{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "title : \"🛒 Ventes et ☔ pluie : agréger, c'est choisir\"\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Les chapitres précédents ont présenté la **mécanique** de l'agrégation : `groupby`, `pivot_table`, `crosstab`. Cet exercice porte sur autre chose : **la lecture du résultat**.\n",
    "\n",
    "Vous allez constater, sur deux jeux de données différents, qu'une même question posée à des données identiques reçoit des réponses **opposées** selon la grandeur que l'on choisit d'agréger. Agréger n'est pas une opération neutre : c'est déjà une décision d'analyse."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "\n",
    "## Exercice\n",
    "\n",
    "### Partie A : Ventes de deux magasins\n",
    "\n",
    "Le fichier `ventes.csv` donne, pour deux magasins et quatre trimestres, les quantités vendues de trois produits et leur prix unitaire.\n",
    "\n",
    "1. Charger le fichier et **vérifier le type de chaque colonne** avant toute chose.\n",
    "2. Ajouter une colonne `montant`, égale à la quantité multipliée par le prix unitaire.\n",
    "3. Quel magasin réalise le plus gros **chiffre d'affaires** ?\n",
    "4. Croiser magasin et produit : quel produit porte le chiffre d'affaires de chaque magasin ?\n",
    "5. Tracer l'évolution du chiffre d'affaires par trimestre pour chaque magasin.\n",
    "6. **Quel magasin vend le plus d'articles ?** Comparer avec la réponse de la question 3 et commenter.\n",
    "\n",
    "### Partie B : Pluviométrie de trois villes\n",
    "\n",
    "Le fichier `pluviometrie.csv` donne, pour Brest, Paris et Nice, la hauteur de pluie et le nombre de jours de pluie de chaque mois.\n",
    "\n",
    "1. Charger le fichier.\n",
    "2. Classer les trois villes par **cumul annuel de pluie**.\n",
    "3. Classer les trois villes par **nombre de jours de pluie**.\n",
    "4. Comparer les deux classements. Que peut-on en conclure ?\n",
    "5. Calculer la hauteur moyenne de pluie **par jour de pluie** dans chaque ville.\n",
    "6. Construire un tableau croisé station × mois. Attention à l'ordre des mois.\n",
    "7. Pour chaque ville, déterminer le mois le plus arrosé.\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-warning\">\n",
    "\n",
    "**Sur les données.** Les ventes sont fictives. Pour la pluviométrie, les **totaux annuels** sont proches des normales publiées par Météo-France (Brest ≈ 1200 mm, Nice ≈ 800 mm, Paris ≈ 640 mm), mais la **répartition mensuelle a été reconstruite** pour les besoins de l'exercice : ne citez pas ces chiffres comme des relevés officiels.\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-30T08:35:25.084514Z",
     "iopub.status.busy": "2026-10-30T08:35:25.083949Z",
     "iopub.status.idle": "2026-10-30T08:35:26.386780Z",
     "shell.execute_reply": "2026-10-30T08:35:26.386222Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "sns.set_theme(context=\"notebook\", style=\"whitegrid\", palette=\"pastel\")\n",
    "plt.rcParams['figure.figsize'] = (12, 5)\n",
    "\n",
    "from IPython.display import display, Markdown"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Partie A : Ventes de deux magasins"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Charger le fichier et vérifier les types"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Premier réflexe, avant même de lire les données : regarder à quoi ressemble le fichier. Ici le séparateur est `;` et le marqueur décimal est la virgule."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-30T08:35:26.389010Z",
     "iopub.status.busy": "2026-10-30T08:35:26.388750Z",
     "iopub.status.idle": "2026-10-30T08:35:26.397362Z",
     "shell.execute_reply": "2026-10-30T08:35:26.396871Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "```\n",
       "magasin;trimestre;produit;quantite;prix_unitaire\n",
       "Nord;T1;stylo;400;1,50\n",
       "Nord;T1;cahier;120;3,20\n",
       "Nord;T1;classeur;30;8,90\n",
       "Nord;T2;stylo;420;1,50\n",
       "Nord;T2;cahier;130;3,20\n",
       "Nord;T2;classeur;25;8,90\n",
       "Nord;T3;stylo;380;1,50\n",
       "Nord\n",
       "```"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(Markdown(\"```\\n\" + open('ventes.csv', encoding='utf-8').read()[:220] + \"\\n```\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-30T08:35:26.434845Z",
     "iopub.status.busy": "2026-10-30T08:35:26.434652Z",
     "iopub.status.idle": "2026-10-30T08:35:26.447992Z",
     "shell.execute_reply": "2026-10-30T08:35:26.447508Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>magasin</th>\n",
