diff --git a/code_valeur manquante.ipynb b/code_valeur manquante.ipynb
new file mode 100644
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--- /dev/null
+++ b/code_valeur manquante.ipynb	
@@ -0,0 +1,2116 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "c4205b5d-e052-4863-a46b-20e4757052a7",
+   "metadata": {},
+   "source": [
+    "# Business Data Challenge - Team 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "ae3af8e6-ced8-4994-8877-fa98d4297cc0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dd3184e7-54a1-4463-af42-5850d9517a41",
+   "metadata": {},
+   "source": [
+    "Configuration de l'accès aux données"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "b6035982-9ff4-4013-9792-2d50e10db3d1",
+   "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": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import s3fs\n",
+    "# Create filesystem object\n",
+    "S3_ENDPOINT_URL = \"https://\" + os.environ[\"AWS_S3_ENDPOINT\"]\n",
+    "fs = s3fs.S3FileSystem(client_kwargs={'endpoint_url': S3_ENDPOINT_URL})\n",
+    "\n",
+    "BUCKET = \"bdc2324-data/1\"\n",
+    "fs.ls(BUCKET)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "b86c935d-124f-453f-80dd-83ea6770d09c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dic_base=['campaign_stats','campaigns','categories','countries','currencies','customer_target_mappings','customersplus','event_types','events','facilities','link_stats','pricing_formulas','product_packs','products','products_groups','purchases','representation_category_capacities','representations','seasons','structure_tag_mappings','suppliers','tags','target_types','targets','tickets','type_of_categories','type_of_pricing_formulas','type_ofs']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "f6d0b27c-0ecd-406b-b042-6c3802dd68fd",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_438/1008972637.py:5: DtypeWarning: Columns (1) have mixed types. Specify dtype option on import or set low_memory=False.\n",
+      "  globals()[nom_base] = pd.read_csv(file_in, sep=\",\")\n"
+     ]
+    }
+   ],
+   "source": [
+    "dic_base=['campaign_stats','campaigns','categories','countries','currencies','customer_target_mappings','customersplus','event_types','events','facilities','link_stats','pricing_formulas','product_packs','products','products_groups','purchases','representation_category_capacities','representations','seasons','structure_tag_mappings','suppliers','tags','target_types','targets','tickets','type_of_categories','type_of_pricing_formulas','type_ofs']\n",
+    "for nom_base in dic_base:\n",
+    "    FILE_PATH_S3_fanta = 'bdc2324-data/1/1' + nom_base + '.csv'\n",
+    "    with fs.open(FILE_PATH_S3_fanta, mode=\"rb\") as file_in:\n",
+    "        globals()[nom_base] = pd.read_csv(file_in, sep=\",\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "2a6b5e22-3370-457f-83b7-dd1e13663229",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'bdc2324-data/1/1type_ofs.csv'"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "FILE_PATH_S3_fanta"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "79012186-ea51-4252-843e-36a9bbe3847e",
+   "metadata": {},
+   "source": [
+    "# Analyse exploratoire "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1a365f29-4766-47d8-9796-24a5271867b2",
+   "metadata": {},
+   "source": [
+    "## I. Base  type_of_pricing_formulas"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bcc14f93-2289-44eb-816b-a51049b258df",
+   "metadata": {},
+   "source": [
+    "## Detection des valeur manquantes"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "id": "ab2ec4c4-9d38-4aeb-8202-9116df3cdd66",
+   "metadata": {},
+   "source": [
+    "dic_prod_princing=['type_of_pricing_formulas','products_groups','pricing_formulas','product_packs','products']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "88759b4a-2633-478d-abce-29abeac376d1",
+   "metadata": {},
+   "source": [
+    "def verifier_donnees_manquantes(base):\n",
+    "    donnees_manquantes = base.isna().sum()\n",
+    "    print(\"Données manquantes pour la base :\")\n",
+    "    print(donnees_manquantes)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "df3075b4-1490-4cf2-a3fe-c6d4e2144ae3",
+   "metadata": {},
+   "source": [
+    "for nom_base in dic_prod_princing:\n",
+    "    verifier_donnees_manquantes(nom_base)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "e0c67c01-e837-4772-b070-d1be0d895a36",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "id                    0\n",
+       "type_of_id            0\n",
+       "pricing_formula_id    0\n",
+       "created_at            0\n",
+       "updated_at            0\n",
+       "identifier            0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#detection des Nan d\n",
+    "\n",
+    "type_of_pricing_formulas.isna().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "83a6a48d-effe-4537-b4bb-d5a540b610f1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#variable retenu:[[\"id\",\"type_of_id\",\"pricing_formula_id\"]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "3eaffaa6-1164-4ee9-a671-8b5eb3df797d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>type_of_id</th>\n",
+       "      <th>pricing_formula_id</th>\n",
+       "      <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>1</td>\n",
+       "      <td>127</td>\n",
+       "      <td>2021-01-05 11:55:51.226960+01:00</td>\n",
+       "      <td>2021-01-05 11:55:51.226960+01:00</td>\n",
+       "      <td>cf2918b25e6dcf8c30798ca05c8ec8ed</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2425</td>\n",
+       "      <td>2021-01-05 11:55:51.235606+01:00</td>\n",
+       "      <td>2021-01-05 11:55:51.235606+01:00</td>\n",
+       "      <td>2c8ee3f7c1487d792b6c946314e681f2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2937</td>\n",
+       "      <td>2021-01-05 11:55:51.240114+01:00</td>\n",
+       "      <td>2021-01-05 11:55:51.240114+01:00</td>\n",
+       "      <td>44e55c85e4eb59b3c3c01c137a6b25fc</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>48</td>\n",
+       "      <td>2021-01-05 11:55:51.244638+01:00</td>\n",
+       "      <td>2021-01-05 11:55:51.244638+01:00</td>\n",
+       "      <td>ee3bb93b7e2217cd86a49d547fedf6c6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>7</td>\n",
+       "      <td>2021-01-05 11:55:51.249409+01:00</td>\n",
+       "      <td>2021-01-05 11:55:51.249409+01:00</td>\n",
+       "      <td>ae701668574f1a653d2b21ddfd250620</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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>563</th>\n",
+       "      <td>564</td>\n",
+       "      <td>4</td>\n",
+       "      <td>6656</td>\n",
+       "      <td>2022-02-18 16:15:58.872249+01:00</td>\n",
+       "      <td>2022-02-18 16:15:58.872249+01:00</td>\n",
+       "      <td>f669824cdca9de9697f07ff3ba365a8d</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>564</th>\n",
+       "      <td>565</td>\n",
+       "      <td>4</td>\n",
