Bottleneck noteboock python.ipynb

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   "source": [
    "<span style=\"color: red; font-weight: bold; text-align: center; display: block; margin: auto; font-size: 2em;\">BottleNeck</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Résumé de la demande :**\n",
    "\n",
    "La mission consiste en trois étapes principales :\n",
    "\n",
    "1. **Rapprochement des exports :**\n",
    "   - Rapprocher les données de l'ERP (références produit, prix, stock) avec celles de la boutique en ligne (nom, description, ventes).\n",
    "   - Utiliser un tableau Excel créé par Sylvie pour établir le lien entre les références du produit dans l'ERP (product_id) et celles de la boutique en ligne (SKU).\n",
    "\n",
    "2. **Calcul du chiffre d'affaires :**\n",
    "   - Une fois le rapprochement effectué, fournir le chiffre d'affaires par produit.\n",
    "   - Calculer le total du chiffre d'affaires réalisé en ligne.\n",
    "\n",
    "3. **Analyse des prix des produits :**\n",
    "   - Détecter d'éventuelles valeurs aberrantes dans les prix des produits.\n",
    "   - Liste des valeurs aberrantes et représentation graphique pour une meilleure lisibilité.\n",
    "\n",
    "Les exports nécessaires ainsi qu'un tableau Excel pour le lien entre les références seront fournis. La présentation des résultats se fera lors de la prochaine réunion de COPIL, pouvant être sous forme de présentation ou d'un notebook utilisant R ou Python."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span style=\"color:blue; font-weight:bold; font-size:2em;\">I] Installation</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Importations des librairies nécessaires\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
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       "   product_id  onsale_web price  stock_quantity stock_status\n",
       "0        3847           1  24,2               0   outofstock\n",
       "1        3849           1  34,3               0   outofstock\n",
       "2        3850           1  20,8               0   outofstock\n",
       "3        4032           1  14,1               0   outofstock\n",
       "4        4039           1    46               0   outofstock"
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sku</th>\n",
       "      <th>virtual</th>\n",
       "      <th>downloadable</th>\n",
       "      <th>rating_count</th>\n",
       "      <th>average_rating</th>\n",
       "      <th>total_sales</th>\n",
       "      <th>tax_status</th>\n",
       "      <th>tax_class</th>\n",
       "      <th>post_author</th>\n",
       "      <th>post_date</th>\n",
       "      <th>...</th>\n",
       "      <th>post_name</th>\n",
       "      <th>post_modified</th>\n",
       "      <th>post_modified_gmt</th>\n",
       "      <th>post_content_filtered</th>\n",
       "      <th>post_parent</th>\n",
       "      <th>guid</th>\n",
       "      <th>menu_order</th>\n",
       "      <th>post_type</th>\n",
       "      <th>post_mime_type</th>\n",
       "      <th>comment_count</th>\n",
       "    </tr>\n",
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       "      <th>0</th>\n",
       "      <td>16004</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>2018-06-07 16:27:25</td>\n",
       "      <td>...</td>\n",
       "      <td>chateau-du-couvent-pomerol-2017</td>\n",
       "      <td>2020-08-25 18:35:02</td>\n",
       "      <td>2020-08-25 16:35:02</td>\n",
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       "      <td>https://www.bottle-neck.fr/wp-content/uploads/...</td>\n",
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       "      <td>2018-02-14 15:39:43</td>\n",
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       "      <td>pares-balta-penedes-indigena-2017</td>\n",
       "      <td>2020-08-20 15:35:02</td>\n",
       "      <td>2020-08-20 13:35:02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>https://www.bottle-neck.fr/?post_type=product&amp;...</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16209</td>\n",
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       "      <td>2020-01-25 14:08:16</td>\n",
       "      <td>...</td>\n",
       "      <td>domaine-de-la-monardiere-vacqueyras-les-vieill...</td>\n",
       "      <td>2020-08-21 11:35:02</td>\n",
       "      <td>2020-08-21 09:35:02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>https://www.bottle-neck.fr/wp-content/uploads/...</td>\n",
       "      <td>0.0</td>\n",
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       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
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      "text/plain": [
       "     sku  virtual  downloadable  rating_count  average_rating  total_sales  \\\n",
       "0  16004        0             0             0             0.0          5.0   \n",
       "1    NaN        0             0             0             NaN          NaN   \n",
       "2  15075        0             0             0             0.0          3.0   \n",
       "3  16209        0             0             0             0.0          6.0   \n",
       "4  15763        0             0             0             0.0          1.0   \n",
       "\n",
       "  tax_status  tax_class  post_author            post_date  ...  \\\n",
       "0        NaN        NaN          2.0  2018-06-07 16:27:25  ...   \n",
       "1        NaN        NaN          NaN                  NaN  ...   \n",
       "2    taxable        NaN          2.0  2018-02-14 15:39:43  ...   \n",
       "3    taxable        NaN          2.0  2018-02-14 17:15:31  ...   \n",
       "4        NaN        NaN          2.0  2020-01-25 14:08:16  ...   \n",
       "\n",
       "                                           post_name        post_modified  \\\n",
       "0                    chateau-du-couvent-pomerol-2017  2020-08-25 18:35:02   \n",
       "1                                                NaN                  NaN   \n",
       "2                  pares-balta-penedes-indigena-2017  2020-08-20 15:35:02   \n",
       "3                     maurel-cabardes-tradition-2017  2020-08-05 18:05:03   \n",
       "4  domaine-de-la-monardiere-vacqueyras-les-vieill...  2020-08-21 11:35:02   \n",
       "\n",
       "     post_modified_gmt post_content_filtered post_parent  \\\n",
       "0  2020-08-25 16:35:02                   NaN         0.0   \n",
       "1                  NaN                   NaN         NaN   \n",
       "2  2020-08-20 13:35:02                   NaN         0.0   \n",
       "3  2020-08-05 16:05:03                   NaN         0.0   \n",
       "4  2020-08-21 09:35:02                   NaN         0.0   \n",
       "\n",
       "                                                guid menu_order   post_type  \\\n",
       "0  https://www.bottle-neck.fr/wp-content/uploads/...        0.0  attachment   \n",
       "1                                                NaN        NaN         NaN   \n",
       "2  https://www.bottle-neck.fr/?post_type=product&...        0.0     product   \n",
       "3  https://www.bottle-neck.fr/?post_type=product&...        0.0     product   \n",
       "4  https://www.bottle-neck.fr/wp-content/uploads/...        0.0  attachment   \n",
       "\n",
       "  post_mime_type comment_count  \n",
       "0     image/jpeg           0.0  \n",
       "1            NaN           NaN  \n",
       "2            NaN           0.0  \n",
       "3            NaN           0.0  \n",
       "4     image/jpeg           0.0  \n",
       "\n",
       "[5 rows x 28 columns]"
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>product_id</th>\n",
       "      <th>id_web</th>\n",
       "    </tr>\n",
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       "      <td>3847</td>\n",
       "      <td>15298</td>\n",
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       "      <th>1</th>\n",
       "      <td>3849</td>\n",
       "      <td>15296</td>\n",
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       "      <th>2</th>\n",
       "      <td>3850</td>\n",
       "      <td>15300</td>\n",
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       "      <th>3</th>\n",
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       "      <td>19814</td>\n",
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      "text/plain": [
       "   product_id id_web\n",
       "0        3847  15298\n",
       "1        3849  15296\n",
       "2        3850  15300\n",
       "3        4032  19814\n",
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   "source": [
    "#chemin_fichier1_csv = 'C:\\Users\\Rorsharks\\Desktop\\P5\\Fichier_erp.csv'\n",
    "\n",
    "# Chargez les données depuis le fichier CSV dans un DataFrame\n",
    "stock = pd.read_csv('C:\\\\Users\\\\Rorsharks\\\\Desktop\\\\P5\\\\Fichier_erp.csv', sep=';')\n",
    "\n",
    "produits = pd.read_csv('C:\\\\Users\\\\Rorsharks\\\\Desktop\\\\P5\\\\Fichier_web.csv', sep=';', encoding='ISO-8859-1')\n",
    "\n",
    "liaison = pd.read_csv('C:\\\\Users\\\\Rorsharks\\\\Desktop\\\\P5\\\\Fichier_liaison.csv', sep=';')\n",
    "\n",
    "# Affichez les premières lignes du DataFrame\n",
    "display(stock.head())\n",
    "\n",
    "display(produits.head())\n",
    "\n",
    "display(liaison.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span style=\"color: #FF8C00;\">Il semble que certaines données soient marquées comme `NaN` (Not a Number), tandis que d'autres colonnes affichent des valeurs égales à zéro, notamment pour les colonnes \"forerp\" et \"web\".</span>\n",
    "\n",
    "<span style=\"color: #FF8C00;\">Les colonnes suivantes présentent des situations particulières :**</span>\n",
    "\n",
    "- <span style=\"color: #FF8C00;\"> Downloadable**: Il est possible qu'aucun produit n'ait été téléchargé, expliquant ainsi l'absence de données dans cette colonne.</span>\n",
    "\n",
    "- <span style=\"color: #FF8C00;\">Rating_count**: Certains produits peuvent ne pas avoir reçu de notation sur le site web, se traduisant par une valeur nulle dans cette colonne. Une autre possibilité est qu'ils aient reçu une notation de zéro.</span>\n",
    "\n",
    "- <span style=\"color: #FF8C00;\">Post_parent**: Il est envisageable que certains éléments ne possèdent pas de poste parent ou ne soient pas associés à un système post_parent.</span>\n",
    "\n",
    "- <span style=\"color: #FF8C00;\">Post_mime_type**: Tous les produits ne contiennent pas nécessairement de photo sur le site web, ce qui pourrait expliquer des valeurs vides ou spécifiques dans cette colonne.</span>\n",
    "\n",
    "- <span style=\"color: #FF8C00;\">Comment_count**: Il est envisageable que certains produits n'aient reçu aucun commentaire, d'où la présence de valeurs nulles dans cette colonne.</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span style=\"color:blue; font-weight:bold; font-size:2em;\">I.I]Vérification des fichiers</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Aucun doublon détecté.\n",
      "Aucune valeur NaN détectée.\n",
      "\n",
      "Statistiques générales (hors product_id):\n"
     ]
    },
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>onsale_web</th>\n",
       "      <th>stock_quantity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>825.000000</td>\n",
       "      <td>825.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.869091</td>\n",
       "      <td>26.583030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.337506</td>\n",
       "      <td>45.875948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>11.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>34.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>578.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       onsale_web  stock_quantity\n",
       "count  825.000000      825.000000\n",
       "mean     0.869091       26.583030\n",
       "std      0.337506       45.875948\n",
       "min      0.000000       -1.000000\n",
       "25%      1.000000        1.000000\n",
       "50%      1.000000       11.000000\n",
       "75%      1.000000       34.000000\n",
       "max      1.000000      578.000000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Types de données par colonne:\n",
      "product_id         int64\n",
      "onsale_web         int64\n",
      "price             object\n",
      "stock_quantity     int64\n",
      "stock_status      object\n",
      "dtype: object\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(825, 5)"
      ]
     },
     "execution_count": 272,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Charger le fichier CSV dans un DataFrame\n",
    "stock = pd.read_csv(\"fichier_erp.csv\", delimiter=';')\n",
    "\n",
    "# Vérifier les doublons\n",
    "doublons = stock[stock.duplicated()]\n",
    "if not doublons.empty:\n",
    "    print(\"Doublons détectés:\")\n",
    "    print(doublons)\n",
    "else:\n",
    "    print(\"Aucun doublon détecté.\")\n",
    "\n",
    "# Vérifier les valeurs NaN\n",
    "valeurs_nan = stock[stock.isna().any(axis=1)]\n",
    "if not valeurs_nan.empty:\n",
    "    print(\"Valeurs NaN détectées:\")\n",
    "    print(valeurs_nan)\n",
    "else:\n",
    "    print(\"Aucune valeur NaN détectée.\")\n",
    "\n",
    "# Afficher des statistiques générales sur le DataFrame (en excluant la colonne product_id)\n",
    "print(\"\\nStatistiques générales (hors product_id):\")\n",
    "display(stock.drop('product_id', axis=1).describe())\n",
    "\n",
    "print(\"\\nTypes de données par colonne:\")\n",
    "print(stock.dtypes)\n",
    "stock.shape\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span style=\"color: #FF8C00;\"><strong>Analyse statistique préliminaire de la DataFrame avant le nettoyage :</strong></span>\n",
    "<span style=\"color: #FF8C00;\">Cette étape permet d'explorer les données, de détecter d'éventuels problèmes, de planifier le nettoyage, d'orienter le processus, et de se préparer à l'analyse ultérieure.</span>\n",
    "\n",
    "<span style=\"color: #FF8C00;\"><strong>Résumé des résultats :</strong></span>\n",
    "- <span style=\"color: #FF8C00;\">Aucun doublon détecté, aucune valeur NaN présente.</span>\n",
    "\n",
    "<span style=\"color: #FF8C00;\"><strong>Colonnes clés :</strong></span>\n",
    "\n",
    "<span style=\"color: #FF8C00;\">**`onsale_web` :**</span>\n",
    "- <span style=\"color: #FF8C00;\">La plupart des produits sont disponibles en ligne (75e percentile à 1), avec une légère variabilité (écart-type d'environ 0.34).</span>\n",
    "\n",
    "<span style=\"color: #FF8C00;\">**`stock_quantity` :**</span>\n",
    "- <span style=\"color: #FF8C00;\">Une variabilité significative dans les quantités de stock (écart-type d'environ 45.88), allant de -1 à 578.</span>\n",
    "- <span style=\"color: #FF8C00;\">La colonne `price` est de type objet, suggérant peut-être une conversion en type numérique.</span>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Aucun doublon détecté.\n",
      "Valeurs NaN détectées:\n",
      "     product_id id_web\n",
      "19         4055    NaN\n",
      "49         4090    NaN\n",
      "50         4092    NaN\n",
      "119        4195    NaN\n",
      "131        4209    NaN\n",
      "..          ...    ...\n",
      "817        7196    NaN\n",
      "818        7200    NaN\n",
      "819        7201    NaN\n",
      "820        7203    NaN\n",
      "821        7204    NaN\n",
      "\n",
      "[91 rows x 2 columns]\n",
      "\n",
      "Statistiques générales:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe thead th {\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>product_id</th>\n",
       "      <th>id_web</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>825.000000</td>\n",
       "      <td>734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>NaN</td>\n",
       "      <td>734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>NaN</td>\n",
       "      <td>15298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5162.597576</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>902.644635</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3847.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4348.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4907.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5805.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7338.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         product_id id_web\n",
       "count    825.000000    734\n",
       "unique          NaN    734\n",
       "top             NaN  15298\n",
       "freq            NaN      1\n",
       "mean    5162.597576    NaN\n",
       "std      902.644635    NaN\n",
       "min     3847.000000    NaN\n",
       "25%     4348.000000    NaN\n",
       "50%     4907.000000    NaN\n",
       "75%     5805.000000    NaN\n",
       "max     7338.000000    NaN"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Types de données par colonne:\n",
      "product_id     int64\n",
      "id_web        object\n",
      "dtype: object\n",
      "\n",
      "Premières lignes du DataFrame:\n",
      "   product_id id_web\n",
      "0        3847  15298\n",
      "1        3849  15296\n",
      "2        3850  15300\n",
      "3        4032  19814\n",
      "4        4039  19815\n"
     ]
    }
   ],
   "source": [
    "# Vérifier du fichier_liaison\n",
    "\n",
    "# Charger le fichier CSV dans un DataFrame (remplacez \"web.csv\" par le chemin de votre fichier CSV)\n",
    "liaison = pd.read_csv(\"fichier_liaison.csv\", delimiter=';')\n",
    "\n",
    "# Vérifier les doublons\n",
    "doublons = liaison[liaison.duplicated()]\n",
    "if not doublons.empty:\n",
    "    print(\"Doublons détectés:\")\n",
    "    print(doublons)\n",
    "else:\n",
    "    print(\"Aucun doublon détecté.\")\n",
    "\n",
    "# Vérifier les valeurs NaN\n",
    "valeurs_nan = liaison[liaison.isna().any(axis=1)]\n",
    "if not valeurs_nan.empty:\n",
    "    print(\"Valeurs NaN détectées:\")\n",
    "    print(valeurs_nan)\n",
    "else:\n",
    "    print(\"Aucune valeur NaN détectée.\")\n",
    "\n",
    "# Afficher des statistiques générales sur le DataFrame (en excluant les colonnes non numériques)\n",
    "print(\"\\nStatistiques générales:\")\n",
    "display(liaison.describe(include='all'))\n",
    "\n",
    "# Afficher les types de données par colonne\n",
    "print(\"\\nTypes de données par colonne:\")\n",
    "print(liaison.dtypes)\n",
    "\n",
    "# Afficher les premières lignes du DataFrame\n",
    "print(\"\\nPremières lignes du DataFrame:\")\n",
    "print(liaison.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "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>product_id</th>\n",
       "      <th>sku</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3847</td>\n",
       "      <td>15298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3849</td>\n",
       "      <td>15296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3850</td>\n",
       "      <td>15300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4032</td>\n",
       "      <td>19814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4039</td>\n",
       "      <td>19815</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   product_id    sku\n",
       "0        3847  15298\n",
       "1        3849  15296\n",
       "2        3850  15300\n",
       "3        4032  19814\n",
       "4        4039  19815"
      ]
     },
     "execution_count": 274,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# on créé une nouvelle colonne 'sku' égale à la colonne 'id_web', qu'on supprime ensuite\n",
    "liaison['sku'] = liaison['id_web']\n",
    "liaison = liaison.drop(columns = ['id_web'])\n",
    "\n",
    "# on affiche les premières lignes\n",
    "liaison.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span style=\"color: #FF8C00;\"><strong>Résumé des Résultats :</strong></span>\n",
    "\n",
    "<span style=\"color: #FF8C00;\">Pour la colonne <strong>`product_id` :</strong></span>\n",
    "- <span style=\"color: #FF8C00;\">Pas de doublons.</span>\n",
    "- <span style=\"color: #FF8C00;\">Valeur la plus fréquente : Manquante (NaN) avec une fréquence de 1.</span>\n",
    "- <span style=\"color: #FF8C00;\">Moyenne : Environ 5162.60.</span>\n",
    "- <span style=\"color: #FF8C00;\">Écart-type : Environ 902.64, indiquant une variabilité autour de la moyenne.</span>\n",
    "- <span style=\"color: #FF8C00;\">Valeur minimale : 3847, valeur maximale : 7338.</span>\n",
    "\n",
    "<span style=\"color: #FF8C00;\">Pour la colonne <strong>`id_web` :</strong></span>\n",
    "- <span style=\"color: #FF8C00;\">Valeur la plus fréquente : 15298, fréquence : 1.</span>\n",
    "- <span style=\"color: #FF8C00;\">Note : Des valeurs manquantes (NaN) sont présentes dans cette colonne, suggérant un traitement nécessaire.</span>\n",
    "\n",
    "<span style=\"color: #FF8C00;\">Création d'une nouvelle colonne 'sku' dans le DataFrame 'liaison', en copiant les valeurs de la colonne 'id_web', puis supprime la colonne 'id_web', réorganisant ainsi la structure du DataFrame en remplaçant 'id_web' par 'sku'.</span>\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Doublons détectés:\n",
      "      sku  virtual  downloadable  rating_count  average_rating  total_sales  \\\n",
      "17    NaN        0             0             0             NaN          NaN   \n",
      "82    NaN        0             0             0             NaN          NaN   \n",
      "97    NaN        0             0             0             NaN          NaN   \n",
      "121   NaN        0             0             0             NaN          NaN   \n",
      "126   NaN        0             0             0             NaN          NaN   \n",
      "...   ...      ...           ...           ...             ...          ...   \n",
      "1378  NaN        0             0             0             NaN          NaN   \n",
      "1421  NaN        0             0             0             NaN          NaN   \n",
      "1448  NaN        0             0             0             NaN          NaN   \n",
      "1450  NaN        0             0             0             NaN          NaN   \n",
      "1496  NaN        0             0             0             NaN          NaN   \n",
      "\n",
      "     tax_status  tax_class  post_author post_date  ... post_name  \\\n",
      "17          NaN        NaN          NaN       NaN  ...       NaN   \n",
      "82          NaN        NaN          NaN       NaN  ...       NaN   \n",
      "97          NaN        NaN          NaN       NaN  ...       NaN   \n",
      "121         NaN        NaN          NaN       NaN  ...       NaN   \n",
      "126         NaN        NaN          NaN       NaN  ...       NaN   \n",
      "...         ...        ...          ...       ...  ...       ...   \n",
      "1378        NaN        NaN          NaN       NaN  ...       NaN   \n",
      "1421        NaN        NaN          NaN       NaN  ...       NaN   \n",
      "1448        NaN        NaN          NaN       NaN  ...       NaN   \n",
      "1450        NaN        NaN          NaN       NaN  ...       NaN   \n",
      "1496        NaN        NaN          NaN       NaN  ...       NaN   \n",
      "\n",
      "      post_modified post_modified_gmt post_content_filtered post_parent guid  \\\n",
      "17              NaN               NaN                   NaN         NaN  NaN   \n",
      "82              NaN               NaN                   NaN         NaN  NaN   \n",
      "97              NaN               NaN                   NaN         NaN  NaN   \n",
      "121             NaN               NaN                   NaN         NaN  NaN   \n",