       "      <th>trimestre</th>\n",
       "      <th>produit</th>\n",
       "      <th>quantite</th>\n",
       "      <th>prix_unitaire</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Nord</td>\n",
       "      <td>T1</td>\n",
       "      <td>stylo</td>\n",
       "      <td>400</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Nord</td>\n",
       "      <td>T1</td>\n",
       "      <td>cahier</td>\n",
       "      <td>120</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Nord</td>\n",
       "      <td>T1</td>\n",
       "      <td>classeur</td>\n",
       "      <td>30</td>\n",
       "      <td>8.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Nord</td>\n",
       "      <td>T2</td>\n",
       "      <td>stylo</td>\n",
       "      <td>420</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nord</td>\n",
       "      <td>T2</td>\n",
       "      <td>cahier</td>\n",
       "      <td>130</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  magasin trimestre   produit  quantite  prix_unitaire\n",
       "0    Nord        T1     stylo       400            1.5\n",
       "1    Nord        T1    cahier       120            3.2\n",
       "2    Nord        T1  classeur        30            8.9\n",
       "3    Nord        T2     stylo       420            1.5\n",
       "4    Nord        T2    cahier       130            3.2"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "magasin           object\n",
       "trimestre         object\n",
       "produit           object\n",
       "quantite           int64\n",
       "prix_unitaire    float64\n",
       "dtype: object"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ventes = pd.read_csv('ventes.csv', sep=';', decimal=',')\n",
    "\n",
    "display(ventes.head())\n",
    "display(ventes.dtypes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`prix_unitaire` est bien en `float64`. Si nous avions oublié `decimal=','`, la colonne aurait été lue comme du texte et toute la suite aurait été fausse : c'est la vérification à ne jamais sauter."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Ajouter la colonne `montant`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-30T08:35:26.449864Z",
     "iopub.status.busy": "2026-10-30T08:35:26.449689Z",
     "iopub.status.idle": "2026-10-30T08:35:26.457993Z",
     "shell.execute_reply": "2026-10-30T08:35:26.457241Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>magasin</th>\n",
       "      <th>trimestre</th>\n",
       "      <th>produit</th>\n",
       "      <th>quantite</th>\n",
       "      <th>prix_unitaire</th>\n",
       "      <th>montant</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Nord</td>\n",
       "      <td>T1</td>\n",
       "      <td>stylo</td>\n",
       "      <td>400</td>\n",
       "      <td>1.5</td>\n",
       "      <td>600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Nord</td>\n",
       "      <td>T1</td>\n",
       "      <td>cahier</td>\n",
       "      <td>120</td>\n",
       "      <td>3.2</td>\n",
       "      <td>384.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Nord</td>\n",
       "      <td>T1</td>\n",
       "      <td>classeur</td>\n",
       "      <td>30</td>\n",
       "      <td>8.9</td>\n",
       "      <td>267.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Nord</td>\n",
       "      <td>T2</td>\n",
       "      <td>stylo</td>\n",
       "      <td>420</td>\n",
       "      <td>1.5</td>\n",
       "      <td>630.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nord</td>\n",
       "      <td>T2</td>\n",
       "      <td>cahier</td>\n",
       "      <td>130</td>\n",
       "      <td>3.2</td>\n",
       "      <td>416.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  magasin trimestre   produit  quantite  prix_unitaire  montant\n",
       "0    Nord        T1     stylo       400            1.5    600.0\n",
       "1    Nord        T1    cahier       120            3.2    384.0\n",
       "2    Nord        T1  classeur        30            8.9    267.0\n",
       "3    Nord        T2     stylo       420            1.5    630.0\n",
       "4    Nord        T2    cahier       130            3.2    416.0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ventes['montant'] = ventes['quantite'] * ventes['prix_unitaire']\n",
    "ventes.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Quel magasin réalise le plus gros chiffre d'affaires ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-30T08:35:26.460571Z",
     "iopub.status.busy": "2026-10-30T08:35:26.460378Z",
