{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from copy import copy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for every string executing\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "bank_df = pd.read_csv('test_task_data_201808091645.csv', delimiter=';')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "toc-hr-collapsed": false
   },
   "source": [
    "## Наши данные"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 45889 entries, 0 to 45888\n",
      "Data columns (total 8 columns):\n",
      "eventdatetime      45889 non-null object\n",
      "installdatetime    45889 non-null object\n",
      "user_level         45889 non-null int64\n",
      "pack               45889 non-null object\n",
      "price              45889 non-null float64\n",
      "customeruserid     45889 non-null object\n",
      "countrycode        45887 non-null object\n",
      "bank_type          45889 non-null object\n",
      "dtypes: float64(1), int64(1), object(6)\n",
      "memory usage: 2.8+ MB\n"
     ]
    },
    {
     "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>eventdatetime</th>\n",
       "      <th>installdatetime</th>\n",
       "      <th>user_level</th>\n",
       "      <th>pack</th>\n",
       "      <th>price</th>\n",
       "      <th>customeruserid</th>\n",
       "      <th>countrycode</th>\n",
       "      <th>bank_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-06-01 22:47:37</td>\n",
       "      <td>2018-05-22 22:49:44</td>\n",
       "      <td>120</td>\n",
       "      <td>piggy_bank</td>\n",
       "      <td>2.99</td>\n",
       "      <td>045CC293-E490-4F6A-9ED6-B0B1E302F245</td>\n",
       "      <td>AU</td>\n",
       "      <td>old_bank</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-06-01 22:48:48</td>\n",
       "      <td>2018-05-17 15:12:13</td>\n",
       "      <td>59</td>\n",
       "      <td>pack_1</td>\n",
       "      <td>1.99</td>\n",
       "      <td>F7F650C9-D847-46C9-B3A4-9D9C4C75AB56</td>\n",
       "      <td>US</td>\n",
       "      <td>old_bank</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-06-01 22:51:15</td>\n",
       "      <td>2018-05-30 16:02:33</td>\n",
       "      <td>28</td>\n",
       "      <td>pack_2</td>\n",
       "      <td>4.99</td>\n",
       "      <td>4f88616ca3347ee</td>\n",
       "      <td>US</td>\n",
       "      <td>old_bank</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-06-01 22:52:33</td>\n",
       "      <td>2018-05-13 02:10:43</td>\n",
       "      <td>222</td>\n",
       "      <td>adventure_pack</td>\n",
       "      <td>4.99</td>\n",
       "      <td>80C3857A-C205-430D-A889-A1D08830AE50</td>\n",
       "      <td>US</td>\n",
       "      <td>old_bank</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-06-01 22:52:34</td>\n",
       "      <td>2018-05-25 22:53:44</td>\n",
       "      <td>62</td>\n",
       "      <td>pack_1</td>\n",
       "      <td>1.99</td>\n",
       "      <td>5D21A355-AEB7-4141-BE13-554D738929EC</td>\n",
       "      <td>US</td>\n",
       "      <td>old_bank</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        eventdatetime     installdatetime  user_level            pack  price  \\\n",
       "0 2018-06-01 22:47:37 2018-05-22 22:49:44         120      piggy_bank   2.99   \n",
       "1 2018-06-01 22:48:48 2018-05-17 15:12:13          59          pack_1   1.99   \n",
       "2 2018-06-01 22:51:15 2018-05-30 16:02:33          28          pack_2   4.99   \n",
       "3 2018-06-01 22:52:33 2018-05-13 02:10:43         222  adventure_pack   4.99   \n",
       "4 2018-06-01 22:52:34 2018-05-25 22:53:44          62          pack_1   1.99   \n",
       "\n",
       "                         customeruserid countrycode bank_type  \n",
       "0  045CC293-E490-4F6A-9ED6-B0B1E302F245          AU  old_bank  \n",
       "1  F7F650C9-D847-46C9-B3A4-9D9C4C75AB56          US  old_bank  \n",
       "2                       4f88616ca3347ee          US  old_bank  \n",
       "3  80C3857A-C205-430D-A889-A1D08830AE50          US  old_bank  \n",
       "4  5D21A355-AEB7-4141-BE13-554D738929EC          US  old_bank  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank_df.info()\n",
    "bank_df.bank_type.replace('old bank', 'old_bank', inplace=True)\n",
    "bank_df.eventdatetime = pd.to_datetime(bank_df.eventdatetime)\n",
    "bank_df.installdatetime = pd.to_datetime(bank_df.installdatetime)\n",
    "bank_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.989019352033051"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank_df.customeruserid.count()/bank_df.customeruserid.value_counts().count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "toc-hr-collapsed": false
   },
