Pivot tables from dataset.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "###################################################\n",
    "# Сводные таблицы из датасета\n",
    "###################################################\n",
    "\n",
    "\n",
    "###################################################\n",
    "# Описание данных в датасете\n",
    "#\n",
    "# Имеются следующие данные:\n",
    "#\n",
    "# date – дата (2020-03-02 00:00:00 – 2020-03-02 01:29:05),\n",
    "# name – имя пользователя,\n",
    "# transaction – статус транзакции,\n",
    "# minute – сколько минут прошло с начала дня\n",
    "###################################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Импортируем библиотеки\n",
    "\n",
    "import datetime\n",
    "import os\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>date</th>\n",
       "      <th>name</th>\n",
       "      <th>transaction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-03-02 00:37:20</td>\n",
       "      <td>Ekaterina_Petrova</td>\n",
       "      <td>cancelled</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-03-02 00:59:09</td>\n",
       "      <td>Irina_Fedorova</td>\n",
       "      <td>successfull</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-03-02 01:06:14</td>\n",
       "      <td>Ekaterina_Smirnova</td>\n",
       "      <td>successfull</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-03-02 00:02:58</td>\n",
       "      <td>Lolita_Polyanova</td>\n",
       "      <td>cancelled</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-03-02 00:17:52</td>\n",
       "      <td>Julia_Smirnova</td>\n",
       "      <td>cancelled</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  date                name  transaction\n",
       "0  2020-03-02 00:37:20   Ekaterina_Petrova    cancelled\n",
       "1  2020-03-02 00:59:09      Irina_Fedorova  successfull\n",
       "2  2020-03-02 01:06:14  Ekaterina_Smirnova  successfull\n",
       "3  2020-03-02 00:02:58    Lolita_Polyanova    cancelled\n",
       "4  2020-03-02 00:17:52      Julia_Smirnova    cancelled"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Чтение файла CSV с данными \n",
    "\n",
    "df = pd.read_csv('transaction_data.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Проверим размер импортированных таблиц, типы переменных, посмотрим на описательную статистику данных"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5315, 3)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date           object\n",
       "name           object\n",
       "transaction    object\n",
       "dtype: object"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>date</th>\n",
       "      <th>name</th>\n",
       "      <th>transaction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5315</td>\n",
       "      <td>5315</td>\n",
       "      <td>5315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>3373</td>\n",
       "      <td>56</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>2020-03-02 00:35:03</td>\n",
       "      <td>Masha_Ozerova</td>\n",
       "      <td>successfull</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>6</td>\n",
       "      <td>112</td>\n",
       "      <td>3692</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       date           name  transaction\n",
       "count                  5315           5315         5315\n",
       "unique                 3373             56            3\n",
       "top     2020-03-02 00:35:03  Masha_Ozerova  successfull\n",
       "freq                      6            112         3692"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date           3373\n",
       "name             56\n",
       "transaction       3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Определим количество уникальных и пропущенных значений в датасете\n",
    "\n",
    "df.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>date</th>\n",
       "      <th>name</th>\n",
       "      <th>transaction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5310</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5311</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5312</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5313</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5314</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5315 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       date   name  transaction\n",
       "0     False  False        False\n",
       "1     False  False        False\n",
       "2     False  False        False\n",
       "3     False  False        False\n",
       "4     False  False        False\n",
       "...     ...    ...          ...\n",
       "5310  False  False        False\n",
       "5311  False  False        False\n",
       "5312  False  False        False\n",
       "5313  False  False        False\n",
       "5314  False  False        False\n",
       "\n",
       "[5315 rows x 3 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date           0\n",
       "name           0\n",
       "transaction    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Определим, какие значения принимает колонка 'transaction'\n",
    "\n",
    "df.transaction.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         cancelled\n",
       "1       successfull\n",
       "2       successfull\n",
       "3         cancelled\n",
       "4         cancelled\n",
       "           ...     \n",
       "5310    successfull\n",
       "5311    successfull\n",
       "5312    successfull\n",
       "5313    successfull\n",
       "5314      cancelled\n",
       "Name: transaction, Length: 5315, dtype: object"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.transaction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['cancelled', 'successfull', 'error'], dtype=object)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transaction_unique = df.transaction.unique()\n",
    "transaction_unique"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "successfull    3692\n",
       "cancelled      1619\n",
       "error             4\n",
       "Name: transaction, dtype: int64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Найдем количество произошедших в транзакциях ошибок 'error'\n",
    "\n",
    "df.transaction.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_error = df.transaction.value_counts().to_list()[2]\n",
    "num_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Построим график распределения всех возможных вариантов транзакций клиентов\n",
    "\n",
    "def draw_graph_countplot(value):\n",
    "    sns.countplot(value)\n",
