generate graph for jamoat level.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import glob\n",
    "import numpy as np\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['C:\\\\Users\\\\GIC\\\\Desktop\\\\tek\\\\Graphs\\\\notebook\\\\data\\\\Exposure_tables\\\\Asht\\\\Asht_jamoat_exposure_DR.xls', 'C:\\\\Users\\\\GIC\\\\Desktop\\\\tek\\\\Graphs\\\\notebook\\\\data\\\\Exposure_tables\\\\Asht\\\\Asht_jamoat_exposure_EQ.xls', 'C:\\\\Users\\\\GIC\\\\Desktop\\\\tek\\\\Graphs\\\\notebook\\\\data\\\\Exposure_tables\\\\Asht\\\\Asht_jamoat_exposure_FL.xls', 'C:\\\\Users\\\\GIC\\\\Desktop\\\\tek\\\\Graphs\\\\notebook\\\\data\\\\Exposure_tables\\\\Asht\\\\Asht_jamoat_exposure_LS.xls', 'C:\\\\Users\\\\GIC\\\\Desktop\\\\tek\\\\Graphs\\\\notebook\\\\data\\\\Exposure_tables\\\\Asht\\\\Asht_jamoat_exposure_MF.xls', 'C:\\\\Users\\\\GIC\\\\Desktop\\\\tek\\\\Graphs\\\\notebook\\\\data\\\\Exposure_tables\\\\Asht\\\\Asht_jamoat_exposure_SA.xls', 'C:\\\\Users\\\\GIC\\\\Desktop\\\\tek\\\\Graphs\\\\notebook\\\\data\\\\Exposure_tables\\\\Asht\\\\Asht_jamoat_exposure_WS.xls']\n"
     ]
    }
   ],
   "source": [
    "# for Asht district\n",
    "path = r'C:\\Users\\GIC\\Desktop\\tek\\Graphs\\notebook\\data\\Exposure_tables\\Asht'\n",
    "all_files = glob.glob(os.path.join(path, \"*.xls\"))\n",
    "print(all_files)\n",
    "\n",
    "# concate excel data\n",
    "df = pd.concat(map(pd.read_excel, glob.glob(os.path.join(path, '*.xls'))), axis=1)\n",
    "\n",
    "# for output final dataset\n",
    "df.to_csv('data/Exposure_tables/_csv/asht.csv', encoding='utf-8', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def outputCSV(name):\n",
    "    # for Asht district\n",
    "    path = r'C:\\Users\\GIC\\Desktop\\tek\\Graphs\\notebook\\data\\Exposure_tables\\{}'.format(name)\n",
    "    all_files = glob.glob(os.path.join(path, \"*.xls\"))\n",
    "    #print(all_files)\n",
    "\n",
    "    # concate excel data\n",
    "    df = pd.concat(map(pd.read_excel, glob.glob(os.path.join(path, '*.xls'))), axis=1)\n",
    "\n",
    "    # for output final dataset\n",
    "    df.to_csv('data/Exposure_tables/_csv/{}.csv'.format(name), encoding='utf-8', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "outputCSV(\"Ayni\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "outputCSV(\"Bobojon\")\n",
    "outputCSV('Devashtich')\n",
    "outputCSV('Isfara')\n",
    "outputCSV('Istaravshan')\n",
    "outputCSV('Jabbor_Rasulov')\n",
    "outputCSV('Konibodom')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "outputCSV('Kuhistoni_Mastchoh')\n",
    "outputCSV('Mastchoh')\n",
    "outputCSV('Panjakent')\n",
    "outputCSV('Shahriston')\n",
    "outputCSV('Spitamen')\n",
    "outputCSV('Zafarobod')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\GIC\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:3: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
      "of pandas will change to not sort by default.\n",
      "\n",
      "To accept the future behavior, pass 'sort=False'.\n",
      "\n",
      "To retain the current behavior and silence the warning, pass 'sort=True'.\n",
      "\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    }
   ],
   "source": [
    "#making dataframe for all combining datasets\n",
    "path= r'C:\\Users\\GIC\\Desktop\\tek\\Graphs\\notebook\\data\\Exposure_tables\\_csv'\n",
    "df= pd.concat(map(pd.read_csv, glob.glob(os.path.join(path, '*.csv'))), axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
