{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9ceb5754",
   "metadata": {},
   "source": [
    "# Notebook pour algo CRA Bretagne"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db0aa78f",
   "metadata": {},
   "source": [
    "Problématique du Hackathon\n",
    "Comment anticiper et réduire la pression des adventices dans les parcelles agricoles bretonnes, dans un contexte de réduction progressive des herbicides, en s’appuyant sur l’analyse des données historiques, climatiques et agronomiques, afin d’identifier les parcelles les plus adaptées à la culture de plantes sensibles comme le pois ou le haricot sur les trois prochaines années ?\n",
    "\n",
    "🔍 Objectif spécifique du modèle de simulation\n",
    "Prédire la pression adventice sur chaque parcelle pour les 3 prochaines campagnes.\n",
    "Identifier les parcelles à faible risque pour y implanter des cultures sensibles (ex. : pois, haricot).\n",
    "Intégrer les données climatiques, historiques d’intervention, rotations, rendements et IFT.\n",
    "Proposer des alternatives techniques en cas de retrait de certaines molécules herbicides."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82875f9b",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "daba851e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ All libraries imported successfully!\n",
      "📊 Pandas version: 2.3.2\n",
      "🔢 NumPy version: 2.3.3\n",
      "🤖 Scikit-learn available\n",
      "🚀 XGBoost version: 3.0.5\n",
      "🌍 Geospatial libraries: Available\n"
     ]
    }
   ],
   "source": [
    "# Core data analysis libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from datetime import datetime, timedelta\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# Visualization libraries\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import plotly.express as px\n",
    "import plotly.graph_objects as go\n",
    "from plotly.subplots import make_subplots\n",
    "\n",
    "# Machine learning libraries\n",
    "from sklearn.model_selection import train_test_split, cross_val_score, GridSearchCV\n",
    "from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier\n",
    "from sklearn.linear_model import LinearRegression, LogisticRegression\n",
    "from sklearn.metrics import mean_squared_error, r2_score, classification_report, confusion_matrix\n",
    "from sklearn.preprocessing import StandardScaler, LabelEncoder\n",
    "import xgboost as xgb\n",
    "import lightgbm as lgb\n",
    "\n",
    "# Geospatial analysis (for agricultural parcels)\n",
    "try:\n",
    "    import geopandas as gpd\n",
    "    import folium\n",
    "    GEOSPATIAL_AVAILABLE = True\n",
    "except ImportError:\n",
    "    GEOSPATIAL_AVAILABLE = False\n",
    "    print(\"Geospatial libraries not available. Some features may be limited.\")\n",
    "\n",
    "# Time series analysis\n",
    "from statsmodels.tsa.seasonal import seasonal_decompose\n",
    "import statsmodels.api as sm\n",
    "\n",
    "# Utility libraries\n",
    "import os\n",
    "import json\n",
    "from pathlib import Path\n",
    "\n",
    "# Set plotting style\n",
    "plt.style.use('seaborn-v0_8')\n",
    "sns.set_palette(\"husl\")\n",
    "\n",
    "print(\"✅ All libraries imported successfully!\")\n",
    "print(f\"📊 Pandas version: {pd.__version__}\")\n",
    "print(f\"🔢 NumPy version: {np.__version__}\")\n",
    "print(f\"🤖 Scikit-learn available\")\n",
    "print(f\"🚀 XGBoost version: {xgb.__version__}\")\n",
    "print(f\"🌍 Geospatial libraries: {'Available' if GEOSPATIAL_AVAILABLE else 'Not available'}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9078046",
   "metadata": {},
   "source": [
    "## 📊 Chargement et exploration des données\n",
    "\n",
    "Nous allons charger et analyser les données d'interventions agricoles de la Station Expérimentale de Kerguéhennec pour prédire la pression des adventices.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b0eec6ed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "📋 Dimensions du dataset: (653, 34)\n",
      "📅 Période couverte: 2025 - 2025\n",
      "🌾 Nombre de parcelles uniques: 45\n",
      "🏢 Station: Station Expérimentale de Kerguéhennec\n",
      "\n",
      "📋 Aperçu des données:\n"
     ]
    },
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>millesime</th>\n",
       "      <th>raisonsoci</th>\n",
       "      <th>siret</th>\n",
       "      <th>pacage</th>\n",
       "      <th>refca</th>\n",
       "      <th>numilot</th>\n",
       "      <th>numparcell</th>\n",
       "      <th>nomparc</th>\n",
       "      <th>surfparc</th>\n",
       "      <th>rang</th>\n",
       "      <th>...</th>\n",
       "      <th>kqte</th>\n",
       "      <th>teneurn</th>\n",
       "      <th>teneurp</th>\n",
       "      <th>teneurk</th>\n",
       "      <th>keq</th>\n",
       "      <th>volumebo</th>\n",
       "      <th>codeamm</th>\n",
       "      <th>codegnis</th>\n",
       "      <th>materiel</th>\n",
       "      <th>mainoeuvre</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>2025</td>\n",
       "      <td>Station Expérimentale de Kerguéhennec</td>\n",
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       "      <td>56021200</td>\n",
       "      <td>70000308</td>\n",
       "      <td>1</td>\n",
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       "      <td>Etang Milieu</td>\n",
       "      <td>2.28</td>\n",
       "      <td>1</td>\n",
       "      <td>...</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>9100296.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PULVERISATEURS, Pulvérisateur ARLAND Hélium + ...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2025</td>\n",
       "      <td>Station Expérimentale de Kerguéhennec</td>\n",
       "      <td>18560001000016</td>\n",
       "      <td>56021200</td>\n",
       "      <td>70000308</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>Etang Milieu</td>\n",
       "      <td>2.28</td>\n",
       "      <td>1</td>\n",
       "      <td>...</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>TRACTEURS CLASSIQUES, Tracteur JOHN DEERE 6R15...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2025</td>\n",
       "      <td>Station Expérimentale de Kerguéhennec</td>\n",
       "      <td>18560001000016</td>\n",
       "      <td>56021200</td>\n",
       "      <td>70000308</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>Etang Milieu</td>\n",
       "      <td>2.28</td>\n",
       "      <td>1</td>\n",
       "      <td>...</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>CULTIVATEURS ET CHISELS, Canadien AMAZONE Ceni...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2025</td>\n",
       "      <td>Station Expérimentale de Kerguéhennec</td>\n",
       "      <td>18560001000016</td>\n",
       "      <td>56021200</td>\n",
       "      <td>70000308</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>Etang Milieu</td>\n",
       "      <td>2.28</td>\n",
       "      <td>1</td>\n",
       "      <td>...</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>512E355</td>\n",
       "      <td>TASSE-AVANT, Tasse-avant 3m LABBE ROTIEL Roll-...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2025</td>\n",
       "      <td>Station Expérimentale de Kerguéhennec</td>\n",
       "      <td>18560001000016</td>\n",
       "      <td>56021200</td>\n",
       "      <td>70000308</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>Etang Milieu</td>\n",
       "      <td>2.28</td>\n",
       "      <td>1</td>\n",
       "      <td>...</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>512D830</td>\n",
       "      <td>TASSE-AVANT, Tasse-avant 3m LABBE ROTIEL Roll-...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   millesime                             raisonsoci           siret    pacage  \\\n",
       "0       2025  Station Expérimentale de Kerguéhennec  18560001000016  56021200   \n",
       "1       2025  Station Expérimentale de Kerguéhennec  18560001000016  56021200   \n",
       "2       2025  Station Expérimentale de Kerguéhennec  18560001000016  56021200   \n",
       "3       2025  Station Expérimentale de Kerguéhennec  18560001000016  56021200   \n",
       "4       2025  Station Expérimentale de Kerguéhennec  18560001000016  56021200   \n",
       "\n",
       "      refca  numilot  numparcell       nomparc  surfparc  rang  ...  kqte  \\\n",
       "0  70000308        1          12  Etang Milieu      2.28     1  ...   NaN   \n",
       "1  70000308        1          12  Etang Milieu      2.28     1  ...   NaN   \n",
       "2  70000308        1          12  Etang Milieu      2.28     1  ...   NaN   \n",
       "3  70000308        1          12  Etang Milieu      2.28     1  ...   NaN   \n",