     "iopub.status.idle": "2026-10-30T08:35:26.466509Z",
     "shell.execute_reply": "2026-10-30T08:35:26.466013Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "magasin\n",
       "Nord    5264.0\n",
       "Sud     8920.5\n",
       "Name: montant, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "Le magasin Sud réalise le plus gros chiffre d'affaires : 8920.50 €"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ca = ventes.groupby('magasin')['montant'].sum()\n",
    "display(ca)\n",
    "display(Markdown(f\"Le magasin {ca.idxmax()} réalise le plus gros chiffre d'affaires : {ca.max():.2f} €\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Quel produit porte chaque magasin ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-30T08:35:26.468314Z",
     "iopub.status.busy": "2026-10-30T08:35:26.468139Z",
     "iopub.status.idle": "2026-10-30T08:35:26.491928Z",
     "shell.execute_reply": "2026-10-30T08:35:26.490668Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>produit</th>\n",
       "      <th>cahier</th>\n",
       "      <th>classeur</th>\n",
       "      <th>stylo</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>magasin</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Nord</th>\n",
       "      <td>1632.0</td>\n",
       "      <td>1157.0</td>\n",
       "      <td>2475.0</td>\n",
       "      <td>5264.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sud</th>\n",
       "      <td>1360.0</td>\n",
       "      <td>6630.5</td>\n",
       "      <td>930.0</td>\n",
       "      <td>8920.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>2992.0</td>\n",
       "      <td>7787.5</td>\n",
       "      <td>3405.0</td>\n",
       "      <td>14184.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "produit  cahier  classeur   stylo      All\n",
       "magasin                                   \n",
       "Nord     1632.0    1157.0  2475.0   5264.0\n",
       "Sud      1360.0    6630.5   930.0   8920.5\n",
       "All      2992.0    7787.5  3405.0  14184.5"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tableau = ventes.pivot_table(index='magasin', columns='produit',\n",
    "                            values='montant', aggfunc='sum', margins=True)\n",
    "tableau.round(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plus lisible en pourcentage de chaque ligne :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-30T08:35:26.494327Z",
     "iopub.status.busy": "2026-10-30T08:35:26.494147Z",
     "iopub.status.idle": "2026-10-30T08:35:26.504754Z",
     "shell.execute_reply": "2026-10-30T08:35:26.504192Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>produit</th>\n",
       "      <th>cahier</th>\n",
       "      <th>classeur</th>\n",
       "      <th>stylo</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>magasin</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Nord</th>\n",
       "      <td>31.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>47.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sud</th>\n",
       "      <td>15.2</td>\n",
       "      <td>74.3</td>\n",
       "      <td>10.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "produit  cahier  classeur  stylo\n",
       "magasin                         \n",
       "Nord       31.0      22.0   47.0\n",
       "Sud        15.2      74.3   10.4"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "part = ventes.pivot_table(index='magasin', columns='produit', values='montant', aggfunc='sum')\n",
    "(part.div(part.sum(axis=1), axis=0) * 100).round(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La lecture est immédiate : le Sud réalise l'essentiel de son chiffre d'affaires sur les **classeurs**, le Nord est beaucoup plus équilibré, avec une part importante de **stylos**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Évolution trimestrielle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-30T08:35:26.508111Z",
     "iopub.status.busy": "2026-10-30T08:35:26.507855Z",
     "iopub.status.idle": "2026-10-30T08:35:26.719019Z",
     "shell.execute_reply": "2026-10-30T08:35:26.718557Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>magasin</th>\n",
       "      <th>Nord</th>\n",
       "      <th>Sud</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trimestre</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>T1</th>\n",
       "      <td>1251.0</td>\n",
       "      <td>2147.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T2</th>\n",
       "      <td>1268.5</td>\n",
