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   "metadata": {},
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      "Successfully installed contourpy-1.3.3 cycler-0.12.1 fonttools-4.60.1 kiwisolver-1.4.9 matplotlib-3.10.7 plotly-6.3.1 pyparsing-3.2.5\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install requests pandas numpy matplotlib plotly streamlit langchain transformers\n"
   ]
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   "execution_count": 6,
   "id": "42263596",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "🔍 Dataset Loaded Successfully!\n",
      "Total Rows: 203\n",
      "Total Columns: 25\n",
      "\n"
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    },
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state_name</th>\n",
       "      <th>district_name</th>\n",
       "      <th>crop_year</th>\n",
       "      <th>season</th>\n",
       "      <th>crop</th>\n",
       "      <th>area_</th>\n",
       "      <th>production_</th>\n",
       "      <th>subdivision</th>\n",
       "      <th>jan</th>\n",
       "      <th>feb</th>\n",
       "      <th>...</th>\n",
       "      <th>aug</th>\n",
       "      <th>sep</th>\n",
       "      <th>oct</th>\n",
       "      <th>nov</th>\n",
       "      <th>dec</th>\n",
       "      <th>annual</th>\n",
       "      <th>jf</th>\n",
       "      <th>mam</th>\n",
       "      <th>jjas</th>\n",
       "      <th>ond</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>andaman and nicobar islands</td>\n",
       "      <td>NICOBARS</td>\n",
       "      <td>2000</td>\n",
       "      <td>Kharif</td>\n",
       "      <td>Arecanut</td>\n",
       "      <td>1254.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>andaman &amp; nicobar islands</td>\n",
       "      <td>53.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>...</td>\n",
       "      <td>460.8</td>\n",
       "      <td>250.1</td>\n",
       "      <td>321.2</td>\n",
       "      <td>158.3</td>\n",
       "      <td>115.2</td>\n",
       "      <td>2763.2</td>\n",
       "      <td>112.0</td>\n",
       "      <td>812.2</td>\n",
       "      <td>1244.2</td>\n",
       "      <td>594.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>andaman and nicobar islands</td>\n",
       "      <td>NICOBARS</td>\n",
       "      <td>2000</td>\n",
       "      <td>Kharif</td>\n",
       "      <td>Other Kharif pulses</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>andaman &amp; nicobar islands</td>\n",
       "      <td>53.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>...</td>\n",
       "      <td>460.8</td>\n",
       "      <td>250.1</td>\n",
       "      <td>321.2</td>\n",
       "      <td>158.3</td>\n",
       "      <td>115.2</td>\n",
       "      <td>2763.2</td>\n",
       "      <td>112.0</td>\n",
       "      <td>812.2</td>\n",
       "      <td>1244.2</td>\n",
       "      <td>594.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>andaman and nicobar islands</td>\n",
       "      <td>NICOBARS</td>\n",
       "      <td>2000</td>\n",
       "      <td>Kharif</td>\n",
       "      <td>Rice</td>\n",
       "      <td>102.0</td>\n",
       "      <td>321.0</td>\n",
       "      <td>andaman &amp; nicobar islands</td>\n",
       "      <td>53.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>...</td>\n",
       "      <td>460.8</td>\n",
       "      <td>250.1</td>\n",
       "      <td>321.2</td>\n",
       "      <td>158.3</td>\n",
       "      <td>115.2</td>\n",
       "      <td>2763.2</td>\n",
       "      <td>112.0</td>\n",
       "      <td>812.2</td>\n",
       "      <td>1244.2</td>\n",
       "      <td>594.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>andaman and nicobar islands</td>\n",