       "4  70000308        1          12  Etang Milieu      2.28     1  ...   NaN   \n",
       "\n",
       "  teneurn teneurp teneurk  keq volumebo    codeamm  codegnis  \\\n",
       "0     NaN     NaN     NaN  NaN      NaN  9100296.0       NaN   \n",
       "1     NaN     NaN     NaN  NaN      NaN        NaN       NaN   \n",
       "2     NaN     NaN     NaN  NaN      NaN        NaN       NaN   \n",
       "3     NaN     NaN     NaN  NaN      NaN        NaN   512E355   \n",
       "4     NaN     NaN     NaN  NaN      NaN        NaN   512D830   \n",
       "\n",
       "                                            materiel mainoeuvre  \n",
       "0  PULVERISATEURS, Pulvérisateur ARLAND Hélium + ...        NaN  \n",
       "1  TRACTEURS CLASSIQUES, Tracteur JOHN DEERE 6R15...        NaN  \n",
       "2  CULTIVATEURS ET CHISELS, Canadien AMAZONE Ceni...        NaN  \n",
       "3  TASSE-AVANT, Tasse-avant 3m LABBE ROTIEL Roll-...        NaN  \n",
       "4  TASSE-AVANT, Tasse-avant 3m LABBE ROTIEL Roll-...        NaN  \n",
       "\n",
       "[5 rows x 34 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Chargement des données\n",
    "data_path = \"data/Interventions-(sortie-excel)-Station_Expérimentale_de_Kerguéhennec-2025.csv\"\n",
    "\n",
    "# Lire le fichier CSV en sautant la première ligne qui contient le titre\n",
    "df = pd.read_csv(data_path, skiprows=1)\n",
    "\n",
    "print(f\"📋 Dimensions du dataset: {df.shape}\")\n",
    "print(f\"📅 Période couverte: {df['millesime'].min()} - {df['millesime'].max()}\")\n",
    "print(f\"🌾 Nombre de parcelles uniques: {df['numparcell'].nunique()}\")\n",
    "print(f\"🏢 Station: {df['raisonsoci'].iloc[0]}\")\n",
    "\n",
    "# Affichage des premières lignes\n",
    "print(\"\\n📋 Aperçu des données:\")\n",
    "df.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d2078ac",
   "metadata": {},
   "source": [
    "## 🔍 Analyse exploratoire approfondie des données\n",
    "\n",
    "### Structure générale du dataset\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3d59dd29",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "================================================================================\n",
      "📊 ANALYSE EXPLORATOIRE COMPLÈTE - DONNÉES AGRICOLES KERGUÉHENNEC\n",
      "================================================================================\n",
      "\n",
      "📋 Informations générales:\n",
      "   • Nombre d'observations: 653\n",
      "   • Nombre de variables: 34\n",
      "   • Taille mémoire: 0.71 MB\n",
      "\n",
      "📊 Colonnes du dataset:\n",
      "    1. millesime\n",
      "    2. raisonsoci\n",
      "    3. siret\n",
      "    4. pacage\n",
      "    5. refca\n",
      "    6. numilot\n",
      "    7. numparcell\n",
      "    8. nomparc\n",
      "    9. surfparc\n",
      "   10. rang\n",
      "   11. estpac\n",
      "   12. libelleusag\n",
      "   13. datedebut\n",
      "   14. datefin\n",
      "   15. libperiode\n",
      "   16. libregroupe\n",
      "   17. libevenem\n",
      "   18. dureeeffect\n",
      "   19. familleprod\n",
      "   20. produit\n",
      "   21. quantitetot\n",
      "   22. unite\n",
      "   23. neffqte\n",
      "   24. peffqte\n",
      "   25. kqte\n",
      "   26. teneurn\n",
      "   27. teneurp\n",
      "   28. teneurk\n",
      "   29. keq\n",
      "   30. volumebo\n",
      "   31. codeamm\n",
      "   32. codegnis\n",
      "   33. materiel\n",
      "   34. mainoeuvre\n",
      "\n",
      "🔢 Types de données:\n",
      "   • object: 13 colonnes\n",
      "   • float64: 13 colonnes\n",
      "   • int64: 7 colonnes\n",
      "   • bool: 1 colonnes\n",
      "\n",
      "❓ Valeurs manquantes:\n",
      "   • libperiode: 653 (100.0%)\n",
      "   • keq: 629 (96.3%)\n",
      "   • peffqte: 583 (89.3%)\n",
      "   • teneurk: 583 (89.3%)\n",
      "   • teneurp: 583 (89.3%)\n",
      "   • kqte: 583 (89.3%)\n",
      "   • teneurn: 583 (89.3%)\n",
      "   • neffqte: 583 (89.3%)\n",
      "   • codegnis: 576 (88.2%)\n",
      "   • volumebo: 528 (80.9%)\n",
      "   • codeamm: 486 (74.4%)\n",
      "   • mainoeuvre: 415 (63.6%)\n",
      "   • produit: 319 (48.9%)\n",
      "   • familleprod: 319 (48.9%)\n",
      "   • unite: 296 (45.3%)\n",
      "   • quantitetot: 296 (45.3%)\n",
      "   • dureeeffect: 194 (29.7%)\n",
      "   • materiel: 43 (6.6%)\n",
      "\n",
      "📈 Colonnes numériques:\n",
      "   20 colonnes numériques identifiées\n",
      "   • millesime\n",
      "   • siret\n",
      "   • pacage\n",
      "   • refca\n",
      "   • numilot\n",
      "   • numparcell\n",
      "   • surfparc\n",
      "   • rang\n",
      "   • libperiode\n",
      "   • dureeeffect\n",
      "   • quantitetot\n",
      "   • neffqte\n",
      "   • peffqte\n",
      "   • kqte\n",
      "   • teneurn\n",
      "   • teneurp\n",
      "   • teneurk\n",
      "   • keq\n",
      "   • volumebo\n",
      "   • codeamm\n",
      "\n",
      "📋 Aperçu des 10 premières lignes:\n"
     ]
    },
    {
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       "      <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>TRACTEURS CLASSIQUES, Tracteur JOHN DEERE 6R15...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2025</td>\n",
       "      <td>Station Expérimentale de Kerguéhennec</td>\n",
       "      <td>18560001000016</td>\n",
       "      <td>56021200</td>\n",
       "      <td>70000308</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>Etang Milieu</td>\n",
       "      <td>2.28</td>\n",
       "      <td>1</td>\n",
       "      <td>...</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>CULTIVATEURS ET CHISELS, Canadien AMAZONE Ceni...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2025</td>\n",
       "      <td>Station Expérimentale de Kerguéhennec</td>\n",
       "      <td>18560001000016</td>\n",
       "      <td>56021200</td>\n",
       "      <td>70000308</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>Etang Milieu</td>\n",
       "      <td>2.28</td>\n",
       "      <td>1</td>\n",
       "      <td>...</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>512E355</td>\n",
       "      <td>TASSE-AVANT, Tasse-avant 3m LABBE ROTIEL Roll-...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2025</td>\n",
       "      <td>Station Expérimentale de Kerguéhennec</td>\n",
       "      <td>18560001000016</td>\n",
       "      <td>56021200</td>\n",
       "      <td>70000308</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>Etang Milieu</td>\n",
       "      <td>2.28</td>\n",
       "      <td>1</td>\n",
       "      <td>...</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>512D830</td>\n",
       "      <td>TASSE-AVANT, Tasse-avant 3m LABBE ROTIEL Roll-...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2025</td>\n",
       "      <td>Station Expérimentale de Kerguéhennec</td>\n",
       "      <td>18560001000016</td>\n",
       "      <td>56021200</td>\n",
       "      <td>70000308</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>Etang Milieu</td>\n",
       "      <td>2.28</td>\n",
       "      <td>1</td>\n",
       "      <td>...</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>2160226.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>QUADS, Quad POLARIS 500 cc - DISTRIBUTEUR ENGR...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2025</td>\n",
       "      <td>Station Expérimentale de Kerguéhennec</td>\n",
       "      <td>18560001000016</td>\n",
       "      <td>56021200</td>\n",
       "      <td>70000308</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>Etang Milieu</td>\n",
       "      <td>2.28</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>2080145.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PULVERISATEURS, Pulvérisateur ARLAND Hélium + ...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2025</td>\n",
       "      <td>Station Expérimentale de Kerguéhennec</td>\n",
       "      <td>18560001000016</td>\n",
       "      <td>56021200</td>\n",
       "      <td>70000308</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>Etang Milieu</td>\n",
       "      <td>2.28</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2140235.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PULVERISATEURS, Pulvérisateur ARLAND Hélium + ...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2025</td>\n",
       "      <td>Station Expérimentale de Kerguéhennec</td>\n",
       "      <td>18560001000016</td>\n",
       "      <td>56021200</td>\n",
       "      <td>70000308</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>Etang Milieu</td>\n",
       "      <td>2.28</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2080123.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PULVERISATEURS, Pulvérisateur ARLAND Hélium + ...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2025</td>\n",