+       "      <td>6607</td>\n",
+       "      <td>2022-02-18 16:15:59.231018+01:00</td>\n",
+       "      <td>2022-02-18 16:15:59.231018+01:00</td>\n",
+       "      <td>6421c8146a598758139153b0e7b921ea</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>565</th>\n",
+       "      <td>566</td>\n",
+       "      <td>4</td>\n",
+       "      <td>6700</td>\n",
+       "      <td>2022-02-18 16:15:59.724812+01:00</td>\n",
+       "      <td>2022-02-18 16:15:59.724812+01:00</td>\n",
+       "      <td>6823f6d4d80b322fbfb8b83545a9f96d</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>566</th>\n",
+       "      <td>567</td>\n",
+       "      <td>4</td>\n",
+       "      <td>8118</td>\n",
+       "      <td>2022-02-18 16:16:00.163381+01:00</td>\n",
+       "      <td>2022-02-18 16:16:00.163381+01:00</td>\n",
+       "      <td>35cfc12584b4d1b94795d97fd0aa56e8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>567</th>\n",
+       "      <td>569</td>\n",
+       "      <td>7</td>\n",
+       "      <td>48157</td>\n",
+       "      <td>2023-03-13 11:30:29.480161+01:00</td>\n",
+       "      <td>2023-03-13 11:30:29.480161+01:00</td>\n",
+       "      <td>55863541f33fd229ac9b54d9ec1f4874</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>568 rows × 6 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      id  type_of_id  pricing_formula_id                        created_at  \\\n",
+       "0      1           1                 127  2021-01-05 11:55:51.226960+01:00   \n",
+       "1      2           1                2425  2021-01-05 11:55:51.235606+01:00   \n",
+       "2      3           1                2937  2021-01-05 11:55:51.240114+01:00   \n",
+       "3      4           1                  48  2021-01-05 11:55:51.244638+01:00   \n",
+       "4      5           1                   7  2021-01-05 11:55:51.249409+01:00   \n",
+       "..   ...         ...                 ...                               ...   \n",
+       "563  564           4                6656  2022-02-18 16:15:58.872249+01:00   \n",
+       "564  565           4                6607  2022-02-18 16:15:59.231018+01:00   \n",
+       "565  566           4                6700  2022-02-18 16:15:59.724812+01:00   \n",
+       "566  567           4                8118  2022-02-18 16:16:00.163381+01:00   \n",
+       "567  569           7               48157  2023-03-13 11:30:29.480161+01:00   \n",
+       "\n",
+       "                           updated_at                        identifier  \n",
+       "0    2021-01-05 11:55:51.226960+01:00  cf2918b25e6dcf8c30798ca05c8ec8ed  \n",
+       "1    2021-01-05 11:55:51.235606+01:00  2c8ee3f7c1487d792b6c946314e681f2  \n",
+       "2    2021-01-05 11:55:51.240114+01:00  44e55c85e4eb59b3c3c01c137a6b25fc  \n",
+       "3    2021-01-05 11:55:51.244638+01:00  ee3bb93b7e2217cd86a49d547fedf6c6  \n",
+       "4    2021-01-05 11:55:51.249409+01:00  ae701668574f1a653d2b21ddfd250620  \n",
+       "..                                ...                               ...  \n",
+       "563  2022-02-18 16:15:58.872249+01:00  f669824cdca9de9697f07ff3ba365a8d  \n",
+       "564  2022-02-18 16:15:59.231018+01:00  6421c8146a598758139153b0e7b921ea  \n",
+       "565  2022-02-18 16:15:59.724812+01:00  6823f6d4d80b322fbfb8b83545a9f96d  \n",
+       "566  2022-02-18 16:16:00.163381+01:00  35cfc12584b4d1b94795d97fd0aa56e8  \n",
+       "567  2023-03-13 11:30:29.480161+01:00  55863541f33fd229ac9b54d9ec1f4874  \n",
+       "\n",
+       "[568 rows x 6 columns]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type_of_pricing_formulas"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "57298669-8d55-40d5-a5aa-4c5df984eec7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "id                     int64\n",
+       "type_of_id             int64\n",
+       "pricing_formula_id     int64\n",
+       "created_at            object\n",
+       "updated_at            object\n",
+       "identifier            object\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#type des variables\n",
+    "\n",
+    "type_of_pricing_formulas.dtypes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "c11850cb-8833-44c0-a11d-9695d620a42b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>type_of_id</th>\n",
+       "      <th>pricing_formula_id</th>\n",
+       "      <th>created_at</th>\n",
+       "      <th>updated_at</th>\n",
+       "      <th>identifier</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Empty DataFrame\n",
+       "Columns: [id, type_of_id, pricing_formula_id, created_at, updated_at, identifier]\n",
+       "Index: []"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Identification des doublons\n",
+    "type_of_pricing_formulas.loc[type_of_pricing_formulas['id'].duplicated(keep=False),:]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7a40de03-5e18-4d3d-a0f8-da960c29fad8",
+   "metadata": {},
+   "source": [
+    "## II.products_groups"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "89909175-6734-4e8e-8632-d6f8ca812388",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "id                    0\n",
+       "percent_price         0\n",
+       "max_price             0\n",
+       "min_price             0\n",
+       "category_id           0\n",
+       "pricing_formula_id    0\n",
+       "representation_id     0\n",
+       "created_at            0\n",
+       "updated_at            0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#detection des Nan \n",
+    "\n",
+    "products_groups.isna().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e0518684-c83c-4f0a-89ea-d7dcfd60051d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#variable retenu:[[\"id\",\"percent_price\",\"max_price\",\"min_price\",\"category_id\",\"pricing_formula_id\",\"representation_id\"]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "6a187170-96c4-48d2-9568-b270f67e2c27",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "id                      int64\n",
+       "percent_price         float64\n",
+       "max_price             float64\n",
+       "min_price             float64\n",
+       "category_id             int64\n",
+       "pricing_formula_id      int64\n",
+       "representation_id       int64\n",
+       "created_at             object\n",
+       "updated_at             object\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#type des variables\n",
+    "\n",
+    "products_groups.dtypes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "2fba2cb0-a6a4-43b2-a854-3be07939c28b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>percent_price</th>\n",
+       "      <th>max_price</th>\n",
+       "      <th>min_price</th>\n",
+       "      <th>category_id</th>\n",
+       "      <th>pricing_formula_id</th>\n",
+       "      <th>representation_id</th>\n",
+       "      <th>created_at</th>\n",
+       "      <th>updated_at</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Empty DataFrame\n",
