>\n",
       "      <th>cmlt_hard_buy</th>\n",
       "      <th>cmlt_hard_earn</th>\n",
       "      <th>cmlt_hard_gift</th>\n",
       "      <th>cmlt_hard_spent</th>\n",
       "      <th>cmlt_hard_max_spent</th>\n",
       "      <th>country_top_tier</th>\n",
       "      <th>cmlt_time_4grind</th>\n",
       "      <th>cmlt_time_with_us_4grind</th>\n",
       "      <th>cmlt_avg_time_for_level</th>\n",
       "      <th>cmlt_avg_duration</th>\n",
       "      <th>cmlt_avg_grind_duration</th>\n",
       "      <th>cmlt_winrate</th>\n",
       "      <th>cmlt_spent_hc_per_grind</th>\n",
       "      <th>cmlt_spent_hc_div_active_time</th>\n",
       "      <th>cmlt_seconds_div_active_time</th>\n",
       "      <th>cmlt_hard_med_spent</th>\n",
       "      <th>target_game_currency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>250006.000000</td>\n",
       "      <td>250006.000000</td>\n",
       "      <td>250006.000000</td>\n",
       "      <td>250006.000000</td>\n",
       "      <td>250006.000000</td>\n",
       "      <td>250006.000000</td>\n",
       "      <td>250006.000000</td>\n",
       "      <td>250006.000000</td>\n",
       "      <td>250006.000000</td>\n",
       "      <td>250006.000000</td>\n",
       "      <td>250006.000000</td>\n",
       "      <td>250006.000000</td>\n",
       "      <td>250006.000000</td>\n",
       "      <td>250006.000000</td>\n",
       "      <td>250006.000000</td>\n",
       "      <td>250006.000000</td>\n",
       "      <td>2.500060e+05</td>\n",
       "      <td>2.500060e+05</td>\n",
       "      <td>250006.000000</td>\n",
       "      <td>250006.000000</td>\n",
       "      <td>250006.000000</td>\n",
       "      <td>250006.000000</td>\n",
       "      <td>250006.000000</td>\n",
       "      <td>250006.000000</td>\n",
       "      <td>250006.000000</td>\n",
       "      <td>2.500060e+05</td>\n",
       "      <td>2.500060e+05</td>\n",
       "      <td>250006.000000</td>\n",
       "      <td>2.500060e+05</td>\n",
       "      <td>2.500060e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>34.292041</td>\n",
       "      <td>33960.597330</td>\n",
       "      <td>2450.241610</td>\n",
       "      <td>9583.622937</td>\n",
       "      <td>5.331756</td>\n",
       "      <td>25140.172560</td>\n",
       "      <td>79.080718</td>\n",
       "      <td>314.431366</td>\n",
       "      <td>304.991020</td>\n",
       "      <td>6.233966</td>\n",
       "      <td>44.002416</td>\n",
       "      <td>112.593694</td>\n",
       "      <td>29.681140</td>\n",
       "      <td>5.623445</td>\n",
       "      <td>5.227767</td>\n",
       "      <td>14.245014</td>\n",
       "      <td>8.451479e+03</td>\n",
       "      <td>8.445564e+03</td>\n",
       "      <td>0.493296</td>\n",
       "      <td>0.005916</td>\n",
       "      <td>0.008280</td>\n",
       "      <td>1018.602076</td>\n",
       "      <td>1445.571733</td>\n",
       "      <td>193.668030</td>\n",
       "      <td>0.703148</td>\n",
       "      <td>8.413403e+03</td>\n",
       "      <td>8.413403e+03</td>\n",
       "      <td>430.247223</td>\n",
       "      <td>1.442863e+03</td>\n",
       "      <td>4.783516e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>654.358101</td>\n",
       "      <td>73713.498159</td>\n",
       "      <td>4697.404027</td>\n",
       "      <td>19053.876939</td>\n",
       "      <td>8.452232</td>\n",
       "      <td>47450.271153</td>\n",
       "      <td>138.372150</td>\n",
       "      <td>715.251877</td>\n",
       "      <td>461.380456</td>\n",
       "      <td>8.418643</td>\n",
       "      <td>77.639872</td>\n",
       "      <td>470.382938</td>\n",
       "      <td>47.596697</td>\n",
       "      <td>117.263139</td>\n",
       "      <td>7.509561</td>\n",
       "      <td>5.185956</td>\n",
       "      <td>4.200014e+06</td>\n",
       "      <td>4.200013e+06</td>\n",
       "      <td>0.499956</td>\n",
       "      <td>0.159049</td>\n",
       "      <td>0.299056</td>\n",
       "      <td>1820.324953</td>\n",
       "      <td>2326.942912</td>\n",
       "      <td>584.202320</td>\n",
       "      <td>0.372332</td>\n",
       "      <td>4.199972e+06</td>\n",
       "      <td>4.199972e+06</td>\n",
       "      <td>918.680315</td>\n",
       "      <td>7.002800e+05</td>\n",
       "      <td>8.782120e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.081967</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>266.000000</td>\n",
       "      <td>322.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.003119</td>\n",
       "      <td>0.000108</td>\n",
       "      <td>209.000000</td>\n",
       "      <td>239.000000</td>\n",
       "      <td>118.155729</td>\n",
       "      <td>0.518750</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>26.666667</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1511.500000</td>\n",
       "      <td>1086.000000</td>\n",
       "      <td>1707.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3684.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>166.000000</td>\n",
       "      <td>127.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.005128</td>\n",
       "      <td>0.002156</td>\n",
       "      <td>474.000000</td>\n",
       "      <td>829.500000</td>\n",