   "source": [
    "### Немного общей инфы\n",
    "**По каждому предложению сколько раз его купили**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pack_1            19741\n",
       "pack_2            10192\n",
       "pack_3             6118\n",
       "pack_4             2508\n",
       "piggy_bank         2235\n",
       "starter_pack       1718\n",
       "adventure_pack     1496\n",
       "smart_offer_2       585\n",
       "smart_offer_3       325\n",
       "pack_5              261\n",
       "smart_offer_1       222\n",
       "pack_6              207\n",
       "smart_offer_4       203\n",
       "smart_offer_5        59\n",
       "smart_offer_6        19\n",
       "Name: pack, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank_df.pack.value_counts(sort=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Сколько было покупок для каждой цены вообще**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.99     19741\n",
       "4.99     11688\n",
       "9.99      6118\n",
       "2.99      3953\n",
       "19.99     2508\n",
       "3.99       585\n",
       "7.99       325\n",
       "49.99      261\n",
       "0.99       222\n",
       "99.99      207\n",
       "14.99      203\n",
       "34.99       59\n",
       "74.99       19\n",
       "Name: price, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2775bb09550>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={'figure.figsize':(20,10)})\n",
    "bank_df.price.value_counts()\n",
    "bank_df.price.value_counts().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2775a99bbe0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot('price', data=bank_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Видно, что прайс в 35 и 75 совсем почти не покупается. Либо потому, что в банке во всплывающем окошке не появляются эти карточки, либо это никому не интересно. Скорее первое. Потому что остальные карточки с большим колвом продаж есть на экране. В общем с этой штукой можно поиграться и посмотреть. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bank_df.price.plot(kind ='kde', figsize=(20,10), logy=False);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pack\n",
       "adventure_pack     4.99\n",
       "pack_1             1.99\n",
       "pack_2             4.99\n",
       "pack_3             9.99\n",
       "pack_4            19.99\n",
       "pack_5            49.99\n",
       "pack_6            99.99\n",
       "piggy_bank         2.99\n",
       "smart_offer_1      0.99\n",
       "smart_offer_2      3.99\n",
       "smart_offer_3      7.99\n",
       "smart_offer_4     14.99\n",
       "smart_offer_5     34.99\n",
       "smart_offer_6     74.99\n",
       "starter_pack       2.99\n",
       "Name: price, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank_df.groupby('pack').price.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "smart pack exists only in new bank\n",
      "\n",
      "new_bank    1413\n",
      "Name: bank_type, dtype: int64\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print('smart pack exists only in new bank\\n\\n{}'.format(bank_df[bank_df.pack.str.contains('smart')].bank_type.value_counts()), end='\\n\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Посчитаем в какой ценовой категории соотношение покупок больше всего** \n",
    "\n",
    "Тут получается, что покупки с ценником больше 10 не так уж и много. Опять же прайс меньше 10 делают в два раза больше выручки, чем прайс больше."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count of goods for price < 10 is 42632 with all over amount of money 175696.68000000002\n",
      "count of goods for price > 10 is 3257 with all over amount of money 90412.43\n",
      "count of goods for price between 10 and 20 is 2711 with all over amount of money 53177.89000000001\n"
     ]
    }
   ],
   "source": [
    "print('count of goods for price < 10 is', bank_df[bank_df.price < 10].price.count(), \n",
    "      'with all over amount of money', bank_df[bank_df.price < 10].price.sum())\n",
    "print('count of goods for price > 10 is', bank_df[bank_df.price > 10].price.count(), \n",
    "      'with all over amount of money', bank_df[bank_df.price > 10].price.sum())\n",
    "print('count of goods for price between 10 and 20 is', bank_df[(bank_df.price > 10) & (bank_df.price < 20)].price.count(), \n",
    "      'with all over amount of money', bank_df[(bank_df.price > 10) & (bank_df.price < 20)].price.sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Банк старый, банк новый\n",
    "\n",
    "Сколько операций было совершено в новом и старом банке? \n",