+       "Columns: [id, percent_price, max_price, min_price, category_id, pricing_formula_id, representation_id, created_at, updated_at]\n",
+       "Index: []"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Identification des doublons\n",
+    "products_groups.loc[products_groups[['id','pricing_formula_id','representation_id']].duplicated(keep=False),:]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5312ac13-8fbd-4c3f-a98a-8c28f079a599",
+   "metadata": {},
+   "source": [
+    "## III.pricing_formulas"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "3383a773-0817-4b23-84e7-8d5d0c74b179",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>name</th>\n",
+       "      <th>created_at</th>\n",
+       "      <th>updated_at</th>\n",
+       "      <th>extra_field</th>\n",
+       "      <th>identifier</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>41909</td>\n",
+       "      <td>visite mécènes 1h30</td>\n",
+       "      <td>2022-07-08 07:08:26.802266+02:00</td>\n",
+       "      <td>2022-07-08 07:08:26.802266+02:00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>21d4b0043c12b21952b0797d140991a1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>502</td>\n",
+       "      <td>entree mucem tp( expo picasso)</td>\n",
+       "      <td>2020-09-03 13:43:59.816765+02:00</td>\n",
+       "      <td>2022-02-18 15:57:55.792581+01:00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>223b09e6c3f1f75dbf8df019af97a555</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>504</td>\n",
+       "      <td>nombre de personnes cinema</td>\n",
+       "      <td>2020-09-03 13:43:59.818198+02:00</td>\n",
+       "      <td>2021-01-25 19:16:05.187114+01:00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>ba33b7b6d225a75d713a356b49c4d915</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>117</td>\n",
+       "      <td>spectacle tarif e famille tr</td>\n",
+       "      <td>2020-09-03 13:21:21.400249+02:00</td>\n",
+       "      <td>2023-03-13 11:30:29.525335+01:00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>a00b61ad933518856f86e63ca91a5750</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1496</td>\n",
+       "      <td>billet nb famille mecene 1a</td>\n",
+       "      <td>2020-09-03 14:29:33.320952+02:00</td>\n",
+       "      <td>2021-01-25 19:23:06.816402+01:00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>7f6013803c242253a5ccde80f780984f</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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>551</th>\n",
+       "      <td>529</td>\n",
+       "      <td>billet nb expo gr</td>\n",
+       "      <td>2020-09-03 13:43:59.835944+02:00</td>\n",
+       "      <td>2022-02-18 15:57:55.792581+01:00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>7d888e42abe101fc8b21dc88948c8b74</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>552</th>\n",
+       "      <td>3153</td>\n",
+       "      <td>nb pers visite scolaire rep</td>\n",
+       "      <td>2020-09-03 16:32:37.068864+02:00</td>\n",
+       "      <td>2022-02-18 15:57:55.792581+01:00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3cf21731c25eee650d5b232ee4780563</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>553</th>\n",
+       "      <td>5847</td>\n",
+       "      <td>visite scolaire rep1h00</td>\n",
+       "      <td>2021-06-09 18:10:49.742531+02:00</td>\n",
+       "      <td>2022-02-18 15:55:03.576236+01:00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>a7bb5a6892d55f0d5ee4ce5786ae5fc6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>554</th>\n",
+       "      <td>5840</td>\n",
+       "      <td>france billet - entree ts</td>\n",
+       "      <td>2021-06-09 18:10:49.737576+02:00</td>\n",
+       "      <td>2022-02-18 16:16:00.199543+01:00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4c53016fc65847646f600eff853593e5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>555</th>\n",
+       "      <td>5863</td>\n",
+       "      <td>france billet - entree tp</td>\n",
+       "      <td>2021-06-09 18:12:49.269924+02:00</td>\n",
+       "      <td>2022-02-18 16:16:00.199543+01:00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>90e642c0e1ef6bc9f2bc43089798de00</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>556 rows × 6 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        id                            name                        created_at  \\\n",
+       "0    41909             visite mécènes 1h30  2022-07-08 07:08:26.802266+02:00   \n",
+       "1      502  entree mucem tp( expo picasso)  2020-09-03 13:43:59.816765+02:00   \n",
+       "2      504      nombre de personnes cinema  2020-09-03 13:43:59.818198+02:00   \n",
+       "3      117    spectacle tarif e famille tr  2020-09-03 13:21:21.400249+02:00   \n",
+       "4     1496     billet nb famille mecene 1a  2020-09-03 14:29:33.320952+02:00   \n",
+       "..     ...                             ...                               ...   \n",
+       "551    529               billet nb expo gr  2020-09-03 13:43:59.835944+02:00   \n",
+       "552   3153     nb pers visite scolaire rep  2020-09-03 16:32:37.068864+02:00   \n",
+       "553   5847         visite scolaire rep1h00  2021-06-09 18:10:49.742531+02:00   \n",
+       "554   5840       france billet - entree ts  2021-06-09 18:10:49.737576+02:00   \n",
+       "555   5863       france billet - entree tp  2021-06-09 18:12:49.269924+02:00   \n",
+       "\n",
+       "                           updated_at  extra_field  \\\n",
+       "0    2022-07-08 07:08:26.802266+02:00          NaN   \n",
+       "1    2022-02-18 15:57:55.792581+01:00          NaN   \n",
+       "2    2021-01-25 19:16:05.187114+01:00          NaN   \n",
+       "3    2023-03-13 11:30:29.525335+01:00          NaN   \n",
+       "4    2021-01-25 19:23:06.816402+01:00          NaN   \n",
+       "..                                ...          ...   \n",
+       "551  2022-02-18 15:57:55.792581+01:00          NaN   \n",
+       "552  2022-02-18 15:57:55.792581+01:00          NaN   \n",
+       "553  2022-02-18 15:55:03.576236+01:00          NaN   \n",
+       "554  2022-02-18 16:16:00.199543+01:00          NaN   \n",
+       "555  2022-02-18 16:16:00.199543+01:00          NaN   \n",
+       "\n",
+       "                           identifier  \n",
+       "0    21d4b0043c12b21952b0797d140991a1  \n",
+       "1    223b09e6c3f1f75dbf8df019af97a555  \n",
+       "2    ba33b7b6d225a75d713a356b49c4d915  \n",
+       "3    a00b61ad933518856f86e63ca91a5750  \n",
+       "4    7f6013803c242253a5ccde80f780984f  \n",
+       "..                                ...  \n",
+       "551  7d888e42abe101fc8b21dc88948c8b74  \n",
+       "552  3cf21731c25eee650d5b232ee4780563  \n",
+       "553  a7bb5a6892d55f0d5ee4ce5786ae5fc6  \n",
+       "554  4c53016fc65847646f600eff853593e5  \n",
+       "555  90e642c0e1ef6bc9f2bc43089798de00  \n",
+       "\n",
