BDC-team-1/Exploration_billet_AJ.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "56b3d44e-1e3f-4726-9916-0f9af107860e",
   "metadata": {},
   "source": [
    "# Business Data Challenge - Team 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "15103481-8d74-404c-aa09-7601fe7730da",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import s3fs\n",
    "import re"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3bb0d13-34b2-4e1c-9985-468cd87c5a0e",
   "metadata": {},
   "source": [
    "Configuration de l'accès aux données"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5d83bb1a-d341-446e-91f6-1c428607f6d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create filesystem object\n",
    "S3_ENDPOINT_URL = \"https://\" + os.environ[\"AWS_S3_ENDPOINT\"]\n",
    "fs = s3fs.S3FileSystem(client_kwargs={'endpoint_url': S3_ENDPOINT_URL})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f99da24f-0d93-4618-92bc-3ba81dc0445c",
   "metadata": {},
   "source": [
    "# Exemple sur Company 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d74b68f-ba07-4a15-9a27-dae931762d70",
   "metadata": {},
   "source": [
    "## Chargement données"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "699664b9-eee4-4f8d-a207-e524526560c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "BUCKET = \"bdc2324-data/1\"\n",
    "liste_database = fs.ls(BUCKET)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "aaf64d60-bf92-470c-8210-d09abd6a653e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['bdc2324-data/1/1campaign_stats.csv',\n",
       " 'bdc2324-data/1/1campaigns.csv',\n",
       " 'bdc2324-data/1/1categories.csv',\n",
       " 'bdc2324-data/1/1countries.csv',\n",
       " 'bdc2324-data/1/1currencies.csv',\n",
       " 'bdc2324-data/1/1customer_target_mappings.csv',\n",
       " 'bdc2324-data/1/1customersplus.csv',\n",
       " 'bdc2324-data/1/1event_types.csv',\n",
       " 'bdc2324-data/1/1events.csv',\n",
       " 'bdc2324-data/1/1facilities.csv',\n",
       " 'bdc2324-data/1/1link_stats.csv',\n",
       " 'bdc2324-data/1/1pricing_formulas.csv',\n",
       " 'bdc2324-data/1/1product_packs.csv',\n",
       " 'bdc2324-data/1/1products.csv',\n",
       " 'bdc2324-data/1/1products_groups.csv',\n",
       " 'bdc2324-data/1/1purchases.csv',\n",
       " 'bdc2324-data/1/1representation_category_capacities.csv',\n",
       " 'bdc2324-data/1/1representations.csv',\n",
       " 'bdc2324-data/1/1seasons.csv',\n",
       " 'bdc2324-data/1/1structure_tag_mappings.csv',\n",
       " 'bdc2324-data/1/1suppliers.csv',\n",
       " 'bdc2324-data/1/1tags.csv',\n",
       " 'bdc2324-data/1/1target_types.csv',\n",
       " 'bdc2324-data/1/1targets.csv',\n",
       " 'bdc2324-data/1/1tickets.csv',\n",
       " 'bdc2324-data/1/1type_of_categories.csv',\n",
       " 'bdc2324-data/1/1type_of_pricing_formulas.csv',\n",
       " 'bdc2324-data/1/1type_ofs.csv']"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "liste_database"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0cb92854-903b-4efd-ac1b-197e29f044b4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['bdc2324-data/1/1purchases.csv', 'bdc2324-data/1/1suppliers.csv', 'bdc2324-data/1/1tickets.csv', 'bdc2324-data/1/1type_ofs.csv']\n"
     ]
    }
   ],
   "source": [
    "liste_database_select = ['suppliers', 'ticket', 'purchase', 'consumption', 'type_ofs']\n",
    "\n",
    "# Filtrer la liste pour les éléments contenant au moins un élément de la liste à tester\n",
    "liste_database_filtered = [element for element in liste_database if any(element_part in element for element_part in liste_database_select)]\n",
    "\n",
    "# Afficher le résultat\n",
    "print(liste_database_filtered)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "dd6a3518-b752-4a1e-b77b-9e03e853c3ed",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_9792/4081512283.py:10: DtypeWarning: Columns (1) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  df = pd.read_csv(file_in)\n"
     ]
    }
   ],
   "source": [
    "# loop to create dataframes from liste\n",
    "files_path = liste_database\n",
    "\n",
    "client_number = files_path[0].split(\"/\")[1]\n",
    "df_prefix = \"df\" + str(client_number) + \"_\"\n",
    "\n",
    "for i in range(len(files_path)) :\n",
    "    current_path = files_path[i]\n",
    "    with fs.open(current_path, mode=\"rb\") as file_in:\n",
    "        df = pd.read_csv(file_in)\n",
    "        # the pattern of the name is df1xxx\n",
    "        nom_dataframe = df_prefix + re.search(r'\\/(\\d+)\\/(\\d+)([a-zA-Z_]+)\\.csv$', current_path).group(3)\n",
    "        globals()[nom_dataframe] = df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e908f516-2a74-45d6-8492-7dcdc3afbe1f",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "## tickets.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "14f4158e-c9c0-4beb-826a-5e0f949434a4",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>number</th>\n",
       "      <th>created_at</th>\n",
       "      <th>updated_at</th>\n",
       "      <th>purchase_id</th>\n",
       "      <th>product_id</th>\n",
       "      <th>is_from_subscription</th>\n",
       "      <th>type_of</th>\n",
       "      <th>supplier_id</th>\n",
       "      <th>barcode</th>\n",
       "      <th>identifier</th>\n",
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       "  </thead>\n",
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       "<p>1826672 rows × 11 columns</p>\n",
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      ],
      "text/plain": [
       "               id          number                        created_at  \\\n",
       "0        13070859  13593002661288  2021-12-28 20:47:10.320641+01:00   \n",
       "1        13070860  13593002661399  2021-12-28 20:47:10.321037+01:00   \n",
       "2        13070861  13593002661419  2021-12-28 20:47:10.321629+01:00   \n",
       "3        13070862  13593002661508  2021-12-28 20:47:10.322029+01:00   \n",
       "4        13070863  13593002661689  2021-12-28 20:47:10.322449+01:00   \n",
       "...           ...             ...                               ...   \n",
       "1826667  20662815  13593016154390  2023-11-09 07:51:34.935983+01:00   \n",
       "1826668  20662816  13593016154501  2023-11-09 07:51:34.937038+01:00   \n",
       "1826669  20662817  13593016154680  2023-11-09 07:51:34.938224+01:00   \n",
       "1826670  20662818  13593016154899  2023-11-09 07:51:34.939328+01:00   \n",
       "1826671  20662819  13593016154988  2023-11-09 07:51:34.940680+01:00   \n",
       "\n",
       "                               updated_at  purchase_id  product_id  \\\n",
       "0        2022-02-14 18:46:53.614229+01:00      5107462      225251   \n",
       "1        2022-02-14 18:46:53.614761+01:00      5107462      224914   \n",
       "2        2022-02-14 18:46:53.615521+01:00      5107462      224914   \n",
       "3        2022-02-14 18:46:53.616000+01:00      5107462      224914   \n",
       "4        2022-02-14 18:46:53.616447+01:00      5107462      224914   \n",
       "...                                   ...          ...         ...   \n",
       "1826667  2023-11-09 07:51:34.935983+01:00      8007697      405689   \n",
       "1826668  2023-11-09 07:51:34.937038+01:00      8007698      403658   \n",
       "1826669  2023-11-09 07:51:34.938224+01:00      8007698      403658   \n",
       "1826670  2023-11-09 07:51:34.939328+01:00      8007699      403658   \n",
       "1826671  2023-11-09 07:51:34.940680+01:00      8007699      403658   \n",
       "\n",
       "         is_from_subscription  type_of  supplier_id  barcode  \\\n",
       "0                       False        1            3      NaN   \n",
       "1                       False        1            3      NaN   \n",
       "2                       False        1            3      NaN   \n",
       "3                       False        1            3      NaN   \n",
       "4                       False        1            3      NaN   \n",
       "...                       ...      ...          ...      ...   \n",
       "1826667                 False        1            3      NaN   \n",
       "1826668                 False        1            3      NaN   \n",
       "1826669                 False        1            3      NaN   \n",
       "1826670                 False        1            3      NaN   \n",
       "1826671                 False        1            3      NaN   \n",
       "\n",
       "                               identifier  \n",
       "0        b6ad7fc36f33b5e05f58c7fca06688a6  \n",
       "1        b0903af480266f27802fe5c38c277c9e  \n",
       "2        64ca12b7e26a65b90335c0702ea0faba  \n",
       "3        5ac2f8150aa9f3a6b1599df08cc2f0c7  \n",
       "4        dfe30081bae020d12094279926136b9c  \n",
       "...                                   ...  \n",
       "1826667  dba9aa428f843b79ae69dfacfe8fc579  \n",
       "1826668  93f1fcfc6ba4fa68f92eb4b4a619fcf0  \n",
       "1826669  c8bbbd25df2c158767ceef42c3237f23  \n",
       "1826670  738f0a8b5088b5056bc3b32eff2dca1f  \n",
       "1826671  4c5a6195434377380b4e6ae63b2e9cf6  \n",
       "\n",
       "[1826672 rows x 11 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1_tickets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f3c35394-b586-4ae4-b5ab-b03bb01bb618",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1826672 entries, 0 to 1826671\n",
      "Data columns (total 11 columns):\n",
      " #   Column                Dtype  \n",
      "---  ------                -----  \n",
      " 0   id                    int64  \n",
      " 1   number                object \n",
      " 2   created_at            object \n",
      " 3   updated_at            object \n",
      " 4   purchase_id           int64  \n",
      " 5   product_id            int64  \n",
      " 6   is_from_subscription  bool   \n",
      " 7   type_of               int64  \n",
