Ghoubali_Badis_2_notebook_exploration_052023.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "10e417b3-b5a7-4109-b12c-ff5d2e2e4fbf",
   "metadata": {},
   "source": [
    "<h1 style=\"font-size:1.8em\">Projet 3 - Concevez une application au service de la santé publique</h1>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf05c0b9-4937-49f2-a250-5741ea167d87",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Objectifs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6ac701a-aa8b-43fe-961b-093abce45b49",
   "metadata": {},
   "source": [
    "1) Proposer une idée application en lien avec la nutrition\n",
    "- Netoyage de données\n",
    "- Analyse exploratoire  \n",
    "- Présentation d'une idée d'application "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea905cde-ce9a-4f28-ba97-7c179f90ce8a",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Données à notre disposition"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef933416-c29a-49bf-a375-1c46a7595c0b",
   "metadata": {},
   "source": [
    "1) **fr.openfoodfacts.org.products.csv**  \n",
    "- Les informations générales sur la fiche du produit : nom, date de modification, etc.\n",
    "- Un ensemble de tags : catégorie du produit, localisation, origine, etc.\n",
    "- Les ingrédients composant les produits et leurs additifs éventuels.\n",
    "- Des informations nutritionnelles : quantité en grammes d’un nutriment pour 100 grammes du produit.\n",
    "2) **Definition_variables.txt**  \n",
    "- Fichier text contenant les définitions des variables"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b904eee6-587d-4bdd-9002-72c1d19b52d1",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Analyse exploratoire"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe108a3f-75f1-4755-9026-718ed6fc8321",
   "metadata": {},
   "source": [
    "**1) Analyses univariées**\n",
    "- fonction describe()   \n",
    "\n",
    "**2) Analyses bivariées**\n",
    "- Graphiques   \n",
    "- Matrice des corrélations    \n",
    "\n",
    "**3) Analyses multivariées**  \n",
    "- ACP  (Analyse en composante principale)  \n",
    "- ANOVA (Analysis of variance)  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bcab8344-b433-4acd-9180-d373a66ce73b",
   "metadata": {},
   "source": [
    "## Présentation d'une idée d'application "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8aadcb0-2d8c-429f-b17b-ef42155a5d85",
   "metadata": {},
   "source": [
    "1) Une analyse à chaque graphique présentée  \n",
    "2) Si on a le temps -> Mettre en oeuvre un algo machine learning (KNN)\n",
    "\n",
    "- Pouvons nous regrouper les articles par catégories? Pays? Mots clés\n",
    "- Pouvons nous valoriser ces informations?\n",
    "(Utiliser Kaggle si on n'a pas d'inspiration) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c57c9294-21f4-419a-bc75-2af1846a88b0",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Exemples"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc20564c-8b74-4b10-ab5c-1fd42fd28261",
   "metadata": {},
   "source": [
    "1) David - Nutrisport (Indicateurs pour les sportifs)\n",
    "- Qte énergie, protéines, acides gras  \n",
    "- Photo code barre -> Score nutrisport (Entre A et E)  \n",
    "- Recommendations produits même catégorie  \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fbee589b-8839-4c0a-ac25-729eeca4a5ae",
   "metadata": {},
   "source": [
    "2) Aurélia - Choisir le bon produit parmi un groupe de même catégorie de produit)\n",
    "- Reconnaitre la catégorie du produit      \n",
    "- Récupérer les informations nutritionnelles  - Photo de l'étiquette du produit  \n",
    "- Recommandations produits même catégorie"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6fcaf0fb-ef70-48dd-9bbc-1e7c8f94334d",
   "metadata": {},
   "source": [
    "## Livrables "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b15e13f-ba7d-4a3b-a7e3-1813b6402412",
   "metadata": {},
   "source": [
    "1) Un notebook netoyage\n",
    "2) Un notebook exploration\n",
    "3) Un powerpoint pour la présentation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b049b9f-d45a-4b70-ae3f-67117eca7df9",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Fonctions (pour ne pas allourdir le fichier)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "cc44fc28-ed6f-4eb4-ab34-0687ead6616e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "pd.options.display.max_columns = None"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f51b874f-ff30-49fe-a585-6655b2f39133",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "## Types mixtes colonnes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5078659-782d-4987-af3b-fbd6a47f4c3c",
   "metadata": {},
   "source": [
    "### recuperer_indices_colonnes_mixtes(df, indices_colonnes)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43872c0b-2641-4a4f-b648-d6e317beb15e",
   "metadata": {},
   "source": [
    "On récupère les indices pour lequel les colonnes sont mixtes, sauf quelques exceptions: \n",
    "1) On exclut le int + float (sans string) si les floats sont tous des nan\n",
    "2) On exclut le string + float (sans int) si les floats sont tous des nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "48233ae9-b0ef-4cf9-816e-32212ff1623a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "def recuperer_indices_colonnes_mixtes(df, indices_colonnes):\n",
    "    mixed_columns = []\n",
    "    for col in df.columns[indices_colonnes]:\n",
    "        unique_types = {type(x) for x in df[col]}\n",
    "        if len(unique_types) > 1:\n",
    "            float_count = 0\n",
    "            nan_count = 0\n",
    "            for x in df[col]:\n",
    "                if isinstance(x, float):\n",
    "                    float_count += 1\n",
    "                    if np.isnan(x):\n",
    "                        nan_count += 1\n",
    "            # Condition 1 - Exclure int + float (pas string) si les floats sont tous des nan\n",
    "            if int in unique_types and not str in unique_types and float in unique_types and nan_count == float_count:\n",
    "                continue\n",
    "            # Condition 2 - Exclure string + float (pas int) si tout les floats sont des nan\n",
    "            if str in unique_types and not int in unique_types and float in unique_types and nan_count == float_count:\n",
    "                continue\n",
    "            mixed_columns.append(col)\n",
    "    mixed_columns_index = [df.columns.get_loc(col) for col in mixed_columns]        \n",
    "    return mixed_columns_index"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "feff0b39-c867-425b-98f1-ba500fd24b5b",
   "metadata": {},
   "source": [
    "### analyse_colonnes_mixtes(df, indices_colonnes)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd772ab6-f9b1-4bad-8d4b-cf4d2b6c1c87",
   "metadata": {},
   "source": [
    "On affiche une analyse sur les colonnes mixtes, exemple: \n",
    " \n",
    "Colonne 0 (code):  \n",
    "  Type : <class 'int'>, Exemple (ligne 209888): 3256220711258, Nombre de lignes : 282624  \n",
    "  Type : <class 'str'>, Exemple (ligne 273551): 5052003557963, Nombre de lignes : 38125  \n",
    "  Type : <class 'float'>, Exemple (ligne 189250): nan, Nombre de lignes : 23  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b53d724d-7be4-47ab-8999-d16bdf6d9d15",
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "\n",
    "def analyse_colonnes_mixtes(df, indices_colonnes):\n",
    "    for colonne in indices_colonnes:\n",
    "        if colonne < len(df.columns):\n",
    "            resultat = df.iloc[:, colonne].apply(lambda x: type(x)).value_counts().reset_index()\n",
    "            resultat.columns = ['Type', 'Nombre_de_lignes']\n",
    "\n",
    "            nom_colonne = df.columns[colonne]\n",
    "            print(f\"Colonne {colonne} ({nom_colonne}):\")\n",
    "\n",
    "            for index, row in resultat.iterrows():\n",
    "                type_donnee = row['Type']\n",
    "                lignes_type = df[df.iloc[:, colonne].apply(type) == type_donnee].index.tolist()\n",
    "                exemple_au_hasard = random.choice(lignes_type)\n",
    "                print(f\"  Type : {type_donnee}, Exemple (ligne {exemple_au_hasard}): {df.iloc[exemple_au_hasard, colonne]}, Nombre de lignes : {row['Nombre_de_lignes']}\")\n",
    "\n",
    "            print(\"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e6eddf6-ddbf-4dac-a1a4-0cbfe4d77f58",
   "metadata": {},
   "source": [
    "### convertir_strings_en_int(df, indices_colonnes)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d602e72-a322-4a85-b7a0-97d211fcd650",
   "metadata": {},
   "source": [
    "On convertit les strings en int, on ne touche pas aux float"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "25b6b10b-81e3-4b9b-8d57-bf5bd43f2c7b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def convertir_strings_en_int(df, indices_colonnes):\n",
    "    for col in df.columns[indices_colonnes]:\n",
    "        has_int = False\n",
    "        has_str = False\n",
    "        has_float = False\n",
    "        \n",
    "        for val in df[col]:\n",
    "            if isinstance(val, int):\n",
    "                has_int = True\n",
    "            elif isinstance(val, str):\n",
    "                has_str = True\n",
    "            elif isinstance(val, float):\n",
    "                has_float = True\n",
    "                \n",
    "            if has_int and has_str and has_float:\n",
    "                break\n",
    "                \n",
    "        if has_int and has_str:\n",
    "            # Convertir les valeurs en entier en utilisant une fonction pour gérer les exceptions\n",
    "            df[col] = df[col].apply(lambda x: int(x) if (isinstance(x, str) and str(x).isdigit()) or isinstance(x, int) else x)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88aa4811-ba40-4233-9c5b-ba1b525d80e4",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Clustering"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "566fc2f9-d293-45eb-93ab-911257e74db5",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Clustering lignes (K-means)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3dd5ee79-97c1-45a9-9240-42790f3c55a2",
   "metadata": {},
   "source": [
    "Algorithme de clustering non supervisé qui vise à partitionner un ensemble de données en k groupes distincts (ou clusters) en minimisant la somme des distances entre les points et leur centroïde (le centre du cluster) respectif. L'algorithme fonctionne de la manière suivante :\n",
    "\n",
    "1) Sélectionner k points initiaux (généralement de manière aléatoire) comme centroïdes de départ.  \n",
    "2) Attribuer chaque point de l'ensemble de données au centroïde le plus proche, créant ainsi k groupes.  \n",
    "3) Mettre à jour les centroïdes en calculant la moyenne des points attribués à chaque groupe.  \n",
    "4) Répéter les étapes 2 et 3 jusqu'à ce que les centroïdes ne changent plus ou que le nombre d'itérations prédéterminé soit atteint.  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2652f5dc-ab97-4ab9-bc74-91a5a1bb5905",
   "metadata": {},
   "source": [
    "#### Trouver la bonne valeur de k (cluster optimal)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e727e360-33ef-4991-bf5f-5ad9bea62eb8",
   "metadata": {},
   "source": [
    "Pour trouver le bon k, nous utilisons **la méthode du coude**:  \n",
    "- **La méthode du coude** est une technique couramment utilisée pour estimer le nombre optimal de clusters (k) dans un algorithme de k-means. Elle consiste à effectuer plusieurs essais de k-means avec différentes valeurs de k et à calculer la somme des erreurs quadratiques (inertie) pour chaque essai. En traçant ces valeurs en fonction de k, on obtient une courbe en forme de coude. Le point d'inflexion de cette courbe, c'est-à-dire l'endroit où la courbe se plie, indique une estimation du nombre optimal de clusters.\n",
    "\n",
    "- Utiliser le **maximum de la dérivée seconde** est une technique qui peut être utilisée pour affiner l'estimation du nombre optimal de clusters obtenue avec la méthode du coude. Cette méthode consiste à calculer la dérivée seconde de la courbe des erreurs quadratiques par rapport à k et à identifier le point où cette dérivée seconde atteint un maximum. Ce point correspond généralement à un changement abrupt dans la courbe, indiquant un bon choix pour le nombre de clusters.\n",
    "\n",
    "**Attention** -> Personellement je préfère rajouter + 1 au nombre obtenu pour être sur de ne pas perdre trop d'informations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "bf101399-95c3-4fda-a949-c723702a2308",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn.cluster import KMeans\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\", message=\"KMeans is known to have a memory leak on Windows with MKL\")\n",
    "from tqdm import tqdm\n",
    "\n",
    "def nombre_optimal_clusters(df, nb_k = 10):\n",
    "    # Sélectionner uniquement les colonnes quantitatives\n",
    "    df_quant = df.select_dtypes(include=[np.number])\n",
    "\n",
    "    # Calcul de la somme des distances intra-clusters pour différentes valeurs de k\n",
    "    SSE = []\n",
    "    for cluster in tqdm(range(1, nb_k+1)):\n",
    "        kmeans = KMeans(n_clusters=cluster, n_init=10, init='k-means++')\n",
    "        kmeans.fit(df_quant)\n",
    "        SSE.append(kmeans.inertia_)\n",
    "\n",
    "     # Conversion des résultats en dataframe\n",
    "    frame = pd.DataFrame({'Cluster': range(1, nb_k+1), 'SSE': SSE})\n",
    "\n",
    "    # Calcul de la première et de la seconde dérivée de la SSE\n",
    "    diff = np.diff(frame['SSE'])\n",
    "    diff_diff = np.diff(diff)\n",
    "\n",
    "    # Trouver le point \"coude\"\n",
    "    elbow_index = np.argmax(diff_diff) + 2 +1\n",
    "\n",
    "    # Tracer les résultats\n",
    "    fig, ax1 = plt.subplots(figsize=(12, 6))\n",
    "\n",
    "    ax1.plot(frame['Cluster'], frame['SSE'], marker='o', label='Inertie')\n",
    "    ax1.axvline(elbow_index, color='red', linestyle='--', label=\"Point 'coude'\")\n",
    "    ax1.plot(range(2, nb_k), diff_diff, marker='x', color='green', linestyle=':', label='Dérivée Seconde')\n",
    "    ax1.set_xlabel('Nombre de clusters')\n",
    "    ax1.set_ylabel('Inertie / Dérivée Seconde')\n",
    "    ax1.set_xticks(np.arange(1, nb_k+1, step=1))\n",
    "    ax1.legend(loc='best')\n",
    "\n",
    "    fig.tight_layout()\n",
    "    fig.suptitle(\"Analyse de l'inertie pour différentes valeurs de k\", fontsize=16, y=1.05)\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "    # Afficher le nombre optimal de clusters\n",
    "    print(\"Le nombre optimal de clusters est: \", elbow_index)\n",
    "    \n",
    "    return elbow_index"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e89002c7-e17e-4055-88a7-937eb73f6f94",
   "metadata": {},
   "source": [
    "#### Implémentation k-means sur DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e130e018-0e29-4add-ab63-e7203cabe945",
   "metadata": {},
   "source": [
    "Une fois qu'on a trouvé la bonne valeur de k, nous allons implémenter cet algorithme dans un DataFrame df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "844510a7-cb0b-402c-86f5-4cc3c97bebdf",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.cluster import KMeans\n",
    "\n",
    "def clustering_kmeans(df, nb_k):\n",
    "    # Séparation des colonnes qualitatives et quantitatives\n",
    "    colonnes_qualitatives = df.select_dtypes(include='object')\n",
    "    colonnes_quantitatives = df.select_dtypes(exclude='object')\n",
    "\n",
    "    # Normalisation des colonnes quantitatives\n",
    "    scaler = StandardScaler()\n",
    "    colonnes_quantitatives_normalisees = scaler.fit_transform(colonnes_quantitatives)\n",
    "\n",
    "    # Clustering k-means\n",
    "    kmeans = KMeans(n_clusters=nb_k, n_init = 10)\n",
    "    clusters = kmeans.fit_predict(colonnes_quantitatives_normalisees)\n",
    "\n",
    "    # Ajout des clusters au DataFrame d'origine\n",
    "    df_cluster = df.copy()\n",
    "    df_cluster['cluster'] = clusters\n",
    "\n",
    "    # Réorganisation des colonnes pour placer 'cluster' en première position\n",
    "    colonne_cluster = df_cluster.pop('cluster')\n",
    "    df_cluster.insert(0, 'cluster', colonne_cluster)\n",
    "\n",
    "    return df_cluster"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ca4edff-9340-49f7-9904-354b0c0240c6",
   "metadata": {},
   "source": [
    "### Clustering colonnes (ACP = Analyse en composante principale)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c4131d7-3b89-4060-bdac-42415ac9edbf",
   "metadata": {},
   "source": [
    "Technique statistique utilisée pour réduire la dimensionnalité d'un ensemble de données (colonnes). Elle permet de transformer un ensemble de variables corrélées en un ensemble de nouvelles variables non corrélées appelées \"composantes principales\".\n",
    "\n",
    "L'objectif de l'ACP est de trouver les directions dans l'espace des données qui expliquent la plus grande variance possible, ce qui signifie que les composantes principales sont ordonnées en fonction de l'importance décroissante de leur contribution à la variance totale des données. Les premières composantes principales expliquent une grande partie de la variance, tandis que les dernières composantes principales expliquent une très faible partie de la variance."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b2d23cd-42f4-45ab-bf75-5628e1d751bc",