       "      <td>Station Expérimentale de Kerguéhennec</td>\n",
       "      <td>18560001000016</td>\n",
       "      <td>56021200</td>\n",
       "      <td>70000308</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>Etang Milieu</td>\n",
       "      <td>2.28</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>33.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TRACTEURS CLASSIQUES, Tracteur CLAAS Arion 440...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   millesime                             raisonsoci           siret    pacage  \\\n",
       "0       2025  Station Expérimentale de Kerguéhennec  18560001000016  56021200   \n",
       "1       2025  Station Expérimentale de Kerguéhennec  18560001000016  56021200   \n",
       "2       2025  Station Expérimentale de Kerguéhennec  18560001000016  56021200   \n",
       "3       2025  Station Expérimentale de Kerguéhennec  18560001000016  56021200   \n",
       "4       2025  Station Expérimentale de Kerguéhennec  18560001000016  56021200   \n",
       "5       2025  Station Expérimentale de Kerguéhennec  18560001000016  56021200   \n",
       "6       2025  Station Expérimentale de Kerguéhennec  18560001000016  56021200   \n",
       "7       2025  Station Expérimentale de Kerguéhennec  18560001000016  56021200   \n",
       "8       2025  Station Expérimentale de Kerguéhennec  18560001000016  56021200   \n",
       "9       2025  Station Expérimentale de Kerguéhennec  18560001000016  56021200   \n",
       "\n",
       "      refca  numilot  numparcell       nomparc  surfparc  rang  ...  kqte  \\\n",
       "0  70000308        1          12  Etang Milieu      2.28     1  ...   NaN   \n",
       "1  70000308        1          12  Etang Milieu      2.28     1  ...   NaN   \n",
       "2  70000308        1          12  Etang Milieu      2.28     1  ...   NaN   \n",
       "3  70000308        1          12  Etang Milieu      2.28     1  ...   NaN   \n",
       "4  70000308        1          12  Etang Milieu      2.28     1  ...   NaN   \n",
       "5  70000308        1          12  Etang Milieu      2.28     1  ...   NaN   \n",
       "6  70000308        1          12  Etang Milieu      2.28     1  ...   NaN   \n",
       "7  70000308        1          12  Etang Milieu      2.28     1  ...   NaN   \n",
       "8  70000308        1          12  Etang Milieu      2.28     1  ...   NaN   \n",
       "9  70000308        1          12  Etang Milieu      2.28     1  ...   0.0   \n",
       "\n",
       "  teneurn teneurp teneurk  keq volumebo    codeamm  codegnis  \\\n",
       "0     NaN     NaN     NaN  NaN      NaN  9100296.0       NaN   \n",
       "1     NaN     NaN     NaN  NaN      NaN        NaN       NaN   \n",
       "2     NaN     NaN     NaN  NaN      NaN        NaN       NaN   \n",
       "3     NaN     NaN     NaN  NaN      NaN        NaN   512E355   \n",
       "4     NaN     NaN     NaN  NaN      NaN        NaN   512D830   \n",
       "5     NaN     NaN     NaN  NaN      NaN  2160226.0       NaN   \n",
       "6     NaN     NaN     NaN  NaN    150.0  2080145.0       NaN   \n",
       "7     NaN     NaN     NaN  NaN    100.0  2140235.0       NaN   \n",
       "8     NaN     NaN     NaN  NaN    100.0  2080123.0       NaN   \n",
       "9    33.5     0.0     0.0  NaN      NaN        NaN       NaN   \n",
       "\n",
       "                                            materiel mainoeuvre  \n",
       "0  PULVERISATEURS, Pulvérisateur ARLAND Hélium + ...        NaN  \n",
       "1  TRACTEURS CLASSIQUES, Tracteur JOHN DEERE 6R15...        NaN  \n",
       "2  CULTIVATEURS ET CHISELS, Canadien AMAZONE Ceni...        NaN  \n",
       "3  TASSE-AVANT, Tasse-avant 3m LABBE ROTIEL Roll-...        NaN  \n",
       "4  TASSE-AVANT, Tasse-avant 3m LABBE ROTIEL Roll-...        NaN  \n",
       "5  QUADS, Quad POLARIS 500 cc - DISTRIBUTEUR ENGR...        NaN  \n",
       "6  PULVERISATEURS, Pulvérisateur ARLAND Hélium + ...        NaN  \n",
       "7  PULVERISATEURS, Pulvérisateur ARLAND Hélium + ...        NaN  \n",
       "8  PULVERISATEURS, Pulvérisateur ARLAND Hélium + ...        NaN  \n",
       "9  TRACTEURS CLASSIQUES, Tracteur CLAAS Arion 440...        NaN  \n",
       "\n",
       "[10 rows x 34 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1. EXPLORATION GÉNÉRALE DES DONNÉES\n",
    "print(\"=\"*80)\n",
    "print(\"📊 ANALYSE EXPLORATOIRE COMPLÈTE - DONNÉES AGRICOLES KERGUÉHENNEC\")\n",
    "print(\"=\"*80)\n",
    "\n",
    "# Informations de base\n",
    "print(f\"\\n📋 Informations générales:\")\n",
    "print(f\"   • Nombre d'observations: {df.shape[0]:,}\")\n",
    "print(f\"   • Nombre de variables: {df.shape[1]}\")\n",
    "print(f\"   • Taille mémoire: {df.memory_usage(deep=True).sum() / 1024**2:.2f} MB\")\n",
    "\n",
    "# Aperçu de la structure\n",
    "print(f\"\\n📊 Colonnes du dataset:\")\n",
    "for i, col in enumerate(df.columns, 1):\n",
    "    print(f\"   {i:2d}. {col}\")\n",
    "\n",
    "# Types de données\n",
    "print(f\"\\n🔢 Types de données:\")\n",
    "type_counts = df.dtypes.value_counts()\n",
    "for dtype, count in type_counts.items():\n",
    "    print(f\"   • {dtype}: {count} colonnes\")\n",
    "\n",
    "# Valeurs manquantes\n",
    "print(f\"\\n❓ Valeurs manquantes:\")\n",
    "missing_data = df.isnull().sum()\n",
    "missing_data = missing_data[missing_data > 0].sort_values(ascending=False)\n",
    "if len(missing_data) > 0:\n",
    "    for col, count in missing_data.items():\n",
    "        pct = (count / len(df)) * 100\n",
    "        print(f\"   • {col}: {count} ({pct:.1f}%)\")\n",
    "else:\n",
    "    print(\"   ✅ Aucune valeur manquante détectée\")\n",
    "\n",
    "# Statistiques descriptives pour les colonnes numériques\n",
    "print(f\"\\n📈 Colonnes numériques:\")\n",
    "numeric_cols = df.select_dtypes(include=[np.number]).columns\n",
    "print(f\"   {len(numeric_cols)} colonnes numériques identifiées\")\n",
    "for col in numeric_cols:\n",
    "    print(f\"   • {col}\")\n",
    "\n",
    "print(f\"\\n📋 Aperçu des 10 premières lignes:\")\n",
    "df.head(10)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68cb5093",
   "metadata": {},
   "source": [
    "### 🌾 Analyse des parcelles agricoles\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "18ef7022",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==================================================\n",
      "🌾 ANALYSE DES PARCELLES AGRICOLES\n",
      "==================================================\n",
      "\n",
      "📊 Répartition des parcelles:\n",
      "   • Nombre total de parcelles: 45\n",
      "   • Surface totale couverte: 1183.03 hectares\n",
      "   • Surface moyenne par parcelle: 1.81 hectares\n",
      "   • Surface médiane par parcelle: 0.98 hectares\n",
      "\n",
      "📋 Top 10 des parcelles par surface:\n",
      "                                 Surface_ha                 Culture  \\\n",
      "numparcell nomparc                                                    \n",
      "1102       Bourg bas                   6.73        blé tendre hiver   \n",
      "1301       Bois Guillemin              5.97        blé tendre hiver   \n",
      "1101       Bourg Haut                  5.55              maïs grain   \n",
      "1001       Carancier Ht                5.46             colza hiver   \n",
      "48         Etang Bois                  3.36  haricot vert industrie   \n",
      "44         La Défriche                 3.25             CIPAN autre   \n",
      "2          Kersuzan Bas                3.05             CIPAN autre   \n",
      "81          Charbonnerie Entrée        3.01             CIPAN autre   \n",
      "11         Cléhury                     2.97              orge hiver   \n",
      "5          Etang Moulin                2.85             CIPAN autre   \n",
      "\n",
      "                                 Nb_interventions  Types_evenements  \\\n",
      "numparcell nomparc                                                    \n",
      "1102       Bourg bas                           14                 8   \n",
      "1301       Bois Guillemin                      16                 7   \n",
      "1101       Bourg Haut                          12                 7   \n",
      "1001       Carancier Ht                        18                 8   \n",
      "48         Etang Bois                          26                 6   \n",
      "44         La Défriche                          8                 6   \n",
      "2          Kersuzan Bas                        17                 9   \n",
      "81          Charbonnerie Entrée                18                 9   \n",
      "11         Cléhury                             21                11   \n",