      " 8   supplier_id           int64  \n",
      " 9   barcode               float64\n",
      " 10  identifier            object \n",
      "dtypes: bool(1), float64(1), int64(5), object(4)\n",
      "memory usage: 141.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df1_tickets.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c1b42769-03c7-4785-92ce-5e1e6b41908d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                        0.0\n",
       "number                    0.0\n",
       "created_at                0.0\n",
       "updated_at                0.0\n",
       "purchase_id               0.0\n",
       "product_id                0.0\n",
       "is_from_subscription      0.0\n",
       "type_of                   0.0\n",
       "supplier_id               0.0\n",
       "barcode                 100.0\n",
       "identifier                0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1_tickets.isna().sum()/len(df1_tickets)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "42896791-2d93-4725-a50b-6c7cbe535ec7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_619/232847087.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df1_tickets_clean.rename(columns = {'id' : 'ticket_id'}, inplace = True)\n"
     ]
    }
   ],
   "source": [
    "# Selection des variables\n",
    "df1_tickets_clean = df1_tickets[['id', 'purchase_id', 'product_id', 'is_from_subscription', 'type_of', 'supplier_id']]\n",
    "df1_tickets_clean.rename(columns = {'id' : 'ticket_id'}, inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78453f3c-4f89-44ed-a6c6-2a7443b72b52",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "## suppliers.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2e0dada0-9457-484c-aa55-77e44613ecca",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>name</th>\n",
       "      <th>manually_added</th>\n",
       "      <th>label</th>\n",
       "      <th>itr</th>\n",
       "      <th>updated_at</th>\n",
       "      <th>created_at</th>\n",
       "      <th>commission</th>\n",
       "      <th>identifier</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1617</td>\n",
       "      <td>j4 administration</td>\n",
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       "      <td>NaN</td>\n",
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       "      <th>1</th>\n",
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       "      <td>2020-09-03 13:16:35.329062+02:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>52ff3466787b4d538407372e5f7afe0f</td>\n",
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       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>vad</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>2020-09-03 13:11:23.896992+02:00</td>\n",
       "      <td>2020-09-03 13:11:23.896992+02:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1225483c97b36018cab2bea14ab78ea6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>fort saint jean</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-09-03 13:11:23.833073+02:00</td>\n",
       "      <td>2020-09-03 13:11:23.833073+02:00</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>j4</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-09-03 13:11:23.888993+02:00</td>\n",
       "      <td>2020-09-03 13:11:23.888993+02:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6a0cf6edf20060344b465706b61719aa</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>revendeur</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-09-03 13:11:23.900987+02:00</td>\n",
       "      <td>2020-09-03 13:11:23.900987+02:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>931239d4acb6214d7e5c98edecfb4916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3</td>\n",
       "      <td>vente en ligne</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-09-03 13:11:23.893097+02:00</td>\n",
       "      <td>2020-09-03 13:11:23.893097+02:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bde8f2ccff510df8572d3214d86b837d</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>6</td>\n",
       "      <td>ccr</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-09-03 13:11:23.904974+02:00</td>\n",
       "      <td>2020-09-03 13:11:23.904974+02:00</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>8</th>\n",
       "      <td>7</td>\n",
       "      <td>dab</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-09-03 13:11:23.908970+02:00</td>\n",
       "      <td>2020-09-03 13:11:23.908970+02:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11c6d471fa4e354e62e684d293694202</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     id               name  manually_added  label  itr  \\\n",
       "0  1617  j4 administration           False    NaN  NaN   \n",
       "1     8         non défini           False    NaN  NaN   \n",
       "2     4                vad           False    NaN  NaN   \n",
       "3     1    fort saint jean           False    NaN  NaN   \n",
       "4     2                 j4           False    NaN  NaN   \n",
       "5     5          revendeur           False    NaN  NaN   \n",
       "6     3     vente en ligne           False    NaN  NaN   \n",
       "7     6                ccr           False    NaN  NaN   \n",
       "8     7                dab           False    NaN  NaN   \n",
       "\n",
       "                         updated_at                        created_at  \\\n",
       "0  2021-07-29 09:21:37.325772+02:00  2021-07-29 09:21:37.325772+02:00   \n",
       "1  2020-09-03 13:16:35.329062+02:00  2020-09-03 13:16:35.329062+02:00   \n",
       "2  2020-09-03 13:11:23.896992+02:00  2020-09-03 13:11:23.896992+02:00   \n",
       "3  2020-09-03 13:11:23.833073+02:00  2020-09-03 13:11:23.833073+02:00   \n",
       "4  2020-09-03 13:11:23.888993+02:00  2020-09-03 13:11:23.888993+02:00   \n",
       "5  2020-09-03 13:11:23.900987+02:00  2020-09-03 13:11:23.900987+02:00   \n",
       "6  2020-09-03 13:11:23.893097+02:00  2020-09-03 13:11:23.893097+02:00   \n",
       "7  2020-09-03 13:11:23.904974+02:00  2020-09-03 13:11:23.904974+02:00   \n",
       "8  2020-09-03 13:11:23.908970+02:00  2020-09-03 13:11:23.908970+02:00   \n",
       "\n",
       "   commission                        identifier  \n",
       "0         NaN  5958b2a060ac3e31678b438892a1bd2e  \n",
       "1         NaN  52ff3466787b4d538407372e5f7afe0f  \n",
       "2         NaN  1225483c97b36018cab2bea14ab78ea6  \n",
       "3         NaN  001b9b4a524fe407150b8235b304d4ec  \n",
       "4         NaN  6a0cf6edf20060344b465706b61719aa  \n",
       "5         NaN  931239d4acb6214d7e5c98edecfb4916  \n",
       "6         NaN  bde8f2ccff510df8572d3214d86b837d  \n",
       "7         NaN  b48ec279411f7dbbb68393c61a9724d9  \n",
       "8         NaN  11c6d471fa4e354e62e684d293694202  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1_suppliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b583be02-ab60-4e14-9325-0204f203a1af",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 9 entries, 0 to 8\n",
      "Data columns (total 9 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   id              9 non-null      int64  \n",
      " 1   name            9 non-null      object \n",
      " 2   manually_added  9 non-null      bool   \n",
      " 3   label           0 non-null      float64\n",
      " 4   itr             0 non-null      float64\n",
      " 5   updated_at      9 non-null      object \n",
      " 6   created_at      9 non-null      object \n",
      " 7   commission      0 non-null      float64\n",
      " 8   identifier      9 non-null      object \n",
      "dtypes: bool(1), float64(3), int64(1), object(4)\n",
      "memory usage: 713.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "df1_suppliers.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6d7f338e-e4d3-422b-9cdc-dec967c0b28e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                  0.0\n",
       "name                0.0\n",
       "manually_added      0.0\n",
       "label             100.0\n",
       "itr               100.0\n",
       "updated_at          0.0\n",
       "created_at          0.0\n",
       "commission        100.0\n",
       "identifier          0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1_suppliers.isna().sum()/len(df1_suppliers)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3c645ab7-16bf-4054-9ae2-15a8c32e29c6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_619/302783287.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df1_suppliers_clean.rename(columns = {'name' : 'supplier_name'}, inplace = True)\n"
     ]
    }
   ],
   "source": [
    "# Selection des variables\n",
    "df1_suppliers_clean = df1_suppliers[['id', 'name']]\n",
    "df1_suppliers_clean.rename(columns = {'name' : 'supplier_name'}, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4de7e2e2-6da4-4618-8444-b524399c5493",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>supplier_name</th>\n",
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       "      <td>vad</td>\n",
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       "      <th>3</th>\n",
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       "      <td>fort saint jean</td>\n",
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       "      <th>4</th>\n",
       "      <td>2</td>\n",
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       "      <td>revendeur</td>\n",
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       "      <th>6</th>\n",
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       "      <td>vente en ligne</td>\n",
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       "      <th>7</th>\n",
       "      <td>6</td>\n",
       "      <td>ccr</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>7</td>\n",
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      "text/plain": [
       "     id      supplier_name\n",
       "0  1617  j4 administration\n",