       "      <td>NICOBARS</td>\n",
       "      <td>2000</td>\n",
       "      <td>Whole Year</td>\n",
       "      <td>Banana</td>\n",
       "      <td>176.0</td>\n",
       "      <td>641.0</td>\n",
       "      <td>andaman &amp; nicobar islands</td>\n",
       "      <td>53.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>...</td>\n",
       "      <td>460.8</td>\n",
       "      <td>250.1</td>\n",
       "      <td>321.2</td>\n",
       "      <td>158.3</td>\n",
       "      <td>115.2</td>\n",
       "      <td>2763.2</td>\n",
       "      <td>112.0</td>\n",
       "      <td>812.2</td>\n",
       "      <td>1244.2</td>\n",
       "      <td>594.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>andaman and nicobar islands</td>\n",
       "      <td>NICOBARS</td>\n",
       "      <td>2000</td>\n",
       "      <td>Whole Year</td>\n",
       "      <td>Cashewnut</td>\n",
       "      <td>720.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>andaman &amp; nicobar islands</td>\n",
       "      <td>53.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>...</td>\n",
       "      <td>460.8</td>\n",
       "      <td>250.1</td>\n",
       "      <td>321.2</td>\n",
       "      <td>158.3</td>\n",
       "      <td>115.2</td>\n",
       "      <td>2763.2</td>\n",
       "      <td>112.0</td>\n",
       "      <td>812.2</td>\n",
       "      <td>1244.2</td>\n",
       "      <td>594.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    state_name district_name  crop_year      season  \\\n",
       "0  andaman and nicobar islands      NICOBARS       2000      Kharif   \n",
       "1  andaman and nicobar islands      NICOBARS       2000      Kharif   \n",
       "2  andaman and nicobar islands      NICOBARS       2000      Kharif   \n",
       "3  andaman and nicobar islands      NICOBARS       2000  Whole Year   \n",
       "4  andaman and nicobar islands      NICOBARS       2000  Whole Year   \n",
       "\n",
       "                  crop   area_  production_                subdivision   jan  \\\n",
       "0             Arecanut  1254.0       2000.0  andaman & nicobar islands  53.0   \n",
       "1  Other Kharif pulses     2.0          1.0  andaman & nicobar islands  53.0   \n",
       "2                 Rice   102.0        321.0  andaman & nicobar islands  53.0   \n",
       "3               Banana   176.0        641.0  andaman & nicobar islands  53.0   \n",
       "4            Cashewnut   720.0        165.0  andaman & nicobar islands  53.0   \n",
       "\n",
       "    feb  ...    aug    sep    oct    nov    dec  annual     jf    mam    jjas  \\\n",
       "0  59.0  ...  460.8  250.1  321.2  158.3  115.2  2763.2  112.0  812.2  1244.2   \n",
       "1  59.0  ...  460.8  250.1  321.2  158.3  115.2  2763.2  112.0  812.2  1244.2   \n",
       "2  59.0  ...  460.8  250.1  321.2  158.3  115.2  2763.2  112.0  812.2  1244.2   \n",
       "3  59.0  ...  460.8  250.1  321.2  158.3  115.2  2763.2  112.0  812.2  1244.2   \n",
       "4  59.0  ...  460.8  250.1  321.2  158.3  115.2  2763.2  112.0  812.2  1244.2   \n",
       "\n",
       "     ond  \n",
       "0  594.7  \n",
       "1  594.7  \n",
       "2  594.7  \n",
       "3  594.7  \n",
       "4  594.7  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "📊 Columns in dataset:\n",
      "['state_name', 'district_name', 'crop_year', 'season', 'crop', 'area_', 'production_', 'subdivision', 'jan', 'feb', 'mar', 'apr', 'may', 'jun', 'jul', 'aug', 'sep', 'oct', 'nov', 'dec', 'annual', 'jf', 'mam', 'jjas', 'ond']\n",
      "\n",
      "🏛️ Unique States in Dataset:\n",
      "- andaman and nicobar islands\n",
      "\n",
      "🌾 Unique Crops in Dataset:\n",
      "- Arecanut\n",
      "- Arhar/Tur\n",