       "      <td>2235.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T3</th>\n",
       "      <td>1233.5</td>\n",
       "      <td>2119.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T4</th>\n",
       "      <td>1511.0</td>\n",
       "      <td>2419.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "magasin      Nord     Sud\n",
       "trimestre                \n",
       "T1         1251.0  2147.0\n",
       "T2         1268.5  2235.0\n",
       "T3         1233.5  2119.5\n",
       "T4         1511.0  2419.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "evolution = ventes.pivot_table(index='trimestre', columns='magasin',\n",
    "                               values='montant', aggfunc='sum')\n",
    "display(evolution.round(2))\n",
    "\n",
    "evolution.plot(marker='o')\n",
    "plt.ylabel(\"Chiffre d'affaires (€)\")\n",
    "plt.title(\"Chiffre d'affaires trimestriel par magasin\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Et en nombre d'articles ?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Jusqu'ici nous avons agrégé les **euros**. Agrégeons maintenant les **articles** :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-30T08:35:26.721115Z",
     "iopub.status.busy": "2026-10-30T08:35:26.720807Z",
     "iopub.status.idle": "2026-10-30T08:35:26.735536Z",
     "shell.execute_reply": "2026-10-30T08:35:26.735013Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>articles</th>\n",
       "      <th>chiffre_affaires</th>\n",
       "      <th>panier_moyen</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>magasin</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Nord</th>\n",
       "      <td>2290</td>\n",
       "      <td>5264.0</td>\n",
       "      <td>2.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sud</th>\n",
       "      <td>1790</td>\n",
       "      <td>8920.5</td>\n",
       "      <td>4.98</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         articles  chiffre_affaires  panier_moyen\n",
       "magasin                                          \n",
       "Nord         2290            5264.0          2.30\n",
       "Sud          1790            8920.5          4.98"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bilan = ventes.groupby('magasin').agg(\n",
    "    articles          = ('quantite', 'sum'),\n",
    "    chiffre_affaires  = ('montant',  'sum')\n",
    ")\n",
    "bilan['panier_moyen'] = (bilan['chiffre_affaires'] / bilan['articles']).round(2)\n",
    "bilan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-30T08:35:26.737303Z",
     "iopub.status.busy": "2026-10-30T08:35:26.737150Z",
     "iopub.status.idle": "2026-10-30T08:35:26.743010Z",
     "shell.execute_reply": "2026-10-30T08:35:26.742587Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "Plus d'articles vendus     : Nord"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "Plus gros chiffre d'affaires : Sud"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(Markdown(f\"Plus d'articles vendus     : {bilan['articles'].idxmax()}\"))\n",
    "display(Markdown(f\"Plus gros chiffre d'affaires : {bilan['chiffre_affaires'].idxmax()}\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-danger\">\n",
    "\n",
    "**Les deux classements s'opposent.** Le magasin **Nord** vend 2 290 articles contre 1 790 pour le Sud, mais le Sud réalise un chiffre d'affaires de 8 920,50 € contre 5 264 € pour le Nord.\n",
    "\n",
    "Il n'y a aucune contradiction : le Nord écoule des stylos à 1,50 €, le Sud des classeurs à 8,90 €. La dernière colonne le résume : le prix moyen d'un article vendu est près de deux fois plus élevé au Sud.\n",
    "\n",
    "La question « quel est le meilleur magasin ? » n'a donc **pas de réponse** tant qu'on n'a pas dit ce qu'on mesure. C'est à l'analyste de le préciser, pas à `groupby`.\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Partie B : Pluviométrie de trois villes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Charger le fichier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-30T08:35:26.744762Z",
     "iopub.status.busy": "2026-10-30T08:35:26.744596Z",
     "iopub.status.idle": "2026-10-30T08:35:26.752343Z",
     "shell.execute_reply": "2026-10-30T08:35:26.751835Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>station</th>\n",
       "      <th>mois</th>\n",
       "      <th>pluie_mm</th>\n",
       "      <th>jours_pluie</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Brest</td>\n",
       "      <td>janvier</td>\n",
       "      <td>150</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Brest</td>\n",
