data_analytics_46_errors_date.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from datetime import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('real_data_no_header.txt', sep='\\t', names = ['id', 'date', 'user_id', 'duration', 'medium', 'source', 'cost', 'order_id', 'amount_paid'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>id</th>\n",
       "      <th>date</th>\n",
       "      <th>user_id</th>\n",
       "      <th>duration</th>\n",
       "      <th>medium</th>\n",
       "      <th>source</th>\n",
       "      <th>cost</th>\n",
       "      <th>order_id</th>\n",
       "      <th>amount_paid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40443</td>\n",
       "      <td>05.10.2016 23:18</td>\n",
       "      <td>1010</td>\n",
       "      <td>0,000925926</td>\n",
       "      <td>seo</td>\n",
       "      <td>google</td>\n",
       "      <td>0</td>\n",
       "      <td>6243</td>\n",
       "      <td>20,20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>35044</td>\n",
       "      <td>09.10.2016T 21:40:00</td>\n",
       "      <td>1036</td>\n",
       "      <td>0,006493056</td>\n",
       "      <td>sem</td>\n",
       "      <td>yandex</td>\n",
       "      <td>0,07</td>\n",
       "      <td>6145</td>\n",
       "      <td>15,60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>40177</td>\n",
       "      <td>05.10.2016 3:23</td>\n",
       "      <td>1041</td>\n",
       "      <td>0,00337963</td>\n",
       "      <td>email</td>\n",
       "      <td>promo</td>\n",
       "      <td>0</td>\n",
       "      <td>6128</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>39401</td>\n",
       "      <td>05.10.2016 23:19</td>\n",
       "      <td>1041</td>\n",
       "      <td>0,000462963</td>\n",
       "      <td>sem</td>\n",
       "      <td>yandex</td>\n",
       "      <td>0,03</td>\n",
       "      <td>6697</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>41545</td>\n",
       "      <td>01.10.2016 4:57</td>\n",
       "      <td>1042</td>\n",
       "      <td>0,006493056</td>\n",
       "      <td>sem</td>\n",
       "      <td>google</td>\n",
       "      <td>0,06</td>\n",
       "      <td>4510</td>\n",
       "      <td>14,80</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id                  date  user_id     duration medium  source  cost  \\\n",
       "0  40443      05.10.2016 23:18     1010  0,000925926    seo  google     0   \n",
       "1  35044  09.10.2016T 21:40:00     1036  0,006493056    sem  yandex  0,07   \n",
       "2  40177       05.10.2016 3:23     1041   0,00337963  email   promo     0   \n",
       "3  39401      05.10.2016 23:19     1041  0,000462963    sem  yandex  0,03   \n",
       "4  41545       01.10.2016 4:57     1042  0,006493056    sem  google  0,06   \n",
       "\n",
       "   order_id amount_paid  \n",
       "0      6243       20,20  \n",
       "1      6145       15,60  \n",
       "2      6128         NaN  \n",
       "3      6697         NaN  \n",
       "4      4510       14,80  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def datetime_to_date(date_string):\n",
    "    \"\"\"Перевод даты покупки а формат %Y-%m-%d\"\"\"\n",
    "    try:\n",
    "        date_string = str(date_string)\n",
    "        date_datetime = datetime.strptime(date_string, '%d.%m.%Y %H:%M')\n",
    "    except ValueError:\n",
    "        try:\n",
    "            date_datetime = datetime.strptime(date_string, '%d.%m.%YT %H:%M:%S')    \n",
    "        except:\n",
    "            return 'undefined'\n",
    "    return date_datetime.strftime('%Y-%m-%d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2016-10-05'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datetime_to_date('05.10.2016 23:18')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['dateTime'] = data['date'].apply(datetime_to_date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "      <th>source</th>\n",
       "      <th>cost</th>\n",
       "      <th>order_id</th>\n",
       "      <th>amount_paid</th>\n",
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       "      <td>40443</td>\n",
       "      <td>05.10.2016 23:18</td>\n",
       "      <td>1010</td>\n",
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       "      <td>seo</td>\n",