      "126             NaN               NaN                   NaN         NaN  NaN   \n",
      "...             ...               ...                   ...         ...  ...   \n",
      "1378            NaN               NaN                   NaN         NaN  NaN   \n",
      "1421            NaN               NaN                   NaN         NaN  NaN   \n",
      "1448            NaN               NaN                   NaN         NaN  NaN   \n",
      "1450            NaN               NaN                   NaN         NaN  NaN   \n",
      "1496            NaN               NaN                   NaN         NaN  NaN   \n",
      "\n",
      "     menu_order  post_type post_mime_type comment_count  \n",
      "17          NaN        NaN            NaN           NaN  \n",
      "82          NaN        NaN            NaN           NaN  \n",
      "97          NaN        NaN            NaN           NaN  \n",
      "121         NaN        NaN            NaN           NaN  \n",
      "126         NaN        NaN            NaN           NaN  \n",
      "...         ...        ...            ...           ...  \n",
      "1378        NaN        NaN            NaN           NaN  \n",
      "1421        NaN        NaN            NaN           NaN  \n",
      "1448        NaN        NaN            NaN           NaN  \n",
      "1450        NaN        NaN            NaN           NaN  \n",
      "1496        NaN        NaN            NaN           NaN  \n",
      "\n",
      "[82 rows x 28 columns]\n",
      "Valeurs NaN détectées:\n",
      "        sku  virtual  downloadable  rating_count  average_rating  total_sales  \\\n",
      "0     16004        0             0             0             0.0          5.0   \n",
      "1       NaN        0             0             0             NaN          NaN   \n",
      "2     15075        0             0             0             0.0          3.0   \n",
      "3     16209        0             0             0             0.0          6.0   \n",
      "4     15763        0             0             0             0.0          1.0   \n",
      "...     ...      ...           ...           ...             ...          ...   \n",
      "1508  12881        0             0             0             0.0          2.0   \n",
      "1509  15663        0             0             0             0.0          3.0   \n",
      "1510  15910        0             0             0             0.0          0.0   \n",
      "1511     38        0             0             0             0.0         38.0   \n",
      "1512  13599        0             0             0             0.0          1.0   \n",
      "\n",
      "     tax_status  tax_class  post_author            post_date  ...  \\\n",
      "0           NaN        NaN          2.0  2018-06-07 16:27:25  ...   \n",
      "1           NaN        NaN          NaN                  NaN  ...   \n",
      "2       taxable        NaN          2.0  2018-02-14 15:39:43  ...   \n",
      "3       taxable        NaN          2.0  2018-02-14 17:15:31  ...   \n",
      "4           NaN        NaN          2.0  2020-01-25 14:08:16  ...   \n",
      "...         ...        ...          ...                  ...  ...   \n",
      "1508        NaN        NaN          2.0  2019-03-28 15:25:14  ...   \n",
      "1509    taxable        NaN          2.0  2018-02-27 10:27:01  ...   \n",
      "1510    taxable        NaN          2.0  2019-03-28 10:59:43  ...   \n",
      "1511        NaN        NaN          2.0  2018-04-18 12:25:58  ...   \n",
      "1512        NaN        NaN          2.0  2018-03-01 14:12:39  ...   \n",
      "\n",
      "                                              post_name        post_modified  \\\n",
      "0                       chateau-du-couvent-pomerol-2017  2020-08-25 18:35:02   \n",
      "1                                                   NaN                  NaN   \n",
      "2                     pares-balta-penedes-indigena-2017  2020-08-20 15:35:02   \n",
      "3                        maurel-cabardes-tradition-2017  2020-08-05 18:05:03   \n",
      "4     domaine-de-la-monardiere-vacqueyras-les-vieill...  2020-08-21 11:35:02   \n",
      "...                                                 ...                  ...   \n",
      "1508                 montbourgeau-etoile-vin-jaune-2009  2019-12-30 10:30:01   \n",
      "1509  chermette-domaine-du-vissoux-brouilly-pierreux...  2020-08-01 09:35:02   \n",
      "1510     thevenet-quintaine-vire-clesse-la-bongran-2015  2020-08-14 10:45:02   \n",
      "1511         emile-boeckel-cremant-brut-blanc-de-blancs  2020-08-27 17:15:03   \n",
      "1512     champagne-mailly-grand-cru-blanc-de-pinot-noir  2020-08-26 18:05:02   \n",
      "\n",
      "        post_modified_gmt post_content_filtered post_parent  \\\n",
      "0     2020-08-25 16:35:02                   NaN         0.0   \n",
      "1                     NaN                   NaN         NaN   \n",
      "2     2020-08-20 13:35:02                   NaN         0.0   \n",
      "3     2020-08-05 16:05:03                   NaN         0.0   \n",
      "4     2020-08-21 09:35:02                   NaN         0.0   \n",
      "...                   ...                   ...         ...   \n",
      "1508  2019-12-30 09:30:01                   NaN         0.0   \n",
      "1509  2020-08-01 07:35:02                   NaN         0.0   \n",
      "1510  2020-08-14 08:45:02                   NaN         0.0   \n",
      "1511  2020-08-27 15:15:03                   NaN         0.0   \n",
      "1512  2020-08-26 16:05:02                   NaN         0.0   \n",
      "\n",
      "                                                   guid menu_order  \\\n",
      "0     https://www.bottle-neck.fr/wp-content/uploads/...        0.0   \n",
      "1                                                   NaN        NaN   \n",
      "2     https://www.bottle-neck.fr/?post_type=product&...        0.0   \n",
      "3     https://www.bottle-neck.fr/?post_type=product&...        0.0   \n",
      "4     https://www.bottle-neck.fr/wp-content/uploads/...        0.0   \n",
      "...                                                 ...        ...   \n",
      "1508  https://www.bottle-neck.fr/wp-content/uploads/...        0.0   \n",
      "1509  https://www.bottle-neck.fr/?post_type=product&...        0.0   \n",
      "1510  https://www.bottle-neck.fr/?post_type=product&...        0.0   \n",
      "1511  https://www.bottle-neck.fr/wp-content/uploads/...        0.0   \n",
      "1512  https://www.bottle-neck.fr/wp-content/uploads/...        0.0   \n",
      "\n",
      "       post_type post_mime_type comment_count  \n",
      "0     attachment     image/jpeg           0.0  \n",
      "1            NaN            NaN           NaN  \n",
      "2        product            NaN           0.0  \n",
      "3        product            NaN           0.0  \n",
      "4     attachment     image/jpeg           0.0  \n",
      "...          ...            ...           ...  \n",
      "1508  attachment     image/jpeg           0.0  \n",
      "1509     product            NaN           0.0  \n",
      "1510     product            NaN           0.0  \n",
      "1511  attachment     image/jpeg           0.0  \n",
      "1512  attachment     image/jpeg           0.0  \n",
      "\n",
      "[1513 rows x 28 columns]\n",
      "\n",
      "Statistiques générales:\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>virtual</th>\n",
       "      <th>downloadable</th>\n",
       "      <th>rating_count</th>\n",
       "      <th>average_rating</th>\n",
       "      <th>total_sales</th>\n",
       "      <th>tax_class</th>\n",
       "      <th>post_author</th>\n",
       "      <th>post_content</th>\n",
       "      <th>post_password</th>\n",
       "      <th>post_content_filtered</th>\n",
       "      <th>post_parent</th>\n",
       "      <th>menu_order</th>\n",
       "      <th>comment_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1513.0</td>\n",
       "      <td>1513.0</td>\n",
       "      <td>1513.0</td>\n",
       "      <td>1430.0</td>\n",
       "      <td>1430.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1430.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1430.0</td>\n",
       "      <td>1430.0</td>\n",
       "      <td>1430.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.855245</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.998601</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.702346</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.037385</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       virtual  downloadable  rating_count  average_rating  total_sales  \\\n",
       "count   1513.0        1513.0        1513.0          1430.0  1430.000000   \n",
       "mean       0.0           0.0           0.0             0.0     3.855245   \n",
       "std        0.0           0.0           0.0             0.0     7.702346   \n",
       "min        0.0           0.0           0.0             0.0     0.000000   \n",
       "25%        0.0           0.0           0.0             0.0     0.000000   \n",
       "50%        0.0           0.0           0.0             0.0     1.000000   \n",
       "75%        0.0           0.0           0.0             0.0     4.000000   \n",
       "max        0.0           0.0           0.0             0.0    96.000000   \n",
       "\n",
       "       tax_class  post_author  post_content  post_password  \\\n",
       "count        0.0  1430.000000           0.0            0.0   \n",
       "mean         NaN     1.998601           NaN            NaN   \n",
       "std          NaN     0.037385           NaN            NaN   \n",
       "min          NaN     1.000000           NaN            NaN   \n",
       "25%          NaN     2.000000           NaN            NaN   \n",
       "50%          NaN     2.000000           NaN            NaN   \n",
       "75%          NaN     2.000000           NaN            NaN   \n",
       "max          NaN     2.000000           NaN            NaN   \n",
       "\n",
       "       post_content_filtered  post_parent  menu_order  comment_count  \n",
       "count                    0.0       1430.0      1430.0         1430.0  \n",
       "mean                     NaN          0.0         0.0            0.0  \n",
       "std                      NaN          0.0         0.0            0.0  \n",
       "min                      NaN          0.0         0.0            0.0  \n",
       "25%                      NaN          0.0         0.0            0.0  \n",
       "50%                      NaN          0.0         0.0            0.0  \n",
       "75%                      NaN          0.0         0.0            0.0  \n",
       "max                      NaN          0.0         0.0            0.0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(1513, 28)"
      ]
     },
     "execution_count": 275,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Vérifier du fichier_web\n",
    "\n",
    "# Charger le fichier CSV dans un DataFrame (remplacez \"web.csv\" par le chemin de votre fichier CSV)\n",
    "produits = pd.read_csv(\"fichier_web.csv\", encoding='ISO-8859-1', on_bad_lines='skip', skip_blank_lines=True, delimiter=';')\n",
    "\n",
    "# Vérifier les doublons\n",
    "doublons = produits[produits.duplicated()]\n",
    "if not doublons.empty:\n",
    "    print(\"Doublons détectés:\")\n",
    "    print(doublons)\n",
    "else:\n",
    "    print(\"Aucun doublon détecté.\")\n",
    "\n",
    "# Vérifier les valeurs NaN\n",
    "valeurs_nan = produits[produits.isna().any(axis=1)]\n",
    "if not valeurs_nan.empty:\n",
    "    print(\"Valeurs NaN détectées:\")\n",
    "    print(valeurs_nan)\n",
    "else:\n",
    "    print(\"Aucune valeur NaN détectée.\")\n",
    "\n",
    "\n",
    "# Afficher des statistiques générales sur le DataFrame\n",
    "print(\"\\nStatistiques générales:\")\n",
    "display(produits.describe())\n",
    "produits.shape\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span style=\"color: #FF8C00;\"><strong>Résumé des Résultats :</strong></span>\n",
    "- <span style=\"color: #FF8C00;\">Des doublons et des valeurs NaN sont présents dans les données.</span>\n",
    "\n",
    "<span style=\"color: #FF8C00;\"><strong>Résumé des Statistiques :</strong></span>\n",
    "- <span style=\"color: #FF8C00;\">Les colonnes telles que 'virtual', 'downloadable', 'rating_count', 'average_rating', etc., affichent des variations dans le nombre total d'entrées non manquantes.</span>\n",
    "- <span style=\"color: #FF8C00;\">La moyenne de 'total_sales' est d'environ 3.86, présentant une certaine variabilité autour de la moyenne.</span>\n",
    "- <span style=\"color: #FF8C00;\">Des valeurs constantes de zéro dans plusieurs colonnes suggèrent une possible absence de diversité.</span>\n",
    "- <span style=\"color: #FF8C00;\">La colonne 'average_rating' contient des valeurs NaN, indiquant des données manquantes ou indéfinies.</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### A.I) Analyse du Fichier ERP\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>1</th>\n",
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       "      <th>2</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>outofstock</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4039</td>\n",
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       "      <td>46.0</td>\n",
       "      <td>0</td>\n",
       "      <td>outofstock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>820</th>\n",
       "      <td>7203</td>\n",
       "      <td>0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>30</td>\n",
       "      <td>instock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>821</th>\n",
       "      <td>7204</td>\n",
       "      <td>0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>9</td>\n",
       "      <td>instock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>822</th>\n",
       "      <td>7247</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23</td>\n",
       "      <td>instock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>823</th>\n",
       "      <td>7329</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14</td>\n",
       "      <td>instock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>824</th>\n",
       "      <td>7338</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>45</td>\n",
       "      <td>instock</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>825 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     product_id  onsale_web  price  stock_quantity stock_status\n",
       "0          3847           1    NaN               0   outofstock\n",
       "1          3849           1    NaN               0   outofstock\n",
       "2          3850           1    NaN               0   outofstock\n",
       "3          4032           1    NaN               0   outofstock\n",
       "4          4039           1   46.0               0   outofstock\n",
       "..          ...         ...    ...             ...          ...\n",
       "820        7203           0   45.0              30      instock\n",
       "821        7204           0   45.0               9      instock\n",
       "822        7247           1    NaN              23      instock\n",
       "823        7329           0    NaN              14      instock\n",
       "824        7338           1    NaN              45      instock\n",
       "\n",
       "[825 rows x 5 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Nettoyage du fichier_erp\n",
    "\n",
    "# Conversion de la colonne \"price\" en type numérique \n",
    "stock['price'] = pd.to_numeric(stock['price'], errors='coerce')\n",
    "\n",
    "display(stock)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lignes où la conversion a échoué :\n",
      "     product_id  onsale_web price  stock_quantity stock_status  price_numeric\n",
      "0          3847           1  24,2               0   outofstock            NaN\n",
      "1          3849           1  34,3               0   outofstock            NaN\n",
      "2          3850           1  20,8               0   outofstock            NaN\n",
      "3          4032           1  14,1               0   outofstock            NaN\n",
      "5          4040           1  34,3               0   outofstock            NaN\n",
      "..          ...         ...   ...             ...          ...            ...\n",
      "787        6930           1   8,4              83      instock            NaN\n",
      "792        7023           1  27,5              15      instock            NaN\n",
      "822        7247           1  54,8              23      instock            NaN\n",
      "823        7329           0  26,5              14      instock            NaN\n",
      "824        7338           1  16,3              45      instock            NaN\n",
      "\n",
      "[609 rows x 6 columns]\n"
     ]
    }
   ],
   "source": [
    "# Charger le fichier CSV dans un DataFrame\n",
    "stock = pd.read_csv(\"fichier_erp.csv\", delimiter=';')\n",
    "\n",
    "# Convertir la colonne \"price\" en numérique en conservant les valeurs originales si la conversion échoue\n",
    "stock['price_numeric'] = pd.to_numeric(stock['price'], errors='coerce')\n",
    "\n",
    "# Afficher les lignes où la conversion a échoué\n",
    "failed_conversion_rows = stock[stock['price_numeric'].isnull()]\n",
    "print(\"Lignes où la conversion a échoué :\")\n",
    "print(failed_conversion_rows)\n",
    "\n",
    "# Conserver les valeurs originales là où la conversion a échoué\n",
    "stock['price_numeric'] = stock['price_numeric'].combine_first(stock['price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lignes où la conversion a échoué :\n",
      "Empty DataFrame\n",
      "Columns: [product_id, onsale_web, price, stock_quantity, stock_status, price_numeric]\n",
      "Index: []\n"
     ]
    },
    {
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       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>820</th>\n",
       "      <td>7203</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>30</td>\n",
       "      <td>instock</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>821</th>\n",
       "      <td>7204</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>9</td>\n",
       "      <td>instock</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>822</th>\n",
       "      <td>7247</td>\n",
       "      <td>1</td>\n",
       "      <td>54.8</td>\n",
       "      <td>23</td>\n",
       "      <td>instock</td>\n",
       "      <td>54.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>823</th>\n",
       "      <td>7329</td>\n",
       "      <td>0</td>\n",
       "      <td>26.5</td>\n",
       "      <td>14</td>\n",
       "      <td>instock</td>\n",
       "      <td>26.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>824</th>\n",
       "      <td>7338</td>\n",
       "      <td>1</td>\n",
       "      <td>16.3</td>\n",
       "      <td>45</td>\n",
       "      <td>instock</td>\n",
       "      <td>16.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>825 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     product_id  onsale_web price  stock_quantity stock_status  price_numeric\n",
       "0          3847           1  24.2               0   outofstock           24.2\n",
       "1          3849           1  34.3               0   outofstock           34.3\n",
       "2          3850           1  20.8               0   outofstock           20.8\n",
       "3          4032           1  14.1               0   outofstock           14.1\n",
       "4          4039           1    46               0   outofstock           46.0\n",
       "..          ...         ...   ...             ...          ...            ...\n",
       "820        7203           0    45              30      instock           45.0\n",
       "821        7204           0    45               9      instock           45.0\n",
       "822        7247           1  54.8              23      instock           54.8\n",
       "823        7329           0  26.5              14      instock           26.5\n",
       "824        7338           1  16.3              45      instock           16.3\n",
       "\n",
       "[825 rows x 6 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Remplacer les virgules par des points dans la colonne \"price\"\n",
    "stock['price'] = stock['price'].str.replace(',', '.')\n",
    "\n",
    "# Tenter la conversion en numérique\n",
    "stock['price_numeric'] = pd.to_numeric(stock['price'], errors='coerce')\n",
    "\n",
    "# Afficher les lignes où la conversion a échoué\n",
    "failed_conversion_rows = stock[stock['price_numeric'].isnull()]\n",
    "print(\"Lignes où la conversion a échoué :\")\n",
    "print(failed_conversion_rows)\n",
    "\n",
    "# Conserver les valeurs originales là où la conversion a échoué\n",
    "stock['price_numeric'] = stock['price_numeric'].combine_first(stock['price'])\n",
    "display(stock)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### B.I) Analyse du Fichier liaison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "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>product_id</th>\n",
       "      <th>sku</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3847</td>\n",
       "      <td>15298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3849</td>\n",
       "      <td>15296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3850</td>\n",
       "      <td>15300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4032</td>\n",
       "      <td>19814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4039</td>\n",
       "      <td>19815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>820</th>\n",
       "      <td>7203</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>821</th>\n",
       "      <td>7204</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>822</th>\n",
       "      <td>7247</td>\n",
       "      <td>13127-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>823</th>\n",
       "      <td>7329</td>\n",
       "      <td>14680-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>824</th>\n",
       "      <td>7338</td>\n",
       "      <td>16230</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>825 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     product_id      sku\n",
       "0          3847    15298\n",
       "1          3849    15296\n",
       "2          3850    15300\n",
       "3          4032    19814\n",
       "4          4039    19815\n",
       "..          ...      ...\n",
       "820        7203        0\n",
       "821        7204        0\n",
       "822        7247  13127-1\n",
       "823        7329  14680-1\n",
       "824        7338    16230\n",
       "\n",
       "[825 rows x 2 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Remplacer les NaN par 0 dans tout le DataFrame\n",
    "liaison = liaison.fillna(0)\n",
    "display(liaison)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['15298', '15296', '15300', '19814', '19815', '15303', '14975',\n",
       "       '16042', '14980', '16041', '15269', '14977', '16044', '16043',\n",
       "       '16449', '16045', '16030', '13127', '19816', 0, '16029', '16039',\n",
       "       '16318', '16275', '16498', '16320', '16319', '15966', '15022',\n",
       "       '15967', '15490', '16416', '11862', '15444', '15953', '12045',\n",
       "       '13074', '15941', '16069', '13072', '15440', '13435', '13078',\n",
       "       '13117', '16296', '16014', '16462', '16013', '16180', '15676',\n",
       "       '16120', '15564', '15675', '15378', '15813', '13416', '14905',\n",