       "      <td>février</td>\n",
       "      <td>110</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Brest</td>\n",
       "      <td>mars</td>\n",
       "      <td>100</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Brest</td>\n",
       "      <td>avril</td>\n",
       "      <td>85</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Brest</td>\n",
       "      <td>mai</td>\n",
       "      <td>75</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  station     mois  pluie_mm  jours_pluie\n",
       "0   Brest  janvier       150           17\n",
       "1   Brest  février       110           14\n",
       "2   Brest     mars       100           14\n",
       "3   Brest    avril        85           13\n",
       "4   Brest      mai        75           12"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(36, 4)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pluie = pd.read_csv('pluviometrie.csv', sep=';')\n",
    "\n",
    "display(pluie.head())\n",
    "display(pluie.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. et 3. Deux classements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-30T08:35:26.754218Z",
     "iopub.status.busy": "2026-10-30T08:35:26.754056Z",
     "iopub.status.idle": "2026-10-30T08:35:26.764247Z",
     "shell.execute_reply": "2026-10-30T08:35:26.763898Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cumul_mm</th>\n",
       "      <th>jours</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>station</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Brest</th>\n",
       "      <td>1225</td>\n",
       "      <td>162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nice</th>\n",
       "      <td>797</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Paris</th>\n",
       "      <td>655</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         cumul_mm  jours\n",
       "station                 \n",
       "Brest        1225    162\n",
       "Nice          797     63\n",
       "Paris         655    111"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bilan_villes = pluie.groupby('station').agg(\n",
    "    cumul_mm = ('pluie_mm',    'sum'),\n",
    "    jours    = ('jours_pluie', 'sum')\n",
    ")\n",
    "bilan_villes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-30T08:35:26.765931Z",
     "iopub.status.busy": "2026-10-30T08:35:26.765758Z",
     "iopub.status.idle": "2026-10-30T08:35:26.770619Z",
     "shell.execute_reply": "2026-10-30T08:35:26.770146Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "**Classement par hauteur de pluie :** Brest > Nice > Paris"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Classement par jours de pluie :** Brest > Paris > Nice"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(Markdown(\"**Classement par hauteur de pluie :** \"\n",
    "                 + \" > \".join(bilan_villes['cumul_mm'].sort_values(ascending=False).index)))\n",
    "display(Markdown(\"**Classement par jours de pluie :** \"\n",
    "                 + \" > \".join(bilan_villes['jours'].sort_values(ascending=False).index)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Comparaison des deux classements"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-danger\">\n",
    "\n",
    "**Paris et Nice échangent leur place.**\n",
    "\n",
    "- En **hauteur de pluie**, il tombe nettement plus d'eau à Nice qu'à Paris : environ 800 mm contre 655 mm.\n",
    "- En **nombre de jours**, c'est l'inverse : il pleut à Paris environ 111 jours par an, contre 63 à Nice.\n",
    "\n",
    "« Il pleut plus à Paris qu'à Nice » est donc **vrai et faux à la fois**, selon la grandeur choisie. Les deux affirmations décrivent la même réalité : à Nice il pleut rarement, mais fort ; à Paris il pleut souvent, mais peu.\n",
    "\n",
    "Brest, elle, est première dans les deux classements : il n'y a pas toujours d'ambiguïté, mais il faut toujours vérifier.\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. L'intensité des pluies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Le rapport des deux grandeurs donne directement la hauteur moyenne tombée un jour de pluie :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-30T08:35:26.772800Z",
     "iopub.status.busy": "2026-10-30T08:35:26.772486Z",
     "iopub.status.idle": "2026-10-30T08:35:26.779482Z",
     "shell.execute_reply": "2026-10-30T08:35:26.778969Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cumul_mm</th>\n",
       "      <th>jours</th>\n",