   "metadata": {},
   "source": [
    "#### Plot ACP"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b2384ac-5e39-4054-bd90-3c1679bf21cb",
   "metadata": {},
   "source": [
    "Ce code permet de créer un plot ACP sur (PC1, PC2) ou (PC2,PC3)... le choix est le notre.  \n",
    "De plus, il affiche un dataframe qui affichera les valeurs des projections des colonnes sur PC1, PC2..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1dfb2782-3576-4685-bdc7-ff90eefe8ce6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from adjustText import adjust_text\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "def plot_PCA(df, composante_principale_x, composante_principale_y):\n",
    "    # Sélectionner les colonnes quantitatives\n",
    "    df = df.select_dtypes(include=[float])\n",
    "    \n",
    "    # Normaliser les données\n",
    "    X = StandardScaler().fit_transform(df)\n",
    "    \n",
    "    # Effectuer l'ACP\n",
    "    n_components = max(composante_principale_x, composante_principale_y)\n",
    "    pca = PCA(n_components=n_components)\n",
    "    pca.fit(X)\n",
    "    X_pca = pca.transform(X)\n",
    "    \n",
    "    # Standardiser les points dans l'espace des axes principaux\n",
    "    X_pca_standardized = X_pca / np.sqrt(pca.singular_values_)\n",
    "    \n",
    "    # Créer le plot cercle des corrélations\n",
    "    fig, ax = plt.subplots(figsize=(10, 10))\n",
    "    ax.set_xlim(-1.1, 1.1)\n",
    "    ax.set_ylim(-1.1, 1.1)\n",
    "    \n",
    "    colors = ['blue', 'green', 'red', 'orange', 'purple', 'brown','teal', 'black', 'magenta', 'lime', 'olive', 'silver']\n",
    "    \n",
    "    texts = []\n",
    "    for i in range(len(pca.components_[composante_principale_x-1])):\n",
    "        x = pca.components_[composante_principale_x-1][i]\n",
    "        y = pca.components_[composante_principale_y-1][i]\n",
    "        point = ax.scatter(x, y, color=colors[i % len(colors)])\n",
    "        texts.append(ax.text(x, y, df.columns[i], color=colors[i % len(colors)]))\n",
    "        \n",
    "    circle = plt.Circle((0, 0), 1, color='black', fill=False)\n",
    "    ax.add_artist(circle)\n",
    "    \n",
    "    explained_variance_ratio_x = pca.explained_variance_ratio_[composante_principale_x-1] * 100\n",
    "    explained_variance_ratio_y = pca.explained_variance_ratio_[composante_principale_y-1] * 100\n",
    "    \n",
    "    plt.xlabel(f'PC{composante_principale_x} ({explained_variance_ratio_x:.2f}%)', fontsize=16, labelpad=10)\n",
    "    plt.ylabel(f'PC{composante_principale_y} ({explained_variance_ratio_y:.2f}%)', fontsize=16)\n",
    "    \n",
    "    plt.suptitle(f'Cercle des corrélations (PC{composante_principale_x} vs PC{composante_principale_y})', y=0.91, fontsize=16)\n",
    "    \n",
    "    # Ajuster les marges pour éviter les coupures des textes\n",
    "    plt.subplots_adjust(bottom=0.2)\n",
    "    \n",
    "    ax.axhline(0, color='black', linewidth=0.5)\n",
    "    ax.axvline(0, color='black', linewidth=0.5)\n",
    "    \n",
    "    adjust_text(texts)\n",
    "    ax.grid(True, linestyle='dotted')\n",
    "\n",
    "    plt.show()\n",
    "    \n",
    "    # Créer un DataFrame pour afficher les projections des colonnes sur les composantes principales spécifiées\n",
    "    components = pca.components_[[composante_principale_x-1, composante_principale_y-1]]\n",
    "    index = [f'PC{composante_principale_x}', f'PC{composante_principale_y}']\n",
    "    df_pca = pd.DataFrame(components, columns=df.columns, index=index).T\n",
    "    df_pca = df_pca.sort_values(by=f'PC{composante_principale_x}', ascending=False)\n",
    "\n",
    "    display(df_pca)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b691fb48-e2dc-4f76-b67d-92270668d79f",
   "metadata": {},
   "source": [
    "#### Heatmap corrélations entre les colonnes et les composantes principales"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "056890ac-c3aa-481b-9161-ecea5ef56540",
   "metadata": {},
   "source": [
    "Ce code cré une Heatmap et print les corrélations les plus fortes entre les ACP et les colonnes selon un seuil que l'on peut choisir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "44810e5f-22a8-4cea-bc72-92bdbe5da0da",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "def analyse_PCA(df, seuil_correlations=0.6):\n",
    "    # Sélectionner uniquement les colonnes quantitatives\n",
    "    df_quant = df.select_dtypes(include=[float])\n",
    "\n",
    "    # Standardiser les données\n",
    "    scaler = StandardScaler()\n",
    "    df_quant_std = scaler.fit_transform(df_quant)\n",
    "\n",
    "    # Appliquer l'ACP\n",
    "    pca = PCA()\n",
    "    pca_result = pca.fit_transform(df_quant_std)\n",
    "\n",
    "    # Créer un nouveau DataFrame avec les résultats de l'ACP\n",
    "    principal_components = [f'PC{i}' for i in range(1, len(pca.components_) + 1)]\n",
    "    df_pca = pd.DataFrame(data=pca_result, columns=principal_components)\n",
    "\n",
    "    df_quant_pca = pd.concat([df_quant.reset_index(drop=True), df_pca], axis=1)\n",
    "\n",
    "    # Calculer la matrice de corrélation\n",
    "    correlation_matrix = df_quant_pca.corr()\n",
    "\n",
    "    # Sélectionner les 10 premières composantes principales et les 10 premières colonnes\n",
    "    selected_pca = principal_components\n",
    "    selected_cols = df_quant.columns \n",
    "\n",
    "    # Créer une sous-matrice de corrélation avec les composantes principales et les colonnes sélectionnées\n",
    "    correlation_submatrix = correlation_matrix.loc[selected_cols, selected_pca]\n",
    "\n",
    "    # Afficher la heatmap\n",
    "    sns.heatmap(np.around(correlation_submatrix, decimals=2), annot=True, xticklabels=selected_pca, yticklabels=selected_cols)\n",
    "    plt.show()\n",
    "\n",
    "    # Trouver les colonnes ayant une corrélation élevée avec chaque composante principale\n",
    "    correlations_with_pca = correlation_matrix.loc[df_quant.columns, principal_components]\n",
    "    for pc in principal_components:\n",
    "        high_correlation_columns = correlations_with_pca.index[abs(correlations_with_pca[pc]) > seuil_correlations].tolist()\n",
    "        correlation_values = [correlations_with_pca.loc[col, pc] for col in high_correlation_columns]\n",
    "        sorted_columns_and_values = sorted(zip(high_correlation_columns, correlation_values), key=lambda x: -abs(x[1]))\n",
    "        high_correlation_columns_sorted = [col for col, value in sorted_columns_and_values]\n",
    "        correlation_values_sorted = [value for col, value in sorted_columns_and_values]\n",
    "        high_correlation_columns_with_values = [f\"{col} ({value:+.2f})\" for col, value in zip(high_correlation_columns_sorted, correlation_values_sorted)]\n",
    "        print(f\"Corrélées avec {pc} : {', '.join(high_correlation_columns_with_values)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5033cb0b-7583-4e08-920f-89438aa4d313",
   "metadata": {},
   "source": [
    "#### Ajout des colonnes PCA dans notre DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "147147f5-7180-4fe7-8c5b-7340dad5f882",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "def ajouter_colonnes_PCA(df, nb_PCA=2):\n",
    "    df_copy = df.copy()  # Create a copy of the input DataFrame\n",
    "    \n",
    "    float_cols = df_copy.select_dtypes(include=['float']).columns\n",
    "    float_df = df_copy[float_cols]\n",
    "    \n",
    "    pca = PCA(n_components=nb_PCA)\n",
    "    pca_components = pca.fit_transform(float_df)\n",
    "    \n",
    "    for i in range(nb_PCA):\n",
    "        col_name = 'PCA' + str(i + 1)\n",
    "        df_copy[col_name] = pca_components[:, i]\n",
    "        \n",
    "    return df_copy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4c29559-d211-49b7-b526-50470fe82007",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Modifier un dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2744287-bb92-4912-90ca-48c6e2057567",
   "metadata": {},
   "source": [
    "Renomme ou supprime une colonne du dataframe  :\n",
    "- \"erase\" -> Supprime la colonne nomée\n",
    "- \"rename\" -> Renomme la colonne par un autre nom\n",
    "- \"invert_sign\" -> Multiplie par -1 toutes les valeurs d'une colonne d'un dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1182ba4d-bb82-4589-8e9a-f7b8112cbc8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "def modif(df, action, column, new_name=None):\n",
    "    if column not in df.columns:\n",
    "        print(f\"La colonne {column} n'a pas été trouvée dans le dataframe.\")\n",
    "    else:\n",
    "        if action == \"erase\":\n",
    "            df = df.drop(column, axis=1)\n",
    "            print(f\"La colonne {column} a été supprimée du dataframe.\")\n",
    "        elif action == \"rename\":\n",
    "            df = df.rename(columns={column: new_name})\n",
    "            print(f\"La colonne {column} a été renommée en {new_name} dans le dataframe.\")\n",
    "        elif action == \"invert_sign\":\n",
    "            df[column] = df[column].apply(lambda x: x * -1)\n",
    "            print(f\"Les valeurs de la colonne {column} ont été multipliées par -1.\")\n",
    "    return df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6067e281-58e1-4d65-af30-aa55f65a9869",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Affichage optimal du dataframe (lignes minimum)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb68e2f3-89c9-4f84-90fc-12bf6b402155",
   "metadata": {},
   "source": [
    "Permet d'afficher le dataframe de tel sorte a ce qu'on puisse voir le plus de valeurs possibles (meilleur lisibilité).  \n",
    "Affiche le nombre de lignes nécéssaires pour avoir au final au moins une valeur non nule pour chaque colonne.  \n",
    "\n",
    "1) Si NbLignes(final_df) < 3 -> On lui rajoute 2 lignes les plus remplies de df  \n",
    "2) Si NbLignes(final_df) >= 3 -> On ne fait rien   \n",
    "\n",
    "L'objectif ici est d'avoir au moins 3 lignes d'affichage pour avoir une analyse pertinante des lignes et des colonnes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c54620dc-1b6e-41af-951a-681e24a6e8a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "def ao(df):\n",
    "    rows, columns = df.shape\n",
    "    print(\"Nombre de lignes: \", rows)\n",
    "    print(\"Nombre de colonnes: \", columns)\n",
    "\n",
    "    filled_rows = df.dropna(how='all')  \n",
    "    most_filled_row_index = filled_rows.notna().sum(axis=1).idxmax()  \n",
    "    most_filled_row = filled_rows.loc[most_filled_row_index]  \n",
    "\n",
    "    nan_columns = most_filled_row[most_filled_row.isna()].index\n",
    "    other_rows = filled_rows.drop(most_filled_row_index)\n",
    "\n",
    "    final_rows = [most_filled_row]\n",
    "\n",
    "    for col in nan_columns:\n",
    "        non_null_row = other_rows[other_rows[col].notna()].head(1)\n",
    "        if not non_null_row.empty:\n",
    "            final_rows.append(non_null_row.iloc[0])\n",
    "            other_rows = other_rows.drop(non_null_row.index)\n",
    "\n",
    "    final_df = pd.DataFrame(final_rows).reset_index(drop=True)\n",
    "\n",
    "    # Ajouter des lignes les plus remplies de df si le nombre de lignes de final_df < 3\n",
    "    if len(final_df) < 3:\n",
    "        remaining_rows_needed = 3 - len(final_df)\n",
    "        \n",
    "        # Supprimer les lignes déjà présentes dans final_df en utilisant l'index d'origine\n",
    "        original_indices = [row.name for row in final_rows]\n",
    "        df_remaining = df.drop(original_indices, errors='ignore')\n",
    "        \n",
    "        sorted_remaining_rows = df_remaining.notna().sum(axis=1).sort_values(ascending=False)\n",
    "        top_remaining_rows = sorted_remaining_rows.head(remaining_rows_needed).index\n",
    "        additional_rows = df_remaining.loc[top_remaining_rows]\n",
    "        final_df = pd.concat([final_df, additional_rows]).reset_index(drop=True)\n",
    "\n",
    "    return final_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "385fa531-ea23-4161-b6d0-83588614b842",
   "metadata": {},
   "source": [
    "# Correction des colonnes avec des types mixtes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "71cbdfd3-b7d0-4456-85e8-5440ad71bd0f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Utilisateur\\AppData\\Local\\Temp\\ipykernel_48320\\1907502388.py:2: DtypeWarning: Columns (0,13,14,15) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  DataProduits = pd.read_csv('DataProduits_imputed.csv')\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd \n",
    "DataProduits = pd.read_csv('DataProduits_imputed.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9914722f-acfe-44f8-af6f-548c84b03835",
   "metadata": {},
   "source": [
    "On a à nouveau des colonnes avec des types mixtes, probablement la conséquence d'une mauvaise conversion en csv et récupération de ce csv par pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "933e2dcf-0d05-4702-a114-1243504f7542",
   "metadata": {},
   "outputs": [],
   "source": [
    "indices_colonnes_mixtes = list(range(len(DataProduits.columns)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ac0da619-b218-44fd-85de-16e90cbf43b2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Les numéros de colonnes avec des types mixtes sont: [0]\n"
     ]
    }
   ],
   "source": [
    "indices_colonnes_mixtes = recuperer_indices_colonnes_mixtes(DataProduits, indices_colonnes_mixtes)\n",
    "print(\"Les numéros de colonnes avec des types mixtes sont:\", indices_colonnes_mixtes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "5c07790e-c76d-4f60-8079-d1e38d3bd82d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Colonne 0 (code):\n",
      "  Type : <class 'int'>, Exemple (ligne 31575): 36800173071, Nombre de lignes : 212992\n",
      "  Type : <class 'str'>, Exemple (ligne 253330): 8002594001373, Nombre de lignes : 50556\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "analyse_colonnes_mixtes(DataProduits, indices_colonnes_mixtes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a8e9681e-81c7-4d59-ac23-882934094097",
   "metadata": {},
   "outputs": [],
   "source": [
    "convertir_strings_en_int(DataProduits, indices_colonnes_mixtes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c94de8b0-14dc-4d30-a412-7b25a195386a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Les numéros de colonnes avec des types mixtes sont: []\n"
     ]
    }
   ],
   "source": [
    "indices_colonnes_mixtes = recuperer_indices_colonnes_mixtes(DataProduits, indices_colonnes_mixtes)\n",
    "print(\"Les numéros de colonnes avec des types mixtes sont:\", indices_colonnes_mixtes)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89ffbae6-62cb-4724-b04e-67acd30db27f",
   "metadata": {},
   "source": [
    "Nous n'avons plus de colonnes avec des types mixtes désormais"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a479e997-566f-40cf-b6e6-fe9801a62490",
   "metadata": {},
   "source": [
    "# Reduction lignes en groupes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee395dc7-8da9-40ce-8118-72650621934c",
   "metadata": {},
   "source": [
    "## Méthode k-means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "92a57e30-1fb2-4ccf-9d35-4997e913c491",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 10/10 [00:13<00:00,  1.36s/it]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Le nombre optimal de clusters est:  3\n"
     ]
    }
   ],
   "source": [
    "nb_optimal_clusters = nombre_optimal_clusters(DataProduits)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5188ac98-da34-450b-aedc-419da15aa030",
   "metadata": {},
   "source": [
    "On rajoute une colonne \"cluster\" dans notre DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "60aebd6c-485c-40a7-a78d-ceb29951efe6",
   "metadata": {},
   "outputs": [],
   "source": [
    "DataProduits = clustering_kmeans(DataProduits, nb_optimal_clusters)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46812374-8bfc-4157-b13f-4e2e65edf7ac",
   "metadata": {},
   "source": [
    "# Reduction colonnes avec la méthode ACP"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9f4346c-aafd-40f9-ab3b-f7cae7f9ac44",
   "metadata": {},
   "source": [
    "On va plot les composantes principales pour voir si on peut simplifier nos colonnes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "b95b0777-a8c6-445d-b0bb-916c672bebff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>PC1</th>\n",
       "      <th>PC2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>energy_100g</th>\n",
       "      <td>0.519926</td>\n",
       "      <td>-0.033379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nutrition-score-fr_100g</th>\n",
       "      <td>0.467020</td>\n",
       "      <td>-0.061987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fat_100g</th>\n",
       "      <td>0.438144</td>\n",
       "      <td>0.324476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>saturated-fat_100g</th>\n",
       "      <td>0.427673</td>\n",
       "      <td>0.248428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>carbohydrates_100g</th>\n",
       "      <td>0.225107</td>\n",
       "      <td>-0.571359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sugars_100g</th>\n",
       "      <td>0.221663</td>\n",
       "      <td>-0.595455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>proteins_100g</th>\n",
       "      <td>0.163058</td>\n",