      "- Banana\n",
      "- Black pepper\n",
      "- Cashewnut\n",
      "- Coconut\n",
      "- Dry chillies\n",
      "- Dry ginger\n",
      "- Groundnut\n",
      "- Maize\n",
      "- Moong(Green Gram)\n",
      "- Other Kharif pulses\n",
      "- Rice\n",
      "- Sugarcane\n",
      "- Sunflower\n",
      "- Sweet potato\n",
      "- Tapioca\n",
      "- Turmeric\n",
      "- Urad\n",
      "- other oilseeds\n",
      "... (Total 20 unique crops)\n",
      "\n",
      "📅 Crop Year Range: 2000 - 2010\n",
      "\n",
      "📈 Number of unique crops per state:\n"
     ]
    },
    {
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       "                    state_name  unique_crops\n",
       "0  andaman and nicobar islands            20"
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   ],
   "source": [
    "# -----------------------------------------------\n",
    "# 📘 Project Samarth - Phase 1: Data Discovery\n",
    "# -----------------------------------------------\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "# ✅ Load merged dataset\n",
    "file_path = \"../hybrid_dataset/merged_agri_rainfall.csv\"  # adjust if needed\n",
    "df = pd.read_csv(file_path)\n",
    "\n",
    "# ✅ Basic info\n",
    "print(\"🔍 Dataset Loaded Successfully!\")\n",
    "print(f\"Total Rows: {len(df)}\")\n",
    "print(f\"Total Columns: {len(df.columns)}\\n\")\n",
    "\n",
    "# ✅ Display first few rows\n",
    "display(df.head())\n",
    "\n",
    "# ✅ Show all available columns\n",
    "print(\"📊 Columns in dataset:\")\n",
    "print(df.columns.tolist())\n",
    "\n",
    "# ✅ Check unique states\n",
    "if \"state_name\" in df.columns:\n",
    "    states = sorted(df[\"state_name\"].dropna().unique().tolist())\n",
    "    print(\"\\n🏛️ Unique States in Dataset:\")\n",
    "    for s in states:\n",
    "        print(\"-\", s)\n",
    "\n",
    "# ✅ Check unique crops\n",
    "if \"crop\" in df.columns:\n",
    "    crops = sorted(df[\"crop\"].dropna().unique().tolist())\n",
    "    print(\"\\n🌾 Unique Crops in Dataset:\")\n",
    "    for c in crops[:20]:  # limit to first 20\n",
    "        print(\"-\", c)\n",
    "    print(f\"... (Total {len(crops)} unique crops)\")\n",
    "\n",
    "# ✅ Check year range\n",
    "if \"crop_year\" in df.columns:\n",
    "    min_year, max_year = int(df[\"crop_year\"].min()), int(df[\"crop_year\"].max())\n",
    "    print(f\"\\n📅 Crop Year Range: {min_year} - {max_year}\")\n",
    "\n",
    "# ✅ Quick count by state and crop\n",
    "if {\"state_name\", \"crop\"} <= set(df.columns):\n",
    "    summary = (\n",
    "        df.groupby(\"state_name\")[\"crop\"]\n",
    "        .nunique()\n",
    "        .sort_values(ascending=False)\n",
    "        .reset_index()\n",
    "        .rename(columns={\"crop\": \"unique_crops\"})\n",
    "    )\n",
    "    print(\"\\n📈 Number of unique crops per state:\")\n",
    "    display(summary)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "49e9d168",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Columns available in this file:\n",
      "['subdivision', 'year', 'jan', 'feb', 'mar', 'apr', 'may', 'jun', 'jul', 'aug', 'sep', 'oct', 'nov', 'dec', 'annual', 'jf', 'mam', 'jjas', 'ond']\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv(r\"C:\\Users\\satya\\Downloads\\Project_Samarth\\task\\hybrid_dataset\\imd_rainfall_data.csv\")\n",
    "\n",
    "print(\"Columns available in this file:\")\n",
    "print(df.columns.tolist())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1f616bd6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ Datasets Loaded Successfully!\n",
      "\n",
      "Agriculture Data Shape: (5000, 7)\n",