       "      <th>mm_par_jour_de_pluie</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>station</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Nice</th>\n",
       "      <td>797</td>\n",
       "      <td>63</td>\n",
       "      <td>12.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brest</th>\n",
       "      <td>1225</td>\n",
       "      <td>162</td>\n",
       "      <td>7.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Paris</th>\n",
       "      <td>655</td>\n",
       "      <td>111</td>\n",
       "      <td>5.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         cumul_mm  jours  mm_par_jour_de_pluie\n",
       "station                                       \n",
       "Nice          797     63                  12.7\n",
       "Brest        1225    162                   7.6\n",
       "Paris         655    111                   5.9"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bilan_villes['mm_par_jour_de_pluie'] = (bilan_villes['cumul_mm'] / bilan_villes['jours']).round(1)\n",
    "bilan_villes.sort_values('mm_par_jour_de_pluie', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Une pluie niçoise apporte en moyenne **plus du double** d'une pluie parisienne. C'est cette troisième grandeur, construite à partir des deux premières, qui réconcilie les classements et explique le phénomène."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Tableau croisé station × mois"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Première tentative, et premier piège :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-30T08:35:26.781174Z",
     "iopub.status.busy": "2026-10-30T08:35:26.781013Z",
     "iopub.status.idle": "2026-10-30T08:35:26.796276Z",
     "shell.execute_reply": "2026-10-30T08:35:26.795761Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>mois</th>\n",
       "      <th>août</th>\n",
       "      <th>avril</th>\n",
       "      <th>décembre</th>\n",
       "      <th>février</th>\n",
       "      <th>janvier</th>\n",
       "      <th>juillet</th>\n",
       "      <th>juin</th>\n",
       "      <th>mai</th>\n",
       "      <th>mars</th>\n",
       "      <th>novembre</th>\n",
       "      <th>octobre</th>\n",
       "      <th>septembre</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>station</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Brest</th>\n",
       "      <td>55.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>155.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>85.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nice</th>\n",
       "      <td>20.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Paris</th>\n",
       "      <td>60.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>48.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "mois     août  avril  décembre  février  janvier  juillet  juin   mai   mars  \\\n",
       "station                                                                        \n",
       "Brest    55.0   85.0     165.0    110.0    150.0     50.0  55.0  75.0  100.0   \n",
       "Nice     20.0   70.0      95.0     45.0     70.0     12.0  35.0  45.0   40.0   \n",
       "Paris    60.0   52.0      58.0     45.0     50.0     60.0  55.0  65.0   48.0   \n",
       "\n",
       "mois     novembre  octobre  septembre  \n",
       "station                                \n",
       "Brest       155.0    140.0       85.0  \n",
       "Nice        140.0    150.0       75.0  \n",
       "Paris        52.0     62.0       48.0  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pluie.pivot_table(index='station', columns='mois', values='pluie_mm')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Les mois sont rangés par ordre **alphabétique** : août, avril, décembre… Pandas ne peut pas deviner qu'il s'agit d'un calendrier. Il faut le lui dire, en déclarant la colonne comme une variable **catégorielle ordonnée** :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-30T08:35:26.798073Z",
     "iopub.status.busy": "2026-10-30T08:35:26.797899Z",
     "iopub.status.idle": "2026-10-30T08:35:26.822992Z",
     "shell.execute_reply": "2026-10-30T08:35:26.822285Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>mois</th>\n",
       "      <th>janvier</th>\n",
       "      <th>février</th>\n",
       "      <th>mars</th>\n",
       "      <th>avril</th>\n",
       "      <th>mai</th>\n",
       "      <th>juin</th>\n",
       "      <th>juillet</th>\n",
       "      <th>août</th>\n",
       "      <th>septembre</th>\n",
       "      <th>octobre</th>\n",
       "      <th>novembre</th>\n",