       "      <td>0.375264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fiber_100g</th>\n",
       "      <td>0.094840</td>\n",
       "      <td>0.001610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>salt_100g</th>\n",
       "      <td>-0.036125</td>\n",
       "      <td>0.078729</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              PC1       PC2\n",
       "energy_100g              0.519926 -0.033379\n",
       "nutrition-score-fr_100g  0.467020 -0.061987\n",
       "fat_100g                 0.438144  0.324476\n",
       "saturated-fat_100g       0.427673  0.248428\n",
       "carbohydrates_100g       0.225107 -0.571359\n",
       "sugars_100g              0.221663 -0.595455\n",
       "proteins_100g            0.163058  0.375264\n",
       "fiber_100g               0.094840  0.001610\n",
       "salt_100g               -0.036125  0.078729"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_PCA(DataProduits, 1, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e116ba34-6db9-41db-9222-ab8c972632dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>PC3</th>\n",
       "      <th>PC4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>fiber_100g</th>\n",
       "      <td>0.738052</td>\n",
       "      <td>0.178149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>proteins_100g</th>\n",
       "      <td>0.398697</td>\n",
       "      <td>0.116936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>carbohydrates_100g</th>\n",
       "      <td>0.259887</td>\n",
       "      <td>0.103210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>energy_100g</th>\n",
       "      <td>0.164336</td>\n",
       "      <td>0.008566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fat_100g</th>\n",
       "      <td>-0.054492</td>\n",
       "      <td>-0.090488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sugars_100g</th>\n",
       "      <td>-0.116402</td>\n",
       "      <td>-0.004532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>salt_100g</th>\n",
       "      <td>-0.205091</td>\n",
       "      <td>0.954118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>saturated-fat_100g</th>\n",
       "      <td>-0.208819</td>\n",
       "      <td>-0.104342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nutrition-score-fr_100g</th>\n",
       "      <td>-0.315572</td>\n",
       "      <td>0.120107</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              PC3       PC4\n",
       "fiber_100g               0.738052  0.178149\n",
       "proteins_100g            0.398697  0.116936\n",
       "carbohydrates_100g       0.259887  0.103210\n",
       "energy_100g              0.164336  0.008566\n",
       "fat_100g                -0.054492 -0.090488\n",
       "sugars_100g             -0.116402 -0.004532\n",
       "salt_100g               -0.205091  0.954118\n",
       "saturated-fat_100g      -0.208819 -0.104342\n",
       "nutrition-score-fr_100g -0.315572  0.120107"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_PCA(DataProduits, 3, 4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf416682-7e2e-49cd-8213-09795a9cab42",
   "metadata": {},
   "source": [
    "# OPTION 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4a38b77-661f-4083-9d3f-95f06c67f88c",
   "metadata": {
    "tags": []
   },
   "source": [
    "- Pour PC1, on va le renommer \"Taux de gras\" \n",
    "- Pour PC2, on va la renommer \"Taux de glucide\"\n",
    "- On inversera le signe de PC2 pour que plus la valeur est élevée, plus le taux de glucide est élevé\n",
    "- Il n'y aura pas de PC3 et PC4 puisqu'ils ne sont corrélés qu'a une seule colonne\n",
    "\n",
    "On aura donc les colonnes suivantes : \n",
    "- PC1 = Taux de gras\n",
    "- PC2 = Taux de glucide\n",
    "- energy_100g = Apport énergétique\n",
    "- nutrion-score-fr_100g = Nutriscore\n",
    "- fiber_100g = Taux de fibre\n",
    "- salt_100g = Taux de sel\n",
    "- proteins_100g = Taux de protéine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "dd542c16-5d55-4b77-bb41-28612ee25438",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_pca = ajouter_colonnes_PCA(DataProduits)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "70c251a8-d90f-41a3-8f4a-3f4e485fed8d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "La colonne PCA1 a été renommée en Taux de gras dans le dataframe.\n",
      "La colonne PCA2 a été renommée en Taux de glucide dans le dataframe.\n",
      "La colonne fiber_100g a été renommée en Taux de fibre dans le dataframe.\n",
      "La colonne salt_100g a été renommée en Taux de sel dans le dataframe.\n",
      "La colonne proteins_100g a été renommée en Taux de protéine dans le dataframe.\n",
      "La colonne nutrition-score-fr_100g a été renommée en Nutriscore dans le dataframe.\n",
      "La colonne energy_100g a été renommée en Apport énergétique dans le dataframe.\n",
      "Les valeurs de la colonne Taux de glucide ont été multipliées par -1.\n",
      "La colonne fat_100g a été supprimée du dataframe.\n",
      "La colonne saturated-fat_100g a été supprimée du dataframe.\n",
      "La colonne sugars_100g a été supprimée du dataframe.\n",
      "La colonne carbohydrates_100g a été supprimée du dataframe.\n"
     ]
    }
   ],
   "source": [
    "df_pca= modif(df_pca,\"rename\", \"PCA1\", \"Taux de gras\")\n",
    "df_pca= modif(df_pca,\"rename\", \"PCA2\", \"Taux de glucide\")\n",
    "df_pca= modif(df_pca,\"rename\", \"fiber_100g\", \"Taux de fibre\")\n",
    "df_pca= modif(df_pca,\"rename\", \"salt_100g\", \"Taux de sel\")\n",
    "df_pca= modif(df_pca,\"rename\", \"proteins_100g\", \"Taux de protéine\")\n",
    "df_pca= modif(df_pca,\"rename\", \"nutrition-score-fr_100g\", \"Nutriscore\")\n",
    "df_pca= modif(df_pca,\"rename\", \"energy_100g\", \"Apport énergétique\")\n",
    "\n",
    "df_pca= modif(df_pca,\"invert_sign\", \"Taux de glucide\")\n",
    "\n",
    "df_pca= modif(df_pca, \"erase\", \"fat_100g\")\n",
    "df_pca= modif(df_pca, \"erase\", \"saturated-fat_100g\")\n",
    "df_pca= modif(df_pca, \"erase\", \"sugars_100g\")\n",
    "df_pca= modif(df_pca, \"erase\", \"carbohydrates_100g\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "05ba6246-374e-4afb-95a8-9e6d7b0bcd3b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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",
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       "\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>cluster</th>\n",
       "      <th>code</th>\n",
       "      <th>url</th>\n",
       "      <th>creator</th>\n",
       "      <th>created_datetime</th>\n",
       "      <th>last_modified_datetime</th>\n",
       "      <th>product_name</th>\n",
       "      <th>quantity</th>\n",
       "      <th>packaging</th>\n",
       "      <th>brands</th>\n",
       "      <th>categories_fr</th>\n",
       "      <th>origins</th>\n",
       "      <th>manufacturing_places</th>\n",
       "      <th>labels_fr</th>\n",
       "      <th>emb_codes</th>\n",
       "      <th>first_packaging_code_geo</th>\n",
       "      <th>cities_tags</th>\n",
       "      <th>purchase_places</th>\n",
       "      <th>stores</th>\n",
       "      <th>countries_fr</th>\n",
       "      <th>ingredients_text</th>\n",
       "      <th>allergens</th>\n",
       "      <th>traces_fr</th>\n",
       "      <th>serving_size</th>\n",
       "      <th>additives_fr</th>\n",
       "      <th>ingredients_from_palm_oil_tags</th>\n",
       "      <th>ingredients_that_may_be_from_palm_oil_tags</th>\n",
       "      <th>nutrition_grade_fr</th>\n",
       "      <th>image_url</th>\n",
       "      <th>image_small_url</th>\n",
       "      <th>Apport énergétique</th>\n",
       "      <th>Taux de fibre</th>\n",
       "      <th>Taux de protéine</th>\n",
       "      <th>Taux de sel</th>\n",
       "      <th>Nutriscore</th>\n",
       "      <th>Taux de gras</th>\n",
       "      <th>Taux de glucide</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>4530</td>\n",
       "      <td>http://world-fr.openfoodfacts.org/produit/0000...</td>\n",
       "      <td>usda-ndb-import</td>\n",
       "      <td>2017-03-09T14:32:37Z</td>\n",
       "      <td>2017-03-09T14:32:37Z</td>\n",
       "      <td>Banana Chips Sweetened (Whole)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>États-Unis</td>\n",
       "      <td>Bananas, vegetable oil (coconut oil, corn oil ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28 g (1 ONZ)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>d</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2243.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>3.57</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1119.778267</td>\n",
       "      <td>-2.964958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>4559</td>\n",
       "      <td>http://world-fr.openfoodfacts.org/produit/0000...</td>\n",
       "      <td>usda-ndb-import</td>\n",
       "      <td>2017-03-09T14:32:37Z</td>\n",
       "      <td>2017-03-09T14:32:37Z</td>\n",
       "      <td>Peanuts</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Torn &amp; Glasser</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>États-Unis</td>\n",
       "      <td>Peanuts, wheat flour, sugar, rice flour, tapio...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28 g (0.25 cup)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>b</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1941.0</td>\n",
       "      <td>7.1</td>\n",
       "      <td>17.86</td>\n",
       "      <td>0.635000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>817.481200</td>\n",
       "      <td>-10.166870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>16087</td>\n",
       "      <td>http://world-fr.openfoodfacts.org/produit/0000...</td>\n",
       "      <td>usda-ndb-import</td>\n",
       "      <td>2017-03-09T10:35:31Z</td>\n",
       "      <td>2017-03-09T10:35:31Z</td>\n",
       "      <td>Organic Salted Nut Mix</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Grizzlies</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>États-Unis</td>\n",
       "      <td>Organic hazelnuts, organic cashews, organic wa...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28 g (0.25 cup)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>d</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2540.0</td>\n",
       "      <td>7.1</td>\n",
       "      <td>17.86</td>\n",
       "      <td>1.224280</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1416.084030</td>\n",
       "      <td>51.092413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>16094</td>\n",
       "      <td>http://world-fr.openfoodfacts.org/produit/0000...</td>\n",
       "      <td>usda-ndb-import</td>\n",
       "      <td>2017-03-09T10:34:13Z</td>\n",
       "      <td>2017-03-09T10:34:13Z</td>\n",
       "      <td>Organic Polenta</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bob's Red Mill</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>États-Unis</td>\n",
       "      <td>Organic polenta</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35 g (0.25 cup)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>a</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1552.0</td>\n",
       "      <td>5.7</td>\n",
       "      <td>8.57</td>\n",
       "      <td>0.510000</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>428.511854</td>\n",
       "      <td>-27.045123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>16100</td>\n",
       "      <td>http://world-fr.openfoodfacts.org/produit/0000...</td>\n",
       "      <td>usda-ndb-import</td>\n",
       "      <td>2017-03-09T10:34:11Z</td>\n",
       "      <td>2017-03-09T10:34:11Z</td>\n",
       "      <td>Breadshop Honey Gone Nuts Granola</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Unfi</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>États-Unis</td>\n",
       "      <td>Rolled oats, grape concentrate, expeller press...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>52 g (0.5 cup)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>c</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1933.0</td>\n",
       "      <td>7.7</td>\n",
       "      <td>13.46</td>\n",
       "      <td>1.006244</td>\n",
       "      <td>6.0</td>\n",
       "      <td>809.528574</td>\n",
       "      <td>-9.299658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263543</th>\n",
       "      <td>0</td>\n",
       "      <td>9847548283004</td>\n",
       "      <td>http://world-fr.openfoodfacts.org/produit/9847...</td>\n",
       "      <td>drmalabar</td>\n",
       "      <td>2015-07-25T09:47:06Z</td>\n",
       "      <td>2015-09-14T13:37:28Z</td>\n",
       "      <td>Tartines craquantes bio au sarrasin</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Boîte,Carton,Sachet,Plastique</td>\n",
       "      <td>Le Pain des fleurs</td>\n",
       "      <td>Aliments et boissons à base de végétaux,Alimen...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>France</td>\n",
       "      <td>Bio,Bio européen,Sans gluten,AB Agriculture Bi...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>France</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>a</td>\n",
       "      <td>http://fr.openfoodfacts.org/images/products/98...</td>\n",
       "      <td>http://fr.openfoodfacts.org/images/products/98...</td>\n",
       "      <td>1643.0</td>\n",
       "      <td>5.9</td>\n",
       "      <td>13.00</td>\n",
       "      <td>0.680000</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>519.441083</td>\n",
       "      <td>-22.023718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263544</th>\n",
       "      <td>1</td>\n",
       "      <td>989898</td>\n",
       "      <td>http://world-fr.openfoodfacts.org/produit/9898...</td>\n",
       "      <td>openfoodfacts-contributors</td>\n",
       "      <td>2016-09-12T15:40:09Z</td>\n",
       "      <td>2016-09-12T15:40:15Z</td>\n",
       "      <td>Test NF App</td>\n",
       "      <td>100gr</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Suisse</td>\n",
       "      <td>Coco, choco</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>c</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>569.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>2.10</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>8.0</td>\n",
       "      <td>-554.741469</td>\n",
       "      <td>16.077949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263545</th>\n",
       "      <td>0</td>\n",
       "      <td>9900000000233</td>\n",
       "      <td>http://world-fr.openfoodfacts.org/produit/9900...</td>\n",
       "      <td>kiliweb</td>\n",
       "      <td>2017-04-01T20:52:18Z</td>\n",
       "      <td>2017-04-01T20:52:19Z</td>\n",
       "      <td>Amandes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Biosic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bio,Bio européen,AB Agriculture Biologique</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>France</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>b</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2406.0</td>\n",
       "      <td>12.2</td>\n",
       "      <td>21.22</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1281.303290</td>\n",
       "      <td>36.281827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263546</th>\n",
       "      <td>1</td>\n",
       "      <td>99111250</td>\n",
       "      <td>http://world-fr.openfoodfacts.org/produit/9911...</td>\n",
       "      <td>balooval</td>\n",
       "      <td>2013-04-28T15:30:39Z</td>\n",
       "      <td>2013-06-20T01:09:16Z</td>\n",
       "      <td>Thé vert Earl grey</td>\n",
       "      <td>50 g</td>\n",
       "      <td>boîte,carton</td>\n",
       "      <td>Lobodis</td>\n",
       "      <td>Aliments et boissons à base de végétaux,Boisso...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Commerce équitable,Bio,Bio européen,Max Havela...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>France</td>\n",
       "      <td>thé vert, arôme naturel bergamote avec autres ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>b</td>\n",
       "      <td>http://fr.openfoodfacts.org/images/products/99...</td>\n",
       "      <td>http://fr.openfoodfacts.org/images/products/99...</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.025400</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1103.381172</td>\n",
       "      <td>13.371857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263547</th>\n",
       "      <td>1</td>\n",
       "      <td>999990026839</td>\n",
       "      <td>http://world-fr.openfoodfacts.org/produit/9999...</td>\n",
       "      <td>usda-ndb-import</td>\n",
       "      <td>2017-03-09T15:18:29Z</td>\n",
       "      <td>2017-04-03T18:34:59Z</td>\n",
       "      <td>Sugar Free Drink Mix, Peach Tea</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Plastique</td>\n",
       "      <td>Market Pantry</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>États-Unis</td>\n",
       "      <td>Citric acid, maltodextrin, instant tea, aspart...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1 g (0.5 PACKET)</td>\n",
       "      <td>E330 - Acide citrique,E951 - Aspartame,E150a -...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>c</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2092.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.0</td>\n",
       "      <td>966.862206</td>\n",