       "      <td>google</td>\n",
       "      <td>0</td>\n",
       "      <td>6243</td>\n",
       "      <td>20,20</td>\n",
       "      <td>2016-10-05</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>35044</td>\n",
       "      <td>09.10.2016T 21:40:00</td>\n",
       "      <td>1036</td>\n",
       "      <td>0,006493056</td>\n",
       "      <td>sem</td>\n",
       "      <td>yandex</td>\n",
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       "      <td>2016-10-09</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>40177</td>\n",
       "      <td>05.10.2016 3:23</td>\n",
       "      <td>1041</td>\n",
       "      <td>0,00337963</td>\n",
       "      <td>email</td>\n",
       "      <td>promo</td>\n",
       "      <td>0</td>\n",
       "      <td>6128</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-10-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>39401</td>\n",
       "      <td>05.10.2016 23:19</td>\n",
       "      <td>1041</td>\n",
       "      <td>0,000462963</td>\n",
       "      <td>sem</td>\n",
       "      <td>yandex</td>\n",
       "      <td>0,03</td>\n",
       "      <td>6697</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-10-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>41545</td>\n",
       "      <td>01.10.2016 4:57</td>\n",
       "      <td>1042</td>\n",
       "      <td>0,006493056</td>\n",
       "      <td>sem</td>\n",
       "      <td>google</td>\n",
       "      <td>0,06</td>\n",
       "      <td>4510</td>\n",
       "      <td>14,80</td>\n",
       "      <td>2016-10-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id                  date  user_id     duration medium  source  cost  \\\n",
       "0  40443      05.10.2016 23:18     1010  0,000925926    seo  google     0   \n",
       "1  35044  09.10.2016T 21:40:00     1036  0,006493056    sem  yandex  0,07   \n",
       "2  40177       05.10.2016 3:23     1041   0,00337963  email   promo     0   \n",
       "3  39401      05.10.2016 23:19     1041  0,000462963    sem  yandex  0,03   \n",
       "4  41545       01.10.2016 4:57     1042  0,006493056    sem  google  0,06   \n",
       "\n",
       "   order_id amount_paid    dateTime  \n",
       "0      6243       20,20  2016-10-05  \n",
       "1      6145       15,60  2016-10-09  \n",
       "2      6128         NaN  2016-10-05  \n",
       "3      6697         NaN  2016-10-05  \n",
       "4      4510       14,80  2016-10-01  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.dropna(axis=0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>05.10.2016 23:18</td>\n",
       "      <td>1010</td>\n",
       "      <td>0,000925926</td>\n",
       "      <td>seo</td>\n",
       "      <td>google</td>\n",
       "      <td>0</td>\n",
       "      <td>6243</td>\n",
       "      <td>20,20</td>\n",
       "      <td>2016-10-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>35044</td>\n",
       "      <td>09.10.2016T 21:40:00</td>\n",
       "      <td>1036</td>\n",
       "      <td>0,006493056</td>\n",
       "      <td>sem</td>\n",
       "      <td>yandex</td>\n",
       "      <td>0,07</td>\n",
       "      <td>6145</td>\n",
       "      <td>15,60</td>\n",
       "      <td>2016-10-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>41545</td>\n",
       "      <td>01.10.2016 4:57</td>\n",
       "      <td>1042</td>\n",
       "      <td>0,006493056</td>\n",
       "      <td>sem</td>\n",
       "      <td>google</td>\n",
       "      <td>0,06</td>\n",
       "      <td>4510</td>\n",
       "      <td>14,80</td>\n",
       "      <td>2016-10-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>38308</td>\n",
       "      <td>01.10.2016 23:29</td>\n",
       "      <td>1042</td>\n",
       "      <td>0,00337963</td>\n",
       "      <td>sem</td>\n",
       "      <td>google</td>\n",
       "      <td>0,06</td>\n",
       "      <td>2622</td>\n",
       "      <td>14,40</td>\n",
       "      <td>2016-10-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>35921</td>\n",
       "      <td>03.10.2016 20:20</td>\n",
       "      <td>1042</td>\n",
       "      <td>0,001388889</td>\n",
       "      <td>sem</td>\n",
       "      <td>google</td>\n",
       "      <td>0,12</td>\n",