       "       '15767', '16505', '15683', '16504', '15787', '14800', '15353',\n",
       "       '15382', '15339', '11668', '13209', '15341', '13217', '304',\n",
       "       '11641', '1662', '1360', '15648', '1364', '7086', '1366', '15140',\n",
       "       '16238', '16237', '15141', '14944', '14941', '14751', '16093',\n",
       "       '15668', '15373', '15375', '14474', '15482', '13453', '15075',\n",
       "       '16124', '15785', '15784', '15786', '14332', '16210', '16211',\n",
       "       '16209', '15629', '15583', '16160', '16166', '15783', '16560',\n",
       "       '15747', '15746', '16190', '16189', '16265', '16191', '16263',\n",
       "       '15605', '16529', '15441', '13032', '16256', '16322', '16295',\n",
       "       '15656', '15655', '15415', '15414', '15413', '16023', '16024',\n",
       "       '15720', '15714', '15717', '15718', '15480', '15213', '15672',\n",
       "       '12599', '15758', '15829', '15759', '16585', '15306', '16497',\n",
       "       '15261', '12657', '15403', '15461', '16269', '13905', '16567',\n",
       "       '15436', '14725', '15310', '15770', '16097', '15428', '15033',\n",
       "       '16317', '15032', '6616', '12203', '14253', '12476', '14485',\n",
       "       '14945', '15662', '15663', '15664', '15665', '15136', '16537',\n",
       "       '16307', '16244', '15839', '13460', '13089', '12942', '14864',\n",
       "       '14527', '14865', '15690', '16330', '16154', '16153', '16066',\n",
       "       '16065', '15292', '13771', '16246', '16501', '16578', '15567',\n",
       "       '16553', '13172', '15120', '15949', '15946', '7818', '13599',\n",
       "       '4679', '12586', '12588', '15940', '12587', '12589', '12585',\n",
       "       '9562', '13854', '13853', '11585', '11467', '11586', '13765',\n",
       "       '13766', '11587', '9636', '12639', '12641', '12640', '14768',\n",
       "       '3506', '3510', '3507', '13230', '7819', '3509', '15426', '15621',\n",
       "       '15457', '15065', '13604', '12857', '14785', '15476', '14000',\n",
       "       '15478', '15475', '16151', '15659', '15147', '15660', '15148',\n",
       "       '15149', '15146', '15145', '15801', '15452', '15038', '15030',\n",
       "       '15875', '16186', '14371', '10459', '14372', '11049', '15850',\n",
       "       '15849', '812', '807', '805', '802', '2534', '793', '791', '2179',\n",
       "       '804', '41', '798', '2361', '15848', '16525', '16262', '16261',\n",
       "       '15206', '11849', '13515', '13514', '13516', '10814', '11847',\n",
       "       '13517', '16081', '15402', '15404', '13647', '14657', '16053',\n",
       "       '15525', '15527', '14676', '16057', '16056', '13762', '15280',\n",
       "       '15282', '15281', '15283', '15934', '15933', '15575', '16239',\n",
       "       '14451', '16324', '15582', '13736', '13659', '15465', '15004',\n",
       "       '14699', '15349', '15466', '14700', '10775', '16119', '15667',\n",
       "       '14746', '15361', '15196', '15657', '15658', '15670', '16527',\n",
       "       '16513', '15880', '15879', '16010', '14950', '16540', '15729',\n",
       "       '38', '5646', '8344', '15576', '16138', '14366', '13412', '12601',\n",
       "       '14632', '15315', '13627', '14184', '15429', '16132', '14680',\n",
       "       '15859', '16229', '14302', '16072', '14300', '13096', '16564',\n",
       "       '13754', '15734', '15448', '15881', '15731', '15316', '15732',\n",
       "       '14599', '15733', '15730', '12771', '3568', '14506', '15811',\n",
       "       '16342', '16292', '15307', '16047', '16255', '15154', '16274',\n",
       "       '16148', '14360', '16149', '16289', '14981', '15773', '15776',\n",
       "       '16037', '16038', '15807', '15952', '15808', '16062', '16063',\n",
       "       '14802', '13052', '14805', '14220', '14374', '14395', '15614',\n",
       "       '13809', '15612', '13814', '15613', '15615', '15533', '15531',\n",
       "       '15530', '15608', '15586', '15928', '16276', '16277', '15456',\n",
       "       '15425', '15047', '15927', '16155', '16280', '9937', '16281',\n",
       "       '15554', '15106', '16283', '13379', '15338', '15337',\n",
       "       'bon-cadeau-25-euros', '15737', '15958', '16515', '16586', '11225',\n",
       "       '16004', '14756', '16005', '14930', '13313', '15229', '14507',\n",
       "       '14509', '14508', '15868', '14581', '14580', '15869', '15871',\n",
       "       '15870', '12791', '11602', '15073', '14839', '15272', '14696',\n",
       "       '15630', '11996', '13914', '13913', '11997', '531', '13531',\n",
       "       '15711', '15713', '15715', '15346', '15345', '15344', '15755',\n",
       "       '15677', '14561', '16022', '16011', '3383', '14149', '13904',\n",
       "       '14141', '12494', '15462', '15095', '14626', '12496', '12315',\n",
       "       '15649', '14809', '15155', '12194', '16328', '14469', '16034',\n",
       "       '14679', '15526', '16305', '16306', '15138', '15753', '15756',\n",
       "       '16131', '16130', '16129', '14712', '15481', '16146', '14648',\n",
       "       '14192', '15860', '15863', '15861', '15862', '15864', '14819',\n",
       "       '14828', '14827', '15202', '13959', '13965', '13958', '13957',\n",
       "       '13520', '13969', '14715', '19820', '19821', '15748', '19822',\n",
       "       '16192', '14730', '14729', '8463', '13982', '15944', '15930',\n",
       "       '14912', '15945', '14915', '14855', '14856', '15923', '14845',\n",
       "       '14844', '15921', '15922', '12366', '8365', '12365', '14647',\n",
       "       '15812', '14661', '16304', '15797', '16094', '14736', '11736',\n",
       "       '15036', '15360', '15674', '13557', '15035', '16121', '14241',\n",
       "       '14982', '15026', '15116', '15369', '15566', '16003', '15127',\n",
       "       '15125', '14323', '15631', '16147', '7033', '11258', '13849',\n",
       "       '15818', '15179', '15185', '15183', '15254', '15178', '15184',\n",
       "       '15180', '15264', '14338', '15561', '16213', '14692', '13291',\n",
       "       '13895', '15688', '14461', '14689', '11277', '15399', '13572',\n",
       "       '14955', '13567', '15471', '15080', '14429', '15238', '15237',\n",
       "       '14600', '15241', '11933', '15240', '15325', '15328', '15329',\n",
       "       '15775', '15774', '14983', '13910', '16539', '15910', '12339',\n",
       "       '12869', '14095', '14099', '15856', '12881', '15857', '12882',\n",
       "       '15227', '10014', '14265', '14774', '14775', '14773', '15343',\n",
       "       '15351', '16323', '523', '15432', '16472', '14379', '15609',\n",
       "       '14377', '15895', '13577', '15577', '15766', '15892', '16326',\n",
       "       '15574', '13662', '11669', '13215', '13211', '15342', '15318',\n",
       "       '13073', '16159', '16264', '14899', '15134', '16133', '16028',\n",
       "       '15951', '15487', '15486', '15489', '15529', '14089', '14100',\n",
       "       '14092', '14090', '14106', '14101', '14797', '15201', '14923',\n",
       "       '14573', '14569', '14570', '15834', '14596', '15126', '14604',\n",
       "       '16565', '16580', '16077', '13996', '15072', '11601', '12790',\n",
       "       '15070', '16096', '7032', '15324', '15162', '15161', '15163',\n",
       "       '16273', '16247', '15654', '15710', '15745', '15678', '15810',\n",
       "       '15779', '15707', '15705', '15706', '15704', '15473', '15479',\n",
       "       '15647', '15769', '15434', '15764', '16071', '15781', '16031',\n",
       "       '15539', '16046', '15204', '15205', '15790', '15791', '15792',\n",
       "       '15793', '15795', '15794', '15763', '16152', '15661', '16068',\n",
       "       '16067', '8193', '16144', '15256', '15735', '14897', '15736',\n",
       "       '15740', '15845', '15741', '16135', '15891', '15887', '13127-1',\n",
       "       '14680-1', '16230'], dtype=object)"
      ]
     },
     "execution_count": 280,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "liaison['sku'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "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>product_id</th>\n",
       "      <th>sku</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>443</th>\n",
       "      <td>4954</td>\n",
       "      <td>bon-cadeau-25-euros</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     product_id                  sku\n",
       "443        4954  bon-cadeau-25-euros"
      ]
     },
     "execution_count": 281,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# on recherche la valeur 'bon-cadeau-25-euros' \n",
    "liaison.loc[liaison['sku'] == 'bon-cadeau-25-euros']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "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>product_id</th>\n",
       "      <th>sku</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>443</th>\n",
       "      <td>4954</td>\n",
       "      <td>bon-cadeau-25-euros</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     product_id                  sku\n",
       "443        4954  bon-cadeau-25-euros"
      ]
     },
     "execution_count": 282,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "liaison.loc[liaison['sku'] == 'bon-cadeau-25-euros']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3847, 3849, 3850, 4032, 4039, 4040, 4041, 4042, 4043, 4045, 4046,\n",
       "       4047, 4048, 4049, 4050, 4051, 4052, 4053, 4054, 4055, 4056, 4057,\n",
       "       4058, 4059, 4060, 4062, 4063, 4064, 4065, 4066, 4067, 4068, 4069,\n",
       "       4070, 4071, 4072, 4073, 4074, 4075, 4076, 4077, 4078, 4079, 4081,\n",
       "       4083, 4084, 4085, 4086, 4087, 4090, 4092, 4094, 4095, 4096, 4097,\n",
       "       4098, 4099, 4100, 4101, 4102, 4103, 4104, 4105, 4106, 4107, 4108,\n",
       "       4115, 4130, 4132, 4137, 4138, 4139, 4141, 4142, 4144, 4146, 4147,\n",
       "       4148, 4149, 4150, 4151, 4152, 4153, 4154, 4155, 4156, 4157, 4158,\n",
       "       4159, 4160, 4161, 4162, 4163, 4164, 4165, 4166, 4167, 4168, 4170,\n",
       "       4171, 4172, 4173, 4174, 4176, 4177, 4178, 4179, 4180, 4181, 4182,\n",
       "       4183, 4186, 4187, 4188, 4190, 4191, 4192, 4193, 4194, 4195, 4196,\n",
       "       4197, 4198, 4200, 4201, 4202, 4203, 4204, 4205, 4207, 4208, 4209,\n",
       "       4210, 4211, 4212, 4213, 4215, 4216, 4217, 4219, 4220, 4221, 4222,\n",
       "       4223, 4224, 4225, 4227, 4228, 4229, 4231, 4232, 4233, 4235, 4239,\n",
       "       4240, 4241, 4242, 4244, 4245, 4246, 4248, 4250, 4251, 4253, 4254,\n",
       "       4256, 4257, 4258, 4260, 4261, 4262, 4263, 4264, 4265, 4267, 4268,\n",
       "       4269, 4270, 4271, 4272, 4274, 4275, 4276, 4277, 4278, 4279, 4280,\n",
       "       4281, 4283, 4285, 4286, 4287, 4288, 4289, 4297, 4298, 4299, 4300,\n",
       "       4301, 4303, 4304, 4306, 4307, 4334, 4336, 4337, 4348, 4350, 4352,\n",
       "       4353, 4355, 4356, 4357, 4358, 4359, 4364, 4391, 4392, 4393, 4394,\n",
       "       4395, 4396, 4397, 4398, 4399, 4400, 4401, 4402, 4404, 4405, 4406,\n",
       "       4407, 4558, 4564, 4565, 4566, 4568, 4573, 4577, 4578, 4582, 4584,\n",
       "       4594, 4596, 4597, 4598, 4599, 4600, 4601, 4602, 4603, 4604, 4605,\n",
       "       4606, 4607, 4609, 4610, 4611, 4612, 4613, 4614, 4615, 4616, 4617,\n",
       "       4618, 4619, 4620, 4621, 4625, 4626, 4627, 4628, 4629, 4630, 4631,\n",
       "       4632, 4633, 4634, 4635, 4636, 4646, 4647, 4648, 4649, 4650, 4651,\n",
       "       4653, 4654, 4655, 4656, 4657, 4658, 4659, 4662, 4664, 4665, 4666,\n",
       "       4668, 4669, 4670, 4671, 4672, 4673, 4674, 4675, 4676, 4677, 4678,\n",
       "       4679, 4680, 4681, 4682, 4683, 4684, 4686, 4687, 4689, 4690, 4692,\n",
       "       4693, 4697, 4698, 4702, 4703, 4704, 4705, 4706, 4707, 4708, 4709,\n",
       "       4711, 4712, 4713, 4714, 4715, 4716, 4717, 4718, 4719, 4720, 4721,\n",
       "       4722, 4723, 4725, 4726, 4727, 4728, 4729, 4730, 4731, 4733, 4734,\n",
       "       4738, 4739, 4740, 4741, 4744, 4748, 4749, 4750, 4752, 4753, 4755,\n",
       "       4757, 4758, 4759, 4776, 4778, 4779, 4780, 4782, 4783, 4784, 4785,\n",
       "       4786, 4788, 4789, 4790, 4791, 4792, 4793, 4794, 4795, 4797, 4798,\n",
       "       4799, 4858, 4860, 4861, 4862, 4863, 4864, 4865, 4867, 4869, 4870,\n",
       "       4874, 4876, 4885, 4886, 4888, 4889, 4890, 4891, 4892, 4893, 4899,\n",
       "       4900, 4901, 4902, 4903, 4904, 4907, 4908, 4909, 4910, 4911, 4912,\n",
       "       4913, 4914, 4915, 4918, 4919, 4920, 4921, 4922, 4923, 4924, 4925,\n",
       "       4926, 4927, 4928, 4929, 4930, 4931, 4932, 4933, 4934, 4936, 4937,\n",
       "       4938, 4939, 4940, 4954, 4962, 4963, 4964, 4965, 4970, 4973, 4974,\n",
       "       4975, 4976, 4977, 4978, 4980, 4994, 4995, 4996, 5000, 5001, 5002,\n",
       "       5003, 5004, 5006, 5007, 5008, 5010, 5016, 5017, 5018, 5019, 5020,\n",
       "       5021, 5024, 5025, 5026, 5027, 5047, 5056, 5061, 5062, 5063, 5067,\n",
       "       5068, 5069, 5070, 5075, 5375, 5377, 5379, 5380, 5382, 5383, 5384,\n",
       "       5389, 5391, 5393, 5394, 5395, 5396, 5397, 5398, 5439, 5443, 5444,\n",
       "       5445, 5446, 5448, 5465, 5474, 5477, 5479, 5480, 5481, 5483, 5484,\n",
       "       5485, 5486, 5487, 5488, 5491, 5504, 5505, 5506, 5519, 5520, 5522,\n",
       "       5523, 5524, 5525, 5544, 5545, 5546, 5547, 5548, 5550, 5551, 5552,\n",
       "       5554, 5559, 5560, 5561, 5563, 5564, 5565, 5566, 5569, 5570, 5573,\n",
       "       5574, 5580, 5608, 5609, 5610, 5611, 5612, 5613, 5614, 5615, 5616,\n",
       "       5617, 5618, 5619, 5628, 5629, 5630, 5690, 5693, 5694, 5695, 5696,\n",
       "       5697, 5700, 5703, 5704, 5705, 5706, 5707, 5709, 5711, 5712, 5715,\n",
       "       5722, 5736, 5737, 5738, 5739, 5741, 5742, 5743, 5747, 5753, 5756,\n",
       "       5760, 5761, 5764, 5766, 5767, 5768, 5769, 5770, 5771, 5772, 5773,\n",
       "       5777, 5778, 5779, 5794, 5795, 5796, 5797, 5799, 5800, 5801, 5802,\n",
       "       5803, 5804, 5805, 5806, 5807, 5808, 5809, 5810, 5815, 5816, 5817,\n",
       "       5818, 5819, 5820, 5826, 5827, 5829, 5890, 5891, 5892, 5893, 5894,\n",
       "       5896, 5899, 5900, 5902, 5903, 5904, 5905, 5906, 5907, 5912, 5913,\n",
       "       5914, 5916, 5917, 5918, 5922, 5925, 5930, 5932, 5950, 5951, 5952,\n",
       "       5953, 5954, 5955, 5956, 5957, 5958, 5959, 5960, 5962, 5963, 5964,\n",
       "       5967, 5968, 5969, 6035, 6038, 6041, 6042, 6047, 6049, 6050, 6070,\n",
       "       6072, 6073, 6093, 6094, 6095, 6100, 6101, 6103, 6104, 6105, 6106,\n",
       "       6107, 6108, 6109, 6125, 6126, 6127, 6128, 6129, 6137, 6201, 6202,\n",
       "       6204, 6205, 6206, 6207, 6212, 6213, 6214, 6215, 6216, 6221, 6222,\n",
       "       6223, 6225, 6226, 6227, 6278, 6279, 6280, 6299, 6301, 6324, 6325,\n",
       "       6327, 6328, 6567, 6568, 6569, 6570, 6572, 6573, 6575, 6578, 6584,\n",
       "       6585, 6592, 6594, 6615, 6616, 6617, 6618, 6620, 6621, 6622, 6626,\n",
       "       6627, 6628, 6629, 6631, 6632, 6635, 6663, 6664, 6665, 6666, 6738,\n",
       "       6751, 6753, 6821, 6824, 6825, 6826, 6864, 6866, 6869, 6875, 6884,\n",
       "       6886, 6887, 6898, 6899, 6900, 6901, 6902, 6903, 6904, 6905, 6906,\n",
       "       6907, 6908, 6909, 6920, 6926, 6928, 6930, 7008, 7009, 7010, 7015,\n",
       "       7023, 7025, 7081, 7084, 7085, 7086, 7087, 7088, 7131, 7132, 7133,\n",
       "       7136, 7137, 7159, 7161, 7162, 7163, 7164, 7168, 7169, 7170, 7192,\n",
       "       7193, 7194, 7195, 7196, 7200, 7201, 7203, 7204, 7247, 7329, 7338],\n",
       "      dtype=int64)"
      ]
     },
     "execution_count": 283,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# on affiche les valeurs uniques prises par la variable\n",
    "liaison['product_id'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['15298', '15296', '15300', '19814', '19815', '15303', '14975',\n",
       "       '16042', '14980', '16041', '15269', '14977', '16044', '16043',\n",
       "       '16449', '16045', '16030', '13127', '19816', 0, '16029', '16039',\n",
       "       '16318', '16275', '16498', '16320', '16319', '15966', '15022',\n",
       "       '15967', '15490', '16416', '11862', '15444', '15953', '12045',\n",
       "       '13074', '15941', '16069', '13072', '15440', '13435', '13078',\n",
       "       '13117', '16296', '16014', '16462', '16013', '16180', '15676',\n",
       "       '16120', '15564', '15675', '15378', '15813', '13416', '14905',\n",
       "       '15767', '16505', '15683', '16504', '15787', '14800', '15353',\n",
       "       '15382', '15339', '11668', '13209', '15341', '13217', '304',\n",
       "       '11641', '1662', '1360', '15648', '1364', '7086', '1366', '15140',\n",
       "       '16238', '16237', '15141', '14944', '14941', '14751', '16093',\n",
       "       '15668', '15373', '15375', '14474', '15482', '13453', '15075',\n",
       "       '16124', '15785', '15784', '15786', '14332', '16210', '16211',\n",
       "       '16209', '15629', '15583', '16160', '16166', '15783', '16560',\n",
       "       '15747', '15746', '16190', '16189', '16265', '16191', '16263',\n",
       "       '15605', '16529', '15441', '13032', '16256', '16322', '16295',\n",
       "       '15656', '15655', '15415', '15414', '15413', '16023', '16024',\n",
       "       '15720', '15714', '15717', '15718', '15480', '15213', '15672',\n",
       "       '12599', '15758', '15829', '15759', '16585', '15306', '16497',\n",
       "       '15261', '12657', '15403', '15461', '16269', '13905', '16567',\n",
       "       '15436', '14725', '15310', '15770', '16097', '15428', '15033',\n",
       "       '16317', '15032', '6616', '12203', '14253', '12476', '14485',\n",
       "       '14945', '15662', '15663', '15664', '15665', '15136', '16537',\n",
       "       '16307', '16244', '15839', '13460', '13089', '12942', '14864',\n",
       "       '14527', '14865', '15690', '16330', '16154', '16153', '16066',\n",
       "       '16065', '15292', '13771', '16246', '16501', '16578', '15567',\n",
       "       '16553', '13172', '15120', '15949', '15946', '7818', '13599',\n",
       "       '4679', '12586', '12588', '15940', '12587', '12589', '12585',\n",
       "       '9562', '13854', '13853', '11585', '11467', '11586', '13765',\n",
       "       '13766', '11587', '9636', '12639', '12641', '12640', '14768',\n",
       "       '3506', '3510', '3507', '13230', '7819', '3509', '15426', '15621',\n",
       "       '15457', '15065', '13604', '12857', '14785', '15476', '14000',\n",
       "       '15478', '15475', '16151', '15659', '15147', '15660', '15148',\n",
       "       '15149', '15146', '15145', '15801', '15452', '15038', '15030',\n",
       "       '15875', '16186', '14371', '10459', '14372', '11049', '15850',\n",
       "       '15849', '812', '807', '805', '802', '2534', '793', '791', '2179',\n",
       "       '804', '41', '798', '2361', '15848', '16525', '16262', '16261',\n",
       "       '15206', '11849', '13515', '13514', '13516', '10814', '11847',\n",
       "       '13517', '16081', '15402', '15404', '13647', '14657', '16053',\n",
       "       '15525', '15527', '14676', '16057', '16056', '13762', '15280',\n",
       "       '15282', '15281', '15283', '15934', '15933', '15575', '16239',\n",
       "       '14451', '16324', '15582', '13736', '13659', '15465', '15004',\n",
       "       '14699', '15349', '15466', '14700', '10775', '16119', '15667',\n",
       "       '14746', '15361', '15196', '15657', '15658', '15670', '16527',\n",
       "       '16513', '15880', '15879', '16010', '14950', '16540', '15729',\n",
       "       '38', '5646', '8344', '15576', '16138', '14366', '13412', '12601',\n",
       "       '14632', '15315', '13627', '14184', '15429', '16132', '14680',\n",
       "       '15859', '16229', '14302', '16072', '14300', '13096', '16564',\n",
       "       '13754', '15734', '15448', '15881', '15731', '15316', '15732',\n",
       "       '14599', '15733', '15730', '12771', '3568', '14506', '15811',\n",
       "       '16342', '16292', '15307', '16047', '16255', '15154', '16274',\n",
       "       '16148', '14360', '16149', '16289', '14981', '15773', '15776',\n",
       "       '16037', '16038', '15807', '15952', '15808', '16062', '16063',\n",
       "       '14802', '13052', '14805', '14220', '14374', '14395', '15614',\n",
       "       '13809', '15612', '13814', '15613', '15615', '15533', '15531',\n",
       "       '15530', '15608', '15586', '15928', '16276', '16277', '15456',\n",
       "       '15425', '15047', '15927', '16155', '16280', '9937', '16281',\n",
       "       '15554', '15106', '16283', '13379', '15338', '15337',\n",
       "       'bon-cadeau-25-euros', '15737', '15958', '16515', '16586', '11225',\n",
       "       '16004', '14756', '16005', '14930', '13313', '15229', '14507',\n",
       "       '14509', '14508', '15868', '14581', '14580', '15869', '15871',\n",
       "       '15870', '12791', '11602', '15073', '14839', '15272', '14696',\n",
       "       '15630', '11996', '13914', '13913', '11997', '531', '13531',\n",
       "       '15711', '15713', '15715', '15346', '15345', '15344', '15755',\n",
       "       '15677', '14561', '16022', '16011', '3383', '14149', '13904',\n",
       "       '14141', '12494', '15462', '15095', '14626', '12496', '12315',\n",
       "       '15649', '14809', '15155', '12194', '16328', '14469', '16034',\n",
       "       '14679', '15526', '16305', '16306', '15138', '15753', '15756',\n",
       "       '16131', '16130', '16129', '14712', '15481', '16146', '14648',\n",
       "       '14192', '15860', '15863', '15861', '15862', '15864', '14819',\n",
       "       '14828', '14827', '15202', '13959', '13965', '13958', '13957',\n",
       "       '13520', '13969', '14715', '19820', '19821', '15748', '19822',\n",
       "       '16192', '14730', '14729', '8463', '13982', '15944', '15930',\n",
       "       '14912', '15945', '14915', '14855', '14856', '15923', '14845',\n",
       "       '14844', '15921', '15922', '12366', '8365', '12365', '14647',\n",
       "       '15812', '14661', '16304', '15797', '16094', '14736', '11736',\n",
       "       '15036', '15360', '15674', '13557', '15035', '16121', '14241',\n",
       "       '14982', '15026', '15116', '15369', '15566', '16003', '15127',\n",
       "       '15125', '14323', '15631', '16147', '7033', '11258', '13849',\n",
       "       '15818', '15179', '15185', '15183', '15254', '15178', '15184',\n",
       "       '15180', '15264', '14338', '15561', '16213', '14692', '13291',\n",
       "       '13895', '15688', '14461', '14689', '11277', '15399', '13572',\n",
       "       '14955', '13567', '15471', '15080', '14429', '15238', '15237',\n",
       "       '14600', '15241', '11933', '15240', '15325', '15328', '15329',\n",
       "       '15775', '15774', '14983', '13910', '16539', '15910', '12339',\n",
       "       '12869', '14095', '14099', '15856', '12881', '15857', '12882',\n",
       "       '15227', '10014', '14265', '14774', '14775', '14773', '15343',\n",
       "       '15351', '16323', '523', '15432', '16472', '14379', '15609',\n",
       "       '14377', '15895', '13577', '15577', '15766', '15892', '16326',\n",
       "       '15574', '13662', '11669', '13215', '13211', '15342', '15318',\n",
       "       '13073', '16159', '16264', '14899', '15134', '16133', '16028',\n",
       "       '15951', '15487', '15486', '15489', '15529', '14089', '14100',\n",
       "       '14092', '14090', '14106', '14101', '14797', '15201', '14923',\n",
       "       '14573', '14569', '14570', '15834', '14596', '15126', '14604',\n",
       "       '16565', '16580', '16077', '13996', '15072', '11601', '12790',\n",
       "       '15070', '16096', '7032', '15324', '15162', '15161', '15163',\n",