       "      <th>décembre</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>station</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Brest</th>\n",
       "      <td>150.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>155.0</td>\n",
       "      <td>165.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nice</th>\n",
       "      <td>70.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>95.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Paris</th>\n",
       "      <td>50.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>58.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "mois     janvier  février   mars  avril   mai  juin  juillet  août  septembre  \\\n",
       "station                                                                         \n",
       "Brest      150.0    110.0  100.0   85.0  75.0  55.0     50.0  55.0       85.0   \n",
       "Nice        70.0     45.0   40.0   70.0  45.0  35.0     12.0  20.0       75.0   \n",
       "Paris       50.0     45.0   48.0   52.0  65.0  55.0     60.0  60.0       48.0   \n",
       "\n",
       "mois     octobre  novembre  décembre  \n",
       "station                               \n",
       "Brest      140.0     155.0     165.0  \n",
       "Nice       150.0     140.0      95.0  \n",
       "Paris       62.0      52.0      58.0  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calendrier = [\"janvier\",\"février\",\"mars\",\"avril\",\"mai\",\"juin\",\n",
    "              \"juillet\",\"août\",\"septembre\",\"octobre\",\"novembre\",\"décembre\"]\n",
    "\n",
    "pluie['mois'] = pd.Categorical(pluie['mois'], categories=calendrier, ordered=True)\n",
    "\n",
    "croise = pluie.pivot_table(index='station', columns='mois', values='pluie_mm', observed=True)\n",
    "croise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-30T08:35:26.826040Z",
     "iopub.status.busy": "2026-10-30T08:35:26.825589Z",
     "iopub.status.idle": "2026-10-30T08:35:27.135778Z",
     "shell.execute_reply": "2026-10-30T08:35:27.135300Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(croise, annot=True, fmt='.0f', cmap='Blues', cbar_kws={'label': 'mm'})\n",
    "plt.title(\"Hauteur de pluie mensuelle (mm)\")\n",
    "plt.ylabel(\"\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La carte de chaleur rend les trois régimes immédiatement visibles : Brest arrosée toute l'année avec un creux estival, Paris remarquablement régulière, et Nice avec un été quasi sec suivi d'un automne très violent."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. Le mois le plus arrosé de chaque ville"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-30T08:35:27.137579Z",
     "iopub.status.busy": "2026-10-30T08:35:27.137283Z",
     "iopub.status.idle": "2026-10-30T08:35:27.146412Z",
     "shell.execute_reply": "2026-10-30T08:35:27.145891Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mois le plus arrosé</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>station</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Brest</th>\n",
       "      <td>décembre</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nice</th>\n",
       "      <td>octobre</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Paris</th>\n",
       "      <td>mai</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        mois le plus arrosé\n",
       "station                    \n",
       "Brest              décembre\n",
       "Nice                octobre\n",
       "Paris                   mai"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "croise.idxmax(axis=1).to_frame('mois le plus arrosé')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion\n",
    "\n",
    "<div class=\"alert alert-block alert-info\">\n",
    "\n",
    "**Ce que cet exercice voulait montrer**\n",
    "\n",
    "Dans les deux cas, le code était trivial : un `groupby` et une somme. C'est l'**interprétation** qui demandait du travail.\n",
    "\n",
    "- Partie A : « quel est le meilleur magasin ? » dépend de ce qu'on somme, les euros ou les articles.\n",
    "- Partie B : « où pleut-il le plus ? » dépend de ce qu'on somme, les millimètres ou les jours.\n",
    "\n",
    "Retenez le réflexe : avant d'agréger, demandez-vous **quelle question vous posez réellement**, et après avoir agrégé, demandez-vous si **une autre grandeur donnerait une autre réponse**. C'est précisément ce qu'on attend de vous à l'examen : non pas produire un tableau, mais dire ce qu'il signifie.\n",
    "\n",
    "</div>"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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