       "1     8         non défini\n",
       "2     4                vad\n",
       "3     1    fort saint jean\n",
       "4     2                 j4\n",
       "5     5          revendeur\n",
       "6     3     vente en ligne\n",
       "7     6                ccr\n",
       "8     7                dab"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1_suppliers_clean"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a6df975-c7fc-45bc-92af-a0bdab17d795",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "## type_ofs.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a02f6594-3e91-4e87-bbb6-649c28d4f7e9",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>name</th>\n",
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       "      <th>created_at</th>\n",
       "      <th>updated_at</th>\n",
       "      <th>identifier</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Atelier</td>\n",
       "      <td>pricing_formula</td>\n",
       "      <td>2021-01-05 11:55:51.188106+01:00</td>\n",
       "      <td>2021-01-05 11:55:51.188106+01:00</td>\n",
       "      <td>623ec4067827558b28972cf39fe81ee7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Billet en nombre</td>\n",
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       "      <td>2021-01-11 12:13:19.286301+01:00</td>\n",
       "      <td>2021-01-11 12:13:19.286301+01:00</td>\n",
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       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Groupe</td>\n",
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       "      <td>2021-01-11 12:19:22.842917+01:00</td>\n",
       "      <td>2021-01-11 12:19:22.842917+01:00</td>\n",
       "      <td>1ab143efc3b85acbbc752fe8eb2b0b86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Revendeur</td>\n",
       "      <td>pricing_formula</td>\n",
       "      <td>2021-01-12 12:34:20.481236+01:00</td>\n",
       "      <td>2021-01-12 12:34:20.481236+01:00</td>\n",
       "      <td>8b332723366a07e1eef5f1c92f9ae067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Cinéma scolaire</td>\n",
       "      <td>pricing_formula</td>\n",
       "      <td>2021-01-25 19:16:05.141719+01:00</td>\n",
       "      <td>2021-01-25 19:16:05.141719+01:00</td>\n",
       "      <td>a12e62cb4c4f47e7406bd8fbff2bfe30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>Musée famille</td>\n",
       "      <td>pricing_formula</td>\n",
       "      <td>2021-01-25 19:23:06.692627+01:00</td>\n",
       "      <td>2021-01-25 19:23:06.692627+01:00</td>\n",
       "      <td>1ec6c19283111ccb3ed67f52d414470e</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>Spectacle famille</td>\n",
       "      <td>pricing_formula</td>\n",
       "      <td>2021-01-25 19:28:21.390016+01:00</td>\n",
       "      <td>2021-01-25 19:28:21.390016+01:00</td>\n",
       "      <td>05e2104f1b74ced229c06847d6e91938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>Masterclass</td>\n",
       "      <td>pricing_formula</td>\n",
       "      <td>2021-01-25 19:31:05.076904+01:00</td>\n",
       "      <td>2021-01-25 19:31:05.076904+01:00</td>\n",
       "      <td>9cc946edfb25e11b4282f58db16e6ae9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>Spectacle</td>\n",
       "      <td>pricing_formula</td>\n",
       "      <td>2021-01-25 19:38:41.260535+01:00</td>\n",
       "      <td>2021-01-25 19:38:41.260535+01:00</td>\n",
       "      <td>d88321c347f0e0ab101184cdf25c94bf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>Cinema</td>\n",
       "      <td>pricing_formula</td>\n",
       "      <td>2021-02-05 11:12:31.932576+01:00</td>\n",
       "      <td>2021-02-05 11:12:31.932576+01:00</td>\n",
       "      <td>0870fef2bfcd5b30a12e4f5c7f4aaba7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>Musee</td>\n",
       "      <td>pricing_formula</td>\n",
       "      <td>2021-02-05 11:52:05.468207+01:00</td>\n",
       "      <td>2021-02-05 11:52:05.468207+01:00</td>\n",
       "      <td>8ba8934454cc62c7cdb3eb6e1b39df0c</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>Tarifs plein</td>\n",
       "      <td>category</td>\n",
       "      <td>2023-03-13 11:31:50.528331+01:00</td>\n",
       "      <td>2023-03-13 11:31:50.528331+01:00</td>\n",
       "      <td>a6969df76efc15d157be48e87a7bcf9a</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    id               name         children                        created_at  \\\n",
       "0    1            Atelier  pricing_formula  2021-01-05 11:55:51.188106+01:00   \n",
       "1    2   Billet en nombre  pricing_formula  2021-01-11 12:13:19.286301+01:00   \n",
       "2    3             Groupe  pricing_formula  2021-01-11 12:19:22.842917+01:00   \n",
       "3    4          Revendeur  pricing_formula  2021-01-12 12:34:20.481236+01:00   \n",
       "4    5    Cinéma scolaire  pricing_formula  2021-01-25 19:16:05.141719+01:00   \n",
       "5    6      Musée famille  pricing_formula  2021-01-25 19:23:06.692627+01:00   \n",
       "6    7  Spectacle famille  pricing_formula  2021-01-25 19:28:21.390016+01:00   \n",
       "7    8        Masterclass  pricing_formula  2021-01-25 19:31:05.076904+01:00   \n",
       "8    9          Spectacle  pricing_formula  2021-01-25 19:38:41.260535+01:00   \n",
       "9   10             Cinema  pricing_formula  2021-02-05 11:12:31.932576+01:00   \n",
       "10  11              Musee  pricing_formula  2021-02-05 11:52:05.468207+01:00   \n",
       "11  12       Tarifs plein         category  2023-03-13 11:31:50.528331+01:00   \n",
       "\n",
       "                          updated_at                        identifier  \n",
       "0   2021-01-05 11:55:51.188106+01:00  623ec4067827558b28972cf39fe81ee7  \n",
       "1   2021-01-11 12:13:19.286301+01:00  a53d313a97296ee37caa066dbfe7a45c  \n",
       "2   2021-01-11 12:19:22.842917+01:00  1ab143efc3b85acbbc752fe8eb2b0b86  \n",
       "3   2021-01-12 12:34:20.481236+01:00  8b332723366a07e1eef5f1c92f9ae067  \n",
       "4   2021-01-25 19:16:05.141719+01:00  a12e62cb4c4f47e7406bd8fbff2bfe30  \n",
       "5   2021-01-25 19:23:06.692627+01:00  1ec6c19283111ccb3ed67f52d414470e  \n",
       "6   2021-01-25 19:28:21.390016+01:00  05e2104f1b74ced229c06847d6e91938  \n",
       "7   2021-01-25 19:31:05.076904+01:00  9cc946edfb25e11b4282f58db16e6ae9  \n",
       "8   2021-01-25 19:38:41.260535+01:00  d88321c347f0e0ab101184cdf25c94bf  \n",
       "9   2021-02-05 11:12:31.932576+01:00  0870fef2bfcd5b30a12e4f5c7f4aaba7  \n",
       "10  2021-02-05 11:52:05.468207+01:00  8ba8934454cc62c7cdb3eb6e1b39df0c  \n",
       "11  2023-03-13 11:31:50.528331+01:00  a6969df76efc15d157be48e87a7bcf9a  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1_type_ofs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e9c8d32b-22f4-4581-8af7-31cc1c31fa0e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 12 entries, 0 to 11\n",
      "Data columns (total 6 columns):\n",
      " #   Column      Non-Null Count  Dtype \n",
      "---  ------      --------------  ----- \n",
      " 0   id          12 non-null     int64 \n",
      " 1   name        12 non-null     object\n",
      " 2   children    12 non-null     object\n",
      " 3   created_at  12 non-null     object\n",
      " 4   updated_at  12 non-null     object\n",
      " 5   identifier  12 non-null     object\n",
      "dtypes: int64(1), object(5)\n",
      "memory usage: 704.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "df1_type_ofs.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "cbb5e614-1fe5-4da0-bca0-8a242e0885da",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_619/81842251.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df1_type_ofs_clean.rename(columns = {'name' : 'type_of_ticket_name'}, inplace = True)\n"
     ]
    }
   ],
   "source": [
    "# Selection des variables\n",
    "df1_type_ofs_clean = df1_type_ofs[['id', 'name', 'children']]\n",
    "df1_type_ofs_clean.rename(columns = {'name' : 'type_of_ticket_name'}, inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "676a9869-9a8b-4cd2-8b1c-0644b5229c72",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "## purchases.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f8d36b72-f8e7-45e5-b4fa-e0803493fd3c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>purchase_date</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>created_at</th>\n",
       "      <th>updated_at</th>\n",
       "      <th>number</th>\n",
       "      <th>identifier</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5145662</td>\n",
       "      <td>2019-07-17 11:17:53+02:00</td>\n",
       "      <td>6632</td>\n",
       "      <td>2021-12-28 20:48:51.569237+01:00</td>\n",
       "      <td>2021-12-28 20:48:51.569237+01:00</td>\n",
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       "      <td>4941642</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <th>742245</th>\n",
       "      <td>8007695</td>\n",
       "      <td>2023-11-08 17:51:19+01:00</td>\n",
       "      <td>1256133</td>\n",
       "      <td>2023-11-09 07:51:33.920187+01:00</td>\n",
       "      <td>2023-11-09 07:51:33.920187+01:00</td>\n",
       "      <td>99ad774dedbad43feb73514765d2f0ba</td>\n",
       "      <td>d68558180b4bf2e8a945724843655775</td>\n",
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       "      <th>742246</th>\n",
       "      <td>8007696</td>\n",
       "      <td>2023-11-08 18:17:51+01:00</td>\n",
       "      <td>1256134</td>\n",
       "      <td>2023-11-09 07:51:33.921967+01:00</td>\n",
       "      <td>2023-11-09 07:51:33.921967+01:00</td>\n",
       "      <td>c1511614c511c5f95980172690179102</td>\n",
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       "      <td>2023-11-09 07:51:33.923034+01:00</td>\n",
       "      <td>2023-11-09 07:51:33.923034+01:00</td>\n",
       "      <td>33b64b39cc53428b4f17d65ff5b93104</td>\n",
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       "      <th>742248</th>\n",