       "      <td>2663</td>\n",
       "      <td>14,00</td>\n",
       "      <td>2016-10-03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id                  date  user_id     duration medium  source  cost  \\\n",
       "0  40443      05.10.2016 23:18     1010  0,000925926    seo  google     0   \n",
       "1  35044  09.10.2016T 21:40:00     1036  0,006493056    sem  yandex  0,07   \n",
       "4  41545       01.10.2016 4:57     1042  0,006493056    sem  google  0,06   \n",
       "5  38308      01.10.2016 23:29     1042   0,00337963    sem  google  0,06   \n",
       "6  35921      03.10.2016 20:20     1042  0,001388889    sem  google  0,12   \n",
       "\n",
       "   order_id amount_paid    dateTime  \n",
       "0      6243       20,20  2016-10-05  \n",
       "1      6145       15,60  2016-10-09  \n",
       "4      4510       14,80  2016-10-01  \n",
       "5      2622       14,40  2016-10-01  \n",
       "6      2663       14,00  2016-10-03  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def change_cpa_revenue(row): \n",
    "    if row['medium'] == 'cpa-partners':\n",
    "        amount_paid = float(row['amount_paid'].replace(',', '.'))\n",
    "        return amount_paid / 1000000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['amount_paid_corrected'] = data.apply(change_cpa_revenue, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
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       "      <th>id</th>\n",
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       "      <th>user_id</th>\n",
       "      <th>duration</th>\n",
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       "      <th>order_id</th>\n",
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       "      <th>amount_paid_corrected</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40443</td>\n",
       "      <td>05.10.2016 23:18</td>\n",
       "      <td>1010</td>\n",
       "      <td>0,000925926</td>\n",
       "      <td>seo</td>\n",
       "      <td>google</td>\n",
       "      <td>0</td>\n",
       "      <td>6243</td>\n",
       "      <td>20,20</td>\n",
       "      <td>2016-10-05</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>35044</td>\n",
       "      <td>09.10.2016T 21:40:00</td>\n",
       "      <td>1036</td>\n",
       "      <td>0,006493056</td>\n",
       "      <td>sem</td>\n",
       "      <td>yandex</td>\n",
       "      <td>0,07</td>\n",
       "      <td>6145</td>\n",
       "      <td>15,60</td>\n",
       "      <td>2016-10-09</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>41545</td>\n",
       "      <td>01.10.2016 4:57</td>\n",
       "      <td>1042</td>\n",
       "      <td>0,006493056</td>\n",
       "      <td>sem</td>\n",
       "      <td>google</td>\n",
       "      <td>0,06</td>\n",
       "      <td>4510</td>\n",
       "      <td>14,80</td>\n",
       "      <td>2016-10-01</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>38308</td>\n",
       "      <td>01.10.2016 23:29</td>\n",
       "      <td>1042</td>\n",
       "      <td>0,00337963</td>\n",
       "      <td>sem</td>\n",
       "      <td>google</td>\n",
       "      <td>0,06</td>\n",
       "      <td>2622</td>\n",
       "      <td>14,40</td>\n",
       "      <td>2016-10-01</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>35921</td>\n",
       "      <td>03.10.2016 20:20</td>\n",
       "      <td>1042</td>\n",
       "      <td>0,001388889</td>\n",
       "      <td>sem</td>\n",
       "      <td>google</td>\n",
       "      <td>0,12</td>\n",
       "      <td>2663</td>\n",
       "      <td>14,00</td>\n",
       "      <td>2016-10-03</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id                  date  user_id     duration medium  source  cost  \\\n",
       "0  40443      05.10.2016 23:18     1010  0,000925926    seo  google     0   \n",
       "1  35044  09.10.2016T 21:40:00     1036  0,006493056    sem  yandex  0,07   \n",
       "4  41545       01.10.2016 4:57     1042  0,006493056    sem  google  0,06   \n",
       "5  38308      01.10.2016 23:29     1042   0,00337963    sem  google  0,06   \n",
       "6  35921      03.10.2016 20:20     1042  0,001388889    sem  google  0,12   \n",
       "\n",