    "Операций в новом банке в полтора раза больше, чем в старом. Надо запомнить, это существенное различие. Также видно, что за счет новых предложений появился больше выбор. И люди этот выбор делают."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "new_bank    27000\n",
       "old_bank    18889\n",
       "Name: bank_type, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "coef for comparing = 1.4294033564508444\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2775af5b7f0>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bank_df.bank_type.value_counts()\n",
    "norm_coef = (bank_df.bank_type.value_counts()/bank_df.bank_type.value_counts()['old_bank'])[0]\n",
    "print('coef for comparing = {}'.format(norm_coef))\n",
    "sns.countplot('price', data=bank_df, hue='bank_type')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Что там со средним чеком?** \n",
    "\n",
    "Кажется новый банк получше все таки продает. На графике плохо, но видно, что средний чек поднялся и распределение чека у нового банка похудело внизу и потолстело вверху, и можно предположить, что игроки теперь чаще покупают карточки дороже. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "average bill\n",
      "bank_type\n",
      "new_bank    5.996889\n",
      "old_bank    5.516073\n",
      "Name: price, dtype: float64\n",
      "\n",
      "median bill\n",
      "bank_type\n",
      "new_bank    3.99\n",
      "old_bank    2.99\n",
      "Name: price, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('average bill')\n",
    "print(bank_df.groupby(bank_df.bank_type).price.mean())\n",
    "print('\\nmedian bill')\n",
    "print(bank_df.groupby(bank_df.bank_type).price.median())\n",
    "sns.violinplot(data=bank_df, y='price', x='bank_type', gridsize=300, cut=5);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Как мы видим и действительно, для дешевых покупок ничего не изменилось в распределениях трат между банками. \n",
    "\n",
    "1.4 ~ 1.42\n",
    "\n",
    "А вот в дорогом сегменте карточек покупок стало существенно больше. \n",
    "\n",
    "1.7 > 1.4 почти в 1.3 раза."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "for prices < 10 counts of sale of new and old bank\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "new_bank    24913\n",
       "old_bank    17719\n",
       "Name: bank_type, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "new_bank    1.000000\n",
       "old_bank    1.406005\n",
       "Name: bank_type, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# how much new bank best sale low prices packs vs old bank\n",
    "print('for prices < 10 counts of sale of new and old bank')\n",
    "bank_df[bank_df.price < 10].bank_type.value_counts()\n",
    "bank_df[bank_df.price < 10].bank_type.value_counts().new_bank / bank_df[bank_df.price < 10].bank_type.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "for prices > 10 counts of sale of new and old bank\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "new_bank    2087\n",
       "old_bank    1170\n",
       "Name: bank_type, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "new_bank    1.000000\n",
       "old_bank    1.783761\n",
       "Name: bank_type, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "1.2479057613168725"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('for prices > 10 counts of sale of new and old bank')\n",
    "bank_df[bank_df.price > 10].bank_type.value_counts()\n",
    "bank_df[bank_df.price > 10].bank_type.value_counts().new_bank / bank_df[bank_df.price > 10].bank_type.value_counts()\n",
    "bank_df[bank_df.price > 10].bank_type.value_counts().new_bank / bank_df[bank_df.price > 10].bank_type.value_counts()[1] / norm_coef"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "На спецальные паки кажется тоже существенно повлияло смена банка. Причем, тут разница от двух до трех раз, что опять же существенно больше, чем 1.4."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "piggy distribution by bank type\n",
      "\n",
      "new_bank    1725\n",
      "old_bank     510\n",
      "Name: bank_type, dtype: int64\n",
      "\n",
      "adventure distibution by bank type\n",
      "\n",
      "new_bank    975\n",
      "old_bank    521\n",
      "Name: bank_type, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print('piggy distribution by bank type\\n\\n{}'.format(bank_df[bank_df.pack.str.contains('piggy')].bank_type.value_counts()), end='\\n\\n')\n",
    "\n",
    "print('adventure distibution by bank type\\n\\n{}'.format(bank_df[bank_df.pack.str.contains('advent')].bank_type.value_counts()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "toc-hr-collapsed": false
   },
   "source": [
    "### Умные скидки?\n",
    "\n",
    "**Посчитаем, как влияет умное предложение на постоянных покупателей**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "amount of good customers 3006\n",