       "      <td>8007698</td>\n",
       "      <td>2023-11-08 19:32:18+01:00</td>\n",
       "      <td>1256136</td>\n",
       "      <td>2023-11-09 07:51:33.924135+01:00</td>\n",
       "      <td>2023-11-09 07:51:33.924135+01:00</td>\n",
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       "      <td>1256137</td>\n",
       "      <td>2023-11-09 07:51:33.925382+01:00</td>\n",
       "      <td>2023-11-09 07:51:33.925382+01:00</td>\n",
       "      <td>d31ced089c2b1f90479257a4686f9306</td>\n",
       "      <td>d86b1e0de3ff01eaf04fbcd031ac5fef</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>742250 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             id              purchase_date  customer_id  \\\n",
       "0       5145662  2019-07-17 11:17:53+02:00         6632   \n",
       "1       4941642  2018-10-31 11:59:00+01:00            1   \n",
       "2       5088860  2018-10-31 12:45:12+01:00            1   \n",
       "3       5088862  2018-10-31 13:07:12+01:00            1   \n",
       "4       5088863  2018-10-31 13:08:50+01:00            1   \n",
       "...         ...                        ...          ...   \n",
       "742245  8007695  2023-11-08 17:51:19+01:00      1256133   \n",
       "742246  8007696  2023-11-08 18:17:51+01:00      1256134   \n",
       "742247  8007697  2023-11-08 18:23:54+01:00      1256135   \n",
       "742248  8007698  2023-11-08 19:32:18+01:00      1256136   \n",
       "742249  8007699  2023-11-08 20:30:28+01:00      1256137   \n",
       "\n",
       "                              created_at                        updated_at  \\\n",
       "0       2021-12-28 20:48:51.569237+01:00  2021-12-28 20:48:51.569237+01:00   \n",
       "1       2021-12-28 20:31:48.196681+01:00  2022-03-03 17:52:21.958861+01:00   \n",
       "2       2021-12-28 20:46:34.703542+01:00  2021-12-28 20:46:34.703542+01:00   \n",
       "3       2021-12-28 20:46:34.704773+01:00  2021-12-28 20:46:34.704773+01:00   \n",
       "4       2021-12-28 20:46:34.705453+01:00  2021-12-28 20:46:34.705453+01:00   \n",
       "...                                  ...                               ...   \n",
       "742245  2023-11-09 07:51:33.920187+01:00  2023-11-09 07:51:33.920187+01:00   \n",
       "742246  2023-11-09 07:51:33.921967+01:00  2023-11-09 07:51:33.921967+01:00   \n",
       "742247  2023-11-09 07:51:33.923034+01:00  2023-11-09 07:51:33.923034+01:00   \n",
       "742248  2023-11-09 07:51:33.924135+01:00  2023-11-09 07:51:33.924135+01:00   \n",
       "742249  2023-11-09 07:51:33.925382+01:00  2023-11-09 07:51:33.925382+01:00   \n",
       "\n",
       "                                  number                        identifier  \n",
       "0       fa80c83b29a268b45728c910a8afcf79  82877c41df26f832eb823a83acd1a172  \n",
       "1       597b6c06adfe6acc539b29b657b80da0  e7102ebe65526c427245533ebabe66e5  \n",
       "2       4a7f6baaf9be6a99e3fead7f7e981fa8  af75c4ae53d1b6957875538355b162e1  \n",
       "3       1d83dfad44b73070d1c6d5875d0edd2d  4b2fe34659b177209b07270ae1043b40  \n",
       "4       7bfe2bc9c1670c973d0960e3fd408cf8  b115f04a99b94df9e4a32185844f0998  \n",
       "...                                  ...                               ...  \n",
       "742245  99ad774dedbad43feb73514765d2f0ba  d68558180b4bf2e8a945724843655775  \n",
       "742246  c1511614c511c5f95980172690179102  f5102d910a7731091f239ad7b0df35b4  \n",
       "742247  33b64b39cc53428b4f17d65ff5b93104  e2b917626be60cc2c3207cc037fe69e4  \n",
       "742248  9ae0b129e704b3d9c093ce9c7c4e5039  5bfa23236c31f8562c3a0233c1b53b31  \n",
       "742249  d31ced089c2b1f90479257a4686f9306  d86b1e0de3ff01eaf04fbcd031ac5fef  \n",
       "\n",
       "[742250 rows x 7 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1_purchases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "3f266a9d-6eee-4b27-b6cc-d401bc2fa0b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 742250 entries, 0 to 742249\n",
      "Data columns (total 7 columns):\n",
      " #   Column         Non-Null Count   Dtype \n",
      "---  ------         --------------   ----- \n",
      " 0   id             742250 non-null  int64 \n",
      " 1   purchase_date  742250 non-null  object\n",
      " 2   customer_id    742250 non-null  int64 \n",
      " 3   created_at     742250 non-null  object\n",
      " 4   updated_at     742250 non-null  object\n",
      " 5   number         742250 non-null  object\n",
      " 6   identifier     742250 non-null  object\n",
      "dtypes: int64(2), object(5)\n",
      "memory usage: 39.6+ MB\n"
     ]
    }
   ],
   "source": [
    "df1_purchases.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "8b24ccbc-ccf0-4722-8cd9-8ee8aa90d1fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Nettoyage purchase_date\n",
    "df1_purchases['purchase_date'] = pd.to_datetime(df1_purchases['purchase_date'], utc = True)\n",
    "df1_purchases['purchase_date'] = pd.to_datetime(df1_purchases['purchase_date'], format = 'ISO8601')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "27d18584-228f-4698-85d6-4d23151ea5ed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 742250 entries, 0 to 742249\n",
      "Data columns (total 7 columns):\n",
      " #   Column         Non-Null Count   Dtype              \n",
      "---  ------         --------------   -----              \n",
      " 0   id             742250 non-null  int64              \n",
      " 1   purchase_date  742250 non-null  datetime64[ns, UTC]\n",
      " 2   customer_id    742250 non-null  int64              \n",
      " 3   created_at     742250 non-null  object             \n",
      " 4   updated_at     742250 non-null  object             \n",
      " 5   number         742250 non-null  object             \n",
      " 6   identifier     742250 non-null  object             \n",
      "dtypes: datetime64[ns, UTC](1), int64(2), object(4)\n",
      "memory usage: 39.6+ MB\n"
     ]
    }
   ],
   "source": [
    "df1_purchases.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ea22e3a2-2b25-481d-8ebc-194e11a06cd9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Selection des variables\n",
    "df1_purchases_clean = df1_purchases[['id', 'purchase_date', 'customer_id']]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53227600-c1c5-48aa-9f5d-db5a23a8a22a",
   "metadata": {},
   "source": [
    "## Fusion de l'ensemble des données billétiques"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e0b8b47a-b321-4a79-823c-36a131a78ac7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fusion avec fournisseurs\n",
    "df1_ticket_information = pd.merge(df1_tickets_clean, df1_suppliers_clean, left_on = 'supplier_id', right_on = 'id', how = 'inner')\n",
    "df1_ticket_information.drop(['supplier_id', 'id'], axis = 1, inplace=True)\n",
    "\n",
    "# Fusion avec type de tickets\n",
    "df1_ticket_information = pd.merge(df1_ticket_information, df1_type_ofs_clean, left_on = 'type_of', right_on = 'id', how = 'inner')\n",
    "df1_ticket_information.drop(['type_of', 'id'], axis = 1, inplace=True)\n",
    "\n",
    "# Fusion avec achats\n",
    "df1_ticket_information = pd.merge(df1_ticket_information, df1_purchases_clean, left_on = 'purchase_id', right_on = 'id', how = 'inner')\n",
    "df1_ticket_information.drop(['purchase_id', 'id'], axis = 1, inplace=True)"
   ]
  },
  {
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   "id": "7572e6e7-f28d-43ba-b045-b9fa09e68e1d",
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       "<p>1826672 rows × 8 columns</p>\n",
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      ],
      "text/plain": [
       "         ticket_id  product_id  is_from_subscription   supplier_name  \\\n",
       "0         13070859      225251                 False  vente en ligne   \n",
       "1         13070860      224914                 False  vente en ligne   \n",
       "2         13070861      224914                 False  vente en ligne   \n",
       "3         13070862      224914                 False  vente en ligne   \n",
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       "...            ...         ...                   ...             ...   \n",
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       "1826669   20662817      403658                 False  vente en ligne   \n",
       "1826670   20662818      403658                 False  vente en ligne   \n",
       "1826671   20662819      403658                 False  vente en ligne   \n",
       "\n",
       "        type_of_ticket_name         children             purchase_date  \\\n",
       "0                   Atelier  pricing_formula 2018-12-28 14:47:50+00:00   \n",
       "1                   Atelier  pricing_formula 2018-12-28 14:47:50+00:00   \n",
       "2                   Atelier  pricing_formula 2018-12-28 14:47:50+00:00   \n",
       "3                   Atelier  pricing_formula 2018-12-28 14:47:50+00:00   \n",
       "4                   Atelier  pricing_formula 2018-12-28 14:47:50+00:00   \n",
       "...                     ...              ...                       ...   \n",
       "1826667             Atelier  pricing_formula 2023-11-08 17:23:54+00:00   \n",
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       "1826669             Atelier  pricing_formula 2023-11-08 18:32:18+00:00   \n",
       "1826670             Atelier  pricing_formula 2023-11-08 19:30:28+00:00   \n",
       "1826671             Atelier  pricing_formula 2023-11-08 19:30:28+00:00   \n",
       "\n",
       "         customer_id  \n",
       "0              48187  \n",
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       "3              48187  \n",
       "4              48187  \n",
       "...              ...  \n",
       "1826667      1256135  \n",
       "1826668      1256136  \n",
       "1826669      1256136  \n",
       "1826670      1256137  \n",
       "1826671      1256137  \n",