       "   order_id amount_paid    dateTime  amount_paid_corrected  \n",
       "0      6243       20,20  2016-10-05                    NaN  \n",
       "1      6145       15,60  2016-10-09                    NaN  \n",
       "4      4510       14,80  2016-10-01                    NaN  \n",
       "5      2622       14,40  2016-10-01                    NaN  \n",
       "6      2663       14,00  2016-10-03                    NaN  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.dropna(axis=0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
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       "      <th>10</th>\n",
       "      <td>40986</td>\n",
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       "      <td>1042</td>\n",
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       "      <td>cpa-partners</td>\n",
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       "      <td>2016-10-05</td>\n",
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       "      <td>0,000462963</td>\n",
       "      <td>cpa-partners</td>\n",
       "      <td>burgerclub</td>\n",
       "      <td>1,312</td>\n",
       "      <td>3474</td>\n",
       "      <td>20000000</td>\n",
       "      <td>2016-10-02</td>\n",
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       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>35885</td>\n",
       "      <td>09.10.2016T 8:33:00</td>\n",
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       "      <td>0,006759259</td>\n",
       "      <td>cpa-partners</td>\n",
       "      <td>food-delivery</td>\n",
       "      <td>2,49</td>\n",
       "      <td>3659</td>\n",
       "      <td>24000000</td>\n",
       "      <td>2016-10-09</td>\n",
       "      <td>24.0</td>\n",
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       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>35544</td>\n",
       "      <td>08.10.2016T 22:55:00</td>\n",
       "      <td>1149</td>\n",
       "      <td>0,000462963</td>\n",
       "      <td>cpa-partners</td>\n",
       "      <td>burgerclub</td>\n",
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       "      <td>2016-10-08</td>\n",
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       "    <tr>\n",
       "      <th>99</th>\n",
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       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>37597</td>\n",
       "      <td>01.10.2016 16:52</td>\n",
       "      <td>1218</td>\n",
       "      <td>0,000821759</td>\n",
       "      <td>cpa-partners</td>\n",
       "      <td>burgerclub</td>\n",
       "      <td>1,792</td>\n",
       "      <td>5308</td>\n",
       "      <td>12200000</td>\n",
       "      <td>2016-10-01</td>\n",
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       "    <tr>\n",
       "      <th>126</th>\n",
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       "      <td>0,006759259</td>\n",
       "      <td>cpa-partners</td>\n",
       "      <td>burgerclub</td>\n",
       "      <td>1,232</td>\n",
       "      <td>4669</td>\n",
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       "      <td>2016-10-09</td>\n",
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       "      <td>1223</td>\n",
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       "      <td>burgerclub</td>\n",
       "      <td>1,312</td>\n",
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       "      <td>17600000</td>\n",
       "      <td>2016-10-06</td>\n",
       "      <td>17.6</td>\n",
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       "    <tr>\n",
       "      <th>220</th>\n",
       "      <td>37107</td>\n",
       "      <td>03.10.2016 18:28</td>\n",
       "      <td>1302</td>\n",
       "      <td>0,001643519</td>\n",
       "      <td>cpa-partners</td>\n",
       "      <td>burgerclub</td>\n",
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       "      <td>7394</td>\n",
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       "        id                  date  user_id     duration        medium  \\\n",
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       "43   41581       02.10.2016 2:58     1108  0,000462963  cpa-partners   \n",