      "\n",
      "distribution and amount of smart_offers of good customers:\n",
      "\n",
      "smart_offer_2    413\n",
      "smart_offer_3    274\n",
      "smart_offer_4    177\n",
      "smart_offer_5     57\n",
      "smart_offer_1     45\n",
      "smart_offer_6     18\n",
      "Name: pack, dtype: int64\n",
      "\n",
      "distribution and amount of smart_offers of bad customers:\n",
      "\n",
      "smart_offer_1    177\n",
      "smart_offer_2    172\n",
      "smart_offer_3     51\n",
      "smart_offer_4     26\n",
      "smart_offer_5      2\n",
      "smart_offer_6      1\n",
      "Name: pack, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# define good customers as who buy more then 3 times\n",
    "good_customers = bank_df.customeruserid.value_counts()[(bank_df.customeruserid.value_counts() > 3)].index\n",
    "print('amount of good customers {}'.format(len(good_customers)))\n",
    "\n",
    "# and see what if smart offers affect on good and bad customers\n",
    "print('\\ndistribution and amount of smart_offers of good customers:\\n')\n",
    "print(bank_df[bank_df.customeruserid.isin(good_customers)].pack.value_counts() \\\n",
    "[bank_df[bank_df.customeruserid.isin(good_customers)].pack.value_counts().index.str.contains('smart')])\n",
    "print('\\ndistribution and amount of smart_offers of bad customers:\\n')\n",
    "print(bank_df[~bank_df.customeruserid.isin(good_customers)].pack.value_counts() \\\n",
    "[bank_df[~bank_df.customeruserid.isin(good_customers)].pack.value_counts().index.str.contains('smart')])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Фан факт.** Клиент который заплатил через 6 секунд после установки приложения, причем будучи сразу 9 уровня, платил всего три раза и в старом банке, правда второй платеж был на крупную сумму. А кто ждал почти два года до первой оплаты заплатил уже в новом банке и тоже всего три раза."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "min time between install and pay 0 days 00:00:06\n",
      "max time between install and pay 533 days 08:31:58\n"
     ]
    },
    {
     "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>eventdatetime</th>\n",
       "      <th>installdatetime</th>\n",
       "      <th>user_level</th>\n",
       "      <th>pack</th>\n",
       "      <th>price</th>\n",
       "      <th>customeruserid</th>\n",
       "      <th>countrycode</th>\n",
       "      <th>bank_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>24322</th>\n",
       "      <td>2018-07-24 09:33:01</td>\n",
       "      <td>2017-02-18 20:51:00</td>\n",
       "      <td>100</td>\n",
       "      <td>pack_2</td>\n",
       "      <td>4.99</td>\n",
       "      <td>7B057292-CA40-4491-A5DE-40B395C3A304</td>\n",
       "      <td>CA</td>\n",
       "      <td>new_bank</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27703</th>\n",
       "      <td>2018-07-29 09:31:37</td>\n",
       "      <td>2017-02-18 20:51:00</td>\n",
       "      <td>103</td>\n",
       "      <td>pack_3</td>\n",
       "      <td>9.99</td>\n",
       "      <td>7B057292-CA40-4491-A5DE-40B395C3A304</td>\n",
       "      <td>CA</td>\n",
       "      <td>new_bank</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32962</th>\n",
       "      <td>2018-08-06 05:22:58</td>\n",
       "      <td>2017-02-18 20:51:00</td>\n",
       "      <td>103</td>\n",
       "      <td>adventure_pack</td>\n",
       "      <td>4.99</td>\n",
       "      <td>7B057292-CA40-4491-A5DE-40B395C3A304</td>\n",
       "      <td>CA</td>\n",
       "      <td>new_bank</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34801</th>\n",
       "      <td>2018-05-18 14:18:43</td>\n",
       "      <td>2018-05-18 14:18:37</td>\n",
       "      <td>9</td>\n",
       "      <td>pack_3</td>\n",
       "      <td>9.99</td>\n",
       "      <td>31F22D78-5C12-4E79-87CB-B870EF5FE171</td>\n",
       "      <td>CN</td>\n",
       "      <td>old_bank</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35631</th>\n",
       "      <td>2018-05-20 13:54:24</td>\n",
       "      <td>2018-05-18 14:18:37</td>\n",
       "      <td>17</td>\n",
       "      <td>pack_6</td>\n",
       "      <td>99.99</td>\n",
       "      <td>31F22D78-5C12-4E79-87CB-B870EF5FE171</td>\n",
       "      <td>CN</td>\n",
       "      <td>old_bank</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37995</th>\n",
       "      <td>2018-05-22 15:19:59</td>\n",
       "      <td>2018-05-18 14:18:37</td>\n",
       "      <td>32</td>\n",
       "      <td>starter_pack</td>\n",
       "      <td>2.99</td>\n",
       "      <td>31F22D78-5C12-4E79-87CB-B870EF5FE171</td>\n",