       "       '16273', '16247', '15654', '15710', '15745', '15678', '15810',\n",
       "       '15779', '15707', '15705', '15706', '15704', '15473', '15479',\n",
       "       '15647', '15769', '15434', '15764', '16071', '15781', '16031',\n",
       "       '15539', '16046', '15204', '15205', '15790', '15791', '15792',\n",
       "       '15793', '15795', '15794', '15763', '16152', '15661', '16068',\n",
       "       '16067', '8193', '16144', '15256', '15735', '14897', '15736',\n",
       "       '15740', '15845', '15741', '16135', '15891', '15887', '13127-1',\n",
       "       '14680-1', '16230'], dtype=object)"
      ]
     },
     "execution_count": 284,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# on affiche les valeurs uniques prises par la variable\n",
    "liaison['sku'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### C.I) Analyse du Fichier WEB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1513, 28)"
      ]
     },
     "execution_count": 285,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remplacer les valeurs NaN par une valeur par défaut (par exemple, 0) dans le fichier produit\n",
    "# produits = produits.fillna(0)\n",
    "\n",
    "# Sélectionne les lignes duplicatées basées sur la colonne 'product_id' dans le DataFrame 'liaison'.\n",
    "liaison_duplications = liaison.loc[liaison['product_id'].duplicated(keep=False), :]\n",
    "\n",
    "# Affiche les premières lignes du DataFrame 'produits'.\n",
    "produits.head()\n",
    "produits.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### A.II) Merge fichier liaison et erp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(825, 6)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>product_id</th>\n",
       "      <th>onsale_web</th>\n",
       "      <th>stock_quantity</th>\n",
       "      <th>stock_status</th>\n",
       "      <th>price_numeric</th>\n",
       "      <th>sku</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3847</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>outofstock</td>\n",
       "      <td>24.2</td>\n",
       "      <td>15298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3849</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>outofstock</td>\n",
       "      <td>34.3</td>\n",
       "      <td>15296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3850</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>outofstock</td>\n",
       "      <td>20.8</td>\n",
       "      <td>15300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4032</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>outofstock</td>\n",
       "      <td>14.1</td>\n",
       "      <td>19814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4039</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>outofstock</td>\n",
       "      <td>46.0</td>\n",
       "      <td>19815</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   product_id  onsale_web  stock_quantity stock_status  price_numeric    sku\n",
       "0        3847           1               0   outofstock           24.2  15298\n",
       "1        3849           1               0   outofstock           34.3  15296\n",
       "2        3850           1               0   outofstock           20.8  15300\n",
       "3        4032           1               0   outofstock           14.1  19814\n",
       "4        4039           1               0   outofstock           46.0  19815"
      ]
     },
     "execution_count": 286,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fusionner les DataFrames 'stock' et 'liaison' sur la colonne 'product_id'\n",
    "liaison_stock = pd.merge(stock, liaison, on = 'product_id')\n",
    "liaison_stock = liaison_stock.drop('price', axis=1)\n",
    "\n",
    "# Afficher les premières lignes du DataFrame\n",
    "print(liaison_stock.shape)\n",
    "\n",
    "liaison_stock.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### B.II) Merge fichier liaison et stock"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1428, 28)"
      ]
     },
     "execution_count": 287,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Supprime les lignes du DataFrame où la référence SKU ('sku') est manquante.\n",
    "produits_no_nan_sku = produits.dropna(subset=['sku'])\n",
    "produits_no_nan_sku.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1428, 33)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0       False\n",
       "1        True\n",
       "2       False\n",
       "3        True\n",
       "4       False\n",
       "        ...  \n",
       "1423     True\n",
       "1424    False\n",
       "1425     True\n",
       "1426    False\n",
       "1427     True\n",
       "Length: 1428, dtype: bool"
      ]
     },
     "execution_count": 288,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fusionner les DataFrames après la conversion en minuscules\n",
    "resultat_final = pd.merge(liaison_stock, produits_no_nan_sku, on='sku')\n",
    "\n",
    "print(resultat_final.shape)\n",
    "resultat_final.head()\n",
    "resultat_final.duplicated(subset = 'product_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       False\n",
       "1        True\n",
       "2       False\n",
       "3        True\n",
       "4       False\n",
       "        ...  \n",
       "1423     True\n",
       "1424    False\n",
       "1425     True\n",
       "1426    False\n",
       "1427     True\n",
       "Length: 1428, dtype: bool"
      ]
     },
     "execution_count": 289,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resultat_final.duplicated(subset = 'product_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "product       714\n",
      "attachment    714\n",
      "Name: post_type, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(resultat_final['post_type'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1428, 33)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>product_id</th>\n",
       "      <th>onsale_web</th>\n",
       "      <th>stock_quantity</th>\n",
       "      <th>stock_status</th>\n",
       "      <th>price_numeric</th>\n",
       "      <th>sku</th>\n",
       "      <th>virtual</th>\n",
       "      <th>downloadable</th>\n",
       "      <th>rating_count</th>\n",
       "      <th>average_rating</th>\n",
       "      <th>...</th>\n",
       "      <th>post_name</th>\n",
       "      <th>post_modified</th>\n",
       "      <th>post_modified_gmt</th>\n",
       "      <th>post_content_filtered</th>\n",
       "      <th>post_parent</th>\n",
       "      <th>guid</th>\n",
       "      <th>menu_order</th>\n",
       "      <th>post_type</th>\n",
       "      <th>post_mime_type</th>\n",
       "      <th>comment_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3847</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>outofstock</td>\n",
       "      <td>24.2</td>\n",
       "      <td>15298</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>pierre-jean-villa-saint-joseph-preface-2018</td>\n",
       "      <td>2019-12-30 09:30:29</td>\n",
       "      <td>2019-12-30 08:30:29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>https://www.bottle-neck.fr/?post_type=product&amp;...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>product</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3847</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>outofstock</td>\n",
       "      <td>24.2</td>\n",
       "      <td>15298</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>pierre-jean-villa-saint-joseph-preface-2018</td>\n",
       "      <td>2019-12-30 09:30:29</td>\n",
       "      <td>2019-12-30 08:30:29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>https://www.bottle-neck.fr/wp-content/uploads/...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>attachment</td>\n",
       "      <td>image/jpeg</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3849</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>outofstock</td>\n",
       "      <td>34.3</td>\n",
       "      <td>15296</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>pierre-jean-villa-saint-joseph-tilde-2017</td>\n",
       "      <td>2019-12-21 09:00:17</td>\n",
       "      <td>2019-12-21 08:00:17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>https://www.bottle-neck.fr/wp-content/uploads/...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>attachment</td>\n",
       "      <td>image/jpeg</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3849</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>outofstock</td>\n",
       "      <td>34.3</td>\n",
       "      <td>15296</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>pierre-jean-villa-saint-joseph-tilde-2017</td>\n",
       "      <td>2019-12-21 09:00:17</td>\n",
       "      <td>2019-12-21 08:00:17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>https://www.bottle-neck.fr/?post_type=product&amp;...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>product</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3850</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>outofstock</td>\n",
       "      <td>20.8</td>\n",
       "      <td>15300</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>pierre-jean-villa-croze-hermitage-accroche-coe...</td>\n",
       "      <td>2020-06-26 18:15:03</td>\n",
       "      <td>2020-06-26 16:15:03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>https://www.bottle-neck.fr/wp-content/uploads/...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>attachment</td>\n",
       "      <td>image/jpeg</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   product_id  onsale_web  stock_quantity stock_status  price_numeric    sku  \\\n",
       "0        3847           1               0   outofstock           24.2  15298   \n",
       "1        3847           1               0   outofstock           24.2  15298   \n",
       "2        3849           1               0   outofstock           34.3  15296   \n",
       "3        3849           1               0   outofstock           34.3  15296   \n",
       "4        3850           1               0   outofstock           20.8  15300   \n",
       "\n",
       "   virtual  downloadable  rating_count  average_rating  ...  \\\n",
       "0        0             0             0             0.0  ...   \n",
       "1        0             0             0             0.0  ...   \n",
       "2        0             0             0             0.0  ...   \n",
       "3        0             0             0             0.0  ...   \n",
       "4        0             0             0             0.0  ...   \n",
       "\n",
       "                                           post_name        post_modified  \\\n",
       "0        pierre-jean-villa-saint-joseph-preface-2018  2019-12-30 09:30:29   \n",
       "1        pierre-jean-villa-saint-joseph-preface-2018  2019-12-30 09:30:29   \n",
       "2          pierre-jean-villa-saint-joseph-tilde-2017  2019-12-21 09:00:17   \n",
       "3          pierre-jean-villa-saint-joseph-tilde-2017  2019-12-21 09:00:17   \n",
       "4  pierre-jean-villa-croze-hermitage-accroche-coe...  2020-06-26 18:15:03   \n",
       "\n",
       "     post_modified_gmt  post_content_filtered post_parent  \\\n",
       "0  2019-12-30 08:30:29                    NaN         0.0   \n",
       "1  2019-12-30 08:30:29                    NaN         0.0   \n",
       "2  2019-12-21 08:00:17                    NaN         0.0   \n",
       "3  2019-12-21 08:00:17                    NaN         0.0   \n",
       "4  2020-06-26 16:15:03                    NaN         0.0   \n",
       "\n",
       "                                                guid  menu_order   post_type  \\\n",
       "0  https://www.bottle-neck.fr/?post_type=product&...         0.0     product   \n",
       "1  https://www.bottle-neck.fr/wp-content/uploads/...         0.0  attachment   \n",
       "2  https://www.bottle-neck.fr/wp-content/uploads/...         0.0  attachment   \n",
       "3  https://www.bottle-neck.fr/?post_type=product&...         0.0     product   \n",
       "4  https://www.bottle-neck.fr/wp-content/uploads/...         0.0  attachment   \n",
       "\n",
       "  post_mime_type comment_count  \n",
       "0            NaN           0.0  \n",
       "1     image/jpeg           0.0  \n",
       "2     image/jpeg           0.0  \n",
       "3            NaN           0.0  \n",
       "4     image/jpeg           0.0  \n",
       "\n",
       "[5 rows x 33 columns]"
      ]
     },
     "execution_count": 291,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(resultat_final.shape)\n",
    "resultat_final.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span style=\"color: #FF8C00;\">Filtrage sur la DataFrame resultat_final, en ne conservant que les lignes où la colonne 'post_type' a la valeur 'product'.</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span style=\"color: #FF8C00;\">Par ailleurs, dans le traitement de nos fichiers et l'élaboration des calcul, j'ai procédé à un flitre sur la DataFrame resultat_final, en ne conservant que les lignes où la colonne 'post_type' a la valeur 'product'. Je me suis rendu compte que si je n'effectuer pas ce filtre, j'avais des valeurs négatives dans le CA, dans le stocks ainsi que dans les prix. Ce filtrage spécifique permet de me concentrer uniquement par produits et permet donc d'éliminer des données non pertinentes et assuré la cohérence des calculs.</span>\n",
    "<span style=\"color: #FF8C00;\">Les avantages sont de conserver uniquement les produits néanmoins le risque et la perte de quelques élements de valeurs.</span>\n",
    "\n",
    "<span style=\"color: #FF8C00;\">Si on utliser pas ce filtre on aurait par exemple des pertes d'informations importantes notamment dans le calcul du chiffre d'affaire (exemple : resultat_final = resultat_final.dropna(axis=1))</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "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>product_id</th>\n",
       "      <th>onsale_web</th>\n",
       "      <th>stock_quantity</th>\n",
       "      <th>stock_status</th>\n",
       "      <th>price_numeric</th>\n",
       "      <th>sku</th>\n",
       "      <th>virtual</th>\n",
       "      <th>downloadable</th>\n",
       "      <th>rating_count</th>\n",
       "      <th>average_rating</th>\n",
       "      <th>...</th>\n",
       "      <th>comment_status</th>\n",
       "      <th>ping_status</th>\n",
       "      <th>post_name</th>\n",
       "      <th>post_modified</th>\n",
       "      <th>post_modified_gmt</th>\n",
       "      <th>post_parent</th>\n",
       "      <th>guid</th>\n",
       "      <th>menu_order</th>\n",
       "      <th>post_type</th>\n",
       "      <th>comment_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3847</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>closed</td>\n",
       "      <td>closed</td>\n",
       "      <td>pierre-jean-villa-saint-joseph-preface-2018</td>\n",
       "      <td>2019-12-30 09:30:29</td>\n",
       "      <td>2019-12-30 08:30:29</td>\n",
       "      <td>0.0</td>\n",
       "      <td>https://www.bottle-neck.fr/?post_type=product&amp;...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>product</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3849</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>outofstock</td>\n",
       "      <td>34.3</td>\n",
       "      <td>15296</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>closed</td>\n",
       "      <td>closed</td>\n",
       "      <td>pierre-jean-villa-saint-joseph-tilde-2017</td>\n",
       "      <td>2019-12-21 09:00:17</td>\n",
       "      <td>2019-12-21 08:00:17</td>\n",
       "      <td>0.0</td>\n",
       "      <td>https://www.bottle-neck.fr/?post_type=product&amp;...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>product</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3850</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>outofstock</td>\n",
       "      <td>20.8</td>\n",
       "      <td>15300</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>closed</td>\n",
       "      <td>closed</td>\n",
       "      <td>pierre-jean-villa-croze-hermitage-accroche-coe...</td>\n",
       "      <td>2020-06-26 18:15:03</td>\n",
       "      <td>2020-06-26 16:15:03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>https://www.bottle-neck.fr/?post_type=product&amp;...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>product</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4032</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>outofstock</td>\n",
       "      <td>14.1</td>\n",
       "      <td>19814</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>closed</td>\n",
       "      <td>closed</td>\n",
       "      <td>pierre-jean-villa-igp-gamine-2018</td>\n",
       "      <td>2020-01-04 16:36:01</td>\n",
       "      <td>2020-01-04 15:36:01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>https://www.bottle-neck.fr/?post_type=product&amp;...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>product</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4039</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>outofstock</td>\n",
       "      <td>46.0</td>\n",
       "      <td>19815</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>closed</td>\n",
       "      <td>closed</td>\n",
       "      <td>pierre-jean-villa-cote-rotie-carmina-2017</td>\n",
       "      <td>2020-01-04 16:36:10</td>\n",
       "      <td>2020-01-04 15:36:10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>https://www.bottle-neck.fr/?post_type=product&amp;...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>product</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   product_id  onsale_web  stock_quantity stock_status  price_numeric    sku  \\\n",
       "0        3847           1               0   outofstock           24.2  15298   \n",
       "3        3849           1               0   outofstock           34.3  15296   \n",
       "5        3850           1               0   outofstock           20.8  15300   \n",
       "6        4032           1               0   outofstock           14.1  19814   \n",
       "8        4039           1               0   outofstock           46.0  19815   \n",
       "\n",
       "   virtual  downloadable  rating_count  average_rating  ...  comment_status  \\\n",
       "0        0             0             0             0.0  ...          closed   \n",
       "3        0             0             0             0.0  ...          closed   \n",
       "5        0             0             0             0.0  ...          closed   \n",
       "6        0             0             0             0.0  ...          closed   \n",
       "8        0             0             0             0.0  ...          closed   \n",
       "\n",
       "   ping_status                                          post_name  \\\n",
       "0       closed        pierre-jean-villa-saint-joseph-preface-2018   \n",
       "3       closed          pierre-jean-villa-saint-joseph-tilde-2017   \n",
       "5       closed  pierre-jean-villa-croze-hermitage-accroche-coe...   \n",
       "6       closed                  pierre-jean-villa-igp-gamine-2018   \n",
       "8       closed          pierre-jean-villa-cote-rotie-carmina-2017   \n",
       "\n",
       "         post_modified    post_modified_gmt post_parent  \\\n",
       "0  2019-12-30 09:30:29  2019-12-30 08:30:29         0.0   \n",
       "3  2019-12-21 09:00:17  2019-12-21 08:00:17         0.0   \n",
       "5  2020-06-26 18:15:03  2020-06-26 16:15:03         0.0   \n",
       "6  2020-01-04 16:36:01  2020-01-04 15:36:01         0.0   \n",
       "8  2020-01-04 16:36:10  2020-01-04 15:36:10         0.0   \n",
       "\n",
       "                                                guid menu_order post_type  \\\n",
       "0  https://www.bottle-neck.fr/?post_type=product&...        0.0   product   \n",
       "3  https://www.bottle-neck.fr/?post_type=product&...        0.0   product   \n",
       "5  https://www.bottle-neck.fr/?post_type=product&...        0.0   product   \n",
       "6  https://www.bottle-neck.fr/?post_type=product&...        0.0   product   \n",
       "8  https://www.bottle-neck.fr/?post_type=product&...        0.0   product   \n",
       "\n",
       "  comment_count  \n",
       "0           0.0  \n",
       "3           0.0  \n",
       "5           0.0  \n",
       "6           0.0  \n",
       "8           0.0  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 292,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Supprimer la colonne \"product_id_y\" de la DataFrame \"resultat_final\"\n",
    "resultat_final = resultat_final.dropna(axis=1)\n",
    "\n",
    "# Filtrer et ne garder que les post_type product\n",
    "resultat_final = resultat_final.loc[resultat_final['post_type'] == 'product']\n",
    "\n",
    "resultat_final.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(714, 26)"
      ]
     },
     "execution_count": 293,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resultat_final.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span style=\"color:blue; font-weight:bold; font-size:2em;\">III]Calculs</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### A.III) Calcul du chiffre d'affaires"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "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>product_id</th>\n",
       "      <th>onsale_web</th>\n",
       "      <th>stock_quantity</th>\n",
       "      <th>stock_status</th>\n",
       "      <th>price_numeric</th>\n",
       "      <th>sku</th>\n",
       "      <th>virtual</th>\n",
       "      <th>downloadable</th>\n",
       "      <th>rating_count</th>\n",
       "      <th>average_rating</th>\n",
       "      <th>...</th>\n",
       "      <th>ping_status</th>\n",
       "      <th>post_name</th>\n",
       "      <th>post_modified</th>\n",
       "      <th>post_modified_gmt</th>\n",
       "      <th>post_parent</th>\n",
       "      <th>guid</th>\n",
       "      <th>menu_order</th>\n",
       "      <th>post_type</th>\n",
       "      <th>comment_count</th>\n",
       "      <th>ca</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3847</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>outofstock</td>\n",
       "      <td>24.2</td>\n",
       "      <td>15298</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>closed</td>\n",
       "      <td>pierre-jean-villa-saint-joseph-preface-2018</td>\n",
       "      <td>2019-12-30 09:30:29</td>\n",
       "      <td>2019-12-30 08:30:29</td>\n",
       "      <td>0.0</td>\n",
       "      <td>https://www.bottle-neck.fr/?post_type=product&amp;...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>product</td>\n",
       "      <td>0.0</td>\n",
       "      <td>145.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3849</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>outofstock</td>\n",
       "      <td>34.3</td>\n",
       "      <td>15296</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>closed</td>\n",
       "      <td>pierre-jean-villa-saint-joseph-tilde-2017</td>\n",
       "      <td>2019-12-21 09:00:17</td>\n",
       "      <td>2019-12-21 08:00:17</td>\n",
       "      <td>0.0</td>\n",
       "      <td>https://www.bottle-neck.fr/?post_type=product&amp;...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>product</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3850</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>outofstock</td>\n",
       "      <td>20.8</td>\n",
       "      <td>15300</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>closed</td>\n",
       "      <td>pierre-jean-villa-croze-hermitage-accroche-coe...</td>\n",