      "IMD Rainfall Data Shape: (2000, 19)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Paths to your saved files (use raw string format to avoid escape issues)\n",
    "agri_path = r\"C:\\Users\\satya\\Downloads\\Project_Samarth\\task\\hybrid_dataset\\agriculture_data.csv\"\n",
    "imd_path = r\"C:\\Users\\satya\\Downloads\\Project_Samarth\\task\\hybrid_dataset\\imd_rainfall_data.csv\"\n",
    "\n",
    "# Load both datasets\n",
    "agri_df = pd.read_csv(agri_path)\n",
    "imd_df = pd.read_csv(imd_path)\n",
    "\n",
    "print(\"✅ Datasets Loaded Successfully!\\n\")\n",
    "print(f\"Agriculture Data Shape: {agri_df.shape}\")\n",
    "print(f\"IMD Rainfall Data Shape: {imd_df.shape}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "485e1cd8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "🌾 Agriculture Data Columns:\n",
      "['state_name', 'district_name', 'crop_year', 'season', 'crop', 'area_', 'production_']\n",
      "\n",
      "☁️ IMD Rainfall Data Columns:\n",
      "['subdivision', 'year', 'jan', 'feb', 'mar', 'apr', 'may', 'jun', 'jul', 'aug', 'sep', 'oct', 'nov', 'dec', 'annual', 'jf', 'mam', 'jjas', 'ond']\n",
      "\n",
      "📊 Agriculture Data Sample:\n"
     ]
    },
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       "                    state_name district_name  crop_year      season  \\\n",
       "0  Andaman and Nicobar Islands      NICOBARS       2000      Kharif   \n",
       "1  Andaman and Nicobar Islands      NICOBARS       2000      Kharif   \n",
       "2  Andaman and Nicobar Islands      NICOBARS       2000      Kharif   \n",
       "3  Andaman and Nicobar Islands      NICOBARS       2000  Whole Year   \n",
       "4  Andaman and Nicobar Islands      NICOBARS       2000  Whole Year   \n",
       "\n",
       "                  crop   area_  production_  \n",
       "0             Arecanut  1254.0       2000.0  \n",
       "1  Other Kharif pulses     2.0          1.0  \n",
       "2                 Rice   102.0        321.0  \n",
       "3               Banana   176.0        641.0  \n",
       "4            Cashewnut   720.0        165.0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "🌦️ IMD Rainfall Data Sample:\n"
     ]
    },
    {
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         "517.5",
         "365.1",
         "481.1",
         "332.6",
         "388.5",
         "558.2",
         "33.6",
         "3373.2",
         "136.3",
         "560.3",
         "1696.3",
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         "Andaman & Nicobar Islands",
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         "359.0",
         "160.5",
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         "458.3",
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         "479.9",
         "728.4",
         "326.7",
         "339.0",
         "181.2",
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         "225.0",
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         "156.7",
         "236.1",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>subdivision</th>\n",
       "      <th>year</th>\n",
       "      <th>jan</th>\n",
       "      <th>feb</th>\n",
       "      <th>mar</th>\n",
       "      <th>apr</th>\n",
       "      <th>may</th>\n",
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       "      <th>ond</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Andaman &amp; Nicobar Islands</td>\n",
       "      <td>1901</td>\n",
       "      <td>49.2</td>\n",
       "      <td>87.1</td>\n",
       "      <td>29.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>528.8</td>\n",
       "      <td>517.5</td>\n",
       "      <td>365.1</td>\n",
       "      <td>481.1</td>\n",
       "      <td>332.6</td>\n",
       "      <td>388.5</td>\n",