       "      <td>CN</td>\n",
       "      <td>old_bank</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            eventdatetime     installdatetime  user_level            pack  \\\n",
       "24322 2018-07-24 09:33:01 2017-02-18 20:51:00         100          pack_2   \n",
       "27703 2018-07-29 09:31:37 2017-02-18 20:51:00         103          pack_3   \n",
       "32962 2018-08-06 05:22:58 2017-02-18 20:51:00         103  adventure_pack   \n",
       "34801 2018-05-18 14:18:43 2018-05-18 14:18:37           9          pack_3   \n",
       "35631 2018-05-20 13:54:24 2018-05-18 14:18:37          17          pack_6   \n",
       "37995 2018-05-22 15:19:59 2018-05-18 14:18:37          32    starter_pack   \n",
       "\n",
       "       price                        customeruserid countrycode bank_type  \n",
       "24322   4.99  7B057292-CA40-4491-A5DE-40B395C3A304          CA  new_bank  \n",
       "27703   9.99  7B057292-CA40-4491-A5DE-40B395C3A304          CA  new_bank  \n",
       "32962   4.99  7B057292-CA40-4491-A5DE-40B395C3A304          CA  new_bank  \n",
       "34801   9.99  31F22D78-5C12-4E79-87CB-B870EF5FE171          CN  old_bank  \n",
       "35631  99.99  31F22D78-5C12-4E79-87CB-B870EF5FE171          CN  old_bank  \n",
       "37995   2.99  31F22D78-5C12-4E79-87CB-B870EF5FE171          CN  old_bank  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('min time between install and pay {}'.format((bank_df.eventdatetime - bank_df.installdatetime).min()))\n",
    "print('max time between install and pay {}'.format((bank_df.eventdatetime - bank_df.installdatetime).max()))\n",
    "lcind = bank_df.iloc[0:,0:2].diff(periods = 1, axis=1).nsmallest(1, 'installdatetime').index\n",
    "fcind = bank_df.iloc[0:,0:2].diff(periods = -1, axis=1).nsmallest(1, 'eventdatetime').index\n",
    "bank_df[bank_df.customeruserid.isin(bank_df.iloc[[lcind[0], fcind[0]]].customeruserid)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Хорошо, найдем всех пользователей, которые покупали и в старом банке и в новом банке. И посмотрим у них средний чек по банкам."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "temptable = bank_df[bank_df.customeruserid.duplicated(False)].sort_values(by='customeruserid').reset_index().drop('index',axis=1)\n",
    "custid = set()\n",
    "endofindex = temptable.index.max()\n",
    "for ind in temptable.index:\n",
    "    if ind == endofindex:\n",
    "        pass\n",
    "    elif ((temptable.iat[ind, 5] == temptable.iat[ind+1, 5]) & (temptable.iat[ind, 7] != temptable.iat[ind+1, 7])):\n",
    "        custid.add(temptable.iat[ind, 5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "avg check \n",
      "bank_type\n",
      "new_bank    6.350488\n",
      "old_bank    5.657405\n",
      "Name: price, dtype: float64\n",
      "\n",
      "median check \n",
      "bank_type\n",
      "new_bank    4.99\n",
      "old_bank    1.99\n",
      "Name: price, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print('avg check \\n{}'.format(\n",
    "bank_df[bank_df.customeruserid.isin(custid)].groupby('bank_type').price.mean()))\n",
    "print('\\nmedian check \\n{}'.format(\n",
    "bank_df[bank_df.customeruserid.isin(custid)].groupby('bank_type').price.median()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Как видно, новый банк положительно повлиял на средний чек, а также медиана выросла, что говорит нам о том, что больше половины людей стали покупать более дорогие карточки"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Итоги\n",
    "Вроде вполне понятно, что новый банк привел к увеличению выручки, а также увеличению среднего чека и пользовательского поведения, в сторону покупок более дорогих карточек с валютой. \n",
    "Можно все проверить статистически, но на мой взгляд все наглядно и так.\n",
    "Умные предложения не вносят пока существенной разницы в поведении кажется, тем более рано еще судить, так как их относительно недавно ввели, первая покупка по ним была в августе. \n",
    "Еще не накопилось достаточно данных, хотя уже можно сказать, что те платил раньше с охотой пользуются этими предложениями, а те, \n",
    "кто раньше не платил, некоторые все таки начали именно с этих умных предложений. \n",
    "Понятно, что из данных не выжато еще все. Можно еще проверить некоторые гипотезы, посмотреть зависимость от времени в покупках, от страны, и того как растет уровень в зависимости от оплат. \n",
    "Если же добавить данных, то станет что то еще более понятным. \n",
    "В остальном же, гипотезы первоначально проверены, стоит копать дальше в конкретное какое то поведени."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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