+       "[556 rows x 6 columns]"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pricing_formulas"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "d8130c73-6c5f-45b1-93ae-db7679c8ca56",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "id             0.0\n",
+       "name           0.0\n",
+       "created_at     0.0\n",
+       "updated_at     0.0\n",
+       "extra_field    1.0\n",
+       "identifier     0.0\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#detection des Nan \n",
+    "\n",
+    "pricing_formulas.isna().sum()/pricing_formulas.shape[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9f2909c1-bc6a-443f-a077-84f6ce6b7ab5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#variable retenu: [[\"id\",\"name\"]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "44f1dbfd-c3cf-464b-9877-f37fcc61da92",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "id               int64\n",
+       "name            object\n",
+       "created_at      object\n",
+       "updated_at      object\n",
+       "extra_field    float64\n",
+       "identifier      object\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#type des variables\n",
+    "\n",
+    "pricing_formulas.dtypes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "6784b41b-da74-4fae-832e-16641ae710c1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>name</th>\n",
+       "      <th>created_at</th>\n",
+       "      <th>updated_at</th>\n",
+       "      <th>extra_field</th>\n",
+       "      <th>identifier</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Empty DataFrame\n",
+       "Columns: [id, name, created_at, updated_at, extra_field, identifier]\n",
+       "Index: []"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Identification des doublons\n",
+    "pricing_formulas.loc[pricing_formulas[['id']].duplicated(keep=False),:]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2145b0a4-b73d-4530-8c12-a78b1cf86eae",
+   "metadata": {},
+   "source": [
+    "## IV. product_packs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "e36b07a7-4f0b-4711-86a0-12a1d8158eef",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "id            0.0\n",
+       "name          1.0\n",
+       "type_of       0.0\n",
+       "created_at    0.0\n",
+       "updated_at    0.0\n",
+       "identifier    0.0\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#detection des Nan \n",
+    "\n",
+    "product_packs.isna().sum()/product_packs.shape[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e0887a01-51ea-4034-84fe-dc4dbf2ad949",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#variable retenu:[[\"id\",\"name\"]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "8707396a-f86b-476d-a9f9-c39f8de1d02e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "id              int64\n",
+       "name          float64\n",
+       "type_of         int64\n",
+       "created_at     object\n",
+       "updated_at     object\n",
+       "identifier     object\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#type des variables\n",
+    "\n",
+    "product_packs.dtypes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "4b102bd3-924b-43da-8915-be7664c23f97",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>name</th>\n",
+       "      <th>type_of</th>\n",
+       "      <th>created_at</th>\n",
+       "      <th>updated_at</th>\n",
+       "      <th>identifier</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Empty DataFrame\n",
+       "Columns: [id, name, type_of, created_at, updated_at, identifier]\n",
+       "Index: []"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Identification des doublons\n",
+    "product_packs.loc[product_packs[['id']].duplicated(keep=False),:]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cfe0c525-896b-4731-b38e-306ff6ea0c65",
+   "metadata": {},
+   "source": [
+    "## V.products"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "968beb24-f70c-4eb6-8b1e-4b04bc7fe9c9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "id                    0.0\n",
+       "amount                0.0\n",
+       "is_full_price         0.0\n",
+       "representation_id     0.0\n",
+       "pricing_formula_id    0.0\n",
+       "created_at            0.0\n",
+       "updated_at            0.0\n",
+       "category_id           0.0\n",
+       "apply_price           0.0\n",
+       "products_group_id     0.0\n",
+       "product_pack_id       0.0\n",
+       "extra_field           1.0\n",
+       "amount_consumption    1.0\n",
+       "identifier            0.0\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#detection des Nan \n",
+    "\n",
+    "products.isna().sum()/products.shape[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "15bc6ac6-67e8-4e2c-9641-7ee8bb2581a3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "id                      int64\n",
+       "amount                float64\n",
+       "is_full_price            bool\n",
+       "representation_id       int64\n",
+       "pricing_formula_id      int64\n",
+       "created_at             object\n",
+       "updated_at             object\n",
+       "category_id             int64\n",
+       "apply_price           float64\n",
+       "products_group_id       int64\n",
+       "product_pack_id         int64\n",
+       "extra_field           float64\n",
+       "amount_consumption    float64\n",
+       "identifier             object\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#type des variables\n",
+    "\n",
+    "products.dtypes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "7daa4f1a-e429-4daf-a2e1-1e311b487e09",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#dic_prod_princing=['type_of_pricing_formulas','products_groups','pricing_formulas','product_packs','products']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "dc12b746-6708-4708-826a-acb5a8e665a1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
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+       "\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>name</th>\n",
+       "      <th>created_at</th>\n",
+       "      <th>updated_at</th>\n",
+       "      <th>extra_field</th>\n",
+       "      <th>identifier</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>41909</td>\n",
+       "      <td>visite mécènes 1h30</td>\n",
+       "      <td>2022-07-08 07:08:26.802266+02:00</td>\n",
+       "      <td>2022-07-08 07:08:26.802266+02:00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>21d4b0043c12b21952b0797d140991a1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>502</td>\n",
+       "      <td>entree mucem tp( expo picasso)</td>\n",
+       "      <td>2020-09-03 13:43:59.816765+02:00</td>\n",
+       "      <td>2022-02-18 15:57:55.792581+01:00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>223b09e6c3f1f75dbf8df019af97a555</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>504</td>\n",
+       "      <td>nombre de personnes cinema</td>\n",
+       "      <td>2020-09-03 13:43:59.818198+02:00</td>\n",