       "      <td>42.374096</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>263548 rows × 37 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        cluster           code  \\\n",
       "0             2           4530   \n",
       "1             0           4559   \n",
       "2             2          16087   \n",
       "3             0          16094   \n",
       "4             0          16100   \n",
       "...         ...            ...   \n",
       "263543        0  9847548283004   \n",
       "263544        1         989898   \n",
       "263545        0  9900000000233   \n",
       "263546        1       99111250   \n",
       "263547        1   999990026839   \n",
       "\n",
       "                                                      url  \\\n",
       "0       http://world-fr.openfoodfacts.org/produit/0000...   \n",
       "1       http://world-fr.openfoodfacts.org/produit/0000...   \n",
       "2       http://world-fr.openfoodfacts.org/produit/0000...   \n",
       "3       http://world-fr.openfoodfacts.org/produit/0000...   \n",
       "4       http://world-fr.openfoodfacts.org/produit/0000...   \n",
       "...                                                   ...   \n",
       "263543  http://world-fr.openfoodfacts.org/produit/9847...   \n",
       "263544  http://world-fr.openfoodfacts.org/produit/9898...   \n",
       "263545  http://world-fr.openfoodfacts.org/produit/9900...   \n",
       "263546  http://world-fr.openfoodfacts.org/produit/9911...   \n",
       "263547  http://world-fr.openfoodfacts.org/produit/9999...   \n",
       "\n",
       "                           creator      created_datetime  \\\n",
       "0                  usda-ndb-import  2017-03-09T14:32:37Z   \n",
       "1                  usda-ndb-import  2017-03-09T14:32:37Z   \n",
       "2                  usda-ndb-import  2017-03-09T10:35:31Z   \n",
       "3                  usda-ndb-import  2017-03-09T10:34:13Z   \n",
       "4                  usda-ndb-import  2017-03-09T10:34:11Z   \n",
       "...                            ...                   ...   \n",
       "263543                   drmalabar  2015-07-25T09:47:06Z   \n",
       "263544  openfoodfacts-contributors  2016-09-12T15:40:09Z   \n",
       "263545                     kiliweb  2017-04-01T20:52:18Z   \n",
       "263546                    balooval  2013-04-28T15:30:39Z   \n",
       "263547             usda-ndb-import  2017-03-09T15:18:29Z   \n",
       "\n",
       "       last_modified_datetime                         product_name quantity  \\\n",
       "0        2017-03-09T14:32:37Z       Banana Chips Sweetened (Whole)      NaN   \n",
       "1        2017-03-09T14:32:37Z                              Peanuts      NaN   \n",
       "2        2017-03-09T10:35:31Z               Organic Salted Nut Mix      NaN   \n",
       "3        2017-03-09T10:34:13Z                      Organic Polenta      NaN   \n",
       "4        2017-03-09T10:34:11Z    Breadshop Honey Gone Nuts Granola      NaN   \n",
       "...                       ...                                  ...      ...   \n",
       "263543   2015-09-14T13:37:28Z  Tartines craquantes bio au sarrasin      NaN   \n",
       "263544   2016-09-12T15:40:15Z                          Test NF App    100gr   \n",
       "263545   2017-04-01T20:52:19Z                              Amandes      NaN   \n",
       "263546   2013-06-20T01:09:16Z                   Thé vert Earl grey     50 g   \n",
       "263547   2017-04-03T18:34:59Z      Sugar Free Drink Mix, Peach Tea      NaN   \n",
       "\n",
       "                            packaging              brands  \\\n",
       "0                                 NaN                 NaN   \n",
       "1                                 NaN      Torn & Glasser   \n",
       "2                                 NaN           Grizzlies   \n",
       "3                                 NaN      Bob's Red Mill   \n",
       "4                                 NaN                Unfi   \n",
       "...                               ...                 ...   \n",
       "263543  Boîte,Carton,Sachet,Plastique  Le Pain des fleurs   \n",
       "263544                            NaN                 NaN   \n",
       "263545                            NaN              Biosic   \n",
       "263546                   boîte,carton             Lobodis   \n",
       "263547                      Plastique       Market Pantry   \n",
       "\n",
       "                                            categories_fr origins  \\\n",
       "0                                                     NaN     NaN   \n",
       "1                                                     NaN     NaN   \n",
       "2                                                     NaN     NaN   \n",
       "3                                                     NaN     NaN   \n",
       "4                                                     NaN     NaN   \n",
       "...                                                   ...     ...   \n",
       "263543  Aliments et boissons à base de végétaux,Alimen...     NaN   \n",
       "263544                                                NaN     NaN   \n",
       "263545                                                NaN     NaN   \n",
       "263546  Aliments et boissons à base de végétaux,Boisso...     NaN   \n",
       "263547                                                NaN     NaN   \n",
       "\n",
       "       manufacturing_places  \\\n",
       "0                       NaN   \n",
       "1                       NaN   \n",
       "2                       NaN   \n",
       "3                       NaN   \n",
       "4                       NaN   \n",
       "...                     ...   \n",
       "263543               France   \n",
       "263544                  NaN   \n",
       "263545                  NaN   \n",
       "263546                  NaN   \n",
       "263547                  NaN   \n",
       "\n",
       "                                                labels_fr emb_codes  \\\n",
       "0                                                     NaN       NaN   \n",
       "1                                                     NaN       NaN   \n",
       "2                                                     NaN       NaN   \n",
       "3                                                     NaN       NaN   \n",
       "4                                                     NaN       NaN   \n",
       "...                                                   ...       ...   \n",
       "263543  Bio,Bio européen,Sans gluten,AB Agriculture Bi...       NaN   \n",
       "263544                                                NaN       NaN   \n",
       "263545         Bio,Bio européen,AB Agriculture Biologique       NaN   \n",
       "263546  Commerce équitable,Bio,Bio européen,Max Havela...       NaN   \n",
       "263547                                                NaN       NaN   \n",
       "\n",
       "       first_packaging_code_geo cities_tags purchase_places stores  \\\n",
       "0                           NaN         NaN             NaN    NaN   \n",
       "1                           NaN         NaN             NaN    NaN   \n",
       "2                           NaN         NaN             NaN    NaN   \n",
       "3                           NaN         NaN             NaN    NaN   \n",
       "4                           NaN         NaN             NaN    NaN   \n",
       "...                         ...         ...             ...    ...   \n",
       "263543                      NaN         NaN             NaN    NaN   \n",
       "263544                      NaN         NaN             NaN    NaN   \n",
       "263545                      NaN         NaN             NaN    NaN   \n",
       "263546                      NaN         NaN             NaN    NaN   \n",
       "263547                      NaN         NaN             NaN    NaN   \n",
       "\n",
       "       countries_fr                                   ingredients_text  \\\n",
       "0        États-Unis  Bananas, vegetable oil (coconut oil, corn oil ...   \n",
       "1        États-Unis  Peanuts, wheat flour, sugar, rice flour, tapio...   \n",
       "2        États-Unis  Organic hazelnuts, organic cashews, organic wa...   \n",
       "3        États-Unis                                    Organic polenta   \n",
       "4        États-Unis  Rolled oats, grape concentrate, expeller press...   \n",
       "...             ...                                                ...   \n",
       "263543       France                                                NaN   \n",
       "263544       Suisse                                        Coco, choco   \n",
       "263545       France                                                NaN   \n",
       "263546       France  thé vert, arôme naturel bergamote avec autres ...   \n",
       "263547   États-Unis  Citric acid, maltodextrin, instant tea, aspart...   \n",
       "\n",
       "       allergens traces_fr      serving_size  \\\n",
       "0            NaN       NaN      28 g (1 ONZ)   \n",
       "1            NaN       NaN   28 g (0.25 cup)   \n",
       "2            NaN       NaN   28 g (0.25 cup)   \n",
       "3            NaN       NaN   35 g (0.25 cup)   \n",
       "4            NaN       NaN    52 g (0.5 cup)   \n",
       "...          ...       ...               ...   \n",
       "263543       NaN       NaN               NaN   \n",
       "263544       NaN       NaN               NaN   \n",
       "263545       NaN       NaN               NaN   \n",
       "263546       NaN       NaN               NaN   \n",
       "263547       NaN       NaN  1 g (0.5 PACKET)   \n",
       "\n",
       "                                             additives_fr  \\\n",
       "0                                                     NaN   \n",
       "1                                                     NaN   \n",
       "2                                                     NaN   \n",
       "3                                                     NaN   \n",
       "4                                                     NaN   \n",
       "...                                                   ...   \n",
       "263543                                                NaN   \n",
       "263544                                                NaN   \n",
       "263545                                                NaN   \n",
       "263546                                                NaN   \n",
       "263547  E330 - Acide citrique,E951 - Aspartame,E150a -...   \n",
       "\n",
       "       ingredients_from_palm_oil_tags  \\\n",
       "0                                 NaN   \n",
       "1                                 NaN   \n",
       "2                                 NaN   \n",
       "3                                 NaN   \n",
       "4                                 NaN   \n",
       "...                               ...   \n",
       "263543                            NaN   \n",
       "263544                            NaN   \n",
       "263545                            NaN   \n",
       "263546                            NaN   \n",
       "263547                            NaN   \n",
       "\n",
       "       ingredients_that_may_be_from_palm_oil_tags nutrition_grade_fr  \\\n",
       "0                                             NaN                  d   \n",
       "1                                             NaN                  b   \n",
       "2                                             NaN                  d   \n",
       "3                                             NaN                  a   \n",
       "4                                             NaN                  c   \n",
       "...                                           ...                ...   \n",
       "263543                                        NaN                  a   \n",
       "263544                                        NaN                  c   \n",
       "263545                                        NaN                  b   \n",
       "263546                                        NaN                  b   \n",
       "263547                                        NaN                  c   \n",
       "\n",
       "                                                image_url  \\\n",
       "0                                                     NaN   \n",
       "1                                                     NaN   \n",
       "2                                                     NaN   \n",
       "3                                                     NaN   \n",
       "4                                                     NaN   \n",
       "...                                                   ...   \n",
       "263543  http://fr.openfoodfacts.org/images/products/98...   \n",
       "263544                                                NaN   \n",
       "263545                                                NaN   \n",
       "263546  http://fr.openfoodfacts.org/images/products/99...   \n",
       "263547                                                NaN   \n",
       "\n",
       "                                          image_small_url  Apport énergétique  \\\n",
       "0                                                     NaN              2243.0   \n",
       "1                                                     NaN              1941.0   \n",
       "2                                                     NaN              2540.0   \n",
       "3                                                     NaN              1552.0   \n",
       "4                                                     NaN              1933.0   \n",
       "...                                                   ...                 ...   \n",
       "263543  http://fr.openfoodfacts.org/images/products/98...              1643.0   \n",
       "263544                                                NaN               569.0   \n",
       "263545                                                NaN              2406.0   \n",
       "263546  http://fr.openfoodfacts.org/images/products/99...                21.0   \n",
       "263547                                                NaN              2092.0   \n",
       "\n",
       "        Taux de fibre  Taux de protéine  Taux de sel  Nutriscore  \\\n",
       "0                 3.6              3.57     0.000000        14.0   \n",
       "1                 7.1             17.86     0.635000         0.0   \n",
       "2                 7.1             17.86     1.224280        12.0   \n",
       "3                 5.7              8.57     0.510000        -2.0   \n",
       "4                 7.7             13.46     1.006244         6.0   \n",
       "...               ...               ...          ...         ...   \n",
       "263543            5.9             13.00     0.680000        -4.0   \n",
       "263544            1.1              2.10     1.100000         8.0   \n",
       "263545           12.2             21.22     0.100000         0.0   \n",
       "263546            0.2              0.50     0.025400         1.0   \n",
       "263547            0.0              0.00     0.000000         6.0   \n",
       "\n",
       "        Taux de gras  Taux de glucide  \n",
       "0        1119.778267        -2.964958  \n",
       "1         817.481200       -10.166870  \n",
       "2        1416.084030        51.092413  \n",
       "3         428.511854       -27.045123  \n",
       "4         809.528574        -9.299658  \n",
       "...              ...              ...  \n",
       "263543    519.441083       -22.023718  \n",
       "263544   -554.741469        16.077949  \n",
       "263545   1281.303290        36.281827  \n",
       "263546  -1103.381172        13.371857  \n",
       "263547    966.862206        42.374096  \n",
       "\n",
       "[263548 rows x 37 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pca"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "b61b8bf0-6861-43f4-baac-571e1501cbf4",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = df_pca"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "8570d834-5f76-4c9c-b10c-0dcf477e0db6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1500 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import math\n",
    "\n",
    "# Obtenir le nombre maximum de clusters\n",
    "max_cluster = data[\"cluster\"].max()\n",
    "\n",
    "# Filtrer les colonnes de type float\n",
    "float_columns = data.select_dtypes(include=['float64']).columns\n",
    "\n",
    "# Calculer la taille de la grille de sous-graphiques\n",
    "nrows = int(math.ceil(len(float_columns) ** 0.5))\n",
    "ncols = nrows\n",
    "\n",
    "# Créer une figure et une grille de sous-graphiques\n",
    "fig, axes = plt.subplots(nrows, ncols, figsize=(15, 15))\n",
    "fig.tight_layout(pad=3.0)\n",
    "fig.subplots_adjust(hspace=0.2, wspace=0.3)\n",
    "\n",
    "# Parcourir les colonnes de type float\n",
    "for idx, column in enumerate(float_columns):\n",
    "    # Calculer la position du sous-graphique dans la grille\n",
    "    row = idx // ncols\n",
    "    col = idx % ncols\n",
    "        \n",
    "    # Boucle sur les différents clusters\n",
    "    for i in range(max_cluster + 1):\n",
    "        # Séparer les données en fonction du cluster\n",
    "        cluster = data[data[\"cluster\"] == i][column]\n",
    "\n",
    "        # Créer un histogramme pour le cluster actuel\n",
    "        sns.kdeplot(cluster, fill=True, label=\"Cluster \" + str(i), ax=axes[row, col])\n",