       "      <td>164.726946</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>137.600000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>26246.500000</td>\n",
       "      <td>2989.000000</td>\n",
       "      <td>9923.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>26324.000000</td>\n",
       "      <td>89.000000</td>\n",
       "      <td>316.000000</td>\n",
       "      <td>428.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>9.000000e+00</td>\n",
       "      <td>7.000000e+00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.006574</td>\n",
       "      <td>0.007294</td>\n",
       "      <td>1296.833333</td>\n",
       "      <td>1877.000000</td>\n",
       "      <td>214.363636</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.182036e-01</td>\n",
       "      <td>1.182036e-01</td>\n",
       "      <td>468.496055</td>\n",
       "      <td>2.285714e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>102200.050000</td>\n",
       "      <td>600851.000000</td>\n",
       "      <td>86377.000000</td>\n",
       "      <td>507861.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>495404.000000</td>\n",
       "      <td>1442.000000</td>\n",
       "      <td>31083.000000</td>\n",
       "      <td>8911.000000</td>\n",
       "      <td>159.000000</td>\n",
       "      <td>812.000000</td>\n",
       "      <td>19121.000000</td>\n",
       "      <td>615.000000</td>\n",
       "      <td>22630.000000</td>\n",
       "      <td>587.000000</td>\n",
       "      <td>435.000000</td>\n",
       "      <td>2.100008e+09</td>\n",
       "      <td>2.100008e+09</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>96.500000</td>\n",
       "      <td>230129.000000</td>\n",
       "      <td>85664.000000</td>\n",
       "      <td>72622.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.100008e+09</td>\n",
       "      <td>2.100008e+09</td>\n",
       "      <td>41731.000000</td>\n",
       "      <td>3.500014e+08</td>\n",
       "      <td>1.044268e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       cmlt_daily_game_currency  cmlt_seconds_with_us  \\\n",
       "count             250006.000000         250006.000000   \n",
       "mean                  34.292041          33960.597330   \n",
       "std                  654.358101          73713.498159   \n",
       "min                    0.000000              0.000000   \n",
       "25%                    0.000000             66.000000   \n",
       "50%                    0.000000           1511.500000   \n",
       "75%                    0.000000          26246.500000   \n",
       "max               102200.050000         600851.000000   \n",
       "\n",
       "       cmlt_max_sessions_duration  cmlt_sum_sessions_duration  \\\n",
       "count               250006.000000               250006.000000   \n",
       "mean                  2450.241610                 9583.622937   \n",
       "std                   4697.404027                19053.876939   \n",
       "min                      1.000000                    1.000000   \n",
       "25%                    266.000000                  322.000000   \n",
       "50%                   1086.000000                 1707.000000   \n",
       "75%                   2989.000000                 9923.000000   \n",
       "max                  86377.000000               507861.000000   \n",
       "\n",
       "       cmlt_count_sessions  cmlt_sum_quantum_duration  cmlt_count_quant  \\\n",
       "count        250006.000000              250006.000000     250006.000000   \n",
       "mean              5.331756               25140.172560         79.080718   \n",
       "std               8.452232               47450.271153        138.372150   \n",
       "min               1.000000                   0.000000          0.000000   \n",
       "25%               1.000000                 440.000000          4.000000   \n",
       "50%               2.000000                3684.000000         16.000000   \n",
       "75%               6.000000               26324.000000         89.000000   \n",
       "max             199.000000              495404.000000       1442.000000   \n",
       "\n",
       "       cmlt_max_quantum_duration  cmlt_max_quant  cmlt_final_level  \\\n",
       "count              250006.000000   250006.000000     250006.000000   \n",
       "mean                  314.431366      304.991020          6.233966   \n",
       "std                   715.251877      461.380456          8.418643   \n",
       "min                     0.000000        0.000000          1.000000   \n",
       "25%                    68.000000       28.000000          1.000000   \n",
       "50%                   166.000000      127.000000          4.000000   \n",
       "75%                   316.000000      428.000000          8.000000   \n",
       "max                 31083.000000     8911.000000        159.000000   \n",
       "\n",
       "       cmlt_count_grind  cmlt_max_grind  cmlt_count_won_grind  cmlt_hard_buy  \\\n",
       "count     250006.000000   250006.000000         250006.000000  250006.000000   \n",
       "mean          44.002416      112.593694             29.681140       5.623445   \n",
       "std           77.639872      470.382938             47.596697     117.263139   \n",
       "min            0.000000        0.000000              0.000000       0.000000   \n",