+       "      <td>2021-01-25 19:16:05.187114+01:00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>ba33b7b6d225a75d713a356b49c4d915</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>117</td>\n",
+       "      <td>spectacle tarif e famille tr</td>\n",
+       "      <td>2020-09-03 13:21:21.400249+02:00</td>\n",
+       "      <td>2023-03-13 11:30:29.525335+01:00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>a00b61ad933518856f86e63ca91a5750</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1496</td>\n",
+       "      <td>billet nb famille mecene 1a</td>\n",
+       "      <td>2020-09-03 14:29:33.320952+02:00</td>\n",
+       "      <td>2021-01-25 19:23:06.816402+01:00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>7f6013803c242253a5ccde80f780984f</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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>551</th>\n",
+       "      <td>529</td>\n",
+       "      <td>billet nb expo gr</td>\n",
+       "      <td>2020-09-03 13:43:59.835944+02:00</td>\n",
+       "      <td>2022-02-18 15:57:55.792581+01:00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>7d888e42abe101fc8b21dc88948c8b74</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>552</th>\n",
+       "      <td>3153</td>\n",
+       "      <td>nb pers visite scolaire rep</td>\n",
+       "      <td>2020-09-03 16:32:37.068864+02:00</td>\n",
+       "      <td>2022-02-18 15:57:55.792581+01:00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3cf21731c25eee650d5b232ee4780563</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>553</th>\n",
+       "      <td>5847</td>\n",
+       "      <td>visite scolaire rep1h00</td>\n",
+       "      <td>2021-06-09 18:10:49.742531+02:00</td>\n",
+       "      <td>2022-02-18 15:55:03.576236+01:00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>a7bb5a6892d55f0d5ee4ce5786ae5fc6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>554</th>\n",
+       "      <td>5840</td>\n",
+       "      <td>france billet - entree ts</td>\n",
+       "      <td>2021-06-09 18:10:49.737576+02:00</td>\n",
+       "      <td>2022-02-18 16:16:00.199543+01:00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4c53016fc65847646f600eff853593e5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>555</th>\n",
+       "      <td>5863</td>\n",
+       "      <td>france billet - entree tp</td>\n",
+       "      <td>2021-06-09 18:12:49.269924+02:00</td>\n",
+       "      <td>2022-02-18 16:16:00.199543+01:00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>90e642c0e1ef6bc9f2bc43089798de00</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>556 rows × 6 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        id                            name                        created_at  \\\n",
+       "0    41909             visite mécènes 1h30  2022-07-08 07:08:26.802266+02:00   \n",
+       "1      502  entree mucem tp( expo picasso)  2020-09-03 13:43:59.816765+02:00   \n",
+       "2      504      nombre de personnes cinema  2020-09-03 13:43:59.818198+02:00   \n",
+       "3      117    spectacle tarif e famille tr  2020-09-03 13:21:21.400249+02:00   \n",
+       "4     1496     billet nb famille mecene 1a  2020-09-03 14:29:33.320952+02:00   \n",
+       "..     ...                             ...                               ...   \n",
+       "551    529               billet nb expo gr  2020-09-03 13:43:59.835944+02:00   \n",
+       "552   3153     nb pers visite scolaire rep  2020-09-03 16:32:37.068864+02:00   \n",
+       "553   5847         visite scolaire rep1h00  2021-06-09 18:10:49.742531+02:00   \n",
+       "554   5840       france billet - entree ts  2021-06-09 18:10:49.737576+02:00   \n",
+       "555   5863       france billet - entree tp  2021-06-09 18:12:49.269924+02:00   \n",
+       "\n",
+       "                           updated_at  extra_field  \\\n",
+       "0    2022-07-08 07:08:26.802266+02:00          NaN   \n",
+       "1    2022-02-18 15:57:55.792581+01:00          NaN   \n",
+       "2    2021-01-25 19:16:05.187114+01:00          NaN   \n",
+       "3    2023-03-13 11:30:29.525335+01:00          NaN   \n",
+       "4    2021-01-25 19:23:06.816402+01:00          NaN   \n",
+       "..                                ...          ...   \n",
+       "551  2022-02-18 15:57:55.792581+01:00          NaN   \n",
+       "552  2022-02-18 15:57:55.792581+01:00          NaN   \n",
+       "553  2022-02-18 15:55:03.576236+01:00          NaN   \n",
+       "554  2022-02-18 16:16:00.199543+01:00          NaN   \n",
+       "555  2022-02-18 16:16:00.199543+01:00          NaN   \n",
+       "\n",
+       "                           identifier  \n",
+       "0    21d4b0043c12b21952b0797d140991a1  \n",
+       "1    223b09e6c3f1f75dbf8df019af97a555  \n",
+       "2    ba33b7b6d225a75d713a356b49c4d915  \n",
+       "3    a00b61ad933518856f86e63ca91a5750  \n",
+       "4    7f6013803c242253a5ccde80f780984f  \n",
+       "..                                ...  \n",
+       "551  7d888e42abe101fc8b21dc88948c8b74  \n",
+       "552  3cf21731c25eee650d5b232ee4780563  \n",
+       "553  a7bb5a6892d55f0d5ee4ce5786ae5fc6  \n",
+       "554  4c53016fc65847646f600eff853593e5  \n",
+       "555  90e642c0e1ef6bc9f2bc43089798de00  \n",
+       "\n",
+       "[556 rows x 6 columns]"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pricing_formulas"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "46aad10f-8530-410e-872b-bb253c553a46",
+   "metadata": {},
+   "source": [
+    "# jointure entre les bases"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a4c3edd1-6d58-4c57-b3e4-0ef3529f6b8c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#dic_prod_princing=['type_of_pricing_formulas','products_groups','pricing_formulas','product_packs','products']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "id": "eac537e1-bbad-45bc-a85c-12b675da1088",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Merge1 entre products et pricing_formulas\n",
+    "base1=products.merge(pricing_formulas, how='left', left_on= 'pricing_formula_id', right_on= 'id',  suffixes = (\"_products\", \"_pricing_formula\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "id": "75be3a30-3114-432d-87d6-697533c3c871",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Merge2 entre base1 et products_groups\n",
+    "base2=base1.merge(products_groups, how='left', left_on= 'id_pricing_formula', right_on= 'id',  suffixes = (\"_merge2\", \"_product_group\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "id": "34a169c6-07a8-4ac3-a9e1-d7e7461f7310",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Merge3 entre base2 et type_of_pricing_formulas\n",
+    "base3=base2.merge(type_of_pricing_formulas, how='left', left_on= 'id_pricing_formula', right_on= 'pricing_formula_id',  suffixes = (\"_merge3\", \"_type_of_pricing_f\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "id": "f44f40d2-5304-4931-b7e6-fcc06b2657b6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Merge4 entre base3 et type_of_pricing_formulas\n",