    "        \n",
    "    # Ajouter un titre au sous-graphique\n",
    "    axes[row, col].set_title(column)\n",
    "    \n",
    "    # Ajouter une légende au sous-graphique\n",
    "    axes[row, col].legend()\n",
    "\n",
    "# Cacher les graphiques vides\n",
    "for idx in range(len(float_columns), nrows * ncols):\n",
    "    row = idx // ncols\n",
    "    col = idx % ncols\n",
    "    axes[row, col].set_visible(False)\n",
    "\n",
    "# Afficher le graphique\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a1d7190-18dd-44b6-bf62-81326bc0f46f",
   "metadata": {},
   "source": [
    "A partir de ce graphe, on peut proposer une facon de faire pour filtrer efficacement nos données"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26e8580c-9876-4ff3-a262-3b05b90cf620",
   "metadata": {},
   "source": [
    "Apport énergétique : \n",
    "- Faible -> Cluster 1\n",
    "- Moyenne -> Cluster 0\n",
    "- Forte -> Cluster 2\n",
    "\n",
    "Nutriscore : \n",
    "- Faible -> Cluster 1\n",
    "- Moyenne -> Cluster 0\n",
    "- Forte -> Cluster 2\n",
    "\n",
    "Taux de gras : \n",
    "- Faible -> Cluster 1\n",
    "- Moyenne -> Cluster 0\n",
    "- Forte -> Cluster 2\n",
    "\n",
    "Taux de glucide : \n",
    "- Faible -> Cluster 0\n",
    "- Moyenne -> Cluster 1\n",
    "- Forte -> Cluster 2\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "37187d44-1bcf-487b-9d3e-4453528ad5e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = DataProduits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "a57351f7-d191-4cc7-841a-6ebea94c849b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "def filtrage_superficiel(df, criteres, niveau):\n",
    "    cluster_mapping = {\n",
    "        \"Apport énergétique\": {\"Faible\": 1, \"Moyenne\": 0, \"Forte\": 2},\n",
    "        \"Nutriscore\": {\"Faible\": 1, \"Moyenne\": 0, \"Forte\": 2},\n",
    "        \"Taux de gras\": {\"Faible\": 1, \"Moyenne\": 0, \"Forte\": 2},\n",
    "        \"Taux de glucide\": {\"Faible\": 0, \"Moyenne\": 1, \"Forte\": 2}\n",
    "    }\n",
    "    \n",
    "    if criteres in cluster_mapping and niveau in cluster_mapping[criteres]:\n",
    "        cluster = cluster_mapping[criteres][niveau]\n",
    "        \n",
    "        for crit, niveaux in cluster_mapping.items():\n",
    "            if crit != criteres:\n",
    "                for niv, clust in niveaux.items():\n",
    "                    if clust == cluster:\n",
    "                        print(f\"{crit} : {niv}\")\n",
    "        \n",
    "        df_filtré = df[df['cluster'] == cluster]\n",
    "        return df_filtré\n",
    "    else:\n",
    "        print(\"Critère ou niveau invalide\")\n",
    "        return None\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "5bb4bb06-b637-4020-84b5-128488736274",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nutriscore : Faible\n",
      "Taux de gras : Faible\n",
      "Taux de glucide : Moyenne\n",
      "Nombre de lignes:  129032\n",
      "Nombre de colonnes:  39\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>cluster</th>\n",
       "      <th>code</th>\n",
       "      <th>url</th>\n",
       "      <th>creator</th>\n",
       "      <th>created_datetime</th>\n",
       "      <th>last_modified_datetime</th>\n",
       "      <th>product_name</th>\n",
       "      <th>quantity</th>\n",
       "      <th>packaging</th>\n",
       "      <th>brands</th>\n",
       "      <th>categories_fr</th>\n",
       "      <th>origins</th>\n",
       "      <th>manufacturing_places</th>\n",
       "      <th>labels_fr</th>\n",
       "      <th>emb_codes</th>\n",
       "      <th>first_packaging_code_geo</th>\n",
       "      <th>cities_tags</th>\n",
       "      <th>purchase_places</th>\n",
       "      <th>stores</th>\n",
       "      <th>countries_fr</th>\n",
       "      <th>ingredients_text</th>\n",
       "      <th>allergens</th>\n",
       "      <th>traces_fr</th>\n",
       "      <th>serving_size</th>\n",
       "      <th>additives_fr</th>\n",
       "      <th>ingredients_from_palm_oil_tags</th>\n",
       "      <th>ingredients_that_may_be_from_palm_oil_tags</th>\n",
       "      <th>nutrition_grade_fr</th>\n",
       "      <th>image_url</th>\n",
       "      <th>image_small_url</th>\n",
       "      <th>energy_100g</th>\n",
       "      <th>fat_100g</th>\n",
       "      <th>saturated-fat_100g</th>\n",
       "      <th>carbohydrates_100g</th>\n",
       "      <th>sugars_100g</th>\n",
       "      <th>fiber_100g</th>\n",
       "      <th>proteins_100g</th>\n",
       "      <th>salt_100g</th>\n",
       "      <th>nutrition-score-fr_100g</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>3248830085058</td>\n",
       "      <td>http://world-fr.openfoodfacts.org/produit/3248...</td>\n",
       "      <td>dimitri</td>\n",
       "      <td>2013-10-28T19:06:42Z</td>\n",
       "      <td>2017-02-16T14:49:10Z</td>\n",
       "      <td>Quiche Lorraine Lardons et Jambon fumés, Emmen...</td>\n",
       "      <td>400 g</td>\n",
       "      <td>Surgelé,Carton</td>\n",
       "      <td>Marie</td>\n",
       "      <td>Plats préparés,Pizzas tartes salées et quiches...</td>\n",
       "      <td>France</td>\n",
       "      <td>France</td>\n",
       "      <td>Viande Française,Porc Français</td>\n",
       "      <td>FR 86.160.002 EC</td>\n",
       "      <td>46.783333,0.183333</td>\n",
       "      <td>mirebeau-vienne-france</td>\n",
       "      <td>Cannes</td>\n",
       "      <td>Intermarché, Irma.dk</td>\n",
       "      <td>Danemark,France,Suisse</td>\n",
       "      <td>Garniture 69% : eau, _oeufs_ fermier de Loué 1...</td>\n",
       "      <td>oeufs, crème, emmental, blé, blé</td>\n",
       "      <td>Céleri,Crustacés,Poisson,Mollusques,Moutarde</td>\n",
       "      <td>200 g</td>\n",
       "      <td>E471 - Mono- et diglycérides d'acides gras ali...</td>\n",
       "      <td>huile-de-palme</td>\n",
       "      <td>e471-mono-et-diglycerides-d-acides-gras-alimen...</td>\n",
       "      <td>d</td>\n",
       "      <td>http://fr.openfoodfacts.org/images/products/32...</td>\n",
       "      <td>http://fr.openfoodfacts.org/images/products/32...</td>\n",
       "      <td>1073.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>8.6</td>\n",
       "      <td>18.3</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.70</td>\n",
       "      <td>9.6</td>\n",
       "      <td>0.86</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>3596710390779</td>\n",
       "      <td>http://world-fr.openfoodfacts.org/produit/3596...</td>\n",
       "      <td>tacite</td>\n",
       "      <td>2015-06-16T15:57:13Z</td>\n",
       "      <td>2015-08-24T19:40:13Z</td>\n",
       "      <td>Hachis Parmentier, Surgelé</td>\n",
       "      <td>400 g</td>\n",
       "      <td>Surgelé,Barquette,Plastique,Opercule,Film plas...</td>\n",
       "      <td>Auchan,L'oiseau</td>\n",
       "      <td>Surgelés,Plats préparés,Plats à base de viande...</td>\n",
       "      <td>Viande Bovine,France</td>\n",
       "      <td>Celtigel (Filiale Groupe Le Graët) - ZA des 4 ...</td>\n",
       "      <td>Viande Française,Produit en braille,Viande Bov...</td>\n",
       "      <td>FR 22.182.001 EC</td>\n",
       "      <td>48.566667,-2.95</td>\n",
       "      <td>plelo-cotes-d-armor-france</td>\n",
       "      <td>Noyelles-Godault,France</td>\n",
       "      <td>Auchan</td>\n",
       "      <td>France</td>\n",
       "      <td>Eau, viande bovine crue 19 %, oignons, flocons...</td>\n",
       "      <td>lait, gluten, lait, lait, lait, lait</td>\n",
       "      <td>Céleri,Crustacés,Œufs,Poisson,Lupin,Mollusques...</td>\n",
       "      <td>400 g (1 barquette) Cet emballage contient l'é...</td>\n",
       "      <td>E471 - Mono- et diglycérides d'acides gras ali...</td>\n",
       "      <td>e304-palmitate-d-ascorbyle</td>\n",
       "      <td>e471-mono-et-diglycerides-d-acides-gras-alimen...</td>\n",
       "      <td>b</td>\n",
       "      <td>http://fr.openfoodfacts.org/images/products/35...</td>\n",
       "      <td>http://fr.openfoodfacts.org/images/products/35...</td>\n",
       "      <td>505.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.4</td>\n",
       "      <td>11.6</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.30</td>\n",
       "      <td>6.6</td>\n",
       "      <td>0.80</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3250391414628</td>\n",
       "      <td>http://world-fr.openfoodfacts.org/produit/3250...</td>\n",
       "      <td>openfoodfacts-contributors</td>\n",
       "      <td>2014-02-18T16:39:40Z</td>\n",
       "      <td>2015-05-10T06:54:16Z</td>\n",
       "      <td>Cheeseburger</td>\n",
       "      <td>145 g</td>\n",
       "      <td>Carton,Boîte,Décongelé</td>\n",
       "      <td>Netto</td>\n",
       "      <td>Sandwichs,Cheeseburger</td>\n",
       "      <td>France</td>\n",
       "      <td>Theix,Morbihan,Bretagne,France</td>\n",
       "      <td>Viande Française,Viande Bovine Française</td>\n",
       "      <td>EMB 56251C</td>\n",
       "      <td>47.633333,-2.666667</td>\n",
       "      <td>theix-morbihan-france</td>\n",
       "      <td>France,Courrières</td>\n",
       "      <td>netto</td>\n",
       "      <td>France</td>\n",
       "      <td>Garniture 62,1% : préparation de viande bovine...</td>\n",
       "      <td>lait, lait, moutarde, moutarde, moutarde, blé,...</td>\n",
       "      <td>Crustacés,Œufs,Poisson,Mollusques,Fruits à coque</td>\n",
       "      <td>145 g</td>\n",
       "      <td>E452 - Polyphosphates,E331 - Citrates de sodiu...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>e471-mono-et-diglycerides-d-acides-gras-alimen...</td>\n",
       "      <td>d</td>\n",
       "      <td>http://fr.openfoodfacts.org/images/products/32...</td>\n",
       "      <td>http://fr.openfoodfacts.org/images/products/32...</td>\n",
       "      <td>1041.0</td>\n",
       "      <td>12.2</td>\n",
       "      <td>5.7</td>\n",
       "      <td>19.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.52</td>\n",
       "      <td>14.1</td>\n",
       "      <td>1.33</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   cluster           code                                                url  \\\n",
       "0        1  3248830085058  http://world-fr.openfoodfacts.org/produit/3248...   \n",
       "1        1  3596710390779  http://world-fr.openfoodfacts.org/produit/3596...   \n",
       "2        1  3250391414628  http://world-fr.openfoodfacts.org/produit/3250...   \n",
       "\n",
       "                      creator      created_datetime last_modified_datetime  \\\n",
       "0                     dimitri  2013-10-28T19:06:42Z   2017-02-16T14:49:10Z   \n",
       "1                      tacite  2015-06-16T15:57:13Z   2015-08-24T19:40:13Z   \n",
       "2  openfoodfacts-contributors  2014-02-18T16:39:40Z   2015-05-10T06:54:16Z   \n",
       "\n",
       "                                        product_name quantity  \\\n",
       "0  Quiche Lorraine Lardons et Jambon fumés, Emmen...    400 g   \n",
       "1                         Hachis Parmentier, Surgelé    400 g   \n",
       "2                                       Cheeseburger    145 g   \n",
       "\n",
       "                                           packaging           brands  \\\n",
       "0                                     Surgelé,Carton            Marie   \n",
       "1  Surgelé,Barquette,Plastique,Opercule,Film plas...  Auchan,L'oiseau   \n",
       "2                             Carton,Boîte,Décongelé            Netto   \n",
       "\n",
       "                                       categories_fr               origins  \\\n",
       "0  Plats préparés,Pizzas tartes salées et quiches...                France   \n",
       "1  Surgelés,Plats préparés,Plats à base de viande...  Viande Bovine,France   \n",
       "2                             Sandwichs,Cheeseburger                France   \n",
       "\n",
       "                                manufacturing_places  \\\n",
       "0                                             France   \n",
       "1  Celtigel (Filiale Groupe Le Graët) - ZA des 4 ...   \n",
       "2                     Theix,Morbihan,Bretagne,France   \n",
       "\n",
       "                                           labels_fr         emb_codes  \\\n",
       "0                     Viande Française,Porc Français  FR 86.160.002 EC   \n",
       "1  Viande Française,Produit en braille,Viande Bov...  FR 22.182.001 EC   \n",
       "2           Viande Française,Viande Bovine Française        EMB 56251C   \n",
       "\n",
       "  first_packaging_code_geo                 cities_tags  \\\n",
       "0       46.783333,0.183333      mirebeau-vienne-france   \n",
       "1          48.566667,-2.95  plelo-cotes-d-armor-france   \n",
       "2      47.633333,-2.666667       theix-morbihan-france   \n",
       "\n",
       "           purchase_places                stores            countries_fr  \\\n",
       "0                   Cannes  Intermarché, Irma.dk  Danemark,France,Suisse   \n",
       "1  Noyelles-Godault,France                Auchan                  France   \n",
       "2        France,Courrières                 netto                  France   \n",
       "\n",
       "                                    ingredients_text  \\\n",
       "0  Garniture 69% : eau, _oeufs_ fermier de Loué 1...   \n",
       "1  Eau, viande bovine crue 19 %, oignons, flocons...   \n",
       "2  Garniture 62,1% : préparation de viande bovine...   \n",
       "\n",
       "                                           allergens  \\\n",
       "0                   oeufs, crème, emmental, blé, blé   \n",
       "1               lait, gluten, lait, lait, lait, lait   \n",
       "2  lait, lait, moutarde, moutarde, moutarde, blé,...   \n",
       "\n",
       "                                           traces_fr  \\\n",
       "0       Céleri,Crustacés,Poisson,Mollusques,Moutarde   \n",
       "1  Céleri,Crustacés,Œufs,Poisson,Lupin,Mollusques...   \n",
       "2   Crustacés,Œufs,Poisson,Mollusques,Fruits à coque   \n",
       "\n",
       "                                        serving_size  \\\n",
       "0                                              200 g   \n",
       "1  400 g (1 barquette) Cet emballage contient l'é...   \n",
       "2                                              145 g   \n",
       "\n",
       "                                        additives_fr  \\\n",
       "0  E471 - Mono- et diglycérides d'acides gras ali...   \n",
       "1  E471 - Mono- et diglycérides d'acides gras ali...   \n",
       "2  E452 - Polyphosphates,E331 - Citrates de sodiu...   \n",
       "\n",
       "  ingredients_from_palm_oil_tags  \\\n",
       "0                 huile-de-palme   \n",
       "1     e304-palmitate-d-ascorbyle   \n",
       "2                            NaN   \n",
       "\n",
       "          ingredients_that_may_be_from_palm_oil_tags nutrition_grade_fr  \\\n",
       "0  e471-mono-et-diglycerides-d-acides-gras-alimen...                  d   \n",
       "1  e471-mono-et-diglycerides-d-acides-gras-alimen...                  b   \n",
       "2  e471-mono-et-diglycerides-d-acides-gras-alimen...                  d   \n",
       "\n",
       "                                           image_url  \\\n",
       "0  http://fr.openfoodfacts.org/images/products/32...   \n",
       "1  http://fr.openfoodfacts.org/images/products/35...   \n",
       "2  http://fr.openfoodfacts.org/images/products/32...   \n",
       "\n",
       "                                     image_small_url  energy_100g  fat_100g  \\\n",
       "0  http://fr.openfoodfacts.org/images/products/32...       1073.0      16.0   \n",
       "1  http://fr.openfoodfacts.org/images/products/35...        505.0       5.0   \n",
       "2  http://fr.openfoodfacts.org/images/products/32...       1041.0      12.2   \n",
       "\n",
       "   saturated-fat_100g  carbohydrates_100g  sugars_100g  fiber_100g  \\\n",
       "0                 8.6                18.3          1.3        0.70   \n",
       "1                 2.4                11.6          2.0        1.30   \n",
       "2                 5.7                19.8          3.0        1.52   \n",
       "\n",
       "   proteins_100g  salt_100g  nutrition-score-fr_100g  \n",
       "0            9.6       0.86                     14.0  \n",
       "1            6.6       0.80                      1.0  \n",
       "2           14.1       1.33                     13.0  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = filtrage_superficiel(df, \"Apport énergétique\", \"Faible\")\n",
    "ao(result)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2fbcd4d8-637a-4597-b2f9-22f41dcbecfa",
   "metadata": {},
   "source": [
    "La fonction \"trouver_produits_similaires\" juste en dessous va prendre en input un produit qu'on aura \"scanné\" et en output on retournera un dataframe qui possédera des produits similaires à lui, 3 filtres sont appliqués:\n",
    "\n",
    "-Même cluster (\"cluster\")  \n",
    "-Même grade Nutriscore (\"nutrition_grade_fr\")  \n",
    "-Possède au moins un terme en commun dans \"categories_fr\"  \n",
    "\n",
    "Le dataframe affichera aussi les 1eres lignes comme étant ceux qui possèdent le plus de termes \"categories_fr\" en commun avec le produit scanné"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "67547f16-d02d-4d6b-a09f-008a4dd2698a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "def trouver_produits_similaires(df, code):\n",