       "      <td>558.2</td>\n",
       "      <td>33.6</td>\n",
       "      <td>3373.2</td>\n",
       "      <td>136.3</td>\n",
       "      <td>560.3</td>\n",
       "      <td>1696.3</td>\n",
       "      <td>980.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Andaman &amp; Nicobar Islands</td>\n",
       "      <td>1902</td>\n",
       "      <td>0.0</td>\n",
       "      <td>159.8</td>\n",
       "      <td>12.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>446.1</td>\n",
       "      <td>537.1</td>\n",
       "      <td>228.9</td>\n",
       "      <td>753.7</td>\n",
       "      <td>666.2</td>\n",
       "      <td>197.2</td>\n",
       "      <td>359.0</td>\n",
       "      <td>160.5</td>\n",
       "      <td>3520.7</td>\n",
       "      <td>159.8</td>\n",
       "      <td>458.3</td>\n",
       "      <td>2185.9</td>\n",
       "      <td>716.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Andaman &amp; Nicobar Islands</td>\n",
       "      <td>1903</td>\n",
       "      <td>12.7</td>\n",
       "      <td>144.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>235.1</td>\n",
       "      <td>479.9</td>\n",
       "      <td>728.4</td>\n",
       "      <td>326.7</td>\n",
       "      <td>339.0</td>\n",
       "      <td>181.2</td>\n",
       "      <td>284.4</td>\n",
       "      <td>225.0</td>\n",
       "      <td>2957.4</td>\n",
       "      <td>156.7</td>\n",
       "      <td>236.1</td>\n",
       "      <td>1874.0</td>\n",
       "      <td>690.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Andaman &amp; Nicobar Islands</td>\n",
       "      <td>1904</td>\n",
       "      <td>9.4</td>\n",
       "      <td>14.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>202.4</td>\n",
       "      <td>304.5</td>\n",
       "      <td>495.1</td>\n",
       "      <td>502.0</td>\n",
       "      <td>160.1</td>\n",
       "      <td>820.4</td>\n",
       "      <td>222.2</td>\n",
       "      <td>308.7</td>\n",
       "      <td>40.1</td>\n",
       "      <td>3079.6</td>\n",
       "      <td>24.1</td>\n",
       "      <td>506.9</td>\n",
       "      <td>1977.6</td>\n",
       "      <td>571.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Andaman &amp; Nicobar Islands</td>\n",
       "      <td>1905</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.3</td>\n",
       "      <td>26.9</td>\n",
       "      <td>279.5</td>\n",
       "      <td>628.7</td>\n",
       "      <td>368.7</td>\n",
       "      <td>330.5</td>\n",
       "      <td>297.0</td>\n",
       "      <td>260.7</td>\n",
       "      <td>25.4</td>\n",
       "      <td>344.7</td>\n",
       "      <td>2566.7</td>\n",
       "      <td>1.3</td>\n",
       "      <td>309.7</td>\n",
       "      <td>1624.9</td>\n",
       "      <td>630.8</td>\n",
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      "text/plain": [
       "                 subdivision  year   jan    feb   mar    apr    may    jun  \\\n",
       "0  Andaman & Nicobar Islands  1901  49.2   87.1  29.2    2.3  528.8  517.5   \n",
       "1  Andaman & Nicobar Islands  1902   0.0  159.8  12.2    0.0  446.1  537.1   \n",
       "2  Andaman & Nicobar Islands  1903  12.7  144.0   0.0    1.0  235.1  479.9   \n",
       "3  Andaman & Nicobar Islands  1904   9.4   14.7   0.0  202.4  304.5  495.1   \n",
       "4  Andaman & Nicobar Islands  1905   1.3    0.0   3.3   26.9  279.5  628.7   \n",
       "\n",
       "     jul    aug    sep    oct    nov    dec  annual     jf    mam    jjas  \\\n",
       "0  365.1  481.1  332.6  388.5  558.2   33.6  3373.2  136.3  560.3  1696.3   \n",
       "1  228.9  753.7  666.2  197.2  359.0  160.5  3520.7  159.8  458.3  2185.9   \n",
       "2  728.4  326.7  339.0  181.2  284.4  225.0  2957.4  156.7  236.1  1874.0   \n",