+    "df_product_pricing=base3.merge(product_packs, how='left', left_on= 'product_pack_id', right_on= 'id',  suffixes = (\"_merge4\", \"_product_pack\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "id": "a28772c3-7bc1-46b4-acc8-1388dc60ec98",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "      <td>2022-01-28 19:29:23.525722+01:00</td>\n",
+       "      <td>2022-01-28 19:29:23.525722+01:00</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>156773</td>\n",
+       "      <td>...</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>2021-02-05 11:52:06.107939+01:00</td>\n",
+       "      <td>2021-02-05 11:52:06.107939+01:00</td>\n",
+       "      <td>7d0b25bdfff9f366da8be820608c8191</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2020-09-03 13:11:24.501197+02:00</td>\n",
+       "      <td>2020-09-03 13:11:24.501197+02:00</td>\n",
+       "      <td>a764b4bf13a360c7ac2a35ec4ca96c95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1341</td>\n",
+       "      <td>8.5</td>\n",
+       "      <td>False</td>\n",
+       "      <td>9</td>\n",
+       "      <td>93</td>\n",
+       "      <td>2020-09-03 13:29:30.773089+02:00</td>\n",
+       "      <td>2020-09-03 13:29:30.773089+02:00</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1175</td>\n",
+       "      <td>...</td>\n",
+       "      <td>93.0</td>\n",
+       "      <td>2021-02-05 11:52:06.004162+01:00</td>\n",
+       "      <td>2021-02-05 11:52:06.004162+01:00</td>\n",
+       "      <td>1dbb0795e8f47cb75ba7cdb08c06be5f</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2020-09-03 13:11:24.501197+02:00</td>\n",
+       "      <td>2020-09-03 13:11:24.501197+02:00</td>\n",
+       "      <td>a764b4bf13a360c7ac2a35ec4ca96c95</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 41 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   id_products  amount  is_full_price  representation_id_merge2  \\\n",
+       "0        10682     9.0          False                       914   \n",
+       "1          478     9.5          False                       273   \n",
+       "2        20873    11.5          False                       275   \n",
+       "3       157142     8.0          False                     82519   \n",
+       "4         1341     8.5          False                         9   \n",
+       "\n",
+       "   pricing_formula_id_merge2               created_at_products  \\\n",
+       "0                        114  2020-09-03 14:09:43.119798+02:00   \n",
+       "1                        131  2020-09-03 13:21:22.711773+02:00   \n",
+       "2                        137  2020-09-03 14:46:33.589030+02:00   \n",
+       "3                          9  2022-01-28 19:29:23.525722+01:00   \n",
+       "4                         93  2020-09-03 13:29:30.773089+02:00   \n",
+       "\n",
+       "                updated_at_products  category_id_merge2  apply_price  \\\n",
+       "0  2020-09-03 14:09:43.119798+02:00                  41          0.0   \n",
+       "1  2020-09-03 13:21:22.711773+02:00                   1          0.0   \n",
+       "2  2020-09-03 14:46:33.589030+02:00                   1          0.0   \n",
+       "3  2022-01-28 19:29:23.525722+01:00                   5          0.0   \n",
+       "4  2020-09-03 13:29:30.773089+02:00                   1          0.0   \n",
+       "\n",
+       "   products_group_id  ...  pricing_formula_id  \\\n",
+       "0              10655  ...               114.0   \n",
+       "1                471  ...               131.0   \n",
+       "2              20825  ...               137.0   \n",
+       "3             156773  ...                 9.0   \n",
+       "4               1175  ...                93.0   \n",
+       "\n",
+       "       created_at_type_of_pricing_f      updated_at_type_of_pricing_f  \\\n",
+       "0  2021-02-15 17:02:27.395376+01:00  2021-02-15 17:02:27.395376+01:00   \n",
+       "1  2021-02-05 11:52:05.923905+01:00  2021-02-05 11:52:05.923905+01:00   \n",
+       "2  2021-02-05 11:52:05.939898+01:00  2021-02-05 11:52:05.939898+01:00   \n",
+       "3  2021-02-05 11:52:06.107939+01:00  2021-02-05 11:52:06.107939+01:00   \n",
+       "4  2021-02-05 11:52:06.004162+01:00  2021-02-05 11:52:06.004162+01:00   \n",
+       "\n",
+       "                  identifier_merge4  id name_product_pack type_of  \\\n",
+       "0  3706121eb9f43b635bef1433c06f679c   1               NaN       0   \n",
+       "1  0aceb248607671792298436004b95275   1               NaN       0   \n",
+       "2  93002d4637331edd81ffc28b6e8e89c0   1               NaN       0   \n",
+       "3  7d0b25bdfff9f366da8be820608c8191   1               NaN       0   \n",
+       "4  1dbb0795e8f47cb75ba7cdb08c06be5f   1               NaN       0   \n",
+       "\n",
+       "                         created_at                        updated_at  \\\n",
+       "0  2020-09-03 13:11:24.501197+02:00  2020-09-03 13:11:24.501197+02:00   \n",
+       "1  2020-09-03 13:11:24.501197+02:00  2020-09-03 13:11:24.501197+02:00   \n",
+       "2  2020-09-03 13:11:24.501197+02:00  2020-09-03 13:11:24.501197+02:00   \n",
+       "3  2020-09-03 13:11:24.501197+02:00  2020-09-03 13:11:24.501197+02:00   \n",
+       "4  2020-09-03 13:11:24.501197+02:00  2020-09-03 13:11:24.501197+02:00   \n",
+       "\n",
+       "            identifier_product_pack  \n",
+       "0  a764b4bf13a360c7ac2a35ec4ca96c95  \n",
+       "1  a764b4bf13a360c7ac2a35ec4ca96c95  \n",
+       "2  a764b4bf13a360c7ac2a35ec4ca96c95  \n",
+       "3  a764b4bf13a360c7ac2a35ec4ca96c95  \n",
+       "4  a764b4bf13a360c7ac2a35ec4ca96c95  \n",
+       "\n",
+       "[5 rows x 41 columns]"
+      ]
+     },
+     "execution_count": 90,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_product_pricing.head(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "03442997-806f-4285-a139-3bad46bb4522",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "d22a0d75-53c5-4b54-9060-c9e7c307fb13",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "BUCKET = \"bdc2324-data\"\n",
+    "directory_path = '2'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "7c229dad-6ebd-4f43-99f1-fb330dc29466",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['bdc2324-data/2/2campaign_stats.csv',\n",
+       " 'bdc2324-data/2/2campaigns.csv',\n",
+       " 'bdc2324-data/2/2categories.csv',\n",
+       " 'bdc2324-data/2/2contribution_sites.csv',\n",
+       " 'bdc2324-data/2/2contributions.csv',\n",
+       " 'bdc2324-data/2/2countries.csv',\n",
+       " 'bdc2324-data/2/2currencies.csv',\n",
+       " 'bdc2324-data/2/2customer_target_mappings.csv',\n",
+       " 'bdc2324-data/2/2customersplus.csv',\n",
+       " 'bdc2324-data/2/2event_types.csv',\n",
+       " 'bdc2324-data/2/2events.csv',\n",
+       " 'bdc2324-data/2/2facilities.csv',\n",
+       " 'bdc2324-data/2/2link_stats.csv',\n",
+       " 'bdc2324-data/2/2pricing_formulas.csv',\n",
+       " 'bdc2324-data/2/2product_packs.csv',\n",
+       " 'bdc2324-data/2/2products.csv',\n",
+       " 'bdc2324-data/2/2products_groups.csv',\n",