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     "metadata": {},
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   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "      <td>5857</td>\n",
       "      <td>5857</td>\n",
       "      <td>5857</td>\n",
       "      <td>5857</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>32691.648718</td>\n",
       "      <td>32691.648718</td>\n",
       "      <td>32691.648718</td>\n",
       "      <td>63</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>6566</td>\n",
       "      <td>6566</td>\n",
       "      <td>6566</td>\n",
       "      <td>6566</td>\n",
       "      <td>6566</td>\n",
       "      <td>6566</td>\n",
       "      <td>6566</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>37212.766270</td>\n",
       "      <td>37212.766270</td>\n",
       "      <td>37212.766270</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4928</td>\n",
       "      <td>4928</td>\n",
       "      <td>4928</td>\n",
       "      <td>4928</td>\n",
       "      <td>4928</td>\n",
       "      <td>4928</td>\n",
       "      <td>4928</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>32388.667103</td>\n",
       "      <td>32388.667103</td>\n",
       "      <td>32388.667103</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>11905</td>\n",
       "      <td>11905</td>\n",
       "      <td>11905</td>\n",
       "      <td>11905</td>\n",
       "      <td>11905</td>\n",
       "      <td>11905</td>\n",
       "      <td>11905</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>48921.648995</td>\n",
       "      <td>48921.648995</td>\n",
       "      <td>48921.648995</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>148 rows × 508 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    AGRI_AREA  AGRI_AREA.1  AGRI_AREA.2  AGRI_AREA.3  AGRI_AREA.4  \\\n",
       "0        2992         2992         2992         2992         2992   \n",
       "1        1980         1980         1980         1980         1980   \n",
       "2        1980         1980         1980         1980         1980   \n",
       "3        1064         1064         1064         1064         1064   \n",
       "4       11576        11576        11576        11576        11576   \n",
       "..        ...          ...          ...          ...          ...   \n",
       "0       10636        10636        10636        10636        10636   \n",
       "1        5857         5857         5857         5857         5857   \n",
       "2        6566         6566         6566         6566         6566   \n",
       "3        4928         4928         4928         4928         4928   \n",
       "4       11905        11905        11905        11905        11905   \n",
       "\n",
       "    AGRI_AREA.5  AGRI_AREA.6  AGRI_DR_10  AGRI_DR_11  AGRI_DR_12  ...  \\\n",
       "0          2992         2992           0           0           0  ...   \n",
       "1          1980         1980           0           0           0  ...   \n",
       "2          1980         1980           0           0           0  ...   \n",
       "3          1064         1064           0           0           0  ...   \n",
       "4         11576        11576           0           0           0  ...   \n",
       "..          ...          ...         ...         ...         ...  ...   \n",
       "0         10636        10636           0           0           0  ...   \n",
       "1          5857         5857           0           0           0  ...   \n",