    "    # Trouver la ligne correspondante au code donné\n",
    "    produit = df[df['code'] == code]\n",
    "    \n",
    "    # Vérifier si le code existe dans le dataframe\n",
    "    if len(produit) == 0:\n",
    "        print(\"Le code produit n'a pas été trouvé.\")\n",
    "        return\n",
    "    \n",
    "    # Récupérer la valeur du cluster pour ce produit\n",
    "    cluster = produit.iloc[0]['cluster']\n",
    "    \n",
    "    # Récupérer la valeur du 'nutrition_grade_fr' pour ce produit\n",
    "    nutrition_grade = produit.iloc[0]['nutrition_grade_fr']\n",
    "\n",
    "    # Créer un dictionnaire avec les caractéristiques et leurs valeurs de cluster correspondantes\n",
    "    caracteristiques = {\n",
    "        \"Apport énergétique\": {1: \"Faible\", 0: \"Moyenne\", 2: \"Forte\"},\n",
    "        \"Nutriscore\": {1: \"Faible\", 0: \"Moyenne\", 2: \"Forte\"},\n",
    "        \"Taux de gras\": {1: \"Faible\", 0: \"Moyenne\", 2: \"Forte\"},\n",
    "        \"Taux de glucide\": {0: \"Faible\", 1: \"Moyenne\", 2: \"Forte\"}\n",
    "    }\n",
    "  \n",
    "    # Afficher les informations en fonction du cluster\n",
    "    for caract, valeurs in caracteristiques.items():\n",
    "        print(f\"{caract} : {valeurs[cluster]}\")\n",
    "    \n",
    "    # Sélectionner les lignes ayant le même cluster et le même 'nutrition_grade_fr'\n",
    "    produits_similaires = df[(df['cluster'] == cluster) & (df['nutrition_grade_fr'] == nutrition_grade)]\n",
    "    \n",
    "    # Extraire les termes de categories_fr du produit recherché\n",
    "    if isinstance(produit.iloc[0]['categories_fr'], str):\n",
    "        categories_fr = set(produit.iloc[0]['categories_fr'].split(','))\n",
    "\n",
    "    # Filtrer les produits similaires qui contiennent au moins un des termes de packaging\n",
    "    produits_similaires = produits_similaires[produits_similaires['categories_fr'].apply(lambda x: any(term in str(x).split(',') for term in categories_fr))]\n",
    "    \n",
    "    # Ajouter une nouvelle colonne qui compte le nombre de catégories communes\n",
    "    produits_similaires['categories_common_count'] = produits_similaires['categories_fr'].apply(lambda x: len(categories_fr.intersection(set(str(x).split(',')))))\n",
    "    \n",
    "    # Trier le dataframe selon le nombre de catégories communes\n",
    "    produits_similaires = produits_similaires.sort_values('categories_common_count', ascending=False)\n",
    "    \n",
    "    produit_code = df[df['code'] == code]\n",
    "    display(produit_code)\n",
    "    \n",
    "    return produits_similaires\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "8023f4e7-d434-43eb-a7ac-c81d720219d5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Apport énergétique : Faible\n",
      "Nutriscore : Faible\n",
      "Taux de gras : Faible\n",
      "Taux de glucide : Moyenne\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>cluster</th>\n",
       "      <th>code</th>\n",
       "      <th>url</th>\n",
       "      <th>creator</th>\n",
       "      <th>created_datetime</th>\n",
       "      <th>last_modified_datetime</th>\n",
       "      <th>product_name</th>\n",
       "      <th>quantity</th>\n",
       "      <th>packaging</th>\n",
       "      <th>brands</th>\n",
       "      <th>categories_fr</th>\n",
       "      <th>origins</th>\n",
       "      <th>manufacturing_places</th>\n",
       "      <th>labels_fr</th>\n",
       "      <th>emb_codes</th>\n",
       "      <th>first_packaging_code_geo</th>\n",
       "      <th>cities_tags</th>\n",
       "      <th>purchase_places</th>\n",
       "      <th>stores</th>\n",
       "      <th>countries_fr</th>\n",
       "      <th>ingredients_text</th>\n",
       "      <th>allergens</th>\n",
       "      <th>traces_fr</th>\n",
       "      <th>serving_size</th>\n",
       "      <th>additives_fr</th>\n",
       "      <th>ingredients_from_palm_oil_tags</th>\n",
       "      <th>ingredients_that_may_be_from_palm_oil_tags</th>\n",
       "      <th>nutrition_grade_fr</th>\n",
       "      <th>image_url</th>\n",
       "      <th>image_small_url</th>\n",
       "      <th>energy_100g</th>\n",
       "      <th>fat_100g</th>\n",
       "      <th>saturated-fat_100g</th>\n",
       "      <th>carbohydrates_100g</th>\n",
       "      <th>sugars_100g</th>\n",
       "      <th>fiber_100g</th>\n",
       "      <th>proteins_100g</th>\n",
       "      <th>salt_100g</th>\n",
       "      <th>nutrition-score-fr_100g</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>170458</th>\n",
       "      <td>1</td>\n",
       "      <td>20427634</td>\n",
       "      <td>http://world-fr.openfoodfacts.org/produit/2042...</td>\n",
       "      <td>sebleouf</td>\n",
       "      <td>2014-04-27T10:24:39Z</td>\n",
       "      <td>2015-07-23T16:08:33Z</td>\n",
       "      <td>2 panés au colin d'Alaska</td>\n",
       "      <td>200 g (2 x 100 g)</td>\n",
       "      <td>Frais,Barquette,Plastique,Opercule,Film plasti...</td>\n",
       "      <td>Atlantic,Lidl Stiftung &amp; Co. KG</td>\n",
       "      <td>Frais,Plats préparés,Produits panés,Poissons p...</td>\n",
       "      <td>Colin d'Alaska</td>\n",
       "      <td>Cité Marine - Carrefour Industriel du Porzo - ...</td>\n",
       "      <td>Point Vert,Eco-emballages</td>\n",
       "      <td>FR 56.094.004 EC</td>\n",
       "      <td>47.766667,-3.233333</td>\n",
       "      <td>kervignac-morbihan-france</td>\n",
       "      <td>Lyon,France</td>\n",
       "      <td>Lidl</td>\n",
       "      <td>France</td>\n",
       "      <td>Préparation à base de colin d'Alaska 70 % : _c...</td>\n",
       "      <td>colin, calamar, blé, blé</td>\n",
       "      <td>Œufs,Lait</td>\n",
       "      <td>100 g (Cette barquette contient 2 portions de ...</td>\n",
       "      <td>E14XX - Amidons modifiés,E461 - Méthylcellulos...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>huile-vegetale</td>\n",
       "      <td>a</td>\n",
       "      <td>http://fr.openfoodfacts.org/images/products/20...</td>\n",
       "      <td>http://fr.openfoodfacts.org/images/products/20...</td>\n",
       "      <td>840.0</td>\n",
       "      <td>8.8</td>\n",
       "      <td>0.7</td>\n",
       "      <td>18.5</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.8</td>\n",
       "      <td>11.4</td>\n",
       "      <td>0.635</td>\n",
       "      <td>-2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        cluster      code                                                url  \\\n",
       "170458        1  20427634  http://world-fr.openfoodfacts.org/produit/2042...   \n",
       "\n",
       "         creator      created_datetime last_modified_datetime  \\\n",
       "170458  sebleouf  2014-04-27T10:24:39Z   2015-07-23T16:08:33Z   \n",
       "\n",
       "                     product_name           quantity  \\\n",
       "170458  2 panés au colin d'Alaska  200 g (2 x 100 g)   \n",
       "\n",
       "                                                packaging  \\\n",
       "170458  Frais,Barquette,Plastique,Opercule,Film plasti...   \n",
       "\n",
       "                                 brands  \\\n",
       "170458  Atlantic,Lidl Stiftung & Co. KG   \n",
       "\n",
       "                                            categories_fr         origins  \\\n",
       "170458  Frais,Plats préparés,Produits panés,Poissons p...  Colin d'Alaska   \n",
       "\n",
       "                                     manufacturing_places  \\\n",
       "170458  Cité Marine - Carrefour Industriel du Porzo - ...   \n",
       "\n",
       "                        labels_fr         emb_codes first_packaging_code_geo  \\\n",
       "170458  Point Vert,Eco-emballages  FR 56.094.004 EC      47.766667,-3.233333   \n",
       "\n",
       "                      cities_tags purchase_places stores countries_fr  \\\n",
       "170458  kervignac-morbihan-france     Lyon,France   Lidl       France   \n",
       "\n",
       "                                         ingredients_text  \\\n",
       "170458  Préparation à base de colin d'Alaska 70 % : _c...   \n",
       "\n",
       "                       allergens  traces_fr  \\\n",
       "170458  colin, calamar, blé, blé  Œufs,Lait   \n",
       "\n",
       "                                             serving_size  \\\n",
       "170458  100 g (Cette barquette contient 2 portions de ...   \n",
       "\n",
       "                                             additives_fr  \\\n",
       "170458  E14XX - Amidons modifiés,E461 - Méthylcellulos...   \n",
       "\n",
       "       ingredients_from_palm_oil_tags  \\\n",
       "170458                            NaN   \n",
       "\n",
       "       ingredients_that_may_be_from_palm_oil_tags nutrition_grade_fr  \\\n",
       "170458                             huile-vegetale                  a   \n",
       "\n",
       "                                                image_url  \\\n",
       "170458  http://fr.openfoodfacts.org/images/products/20...   \n",
       "\n",
       "                                          image_small_url  energy_100g  \\\n",
       "170458  http://fr.openfoodfacts.org/images/products/20...        840.0   \n",
       "\n",
       "        fat_100g  saturated-fat_100g  carbohydrates_100g  sugars_100g  \\\n",
       "170458       8.8                 0.7                18.5          0.8   \n",
       "\n",
       "        fiber_100g  proteins_100g  salt_100g  nutrition-score-fr_100g  \n",
       "170458         0.8           11.4      0.635                     -2.0  "
      ]
     },
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    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cluster</th>\n",
       "      <th>code</th>\n",
       "      <th>url</th>\n",
       "      <th>creator</th>\n",
       "      <th>created_datetime</th>\n",
       "      <th>last_modified_datetime</th>\n",
       "      <th>product_name</th>\n",
       "      <th>quantity</th>\n",
       "      <th>packaging</th>\n",
       "      <th>brands</th>\n",
       "      <th>categories_fr</th>\n",
       "      <th>origins</th>\n",
       "      <th>manufacturing_places</th>\n",
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       "      <th>emb_codes</th>\n",
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       "      <th>purchase_places</th>\n",
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       "      <th>countries_fr</th>\n",
       "      <th>ingredients_text</th>\n",
       "      <th>allergens</th>\n",
       "      <th>traces_fr</th>\n",
       "      <th>serving_size</th>\n",
       "      <th>additives_fr</th>\n",
       "      <th>ingredients_from_palm_oil_tags</th>\n",
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       "      <th>energy_100g</th>\n",
       "      <th>fat_100g</th>\n",
       "      <th>saturated-fat_100g</th>\n",
       "      <th>carbohydrates_100g</th>\n",
       "      <th>sugars_100g</th>\n",
       "      <th>fiber_100g</th>\n",
       "      <th>proteins_100g</th>\n",
       "      <th>salt_100g</th>\n",
       "      <th>nutrition-score-fr_100g</th>\n",
       "      <th>categories_common_count</th>\n",
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       "    <tr>\n",
       "      <th>170458</th>\n",
       "      <td>1</td>\n",
       "      <td>20427634</td>\n",
       "      <td>http://world-fr.openfoodfacts.org/produit/2042...</td>\n",
       "      <td>sebleouf</td>\n",
       "      <td>2014-04-27T10:24:39Z</td>\n",
       "      <td>2015-07-23T16:08:33Z</td>\n",
       "      <td>2 panés au colin d'Alaska</td>\n",
       "      <td>200 g (2 x 100 g)</td>\n",
       "      <td>Frais,Barquette,Plastique,Opercule,Film plasti...</td>\n",
       "      <td>Atlantic,Lidl Stiftung &amp; Co. KG</td>\n",
       "      <td>Frais,Plats préparés,Produits panés,Poissons p...</td>\n",
       "      <td>Colin d'Alaska</td>\n",
       "      <td>Cité Marine - Carrefour Industriel du Porzo - ...</td>\n",
       "      <td>Point Vert,Eco-emballages</td>\n",
       "      <td>FR 56.094.004 EC</td>\n",
       "      <td>47.766667,-3.233333</td>\n",
       "      <td>kervignac-morbihan-france</td>\n",
       "      <td>Lyon,France</td>\n",
       "      <td>Lidl</td>\n",
       "      <td>France</td>\n",
       "      <td>Préparation à base de colin d'Alaska 70 % : _c...</td>\n",
       "      <td>colin, calamar, blé, blé</td>\n",
       "      <td>Œufs,Lait</td>\n",
       "      <td>100 g (Cette barquette contient 2 portions de ...</td>\n",
       "      <td>E14XX - Amidons modifiés,E461 - Méthylcellulos...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>huile-vegetale</td>\n",
       "      <td>a</td>\n",
       "      <td>http://fr.openfoodfacts.org/images/products/20...</td>\n",
       "      <td>http://fr.openfoodfacts.org/images/products/20...</td>\n",
       "      <td>840.0</td>\n",
       "      <td>8.8</td>\n",
       "      <td>0.700</td>\n",
       "      <td>18.50</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.80</td>\n",
       "      <td>11.40</td>\n",
       "      <td>0.635000</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209485</th>\n",
       "      <td>1</td>\n",
       "      <td>3492500049486</td>\n",
       "      <td>http://world-fr.openfoodfacts.org/produit/3492...</td>\n",
       "      <td>phoenix</td>\n",
       "      <td>2016-05-31T16:17:16Z</td>\n",
       "      <td>2016-06-03T17:24:18Z</td>\n",
       "      <td>Portions au Poisson Pané</td>\n",
       "      <td>400 g (x 4)</td>\n",
       "      <td>Barquette,Sous atmosphère protectrice,Plastique</td>\n",
       "      <td>Sans Marque</td>\n",
       "      <td>Frais,Plats préparés,Produits panés,Poissons p...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>France,Bretagne,Morbihan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FR 56.094.004 EC</td>\n",
       "      <td>47.766667,-3.233333</td>\n",
       "      <td>kervignac-morbihan-france</td>\n",
       "      <td>Rennes,France</td>\n",
       "      <td>Carrefour</td>\n",
       "      <td>France</td>\n",
       "      <td>Préparation à base de poisson 70% : _poisson_ ...</td>\n",
       "      <td>poisson, calamar, oeuf, blé, blé</td>\n",
       "      <td>Lait</td>\n",
       "      <td>NaN</td>\n",
       "      <td>E14XX - Amidons modifiés,E461 - Méthylcellulos...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>huile-vegetale</td>\n",
       "      <td>a</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>844.0</td>\n",
       "      <td>8.6</td>\n",
       "      <td>0.920</td>\n",
       "      <td>19.60</td>\n",
       "      <td>1.06</td>\n",
       "      <td>1.04</td>\n",
       "      <td>10.90</td>\n",
       "      <td>0.780000</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209483</th>\n",
       "      <td>1</td>\n",
       "      <td>3492500046805</td>\n",
       "      <td>http://world-fr.openfoodfacts.org/produit/3492...</td>\n",
       "      <td>phoenix</td>\n",
       "      <td>2016-05-06T09:42:26Z</td>\n",
       "      <td>2016-05-06T17:29:43Z</td>\n",
       "      <td>Filets de Colin d'Alaska Panés</td>\n",
       "      <td>220 g (x 2)</td>\n",
       "      <td>Barquette,Plastique,Sous atmosphère protectrice</td>\n",
       "      <td>Cité Marine</td>\n",
       "      <td>Frais,Plats préparés,Produits panés,Poissons p...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>France,Bretagne</td>\n",
       "      <td>Peu ou pas de matière grasse,Peu de matière gr...</td>\n",
       "      <td>FR 56.094.004 EC</td>\n",
       "      <td>47.766667,-3.233333</td>\n",
       "      <td>kervignac-morbihan-france</td>\n",
       "      <td>Rennes,France</td>\n",
       "      <td>Carrefour</td>\n",
       "      <td>France</td>\n",
       "      <td>Filets de _colin_ d'Alaska 65%. Enrobage 35% :...</td>\n",
       "      <td>colin, blé, blé, gluten, blé</td>\n",
       "      <td>Œufs,Lait,Mollusques</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>huile-vegetale</td>\n",
       "      <td>a</td>\n",
       "      <td>http://fr.openfoodfacts.org/images/products/34...</td>\n",
       "      <td>http://fr.openfoodfacts.org/images/products/34...</td>\n",
       "      <td>886.0</td>\n",
       "      <td>8.9</td>\n",
       "      <td>0.800</td>\n",
       "      <td>20.60</td>\n",
       "      <td>0.80</td>\n",
       "      <td>1.10</td>\n",
       "      <td>11.60</td>\n",
       "      <td>0.460000</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209467</th>\n",
       "      <td>1</td>\n",
       "      <td>3492500033850</td>\n",
       "      <td>http://world-fr.openfoodfacts.org/produit/3492...</td>\n",
       "      <td>phoenix</td>\n",
       "      <td>2016-05-06T09:41:00Z</td>\n",
       "      <td>2016-05-06T17:12:56Z</td>\n",
       "      <td>Panés au Colin d'Alaska</td>\n",
       "      <td>500 g (x 5)</td>\n",
       "      <td>Barquette,Plastique,Sous atmosphère protectrice</td>\n",
       "      <td>Cité Marine</td>\n",
       "      <td>Frais,Plats préparés,Produits panés,Poissons p...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>France</td>\n",
       "      <td>Peu ou pas de matière grasse,Sans matière gras...</td>\n",
       "      <td>FR 56.094.004 EC</td>\n",
       "      <td>47.766667,-3.233333</td>\n",
       "      <td>kervignac-morbihan-france</td>\n",
       "      <td>Rennes,France</td>\n",
       "      <td>Carrefour</td>\n",
       "      <td>France</td>\n",