+       " 'bdc2324-data/2/2purchases.csv',\n",
+       " 'bdc2324-data/2/2representation_category_capacities.csv',\n",
+       " 'bdc2324-data/2/2representations.csv',\n",
+       " 'bdc2324-data/2/2seasons.csv',\n",
+       " 'bdc2324-data/2/2structure_tag_mappings.csv',\n",
+       " 'bdc2324-data/2/2suppliers.csv',\n",
+       " 'bdc2324-data/2/2tags.csv',\n",
+       " 'bdc2324-data/2/2target_types.csv',\n",
+       " 'bdc2324-data/2/2targets.csv',\n",
+       " 'bdc2324-data/2/2tickets.csv']"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "BUCKET = \"bdc2324-data/2\"\n",
+    "fs.ls(BUCKET)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "df3d3548-3d76-4f07-afa1-e240932bc1c7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dic_base_ent2=['campaign_stats','campaigns','categories','contribution_sites','contributions','countries','currencies','customer_target_mappings','customersplus','event_types','events','facilities','link_stats','pricing_formulas','product_packs','products','products_groups','purchases','representation_category_capacities','representations','seasons','structure_tag_mappings','suppliers','tags','target_types','targets','tickets']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "90f8d5fc-43f3-4f36-b8cc-89a41785f032",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_438/673681459.py:5: DtypeWarning: Columns (20) have mixed types. Specify dtype option on import or set low_memory=False.\n",
+      "  globals()[nom_base] = pd.read_csv(file_in, sep=\",\")\n"
+     ]
+    }
+   ],
+   "source": [
+    "dic_base_ent2=['campaign_stats','campaigns','categories','contribution_sites','contributions','countries','currencies','customer_target_mappings','customersplus','event_types','events','facilities','link_stats','pricing_formulas','product_packs','products','products_groups','purchases','representation_category_capacities','representations','seasons','structure_tag_mappings','suppliers','tags','target_types','targets','tickets']\n",
+    "for nom_base in dic_base_ent2:\n",
+    "    FILE_PATH_S3_fanta = 'bdc2324-data/2/2' + nom_base + '.csv'\n",
+    "    with fs.open(FILE_PATH_S3_fanta, mode=\"rb\") as file_in:\n",
+    "        globals()[nom_base] = pd.read_csv(file_in, sep=\",\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "3e39a584-e02b-41b2-831c-33b920e298e9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "27"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(dic_base_ent2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2b6c6f65",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "def calculer_proportion_valeurs_manquantes_et_exporter(databases, fichier_sortie='proportion_valeurs_manquantes.xlsx'):\n",
+    "    \"\"\"\n",
+    "    Calculer la proportion de valeurs manquantes pour chaque variable dans chaque base de données et exporter les résultats dans un fichier Excel.\n",
+    "\n",
+    "    Paramètres:\n",
+    "    - databases (dict): Un dictionnaire où les clés sont les noms des bases de données et les valeurs sont les DataFrames pandas.\n",
+    "    - fichier_sortie (str): Le chemin du fichier Excel de sortie.\n",
+    "\n",
+    "    Retourne:\n",
+    "    - Un fichier Excel où chaque onglet représente une base de données différente avec la proportion de valeurs manquantes pour chaque variable.\n",
+    "    \"\"\"\n",
+    "    with pd.ExcelWriter(fichier_sortie) as writer:\n",
+    "        for nom_db, df in databases.items():\n",
+    "            # Calculer la proportion de valeurs manquantes pour chaque colonne\n",
+    "            proportion_manquantes = df.isnull().mean()\n",
+    "            # Convertir en DataFrame pour un meilleur affichage\n",
+    "            resultats_df = pd.DataFrame(proportion_manquantes, columns=['ProportionValeursManquantes'])\n",
+    "            resultats_df['ProportionValeursManquantes'] = resultats_df['ProportionValeursManquantes'].map(lambda x: f\"{x:.2%}\")\n",
+    "            # Écrire le DataFrame dans un onglet du fichier Excel\n",
+    "            resultats_df.to_excel(writer, sheet_name=nom_db)\n",
+    "\n",
+    "    print(f\"Les résultats ont été exportés dans le fichier '{fichier_sortie}'.\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "06759646-9419-4841-b12f-bbfceb417f3a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#fonction calcul la proportion de valeur manquante\n",
+    "\n",
+    "import pandas as pd\n",
+    "\n",
+    "def calculer_proportion_valeurs_manquantes(databases):\n",
+    "    \"\"\"\n",
+    "    Calculer la proportion de valeurs manquantes pour chaque variable dans chaque base de données.\n",
+    "\n",
+    "    Paramètres:\n",
+    "    - databases (dict): Un dictionnaire où les clés sont les noms des bases de données et les valeurs sont les DataFrames pandas.\n",
+    "\n",
+    "    Retourne:\n",
+    "    - Un dictionnaire où les clés sont les noms des bases de données et les valeurs sont des DataFrames avec la proportion de valeurs manquantes pour chaque variable.\n",
+    "    \"\"\"\n",
+    "    resultats = {}\n",
+    "    for nom_db, df in databases.items():\n",
+    "        # Calculer la proportion de valeurs manquantes pour chaque colonne\n",
+    "        proportion_manquantes = df.isnull().mean()\n",
+    "        # Convertir en DataFrame pour un meilleur affichage\n",
+    "        resultats_df = pd.DataFrame(proportion_manquantes, columns=['ProportionValeursManquantes'])\n",
+    "        resultats_df['ProportionValeursManquantes'] = resultats_df['ProportionValeursManquantes'].map(lambda x: f\"{x:.2%}\")\n",
+    "        # Ajouter le résultat au dictionnaire\n",
+    "        resultats[nom_db] = resultats_df\n",
+    "    return resultats"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0960daa8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Exemple d'utilisation\n",
+    "\n",
+    "databases = {'Base1': campaign_stats, 'Base2': campaigns}\n",
+    "\n",
+    "resultats = calculer_proportion_valeurs_manquantes(databases)\n",
+    "\n",
+    "for nom_db, resultat in resultats.items():\n",
+    "    print(f\"Base de données: {nom_db}\")\n",
+    "    print(resultat)\n",
+    "    print()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "77dc02bb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Exemple d'utilisation\n",
+    "dict={'campaign_stats': campaign_stats,\n",
+    " 'campaigns': campaigns,\n",
+    " 'categories': categories,\n",
+    " 'contribution_sites': contribution_sites,\n",
+    " 'contributions': contributions,\n",
+    " 'countries': countries,\n",
+    " 'currencies': currencies,\n",
+    " 'customer_target_mappings': customer_target_mappings,\n",
+    " 'customersplus': customersplus,\n",
+    " 'event_types': event_types,\n",
+    " 'events': events,\n",
+    " 'facilities': facilities,\n",
+    " 'link_stats': link_stats,\n",
+    " 'pricing_formulas': pricing_formulas,\n",
+    " 'product_packs': product_packs,\n",
+    " 'products': products,\n",