       "25%            2.000000        2.000000              1.000000       0.000000   \n",
       "50%            9.000000       10.000000              8.000000       0.000000   \n",
       "75%           49.000000       62.000000             38.000000       0.000000   \n",
       "max          812.000000    19121.000000            615.000000   22630.000000   \n",
       "\n",
       "       cmlt_hard_earn  cmlt_hard_gift  cmlt_hard_spent  cmlt_hard_max_spent  \\\n",
       "count   250006.000000   250006.000000     2.500060e+05         2.500060e+05   \n",
       "mean         5.227767       14.245014     8.451479e+03         8.445564e+03   \n",
       "std          7.509561        5.185956     4.200014e+06         4.200013e+06   \n",
       "min          0.000000        0.000000     0.000000e+00         0.000000e+00   \n",
       "25%          0.000000       15.000000     0.000000e+00         0.000000e+00   \n",
       "50%          2.000000       15.000000     0.000000e+00         0.000000e+00   \n",
       "75%          8.000000       15.000000     9.000000e+00         7.000000e+00   \n",
       "max        587.000000      435.000000     2.100008e+09         2.100008e+09   \n",
       "\n",
       "       country_top_tier  cmlt_time_4grind  cmlt_time_with_us_4grind  \\\n",
       "count     250006.000000     250006.000000             250006.000000   \n",
       "mean           0.493296          0.005916                  0.008280   \n",
       "std            0.499956          0.159049                  0.299056   \n",
       "min            0.000000          0.000000                  0.000000   \n",
       "25%            0.000000          0.003119                  0.000108   \n",
       "50%            0.000000          0.005128                  0.002156   \n",
       "75%            1.000000          0.006574                  0.007294   \n",
       "max            1.000000         70.000000                 96.500000   \n",
       "\n",
       "       cmlt_avg_time_for_level  cmlt_avg_duration  cmlt_avg_grind_duration  \\\n",
       "count            250006.000000      250006.000000            250006.000000   \n",
       "mean               1018.602076        1445.571733               193.668030   \n",
       "std                1820.324953        2326.942912               584.202320   \n",
       "min                   0.081967           1.000000                 0.000000   \n",
       "25%                 209.000000         239.000000               118.155729   \n",
       "50%                 474.000000         829.500000               164.726946   \n",
       "75%                1296.833333        1877.000000               214.363636   \n",
       "max              230129.000000       85664.000000             72622.000000   \n",
       "\n",
       "        cmlt_winrate  cmlt_spent_hc_per_grind  cmlt_spent_hc_div_active_time  \\\n",
       "count  250006.000000             2.500060e+05                   2.500060e+05   \n",
       "mean        0.703148             8.413403e+03                   8.413403e+03   \n",
       "std         0.372332             4.199972e+06                   4.199972e+06   \n",
       "min         0.000000             0.000000e+00                   0.000000e+00   \n",
       "25%         0.518750             0.000000e+00                   0.000000e+00   \n",
       "50%         0.888889             0.000000e+00                   0.000000e+00   \n",
       "75%         1.000000             1.182036e-01                   1.182036e-01   \n",
       "max         1.000000             2.100008e+09                   2.100008e+09   \n",
       "\n",
       "       cmlt_seconds_div_active_time  cmlt_hard_med_spent  target_game_currency  \n",
       "count                 250006.000000         2.500060e+05          2.500060e+05  \n",
       "mean                     430.247223         1.442863e+03          4.783516e+02  \n",
       "std                      918.680315         7.002800e+05          8.782120e+03  \n",
       "min                        0.000000         0.000000e+00          0.000000e+00  \n",
       "25%                       26.666667         0.000000e+00          0.000000e+00  \n",
       "50%                      137.600000         0.000000e+00          0.000000e+00  \n",
       "75%                      468.496055         2.285714e+00          0.000000e+00  \n",
       "max                    41731.000000         3.500014e+08          1.044268e+06  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.select_dtypes(include=['int', 'float']).describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Выводы"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1) Решается задача регресси, target_game_currency\n",
    "2) Много наблюдений принимает значения 0.0 до 96 перцентиля включительно\n",
    "3) 3.77% наблюдений не нулевые\n",
    "4) Распределение игроков по возрасту разное, средний возраст(года) 49, медианное 53, max 125, min -2, мода 61\n",
    "5) Самая платежеспособная страна US, отрыв со второй алвтежеспособной страной DE на 80%\n",
    "6) network_name видимо канал от куда скачивали приложение, самое наибольший канал это органика\n",
    "7) Большая часть пропущенных значений приходятся на категориальные колонки "
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