       "\n",
       "[1826672 rows x 8 columns]"
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     },
     "execution_count": 24,
     "metadata": {},
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    }
   ],
   "source": [
    "df1_ticket_information"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad2d0059-76d3-44b9-b0eb-0b0ca4d4ba75",
   "metadata": {},
   "source": [
    "# Utilisation de fonctions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "d237be96-8c86-4a91-b7a1-487e87a16c3d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def cleaning_date(df, column_name):\n",
    "    \"\"\"\n",
    "    Nettoie la colonne spécifiée du DataFrame en convertissant les valeurs en datetime avec le format ISO8601.\n",
    "\n",
    "    Parameters:\n",
    "    - df: DataFrame\n",
    "        Le DataFrame contenant la colonne à nettoyer.\n",
    "    - column_name: str\n",
    "        Le nom de la colonne à nettoyer.\n",
    "\n",
    "    Returns:\n",
    "    - DataFrame\n",
    "        Le DataFrame modifié avec la colonne nettoyée.\n",
    "    \"\"\"\n",
    "    df[column_name] = pd.to_datetime(df[column_name], utc = True, format = 'ISO8601')\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "c1afe322-ff41-4760-819e-0195fed5b27d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 20 entries, 0 to 19\n",
      "Data columns (total 2 columns):\n",
      " #   Column           Non-Null Count  Dtype              \n",
      "---  ------           --------------  -----              \n",
      " 0   opened_at        8 non-null      object             \n",
      " 1   opened_at_clean  8 non-null      datetime64[ns, UTC]\n",
      "dtypes: datetime64[ns, UTC](1), object(1)\n",
      "memory usage: 448.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "# Créer un DataFrame exemple\n",
    "df_not_clean = df1_campaign_stats[['opened_at']].head(20)\n",
    "\n",
    "# Appliquer la fonction pour nettoyer la colonne 'purchase_date' de manière vectorisée\n",
    "df_clean = cleaning_date(df_not_clean, 'opened_at')\n",
    "df_clean.rename(columns = {'opened_at' : 'opened_at_clean'}, inplace = True)\n",
    "\n",
    "test = pd.concat([df1_campaign_stats[['opened_at']].head(20), df_clean], axis=1)\n",
    "\n",
    "test.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27ecf058-23eb-4018-abbd-68c4ebe7c786",
   "metadata": {},
   "source": [
    "## Nettoyage, selection et fusion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d887898c-6a21-41ed-901d-4d6fdbca5372",
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "         ticket_id  product_id  is_from_subscription  type_of   supplier_name  \\\n",
       "0         13070859      225251                 False        1  vente en ligne   \n",
       "1         13070860      224914                 False        1  vente en ligne   \n",
       "2         13070861      224914                 False        1  vente en ligne   \n",
       "3         13070862      224914                 False        1  vente en ligne   \n",
       "4         13070863      224914                 False        1  vente en ligne   \n",
       "...            ...         ...                   ...      ...             ...   \n",
       "1826667   20662815      405689                 False        1  vente en ligne   \n",
       "1826668   20662816      403658                 False        1  vente en ligne   \n",
       "1826669   20662817      403658                 False        1  vente en ligne   \n",
       "1826670   20662818      403658                 False        1  vente en ligne   \n",
       "1826671   20662819      403658                 False        1  vente en ligne   \n",
       "\n",
       "                    purchase_date  customer_id  \n",
       "0       2018-12-28 14:47:50+00:00        48187  \n",
       "1       2018-12-28 14:47:50+00:00        48187  \n",
       "2       2018-12-28 14:47:50+00:00        48187  \n",
       "3       2018-12-28 14:47:50+00:00        48187  \n",
       "4       2018-12-28 14:47:50+00:00        48187  \n",
       "...                           ...          ...  \n",
       "1826667 2023-11-08 17:23:54+00:00      1256135  \n",
       "1826668 2023-11-08 18:32:18+00:00      1256136  \n",
       "1826669 2023-11-08 18:32:18+00:00      1256136  \n",
       "1826670 2023-11-08 19:30:28+00:00      1256137  \n",
       "1826671 2023-11-08 19:30:28+00:00      1256137  \n",
       "\n",
       "[1826672 rows x 7 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1_ticket_information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ac9a6373-c1c6-46b5-873b-dc22f17bcbdb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1826672 entries, 0 to 1826671\n",
      "Data columns (total 7 columns):\n",
      " #   Column                Dtype              \n",
      "---  ------                -----              \n",
      " 0   ticket_id             int64              \n",
      " 1   product_id            int64              \n",
      " 2   is_from_subscription  bool               \n",
      " 3   type_of               int64              \n",
      " 4   supplier_name         object             \n",
      " 5   purchase_date         datetime64[ns, UTC]\n",
      " 6   customer_id           int64              \n",
      "dtypes: bool(1), datetime64[ns, UTC](1), int64(4), object(1)\n",
      "memory usage: 85.4+ MB\n"
     ]
    }
   ],
   "source": [
    "df1_ticket_information.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1719943-89eb-4ba0-a107-2f96d5d01ec9",
   "metadata": {},
   "source": [
    "# Customer information"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2132ee2-3f22-45fd-b65b-72689c8b672c",
   "metadata": {},
   "source": [
    "## Target area"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "da5d4708-7147-4cc8-8686-52d4bcba5a7a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_619/2625134041.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df1_targets_clean.rename(columns = {'id' : 'target_id' , 'name' : 'target_name'}, inplace = True)\n"
     ]
    }
   ],
   "source": [
    "# Target.csv cleaning\n",
    "df1_targets_clean = df1_targets[[\"id\", \"target_type_id\", \"name\"]]\n",
    "df1_targets_clean.rename(columns = {'id' : 'target_id' , 'name' : 'target_name'}, inplace = True)\n",
    "\n",
    "# target_type cleaning\n",
    "df1_target_types_clean = df1_target_types[[\"id\",\"is_import\",\"name\"]].add_prefix(\"target_type_\")\n",
    "\n",
    "#customer_target_mappings cleaning\n",
    "df1_customer_target_mappings_clean = df1_customer_target_mappings[[\"id\", \"customer_id\", \"target_id\"]]\n",
    "\n",
    "# Merge target et target_type\n",
    "df1_targets_full = pd.merge(df1_targets_clean, df1_target_types_clean, left_on='target_type_id', right_on='target_type_id', how='inner')\n",
    "df1_targets_full.drop(['target_type_id'], axis = 1, inplace=True)\n",
    "\n",
    "# Merge\n",
    "df1_targets_full = pd.merge(df1_customer_target_mappings_clean, df1_targets_full, left_on='target_id', right_on='target_id', how='inner')\n",
    "df1_targets_full.drop(['target_id'], axis = 1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "b4fa5fe3-ce8e-4b0a-af94-fb468d241bad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id    5.080902\n",
       "dtype: float64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1_targets_test = df1_targets_full[['id', 'customer_id']].groupby(['customer_id']).count()\n",
    "len(df1_targets_test[df1_targets_test['id'] > 1]) / len(df1_targets_test)\n",
    "\n",
    "# 99,6% des 151 000 client visés sont catégorisés plusieurs fois et en moyenne 5 fois... \n",
    "df1_targets_test.mean()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "8072bbb7-1360-4882-bb2b-2f43b6beea0d",
   "metadata": {
    "scrolled": true
   },
   "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>id</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>target_name</th>\n",
       "      <th>target_type_is_import</th>\n",
       "      <th>target_type_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8793</th>\n",
       "      <td>4584599</td>\n",
       "      <td>1</td>\n",
       "      <td>consentement optin jeune public</td>\n",
       "      <td>False</td>\n",
       "      <td>manual_static_filter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13249</th>\n",
       "      <td>4567465</td>\n",
       "      <td>1</td>\n",
       "      <td>DDCP rentrée culturelle 2023</td>\n",
       "      <td>False</td>\n",
       "      <td>manual_static_filter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21424</th>\n",
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       "      <td>spectateurs cine dimanche_cine concert_2122</td>\n",
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       "    <tr>\n",
       "      <th>21665</th>\n",
       "      <td>4544911</td>\n",
       "      <td>1</td>\n",
       "      <td>DDCP Cine 2023</td>\n",
       "      <td>False</td>\n",
       "      <td>manual_static_filter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22811</th>\n",
       "      <td>4545766</td>\n",
       "      <td>1</td>\n",
       "      <td>DDCP OLBJ! 2023</td>\n",
       "      <td>False</td>\n",
       "      <td>manual_static_filter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57305</th>\n",
       "      <td>4457909</td>\n",
       "      <td>1</td>\n",
       "      <td>ddcp_promo_visiteurs occasionnels_musee_8mois</td>\n",
       "      <td>False</td>\n",
       "      <td>manual_dynamic_filter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58843</th>\n",
       "      <td>3688872</td>\n",
       "      <td>1</td>\n",
       "      <td>DDCP promo livemag</td>\n",
       "      <td>False</td>\n",
       "      <td>manual_static_filter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66813</th>\n",
       "      <td>4313646</td>\n",
       "      <td>1</td>\n",