+    " 'products_groups': products_groups,\n",
+    " 'purchases': purchases,\n",
+    " 'representation_category_capacities': representation_category_capacities,\n",
+    " 'representations': representations,\n",
+    " 'seasons': seasons,\n",
+    " 'structure_tag_mappings': structure_tag_mappings,\n",
+    " 'suppliers': suppliers,\n",
+    " 'tags': tags,\n",
+    " 'target_types': target_types,\n",
+    " 'targets': targets,\n",
+    " 'tickets': tickets}\n",
+    "\n",
+    "resultats = calculer_proportion_valeurs_manquantes(dict)\n",
+    "\n",
+    "for nom_db, resultat in resultats.items():\n",
+    "    print(f\"Base de données: {nom_db}\")\n",
+    "    print(resultat)\n",
+    "    print()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "60be9271",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#MEME CODE mais avec l'exportation de result a en format excel"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "955fe358",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "def calculer_proportion_valeurs_manquantes_et_exporter(databases, fichier_sortie='proportion_valeurs_manquantes.xlsx'):\n",
+    "    \"\"\"\n",
+    "    Calculer la proportion de valeurs manquantes pour chaque variable dans chaque base de données et exporter les résultats dans un fichier Excel.\n",
+    "\n",
+    "    Paramètres:\n",
+    "    - databases (dict): Un dictionnaire où les clés sont les noms des bases de données et les valeurs sont les DataFrames pandas.\n",
+    "    - fichier_sortie (str): Le chemin du fichier Excel de sortie.\n",
+    "\n",
+    "    Retourne:\n",
+    "    - Un fichier Excel où chaque onglet représente une base de données différente avec la proportion de valeurs manquantes pour chaque variable.\n",
+    "    \"\"\"\n",
+    "    with pd.ExcelWriter(fichier_sortie) as writer:\n",
+    "        for nom_db, df in databases.items():\n",
+    "            # Calculer la proportion de valeurs manquantes pour chaque colonne\n",
+    "            proportion_manquantes = df.isnull().mean()\n",
+    "            # Convertir en DataFrame pour un meilleur affichage\n",
+    "            resultats_df = pd.DataFrame(proportion_manquantes, columns=['ProportionValeursManquantes'])\n",
+    "            resultats_df['ProportionValeursManquantes'] = resultats_df['ProportionValeursManquantes'].map(lambda x: f\"{x:.2%}\")\n",
+    "            # Écrire le DataFrame dans un onglet du fichier Excel\n",
+    "            resultats_df.to_excel(writer, sheet_name=nom_db)\n",
+    "\n",
+    "    print(f\"Les résultats ont été exportés dans le fichier '{fichier_sortie}'.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7897b689",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Exemple d'utilisation\n",
+    "dict={'campaign_stats': campaign_stats,\n",
+    " 'campaigns': campaigns,\n",
+    " 'categories': categories,\n",
+    " 'contribution_sites': contribution_sites,\n",
+    " 'contributions': contributions,\n",
+    " 'countries': countries,\n",
+    " 'currencies': currencies,\n",
+    " 'customer_target_mappings': customer_target_mappings,\n",
+    " 'customersplus': customersplus,\n",
+    " 'event_types': event_types,\n",
+    " 'events': events,\n",
+    " 'facilities': facilities,\n",
+    " 'link_stats': link_stats,\n",
+    " 'pricing_formulas': pricing_formulas,\n",
+    " 'product_packs': product_packs,\n",
+    " 'products': products,\n",
+    " 'products_groups': products_groups,\n",
+    " 'purchases': purchases,\n",
+    " 'representation_category_capacities': representation_category_capacities,\n",
+    " 'representations': representations,\n",
+    " 'seasons': seasons,\n",
+    " 'structure_tag_mappings': structure_tag_mappings,\n",
+    " 'suppliers': suppliers,\n",
+    " 'tags': tags,\n",
+    " 'target_types': target_types,\n",
+    " 'targets': targets,\n",
+    " 'tickets': tickets}\n",
+    "\n",
+    "calculer_proportion_valeurs_manquantes_et_exporter(dict, 'proportion_valeurs_manquantes_ent1.xlsx')\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "514273f4",
+   "metadata": {},
+   "source": [
+    "## Entreprise 3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "69b8f59a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "BUCKET = \"bdc2324-data/3\"\n",
+    "fs.ls(BUCKET)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9b4c005f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dic_base_ent3=['campaign_stats','campaigns','categories','consumptions','contribution_sites','contributions','countries','currencies','customer_target_mappings','customersplus','event_types','events','facilities','link_stats','pricing_formulas','product_packs','products','products_groups','purchases','representation_category_capacities','representations','seasons','structure_tag_mappings','suppliers','tags','target_types','targets','tickets']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "aae542d6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dic_base_ent3=['campaign_stats','campaigns','categories','consumptions','contribution_sites','contributions','countries','currencies','customer_target_mappings','customersplus','event_types','events','facilities','link_stats','pricing_formulas','product_packs','products','products_groups','purchases','representation_category_capacities','representations','seasons','structure_tag_mappings','suppliers','tags','target_types','targets','tickets']\n",
+    "for nom_base in dic_base_ent2:\n",
+    "    FILE_PATH_S3_fanta = 'bdc2324-data/3/3' + nom_base + '.csv'\n",
+    "    with fs.open(FILE_PATH_S3_fanta, mode=\"rb\") as file_in:\n",
+    "        globals()[nom_base] = pd.read_csv(file_in, sep=\",\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d4b2df76",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dict4={'campaign_stats': campaign_stats,\n",
+    " 'campaigns': campaigns,\n",
+    " 'categories': categories,\n",
+    "  'consumptions': consumptions,\n",
+    " 'contribution_sites': contribution_sites,\n",
+    " 'contributions': contributions,\n",
+    " 'countries': countries,\n",
+    " 'currencies': currencies,\n",
+    " 'customer_target_mappings': customer_target_mappings,\n",
+    " 'customersplus': customersplus,\n",
+    " 'event_types': event_types,\n",
+    " 'events': events,\n",
+    " 'facilities': facilities,\n",
+    " 'link_stats': link_stats,\n",
+    " 'pricing_formulas': pricing_formulas,\n",
+    " 'product_packs': product_packs,\n",
+    " 'products': products,\n",
+    " 'products_groups': products_groups,\n",
+    " 'purchases': purchases,\n",
+    " 'representation_category_capacities': representation_category_capacities,\n",
+    " 'representations': representations,\n",
+    " 'seasons': seasons,\n",
+    " 'structure_tag_mappings': structure_tag_mappings,\n",
+    " 'suppliers': suppliers,\n",
+    " 'tags': tags,\n",
+    " 'target_types': target_types,\n",
+    " 'targets': targets,\n",
+    " 'tickets': tickets}"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}