      "5          Etang Moulin                        14                 7   \n",
      "\n",
      "                                 Types_produits  Quantite_totale  \n",
      "numparcell nomparc                                                \n",
      "1102       Bourg bas                          4          4105.33  \n",
      "1301       Bois Guillemin                     5          3237.86  \n",
      "1101       Bourg Haut                         5         12259.38  \n",
      "1001       Carancier Ht                       9          2591.22  \n",
      "48         Etang Bois                         4          1297.80  \n",
      "44         La Défriche                        1           113.75  \n",
      "2          Kersuzan Bas                       5          6247.16  \n",
      "81          Charbonnerie Entrée               4          6206.70  \n",
      "11         Cléhury                            8          1571.34  \n",
      "5          Etang Moulin                       3            27.24  \n",
      "\n",
      "🌱 Répartition des cultures:\n",
      "   • blé tendre hiver: 135 interventions (20.7%)\n",
      "   • maïs grain: 127 interventions (19.4%)\n",
      "   • colza hiver: 118 interventions (18.1%)\n",
      "   • orge hiver: 61 interventions (9.3%)\n",
      "   • CIPAN autre: 46 interventions (7.0%)\n",
      "   • feverole printemps: 35 interventions (5.4%)\n",
      "   • haricot vert industrie: 26 interventions (4.0%)\n",
      "   • soja: 18 interventions (2.8%)\n",
      "   • avoine printemps: 15 interventions (2.3%)\n",
      "   • triticale hiver: 14 interventions (2.1%)\n",
      "   • sarrasin: 13 interventions (2.0%)\n",
      "   • luzerne: 13 interventions (2.0%)\n",
      "   • méteil grain céréale <30% légum: 10 interventions (1.5%)\n",
      "   • avoine hiver: 9 interventions (1.4%)\n",
      "   • tournesol: 7 interventions (1.1%)\n",
      "   • lupin bleu printemps: 6 interventions (0.9%)\n",
      "\n",
      "📏 Surfaces par type de culture:\n",
      "                                 Surface_totale_ha  Surface_moyenne_ha  \\\n",
      "libelleusag                                                              \n",
      "CIPAN autre                                  70.00                1.52   \n",
      "avoine hiver                                  4.59                0.51   \n",
      "avoine printemps                             14.44                0.96   \n",
      "blé tendre hiver                            304.50                2.26   \n",
      "colza hiver                                 217.42                1.84   \n",
      "feverole printemps                           30.92                0.88   \n",
      "haricot vert industrie                       87.36                3.36   \n",
      "lupin bleu printemps                          3.66                0.61   \n",
      "luzerne                                      27.30                2.10   \n",
      "maïs grain                                  245.53                1.93   \n",
      "méteil grain céréale <30% légum               3.20                0.32   \n",
      "orge hiver                                  121.65                1.99   \n",
      "sarrasin                                     23.88                1.84   \n",
      "soja                                         12.06                0.67   \n",
      "tournesol                                     5.88                0.84   \n",
      "triticale hiver                              10.64                0.76   \n",
      "\n",
      "                                 Nb_parcelles  \n",
      "libelleusag                                    \n",
      "CIPAN autre                                46  \n",
      "avoine hiver                                9  \n",
      "avoine printemps                           15  \n",
      "blé tendre hiver                          135  \n",
      "colza hiver                               118  \n",
      "feverole printemps                         35  \n",
      "haricot vert industrie                     26  \n",
      "lupin bleu printemps                        6  \n",
      "luzerne                                    13  \n",
      "maïs grain                                127  \n",
      "méteil grain céréale <30% légum            10  \n",
      "orge hiver                                 61  \n",
      "sarrasin                                   13  \n",
      "soja                                       18  \n",
      "tournesol                                   7  \n",
      "triticale hiver                            14  \n",
      "\n",
      "📊 Statistiques des surfaces de parcelles:\n",
      "   • count: 653.00 ha\n",
      "   • mean: 1.81 ha\n",
      "   • std: 1.61 ha\n",
      "   • min: 0.11 ha\n",
      "   • 25%: 0.67 ha\n",
      "   • 50%: 0.98 ha\n",
      "   • 75%: 2.85 ha\n",
      "   • max: 6.73 ha\n"
     ]
    }
   ],
   "source": [
    "# 2. ANALYSE DES PARCELLES\n",
    "print(\"=\"*50)\n",
    "print(\"🌾 ANALYSE DES PARCELLES AGRICOLES\")\n",
    "print(\"=\"*50)\n",
    "\n",
    "# Informations sur les parcelles\n",
    "print(f\"\\n📊 Répartition des parcelles:\")\n",
    "print(f\"   • Nombre total de parcelles: {df['numparcell'].nunique()}\")\n",
    "print(f\"   • Surface totale couverte: {df['surfparc'].sum():.2f} hectares\")\n",
    "print(f\"   • Surface moyenne par parcelle: {df['surfparc'].mean():.2f} hectares\")\n",
    "print(f\"   • Surface médiane par parcelle: {df['surfparc'].median():.2f} hectares\")\n",
    "\n",
    "# Analyse par parcelle avec leurs caractéristiques\n",
    "parcel_summary = df.groupby(['numparcell', 'nomparc']).agg({\n",
    "    'surfparc': 'first',\n",
    "    'libelleusag': 'first',  # Culture principale\n",
    "    'millesime': 'count',    # Nombre d'interventions\n",
    "    'libevenem': lambda x: len(x.unique()),  # Types d'événements\n",
    "    'familleprod': lambda x: len(x.dropna().unique()),  # Types de produits\n",
    "    'quantitetot': 'sum'     # Quantité totale utilisée\n",
    "}).round(2)\n",
    "\n",
    "parcel_summary.columns = ['Surface_ha', 'Culture', 'Nb_interventions', 'Types_evenements', 'Types_produits', 'Quantite_totale']\n",
    "parcel_summary = parcel_summary.sort_values('Surface_ha', ascending=False)\n",
    "\n",
    "print(f\"\\n📋 Top 10 des parcelles par surface:\")\n",
    "print(parcel_summary.head(10))\n",
    "\n",
    "# Répartition des cultures\n",
    "print(f\"\\n🌱 Répartition des cultures:\")\n",
    "culture_repartition = df['libelleusag'].value_counts()\n",
    "for culture, count in culture_repartition.items():\n",
    "    pct = (count / len(df)) * 100\n",
    "    print(f\"   • {culture}: {count} interventions ({pct:.1f}%)\")\n",
    "\n",
    "# Analyse des surfaces par culture\n",
    "surface_par_culture = df.groupby('libelleusag')['surfparc'].agg(['sum', 'mean', 'count']).round(2)\n",
    "surface_par_culture.columns = ['Surface_totale_ha', 'Surface_moyenne_ha', 'Nb_parcelles']\n",
    "print(f\"\\n📏 Surfaces par type de culture:\")\n",
    "print(surface_par_culture)\n",
    "\n",
    "# Statistiques des surfaces\n",
    "print(f\"\\n📊 Statistiques des surfaces de parcelles:\")\n",
    "surface_stats = df['surfparc'].describe()\n",
    "for stat, value in surface_stats.items():\n",
    "    print(f\"   • {stat}: {value:.2f} ha\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3b45cda",
   "metadata": {},
   "source": [
    "### 🧪 Analyse des interventions herbicides (focus sur les adventices)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3608fa75",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "============================================================\n",
      "🧪 ANALYSE DES INTERVENTIONS HERBICIDES - PRESSION ADVENTICES\n",
      "============================================================\n",
      "\n",
      "📊 Statistiques générales herbicides:\n",
      "   • Total interventions herbicides: 103\n",
      "   • Pourcentage du dataset: 15.8%\n",
      "   • Parcelles traitées: 26\n",
      "   • Produits herbicides utilisés: 33\n",
      "\n",
      "🎯 Types d'événements herbicides:\n",
      "   • Traitement et protection des cultures: 103 (100.0%)\n",
      "\n",
      "🧪 Top 10 des produits herbicides:\n",
      "   • LUMEO: 7 applications, 1.34 L/Kg total\n",
      "   • PEAK: 7 applications, 0.07 L/Kg total\n",
      "   • GLISTER ULTRA 360: 6 applications, 36.73 L/Kg total\n",
      "   • BISCOTO: 5 applications, 6.05 L/Kg total\n",
      "   • ISARD: 5 applications, 8.07 L/Kg total\n",
      "   • FREEWAY 480: 5 applications, 4.00 L/Kg total\n",
      "   • NISSHIN PREMIUM 6 OD: 5 applications, 2.10 L/Kg total\n",
      "   • ALABAMA: 4 applications, 3.98 L/Kg total\n",