       "      <td>2020-06-26 18:15:03</td>\n",
       "      <td>2020-06-26 16:15:03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>https://www.bottle-neck.fr/?post_type=product&amp;...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>product</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4032</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>outofstock</td>\n",
       "      <td>14.1</td>\n",
       "      <td>19814</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>closed</td>\n",
       "      <td>pierre-jean-villa-igp-gamine-2018</td>\n",
       "      <td>2020-01-04 16:36:01</td>\n",
       "      <td>2020-01-04 15:36:01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>https://www.bottle-neck.fr/?post_type=product&amp;...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>product</td>\n",
       "      <td>0.0</td>\n",
       "      <td>42.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4039</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>outofstock</td>\n",
       "      <td>46.0</td>\n",
       "      <td>19815</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>closed</td>\n",
       "      <td>pierre-jean-villa-cote-rotie-carmina-2017</td>\n",
       "      <td>2020-01-04 16:36:10</td>\n",
       "      <td>2020-01-04 15:36:10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>https://www.bottle-neck.fr/?post_type=product&amp;...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>product</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   product_id  onsale_web  stock_quantity stock_status  price_numeric    sku  \\\n",
       "0        3847           1               0   outofstock           24.2  15298   \n",
       "3        3849           1               0   outofstock           34.3  15296   \n",
       "5        3850           1               0   outofstock           20.8  15300   \n",
       "6        4032           1               0   outofstock           14.1  19814   \n",
       "8        4039           1               0   outofstock           46.0  19815   \n",
       "\n",
       "   virtual  downloadable  rating_count  average_rating  ...  ping_status  \\\n",
       "0        0             0             0             0.0  ...       closed   \n",
       "3        0             0             0             0.0  ...       closed   \n",
       "5        0             0             0             0.0  ...       closed   \n",
       "6        0             0             0             0.0  ...       closed   \n",
       "8        0             0             0             0.0  ...       closed   \n",
       "\n",
       "                                           post_name        post_modified  \\\n",
       "0        pierre-jean-villa-saint-joseph-preface-2018  2019-12-30 09:30:29   \n",
       "3          pierre-jean-villa-saint-joseph-tilde-2017  2019-12-21 09:00:17   \n",
       "5  pierre-jean-villa-croze-hermitage-accroche-coe...  2020-06-26 18:15:03   \n",
       "6                  pierre-jean-villa-igp-gamine-2018  2020-01-04 16:36:01   \n",
       "8          pierre-jean-villa-cote-rotie-carmina-2017  2020-01-04 16:36:10   \n",
       "\n",
       "     post_modified_gmt post_parent  \\\n",
       "0  2019-12-30 08:30:29         0.0   \n",
       "3  2019-12-21 08:00:17         0.0   \n",
       "5  2020-06-26 16:15:03         0.0   \n",
       "6  2020-01-04 15:36:01         0.0   \n",
       "8  2020-01-04 15:36:10         0.0   \n",
       "\n",
       "                                                guid menu_order post_type  \\\n",
       "0  https://www.bottle-neck.fr/?post_type=product&...        0.0   product   \n",
       "3  https://www.bottle-neck.fr/?post_type=product&...        0.0   product   \n",
       "5  https://www.bottle-neck.fr/?post_type=product&...        0.0   product   \n",
       "6  https://www.bottle-neck.fr/?post_type=product&...        0.0   product   \n",
       "8  https://www.bottle-neck.fr/?post_type=product&...        0.0   product   \n",
       "\n",
       "  comment_count     ca  \n",
       "0           0.0  145.2  \n",
       "3           0.0    0.0  \n",
       "5           0.0    0.0  \n",
       "6           0.0   42.3  \n",
       "8           0.0    0.0  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 294,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# on créé une colonne 'ca_web_par_produit' qui est égale au nb de ventes x le prix\n",
    "\n",
    "resultat_final['ca'] = resultat_final['total_sales'] * resultat_final['price_numeric']\n",
    "\n",
    "# on affiche les premières lignes\n",
    "resultat_final.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(714, 27)"
      ]
     },
     "execution_count": 295,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Vérification\n",
    "resultat_final.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Le chiffre d'affaires réalisé en ligne est de 70568.6 €\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Rorsharks\\AppData\\Local\\Temp\\ipykernel_10664\\1469444159.py:11: 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",
      "  top_10['ca'] = top_10['ca'].round(2)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>post_name</th>\n",
       "      <th>product_id</th>\n",
       "      <th>ca</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>388</th>\n",
       "      <td>champagne-gosset-grand-blanc-de-blanc</td>\n",
       "      <td>4334</td>\n",
       "      <td>4704.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>champagne-gosset-grand-rose</td>\n",
       "      <td>4144</td>\n",
       "      <td>4263.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>436</th>\n",
       "      <td>cognac-frapin-vip-xo</td>\n",
       "      <td>4402</td>\n",
       "      <td>2288.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>champagne-gosset-grand-millesime-2006</td>\n",
       "      <td>4142</td>\n",
       "      <td>1590.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>gosset-champagne-grande-reserve</td>\n",
       "      <td>4141</td>\n",
       "      <td>1560.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>402</th>\n",
       "      <td>champagne-egly-ouriet-grand-cru-brut-blanc-de-...</td>\n",
       "      <td>4355</td>\n",
       "      <td>1391.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399</th>\n",
       "      <td>champagne-egly-ouriet-grand-cru-millesime-2008</td>\n",
       "      <td>4352</td>\n",
       "      <td>1125.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>elian-daros-cotes-du-marmandais-clos-baquey-2015</td>\n",
       "      <td>4153</td>\n",
       "      <td>1044.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1303</th>\n",
       "      <td>domaine-giudicelli-patrimonio-blanc-2019</td>\n",
       "      <td>6206</td>\n",
       "      <td>1033.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>gilles-robin-crozes-hermitage-papillon-2019</td>\n",
       "      <td>4068</td>\n",
       "      <td>1029.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              post_name  product_id      ca\n",
       "388               champagne-gosset-grand-blanc-de-blanc        4334  4704.0\n",
       "143                         champagne-gosset-grand-rose        4144  4263.0\n",
       "436                                cognac-frapin-vip-xo        4402  2288.0\n",
       "141               champagne-gosset-grand-millesime-2006        4142  1590.0\n",
       "138                     gosset-champagne-grande-reserve        4141  1560.0\n",
       "402   champagne-egly-ouriet-grand-cru-brut-blanc-de-...        4355  1391.5\n",
       "399      champagne-egly-ouriet-grand-cru-millesime-2008        4352  1125.0\n",
       "158    elian-daros-cotes-du-marmandais-clos-baquey-2015        4153  1044.0\n",
       "1303           domaine-giudicelli-patrimonio-blanc-2019        6206  1033.2\n",
       "61          gilles-robin-crozes-hermitage-papillon-2019        4068  1029.2"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ca_produits_sum = round(resultat_final['ca'].sum(), 2)\n",
    "print(f\"Le chiffre d'affaires réalisé en ligne est de {ca_produits_sum} €\")\n",
    "\n",
    "# Triez le DataFrame par chiffre d'affaires dans l'ordre décroissant\n",
    "resultat_final = resultat_final.sort_values(by='ca', ascending=False)\n",
    "\n",
    "# Sélectionnez les 10 premières lignes (top 10)\n",
    "top_10 = resultat_final.head(10)\n",
    "\n",
    "# Arrondissez le chiffre d'affaires dans le top 10 à deux décimales\n",
    "top_10['ca'] = top_10['ca'].round(2)\n",
    "\n",
    "# Affichez les informations sur le top 10\n",
    "display(top_10[['post_name', 'product_id', 'ca']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Rorsharks\\AppData\\Local\\Temp\\ipykernel_10664\\4079872414.py:8: 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",
      "  top_5['ca'] = top_5['ca'].round(2)\n"
     ]
    },
    {
     "data": {
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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>post_name</th>\n",
       "      <th>product_id</th>\n",
       "      <th>ca</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>388</th>\n",
       "      <td>champagne-gosset-grand-blanc-de-blanc</td>\n",
       "      <td>4334</td>\n",
       "      <td>4704.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>champagne-gosset-grand-rose</td>\n",
       "      <td>4144</td>\n",
       "      <td>4263.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>436</th>\n",
       "      <td>cognac-frapin-vip-xo</td>\n",
       "      <td>4402</td>\n",
       "      <td>2288.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>champagne-gosset-grand-millesime-2006</td>\n",
       "      <td>4142</td>\n",
       "      <td>1590.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>gosset-champagne-grande-reserve</td>\n",
       "      <td>4141</td>\n",
       "      <td>1560.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 post_name  product_id      ca\n",
       "388  champagne-gosset-grand-blanc-de-blanc        4334  4704.0\n",
       "143            champagne-gosset-grand-rose        4144  4263.0\n",
       "436                   cognac-frapin-vip-xo        4402  2288.0\n",
       "141  champagne-gosset-grand-millesime-2006        4142  1590.0\n",
       "138        gosset-champagne-grande-reserve        4141  1560.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Triez le DataFrame par chiffre d'affaires dans l'ordre décroissant\n",
    "resultat_final = resultat_final.sort_values(by='ca', ascending=False)\n",
    "\n",
    "# Sélectionnez les 5 premières lignes (top 5)\n",
    "top_5 = resultat_final.head(5)\n",
    "\n",
    "# Arrondissez le chiffre d'affaires dans le top 5 à deux décimales\n",
    "top_5['ca'] = top_5['ca'].round(2)\n",
    "\n",
    "# Affichez les informations sur le top 5\n",
    "display(top_5[['post_name', 'product_id', 'ca']])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span style=\"color: #FF8C00;\"><strong>Observations sur les Produits :</strong></span>\n",
    "- <span style=\"color: #FF8C00;\">Les 10 premiers et 5 premiers produits semblent être exclusivement des champagnes, cependant, il est important de noter que le prix élevé des champagnes peut ne pas être un indicateur fiable des meilleures ventes réelles.</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>post_name</th>\n",
       "      <th>product_id</th>\n",
       "      <th>ca</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>718</th>\n",
       "      <td>chateau-turcaud-rose-2019</td>\n",
       "      <td>4858</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>882</th>\n",
       "      <td>domaine-de-montgilet-anjou-rouge-2016-2</td>\n",
       "      <td>5056</td>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>ollieux-romanis-monsieur-pinot-2017</td>\n",
       "      <td>4171</td>\n",
       "      <td>7.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>305</th>\n",
       "      <td>parce-freres-igp-pays-oc-zoe-viognier-2019</td>\n",
       "      <td>4245</td>\n",
       "      <td>8.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>parce-freres-hommage-fernand-blanc-2019</td>\n",
       "      <td>4241</td>\n",
       "      <td>8.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>590</th>\n",
       "      <td>chateau-plaisance-fronton-rose-2019</td>\n",
       "      <td>4683</td>\n",
       "      <td>9.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>346</th>\n",
       "      <td>domaine-serol-cote-roannaise-cabochard-2016</td>\n",
       "      <td>4272</td>\n",
       "      <td>9.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1256</th>\n",
       "      <td>borie-la-vitarele-pays-herault-cigales-2019</td>\n",
       "      <td>6070</td>\n",
       "      <td>9.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>579</th>\n",
       "      <td>domaine-rotier-gaillac-rouge-les-gravels-2016</td>\n",
       "      <td>4677</td>\n",
       "      <td>9.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>730</th>\n",
       "      <td>chateau-de-la-selve-igp-coteaux-de-lardeche-ma...</td>\n",
       "      <td>4867</td>\n",
       "      <td>9.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>327</th>\n",
       "      <td>chermette-domaine-du-vissoux-beaujolais-griott...</td>\n",
       "      <td>4261</td>\n",
       "      <td>9.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>694</th>\n",
       "      <td>maurice-schoech-pinot-auxerrois-2018</td>\n",
       "      <td>4785</td>\n",
       "      <td>10.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>946</th>\n",
       "      <td>domaine-bulliat-chiroubles-2019</td>\n",
       "      <td>5480</td>\n",
       "      <td>10.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>384</th>\n",
       "      <td>domaine-de-lidylle-savoie-roussette-2018</td>\n",
       "      <td>4306</td>\n",
       "      <td>10.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1085</th>\n",
       "      <td>triennes-igp-mediterranee-rouge-merlot-2016</td>\n",
       "      <td>5739</td>\n",
       "      <td>10.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>289</th>\n",
       "      <td>catena-zapata-mendoza-alamos-malbec-2018</td>\n",
       "      <td>4231</td>\n",
       "      <td>11.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>584</th>\n",
       "      <td>domaine-de-joy-cotes-de-gascogne-envie-de-joy-...</td>\n",
       "      <td>4680</td>\n",
       "      <td>12.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1262</th>\n",
       "      <td>chateau-de-villeneuve-saumur-champigny-2017</td>\n",
       "      <td>6093</td>\n",
       "      <td>12.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>598</th>\n",
       "      <td>domaine-plageoles-cotes-du-tarn-blanc-sec-ondenc</td>\n",
       "      <td>4689</td>\n",
       "      <td>12.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>paul-ginglinger-pinot-gris-prelats-2018</td>\n",
       "      <td>4097</td>\n",
       "      <td>12.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              post_name  product_id    ca\n",
       "718                           chateau-turcaud-rose-2019        4858   6.5\n",
       "882             domaine-de-montgilet-anjou-rouge-2016-2        5056   7.5\n",
       "192                 ollieux-romanis-monsieur-pinot-2017        4171   7.8\n",
       "305          parce-freres-igp-pays-oc-zoe-viognier-2019        4245   8.9\n",
       "298             parce-freres-hommage-fernand-blanc-2019        4241   8.9\n",
       "590                 chateau-plaisance-fronton-rose-2019        4683   9.1\n",
       "346         domaine-serol-cote-roannaise-cabochard-2016        4272   9.2\n",
       "1256        borie-la-vitarele-pays-herault-cigales-2019        6070   9.3\n",
       "579       domaine-rotier-gaillac-rouge-les-gravels-2016        4677   9.5\n",
       "730   chateau-de-la-selve-igp-coteaux-de-lardeche-ma...        4867   9.9\n",
       "327   chermette-domaine-du-vissoux-beaujolais-griott...        4261   9.9\n",
       "694                maurice-schoech-pinot-auxerrois-2018        4785  10.1\n",
       "946                     domaine-bulliat-chiroubles-2019        5480  10.4\n",
       "384            domaine-de-lidylle-savoie-roussette-2018        4306  10.7\n",
       "1085        triennes-igp-mediterranee-rouge-merlot-2016        5739  10.7\n",
       "289            catena-zapata-mendoza-alamos-malbec-2018        4231  11.1\n",
       "584   domaine-de-joy-cotes-de-gascogne-envie-de-joy-...        4680  12.6\n",
       "1262        chateau-de-villeneuve-saumur-champigny-2017        6093  12.6\n",
       "598    domaine-plageoles-cotes-du-tarn-blanc-sec-ondenc        4689  12.8\n",
       "103             paul-ginglinger-pinot-gris-prelats-2018        4097  12.8"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Filtrer les valeurs supérieures à 0\n",
    "valeurs_positives = resultat_final[resultat_final['ca'] > 0]\n",
    "\n",
    "# Sélectionner les 20 premières lignes avec les valeurs les plus faibles\n",
    "top_20_faibles = valeurs_positives.nsmallest(20, 'ca')\n",
    "\n",
    "# Afficher toutes les informations triées par chiffre d'affaires croissant\n",
    "display(top_20_faibles[['post_name', 'product_id', 'ca']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>product_id</th>\n",
       "      <th>onsale_web</th>\n",
       "      <th>stock_quantity</th>\n",
       "      <th>stock_status</th>\n",
       "      <th>price_numeric</th>\n",
       "      <th>sku</th>\n",
       "      <th>virtual</th>\n",
       "      <th>downloadable</th>\n",
       "      <th>rating_count</th>\n",
       "      <th>average_rating</th>\n",
       "      <th>...</th>\n",
       "      <th>ping_status</th>\n",
       "      <th>post_name</th>\n",
       "      <th>post_modified</th>\n",
       "      <th>post_modified_gmt</th>\n",
       "      <th>post_parent</th>\n",
       "      <th>guid</th>\n",
       "      <th>menu_order</th>\n",
       "      <th>post_type</th>\n",
       "      <th>comment_count</th>\n",
       "      <th>ca</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>388</th>\n",
       "      <td>4334</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>outofstock</td>\n",
       "      <td>49.0</td>\n",
       "      <td>7818</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>closed</td>\n",
       "      <td>champagne-gosset-grand-blanc-de-blanc</td>\n",
       "      <td>2020-08-12 14:00:03</td>\n",
       "      <td>2020-08-12 12:00:03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>https://www.bottle-neck.fr/?post_type=product&amp;...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>product</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4704.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>4144</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>instock</td>\n",
       "      <td>49.0</td>\n",
       "      <td>1662</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>closed</td>\n",
       "      <td>champagne-gosset-grand-rose</td>\n",
       "      <td>2020-08-14 16:45:03</td>\n",
       "      <td>2020-08-14 14:45:03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>https://www.bottle-neck.fr/?post_type=product&amp;...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>product</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4263.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>436</th>\n",
       "      <td>4402</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>instock</td>\n",
       "      <td>176.0</td>\n",
       "      <td>3510</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>closed</td>\n",
       "      <td>cognac-frapin-vip-xo</td>\n",
       "      <td>2020-08-22 11:35:03</td>\n",
       "      <td>2020-08-22 09:35:03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>https://www.bottle-neck.fr/?post_type=product&amp;...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>product</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2288.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>4142</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>instock</td>\n",
       "      <td>53.0</td>\n",
       "      <td>11641</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>closed</td>\n",
       "      <td>champagne-gosset-grand-millesime-2006</td>\n",
       "      <td>2020-08-20 09:30:03</td>\n",
       "      <td>2020-08-20 07:30:03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>https://www.bottle-neck.fr/?post_type=product&amp;...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>product</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1590.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>4141</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>instock</td>\n",
       "      <td>39.0</td>\n",
       "      <td>304</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>closed</td>\n",
       "      <td>gosset-champagne-grande-reserve</td>\n",
       "      <td>2020-08-27 11:25:02</td>\n",
       "      <td>2020-08-27 09:25:02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>https://www.bottle-neck.fr/?post_type=product&amp;...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>product</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1560.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     product_id  onsale_web  stock_quantity stock_status  price_numeric  \\\n",
       "388        4334           1               0   outofstock           49.0   \n",
       "143        4144           1              11      instock           49.0   \n",
       "436        4402           1               8      instock          176.0   \n",
       "141        4142           1               8      instock           53.0   \n",
       "138        4141           1               1      instock           39.0   \n",
       "\n",
       "       sku  virtual  downloadable  rating_count  average_rating  ...  \\\n",
       "388   7818        0             0             0             0.0  ...   \n",
       "143   1662        0             0             0             0.0  ...   \n",
       "436   3510        0             0             0             0.0  ...   \n",
       "141  11641        0             0             0             0.0  ...   \n",
       "138    304        0             0             0             0.0  ...   \n",
       "\n",
       "     ping_status                              post_name        post_modified  \\\n",
       "388       closed  champagne-gosset-grand-blanc-de-blanc  2020-08-12 14:00:03   \n",
       "143       closed            champagne-gosset-grand-rose  2020-08-14 16:45:03   \n",
       "436       closed                   cognac-frapin-vip-xo  2020-08-22 11:35:03   \n",
       "141       closed  champagne-gosset-grand-millesime-2006  2020-08-20 09:30:03   \n",
       "138       closed        gosset-champagne-grande-reserve  2020-08-27 11:25:02   \n",
       "\n",
       "       post_modified_gmt post_parent  \\\n",
       "388  2020-08-12 12:00:03         0.0   \n",
       "143  2020-08-14 14:45:03         0.0   \n",
       "436  2020-08-22 09:35:03         0.0   \n",
       "141  2020-08-20 07:30:03         0.0   \n",
       "138  2020-08-27 09:25:02         0.0   \n",
       "\n",
       "                                                  guid menu_order post_type  \\\n",
       "388  https://www.bottle-neck.fr/?post_type=product&...        0.0   product   \n",
       "143  https://www.bottle-neck.fr/?post_type=product&...        0.0   product   \n",
       "436  https://www.bottle-neck.fr/?post_type=product&...        0.0   product   \n",