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       "84   35544  08.10.2016T 22:55:00     1149  0,000462963  cpa-partners   \n",
       "99   38248      04.10.2016 13:49     1193  0,001921296  cpa-partners   \n",
       "125  37597      01.10.2016 16:52     1218  0,000821759  cpa-partners   \n",
       "126  36558  09.10.2016T 19:10:00     1218  0,006759259  cpa-partners   \n",
       "129  38976      06.10.2016 22:11     1223  0,002777778  cpa-partners   \n",
       "220  37107      03.10.2016 18:28     1302  0,001643519  cpa-partners   \n",
       "253  36666   08.10.2016T 4:34:00     1350  0,003287037  cpa-partners   \n",
       "262  41706       01.10.2016 5:29     1366  0,003842593  cpa-partners   \n",
       "\n",
       "            source   cost  order_id amount_paid    dateTime  \\\n",
       "10      burgerclub   0,88      3993    10800000  2016-10-05   \n",
       "43      burgerclub  1,312      3474    20000000  2016-10-02   \n",
       "49   food-delivery   2,49      3659    24000000  2016-10-09   \n",
       "84      burgerclub  1,696      4166    18800000  2016-10-08   \n",
       "99   food-delivery   2,49      5215    24800000  2016-10-04   \n",
       "125     burgerclub  1,792      5308    12200000  2016-10-01   \n",
       "126     burgerclub  1,232      4669    22400000  2016-10-09   \n",
       "129     burgerclub  1,312      4731    17600000  2016-10-06   \n",
       "220     burgerclub  2,064      7394    21800000  2016-10-03   \n",
       "253  food-delivery      3      7128    12800000  2016-10-08   \n",
       "262  food-delivery   3,36      7329    13000000  2016-10-01   \n",
       "\n",
       "     amount_paid_corrected  \n",
       "10                    10.8  \n",
       "43                    20.0  \n",
       "49                    24.0  \n",
       "84                    18.8  \n",
       "99                    24.8  \n",
       "125                   12.2  \n",
       "126                   22.4  \n",
       "129                   17.6  \n",
       "220                   21.8  \n",
       "253                   12.8  \n",
       "262                   13.0  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(100)"
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  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>12.6</td>\n",
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       "      <th>2016-10-04</th>\n",
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       "      <th>2016-10-05</th>\n",
       "      <td>40986.0</td>\n",
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       "    <tr>\n",
       "      <th>2016-10-06</th>\n",
       "      <td>38976.0</td>\n",
       "      <td>1223.0</td>\n",
       "      <td>4731.0</td>\n",
       "      <td>17.6</td>\n",
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       "    <tr>\n",
       "      <th>2016-10-08</th>\n",
       "      <td>36105.0</td>\n",
       "      <td>1249.5</td>\n",
       "      <td>5647.0</td>\n",
       "      <td>15.8</td>\n",
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       "    <tr>\n",
       "      <th>2016-10-09</th>\n",
       "      <td>36221.5</td>\n",
       "      <td>1163.0</td>\n",
       "      <td>4164.0</td>\n",
       "      <td>23.2</td>\n",
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       "</table>\n",
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      "text/plain": [
       "                 id  user_id  order_id  amount_paid_corrected\n",
       "dateTime                                                     \n",
       "2016-10-01  39651.5   1292.0    6318.5                   12.6\n",
       "2016-10-02  41581.0   1108.0    3474.0                   20.0\n",
       "2016-10-03  37107.0   1302.0    7394.0                   21.8\n",
       "2016-10-04  38248.0   1193.0    5215.0                   24.8\n",
       "2016-10-05  40986.0   1042.0    3993.0                   10.8\n",
       "2016-10-06  38976.0   1223.0    4731.0                   17.6\n",
       "2016-10-08  36105.0   1249.5    5647.0                   15.8\n",
       "2016-10-09  36221.5   1163.0    4164.0                   23.2"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby('dateTime').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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