      "   • CENT-7: 4 applications, 0.62 L/Kg total\n",
      "   • CORUM: 4 applications, 3.83 L/Kg total\n",
      "\n",
      "📈 Top 10 parcelles avec plus forte intensité herbicide (quantité/ha):\n",
      "                                                                Quantite_totale  \\\n",
      "numparcell nomparc                      libelleusag                               \n",
      "50         Lann Chebot Le Roch          blé tendre hiver                   4.64   \n",
      "16         Champ ferme W du sol parking maïs grain                         2.07   \n",
      "48         Etang Bois                   haricot vert industrie            15.44   \n",
      "39         Champ ferme transfert        blé tendre hiver                   4.34   \n",
      "1301       Bois Guillemin               blé tendre hiver                  22.94   \n",
      "1201       Champ Robert                 blé tendre hiver                   4.83   \n",
      "1102       Bourg bas                    blé tendre hiver                  21.95   \n",
      "1101       Bourg Haut                   maïs grain                        16.73   \n",
      "11         Cléhury                      orge hiver                         7.51   \n",
      "38         Champ ferme W du sol         colza hiver                        1.49   \n",
      "\n",
      "                                                                Nb_applications  \\\n",
      "numparcell nomparc                      libelleusag                               \n",
      "50         Lann Chebot Le Roch          blé tendre hiver                      3   \n",
      "16         Champ ferme W du sol parking maïs grain                            6   \n",
      "48         Etang Bois                   haricot vert industrie                9   \n",
      "39         Champ ferme transfert        blé tendre hiver                      4   \n",
      "1301       Bois Guillemin               blé tendre hiver                      3   \n",
      "1201       Champ Robert                 blé tendre hiver                      4   \n",
      "1102       Bourg bas                    blé tendre hiver                      3   \n",
      "1101       Bourg Haut                   maïs grain                            3   \n",
      "11         Cléhury                      orge hiver                            3   \n",
      "38         Champ ferme W du sol         colza hiver                           4   \n",
      "\n",
      "                                                                Nb_produits_diff  \\\n",
      "numparcell nomparc                      libelleusag                                \n",
      "50         Lann Chebot Le Roch          blé tendre hiver                       3   \n",
      "16         Champ ferme W du sol parking maïs grain                             6   \n",
      "48         Etang Bois                   haricot vert industrie                 6   \n",
      "39         Champ ferme transfert        blé tendre hiver                       4   \n",
      "1301       Bois Guillemin               blé tendre hiver                       3   \n",
      "1201       Champ Robert                 blé tendre hiver                       4   \n",
      "1102       Bourg bas                    blé tendre hiver                       3   \n",
      "1101       Bourg Haut                   maïs grain                             3   \n",
      "11         Cléhury                      orge hiver                             3   \n",
      "38         Champ ferme W du sol         colza hiver                            4   \n",
      "\n",
      "                                                                Surface_ha  \\\n",
      "numparcell nomparc                      libelleusag                          \n",
      "50         Lann Chebot Le Roch          blé tendre hiver              0.90   \n",
      "16         Champ ferme W du sol parking maïs grain                    0.43   \n",
      "48         Etang Bois                   haricot vert industrie        3.36   \n",
      "39         Champ ferme transfert        blé tendre hiver              0.98   \n",
      "1301       Bois Guillemin               blé tendre hiver              5.97   \n",
      "1201       Champ Robert                 blé tendre hiver              1.34   \n",
      "1102       Bourg bas                    blé tendre hiver              6.73   \n",
      "1101       Bourg Haut                   maïs grain                    5.55   \n",
      "11         Cléhury                      orge hiver                    2.97   \n",
      "38         Champ ferme W du sol         colza hiver                   0.60   \n",
      "\n",
      "                                                                Intensite_par_ha  \n",
      "numparcell nomparc                      libelleusag                               \n",
      "50         Lann Chebot Le Roch          blé tendre hiver                    5.16  \n",
      "16         Champ ferme W du sol parking maïs grain                          4.81  \n",
      "48         Etang Bois                   haricot vert industrie              4.60  \n",
      "39         Champ ferme transfert        blé tendre hiver                    4.43  \n",
      "1301       Bois Guillemin               blé tendre hiver                    3.84  \n",
      "1201       Champ Robert                 blé tendre hiver                    3.60  \n",
      "1102       Bourg bas                    blé tendre hiver                    3.26  \n",
      "1101       Bourg Haut                   maïs grain                          3.01  \n",
      "11         Cléhury                      orge hiver                          2.53  \n",
      "38         Champ ferme W du sol         colza hiver                         2.48  \n",
      "\n",
      "📅 Répartition temporelle des traitements herbicides:\n",
      "   • Fév: 8 traitements\n",
      "   • Mar: 5 traitements\n",
      "   • Avr: 10 traitements\n",
      "   • Mai: 24 traitements\n",
      "   • Jun: 18 traitements\n",
      "   • Jul: 4 traitements\n",
      "   • Aoû: 7 traitements\n",
      "   • Sep: 11 traitements\n",
      "   • Oct: 2 traitements\n",
      "   • Nov: 14 traitements\n",
      "\n",
      "🔗 Analyse de corrélation:\n",
      "   • Corrélation Surface vs Intensité herbicide: 0.234\n",
      "   • Corrélation Surface vs Nb applications: 0.132\n"
     ]
    }
   ],
   "source": [
    "# 3. ANALYSE FOCUS HERBICIDES ET ADVENTICES\n",
    "print(\"=\"*60)\n",
    "print(\"🧪 ANALYSE DES INTERVENTIONS HERBICIDES - PRESSION ADVENTICES\")\n",
    "print(\"=\"*60)\n",
    "\n",
    "# Filtrer les interventions herbicides\n",
    "herbicides_df = df[df['familleprod'] == 'Herbicides'].copy()\n",
    "\n",
    "print(f\"\\n📊 Statistiques générales herbicides:\")\n",
    "print(f\"   • Total interventions herbicides: {len(herbicides_df)}\")\n",
    "print(f\"   • Pourcentage du dataset: {(len(herbicides_df)/len(df)*100):.1f}%\")\n",
    "print(f\"   • Parcelles traitées: {herbicides_df['numparcell'].nunique()}\")\n",
    "print(f\"   • Produits herbicides utilisés: {herbicides_df['produit'].nunique()}\")\n",
    "\n",
    "# Analyse des types d'événements herbicides\n",
    "print(f\"\\n🎯 Types d'événements herbicides:\")\n",
    "herbicide_events = herbicides_df['libevenem'].value_counts()\n",
    "for event, count in herbicide_events.items():\n",
    "    pct = (count / len(herbicides_df)) * 100\n",
    "    print(f\"   • {event}: {count} ({pct:.1f}%)\")\n",
    "\n",
    "# Produits herbicides les plus utilisés\n",
    "print(f\"\\n🧪 Top 10 des produits herbicides:\")\n",
    "top_herbicides = herbicides_df['produit'].value_counts().head(10)\n",
    "for produit, count in top_herbicides.items():\n",
    "    # Calculer la quantité totale pour ce produit\n",
    "    qty_total = herbicides_df[herbicides_df['produit'] == produit]['quantitetot'].sum()\n",
    "    print(f\"   • {produit}: {count} applications, {qty_total:.2f} L/Kg total\")\n",
    "\n",
    "# Analyse par parcelle - Intensité herbicide\n",
    "herbicide_intensity = herbicides_df.groupby(['numparcell', 'nomparc', 'libelleusag']).agg({\n",
    "    'quantitetot': 'sum',        # Quantité totale herbicide\n",
    "    'datedebut': 'count',        # Nombre d'applications\n",
    "    'produit': lambda x: len(x.unique()),  # Nombre de produits différents\n",
    "    'surfparc': 'first'          # Surface de la parcelle\n",
    "}).round(2)\n",
    "\n",
    "herbicide_intensity.columns = ['Quantite_totale', 'Nb_applications', 'Nb_produits_diff', 'Surface_ha']\n",
    "\n",
    "# Calculer l'intensité par hectare\n",
    "herbicide_intensity['Intensite_par_ha'] = (herbicide_intensity['Quantite_totale'] / \n",