       "141  https://www.bottle-neck.fr/?post_type=product&...        0.0   product   \n",
       "138  https://www.bottle-neck.fr/?post_type=product&...        0.0   product   \n",
       "\n",
       "    comment_count      ca  \n",
       "388           0.0  4704.0  \n",
       "143           0.0  4263.0  \n",
       "436           0.0  2288.0  \n",
       "141           0.0  1590.0  \n",
       "138           0.0  1560.0  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 299,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Vérification\n",
    "resultat_final.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.58\n"
     ]
    }
   ],
   "source": [
    "# Calcule et affiche l'asymétrie arrondie à deux décimales de la distribution des prix dans le DataFrame 'finalresltat_final'.\n",
    "print(round(resultat_final['price_numeric'].skew(), 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span style=\"color: #FF8C00;\"><strong>Observation sur la Variabilité des Prix :</strong></span>\n",
    "- <span style=\"color: #FF8C00;\">Une variabilité des prix en fonction des produits est observée, ce qui est normal étant donné que le coût financier de l'achat peut varier significativement d'un produit à l'autre.</span>\n",
    "\n",
    "<span style=\"color: #FF8C00;\"><strong>Résumé de l'Asymétrie des Prix :</strong></span>\n",
    "- <span style=\"color: #FF8C00;\">Cela suggère que la majorité des produits ont des prix concentrés du côté des valeurs plus basses, avec quelques produits ayant des prix significativement plus élevés, étirant la distribution vers la droite.</span>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### B.III.) Analyse des produits les plus fréquemment rencontrés"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span style=\"color: #FF8C00;\"><strong>Résumé de la Vérification des Occurrences :</strong></span>\n",
    "- <span style=\"color: #FF8C00;\">La vérification des occurrences dans les fichiers a été effectuée pour identifier d'éventuels problèmes.</span>\n",
    "- <span style=\"color: #FF8C00;\">Une mise en relation a été établie entre les occurrences, les prix des produits, le chiffre d'affaires, et les niveaux de stock.</span>\n",
    "- <span style=\"color: #FF8C00;\">Cette analyse vise à détecter des anomalies, à valider l'intégrité des données, et à fournir des insights pour guider les décisions analytiques.</span>\n",
    "- <span style=\"color: #FF8C00;\">Les résultats obtenus peuvent orienter des actions correctives ou des ajustements dans le traitement des données en fonction des relations observées.</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Occurrences des produits avec 'post_name', 'product_id', et 'ca' :\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>post_name</th>\n",
       "      <th>product_id</th>\n",
       "      <th>ca</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>388</th>\n",
       "      <td>champagne-gosset-grand-blanc-de-blanc</td>\n",
       "      <td>4334</td>\n",
       "      <td>4704.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>759</th>\n",
       "      <td>domaine-des-croix-savigny-les-beaune-1er-cru-l...</td>\n",
       "      <td>4902</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>830</th>\n",
       "      <td>champagne-larmandier-bernier-terre-de-vertus-p...</td>\n",
       "      <td>4970</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>683</th>\n",
       "      <td>clos-du-mont-olivet-cotes-du-rhone-rose-farel-...</td>\n",
       "      <td>4778</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>676</th>\n",
       "      <td>domaine-saint-nicolas-fiefs-vendeens-rouge-cuv...</td>\n",
       "      <td>4758</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>566</th>\n",
       "      <td>pelle-menetou-salon-rouge-les-cris-2015</td>\n",
       "      <td>4671</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>569</th>\n",
       "      <td>pelle-menetou-salon-rouge-morogues-2017</td>\n",
       "      <td>4672</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>570</th>\n",
       "      <td>pelle-sancerre-rouge-la-croix-au-garde-2017</td>\n",
       "      <td>4673</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>575</th>\n",
       "      <td>domaine-rotier-gaillac-blanc-sec-renaissance-2015</td>\n",
       "      <td>4675</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>692</th>\n",
       "      <td>maurice-schoech-riesling-vendanges-tardives-2017</td>\n",
       "      <td>4784</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             post_name  product_id      ca\n",
       "388              champagne-gosset-grand-blanc-de-blanc        4334  4704.0\n",
       "759  domaine-des-croix-savigny-les-beaune-1er-cru-l...        4902     0.0\n",
       "830  champagne-larmandier-bernier-terre-de-vertus-p...        4970     0.0\n",
       "683  clos-du-mont-olivet-cotes-du-rhone-rose-farel-...        4778     0.0\n",
       "676  domaine-saint-nicolas-fiefs-vendeens-rouge-cuv...        4758     0.0\n",
       "566            pelle-menetou-salon-rouge-les-cris-2015        4671     0.0\n",
       "569            pelle-menetou-salon-rouge-morogues-2017        4672     0.0\n",
       "570        pelle-sancerre-rouge-la-croix-au-garde-2017        4673     0.0\n",
       "575  domaine-rotier-gaillac-blanc-sec-renaissance-2015        4675     0.0\n",
       "692   maurice-schoech-riesling-vendanges-tardives-2017        4784     0.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compter les occurrences des produits\n",
    "occurrences_produits = resultat_final['product_id'].value_counts()\n",
    "\n",
    "# Afficher les 10 premières occurrences avec 'post_name', 'product_id', et 'ca'\n",
    "top_occurrences_info = resultat_final[resultat_final['product_id'].isin(occurrences_produits.head(10).index)][['post_name', 'product_id', 'ca']]\n",
    "\n",
    "# Afficher le résultat\n",
    "print(\"Occurrences des produits avec 'post_name', 'product_id', et 'ca' :\")\n",
    "display(top_occurrences_info)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Occurrences des produits avec 'post_name', 'product_id', 'ca' et 'occurrences' :\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>post_name</th>\n",
       "      <th>product_id</th>\n",
       "      <th>ca</th>\n",
       "      <th>occurrences</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>388</th>\n",
       "      <td>champagne-gosset-grand-blanc-de-blanc</td>\n",
       "      <td>4334</td>\n",
       "      <td>4704.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>champagne-gosset-grand-rose</td>\n",
       "      <td>4144</td>\n",
       "      <td>4263.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>436</th>\n",
       "      <td>cognac-frapin-vip-xo</td>\n",
       "      <td>4402</td>\n",
       "      <td>2288.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>champagne-gosset-grand-millesime-2006</td>\n",
       "      <td>4142</td>\n",
       "      <td>1590.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>gosset-champagne-grande-reserve</td>\n",
       "      <td>4141</td>\n",
       "      <td>1560.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1216</th>\n",
       "      <td>cognac-normandin-mercier-vfc</td>\n",
       "      <td>5932</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1383</th>\n",
       "      <td>weingut-besson-strasser-zurich-blauer-zweigelt...</td>\n",
       "      <td>6628</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1221</th>\n",
       "      <td>domaine-de-vaccelli-aop-ajaccio-rouge-granit-1...</td>\n",
       "      <td>5951</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1223</th>\n",
       "      <td>clos-du-prieur-terrasses-du-larzac-2018</td>\n",
       "      <td>5956</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1426</th>\n",
       "      <td>domaine-saint-nicolas-fiefs-vendeens-blanc-les...</td>\n",
       "      <td>7338</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>714 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              post_name  product_id      ca  \\\n",
       "388               champagne-gosset-grand-blanc-de-blanc        4334  4704.0   \n",
       "143                         champagne-gosset-grand-rose        4144  4263.0   \n",
       "436                                cognac-frapin-vip-xo        4402  2288.0   \n",
       "141               champagne-gosset-grand-millesime-2006        4142  1590.0   \n",
       "138                     gosset-champagne-grande-reserve        4141  1560.0   \n",
       "...                                                 ...         ...     ...   \n",
       "1216                       cognac-normandin-mercier-vfc        5932     0.0   \n",
       "1383  weingut-besson-strasser-zurich-blauer-zweigelt...        6628     0.0   \n",
       "1221  domaine-de-vaccelli-aop-ajaccio-rouge-granit-1...        5951     0.0   \n",
       "1223            clos-du-prieur-terrasses-du-larzac-2018        5956     0.0   \n",
       "1426  domaine-saint-nicolas-fiefs-vendeens-blanc-les...        7338     0.0   \n",
       "\n",
       "      occurrences  \n",
       "388             1  \n",
       "143             1  \n",
       "436             1  \n",
       "141             1  \n",
       "138             1  \n",
       "...           ...  \n",
       "1216            1  \n",
       "1383            1  \n",
       "1221            1  \n",
       "1223            1  \n",
       "1426            1  \n",
       "\n",
       "[714 rows x 4 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compter les occurrences des produits\n",
    "occurrences_produits = resultat_final['product_id'].value_counts()\n",
    "\n",
    "# Ajouter une colonne 'occurrences' au DataFrame résultant\n",
    "resultat_final_occurrences = resultat_final.merge(occurrences_produits.rename('occurrences'), left_on='product_id', right_index=True)\n",
    "\n",
    "# Afficher le DataFrame résultant avec 'post_name', 'product_id', 'ca' et 'occurrences'\n",
    "print(\"Occurrences des produits avec 'post_name', 'product_id', 'ca' et 'occurrences' :\")\n",
    "display(resultat_final_occurrences[['post_name', 'product_id', 'ca', 'occurrences']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 10 des occurrences non nulles avec 'post_name', 'product_id', 'ca', 'occurrences', 'stock_quantity', et 'price_numeric' :\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>post_name</th>\n",
       "      <th>product_id</th>\n",
       "      <th>ca</th>\n",
       "      <th>occurrences</th>\n",
       "      <th>stock_quantity</th>\n",
       "      <th>price_numeric</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>553</th>\n",
       "      <td>pierre-martin-sancerre-les-monts-damnes-2018</td>\n",
       "      <td>4662</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>20.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236</th>\n",
       "      <td>moulin-gassac-igp-pays-herault-guilhem-rouge-2019</td>\n",
       "      <td>4198</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>105</td>\n",
       "      <td>5.8</td>\n",
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       "    <tr>\n",
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       "      <td>4149</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>34</td>\n",
       "      <td>69.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>planeta-sicilia-alastro-2017</td>\n",
       "      <td>4157</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>8planeta-sicilia-etna-rosso-2018</td>\n",
       "      <td>4158</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>18.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>planeta-sicilia-plumbago-2017</td>\n",
       "      <td>4161</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>11.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>hortus-la-bergerie-blanc-2018</td>\n",
       "      <td>4177</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>109</td>\n",
       "      <td>13.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208</th>\n",
       "      <td>hortus-la-grande-cuvee-blanc-2018</td>\n",
       "      <td>4180</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>47</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215</th>\n",
       "      <td>borie-la-vitarele-saint-chinian-les-cres-2016</td>\n",
       "      <td>4183</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>48</td>\n",
       "      <td>21.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>mas-de-daumas-gassac-igp-saint-guilhem-le-dese...</td>\n",
       "      <td>4202</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>46</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             post_name  product_id   ca  \\\n",
       "553       pierre-martin-sancerre-les-monts-damnes-2018        4662  0.0   \n",
       "236  moulin-gassac-igp-pays-herault-guilhem-rouge-2019        4198  0.0   \n",
       "150  champagne-mailly-grand-cru-intemporelle-rose-2009        4149  0.0   \n",
       "167                       planeta-sicilia-alastro-2017        4157  0.0   \n",
       "168                   8planeta-sicilia-etna-rosso-2018        4158  0.0   \n",
       "175                      planeta-sicilia-plumbago-2017        4161  0.0   \n",
       "202                      hortus-la-bergerie-blanc-2018        4177  0.0   \n",
       "208                  hortus-la-grande-cuvee-blanc-2018        4180  0.0   \n",
       "215      borie-la-vitarele-saint-chinian-les-cres-2016        4183  0.0   \n",
       "242  mas-de-daumas-gassac-igp-saint-guilhem-le-dese...        4202  0.0   \n",
       "\n",
       "     occurrences  stock_quantity  price_numeric  \n",
       "553            1              24           20.8  \n",
       "236            1             105            5.8  \n",
       "150            1              34           69.0  \n",
       "167            1              10           12.0  \n",
       "168            1               0           18.5  \n",
       "175            1               0           11.6  \n",
       "202            1             109           13.5  \n",
       "208            1              47           24.0  \n",
       "215            1              48           21.4  \n",
       "242            1              46           38.0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Filtrer les produits avec une occurrence différente de 0\n",
    "occurrences_non_nulles = resultat_final_occurrences[resultat_final_occurrences['occurrences'] != 0]\n",
    "\n",
    "# Créer un top 10 des produits avec occurrences non nulles\n",
    "top_10_occurrences_non_nulles = occurrences_non_nulles.sort_values(by=['occurrences', 'ca'], ascending=[False, True]).head(10)\n",
    "\n",
    "# Afficher le top 10 avec 'post_name', 'product_id', 'ca', 'occurrences', 'stock_quantity', et 'price_numeric'\n",
    "print(\"Top 10 des occurrences non nulles avec 'post_name', 'product_id', 'ca', 'occurrences', 'stock_quantity', et 'price_numeric' :\")\n",
    "display(top_10_occurrences_non_nulles[['post_name', 'product_id', 'ca', 'occurrences', 'stock_quantity', 'price_numeric']])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span style=\"color: #FF8C00;\"><strong>Résumé de l'Analyse des Données :</strong></span>\n",
    "\n",
    "- <span style=\"color: #FF8C00;\">La colonne <span style=\"color: #FF8C00;\">`post_name`</span> présente une diversité de noms de produits.</span>\n",
    "  \n",
    "- <span style=\"color: #FF8C00;\">Certains produits ont généré du chiffre d'affaires (<span style=\"color: #FF8C00;\">`ca`</span>), tandis que d'autres affichent un chiffre d'affaires nul.</span>\n",
    "\n",
    "- <span style=\"color: #FF8C00;\">La majorité des produits ont une seule occurrence (<span style=\"color: #FF8C00;\">`1`</span>), bien que certains aient <span style=\"color: #FF8C00;\">`0.0`</span> occurrences.</span>\n",
    "\n",
    "- <span style=\"color: #FF8C00;\">Cette analyse préliminaire vise à identifier des anomalies, à valider l'intégrité des données, et à fournir des insights pour guider les décisions analytiques.</span>\n",
    "\n",
    "- <span style=\"color: #FF8C00;\">Les résultats obtenus peuvent orienter des actions correctives ou des ajustements dans le traitement des données en fonction des relations observées.</span>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 20 des occurrences non nulles et 'ca' non nul trié par 'ca' ascendant avec 'post_name', 'product_id', 'ca', 'occurrences', 'stock_quantity', et 'price_numeric' :\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>post_name</th>\n",
       "      <th>product_id</th>\n",
       "      <th>ca</th>\n",
       "      <th>occurrences</th>\n",
       "      <th>stock_quantity</th>\n",
       "      <th>price_numeric</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>718</th>\n",
       "      <td>chateau-turcaud-rose-2019</td>\n",
       "      <td>4858</td>\n",
       "      <td>6.5</td>\n",
       "      <td>1</td>\n",
       "      <td>257</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>882</th>\n",
       "      <td>domaine-de-montgilet-anjou-rouge-2016-2</td>\n",
       "      <td>5056</td>\n",
       "      <td>7.5</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>ollieux-romanis-monsieur-pinot-2017</td>\n",
       "      <td>4171</td>\n",
       "      <td>7.8</td>\n",
       "      <td>1</td>\n",
       "      <td>65</td>\n",
       "      <td>7.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>305</th>\n",
       "      <td>parce-freres-igp-pays-oc-zoe-viognier-2019</td>\n",
       "      <td>4245</td>\n",
       "      <td>8.9</td>\n",
       "      <td>1</td>\n",
       "      <td>141</td>\n",
       "      <td>8.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>parce-freres-hommage-fernand-blanc-2019</td>\n",
       "      <td>4241</td>\n",
       "      <td>8.9</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>8.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>590</th>\n",
       "      <td>chateau-plaisance-fronton-rose-2019</td>\n",
       "      <td>4683</td>\n",
       "      <td>9.1</td>\n",
       "      <td>1</td>\n",
       "      <td>42</td>\n",
       "      <td>9.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>346</th>\n",
       "      <td>domaine-serol-cote-roannaise-cabochard-2016</td>\n",
       "      <td>4272</td>\n",
       "      <td>9.2</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>9.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1256</th>\n",
       "      <td>borie-la-vitarele-pays-herault-cigales-2019</td>\n",
       "      <td>6070</td>\n",
       "      <td>9.3</td>\n",
       "      <td>1</td>\n",
       "      <td>124</td>\n",
       "      <td>9.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>579</th>\n",
       "      <td>domaine-rotier-gaillac-rouge-les-gravels-2016</td>\n",
       "      <td>4677</td>\n",
       "      <td>9.5</td>\n",
       "      <td>1</td>\n",
       "      <td>161</td>\n",
       "      <td>9.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>730</th>\n",
       "      <td>chateau-de-la-selve-igp-coteaux-de-lardeche-ma...</td>\n",
       "      <td>4867</td>\n",
       "      <td>9.9</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>9.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>327</th>\n",
       "      <td>chermette-domaine-du-vissoux-beaujolais-griott...</td>\n",
       "      <td>4261</td>\n",
       "      <td>9.9</td>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>9.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>694</th>\n",
       "      <td>maurice-schoech-pinot-auxerrois-2018</td>\n",
       "      <td>4785</td>\n",
       "      <td>10.1</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>10.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>946</th>\n",
       "      <td>domaine-bulliat-chiroubles-2019</td>\n",
       "      <td>5480</td>\n",
       "      <td>10.4</td>\n",
       "      <td>1</td>\n",
       "      <td>37</td>\n",
       "      <td>10.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>384</th>\n",
       "      <td>domaine-de-lidylle-savoie-roussette-2018</td>\n",
       "      <td>4306</td>\n",
       "      <td>10.7</td>\n",
       "      <td>1</td>\n",
       "      <td>41</td>\n",
       "      <td>10.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1085</th>\n",
       "      <td>triennes-igp-mediterranee-rouge-merlot-2016</td>\n",
       "      <td>5739</td>\n",
       "      <td>10.7</td>\n",
       "      <td>1</td>\n",
       "      <td>39</td>\n",
       "      <td>10.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>289</th>\n",
       "      <td>catena-zapata-mendoza-alamos-malbec-2018</td>\n",
       "      <td>4231</td>\n",
       "      <td>11.1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>11.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>584</th>\n",
       "      <td>domaine-de-joy-cotes-de-gascogne-envie-de-joy-...</td>\n",
       "      <td>4680</td>\n",
       "      <td>12.6</td>\n",
       "      <td>1</td>\n",
       "      <td>34</td>\n",
       "      <td>6.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1262</th>\n",
       "      <td>chateau-de-villeneuve-saumur-champigny-2017</td>\n",
       "      <td>6093</td>\n",
       "      <td>12.6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>12.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>598</th>\n",
       "      <td>domaine-plageoles-cotes-du-tarn-blanc-sec-ondenc</td>\n",
       "      <td>4689</td>\n",
       "      <td>12.8</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>12.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>paul-ginglinger-pinot-gris-prelats-2018</td>\n",
       "      <td>4097</td>\n",
       "      <td>12.8</td>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "      <td>12.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              post_name  product_id    ca  \\\n",
       "718                           chateau-turcaud-rose-2019        4858   6.5   \n",
       "882             domaine-de-montgilet-anjou-rouge-2016-2        5056   7.5   \n",
       "192                 ollieux-romanis-monsieur-pinot-2017        4171   7.8   \n",
       "305          parce-freres-igp-pays-oc-zoe-viognier-2019        4245   8.9   \n",
       "298             parce-freres-hommage-fernand-blanc-2019        4241   8.9   \n",
       "590                 chateau-plaisance-fronton-rose-2019        4683   9.1   \n",
       "346         domaine-serol-cote-roannaise-cabochard-2016        4272   9.2   \n",
       "1256        borie-la-vitarele-pays-herault-cigales-2019        6070   9.3   \n",
       "579       domaine-rotier-gaillac-rouge-les-gravels-2016        4677   9.5   \n",
       "730   chateau-de-la-selve-igp-coteaux-de-lardeche-ma...        4867   9.9   \n",
       "327   chermette-domaine-du-vissoux-beaujolais-griott...        4261   9.9   \n",
       "694                maurice-schoech-pinot-auxerrois-2018        4785  10.1   \n",
       "946                     domaine-bulliat-chiroubles-2019        5480  10.4   \n",
       "384            domaine-de-lidylle-savoie-roussette-2018        4306  10.7   \n",
       "1085        triennes-igp-mediterranee-rouge-merlot-2016        5739  10.7   \n",
       "289            catena-zapata-mendoza-alamos-malbec-2018        4231  11.1   \n",
       "584   domaine-de-joy-cotes-de-gascogne-envie-de-joy-...        4680  12.6   \n",
       "1262        chateau-de-villeneuve-saumur-champigny-2017        6093  12.6   \n",
       "598    domaine-plageoles-cotes-du-tarn-blanc-sec-ondenc        4689  12.8   \n",
       "103             paul-ginglinger-pinot-gris-prelats-2018        4097  12.8   \n",