       "2          6566         6566           0           0           0  ...   \n",
       "3          4928         4928           0           0           0  ...   \n",
       "4         11905        11905           0           0           0  ...   \n",
       "\n",
       "    Shape_Length.4  Shape_Length.5  Shape_Length.6  TEMP  TEMP.1  TEMP.2  \\\n",
       "0    143639.872484   143639.872484   143639.872484   100       0       0   \n",
       "1     76782.018110    76782.018110    76782.018110    95       0       0   \n",
       "2     31333.528856    31333.528856    31333.528856    95       0       0   \n",
       "3     73993.806147    73993.806147    73993.806147   100       0       0   \n",
       "4    160412.185629   160412.185629   160412.185629   100       0       0   \n",
       "..             ...             ...             ...   ...     ...     ...   \n",
       "0     61089.242522    61089.242522    61089.242522    99       0       0   \n",
       "1     32691.648718    32691.648718    32691.648718    63       0       0   \n",
       "2     37212.766270    37212.766270    37212.766270     0       0       0   \n",
       "3     32388.667103    32388.667103    32388.667103     0       0       0   \n",
       "4     48921.648995    48921.648995    48921.648995     0       0       0   \n",
       "\n",
       "    TEMP.3  TEMP.4  TEMP.5  TEMP.6  \n",
       "0        0       0       0       0  \n",
       "1        0       0       0       0  \n",
       "2        0       0       0       0  \n",
       "3        0       0       0       0  \n",
       "4        0       0       0       0  \n",
       "..     ...     ...     ...     ...  \n",
       "0        0       0       0       0  \n",
       "1        0       0       0       0  \n",
       "2        0       0       0       0  \n",
       "3        0       0       0       0  \n",
       "4        0       0       0       0  \n",
       "\n",
       "[148 rows x 508 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['AGRI_AREA', 'AGRI_AREA.1', 'AGRI_AREA.2', 'AGRI_AREA.3', 'AGRI_AREA.4',\n",
       "       'AGRI_AREA.5', 'AGRI_AREA.6', 'AGRI_DR_10', 'AGRI_DR_11', 'AGRI_DR_12',\n",
       "       ...\n",
       "       'Shape_Length.4', 'Shape_Length.5', 'Shape_Length.6', 'TEMP', 'TEMP.1',\n",
       "       'TEMP.2', 'TEMP.3', 'TEMP.4', 'TEMP.5', 'TEMP.6'],\n",
       "      dtype='object', length=508)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AGRI_AREA\n",
      "AGRI_AREA.1\n",
      "AGRI_AREA.2\n",
      "AGRI_AREA.3\n",
      "AGRI_AREA.4\n",
      "AGRI_AREA.5\n",
      "AGRI_AREA.6\n",
      "AGRI_DR_10\n",
      "AGRI_DR_11\n",
      "AGRI_DR_12\n",
      "AGRI_DR_13\n",
      "AGRI_DR_14\n",
      "AGRI_DR_25\n",
      "AGRI_DR_26\n",
      "AGRI_DR_27\n",
      "AGRI_DR_28\n",
      "AGRI_DR_29\n",
      "AGRI_DR_50\n",
      "AGRI_DR_51\n",
      "AGRI_DR_52\n",
      "AGRI_DR_53\n",
      "AGRI_DR_54\n",
      "AGRI_EQ_10\n",
      "AGRI_EQ_11\n",
      "AGRI_EQ_12\n",
      "AGRI_EQ_13\n",
      "AGRI_EQ_14\n",
      "AGRI_EQ_15\n",
      "AGRI_EQ_16\n",
      "AGRI_EQ_17\n",
      "AGRI_EQ_18\n",
      "AGRI_EQ_19\n",
      "AGRI_EQ_47\n",
      "AGRI_EQ_48\n",
      "AGRI_EQ_49\n",
      "AGRI_EQ_50\n",
      "AGRI_EQ_51\n",
      "AGRI_FL_10\n",
      "AGRI_FL_11\n",
      "AGRI_FL_12\n",
      "AGRI_FL_13\n",
      "AGRI_FL_14\n",
      "AGRI_FL_20\n",
      "AGRI_FL_21\n",
      "AGRI_FL_22\n",
      "AGRI_FL_23\n",