       "      <td>DDCP spectateurs Classique mais pas que 2022</td>\n",
       "      <td>False</td>\n",
       "      <td>manual_static_filter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68367</th>\n",
       "      <td>4547662</td>\n",
       "      <td>1</td>\n",
       "      <td>ddcp_promo_musee_au moins 3 achats_dps8mois</td>\n",
       "      <td>False</td>\n",
       "      <td>manual_dynamic_filter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77320</th>\n",
       "      <td>4285520</td>\n",
       "      <td>1</td>\n",
       "      <td>DDCP spectateurs Iminente</td>\n",
       "      <td>False</td>\n",
       "      <td>manual_static_filter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84350</th>\n",
       "      <td>4037805</td>\n",
       "      <td>1</td>\n",
       "      <td>DDCP spectateurs Marseille Jazz 18-19-21</td>\n",
       "      <td>False</td>\n",
       "      <td>manual_static_filter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85383</th>\n",
       "      <td>4569504</td>\n",
       "      <td>1</td>\n",
       "      <td>DDCP rendez-vous de septembre offre spéciale</td>\n",
       "      <td>False</td>\n",
       "      <td>manual_static_filter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92868</th>\n",
       "      <td>4433064</td>\n",
       "      <td>1</td>\n",
       "      <td>ddcp_promo_plein air_ateliers_jardins</td>\n",
       "      <td>False</td>\n",
       "      <td>manual_static_filter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99670</th>\n",
       "      <td>3858684</td>\n",
       "      <td>1</td>\n",
       "      <td>Acid Arab</td>\n",
       "      <td>False</td>\n",
       "      <td>manual_static_filter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105477</th>\n",
       "      <td>4321810</td>\n",
       "      <td>1</td>\n",
       "      <td>Arenametrix_bascule tel vers sib</td>\n",
       "      <td>False</td>\n",
       "      <td>manual_static_filter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169513</th>\n",
       "      <td>3697992</td>\n",
       "      <td>1</td>\n",
       "      <td>ddcp_achats billets nb dps 19052021</td>\n",
       "      <td>False</td>\n",
       "      <td>manual_static_filter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214421</th>\n",
       "      <td>2925324</td>\n",
       "      <td>1</td>\n",
       "      <td>consentement optout scolaires</td>\n",
       "      <td>False</td>\n",
       "      <td>manual_static_filter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>234546</th>\n",
       "      <td>4575957</td>\n",
       "      <td>1</td>\n",
       "      <td>Portrait de Leila shahid</td>\n",
       "      <td>False</td>\n",
       "      <td>manual_static_filter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259808</th>\n",
       "      <td>3722259</td>\n",
       "      <td>1</td>\n",
       "      <td>consentement optin b2b</td>\n",
       "      <td>False</td>\n",
       "      <td>manual_static_filter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>274380</th>\n",
       "      <td>4510423</td>\n",
       "      <td>1</td>\n",
       "      <td>DDCP_marseille_jazz_2023</td>\n",
       "      <td>False</td>\n",
       "      <td>manual_static_filter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>307511</th>\n",
       "      <td>5174466</td>\n",
       "      <td>1</td>\n",
       "      <td>ddcp actoral 21-22</td>\n",
       "      <td>False</td>\n",
       "      <td>manual_static_filter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>357509</th>\n",
       "      <td>4442526</td>\n",
       "      <td>1</td>\n",
       "      <td>ddcp musique barvalo</td>\n",
       "      <td>False</td>\n",
       "      <td>manual_static_filter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>392920</th>\n",
       "      <td>4390642</td>\n",
       "      <td>1</td>\n",
       "      <td>ddcp_md_promo_spectateurs theatre contempo</td>\n",
       "      <td>False</td>\n",
       "      <td>manual_static_filter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>449620</th>\n",
       "      <td>4411897</td>\n",
       "      <td>1</td>\n",
       "      <td>FORMATION _ acheteurs optin last year</td>\n",
       "      <td>False</td>\n",
       "      <td>manual_dynamic_filter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>503809</th>\n",
       "      <td>4734591</td>\n",
       "      <td>1</td>\n",
       "      <td>consentement optin mediation specialisee</td>\n",
       "      <td>False</td>\n",
       "      <td>manual_static_filter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>651222</th>\n",
       "      <td>3554426</td>\n",
       "      <td>1</td>\n",
       "      <td>consentement optin b2c</td>\n",
       "      <td>False</td>\n",
       "      <td>manual_static_filter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>654246</th>\n",
       "      <td>5182212</td>\n",
       "      <td>1</td>\n",
       "      <td>DDCP spectateurs Festival de Marseille 2023</td>\n",
       "      <td>False</td>\n",
       "      <td>manual_static_filter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>654395</th>\n",
       "      <td>5182456</td>\n",
       "      <td>1</td>\n",
       "      <td>rencontres_echelle_spectateurs_2021_2023</td>\n",
       "      <td>False</td>\n",
       "      <td>manual_static_filter</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             id  customer_id                                    target_name  \\\n",
       "8793    4584599            1                consentement optin jeune public   \n",
       "13249   4567465            1                   DDCP rentrée culturelle 2023   \n",
       "21424   4544805            1    spectateurs cine dimanche_cine concert_2122   \n",
       "21665   4544911            1                                 DDCP Cine 2023   \n",
       "22811   4545766            1                                DDCP OLBJ! 2023   \n",
       "57305   4457909            1  ddcp_promo_visiteurs occasionnels_musee_8mois   \n",
       "58843   3688872            1                             DDCP promo livemag   \n",
       "66813   4313646            1   DDCP spectateurs Classique mais pas que 2022   \n",
       "68367   4547662            1    ddcp_promo_musee_au moins 3 achats_dps8mois   \n",
       "77320   4285520            1                      DDCP spectateurs Iminente   \n",
       "84350   4037805            1       DDCP spectateurs Marseille Jazz 18-19-21   \n",
       "85383   4569504            1   DDCP rendez-vous de septembre offre spéciale   \n",
       "92868   4433064            1          ddcp_promo_plein air_ateliers_jardins   \n",
       "99670   3858684            1                                      Acid Arab   \n",
       "105477  4321810            1               Arenametrix_bascule tel vers sib   \n",
       "169513  3697992            1            ddcp_achats billets nb dps 19052021   \n",
       "214421  2925324            1                  consentement optout scolaires   \n",
       "234546  4575957            1                       Portrait de Leila shahid   \n",
       "259808  3722259            1                         consentement optin b2b   \n",
       "274380  4510423            1                       DDCP_marseille_jazz_2023   \n",
       "307511  5174466            1                             ddcp actoral 21-22   \n",
       "357509  4442526            1                           ddcp musique barvalo   \n",
       "392920  4390642            1     ddcp_md_promo_spectateurs theatre contempo   \n",
       "449620  4411897            1          FORMATION _ acheteurs optin last year   \n",
       "503809  4734591            1       consentement optin mediation specialisee   \n",
       "651222  3554426            1                         consentement optin b2c   \n",
       "654246  5182212            1    DDCP spectateurs Festival de Marseille 2023   \n",
       "654395  5182456            1       rencontres_echelle_spectateurs_2021_2023   \n",
       "\n",
       "        target_type_is_import       target_type_name  \n",
       "8793                    False   manual_static_filter  \n",
       "13249                   False   manual_static_filter  \n",
       "21424                   False   manual_static_filter  \n",
       "21665                   False   manual_static_filter  \n",
       "22811                   False   manual_static_filter  \n",
       "57305                   False  manual_dynamic_filter  \n",
       "58843                   False   manual_static_filter  \n",
       "66813                   False   manual_static_filter  \n",
       "68367                   False  manual_dynamic_filter  \n",
       "77320                   False   manual_static_filter  \n",
       "84350                   False   manual_static_filter  \n",
       "85383                   False   manual_static_filter  \n",
       "92868                   False   manual_static_filter  \n",
       "99670                   False   manual_static_filter  \n",
       "105477                  False   manual_static_filter  \n",
       "169513                  False   manual_static_filter  \n",
       "214421                  False   manual_static_filter  \n",
       "234546                  False   manual_static_filter  \n",
       "259808                  False   manual_static_filter  \n",
       "274380                  False   manual_static_filter  \n",
       "307511                  False   manual_static_filter  \n",
       "357509                  False   manual_static_filter  \n",
       "392920                  False   manual_static_filter  \n",
       "449620                  False  manual_dynamic_filter  \n",
       "503809                  False   manual_static_filter  \n",
       "651222                  False   manual_static_filter  \n",
       "654246                  False   manual_static_filter  \n",