       "\n",
       "      occurrences  stock_quantity  price_numeric  \n",
       "718             1             257            6.5  \n",
       "882             1               9            7.5  \n",
       "192             1              65            7.8  \n",
       "305             1             141            8.9  \n",
       "298             1               7            8.9  \n",
       "590             1              42            9.1  \n",
       "346             1              25            9.2  \n",
       "1256            1             124            9.3  \n",
       "579             1             161            9.5  \n",
       "730             1               0            9.9  \n",
       "327             1              89            9.9  \n",
       "694             1              18           10.1  \n",
       "946             1              37           10.4  \n",
       "384             1              41           10.7  \n",
       "1085            1              39           10.7  \n",
       "289             1               0           11.1  \n",
       "584             1              34            6.3  \n",
       "1262            1               0           12.6  \n",
       "598             1               6           12.8  \n",
       "103             1              26           12.8  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Filtrer les produits avec une occurrence différente de 0 et une colonne 'ca' différente de 0\n",
    "occurrences_non_nulles_et_ca_non_nul = resultat_final_occurrences[(resultat_final_occurrences['occurrences'] != 0) & (resultat_final_occurrences['ca'] != 0)]\n",
    "\n",
    "# Créer un top 20 des produits avec occurrences non nulles et 'ca' non nul\n",
    "top_20_occurrences_non_nulles_et_ca_non_nul = occurrences_non_nulles_et_ca_non_nul.sort_values(by=['occurrences', 'ca'], ascending=[False, True]).head(20)\n",
    "\n",
    "# Afficher le top 20 avec 'post_name', 'product_id', 'ca', 'occurrences', 'stock_quantity', et 'price_numeric'\n",
    "print(\"Top 20 des occurrences non nulles et 'ca' non nul trié par 'ca' ascendant avec 'post_name', 'product_id', 'ca', 'occurrences', 'stock_quantity', et 'price_numeric' :\")\n",
    "display(top_20_occurrences_non_nulles_et_ca_non_nul[['post_name', 'product_id', 'ca', 'occurrences', 'stock_quantity', 'price_numeric']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 10 des occurrences supérieures à 1 avec 'post_name', 'product_id', 'ca', 'occurrences', 'stock_quantity', et 'price_numeric' :\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>post_name</th>\n",
       "      <th>product_id</th>\n",
       "      <th>ca</th>\n",
       "      <th>occurrences</th>\n",
       "      <th>stock_quantity</th>\n",
       "      <th>price_numeric</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [post_name, product_id, ca, occurrences, stock_quantity, price_numeric]\n",
       "Index: []"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Filtrer les produits avec une occurrence strictement supérieure à 1\n",
    "occurrences_sup_1 = resultat_final_occurrences[resultat_final_occurrences['occurrences'] > 1]\n",
    "\n",
    "# Créer un top 10 des produits avec occurrences supérieures à 1\n",
    "top_10_occurrences_sup_1 = occurrences_sup_1.sort_values(by=['occurrences', 'ca'], ascending=[False, True]).head(10)\n",
    "\n",
    "# Afficher le top 10 avec 'post_name', 'product_id', 'ca', 'occurrences', 'stock_quantity', et 'price_numeric'\n",
    "print(\"Top 10 des occurrences supérieures à 1 avec 'post_name', 'product_id', 'ca', 'occurrences', 'stock_quantity', et 'price_numeric' :\")\n",
    "display(top_10_occurrences_sup_1[['post_name', 'product_id', 'ca', 'occurrences', 'stock_quantity', 'price_numeric']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lignes avec des valeurs négatives dans la colonne 'ca' :\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>post_name</th>\n",
       "      <th>product_id</th>\n",
       "      <th>ca</th>\n",
       "      <th>occurrences</th>\n",
       "      <th>stock_quantity</th>\n",
       "      <th>price_numeric</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [post_name, product_id, ca, occurrences, stock_quantity, price_numeric]\n",
       "Index: []"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Filtrer les lignes avec des valeurs négatives dans la colonne 'ca'\n",
    "valeurs_negatives = resultat_final_occurrences[resultat_final_occurrences['ca'] < 0]\n",
    "\n",
    "# Afficher les lignes avec des valeurs négatives dans la colonne 'ca' avec 'post_name', 'product_id', 'ca', 'occurrences', 'stock_quantity', et 'price_numeric'\n",
    "print(\"Lignes avec des valeurs négatives dans la colonne 'ca' :\")\n",
    "display(valeurs_negatives[['post_name', 'product_id', 'ca', 'occurrences', 'stock_quantity', 'price_numeric']])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span style=\"color: #FF8C00;\"><strong>Observations sur les Clés et les Valeurs :</strong></span>\n",
    "- <span style=\"color: #FF8C00;\">Les 'product_id' sont des clés uniques, assurant une identification distincte pour chaque produit.</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### C.III.) Analyse des prix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   index  price_numeric\n",
      "0  count     714.000000\n",
      "1   mean      32.493137\n",
      "2    std      27.810525\n",
      "3    min       5.200000\n",
      "4    25%      14.100000\n",
      "5    50%      23.550000\n",
      "6    75%      42.175000\n",
      "7    max     225.000000\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Configure le style de seaborn pour une meilleure apparence des graphiques\n",
    "sns.set(style=\"whitegrid\")\n",
    "\n",
    "# Crée un histogramme des prix dans le DataFrame 'resultat_final' avec seaborn\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.histplot(resultat_final['price_numeric'], bins=30, kde=True, color='skyblue', edgecolor='black')\n",
    "\n",
    "# Ajoute des labels et un titre au graphique\n",
    "plt.xlabel('Price_numéric')\n",
    "plt.ylabel('Fréquence')\n",
    "plt.title('Distribution des Prix')\n",
    "\n",
    "# Ajoute une grille pour une meilleure lisibilité\n",
    "plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
    "\n",
    "# Affiche le graphique\n",
    "plt.show()\n",
    "\n",
    "\n",
    "# Affiche les statistiques descriptives sur la colonne des prix\n",
    "price_stats = resultat_final['price_numeric'].describe().reset_index()\n",
    "\n",
    "# Affiche le tableau de résultats\n",
    "print(price_stats)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span style=\"color: #FF8C00;\"><strong>Résumé des Statistiques des Prix :</strong></span>\n",
    "\n",
    "- <span style=\"color: #FF8C00;\">Nombre d'observations : 714</span>\n",
    "- <span style=\"color: #FF8C00;\">Moyenne des prix : 32.49</span>\n",
    "- <span style=\"color: #FF8C00;\">Écart type : 27.81</span>\n",
    "- <span style=\"color: #FF8C00;\">Prix minimum : 5.20</span>\n",
    "- <span style=\"color: #FF8C00;\">Premier quartile (Q1) : 14.10</span>\n",
    "- <span style=\"color: #FF8C00;\">Médiane (50%) : 23.55</span>\n",
    "- <span style=\"color: #FF8C00;\">Troisième quartile (Q3) : 42.18</span>\n",
    "- <span style=\"color: #FF8C00;\">Prix maximum : 225.00</span>\n",
    "\n",
    "<span style=\"color: #FF8C00;\">Ces statistiques fournissent une vue détaillée de la distribution des prix, allant de la centralité avec la moyenne et la médiane, à la dispersion avec l'écart type, ainsi que les valeurs minimales et maximales.</span>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span style=\"color: #FF8C00;\"><strong>• Observations sur la Distribution des Prix :</strong></span>\n",
    "\n",
    "<span style=\"color: #FF8C00;\"> La variation importante des prix suggère une diversité de produits dans l'ensemble de données.</span>\n",
    "\n",
    "<span style=\"color: #FF8C00;\"> La majorité des produits présente des prix concentrés dans une fourchette basse.</span>\n",
    "\n",
    "<span style=\"color: #FF8C00;\"> Quelques produits ont des prix significativement plus élevés."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    714.000000\n",
      "mean      32.493137\n",
      "std       27.810525\n",
      "min        5.200000\n",
      "25%       14.100000\n",
      "50%       23.550000\n",
      "75%       42.175000\n",
      "max      225.000000\n",
      "Name: price_numeric, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Crée un boxplot pour visualiser les valeurs atypiques des prix\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.boxplot(x=resultat_final['price_numeric'], color='skyblue')\n",
    "\n",
    "# Ajoute des labels et un titre au graphique\n",
    "plt.xlabel('Prix')\n",
    "plt.title('Boxplot des Prix avec Valeurs Atypiques')\n",
    "\n",
    "# Affiche le graphique\n",
    "plt.show()\n",
    "\n",
    "# Analyse des Valeurs Atypiques des Prix\n",
    "# Utilisation d'un boxplot pour identifier les valeurs atypiques dans la distribution des prix.\n",
    "\n",
    "# Résultats de l'analyse\n",
    "boxplot_results = resultat_final['price_numeric'].describe()\n",
    "print(boxplot_results)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span style=\"color: #FF8C00;\"><strong>Résumé de l'Analyse des Statistiques des Prix :</strong></span>\n",
    "\n",
    "- <span style=\"color: #FF8C00;\">Nombre d'observations : 714</span>\n",
    "- <span style=\"color: #FF8C00;\">Moyenne des prix : 32.49</span>\n",
    "- <span style=\"color: #FF8C00;\">Écart type : 27.81</span>\n",
    "- <span style=\"color: #FF8C00;\">Prix minimum : 5.20</span>\n",
    "- <span style=\"color: #FF8C00;\">Premier quartile (Q1) : 14.10</span>\n",
    "- <span style=\"color: #FF8C00;\">Médiane (50%) : 23.55</span>\n",
    "- <span style=\"color: #FF8C00;\">Troisième quartile (Q3) : 42.18</span>\n",
    "- <span style=\"color: #FF8C00;\">Prix maximum : 225.00</span>\n",
    "\n",
    "<span style=\"color: #FF8C00;\">Cette analyse détaillée offre une vue complète de la distribution des prix, mettant en lumière la centralité, la dispersion, ainsi que les valeurs minimales et maximales. Ces données peuvent orienter des décisions commerciales en fournissant des insights sur la variabilité des prix dans votre ensemble de données.</span>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Borne minimale : 5.2\n",
      "Borne pour les valeurs atypiques : [-28.012500000000003, 84.28750000000001]\n",
      "Borne maximale : 225.0\n",
      "Valeurs atypiques :\n",
      "      product_id  onsale_web  stock_quantity stock_status  price_numeric  \\\n",
      "436         4402           1               8      instock          176.0   \n",
      "402         4355           1               2      instock          126.5   \n",
      "399         4352           1               0   outofstock          225.0   \n",
      "763         4904           1              13      instock          137.0   \n",
      "1174        5892           1              10      instock          191.3   \n",
      "1284        6126           1              10      instock          135.0   \n",
      "1306        6212           1               2      instock          115.0   \n",
      "438         4404           1               2      instock          108.5   \n",
      "445         4407           1               6      instock          104.0   \n",
      "411         4359           1               0   outofstock           85.6   \n",
      "876         5026           1               2      instock           86.8   \n",
      "875         5025           1               0   outofstock          112.0   \n",
      "862         5007           1              17      instock          105.0   \n",
      "865         5008           1              10      instock          105.0   \n",
      "126         4115           1              11      instock          100.0   \n",
      "131         4132           1               5      instock           88.4   \n",
      "853         5001           1              20      instock          217.5   \n",
      "760         4903           1              20      instock          102.3   \n",
      "1005        5565           1               0   outofstock           92.0   \n",
      "1296        6202           1              14      instock          116.4   \n",
      "1309        6213           1               7      instock          121.0   \n",
      "1310        6214           1               7      instock           99.0   \n",
      "1312        6215           1               4      instock          115.0   \n",
      "1315        6216           1               6      instock          121.0   \n",
      "1106        5767           1              12      instock          175.0   \n",
      "1022        5612           1              12      instock          124.8   \n",
      "1294        6201           1               7      instock          105.6   \n",
      "454         4582           1               7      instock          109.6   \n",
      "443         4406           1               3      instock          157.0   \n",
      "1205        5916           1               3      instock           93.0   \n",
      "1206        5917           1               4      instock          122.0   \n",
      "1209        5918           1               8      instock          114.0   \n",
      "\n",
      "        sku  virtual  downloadable  rating_count  average_rating  ...  \\\n",
      "436    3510        0             0             0             0.0  ...   \n",
      "402   12589        0             0             0             0.0  ...   \n",
      "399   15940        0             0             0             0.0  ...   \n",
      "763   14220        0             0             0             0.0  ...   \n",
      "1174  14983        0             0             0             0.0  ...   \n",
      "1284  14923        0             0             0             0.0  ...   \n",
      "1306  13996        0             0             0             0.0  ...   \n",
      "438    3507        0             0             0             0.0  ...   \n",
      "445    3509        0             0             0             0.0  ...   \n",
      "411   13853        0             0             0             0.0  ...   \n",
      "876   13913        0             0             0             0.0  ...   \n",
      "875   13914        0             0             0             0.0  ...   \n",
      "862   12791        0             0             0             0.0  ...   \n",
      "865   11602        0             0             0             0.0  ...   \n",
      "126   15382        0             0             0             0.0  ...   \n",
      "131   11668        0             0             0             0.0  ...   \n",
      "853   14581        0             0             0             0.0  ...   \n",
      "760   14805        0             0             0             0.0  ...   \n",
      "1005  19822        0             0             0             0.0  ...   \n",
      "1296  15126        0             0             0             0.0  ...   \n",
      "1309  15072        0             0             0             0.0  ...   \n",
      "1310  11601        0             0             0             0.0  ...   \n",
      "1312  12790        0             0             0             0.0  ...   \n",
      "1315  15070        0             0             0             0.0  ...   \n",
      "1106  15185        0             0             0             0.0  ...   \n",
      "1022  14915        0             0             0             0.0  ...   \n",
      "1294  14596        0             0             0             0.0  ...   \n",
      "454   12857        0             0             0             0.0  ...   \n",
      "443    7819        0             0             0             0.0  ...   \n",
      "1205  14774        0             0             0             0.0  ...   \n",
      "1206  14775        0             0             0             0.0  ...   \n",
      "1209  14773        0             0             0             0.0  ...   \n",
      "\n",
      "      ping_status                                          post_name  \\\n",
      "436        closed                               cognac-frapin-vip-xo   \n",
      "402        closed  champagne-egly-ouriet-grand-cru-brut-blanc-de-...   \n",
      "399        closed     champagne-egly-ouriet-grand-cru-millesime-2008   \n",
      "763        closed  domaine-des-croix-corton-charlemagne-grand-cru...   \n",
      "1174       closed  coteaux-champenois-egly-ouriet-ambonnay-rouge-...   \n",
      "1284       closed             champagne-gosset-celebris-vintage-2007   \n",
      "1306       closed  domaine-des-comtes-lafon-volnay-1er-cru-santen...   \n",
      "438        closed                         cognac-frapin-fontpinot-xo   \n",
      "445        closed                          cognac-frapin-cigar-blend   \n",
      "411        closed  champagne-larmandier-bernier-grand-cru-vieille...   \n",
      "876        closed  champagne-agrapart-fils-mineral-extra-brut-bla...   \n",
      "875        closed  champagne-agrapart-fils-lavizoise-grand-cru-20...   \n",
      "862        closed  domaine-des-comtes-lafon-volnay-1er-cru-santen...   \n",
      "865        closed  domaine-des-comtes-lafon-volnay-1er-cru-santen...   \n",
      "126        closed  zind-humbrecht-riesling-gc-rangen-thann-clos-s...   \n",
      "131        closed  zind-humbrecht-pinot-gris-grand-cru-rangen-de-...   \n",
      "853        closed     david-duband-charmes-chambertin-grand-cru-2014   \n",
      "760        closed  domaine-des-croix-corton-grand-cru-les-greves-...   \n",
      "1005       closed                      tempier-bandol-cabassaou-2017   \n",
      "1296       closed          domaine-clerget-echezeaux-en-orveaux-2015   \n",
      "1309       closed  domaine-des-comtes-lafon-volnay-1er-cru-santen...   \n",
      "1310       closed  domaine-des-comtes-lafon-volnay-1er-cru-champa...   \n",
      "1312       closed  domaine-des-comtes-lafon-volnay-1er-cru-champa...   \n",
      "1315       closed  domaine-des-comtes-lafon-volnay-1er-cru-champa...   \n",
      "1106       closed                camille-giroud-clos-de-vougeot-2016   \n",
      "1022       closed  domaine-weinbach-gewurztraminer-gc-furstentum-...   \n",
      "1294       closed  david-duband-chambolle-musigny-1er-cru-les-sen...   \n",
      "454        closed  chateau-de-puligny-montrachet-1cru-champ-canet...   \n",
      "443        closed     cognac-frapin-chateau-de-fontpinot-1989-20-ans   \n",
      "1205       closed       wemyss-malts-single-cask-chocolate-moka-cake   \n",
      "1206       closed  wemyss-malts-single-cask-scotch-whisky-choc-n-...   \n",
      "1209       closed  wemyss-malts-single-cask-scotch-whisky-chai-ca...   \n",
      "\n",
      "            post_modified    post_modified_gmt post_parent  \\\n",
      "436   2020-08-22 11:35:03  2020-08-22 09:35:03         0.0   \n",
      "402   2020-08-13 10:15:02  2020-08-13 08:15:02         0.0   \n",
      "399   2020-03-07 11:18:45  2020-03-07 10:18:45         0.0   \n",
      "763   2020-05-19 17:15:02  2020-05-19 15:15:02         0.0   \n",
      "1174  2020-04-01 09:30:09  2020-04-01 07:30:09         0.0   \n",
      "1284  2020-08-27 11:45:02  2020-08-27 09:45:02         0.0   \n",
      "1306  2020-06-16 09:30:16  2020-06-16 07:30:16         0.0   \n",
      "438   2020-08-12 09:30:16  2020-08-12 07:30:16         0.0   \n",
      "445   2020-07-04 09:45:03  2020-07-04 07:45:03         0.0   \n",
      "411   2019-12-23 09:30:11  2019-12-23 08:30:11         0.0   \n",
      "876   2020-05-11 14:35:02  2020-05-11 12:35:02         0.0   \n",
      "875   2020-07-09 17:05:02  2020-07-09 15:05:02         0.0   \n",
      "862   2020-07-02 09:30:03  2020-07-02 07:30:03         0.0   \n",
      "865   2020-06-23 15:35:02  2020-06-23 13:35:02         0.0   \n",
      "126   2020-02-08 11:45:02  2020-02-08 10:45:02         0.0   \n",
      "131   2020-02-20 09:55:02  2020-02-20 08:55:02         0.0   \n",
      "853   2020-05-16 09:00:05  2020-05-16 07:00:05         0.0   \n",
      "760   2020-06-27 09:00:07  2020-06-27 07:00:07         0.0   \n",
      "1005  2020-01-04 13:57:04  2020-01-04 12:57:04         0.0   \n",
      "1296  2020-06-06 15:45:01  2020-06-06 13:45:01         0.0   \n",
      "1309  2020-06-25 09:30:06  2020-06-25 07:30:06         0.0   \n",
      "1310  2020-07-04 11:35:02  2020-07-04 09:35:02         0.0   \n",
      "1312  2019-11-04 09:30:25  2019-11-04 08:30:25         0.0   \n",
      "1315  2020-07-30 09:30:08  2020-07-30 07:30:08         0.0   \n",
      "1106  2020-06-11 15:25:04  2020-06-11 13:25:04         0.0   \n",
      "1022  2019-01-23 09:33:57  2019-01-23 08:33:57         0.0   \n",
      "1294  2020-02-29 15:25:02  2020-02-29 14:25:02         0.0   \n",
      "454   2020-02-06 16:35:02  2020-02-06 15:35:02         0.0   \n",
      "443   2020-03-14 16:05:04  2020-03-14 15:05:04         0.0   \n",
      "1205  2019-12-23 09:30:21  2019-12-23 08:30:21         0.0   \n",
      "1206  2020-03-11 09:30:09  2020-03-11 08:30:09         0.0   \n",
      "1209  2020-07-31 18:25:03  2020-07-31 16:25:03         0.0   \n",
      "\n",
      "                                                   guid menu_order post_type  \\\n",
      "436   https://www.bottle-neck.fr/?post_type=product&...        0.0   product   \n",
      "402   https://www.bottle-neck.fr/?post_type=product&...        0.0   product   \n",
      "399   https://www.bottle-neck.fr/?post_type=product&...        0.0   product   \n",
      "763   https://www.bottle-neck.fr/?post_type=product&...        0.0   product   \n",
      "1174  https://www.bottle-neck.fr/?post_type=product&...        0.0   product   \n",
      "1284  https://www.bottle-neck.fr/?post_type=product&...        0.0   product   \n",
      "1306  https://www.bottle-neck.fr/?post_type=product&...        0.0   product   \n",
      "438   https://www.bottle-neck.fr/?post_type=product&...        0.0   product   \n",
      "445   https://www.bottle-neck.fr/?post_type=product&...        0.0   product   \n",
      "411   https://www.bottle-neck.fr/?post_type=product&...        0.0   product   \n",
      "876   https://www.bottle-neck.fr/?post_type=product&...        0.0   product   \n",
      "875   https://www.bottle-neck.fr/?post_type=product&...        0.0   product   \n",
      "862   https://www.bottle-neck.fr/?post_type=product&...        0.0   product   \n",
      "865   https://www.bottle-neck.fr/?post_type=product&...        0.0   product   \n",
      "126   https://www.bottle-neck.fr/?post_type=product&...        0.0   product   \n",
      "131   https://www.bottle-neck.fr/?post_type=product&...        0.0   product   \n",
      "853   https://www.bottle-neck.fr/?post_type=product&...        0.0   product   \n",
      "760   https://www.bottle-neck.fr/?post_type=product&...        0.0   product   \n",
      "1005  https://www.bottle-neck.fr/?post_type=product&...        0.0   product   \n",
      "1296  https://www.bottle-neck.fr/?post_type=product&...        0.0   product   \n",
      "1309  https://www.bottle-neck.fr/?post_type=product&...        0.0   product   \n",
      "1310  https://www.bottle-neck.fr/?post_type=product&...        0.0   product   \n",
      "1312  https://www.bottle-neck.fr/?post_type=product&...        0.0   product   \n",
      "1315  https://www.bottle-neck.fr/?post_type=product&...        0.0   product   \n",