    "                                         herbicide_intensity['Surface_ha']).round(2)\n",
    "\n",
    "herbicide_intensity = herbicide_intensity.sort_values('Intensite_par_ha', ascending=False)\n",
    "\n",
    "print(f\"\\n📈 Top 10 parcelles avec plus forte intensité herbicide (quantité/ha):\")\n",
    "print(herbicide_intensity.head(10))\n",
    "\n",
    "# Analyse temporelle des traitements herbicides\n",
    "herbicides_df['datedebut'] = pd.to_datetime(herbicides_df['datedebut'], format='%d/%m/%y', errors='coerce')\n",
    "herbicides_df['mois'] = herbicides_df['datedebut'].dt.month\n",
    "herbicides_df['jour_annee'] = herbicides_df['datedebut'].dt.dayofyear\n",
    "\n",
    "print(f\"\\n📅 Répartition temporelle des traitements herbicides:\")\n",
    "monthly_herbicides = herbicides_df['mois'].value_counts().sort_index()\n",
    "mois_noms = ['Jan', 'Fév', 'Mar', 'Avr', 'Mai', 'Jun', 'Jul', 'Aoû', 'Sep', 'Oct', 'Nov', 'Déc']\n",
    "for mois, count in monthly_herbicides.items():\n",
    "    if pd.notna(mois):\n",
    "        print(f\"   • {mois_noms[int(mois)-1]}: {count} traitements\")\n",
    "\n",
    "# Corrélation surface/intensité herbicide\n",
    "print(f\"\\n🔗 Analyse de corrélation:\")\n",
    "if len(herbicide_intensity) > 1:\n",
    "    corr_surface_intensite = herbicide_intensity['Surface_ha'].corr(herbicide_intensity['Intensite_par_ha'])\n",
    "    print(f\"   • Corrélation Surface vs Intensité herbicide: {corr_surface_intensite:.3f}\")\n",
    "    \n",
    "    corr_surface_applications = herbicide_intensity['Surface_ha'].corr(herbicide_intensity['Nb_applications'])\n",
    "    print(f\"   • Corrélation Surface vs Nb applications: {corr_surface_applications:.3f}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "239a3c64",
   "metadata": {},
   "source": [
    "### 📊 Visualisations interactives\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ff6b7f33",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "========================================\n",
      "📊 VISUALISATIONS DES DONNÉES\n",
      "========================================\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1200 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x800 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ Graphiques générés avec succès!\n"
     ]
    }
   ],
   "source": [
    "# 4. VISUALISATIONS INTERACTIVES\n",
    "print(\"=\"*40)\n",
    "print(\"📊 VISUALISATIONS DES DONNÉES\")\n",
    "print(\"=\"*40)\n",
    "\n",
    "# Configuration des graphiques\n",
    "plt.rcParams['figure.figsize'] = (15, 10)\n",
    "\n",
    "# 1. Distribution des surfaces de parcelles\n",
    "fig, axes = plt.subplots(2, 2, figsize=(15, 12))\n",
    "fig.suptitle('📊 Analyse des Parcelles Agricoles - Station Kerguéhennec', fontsize=16, fontweight='bold')\n",
    "\n",
    "# Histogramme des surfaces\n",
    "axes[0,0].hist(df['surfparc'], bins=20, color='skyblue', alpha=0.7, edgecolor='black')\n",
    "axes[0,0].set_title('Distribution des surfaces de parcelles')\n",
    "axes[0,0].set_xlabel('Surface (hectares)')\n",
    "axes[0,0].set_ylabel('Nombre de parcelles')\n",
    "axes[0,0].grid(True, alpha=0.3)\n",
    "\n",
    "# Répartition des cultures\n",
    "culture_counts = df['libelleusag'].value_counts()\n",
    "axes[0,1].pie(culture_counts.values, labels=culture_counts.index, autopct='%1.1f%%', startangle=90)\n",
    "axes[0,1].set_title('Répartition des cultures')\n",
    "\n",
    "# Surface par culture\n",
    "surface_par_culture = df.groupby('libelleusag')['surfparc'].sum().sort_values(ascending=True)\n",
    "axes[1,0].barh(surface_par_culture.index, surface_par_culture.values, color='lightgreen')\n",
    "axes[1,0].set_title('Surface totale par culture')\n",
    "axes[1,0].set_xlabel('Surface totale (hectares)')\n",
    "\n",
    "# Box plot des surfaces par culture\n",
    "df.boxplot(column='surfparc', by='libelleusag', ax=axes[1,1])\n",
    "axes[1,1].set_title('Distribution des surfaces par culture')\n",
    "axes[1,1].set_xlabel('Type de culture')\n",
    "axes[1,1].set_ylabel('Surface (hectares)')\n",
    "plt.setp(axes[1,1].xaxis.get_majorticklabels(), rotation=45)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# 2. Analyse des interventions par famille de produits\n",
    "plt.figure(figsize=(15, 8))\n",
    "\n",
    "# Graphique 1: Nombre d'interventions par famille\n",
    "plt.subplot(2, 2, 1)\n",
    "family_counts = df['familleprod'].value_counts()\n",
    "plt.bar(family_counts.index, family_counts.values, color='coral')\n",
    "plt.title('Nombre d\\'interventions par famille de produits')\n",
    "plt.xlabel('Famille de produits')\n",
    "plt.ylabel('Nombre d\\'interventions')\n",
    "plt.xticks(rotation=45)\n",
    "plt.grid(True, alpha=0.3)\n",
    "\n",
    "# Graphique 2: Quantités utilisées par famille\n",
    "plt.subplot(2, 2, 2)\n",
    "quantity_by_family = df.groupby('familleprod')['quantitetot'].sum().sort_values(ascending=False)\n",
    "plt.bar(quantity_by_family.index, quantity_by_family.values, color='lightblue')\n",
    "plt.title('Quantités totales par famille de produits')\n",
    "plt.xlabel('Famille de produits')\n",
    "plt.ylabel('Quantité totale')\n",
    "plt.xticks(rotation=45)\n",
    "plt.grid(True, alpha=0.3)\n",
    "\n",
    "# Graphique 3: Focus herbicides - Timeline\n",
    "plt.subplot(2, 1, 2)\n",
    "if len(herbicides_df) > 0 and not herbicides_df['datedebut'].isna().all():\n",
    "    # Compter les applications par mois\n",
    "    herbicides_monthly = herbicides_df.groupby('mois').size()\n",
    "    months = ['Jan', 'Fév', 'Mar', 'Avr', 'Mai', 'Jun', 'Jul', 'Aoû', 'Sep', 'Oct', 'Nov', 'Déc']\n",
    "    month_data = [herbicides_monthly.get(i, 0) for i in range(1, 13)]\n",
    "    \n",
    "    plt.bar(months, month_data, color='red', alpha=0.7)\n",
    "    plt.title('🧪 Répartition mensuelle des traitements herbicides')\n",
    "    plt.xlabel('Mois')\n",
    "    plt.ylabel('Nombre de traitements')\n",
    "    plt.grid(True, alpha=0.3)\n",
    "else:\n",
    "    plt.text(0.5, 0.5, 'Données temporelles herbicides non disponibles', \n",
    "             horizontalalignment='center', verticalalignment='center', transform=plt.gca().transAxes)\n",
    "    plt.title('Timeline des herbicides - Données manquantes')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "print(\"✅ Graphiques générés avec succès!\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cca4147a",
   "metadata": {},
   "source": [
    "### 🎯 Indicateur de Fréquence de Traitement (IFT) et analyse des risques\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0c577d07",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "============================================================\n",
      "🎯 CALCUL IFT ET ÉVALUATION DES RISQUES ADVENTICES\n",
      "============================================================\n",
      "\n",
      "📊 Répartition des parcelles par niveau de risque adventice:\n",
      "   • TRÈS FAIBLE: 37 parcelles (55.2%)\n",
      "   • FAIBLE: 8 parcelles (11.9%)\n",
      "   • MODÉRÉ: 6 parcelles (9.0%)\n",
      "   • ÉLEVÉ: 9 parcelles (13.4%)\n",
      "   • TRÈS ÉLEVÉ: 7 parcelles (10.4%)\n",
      "\n",
      "🌾 TOP 10 parcelles à FAIBLE RISQUE pour cultures sensibles (pois, haricot):\n",
      "   • Parcelle 2 (Kersuzan Bas): CIPAN autre, 3.05ha\n",
      "     - Risque: TRÈS FAIBLE, IFT≈0.0, 0 herbicides\n",
      "   • Parcelle 4 (Penderff 1): CIPAN autre, 0.37ha\n",
      "     - Risque: TRÈS FAIBLE, IFT≈0.0, 0 herbicides\n",
      "   • Parcelle 4 (Penderff 1): feverole printemps, 0.37ha\n",
      "     - Risque: TRÈS FAIBLE, IFT≈0.0, 0 herbicides\n",
      "   • Parcelle 6 (Lann Chebot chemin): CIPAN autre, 0.11ha\n",
      "     - Risque: TRÈS FAIBLE, IFT≈0.0, 0 herbicides\n",
      "   • Parcelle 10 (Penderff 7 analytique): CIPAN autre, 1.56ha\n",
      "     - Risque: TRÈS FAIBLE, IFT≈0.0, 0 herbicides\n",
      "   • Parcelle 10 (Penderff 7 analytique): avoine printemps, 1.56ha\n",
      "     - Risque: TRÈS FAIBLE, IFT≈0.0, 0 herbicides\n",
      "   • Parcelle 14 ( Grand-Champ 1 essai soja 25): soja, 0.53ha\n",
      "     - Risque: TRÈS FAIBLE, IFT≈0.0, 0 herbicides\n",
      "   • Parcelle 29 (Charbonnerie PK): CIPAN autre, 1.15ha\n",
      "     - Risque: TRÈS FAIBLE, IFT≈0.0, 0 herbicides\n",
      "   • Parcelle 33 (Penderff Luzerne): luzerne, 2.10ha\n",
      "     - Risque: TRÈS FAIBLE, IFT≈0.0, 0 herbicides\n",
      "   • Parcelle 44 (La Défriche): CIPAN autre, 3.25ha\n",
      "     - Risque: TRÈS FAIBLE, IFT≈0.0, 0 herbicides\n",
      "\n",
      "⚠️  TOP 5 parcelles à RISQUE ÉLEVÉ (surveillance prioritaire):\n",
      "   • Parcelle 5 (Etang Moulin): maïs grain, 2.85ha\n",
      "     - Risque: TRÈS ÉLEVÉ, IFT≈2.11, 6 herbicides\n",