       "654395                  False   manual_static_filter  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1_targets_full[df1_targets_full['customer_id'] == 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f665824-a026-4acd-8358-b408a61854b4",
   "metadata": {},
   "source": [
    "## Campaign area"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "5d05203c-ea30-4208-a29f-fef7737c672e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_9792/1967867975.py:15: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df[column_name] = pd.to_datetime(df[column_name], utc = True, format = 'ISO8601')\n",
      "/tmp/ipykernel_9792/1967867975.py:15: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df[column_name] = pd.to_datetime(df[column_name], utc = True, format = 'ISO8601')\n",
      "/tmp/ipykernel_9792/1967867975.py:15: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df[column_name] = pd.to_datetime(df[column_name], utc = True, format = 'ISO8601')\n"
     ]
    }
   ],
   "source": [
    "# campaign_stats cleaning \n",
    "df1_campaign_stats_clean = df1_campaign_stats[[\"id\", \"campaign_id\", \"customer_id\", \"opened_at\", \"sent_at\", \"delivered_at\"]]\n",
    "cleaning_date(df1_campaign_stats_clean, 'opened_at')\n",
    "cleaning_date(df1_campaign_stats_clean, 'sent_at')\n",
    "cleaning_date(df1_campaign_stats_clean, 'delivered_at')\n",
    "\n",
    "# campaigns cleaning\n",
    "df1_campaigns_clean = df1_campaigns[[\"id\", \"name\", \"service_id\", \"sent_at\"]].add_prefix(\"campaign_\")\n",
    "cleaning_date(df1_campaigns_clean, 'campaign_sent_at')\n",
    "\n",
    "# Merge \n",
    "df1_campaigns_full = pd.merge(df1_campaign_stats_clean, df1_campaigns_clean, on = \"campaign_id\", how = \"left\")\n",
    "df1_campaigns_full.drop(['campaign_id'], axis = 1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "8ac634cf-2a30-4ccc-a34d-0fd401a49aaa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 6214808 entries, 0 to 6214807\n",
      "Data columns (total 8 columns):\n",
      " #   Column               Dtype              \n",
      "---  ------               -----              \n",
      " 0   id                   int64              \n",
      " 1   customer_id          int64              \n",
      " 2   opened_at            datetime64[ns, UTC]\n",
      " 3   sent_at              datetime64[ns, UTC]\n",
      " 4   delivered_at         datetime64[ns, UTC]\n",
      " 5   campaign_name        object             \n",
      " 6   campaign_service_id  int64              \n",
      " 7   campaign_sent_at     datetime64[ns, UTC]\n",
      "dtypes: datetime64[ns, UTC](4), int64(3), object(1)\n",
      "memory usage: 379.3+ MB\n"
     ]
    }
   ],
   "source": [
    "df1_campaigns_full.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "7d22cdd5-2060-4922-8e04-27b613d4ee27",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>opened_at</th>\n",
       "      <th>sent_at</th>\n",
       "      <th>delivered_at</th>\n",
       "      <th>campaign_name</th>\n",
       "      <th>campaign_service_id</th>\n",
       "      <th>campaign_sent_at</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19793</td>\n",
       "      <td>112597</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2021-03-28 16:01:09+00:00</td>\n",
       "      <td>2021-03-28 16:24:18+00:00</td>\n",
       "      <td>Le Mucem chez vous, gardons le lien #22</td>\n",
       "      <td>404</td>\n",
       "      <td>2021-03-27 23:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14211</td>\n",
       "      <td>113666</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2021-03-28 16:01:09+00:00</td>\n",
       "      <td>2021-03-28 16:21:02+00:00</td>\n",
       "      <td>Le Mucem chez vous, gardons le lien #22</td>\n",
       "      <td>404</td>\n",
       "      <td>2021-03-27 23:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13150</td>\n",
       "      <td>280561</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2021-03-28 16:00:59+00:00</td>\n",
       "      <td>2021-03-28 16:08:45+00:00</td>\n",
       "      <td>Le Mucem chez vous, gardons le lien #22</td>\n",
       "      <td>404</td>\n",
       "      <td>2021-03-27 23:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7073</td>\n",
       "      <td>101007</td>\n",
       "      <td>2021-03-28 18:11:06+00:00</td>\n",
       "      <td>2021-03-28 16:00:59+00:00</td>\n",
       "      <td>2021-03-28 16:09:47+00:00</td>\n",
       "      <td>Le Mucem chez vous, gardons le lien #22</td>\n",
       "      <td>404</td>\n",
       "      <td>2021-03-27 23:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5175</td>\n",
       "      <td>103972</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2021-03-28 16:01:06+00:00</td>\n",
       "      <td>2021-03-28 16:05:03+00:00</td>\n",
       "      <td>Le Mucem chez vous, gardons le lien #22</td>\n",
       "      <td>404</td>\n",
       "      <td>2021-03-27 23:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6214803</th>\n",
       "      <td>8302994</td>\n",
       "      <td>266155</td>\n",
       "      <td>2023-10-23 09:43:25+00:00</td>\n",
       "      <td>2023-10-23 09:32:33+00:00</td>\n",
       "      <td>2023-10-23 09:32:34+00:00</td>\n",
       "      <td>dre_nov_2023</td>\n",
       "      <td>1318</td>\n",
       "      <td>2023-10-23 09:31:17+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6214804</th>\n",
       "      <td>8303307</td>\n",
       "      <td>21355</td>\n",
       "      <td>2023-10-23 09:44:02+00:00</td>\n",
       "      <td>2023-10-23 09:32:49+00:00</td>\n",
       "      <td>2023-10-23 09:32:49+00:00</td>\n",
       "      <td>dre_nov_2023</td>\n",
       "      <td>1318</td>\n",
       "      <td>2023-10-23 09:31:17+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6214805</th>\n",
       "      <td>8304346</td>\n",
       "      <td>21849</td>\n",
       "      <td>2023-10-23 09:45:52+00:00</td>\n",
       "      <td>2023-10-23 09:33:28+00:00</td>\n",
       "      <td>2023-10-23 09:33:29+00:00</td>\n",
       "      <td>dre_nov_2023</td>\n",
       "      <td>1318</td>\n",
       "      <td>2023-10-23 09:31:17+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6214806</th>\n",
       "      <td>8302037</td>\n",
       "      <td>667789</td>\n",
       "      <td>2023-10-23 09:47:32+00:00</td>\n",
       "      <td>2023-10-23 09:31:53+00:00</td>\n",
       "      <td>2023-10-23 09:31:54+00:00</td>\n",
       "      <td>dre_nov_2023</td>\n",
       "      <td>1318</td>\n",
       "      <td>2023-10-23 09:31:17+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6214807</th>\n",
       "      <td>8304939</td>\n",
       "      <td>294154</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2023-10-23 09:33:54+00:00</td>\n",
       "      <td>2023-10-23 09:33:55+00:00</td>\n",
       "      <td>dre_nov_2023</td>\n",
       "      <td>1318</td>\n",
       "      <td>2023-10-23 09:31:17+00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6214808 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              id  customer_id                 opened_at  \\\n",
       "0          19793       112597                       NaT   \n",
       "1          14211       113666                       NaT   \n",
       "2          13150       280561                       NaT   \n",
       "3           7073       101007 2021-03-28 18:11:06+00:00   \n",
       "4           5175       103972                       NaT   \n",
       "...          ...          ...                       ...   \n",
       "6214803  8302994       266155 2023-10-23 09:43:25+00:00   \n",
       "6214804  8303307        21355 2023-10-23 09:44:02+00:00   \n",
       "6214805  8304346        21849 2023-10-23 09:45:52+00:00   \n",
       "6214806  8302037       667789 2023-10-23 09:47:32+00:00   \n",
       "6214807  8304939       294154                       NaT   \n",
       "\n",
       "                          sent_at              delivered_at  \\\n",
       "0       2021-03-28 16:01:09+00:00 2021-03-28 16:24:18+00:00   \n",
       "1       2021-03-28 16:01:09+00:00 2021-03-28 16:21:02+00:00   \n",
       "2       2021-03-28 16:00:59+00:00 2021-03-28 16:08:45+00:00   \n",
       "3       2021-03-28 16:00:59+00:00 2021-03-28 16:09:47+00:00   \n",
       "4       2021-03-28 16:01:06+00:00 2021-03-28 16:05:03+00:00   \n",
       "...                           ...                       ...   \n",
       "6214803 2023-10-23 09:32:33+00:00 2023-10-23 09:32:34+00:00   \n",
       "6214804 2023-10-23 09:32:49+00:00 2023-10-23 09:32:49+00:00   \n",
       "6214805 2023-10-23 09:33:28+00:00 2023-10-23 09:33:29+00:00   \n",
       "6214806 2023-10-23 09:31:53+00:00 2023-10-23 09:31:54+00:00   \n",
       "6214807 2023-10-23 09:33:54+00:00 2023-10-23 09:33:55+00:00   \n",
       "\n",
       "                                   campaign_name  campaign_service_id  \\\n",
       "0        Le Mucem chez vous, gardons le lien #22                  404   \n",
       "1        Le Mucem chez vous, gardons le lien #22                  404   \n",
       "2        Le Mucem chez vous, gardons le lien #22                  404   \n",
       "3        Le Mucem chez vous, gardons le lien #22                  404   \n",
       "4        Le Mucem chez vous, gardons le lien #22                  404   \n",
       "...                                          ...                  ...   \n",
       "6214803                             dre_nov_2023                 1318   \n",
       "6214804                             dre_nov_2023                 1318   \n",
       "6214805                             dre_nov_2023                 1318   \n",
       "6214806                             dre_nov_2023                 1318   \n",
       "6214807                             dre_nov_2023                 1318   \n",
       "\n",
       "                 campaign_sent_at  \n",