       "3  502.0  160.1  820.4  222.2  308.7   40.1  3079.6   24.1  506.9  1977.6   \n",
       "4  368.7  330.5  297.0  260.7   25.4  344.7  2566.7    1.3  309.7  1624.9   \n",
       "\n",
       "     ond  \n",
       "0  980.3  \n",
       "1  716.7  \n",
       "2  690.6  \n",
       "3  571.0  \n",
       "4  630.8  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"\\n🌾 Agriculture Data Columns:\")\n",
    "print(agri_df.columns.tolist())\n",
    "\n",
    "print(\"\\n☁️ IMD Rainfall Data Columns:\")\n",
    "print(imd_df.columns.tolist())\n",
    "\n",
    "print(\"\\n📊 Agriculture Data Sample:\")\n",
    "display(agri_df.head(5))\n",
    "\n",
    "print(\"\\n🌦️ IMD Rainfall Data Sample:\")\n",
    "display(imd_df.head(5))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4d48c1f1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "🏛️ Unique States in Agriculture Data:\n",
      "['Andaman and Nicobar Islands', 'Andhra Pradesh']\n",
      "\n",
      "📅 Year Range in Agriculture Data:\n",
      "1997 → 2014\n",
      "\n",
      "🌾 Top 10 Crops:\n",
      "['Arecanut', 'Other Kharif pulses', 'Rice', 'Banana', 'Cashewnut', 'Coconut', 'Dry ginger', 'Sugarcane', 'Sweet potato', 'Tapioca']\n",
      "\n",
      "🏛️ Unique Subdivisions in IMD Rainfall Data:\n",
      "['Andaman & Nicobar Islands', 'Arunachal Pradesh', 'Assam & Meghalaya', 'Naga Mani Mizo Tripura', 'Sub Himalayan West Bengal & Sikkim', 'Gangetic West Bengal', 'Orissa', 'Jharkhand', 'Bihar', 'East Uttar Pradesh', 'West Uttar Pradesh', 'Uttarakhand', 'Haryana Delhi & Chandigarh', 'Punjab', 'Himachal Pradesh', 'Jammu & Kashmir', 'West Rajasthan', 'East Rajasthan']\n",
      "\n",
      "📅 Year Range in IMD Rainfall Data:\n",
      "1901 → 2017\n"
     ]
    }
   ],
   "source": [
    "# ---- AGRICULTURE ----\n",
    "print(\"\\n🏛️ Unique States in Agriculture Data:\")\n",
    "print(agri_df['state_name'].unique().tolist())\n",
    "\n",
    "print(\"\\n📅 Year Range in Agriculture Data:\")\n",
    "if 'crop_year' in agri_df.columns:\n",
    "    print(int(agri_df['crop_year'].min()), \"→\", int(agri_df['crop_year'].max()))\n",
    "\n",
    "print(\"\\n🌾 Top 10 Crops:\")\n",
    "print(agri_df['crop'].unique().tolist()[:10])\n",
    "\n",
    "# ---- IMD RAINFALL ----\n",
    "print(\"\\n🏛️ Unique Subdivisions in IMD Rainfall Data:\")\n",
    "print(imd_df['subdivision'].unique().tolist())\n",
    "\n",
    "print(\"\\n📅 Year Range in IMD Rainfall Data:\")\n",
    "if 'year' in imd_df.columns:\n",
    "    print(int(imd_df['year'].min()), \"→\", int(imd_df['year'].max()))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8cfe6ee8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "✅ Common Names Found Between Agriculture & IMD Data (0):\n",
      "[]\n",
      "\n",
      "⚠️ States in Agriculture but not in IMD (2):\n",
      "['andaman and nicobar islands', 'andhra pradesh']\n"
     ]
    }
   ],
   "source": [
    "# Lowercase and trim for consistency\n",
    "agri_states = set(agri_df['state_name'].str.lower().str.strip().unique())\n",
    "imd_subdiv = set(imd_df['subdivision'].str.lower().str.strip().unique())\n",
    "\n",
    "common = sorted(agri_states.intersection(imd_subdiv))\n",
    "\n",
    "print(f\"\\n✅ Common Names Found Between Agriculture & IMD Data ({len(common)}):\")\n",
    "print(common[:10])\n",
    "\n",
    "missing_from_imd = sorted(list(agri_states - imd_subdiv))\n",
    "print(f\"\\n⚠️ States in Agriculture but not in IMD ({len(missing_from_imd)}):\")\n",
    "print(missing_from_imd[:10])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "21653121",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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