      "   • Parcelle 22 (Champ ferme W du sol): colza hiver, 2.02ha\n",
      "     - Risque: TRÈS ÉLEVÉ, IFT≈2.26, 8 herbicides\n",
      "   • Parcelle 48 (Etang Bois): haricot vert industrie, 3.36ha\n",
      "     - Risque: TRÈS ÉLEVÉ, IFT≈4.59, 9 herbicides\n",
      "   • Parcelle 16 (Champ ferme W du sol parking): maïs grain, 0.43ha\n",
      "     - Risque: TRÈS ÉLEVÉ, IFT≈4.82, 6 herbicides\n",
      "   • Parcelle 50 (Lann Chebot Le Roch): blé tendre hiver, 0.90ha\n",
      "     - Risque: TRÈS ÉLEVÉ, IFT≈5.16, 3 herbicides\n",
      "\n",
      "🌱 Analyse du risque adventice par type de culture:\n",
      "   • avoine hiver: IFT moyen = 0.0, Risque dominant = TRÈS FAIBLE\n",
      "   • avoine printemps: IFT moyen = 0.0, Risque dominant = TRÈS FAIBLE\n",
      "   • lupin bleu printemps: IFT moyen = 0.0, Risque dominant = TRÈS FAIBLE\n",
      "   • luzerne: IFT moyen = 0.0, Risque dominant = TRÈS FAIBLE\n",
      "   • méteil grain céréale <30% légum: IFT moyen = 0.0, Risque dominant = TRÈS FAIBLE\n",
      "   • sarrasin: IFT moyen = 0.0, Risque dominant = TRÈS FAIBLE\n",
      "   • soja: IFT moyen = 0.0, Risque dominant = TRÈS FAIBLE\n",
      "   • tournesol: IFT moyen = 0.0, Risque dominant = TRÈS FAIBLE\n",
      "   • triticale hiver: IFT moyen = 0.0, Risque dominant = TRÈS FAIBLE\n",
      "   • CIPAN autre: IFT moyen = 0.08, Risque dominant = TRÈS FAIBLE\n",
      "   • feverole printemps: IFT moyen = 0.35, Risque dominant = TRÈS FAIBLE\n",
      "   • orge hiver: IFT moyen = 1.24, Risque dominant = MODÉRÉ\n",
      "   • colza hiver: IFT moyen = 1.4, Risque dominant = ÉLEVÉ\n",
      "   • maïs grain: IFT moyen = 1.4, Risque dominant = TRÈS ÉLEVÉ\n",
      "   • blé tendre hiver: IFT moyen = 2.76, Risque dominant = ÉLEVÉ\n",
      "   • haricot vert industrie: IFT moyen = 4.59, Risque dominant = TRÈS ÉLEVÉ\n",
      "\n",
      "📈 Statistiques IFT herbicides:\n",
      "   • count: 67.00\n",
      "   • mean: 0.91\n",
      "   • std: 1.42\n",
      "   • min: 0.00\n",
      "   • 25%: 0.00\n",
      "   • 50%: 0.00\n",
      "   • 75%: 1.65\n",
      "   • max: 5.16\n",
      "\n",
      "💾 Données d'analyse des risques prêtes pour modélisation: 67 parcelles analysées\n"
     ]
    }
   ],
   "source": [
    "# 5. CALCUL DE L'IFT ET ANALYSE DES RISQUES\n",
    "print(\"=\"*60)\n",
    "print(\"🎯 CALCUL IFT ET ÉVALUATION DES RISQUES ADVENTICES\")\n",
    "print(\"=\"*60)\n",
    "\n",
    "# Calcul de l'IFT simplifié (approximation)\n",
    "# IFT = somme des (dose appliquée / dose homologuée) pour chaque traitement\n",
    "\n",
    "# Créer un dataset d'analyse des risques par parcelle\n",
    "# D'abord, calculer les quantités d'herbicides par parcelle\n",
    "herbicide_quantities = df[df['familleprod'] == 'Herbicides'].groupby(['numparcell', 'nomparc', 'libelleusag', 'surfparc'])['quantitetot'].sum().fillna(0)\n",
    "\n",
    "# Ensuite, analyse générale par parcelle\n",
    "risk_analysis = df.groupby(['numparcell', 'nomparc', 'libelleusag', 'surfparc']).agg({\n",
    "    # Comptage des interventions par type\n",
    "    'familleprod': lambda x: (x == 'Herbicides').sum(),  # Nb traitements herbicides\n",
    "    'libevenem': lambda x: len(x.unique()),  # Diversité des événements\n",
    "    'produit': lambda x: len(x.unique()),  # Diversité des produits\n",
    "    'datedebut': 'count'  # Total interventions\n",
    "}).round(2)\n",
    "\n",
    "# Ajouter les quantités d'herbicides\n",
    "risk_analysis['Quantite_herbicides'] = herbicide_quantities.reindex(risk_analysis.index, fill_value=0)\n",
    "\n",
    "risk_analysis.columns = ['Nb_herbicides', 'Diversite_evenements', 'Diversite_produits', 'Total_interventions', 'Quantite_herbicides']\n",
    "\n",
    "# Calcul d'indicateurs de risque\n",
    "risk_analysis['IFT_herbicide_approx'] = (risk_analysis['Quantite_herbicides'] / \n",
    "                                        risk_analysis.index.get_level_values('surfparc')).round(2)\n",
    "\n",
    "risk_analysis['Intensite_intervention'] = (risk_analysis['Total_interventions'] / \n",
    "                                         risk_analysis.index.get_level_values('surfparc')).round(1)\n",
    "\n",
    "# Classification du risque adventice (basée sur l'intensité herbicide)\n",
    "def classify_risk(row):\n",
    "    ift = row['IFT_herbicide_approx']\n",
    "    nb_herb = row['Nb_herbicides']\n",
    "    \n",
    "    if ift == 0 and nb_herb == 0:\n",
    "        return 'TRÈS FAIBLE'  # Aucun herbicide\n",
    "    elif ift < 1 and nb_herb <= 1:\n",
    "        return 'FAIBLE'  # Peu d'herbicides\n",
    "    elif ift < 3 and nb_herb <= 3:\n",
    "        return 'MODÉRÉ'  # Usage modéré\n",
    "    elif ift < 5 and nb_herb <= 5:\n",
    "        return 'ÉLEVÉ'  # Usage important\n",
    "    else:\n",
    "        return 'TRÈS ÉLEVÉ'  # Usage intensif\n",
    "\n",
    "risk_analysis['Risque_adventice'] = risk_analysis.apply(classify_risk, axis=1)\n",
    "\n",
    "# Tri par risque croissant (pour identifier les parcelles favorables)\n",
    "risk_order = ['TRÈS FAIBLE', 'FAIBLE', 'MODÉRÉ', 'ÉLEVÉ', 'TRÈS ÉLEVÉ']\n",
    "risk_analysis['Risk_Score'] = risk_analysis['Risque_adventice'].map({r: i for i, r in enumerate(risk_order)})\n",
    "risk_analysis_sorted = risk_analysis.sort_values(['Risk_Score', 'IFT_herbicide_approx'])\n",
    "\n",
    "print(f\"\\n📊 Répartition des parcelles par niveau de risque adventice:\")\n",
    "risk_distribution = risk_analysis['Risque_adventice'].value_counts()[risk_order]\n",
    "for risk_level, count in risk_distribution.items():\n",
    "    pct = (count / len(risk_analysis)) * 100\n",
    "    print(f\"   • {risk_level}: {count} parcelles ({pct:.1f}%)\")\n",
    "\n",
    "print(f\"\\n🌾 TOP 10 parcelles à FAIBLE RISQUE pour cultures sensibles (pois, haricot):\")\n",
    "low_risk_parcels = risk_analysis_sorted.head(10)\n",
    "for idx, row in low_risk_parcels.iterrows():\n",
    "    parcelle, nom, culture, surface = idx\n",
    "    print(f\"   • Parcelle {parcelle} ({nom}): {culture}, {surface:.2f}ha\")\n",
    "    print(f\"     - Risque: {row['Risque_adventice']}, IFT≈{row['IFT_herbicide_approx']}, {row['Nb_herbicides']} herbicides\")\n",
    "\n",
    "print(f\"\\n⚠️  TOP 5 parcelles à RISQUE ÉLEVÉ (surveillance prioritaire):\")\n",
    "high_risk_parcels = risk_analysis_sorted.tail(5)\n",
    "for idx, row in high_risk_parcels.iterrows():\n",
    "    parcelle, nom, culture, surface = idx\n",
    "    print(f\"   • Parcelle {parcelle} ({nom}): {culture}, {surface:.2f}ha\")\n",
    "    print(f\"     - Risque: {row['Risque_adventice']}, IFT≈{row['IFT_herbicide_approx']}, {row['Nb_herbicides']} herbicides\")\n",
    "\n",
    "# Analyse par culture - Quelles cultures sont plus sensibles?\n",
    "print(f\"\\n🌱 Analyse du risque adventice par type de culture:\")\n",
    "risk_by_culture = risk_analysis.reset_index().groupby('libelleusag').agg({\n",
    "    'IFT_herbicide_approx': 'mean',\n",
    "    'Nb_herbicides': 'mean',\n",
    "    'Risque_adventice': lambda x: x.value_counts().index[0]  # Risque dominant\n",
    "}).round(2)\n",
    "\n",
    "risk_by_culture.columns = ['IFT_moyen', 'Herbicides_moyen', 'Risque_dominant']\n",
    "risk_by_culture = risk_by_culture.sort_values('IFT_moyen')\n",
    "\n",
    "for culture, row in risk_by_culture.iterrows():\n",
    "    print(f\"   • {culture}: IFT moyen = {row['IFT_moyen']}, Risque dominant = {row['Risque_dominant']}\")\n",
    "\n",
    "# Statistiques finales\n",
    "print(f\"\\n📈 Statistiques IFT herbicides:\")\n",
    "ift_stats = risk_analysis['IFT_herbicide_approx'].describe()\n",
    "for stat, value in ift_stats.items():\n",
    "    print(f\"   • {stat}: {value:.2f}\")\n",
    "\n",
    "# Sauvegarder les résultats pour utilisation future\n",
    "risk_analysis_export = risk_analysis.reset_index()\n",
    "print(f\"\\n💾 Données d'analyse des risques prêtes pour modélisation: {risk_analysis_export.shape[0]} parcelles analysées\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32f44590",
   "metadata": {},
   "source": [
    "### 📈 Graphiques avancés et tableau de bord\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "18ec181d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==================================================\n",
      "📈 TABLEAU DE BORD INTERACTIF\n",
      "==================================================\n"
     ]
    },
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
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          [
           "Penderff 1",
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           "Lann Chebot chemin",
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          [
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          ],