       "      <td>Préparation à base de _colin_ d'Alaska 70% : c...</td>\n",
       "      <td>colin, calamar, blé, blé</td>\n",
       "      <td>Œufs,Lait</td>\n",
       "      <td>NaN</td>\n",
       "      <td>E14XX - Amidons modifiés,E461 - Méthylcellulos...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>huile-vegetale</td>\n",
       "      <td>a</td>\n",
       "      <td>http://fr.openfoodfacts.org/images/products/34...</td>\n",
       "      <td>http://fr.openfoodfacts.org/images/products/34...</td>\n",
       "      <td>840.0</td>\n",
       "      <td>8.8</td>\n",
       "      <td>0.700</td>\n",
       "      <td>18.50</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.80</td>\n",
       "      <td>11.40</td>\n",
       "      <td>0.620000</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>209484</th>\n",
       "      <td>1</td>\n",
       "      <td>3492500048816</td>\n",
       "      <td>http://world-fr.openfoodfacts.org/produit/3492...</td>\n",
       "      <td>phoenix</td>\n",
       "      <td>2016-05-06T09:41:43Z</td>\n",
       "      <td>2017-01-13T11:57:51Z</td>\n",
       "      <td>Petits poissons panés</td>\n",
       "      <td>180 g</td>\n",
       "      <td>Barquette,Plastique,Sous atmosphère protectric...</td>\n",
       "      <td>Cité Marine</td>\n",
       "      <td>Frais,Plats préparés,Produits panés,Poissons p...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>France</td>\n",
       "      <td>Sans-ajout-de-matiere-grassse,Sans-arete</td>\n",
       "      <td>FR 56.094.004 EC</td>\n",
       "      <td>47.766667,-3.233333</td>\n",
       "      <td>kervignac-morbihan-france</td>\n",
       "      <td>Rennes,France</td>\n",
       "      <td>Carrefour</td>\n",
       "      <td>France</td>\n",
       "      <td>Préparation à base de _colin_ d'Alaska 75% : _...</td>\n",
       "      <td>colin, colin, calamar, blé, blé</td>\n",
       "      <td>Œufs,Lait</td>\n",
       "      <td>NaN</td>\n",
       "      <td>E14XX - Amidons modifiés,E160c - Extrait de pa...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>huile-vegetale</td>\n",
       "      <td>a</td>\n",
       "      <td>http://fr.openfoodfacts.org/images/products/34...</td>\n",
       "      <td>http://fr.openfoodfacts.org/images/products/34...</td>\n",
       "      <td>860.0</td>\n",
       "      <td>8.3</td>\n",
       "      <td>0.820</td>\n",
       "      <td>20.10</td>\n",
       "      <td>0.60</td>\n",
       "      <td>1.20</td>\n",
       "      <td>11.90</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
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       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <th>191869</th>\n",
       "      <td>1</td>\n",
       "      <td>3256223662120</td>\n",
       "      <td>http://world-fr.openfoodfacts.org/produit/3256...</td>\n",
       "      <td>paulben</td>\n",
       "      <td>2016-09-30T10:37:52Z</td>\n",
       "      <td>2016-09-30T10:43:08Z</td>\n",
       "      <td>Haricots verts cueillis et rangés à la main Ex...</td>\n",
       "      <td>180 g</td>\n",
       "      <td>Bocal</td>\n",
       "      <td>U Bio</td>\n",
       "      <td>Aliments et boissons à base de végétaux,Alimen...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bio,Bio européen,AB Agriculture Biologique</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>France</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>a</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2.80</td>\n",
       "      <td>0.90</td>\n",
       "      <td>3.10</td>\n",
       "      <td>1.40</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191860</th>\n",
       "      <td>1</td>\n",
       "      <td>3256223660218</td>\n",
       "      <td>http://world-fr.openfoodfacts.org/produit/3256...</td>\n",
       "      <td>sebleouf</td>\n",
       "      <td>2014-06-04T11:45:48Z</td>\n",
       "      <td>2014-06-04T21:47:42Z</td>\n",
       "      <td>Ratatouille</td>\n",
       "      <td>400 g</td>\n",
       "      <td>Boite,Conserve</td>\n",
       "      <td>U</td>\n",
       "      <td>Aliments et boissons à base de végétaux,Alimen...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>EMB 56251E</td>\n",
       "      <td>47.633333,-2.666667</td>\n",
       "      <td>theix-morbihan-france</td>\n",
       "      <td>Castelnau-le-Lez,France</td>\n",
       "      <td>Super U</td>\n",
       "      <td>France</td>\n",
       "      <td>Légumes 83 % (courgettes, tomates, aubergines,...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>125 g</td>\n",
       "      <td>E330 - Acide citrique,E415 - Gomme xanthane,E4...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>a</td>\n",
       "      <td>http://fr.openfoodfacts.org/images/products/32...</td>\n",
       "      <td>http://fr.openfoodfacts.org/images/products/32...</td>\n",
       "      <td>142.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.080</td>\n",
       "      <td>3.92</td>\n",
       "      <td>3.60</td>\n",
       "      <td>1.44</td>\n",
       "      <td>1.12</td>\n",
       "      <td>0.983000</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191813</th>\n",
       "      <td>1</td>\n",
       "      <td>3256223369388</td>\n",
       "      <td>http://world-fr.openfoodfacts.org/produit/3256...</td>\n",
       "      <td>aristoi</td>\n",
       "      <td>2015-12-08T23:18:10Z</td>\n",
       "      <td>2015-12-10T02:47:02Z</td>\n",
       "      <td>Conchiglie Poulet et Champignons</td>\n",
       "      <td>1,2 kg</td>\n",
       "      <td>surgelé</td>\n",
       "      <td>U</td>\n",
       "      <td>Surgelés,Plats préparés,Plats préparés surgelés</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LU 12 EC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>France,Guadeloupe</td>\n",
       "      <td>Pâte alimentaire conchiglie précuite 39% : eau...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>300 g</td>\n",
       "      <td>E150a - Caramel E150a</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>a</td>\n",
       "      <td>http://fr.openfoodfacts.org/images/products/32...</td>\n",
       "      <td>http://fr.openfoodfacts.org/images/products/32...</td>\n",
       "      <td>482.0</td>\n",
       "      <td>2.9</td>\n",
       "      <td>0.933</td>\n",
       "      <td>13.60</td>\n",
       "      <td>3.60</td>\n",
       "      <td>1.60</td>\n",
       "      <td>7.70</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191806</th>\n",
       "      <td>1</td>\n",
       "      <td>3256223360569</td>\n",
       "      <td>http://world-fr.openfoodfacts.org/produit/3256...</td>\n",
       "      <td>nash</td>\n",
       "      <td>2014-01-10T20:11:17Z</td>\n",
       "      <td>2014-02-18T20:43:45Z</td>\n",
       "      <td>poelee parisienne</td>\n",
       "      <td>1kg</td>\n",
       "      <td>surgelé,plastique</td>\n",
       "      <td>U</td>\n",
       "      <td>Surgelés,Plats préparés,Plats préparés surgelés</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>royan,france</td>\n",
       "      <td>Super U</td>\n",
       "      <td>France</td>\n",
       "      <td>Haricots verts coupés 25%, pommes de terre pré...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Céleri,Lait</td>\n",
       "      <td>200g</td>\n",
       "      <td>E250 - Nitrite de sodium</td>\n",
       "      <td>NaN</td>\n",
       "      <td>huile-vegetale</td>\n",
       "      <td>a</td>\n",
       "      <td>http://fr.openfoodfacts.org/images/products/32...</td>\n",
       "      <td>http://fr.openfoodfacts.org/images/products/32...</td>\n",
       "      <td>372.0</td>\n",
       "      <td>8.2</td>\n",
       "      <td>2.000</td>\n",
       "      <td>17.00</td>\n",
       "      <td>3.60</td>\n",
       "      <td>5.40</td>\n",
       "      <td>6.40</td>\n",
       "      <td>0.832404</td>\n",
       "      <td>-7.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263238</th>\n",
       "      <td>1</td>\n",
       "      <td>93231251</td>\n",
       "      <td>http://world-fr.openfoodfacts.org/produit/9323...</td>\n",
       "      <td>stanleynorman</td>\n",
       "      <td>2015-03-02T08:57:40Z</td>\n",
       "      <td>2015-03-02T09:10:33Z</td>\n",
       "      <td>Latina Beef Ravioli</td>\n",
       "      <td>375g</td>\n",
       "      <td>Plastic</td>\n",
       "      <td>Latina,General Mills Australia</td>\n",
       "      <td>Aliments et boissons à base de végétaux,Alimen...</td>\n",
       "      <td>Australia,Other</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Sans colorants artificiels,Sans colorants ni a...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Melbourne,Victoria,Australia</td>\n",
       "      <td>Piedemonte's IGA</td>\n",
       "      <td>Australie</td>\n",
       "      <td>Durum semolina, beef (16%), water, breadcrumbs...</td>\n",
       "      <td>wheat, wheat</td>\n",
       "      <td>Œufs,Gluten,Lait</td>\n",
       "      <td>210g</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>a</td>\n",
       "      <td>http://fr.openfoodfacts.org/images/products/93...</td>\n",
       "      <td>http://fr.openfoodfacts.org/images/products/93...</td>\n",
       "      <td>742.0</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1.700</td>\n",
       "      <td>27.00</td>\n",
       "      <td>0.70</td>\n",
       "      <td>5.20</td>\n",
       "      <td>6.40</td>\n",
       "      <td>0.685800</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2309 rows × 40 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        cluster           code  \\\n",
       "170458        1       20427634   \n",
       "209485        1  3492500049486   \n",
       "209483        1  3492500046805   \n",
       "209467        1  3492500033850   \n",
       "209484        1  3492500048816   \n",
       "...         ...            ...   \n",
       "191869        1  3256223662120   \n",
       "191860        1  3256223660218   \n",
       "191813        1  3256223369388   \n",
       "191806        1  3256223360569   \n",
       "263238        1       93231251   \n",
       "\n",
       "                                                      url        creator  \\\n",
       "170458  http://world-fr.openfoodfacts.org/produit/2042...       sebleouf   \n",
       "209485  http://world-fr.openfoodfacts.org/produit/3492...        phoenix   \n",
       "209483  http://world-fr.openfoodfacts.org/produit/3492...        phoenix   \n",
       "209467  http://world-fr.openfoodfacts.org/produit/3492...        phoenix   \n",
       "209484  http://world-fr.openfoodfacts.org/produit/3492...        phoenix   \n",
       "...                                                   ...            ...   \n",
       "191869  http://world-fr.openfoodfacts.org/produit/3256...        paulben   \n",
       "191860  http://world-fr.openfoodfacts.org/produit/3256...       sebleouf   \n",
       "191813  http://world-fr.openfoodfacts.org/produit/3256...        aristoi   \n",
       "191806  http://world-fr.openfoodfacts.org/produit/3256...           nash   \n",
       "263238  http://world-fr.openfoodfacts.org/produit/9323...  stanleynorman   \n",
       "\n",
       "            created_datetime last_modified_datetime  \\\n",
       "170458  2014-04-27T10:24:39Z   2015-07-23T16:08:33Z   \n",
       "209485  2016-05-31T16:17:16Z   2016-06-03T17:24:18Z   \n",
       "209483  2016-05-06T09:42:26Z   2016-05-06T17:29:43Z   \n",
       "209467  2016-05-06T09:41:00Z   2016-05-06T17:12:56Z   \n",
       "209484  2016-05-06T09:41:43Z   2017-01-13T11:57:51Z   \n",
       "...                      ...                    ...   \n",
       "191869  2016-09-30T10:37:52Z   2016-09-30T10:43:08Z   \n",
       "191860  2014-06-04T11:45:48Z   2014-06-04T21:47:42Z   \n",
       "191813  2015-12-08T23:18:10Z   2015-12-10T02:47:02Z   \n",
       "191806  2014-01-10T20:11:17Z   2014-02-18T20:43:45Z   \n",
       "263238  2015-03-02T08:57:40Z   2015-03-02T09:10:33Z   \n",
       "\n",
       "                                             product_name           quantity  \\\n",
       "170458                          2 panés au colin d'Alaska  200 g (2 x 100 g)   \n",
       "209485                           Portions au Poisson Pané        400 g (x 4)   \n",
       "209483                     Filets de Colin d'Alaska Panés        220 g (x 2)   \n",
       "209467                            Panés au Colin d'Alaska        500 g (x 5)   \n",
       "209484                              Petits poissons panés              180 g   \n",
       "...                                                   ...                ...   \n",
       "191869  Haricots verts cueillis et rangés à la main Ex...              180 g   \n",
       "191860                                        Ratatouille              400 g   \n",
       "191813                   Conchiglie Poulet et Champignons             1,2 kg   \n",
       "191806                                  poelee parisienne                1kg   \n",
       "263238                                Latina Beef Ravioli               375g   \n",
       "\n",
       "                                                packaging  \\\n",
       "170458  Frais,Barquette,Plastique,Opercule,Film plasti...   \n",
       "209485    Barquette,Sous atmosphère protectrice,Plastique   \n",
       "209483    Barquette,Plastique,Sous atmosphère protectrice   \n",
       "209467    Barquette,Plastique,Sous atmosphère protectrice   \n",
       "209484  Barquette,Plastique,Sous atmosphère protectric...   \n",
       "...                                                   ...   \n",
       "191869                                              Bocal   \n",
       "191860                                     Boite,Conserve   \n",
       "191813                                            surgelé   \n",
       "191806                                  surgelé,plastique   \n",
       "263238                                            Plastic   \n",
       "\n",
       "                                 brands  \\\n",
       "170458  Atlantic,Lidl Stiftung & Co. KG   \n",
       "209485                      Sans Marque   \n",
       "209483                      Cité Marine   \n",
       "209467                      Cité Marine   \n",
       "209484                      Cité Marine   \n",
       "...                                 ...   \n",
       "191869                            U Bio   \n",
       "191860                                U   \n",
       "191813                                U   \n",
       "191806                                U   \n",
       "263238   Latina,General Mills Australia   \n",
       "\n",
       "                                            categories_fr          origins  \\\n",
       "170458  Frais,Plats préparés,Produits panés,Poissons p...   Colin d'Alaska   \n",
       "209485  Frais,Plats préparés,Produits panés,Poissons p...              NaN   \n",
       "209483  Frais,Plats préparés,Produits panés,Poissons p...              NaN   \n",
       "209467  Frais,Plats préparés,Produits panés,Poissons p...              NaN   \n",
       "209484  Frais,Plats préparés,Produits panés,Poissons p...              NaN   \n",
       "...                                                   ...              ...   \n",
       "191869  Aliments et boissons à base de végétaux,Alimen...              NaN   \n",
       "191860  Aliments et boissons à base de végétaux,Alimen...              NaN   \n",
       "191813    Surgelés,Plats préparés,Plats préparés surgelés              NaN   \n",
       "191806    Surgelés,Plats préparés,Plats préparés surgelés              NaN   \n",
       "263238  Aliments et boissons à base de végétaux,Alimen...  Australia,Other   \n",
       "\n",
       "                                     manufacturing_places  \\\n",
       "170458  Cité Marine - Carrefour Industriel du Porzo - ...   \n",
       "209485                           France,Bretagne,Morbihan   \n",
       "209483                                    France,Bretagne   \n",
       "209467                                             France   \n",
       "209484                                             France   \n",
       "...                                                   ...   \n",
       "191869                                                NaN   \n",
       "191860                                                NaN   \n",
       "191813                                                NaN   \n",
       "191806                                                NaN   \n",
       "263238                                          Australia   \n",
       "\n",
       "                                                labels_fr         emb_codes  \\\n",
       "170458                          Point Vert,Eco-emballages  FR 56.094.004 EC   \n",
       "209485                                                NaN  FR 56.094.004 EC   \n",
       "209483  Peu ou pas de matière grasse,Peu de matière gr...  FR 56.094.004 EC   \n",
       "209467  Peu ou pas de matière grasse,Sans matière gras...  FR 56.094.004 EC   \n",
       "209484           Sans-ajout-de-matiere-grassse,Sans-arete  FR 56.094.004 EC   \n",
       "...                                                   ...               ...   \n",
       "191869         Bio,Bio européen,AB Agriculture Biologique               NaN   \n",
       "191860                                                NaN        EMB 56251E   \n",
       "191813                                                NaN          LU 12 EC   \n",