    "    \n",
    "draw_graph_countplot(df.transaction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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>name</th>\n",
       "      <th>transaction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ekaterina_Fedorova</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ekaterina_Ivanova</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ekaterina_Lesnova</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ekaterina_Ozerova</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ekaterina_Petrova</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 name  transaction\n",
       "0  Ekaterina_Fedorova           72\n",
       "1   Ekaterina_Ivanova           59\n",
       "2   Ekaterina_Lesnova           63\n",
       "3   Ekaterina_Ozerova           72\n",
       "4   Ekaterina_Petrova           68"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Определим сколько транзакций осуществил каждый из пользователей. Посмотрим на характеристики распределения, \n",
    "# а затем построим гистограмму числа успешных транзакций, совершённых каждым пользователем\n",
    "\n",
    "success_client = df.query('transaction == \"successfull\"').groupby('name').agg({'transaction': 'count'}).reset_index()\n",
    "success_client.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3b8a3b5e48>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Построим график распределения количества транзакций по пользователям\n",
    "\n",
    "plt.figure(figsize=(18, 5))\n",
    "sns.barplot(x = success_client.index, y = success_client.transaction, color='blue', alpha = 0.7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "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>transaction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>56.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>65.928571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>7.787769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>46.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>61.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>64.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>72.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>82.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       transaction\n",
       "count    56.000000\n",
       "mean     65.928571\n",
       "std       7.787769\n",
       "min      46.000000\n",
       "25%      61.000000\n",
       "50%      64.000000\n",
       "75%      72.000000\n",
       "max      82.000000"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Посмотрим на характеристики полученного выше распределения\n",
    "\n",
    "success_client.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(56, 2)"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "success_client.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "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</th>\n",
       "      <th>name</th>\n",
       "      <th>transaction</th>\n",
       "      <th>minute</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-03-02 00:37:20</td>\n",
       "      <td>Ekaterina_Petrova</td>\n",
       "      <td>cancelled</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-03-02 00:59:09</td>\n",
       "      <td>Irina_Fedorova</td>\n",
       "      <td>successfull</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-03-02 01:06:14</td>\n",
       "      <td>Ekaterina_Smirnova</td>\n",
       "      <td>successfull</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-03-02 00:02:58</td>\n",
       "      <td>Lolita_Polyanova</td>\n",
       "      <td>cancelled</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-03-02 00:17:52</td>\n",
       "      <td>Julia_Smirnova</td>\n",
       "      <td>cancelled</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  date                name  transaction  minute\n",
       "0  2020-03-02 00:37:20   Ekaterina_Petrova    cancelled      37\n",
       "1  2020-03-02 00:59:09      Irina_Fedorova  successfull      59\n",
       "2  2020-03-02 01:06:14  Ekaterina_Smirnova  successfull       6\n",
       "3  2020-03-02 00:02:58    Lolita_Polyanova    cancelled       2\n",
       "4  2020-03-02 00:17:52      Julia_Smirnova    cancelled      17"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Загрузим датасет с новыми данными, полученными от коллеги\n",
    "\n",
    "df_new = pd.read_csv('https://stepik.org/media/attachments/lesson/361624/transaction_data_updated.csv')\n",
    "df_new.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>minute</th>\n",
       "      <th>transaction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ekaterina_Fedorova</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ekaterina_Fedorova</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ekaterina_Fedorova</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ekaterina_Fedorova</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ekaterina_Fedorova</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2573</th>\n",
       "      <td>Vasilisa_Smirnova</td>\n",
       "      <td>52</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2574</th>\n",
       "      <td>Vasilisa_Smirnova</td>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2575</th>\n",
       "      <td>Vasilisa_Smirnova</td>\n",
       "      <td>54</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2576</th>\n",
       "      <td>Vasilisa_Smirnova</td>\n",
       "      <td>56</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2577</th>\n",
       "      <td>Vasilisa_Smirnova</td>\n",
       "      <td>58</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2578 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    name  minute  transaction\n",
       "0     Ekaterina_Fedorova       0            3\n",
       "1     Ekaterina_Fedorova       1            4\n",
       "2     Ekaterina_Fedorova       2            1\n",
       "3     Ekaterina_Fedorova       3            3\n",
       "4     Ekaterina_Fedorova       4            3\n",
       "...                  ...     ...          ...\n",
       "2573   Vasilisa_Smirnova      52            1\n",
       "2574   Vasilisa_Smirnova      53            1\n",
       "2575   Vasilisa_Smirnova      54            2\n",
       "2576   Vasilisa_Smirnova      56            3\n",
       "2577   Vasilisa_Smirnova      58            1\n",
       "\n",
       "[2578 rows x 3 columns]"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Проведем группировку новых данных и определим общее количество транзакций у каждого из клиентов\n",
    "\n",