      "1106  https://www.bottle-neck.fr/?post_type=product&...        0.0   product   \n",
      "1022  https://www.bottle-neck.fr/?post_type=product&...        0.0   product   \n",
      "1294  https://www.bottle-neck.fr/?post_type=product&...        0.0   product   \n",
      "454   https://www.bottle-neck.fr/?post_type=product&...        0.0   product   \n",
      "443   https://www.bottle-neck.fr/?post_type=product&...        0.0   product   \n",
      "1205  https://www.bottle-neck.fr/?post_type=product&...        0.0   product   \n",
      "1206  https://www.bottle-neck.fr/?post_type=product&...        0.0   product   \n",
      "1209  https://www.bottle-neck.fr/?post_type=product&...        0.0   product   \n",
      "\n",
      "     comment_count      ca  \n",
      "436            0.0  2288.0  \n",
      "402            0.0  1391.5  \n",
      "399            0.0  1125.0  \n",
      "763            0.0   685.0  \n",
      "1174           0.0   573.9  \n",
      "1284           0.0   270.0  \n",
      "1306           0.0   230.0  \n",
      "438            0.0   217.0  \n",
      "445            0.0   104.0  \n",
      "411            0.0    85.6  \n",
      "876            0.0     0.0  \n",
      "875            0.0     0.0  \n",
      "862            0.0     0.0  \n",
      "865            0.0     0.0  \n",
      "126            0.0     0.0  \n",
      "131            0.0     0.0  \n",
      "853            0.0     0.0  \n",
      "760            0.0     0.0  \n",
      "1005           0.0     0.0  \n",
      "1296           0.0     0.0  \n",
      "1309           0.0     0.0  \n",
      "1310           0.0     0.0  \n",
      "1312           0.0     0.0  \n",
      "1315           0.0     0.0  \n",
      "1106           0.0     0.0  \n",
      "1022           0.0     0.0  \n",
      "1294           0.0     0.0  \n",
      "454            0.0     0.0  \n",
      "443            0.0     0.0  \n",
      "1205           0.0     0.0  \n",
      "1206           0.0     0.0  \n",
      "1209           0.0     0.0  \n",
      "\n",
      "[32 rows x 27 columns]\n",
      "Total des valeurs atypiques : 32\n"
     ]
    }
   ],
   "source": [
    "# Calcul des quartiles\n",
    "Q1, Q3 = np.percentile(resultat_final['price_numeric'], [25, 75])\n",
    "\n",
    "# Calcul de l'IQR\n",
    "IQR = Q3 - Q1\n",
    "\n",
    "# Définition des bornes pour les valeurs atypiques\n",
    "borne_inf = Q1 - 1.5 * IQR\n",
    "borne_sup = Q3 + 1.5 * IQR\n",
    "\n",
    "# Définition des bornes minimale et maximale\n",
    "borne_min = resultat_final['price_numeric'].min()\n",
    "borne_max = resultat_final['price_numeric'].max()\n",
    "\n",
    "# Sélection des valeurs atypiques\n",
    "valeurs_atypiques = resultat_final[(resultat_final['price_numeric'] < borne_inf) | (resultat_final['price_numeric'] > borne_sup)]\n",
    "\n",
    "# Affichage des résultats\n",
    "print(f\"Borne minimale : {borne_min}\")\n",
    "print(f\"Borne pour les valeurs atypiques : [{borne_inf}, {borne_sup}]\")\n",
    "print(f\"Borne maximale : {borne_max}\")\n",
    "\n",
    "print(f\"Valeurs atypiques :\")\n",
    "print(valeurs_atypiques)\n",
    "\n",
    "# Affichage du total des valeurs atypiques\n",
    "total_atypiques = len(valeurs_atypiques)\n",
    "print(f\"Total des valeurs atypiques : {total_atypiques}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span style=\"color: #FF8C00;\"><strong>Résumé de l'Analyse des Statistiques :</strong></span>\n",
    "\n",
    "- <span style=\"color: #FF8C00;\">Borne minimale : 5.2</span>\n",
    "- <span style=\"color: #FF8C00;\">Borne pour les valeurs atypiques : [-28.0125, 84.2875]</span>\n",
    "- <span style=\"color: #FF8C00;\">Borne maximale : 225.0</span>\n",
    "\n",
    "<span style=\"color: #FF8C00;\">Ces résultats suggèrent une distribution de données, avec la majorité des valeurs se situant entre 5.2 et 225.0. Des valeurs atypiques, définies entre -28.0125 et 84.2875, ont également été identifiées. Cette analyse pourrait être utile pour comprendre la variabilité et la présence éventuelle d'outliers dans l'ensemble de données.</span>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span style=\"color: #FF8C00;\"><strong>Analyse des Valeurs Atypiques :</strong></span>\n",
    "\n",
    "- <span style=\"color: #FF8C00;\">Nombre total de valeurs atypiques : 32</span>\n",
    "\n",
    "<span style=\"color: #FF8C00;\">Les valeurs atypiques dans votre ensemble de données concernent principalement la colonne \"price_numeric\". Voici quelques exemples de produits avec des prix considérés comme atypiques :</span>\n",
    "\n",
    "1. <span style=\"color: #FF8C00;\">Product ID 4402 - Prix : 176.0</span>\n",
    "2. <span style=\"color: #FF8C00;\">Product ID 4355 - Prix : 126.5</span>\n",
    "3. <span style=\"color: #FF8C00;\">Product ID 4352 - Prix : 225.0 (out of stock)</span>\n",
    "4. <span style=\"color: #FF8C00;\">Product ID 4904 - Prix : 137.0</span>\n",
    "5. <span style=\"color: #FF8C00;\">Product ID 5892 - Prix : 191.3</span>\n",
    "\n",
    "<span style=\"color: #FF8C00;\">Il est important de noter que certaines de ces valeurs atypiques coïncident avec des produits qui sont en rupture de stock. Cela pourrait être un aspect intéressant à explorer davantage, car cela peut avoir un impact sur la perception des clients et les décisions d'achat. Une gestion appropriée des prix et des stocks pour ces produits pourrait être envisagée pour optimiser la performance globale de la boutique en ligne.</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Borne inférieure : -28.01\n",
      "Borne supérieure : 84.29\n",
      "Médiane : 23.55\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# Supposons que vous ayez déjà importé votre ensemble de données dans la variable 'resultat_final'\n",
    "\n",
    "# Filtrer les valeurs supérieures à 0\n",
    "resultat_final_positives = resultat_final[resultat_final['price_numeric'] > 0]\n",
    "\n",
    "# Calcul des quartiles pour les valeurs supérieures à 0\n",
    "Q1, Q3 = np.percentile(resultat_final_positives['price_numeric'], [25, 75])\n",
    "\n",
    "# Calcul de l'IQR pour les valeurs supérieures à 0\n",
    "IQR = Q3 - Q1\n",
    "\n",
    "# Calcul des bornes pour les valeurs atypiques\n",
    "borne_inf = Q1 - 1.5 * IQR\n",
    "borne_sup = Q3 + 1.5 * IQR\n",
    "\n",
    "# Calcul de la médiane pour les valeurs supérieures à 0\n",
    "median_positif = np.median(resultat_final_positives['price_numeric'])\n",
    "\n",
    "# Affichage des résultats\n",
    "print(f\"Borne inférieure : {borne_inf:.2f}\")\n",
    "print(f\"Borne supérieure : {borne_sup:.2f}\")\n",
    "print(f\"Médiane : {median_positif:.2f}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span style=\"color: #FF8C00;\"><strong>Résumé de l'Analyse :</strong></span>\n",
    "\n",
    "- <span style=\"color: #FF8C00;\">Borne inférieure : -28.01</span>\n",
    "- <span style=\"color: #FF8C00;\">Borne supérieure : 84.29</span>\n",
    "- <span style=\"color: #FF8C00;\">Médiane : 23.55</span>\n",
    "\n",
    "<span style=\"color: #FF8C00;\">Ces résultats délimitent les valeurs potentiellement atypiques des prix dans notre ensemble de données, avec une borne inférieure de -28.01, une borne supérieure de 84.29, et une médiane de 23.55. Nous utilisons ces seuils pour identifier et examiner les prix qui pourraient nécessiter une attention particulière.</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      product_id stock_status  price_numeric\n",
      "388         4334   outofstock           49.0\n",
      "143         4144      instock           49.0\n",
      "436         4402      instock          176.0\n",
      "141         4142      instock           53.0\n",
      "138         4141      instock           39.0\n",
      "...          ...          ...            ...\n",
      "1216        5932      instock           59.9\n",
      "1383        6628      instock           32.2\n",
      "1221        5951      instock           74.5\n",
      "1223        5956      instock           17.2\n",
      "1426        7338      instock           16.3\n",
      "\n",
      "[714 rows x 3 columns]\n"
     ]
    }
   ],
   "source": [
    "# Filtrer les valeurs supérieures à 0\n",
    "resultat_final_positives = resultat_final[resultat_final['price_numeric'] > 0]\n",
    "\n",
    "# Affichage des résultats\n",
    "print(resultat_final_positives[['product_id', 'stock_status', 'price_numeric']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Aucune valeur négative dans 'price_numeric'.\n"
     ]
    }
   ],
   "source": [
    "# Filtrer les valeurs inférieures à 0\n",
    "resultat_final_negatives = resultat_final[resultat_final['price_numeric'] < 0]\n",
    "\n",
    "# Vérifier si le DataFrame filtré n'est pas vide\n",
    "if not resultat_final_negatives.empty:\n",
    "    # Calcul des quartiles pour les valeurs inférieures à 0\n",
    "    Q1, Q3 = np.percentile(resultat_final_negatives['price_numeric'], [25, 75])\n",
    "\n",
    "    # Calcul de l'IQR pour les valeurs inférieures à 0\n",
    "    IQR = Q3 - Q1\n",
    "\n",
    "    # Calcul des bornes pour les valeurs atypiques\n",
    "    borne_inf = Q1 - 1.5 * IQR\n",
    "    borne_sup = Q3 + 1.5 * IQR\n",
    "\n",
    "    # Calcul de la médiane pour les valeurs inférieures à 0\n",
    "    median_negatif = np.median(resultat_final_negatives['price_numeric'])\n",
    "\n",
    "    # Affichage des résultats\n",
    "    print(f\"Borne inférieure : {borne_inf:.2f}\")\n",
    "    print(f\"Borne supérieure : {borne_sup:.2f}\")\n",
    "    print(f\"Médiane : {median_negatif:.2f}\")\n",
    "else:\n",
    "    print(\"Aucune valeur négative dans 'price_numeric'.\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Empty DataFrame\n",
      "Columns: [product_id, stock_status, price_numeric]\n",
      "Index: []\n"
     ]
    }
   ],
   "source": [
    "# Filtrer les valeurs inférieures à 0\n",
    "resultat_final_negatives = resultat_final[resultat_final['price_numeric'] < 0]\n",
    "\n",
    "# Affichage des résultats\n",
    "print(resultat_final_negatives[['product_id', 'stock_status', 'price_numeric']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count     385.000000\n",
      "mean      183.295065\n",
      "std       400.324073\n",
      "min         6.500000\n",
      "25%        38.600000\n",
      "50%        81.600000\n",
      "75%       164.400000\n",
      "max      4704.000000\n",
      "Name: ca, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Filtrer les valeurs supérieures à 0\n",
    "ventes_sup_0 = resultat_final[resultat_final['ca'] > 0]\n",
    "\n",
    "# Statistiques descriptives des ventes supérieures à 0\n",
    "ventes_stats_sup_0 = ventes_sup_0['ca'].describe()\n",
    "\n",
    "# Affichage des statistiques descriptives pour les ventes supérieures à 0\n",
    "print(ventes_stats_sup_0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span style=\"color: #FF8C00;\"><strong>Résumé des Statistiques du Chiffre d'Affaires :</strong></span>\n",
    "\n",
    "- <span style=\"color: #FF8C00;\">Nombre d'observations : 385</span>\n",
    "- <span style=\"color: #FF8C00;\">Moyenne du chiffre d'affaires : 183.30</span>\n",
    "- <span style=\"color: #FF8C00;\">Écart type : 400.32</span>\n",
    "- <span style=\"color: #FF8C00;\">Chiffre d'affaires minimum : 6.50</span>\n",
    "- <span style=\"color: #FF8C00;\">Premier quartile (Q1) : 38.60</span>\n",
    "- <span style=\"color: #FF8C00;\">Médiane (50%) : 81.60</span>\n",
    "- <span style=\"color: #FF8C00;\">Troisième quartile (Q3) : 164.40</span>\n",
    "- <span style=\"color: #FF8C00;\">Chiffre d'affaires maximum : 4704.00</span>\n",
    "\n",
    "<span style=\"color: #FF8C00;\">Ces statistiques offrent un aperçu détaillé de la distribution du chiffre d'affaires, mettant en évidence la centralité, la dispersion, ainsi que les valeurs minimales et maximales. La moyenne élevée et l'écart type important indiquent une variabilité significative dans les performances financières, avec des observations allant de valeurs basses à très élevées.</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span style=\"color: blue; font-weight: bold; font-size: 2em;\">IV] Analyse Exploratoire des Données (AED) pour le Chiffre d'Affaires des Produits</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  categorie_ca           ca  price_numeric\n",
      "0          Bas    45.171889      21.321198\n",
      "1        Moyen   193.386331      27.611151\n",
      "2         Haut  1168.468966      61.006897\n"
     ]
    }
   ],
   "source": [
    "# Définir les points de découpe pour la segmentation\n",
    "ca_bins = [0, 100, 500, float('inf')]\n",
    "ca_labels = ['Bas', 'Moyen', 'Haut']\n",
    "\n",
    "# Ajouter une colonne 'categorie_ca' basée sur la segmentation par chiffre d'affaires\n",
    "resultat_final['categorie_ca'] = pd.cut(resultat_final['ca'], bins=ca_bins, labels=ca_labels)\n",
    "\n",
    "# Analyse des ventes et des prix par segment de chiffre d'affaires\n",
    "segmentation_ca_stats = resultat_final.groupby('categorie_ca').agg({'ca': 'mean', 'price_numeric': 'mean'}).reset_index()\n",
    "\n",
    "# Affichage des statistiques de segmentation par chiffre d'affaires\n",
    "print(segmentation_ca_stats)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span style=\"color: #FF8C00;\"><strong>Résumé des Statistiques par Catégorie :</strong></span>\n",
    "\n",
    "**Catégorie \"Bas\" :**\n",
    "- <span style=\"color: #FF8C00;\">CA moyen : 45.17</span>\n",
    "- <span style=\"color: #FF8C00;\">Prix moyen : 21.32</span>\n",
    "\n",
    "**Catégorie \"Moyen\" :**\n",
    "- <span style=\"color: #FF8C00;\">CA moyen : 193.39</span>\n",
    "- <span style=\"color: #FF8C00;\">Prix moyen : 27.61</span>\n",
    "\n",
    "**Catégorie \"Haut\" :**\n",
    "- <span style=\"color: #FF8C00;\">CA moyen : 1168.47</span>\n",
    "- <span style=\"color: #FF8C00;\">Prix moyen : 61.01</span>\n",
    "\n",
    "<span style=\"color: #FF8C00;\">Des différences significatives entre les catégories sont observées, avec des chiffres d'affaires et des prix moyens variant considérablement. La catégorie \"Haut\" se distingue par des performances plus élevées, tandis que la catégorie \"Bas\" affiche des valeurs plus modestes.</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 10 des Produits les Plus Vendus :\n",
      "      product_id      ca  price_numeric  stock_quantity\n",
      "388         4334  4704.0           49.0               0\n",
      "143         4144  4263.0           49.0              11\n",
      "436         4402  2288.0          176.0               8\n",
      "141         4142  1590.0           53.0               8\n",
      "138         4141  1560.0           39.0               1\n",
      "402         4355  1391.5          126.5               2\n",
      "399         4352  1125.0          225.0               0\n",
      "158         4153  1044.0           29.0               0\n",
      "1303        6206  1033.2           25.2             120\n",
      "61          4068  1029.2           16.6             157\n"
     ]
    }
   ],
   "source": [
    "#Analyse des produits les plus vendus\n",
    "# Top 10 des produits avec le chiffre d'affaires le plus élevé\n",
    "top_products = resultat_final.nlargest(10, 'ca')[['product_id', 'ca', 'price_numeric', 'stock_quantity']]\n",
    "\n",
    "# Affichage des résultats\n",
    "print(\"Top 10 des Produits les Plus Vendus :\")\n",
    "print(top_products)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span style=\"color: #FF8C00;\"><strong>Analyse Synthétique des Top 10 Produits les Plus Vendus :</strong></span>\n",
    "\n",
    "- <span style=\"color: #FF8C00;\">Diversité de Performances :Les produits varient considérablement en termes de chiffre d'affaires, de prix et de disponibilité en stock.</span>\n",
    "- <span style=\"color: #FF8C00;\">Épuisement Rapide : Certains produits génèrent un CA élevé malgré des stocks épuisés, suggérant une forte demande initiale.</span>\n",
    "- <span style=\"color: #FF8C00;\">Prix Moyens Élevés : Plusieurs produits affichent des prix moyens élevés, contribuant significativement au chiffre d'affaires.</span>\n",
    "- <span style=\"color: #FF8C00;\">Stock Limité : Certains produits ont des stocks limités, ce qui peut influencer la stratégie de gestion des stocks.</span>\n",
    "- <span style=\"color: #FF8C00;\">Dynamique de Prix : Des variations importantes dans les prix suggèrent des stratégies de tarification différentes pour optimiser les ventes.</span>\n",
    "\n",
    "<span style=\"color: #FF8C00;\">Cette analyse fournit un aperçu rapide des performances des produits les plus vendus, offrant des indications sur la demande, la tarification et la gestion des stocks.</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     product_id     ca  price_numeric\n",
      "0          3847  145.2           24.2\n",
      "1          3849    0.0           34.3\n",
      "2          3850    0.0           20.8\n",
      "3          4032   42.3           14.1\n",
      "4          4039    0.0           46.0\n",
      "..          ...    ...            ...\n",
      "709        6930   42.0            8.4\n",
      "710        7023    0.0           27.5\n",
      "711        7025    0.0           69.0\n",
      "712        7247    0.0           54.8\n",
      "713        7338    0.0           16.3\n",
      "\n",
      "[714 rows x 3 columns]\n"
     ]
    }
   ],
   "source": [
    "# Analyse de la corrélation entre les ventes et les prix\n",
    "sns.scatterplot(x='price_numeric', y='ca', data=resultat_final)\n",
    "plt.title('Corrélation entre Prix et Ventes')\n",
    "plt.xlabel('Prix')\n",
    "plt.ylabel('Ventes')\n",
    "plt.show()\n",
    "\n",
    "# Exemple de comparaison de performances entre différentes catégories\n",
    "performances_categories = resultat_final.groupby('product_id').agg({'ca': 'sum', 'price_numeric': 'mean'}).reset_index()\n",
    "\n",
    "# Affichage des performances par catégorie\n",
    "print(performances_categories)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span style=\"color: #FF8C00;\"> Les résultats indiquent des informations sur le chiffre d'affaires (CA) et les prix pour différents produits identifiés par leur \"product_id\". Voici un résumé synthétique :\n",
    "\n",
    "- <span style=\"color: #FF8C00;\">Diversité des Performances :Certains produits génèrent du chiffre d'affaires, tandis que d'autres n'enregistrent aucune vente (CA de 0.0).</span> \n",
    "- <span style=\"color: #FF8C00;\">Variabilité des Prix : Les prix varient significativement d'un produit à l'autre, allant de 8.4 à 69.0.</span> \n",
    "- <span style=\"color: #FF8C00;\">Potentiel d'Optimisation : Les produits avec un CA nul pourraient nécessiter une attention particulière pour comprendre les raisons de leur faible performance.</span> \n",
    "- <span style=\"color: #FF8C00;\">Segmentation de Prix :La diversité des prix suggère une segmentation du marché, avec des produits adaptés à différentes gammes de prix.</span> \n",
    "\n",
    "<span style=\"color: #FF8C00;\">Cette analyse offre un aperçu de la variabilité des performances des produits, soulignant la nécessité de stratégies différenciées en fonction de la demande et des prix.</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span style=\"color: #FF8C00;\">On remarque plus de vente  pour les produits ayant un prix situé entre 50 et 100.</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"color: #0000FF; font-family: Verdana; font-size: 28px; font-weight: bold; text-align: center;\">Conclusion de l'analyse des données</div>\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "L'examen approfondi de l'ensemble de données dévoile une diversité marquée dans les performances des produits de la boutique en ligne. Les aspects tels que la variabilité des prix, la présence de valeurs atypiques, et la catégorisation par niveau de chiffre d'affaires et de prix fournissent des perspectives clés. Une vigilance particulière est recommandée pour les produits atypiques en rupture de stock, ayant généré un chiffre d'affaires significatif. Cette approche ciblée peut optimiser la gestion des prix, des stocks, et ajuster la stratégie commerciale, favorisant ainsi une amélioration globale des performances de la boutique en ligne.</span>\n",
    "\n",
    "Il est également à noter la détection d'anomalies, telles que la présence de bons cadeaux dans la colonne \"sku\". Ces bons devraient être réaffectés à une modalité de paiement spécifique pour assurer une structuration plus cohérente des données.</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En ce qui concerne le système post_parent, je ne dispose pas d'informations sur son utilisation au sein de l'entreprise. En cas de non-utilisation, il pourrait être intéressant d'explorer cet outil pour une meilleure gestion des catégories de produits. De plus, son utilisation pourrait être bénéfique pour le rating, permettant ainsi de filtrer par commentaire ou d'observer la moyenne des commentaires, par exemple, pour les vins. Il serait également possible d'effectuer une analyse de texte, comme un nuage de mots, afin de déterminer la qualité perçue des vins. Cela pourrait aider à identifier des problèmes potentiels de logistique, tels que des retards mentionnés dans les commentaires. En résumé, l'utilisation du système post_parent pourrait permettre d'établir des corrélations entre les notations des produits et les commentaires associés.</span>\n",
    "\n",
    "On pourrait enrichir notre ensemble de données en ajoutant des colonnes telles que \"moyen de paiement\", \"statut de la commande\" et \"date de vente\". Ces nouvelles colonnes amélioreraient la lisibilité des différentes méthodes de paiement et préviendraient les potentielles erreurs, par exemple, liées aux bons cadeaux. En ce qui concerne le statut de la commande, l'ajout de cette colonne permettrait d'obtenir des retours sur les délais de livraison et l'état des produits. Enfin, l'introduction de la colonne \"date de vente\" faciliterait une segmentation temporelle plus fine, favorisant ainsi une meilleure analyse des données dans le temps.</span>\n",
    "\n",
    "Par aillieurs, il y deux colonne price, il serait judicieux d'en concerver qu'une seule pour une millieure lecture des donnnées.</span>\n",
    "\n",
    "En conclusion, l'incorporation de ces éléments permettrait une meilleure traçabilité de la satisfaction client et une gestion temporelle plus précise.</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: nbconvert in c:\\users\\rorsharks\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (7.12.0)Note: you may need to restart the kernel to use updated packages.\n",
      "\n",
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      "Requirement already satisfied: pyzmq>=23.0 in c:\\users\\rorsharks\\appdata\\roaming\\python\\python310\\site-packages (from jupyter-client>=6.1.12->nbclient>=0.5.0->nbconvert) (25.1.1)\n",
      "Requirement already satisfied: tornado>=6.2 in c:\\users\\rorsharks\\appdata\\roaming\\python\\python310\\site-packages (from jupyter-client>=6.1.12->nbclient>=0.5.0->nbconvert) (6.3.3)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "[notice] A new release of pip is available: 23.3.1 -> 23.3.2\n",
      "[notice] To update, run: python.exe -m pip install --upgrade pip\n"
     ]
    }
   ],
   "source": [
    "pip install nbconvert"
   ]
  }
 ],
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   "display_name": "Python 3",
   "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",
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 },
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