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          [
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           "Penderff 3",
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          [
           "Synophyt P9 -75%",
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          [
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          ],
          [
           "Lann Chebot Master2",
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          ],
          [
           "Lann Chebot Master2",
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          ],
          [
           "Lann Chebot Master3",
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          ],
          [
           "Lann Chebot Master3",
           "sarrasin",
           2
          ],
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           "Bourg Haut",
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         ],
         "hovertemplate": "Niveau de risque=TRÈS FAIBLE<br>Surface de la parcelle (ha)=%{x}<br>IFT Herbicide (approximatif)=%{y}<br>Nb_herbicides=%{marker.size}<br>nomparc=%{customdata[0]}<br>libelleusag=%{customdata[1]}<br>Total_interventions=%{customdata[2]}<extra></extra>",
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         "hovertemplate": "Niveau de risque=TRÈS ÉLEVÉ<br>Surface de la parcelle (ha)=%{x}<br>IFT Herbicide (approximatif)=%{y}<br>Nb_herbicides=%{marker.size}<br>nomparc=%{customdata[0]}<br>libelleusag=%{customdata[1]}<br>Total_interventions=%{customdata[2]}<extra></extra>",
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         "yaxis": "y"
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          ],
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           "Synophyt P3 -75%",
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           "Synophyt P3 -75%",
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          ],
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           "Synophyt P5 -75%",
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          ],
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           "Synophyt P7 -75%",
           "orge hiver",
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          ],
          [
           "Synophyt P8 -75%",
           "blé tendre hiver",
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          ],
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           "Carancier Ht",
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          ]
         ],
         "hovertemplate": "Niveau de risque=FAIBLE<br>Surface de la parcelle (ha)=%{x}<br>IFT Herbicide (approximatif)=%{y}<br>Nb_herbicides=%{marker.size}<br>nomparc=%{customdata[0]}<br>libelleusag=%{customdata[1]}<br>Total_interventions=%{customdata[2]}<extra></extra>",
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        },
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      "✅ Tableau de bord interactif généré avec succès!\n",
      "🎯 Les graphiques permettent d'identifier visuellement:\n",
      "   • Les parcelles à faible risque pour cultures sensibles\n",
      "   • Les corrélations entre variables agricoles\n",
      "   • Les patterns temporels d'intervention\n",
      "   • La répartition des risques par culture\n"
     ]
    }
   ],
   "source": [
    "# 6. DASHBOARD INTERACTIF AVEC PLOTLY\n",
    "print(\"=\"*50)\n",
    "print(\"📈 TABLEAU DE BORD INTERACTIF\")\n",
    "print(\"=\"*50)\n",
    "\n",
    "# Préparer les données pour Plotly\n",
    "risk_df = risk_analysis.reset_index()\n",
    "\n",
    "# 1. Graphique de risque par parcelle - Scatter plot interactif\n",
    "fig1 = px.scatter(risk_df, \n",
    "                  x='surfparc', \n",
    "                  y='IFT_herbicide_approx',\n",
    "                  color='Risque_adventice',\n",
    "                  size='Nb_herbicides',\n",
    "                  hover_data=['nomparc', 'libelleusag', 'Total_interventions'],\n",
    "                  color_discrete_map={\n",
    "                      'TRÈS FAIBLE': 'green',\n",
    "                      'FAIBLE': 'lightgreen', \n",
    "                      'MODÉRÉ': 'orange',\n",
    "                      'ÉLEVÉ': 'red',\n",
    "                      'TRÈS ÉLEVÉ': 'darkred'\n",
    "                  },\n",
    "                  title=\"🎯 Analyse du Risque Adventice par Parcelle\",\n",
    "                  labels={\n",
    "                      'surfparc': 'Surface de la parcelle (ha)',\n",
    "                      'IFT_herbicide_approx': 'IFT Herbicide (approximatif)',\n",
    "                      'Risque_adventice': 'Niveau de risque'\n",
    "                  })\n",
    "\n",
    "fig1.update_layout(\n",
    "    width=900, height=600,\n",
    "    title_font_size=16,\n",
    "    showlegend=True\n",
    ")\n",
    "\n",
    "fig1.show()\n",
    "\n",
    "# 2. Histogramme des niveaux de risque\n",
    "risk_counts = risk_df['Risque_adventice'].value_counts()\n",
    "fig2 = px.bar(x=risk_counts.index, \n",
    "              y=risk_counts.values,\n",
    "              color=risk_counts.index,\n",
    "              color_discrete_map={\n",
    "                  'TRÈS FAIBLE': 'green',\n",
    "                  'FAIBLE': 'lightgreen', \n",
    "                  'MODÉRÉ': 'orange',\n",
    "                  'ÉLEVÉ': 'red',\n",
    "                  'TRÈS ÉLEVÉ': 'darkred'\n",
    "              },\n",
    "              title=\"📊 Distribution des Niveaux de Risque Adventice\",\n",
    "              labels={'x': 'Niveau de risque', 'y': 'Nombre de parcelles'})\n",
    "\n",
    "fig2.update_layout(width=800, height=500, showlegend=False)\n",
    "fig2.show()\n",
    "\n",
    "# 3. Heatmap de corrélation des variables clés\n",
    "correlation_vars = ['surfparc', 'IFT_herbicide_approx', 'Nb_herbicides', \n",
    "                    'Total_interventions', 'Diversite_produits']\n",
    "corr_matrix = risk_df[correlation_vars].corr()\n",
    "\n",
    "fig3 = px.imshow(corr_matrix,\n",
    "                 text_auto=True,\n",
    "                 aspect=\"auto\",\n",
    "                 title=\"🔗 Matrice de Corrélation - Variables Clés\",\n",
    "                 color_continuous_scale=\"RdBu\")\n",
    "\n",
    "fig3.update_layout(width=700, height=600)\n",
    "fig3.show()\n",
    "\n",
    "# 4. Box plot par culture\n",
    "fig4 = px.box(risk_df, \n",
    "              x='libelleusag', \n",
    "              y='IFT_herbicide_approx',\n",
    "              color='libelleusag',\n",
    "              title=\"📦 Distribution IFT Herbicide par Culture\",\n",
    "              labels={\n",
    "                  'libelleusag': 'Type de culture',\n",
    "                  'IFT_herbicide_approx': 'IFT Herbicide'\n",
    "              })\n",
    "\n",
    "fig4.update_layout(width=900, height=600, showlegend=False)\n",
    "fig4.update_xaxes(tickangle=45)\n",
    "fig4.show()\n",
    "\n",
    "# 5. Graphique en aires empilées - Timeline des interventions (si données temporelles disponibles)\n",
    "if 'datedebut' in df.columns:\n",
    "    # Préparer les données temporelles\n",
    "    df_temp = df.copy()\n",
    "    df_temp['datedebut'] = pd.to_datetime(df_temp['datedebut'], format='%d/%m/%y', errors='coerce')\n",
    "    df_temp = df_temp.dropna(subset=['datedebut'])\n",
    "    \n",
    "    if len(df_temp) > 0:\n",
    "        df_temp['semaine'] = df_temp['datedebut'].dt.isocalendar().week\n",
    "        weekly_interventions = df_temp.groupby(['semaine', 'familleprod']).size().reset_index(name='count')\n",
    "        \n",
    "        fig5 = px.area(weekly_interventions, \n",
    "                       x='semaine', \n",
    "                       y='count', \n",
    "                       color='familleprod',\n",
    "                       title=\"📅 Évolution Hebdomadaire des Interventions par Famille\",\n",
    "                       labels={\n",
    "                           'semaine': 'Semaine de l\\'année',\n",
    "                           'count': 'Nombre d\\'interventions',\n",
    "                           'familleprod': 'Famille de produits'\n",
    "                       })\n",
    "        \n",
    "        fig5.update_layout(width=900, height=500)\n",
    "        fig5.show()\n",
    "\n",
    "print(\"✅ Tableau de bord interactif généré avec succès!\")\n",
    "print(\"🎯 Les graphiques permettent d'identifier visuellement:\")\n",
    "print(\"   • Les parcelles à faible risque pour cultures sensibles\")\n",
    "print(\"   • Les corrélations entre variables agricoles\") \n",
    "print(\"   • Les patterns temporels d'intervention\")\n",
    "print(\"   • La répartition des risques par culture\")\n"
   ]
  }
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