       "0       2021-03-27 23:00:00+00:00  \n",
       "1       2021-03-27 23:00:00+00:00  \n",
       "2       2021-03-27 23:00:00+00:00  \n",
       "3       2021-03-27 23:00:00+00:00  \n",
       "4       2021-03-27 23:00:00+00:00  \n",
       "...                           ...  \n",
       "6214803 2023-10-23 09:31:17+00:00  \n",
       "6214804 2023-10-23 09:31:17+00:00  \n",
       "6214805 2023-10-23 09:31:17+00:00  \n",
       "6214806 2023-10-23 09:31:17+00:00  \n",
       "6214807 2023-10-23 09:31:17+00:00  \n",
       "\n",
       "[6214808 rows x 8 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1_campaigns_information"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a5b24f0-4bca-4cde-a6ba-eb130b38cac4",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "## Link area"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "bc63bc4e-6cc1-4d35-9635-faf55339e186",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "          id                                               name  service_id  \\\n",
       "0    1319613                newsletter enseignants janvier 2022         721   \n",
       "1    1319586                                   lsf_janvier_2022         717   \n",
       "2    1319282  Invitation à déjeuner au Mucem | Vernissage « ...         591   \n",
       "3    1319283     Vacances de la Toussaint - centres des loisirs         590   \n",
       "4    1319636                              ddcp_promo_md_livemag         730   \n",
       "..       ...                                                ...         ...   \n",
       "952  1320072                                       dre_gaza0106         881   \n",
       "953   661398          DDCP Plan Bis 4 - Marketing direct - MJ5C         183   \n",
       "954  1320487                 Invitation portes ouvertes amitiés         988   \n",
       "955   906903  DDCP PROMO La méditerranée des philosophes #3 ...         310   \n",
       "956   579313             ddcp_promo_automation_manuel_pre_visit         481   \n",
       "\n",
       "                           created_at                        updated_at  \\\n",
       "0    2022-01-14 16:06:42.586321+01:00  2022-02-03 14:17:27.112963+01:00   \n",
       "1    2022-01-07 11:30:35.315895+01:00  2022-02-03 14:17:27.116171+01:00   \n",
       "2    2021-09-28 12:50:24.448752+02:00  2022-02-03 14:17:27.119582+01:00   \n",
       "3    2021-09-28 18:01:04.692073+02:00  2022-02-03 14:17:27.124408+01:00   \n",
       "4    2022-01-27 18:00:41.053069+01:00  2022-02-03 14:17:27.127607+01:00   \n",
       "..                                ...                               ...   \n",
       "952  2022-05-26 09:01:35.523639+02:00  2022-12-02 17:51:22.614046+01:00   \n",
       "953  2021-06-18 10:30:01.259578+02:00  2021-09-24 11:56:09.082785+02:00   \n",
       "954  2022-09-29 18:01:33.834090+02:00  2022-12-02 17:51:23.258324+01:00   \n",
       "955  2021-07-19 14:07:16.177390+02:00  2021-09-24 11:56:09.086101+02:00   \n",
       "956  2021-06-08 17:38:54.041310+02:00  2021-09-24 11:56:09.089394+02:00   \n",
       "\n",
       "     process_id  report_url  category  to_be_synced  \\\n",
       "0           NaN         NaN       0.0         False   \n",
       "1           NaN         NaN       0.0         False   \n",
       "2           NaN         NaN       0.0         False   \n",
       "3           NaN         NaN       0.0         False   \n",
       "4           NaN         NaN       0.0         False   \n",
       "..          ...         ...       ...           ...   \n",
       "952         NaN         NaN       0.0         False   \n",
       "953         NaN         NaN       0.0         False   \n",
       "954         NaN         NaN       0.0         False   \n",
       "955         NaN         NaN       0.0         False   \n",
       "956         NaN         NaN       0.0         False   \n",
       "\n",
       "                           identifier                    sent_at  \n",
       "0    aba3b6fd5d186d28e06ff97135cade7f  2022-01-14 00:00:00+01:00  \n",
       "1    788d986905533aba051261497ecffcbb  2022-01-07 00:00:00+01:00  \n",
       "2    3493894fa4ea036cfc6433c3e2ee63b0  2021-09-28 00:00:00+02:00  \n",
       "3    08b255a5d42b89b0585260b6f2360bdd  2021-09-28 00:00:00+02:00  \n",
       "4    d5cfead94f5350c12c322b5b664544c1  2022-01-27 00:00:00+01:00  \n",
       "..                                ...                        ...  \n",
       "952  7504adad8bb96320eb3afdd4df6e1f60  2022-05-26 00:00:00+02:00  \n",
       "953  cedebb6e872f539bef8c3f919874e9d7  2020-07-27 00:00:00+02:00  \n",
       "954  9908279ebbf1f9b250ba689db6a0222b  2022-09-29 00:00:00+02:00  \n",
       "955  06eb61b839a0cefee4967c67ccb099dc  2020-12-23 00:00:00+01:00  \n",
       "956  9461cce28ebe3e76fb4b931c35a169b0  2021-06-08 00:00:00+02:00  \n",
       "\n",
       "[957 rows x 11 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1_campaigns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "c19b321f-65f9-4d6c-8c1f-edb2eb9d70e7",
   "metadata": {},
   "outputs": [
    {
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       "            id                 clicked_at  link_id  customer_id  \\\n",
       "0            1  2021-03-26 16:30:36+01:00        1       284033   \n",
       "1            2  2021-03-26 17:16:34+01:00        2       119768   \n",
       "2          272  2021-03-28 20:03:32+02:00       42       113105   \n",
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       "4            5  2021-03-26 17:46:00+01:00        3       105095   \n",
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       "\n",
       "                              created_at                        updated_at  \n",
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       "\n",
       "[151051 rows x 6 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
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    }
   ],
   "source": [
    "df1_link_stats"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96ea2523-38dc-47ef-a49e-2c2d9ad0b1c6",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "## Exploration variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "aaa41688-ea7e-4dba-851c-1f0b0ec43c71",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fonction d'exploration pour suppliers.csv = label itr et commission inconnues\n",
    "def suppliers_exploration(suppliers = None) : \n",
    "    \n",
    "    # Taux de NaN pour ces colonnes\n",
    "    label_na = suppliers['label'].isna().sum()/len(suppliers)*100\n",
    "    itr_na = suppliers['itr'].isna().sum()/len(suppliers)*100\n",
    "    commission_na = suppliers['commission'].isna().sum()/len(suppliers)*100\n",
    "\n",
    "    suppliers_desc = pd.DataFrame({'nb_suppliers' : [suppliers['name'].nunique()],\n",
    "                                  'label_na' : [label_na],\n",
    "                                  'itr_na' : [itr_na],\n",
    "                                  'commission_na' : [commission_na]})\n",
    "\n",
    "    return suppliers_desc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "2fecc2e1-113f-46ed-9065-0b9ee416166e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1_suppliers_desc = suppliers_exploration(suppliers = df1_suppliers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "55f6170a-36fb-4efb-9810-f982883660cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "   nb_suppliers  label_na  itr_na  commission_na\n",
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   ],
   "source": [
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  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "0030fd02-09e3-42f5-9c83-290458a38c29",
   "metadata": {},
   "outputs": [],
   "source": [
    "BUCKET = \"bdc2324-data\"\n",
    "liste_folders = fs.ls(BUCKET)\n",
    "\n",
    "liste_files = []\n",
    "for company_folder in liste_folders : \n",
    "    liste_files.extend(fs.ls(company_folder))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "6b1736d1-8fd7-4fcc-9431-b8bf0c7b4f2b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['bdc2324-data/1/1suppliers.csv', 'bdc2324-data/10/10suppliers.csv', 'bdc2324-data/101/101suppliers.csv', 'bdc2324-data/11/11suppliers.csv', 'bdc2324-data/12/12suppliers.csv', 'bdc2324-data/13/13suppliers.csv', 'bdc2324-data/14/14suppliers.csv', 'bdc2324-data/2/2suppliers.csv', 'bdc2324-data/3/3suppliers.csv', 'bdc2324-data/4/4suppliers.csv', 'bdc2324-data/5/5suppliers.csv', 'bdc2324-data/6/6suppliers.csv', 'bdc2324-data/7/7suppliers.csv', 'bdc2324-data/8/8suppliers.csv', 'bdc2324-data/9/9suppliers.csv']\n"
     ]
    }
   ],
   "source": [
    "liste_database_select = ['suppliers']\n",
    "\n",
    "# Filtrer la liste pour les éléments contenant au moins un élément de la liste à tester\n",
    "liste_suppliers = [element for element in liste_files if any(element_part in element for element_part in liste_database_select)]\n",
    "\n",
    "# Afficher le résultat\n",
    "print(liste_suppliers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "226b694b-0b00-4167-b69f-3178902254eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# loop to create dataframes from file 2\n",
    "def database_loading(database_name = None):\n",
    "    files_path = database_name\n",
    "    \n",
    "    client_number = files_path[0].split(\"/\")[1]\n",
    "    df_prefix = \"df\" + str(client_number) + \"_\"\n",
    "    \n",
    "    for i in range(len(files_path)) :\n",
    "        current_path = files_path[i]\n",
    "        with fs.open(current_path, mode=\"rb\") as file_in:\n",
    "            df = pd.read_csv(file_in)\n",
    "            # the pattern of the name is df1xxx\n",
    "            nom_dataframe = df_prefix + re.search(r'\\/(\\d+)\\/(\\d+)([a-zA-Z_]+)\\.csv$', current_path).group(3)\n",
    "            globals()[nom_dataframe] = df\n",
    "\n",
    "    "
   ]
  }
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