    "df_new_group = df_new.groupby(['name', 'minute'], as_index = False).agg({'transaction': 'count'})\n",
    "df_new_group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th>0</th>\n",
       "      <td>3.0</td>\n",
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       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 56 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "name    Ekaterina_Fedorova  Ekaterina_Ivanova  Ekaterina_Lesnova  \\\n",
       "minute                                                             \n",
       "0                      3.0                4.0                2.0   \n",
       "1                      4.0                4.0                NaN   \n",
       "2                      1.0                2.0                4.0   \n",
       "3                      3.0                NaN                2.0   \n",
       "4                      3.0                NaN                NaN   \n",
       "\n",
       "name    Ekaterina_Ozerova  Ekaterina_Petrova  Ekaterina_Polyanova  \\\n",
       "minute                                                              \n",
       "0                     3.0                4.0                  4.0   \n",
       "1                     4.0                2.0                  5.0   \n",
       "2                     4.0                2.0                  1.0   \n",
       "3                     NaN                5.0                  2.0   \n",
       "4                     3.0                1.0                  1.0   \n",
       "\n",
       "name    Ekaterina_Smirnova  Irina_Fedorova  Irina_Ivanova  Irina_Lesnova  ...  \\\n",
       "minute                                                                    ...   \n",
       "0                      2.0             1.0            2.0            2.0  ...   \n",
       "1                      4.0             4.0            4.0            3.0  ...   \n",
       "2                      3.0             2.0            3.0            3.0  ...   \n",
       "3                      1.0             3.0            2.0            2.0  ...   \n",
       "4                      2.0             3.0            2.0            1.0  ...   \n",
       "\n",
       "name    Svetlana_Petrova  Svetlana_Polyanova  Svetlana_Smirnova  \\\n",
       "minute                                                            \n",
       "0                    NaN                 3.0                1.0   \n",
       "1                    4.0                 5.0                3.0   \n",
       "2                    4.0                 1.0                3.0   \n",
       "3                    1.0                 5.0                5.0   \n",
       "4                    1.0                 2.0                2.0   \n",
       "\n",
       "name    Vasilisa_Fedorova  Vasilisa_Ivanova  Vasilisa_Lesnova  \\\n",
       "minute                                                          \n",
       "0                     1.0               4.0               1.0   \n",
       "1                     2.0               3.0               3.0   \n",
       "2                     3.0               NaN               1.0   \n",
       "3                     2.0               4.0               NaN   \n",
       "4                     1.0               1.0               2.0   \n",
       "\n",
       "name    Vasilisa_Ozerova  Vasilisa_Petrova  Vasilisa_Polyanova  \\\n",
       "minute                                                           \n",
       "0                    3.0               NaN                 NaN   \n",
       "1                    NaN               NaN                 3.0   \n",
       "2                    2.0               1.0                 3.0   \n",
       "3                    3.0               4.0                 1.0   \n",
       "4                    2.0               3.0                 NaN   \n",
       "\n",
       "name    Vasilisa_Smirnova  \n",
       "minute                     \n",
       "0                     1.0  \n",
       "1                     2.0  \n",
       "2                     1.0  \n",
       "3                     NaN  \n",
       "4                     1.0  \n",
       "\n",
       "[5 rows x 56 columns]"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Построим сводную таблицу 'user_vs_minute_pivot', где в качестве столбцов будут использованы имена пользователей, \n",
    "# строк – минуты, значений – число совершенных операций в указанную минуту. Пропущенные значения заполним нулями\n",
    "\n",
    "user_vs_minute_pivot = df_new_group.pivot(index='minute', columns='name', values='transaction').fillna(0)\n",
    "user_vs_minute_pivot.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>name</th>\n",
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       "      <th>true_minute</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-03-02 00:37:20</td>\n",
       "      <td>Ekaterina_Petrova</td>\n",
       "      <td>cancelled</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-03-02 00:59:09</td>\n",
       "      <td>Irina_Fedorova</td>\n",
       "      <td>successfull</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-03-02 01:06:14</td>\n",
       "      <td>Ekaterina_Smirnova</td>\n",
       "      <td>successfull</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-03-02 00:02:58</td>\n",
       "      <td>Lolita_Polyanova</td>\n",
       "      <td>cancelled</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-03-02 00:17:52</td>\n",
       "      <td>Julia_Smirnova</td>\n",
       "      <td>cancelled</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 date                name  transaction  true_minute\n",
       "0 2020-03-02 00:37:20   Ekaterina_Petrova    cancelled           37\n",
       "1 2020-03-02 00:59:09      Irina_Fedorova  successfull           59\n",
       "2 2020-03-02 01:06:14  Ekaterina_Smirnova  successfull           66\n",
       "3 2020-03-02 00:02:58    Lolita_Polyanova    cancelled            2\n",
       "4 2020-03-02 00:17:52      Julia_Smirnova    cancelled           17"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['date'] = pd.to_datetime(df['date'])\n",
    "df['true_minute'] = df['date'].dt.minute + df['date'].dt.hour * 60\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3b88aaa438>"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Построим график по нашей сводной таблице - количества транзакций ко времени её совершения\n",
    "\n",
    "plt.figure(figsize=(18, 5))\n",
    "sns.barplot(x = df.true_minute, y = df_new_group.transaction, color='blue', alpha = 0.7)"
   ]
  }
 ],
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