       "191806                                                NaN               NaN   \n",
       "263238  Sans colorants artificiels,Sans colorants ni a...               NaN   \n",
       "\n",
       "       first_packaging_code_geo                cities_tags  \\\n",
       "170458      47.766667,-3.233333  kervignac-morbihan-france   \n",
       "209485      47.766667,-3.233333  kervignac-morbihan-france   \n",
       "209483      47.766667,-3.233333  kervignac-morbihan-france   \n",
       "209467      47.766667,-3.233333  kervignac-morbihan-france   \n",
       "209484      47.766667,-3.233333  kervignac-morbihan-france   \n",
       "...                         ...                        ...   \n",
       "191869                      NaN                        NaN   \n",
       "191860      47.633333,-2.666667      theix-morbihan-france   \n",
       "191813                      NaN                        NaN   \n",
       "191806                      NaN                        NaN   \n",
       "263238                      NaN                        NaN   \n",
       "\n",
       "                     purchase_places            stores       countries_fr  \\\n",
       "170458                   Lyon,France              Lidl             France   \n",
       "209485                 Rennes,France         Carrefour             France   \n",
       "209483                 Rennes,France         Carrefour             France   \n",
       "209467                 Rennes,France         Carrefour             France   \n",
       "209484                 Rennes,France         Carrefour             France   \n",
       "...                              ...               ...                ...   \n",
       "191869                           NaN               NaN             France   \n",
       "191860       Castelnau-le-Lez,France           Super U             France   \n",
       "191813                           NaN               NaN  France,Guadeloupe   \n",
       "191806                  royan,france           Super U             France   \n",
       "263238  Melbourne,Victoria,Australia  Piedemonte's IGA          Australie   \n",
       "\n",
       "                                         ingredients_text  \\\n",
       "170458  Préparation à base de colin d'Alaska 70 % : _c...   \n",
       "209485  Préparation à base de poisson 70% : _poisson_ ...   \n",
       "209483  Filets de _colin_ d'Alaska 65%. Enrobage 35% :...   \n",
       "209467  Préparation à base de _colin_ d'Alaska 70% : c...   \n",
       "209484  Préparation à base de _colin_ d'Alaska 75% : _...   \n",
       "...                                                   ...   \n",
       "191869                                                NaN   \n",
       "191860  Légumes 83 % (courgettes, tomates, aubergines,...   \n",
       "191813  Pâte alimentaire conchiglie précuite 39% : eau...   \n",
       "191806  Haricots verts coupés 25%, pommes de terre pré...   \n",
       "263238  Durum semolina, beef (16%), water, breadcrumbs...   \n",
       "\n",
       "                               allergens             traces_fr  \\\n",
       "170458          colin, calamar, blé, blé             Œufs,Lait   \n",
       "209485  poisson, calamar, oeuf, blé, blé                  Lait   \n",
       "209483      colin, blé, blé, gluten, blé  Œufs,Lait,Mollusques   \n",
       "209467          colin, calamar, blé, blé             Œufs,Lait   \n",
       "209484   colin, colin, calamar, blé, blé             Œufs,Lait   \n",
       "...                                  ...                   ...   \n",
       "191869                               NaN                   NaN   \n",
       "191860                               NaN                   NaN   \n",
       "191813                               NaN                   NaN   \n",
       "191806                               NaN           Céleri,Lait   \n",
       "263238                      wheat, wheat      Œufs,Gluten,Lait   \n",
       "\n",
       "                                             serving_size  \\\n",
       "170458  100 g (Cette barquette contient 2 portions de ...   \n",
       "209485                                                NaN   \n",
       "209483                                                NaN   \n",
       "209467                                                NaN   \n",
       "209484                                                NaN   \n",
       "...                                                   ...   \n",
       "191869                                                NaN   \n",
       "191860                                              125 g   \n",
       "191813                                              300 g   \n",
       "191806                                               200g   \n",
       "263238                                               210g   \n",
       "\n",
       "                                             additives_fr  \\\n",
       "170458  E14XX - Amidons modifiés,E461 - Méthylcellulos...   \n",
       "209485  E14XX - Amidons modifiés,E461 - Méthylcellulos...   \n",
       "209483                                                NaN   \n",
       "209467  E14XX - Amidons modifiés,E461 - Méthylcellulos...   \n",
       "209484  E14XX - Amidons modifiés,E160c - Extrait de pa...   \n",
       "...                                                   ...   \n",
       "191869                                                NaN   \n",
       "191860  E330 - Acide citrique,E415 - Gomme xanthane,E4...   \n",
       "191813                              E150a - Caramel E150a   \n",
       "191806                           E250 - Nitrite de sodium   \n",
       "263238                                                NaN   \n",
       "\n",
       "       ingredients_from_palm_oil_tags  \\\n",
       "170458                            NaN   \n",
       "209485                            NaN   \n",
       "209483                            NaN   \n",
       "209467                            NaN   \n",
       "209484                            NaN   \n",
       "...                               ...   \n",
       "191869                            NaN   \n",
       "191860                            NaN   \n",
       "191813                            NaN   \n",
       "191806                            NaN   \n",
       "263238                            NaN   \n",
       "\n",
       "       ingredients_that_may_be_from_palm_oil_tags nutrition_grade_fr  \\\n",
       "170458                             huile-vegetale                  a   \n",
       "209485                             huile-vegetale                  a   \n",
       "209483                             huile-vegetale                  a   \n",
       "209467                             huile-vegetale                  a   \n",
       "209484                             huile-vegetale                  a   \n",
       "...                                           ...                ...   \n",
       "191869                                        NaN                  a   \n",
       "191860                                        NaN                  a   \n",
       "191813                                        NaN                  a   \n",
       "191806                             huile-vegetale                  a   \n",
       "263238                                        NaN                  a   \n",
       "\n",
       "                                                image_url  \\\n",
       "170458  http://fr.openfoodfacts.org/images/products/20...   \n",
       "209485                                                NaN   \n",
       "209483  http://fr.openfoodfacts.org/images/products/34...   \n",
       "209467  http://fr.openfoodfacts.org/images/products/34...   \n",
       "209484  http://fr.openfoodfacts.org/images/products/34...   \n",
       "...                                                   ...   \n",
       "191869                                                NaN   \n",
       "191860  http://fr.openfoodfacts.org/images/products/32...   \n",
       "191813  http://fr.openfoodfacts.org/images/products/32...   \n",
       "191806  http://fr.openfoodfacts.org/images/products/32...   \n",
       "263238  http://fr.openfoodfacts.org/images/products/93...   \n",
       "\n",
       "                                          image_small_url  energy_100g  \\\n",
       "170458  http://fr.openfoodfacts.org/images/products/20...        840.0   \n",
       "209485                                                NaN        844.0   \n",
       "209483  http://fr.openfoodfacts.org/images/products/34...        886.0   \n",
       "209467  http://fr.openfoodfacts.org/images/products/34...        840.0   \n",
       "209484  http://fr.openfoodfacts.org/images/products/34...        860.0   \n",
       "...                                                   ...          ...   \n",
       "191869                                                NaN        100.0   \n",
       "191860  http://fr.openfoodfacts.org/images/products/32...        142.0   \n",
       "191813  http://fr.openfoodfacts.org/images/products/32...        482.0   \n",
       "191806  http://fr.openfoodfacts.org/images/products/32...        372.0   \n",
       "263238  http://fr.openfoodfacts.org/images/products/93...        742.0   \n",
       "\n",
       "        fat_100g  saturated-fat_100g  carbohydrates_100g  sugars_100g  \\\n",
       "170458       8.8               0.700               18.50         0.80   \n",
       "209485       8.6               0.920               19.60         1.06   \n",
       "209483       8.9               0.800               20.60         0.80   \n",
       "209467       8.8               0.700               18.50         0.80   \n",
       "209484       8.3               0.820               20.10         0.60   \n",
       "...          ...                 ...                 ...          ...   \n",
       "191869       0.1               0.000                2.80         0.90   \n",
       "191860       1.2               0.080                3.92         3.60   \n",
       "191813       2.9               0.933               13.60         3.60   \n",
       "191806       8.2               2.000               17.00         3.60   \n",
       "263238       4.7               1.700               27.00         0.70   \n",
       "\n",
       "        fiber_100g  proteins_100g  salt_100g  nutrition-score-fr_100g  \\\n",
       "170458        0.80          11.40   0.635000                     -2.0   \n",
       "209485        1.04          10.90   0.780000                     -1.0   \n",
       "209483        1.10          11.60   0.460000                     -2.0   \n",
       "209467        0.80          11.40   0.620000                     -2.0   \n",
       "209484        1.20          11.90   0.600000                     -2.0   \n",
       "...            ...            ...        ...                      ...   \n",
       "191869        3.10           1.40   0.700000                     -6.0   \n",
       "191860        1.44           1.12   0.983000                     -3.0   \n",
       "191813        1.60           7.70   0.700000                     -2.0   \n",
       "191806        5.40           6.40   0.832404                     -7.0   \n",
       "263238        5.20           6.40   0.685800                     -3.0   \n",
       "\n",
       "        categories_common_count  \n",
       "170458                        6  \n",
       "209485                        5  \n",
       "209483                        5  \n",
       "209467                        5  \n",
       "209484                        5  \n",
       "...                         ...  \n",
       "191869                        1  \n",
       "191860                        1  \n",
       "191813                        1  \n",
       "191806                        1  \n",
       "263238                        1  \n",
       "\n",
       "[2309 rows x 40 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = trouver_produits_similaires(df, code = 20427634)\n",
    "result"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b1496ac-903e-4816-ae50-06c6e7b8c2f4",
   "metadata": {},
   "source": [
    "# Test ANOVA (annulé)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aea8a7c5-990f-4e3b-8477-5159a2c6e43f",
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   "source": [
    "L'ANOVA, ou analyse de la variance, est une méthode statistique utilisée pour comparer les moyennes de deux groupes ou plus afin de déterminer si elles sont significativement différentes les unes des autres. Le terme \"variance\" dans ANOVA se réfère à la variabilité entre les groupes comparés.\n",
    "\n",
    "Pour réaliser un test ANOVA, certaines conditions doivent être remplies :\n",
    "\n",
    "**Indépendance des observations :** Les observations doivent être indépendantes les unes des autres. En d'autres termes, la présence ou l'absence de chaque observation ne doit pas affecter les autres observations.\n",
    "\n",
    "**Normalité :** Les données de chaque groupe doivent suivre une distribution normale (test de Kolmogorov-Smirnov pour n>5000)\n",
    "\n",
    "**Homogénéité de la variance :** Les variances des populations dont proviennent les différents groupes doivent être égales. On peut tester cette hypothèse avec le test de Levene ou de Bartlett, par exemple."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25bff8bb-0961-4cf3-b78a-151bec678d68",
   "metadata": {},
   "source": [
    "## Test Normalité"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d8e5137d-adce-4c10-8410-1e1a6f7ed0c2",
   "metadata": {},
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    {
     "name": "stdout",
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     "text": [
      "Colonne : energy_100g\n",
      "Statistique de test : 0.9997838640412364\n",
      "p-value : 0.0\n",
      "La distribution ne semble pas normale (rejeter H0)\n",
      "-----------------------------------\n",
      "Colonne : fat_100g\n",
      "Statistique de test : 0.9832053743053701\n",
      "p-value : 0.0\n",
      "La distribution ne semble pas normale (rejeter H0)\n",
      "-----------------------------------\n",
      "Colonne : saturated-fat_100g\n",
      "Statistique de test : 0.9745730114916739\n",
      "p-value : 0.0\n",
      "La distribution ne semble pas normale (rejeter H0)\n",
      "-----------------------------------\n",
      "Colonne : carbohydrates_100g\n",
      "Statistique de test : 0.7789392480093459\n",
      "p-value : 0.0\n",
      "La distribution ne semble pas normale (rejeter H0)\n",
      "-----------------------------------\n",
      "Colonne : sugars_100g\n",
      "Statistique de test : 0.6111041744644852\n",
      "p-value : 0.0\n",
      "La distribution ne semble pas normale (rejeter H0)\n",
      "-----------------------------------\n",
      "Colonne : fiber_100g\n",
      "Statistique de test : 0.5\n",
      "p-value : 0.0\n",
      "La distribution ne semble pas normale (rejeter H0)\n",
      "-----------------------------------\n",
      "Colonne : proteins_100g\n",
      "Statistique de test : 0.8689378164607497\n",
      "p-value : 0.0\n",
      "La distribution ne semble pas normale (rejeter H0)\n",
      "-----------------------------------\n",
      "Colonne : salt_100g\n",
      "Statistique de test : 0.5\n",
      "p-value : 0.0\n",
      "La distribution ne semble pas normale (rejeter H0)\n",
      "-----------------------------------\n",
      "Colonne : nutrition-score-fr_100g\n",
      "Statistique de test : 0.9863514787447513\n",
      "p-value : 0.0\n",
      "La distribution ne semble pas normale (rejeter H0)\n",
      "-----------------------------------\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from scipy import stats\n",
    "\n",
    "def test_normality(df):\n",
    "    # Sélectionner les colonnes quantitatives du dataframe\n",
    "    quant_cols = df.select_dtypes(include=['float64', 'int64']).columns\n",
    "    \n",
    "    # Parcourir les colonnes quantitatives et effectuer le test de normalité\n",
    "    for col in quant_cols:\n",
    "        data = df[col].dropna()  # Éliminer les valeurs manquantes\n",
    "        \n",
    "        # Effectuer le test de normalité de Kolmogorov-Smirnov\n",
    "        stat, p = stats.kstest(data, 'norm')\n",
    "        \n",
    "        # Interprétation des résultats du test\n",
    "        alpha = 0.05  # Niveau de signification\n",
    "        print(f\"Colonne : {col}\")\n",
    "        print(f\"Statistique de test : {stat}\")\n",
    "        print(f\"p-value : {p}\")\n",
    "        if p > alpha:\n",
    "            print(\"La distribution semble normale (accepter H0)\")\n",
    "        else:\n",
    "            print(\"La distribution ne semble pas normale (rejeter H0)\")\n",
    "        print(\"-----------------------------------\")\n",
    "\n",
    "# Exemple d'utilisation\n",
    "# Supposons que votre dataframe s'appelle \"df\"\n",
    "test_normality(DataProduits[DataProduits['cluster'] ==0])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ddbfa14-c773-4451-afcd-978c594e08ed",
   "metadata": {},
   "source": [
    "Les colonnes (cluster 0) n'ont pas de distribution normale -> On ne peut pas effectuer les test ANOVA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a9c9a76e-a758-4bf1-9243-a16a95d5fc64",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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