      "AGRI_FL_24\n",
      "AGRI_FL_51\n",
      "AGRI_FL_52\n",
      "AGRI_FL_53\n",
      "AGRI_FL_54\n",
      "AGRI_FL_5_\n",
      "AGRI_LS_10\n",
      "AGRI_LS_11\n",
      "AGRI_LS_12\n",
      "AGRI_LS_13\n",
      "AGRI_LS_14\n",
      "AGRI_LS_20\n",
      "AGRI_LS_21\n",
      "AGRI_LS_22\n",
      "AGRI_LS_23\n",
      "AGRI_LS_24\n",
      "AGRI_LS_50\n",
      "AGRI_LS_51\n",
      "AGRI_LS_52\n",
      "AGRI_LS_53\n",
      "AGRI_LS_54\n",
      "AGRI_MF_10\n",
      "AGRI_MF_11\n",
      "AGRI_MF_12\n",
      "AGRI_MF_13\n",
      "AGRI_MF_14\n",
      "AGRI_MF_20\n",
      "AGRI_MF_21\n",
      "AGRI_MF_22\n",
      "AGRI_MF_23\n",
      "AGRI_MF_24\n",
      "AGRI_MF_50\n",
      "AGRI_MF_51\n",
      "AGRI_MF_52\n",
      "AGRI_MF_53\n",
      "AGRI_MF_54\n",
      "AGRI_SA_10\n",
      "AGRI_SA_11\n",
      "AGRI_SA_12\n",
      "AGRI_SA_13\n",
      "AGRI_SA_14\n",
      "AGRI_SA_20\n",
      "AGRI_SA_21\n",
      "AGRI_SA_22\n",
      "AGRI_SA_23\n",
      "AGRI_SA_24\n",
      "AGRI_SA_50\n",
      "AGRI_SA_51\n",
      "AGRI_SA_52\n",
      "AGRI_SA_53\n",
      "AGRI_SA_54\n",
      "AGRI_WS_20\n",
      "AGRI_WS_21\n",
      "AGRI_WS_22\n",
      "AGRI_WS_23\n",
      "AGRI_WS_24\n",
      "AGRI_WS_50\n",
      "AGRI_WS_51\n",
      "AGRI_WS_52\n",
      "AGRI_WS_53\n",
      "AGRI_WS_54\n",
      "AGRI_WS_55\n",
      "AGRI_WS_56\n",
      "AGRI_WS_57\n",
      "AGRI_WS_58\n",
      "AGRI_WS_5_\n",
      "BUAT_AREA\n",
      "BUAT_AREA.1\n",
      "BUAT_AREA.2\n",
      "BUAT_AREA.3\n",
      "BUAT_AREA.4\n",
      "BUAT_AREA.5\n",
      "BUAT_AREA.6\n",
      "BUIL_DR_10\n",
      "BUIL_DR_11\n",
      "BUIL_DR_12\n",
      "BUIL_DR_13\n",
      "BUIL_DR_14\n",
      "BUIL_DR_25\n",
      "BUIL_DR_26\n",
      "BUIL_DR_27\n",
      "BUIL_DR_28\n",
      "BUIL_DR_29\n",
      "BUIL_DR_50\n",
      "BUIL_DR_51\n",
      "BUIL_DR_52\n",
      "BUIL_DR_53\n",
      "BUIL_DR_54\n",
      "BUIL_EQ_10\n",
      "BUIL_EQ_11\n",
      "BUIL_EQ_12\n",
      "BUIL_EQ_13\n",
      "BUIL_EQ_14\n",
      "BUIL_EQ_15\n",
      "BUIL_EQ_16\n",
      "BUIL_EQ_17\n",
      "BUIL_EQ_18\n",
      "BUIL_EQ_19\n",
      "BUIL_EQ_47\n",
      "BUIL_EQ_48\n",
      "BUIL_EQ_49\n",
      "BUIL_EQ_50\n",
      "BUIL_EQ_51\n",
      "BUIL_FL_10\n",
      "BUIL_FL_11\n",
      "BUIL_FL_12\n",
      "BUIL_FL_13\n",
      "BUIL_FL_14\n",
      "BUIL_FL_20\n",
      "BUIL_FL_21\n",
      "BUIL_FL_22\n",
      "BUIL_FL_23\n",
      "BUIL_FL_24\n",
      "BUIL_FL_51\n",
      "BUIL_FL_52\n",
      "BUIL_FL_53\n",
      "BUIL_FL_54\n",
      "BUIL_FL_5_\n",
      "BUIL_LS_10\n",
      "BUIL_LS_11\n",
      "BUIL_LS_12\n",
      "BUIL_LS_13\n",
      "BUIL_LS_14\n",
      "BUIL_LS_20\n",
      "BUIL_LS_21\n",
      "BUIL_LS_22\n",
      "BUIL_LS_23\n",
      "BUIL_LS_24\n",
      "BUIL_LS_50\n",
      "BUIL_LS_51\n",
      "BUIL_LS_52\n",
      "BUIL_LS_53\n",
      "BUIL_LS_54\n",
      "BUIL_MF_10\n",
      "BUIL_MF_11\n",
      "BUIL_MF_12\n",
      "BUIL_MF_13\n",
      "BUIL_MF_14\n",
      "BUIL_MF_20\n",
      "BUIL_MF_21\n",
      "BUIL_MF_22\n",
      "BUIL_MF_23\n",
      "BUIL_MF_24\n",
      "BUIL_MF_50\n",
      "BUIL_MF_51\n",
      "BUIL_MF_52\n",
      "BUIL_MF_53\n",
      "BUIL_MF_54\n",
      "BUIL_SA_10\n",
      "BUIL_SA_11\n",
      "BUIL_SA_12\n",
      "BUIL_SA_13\n",
      "BUIL_SA_14\n",
      "BUIL_SA_20\n",
      "BUIL_SA_21\n",
      "BUIL_SA_22\n",
      "BUIL_SA_23\n",
      "BUIL_SA_24\n",
      "BUIL_SA_50\n",
      "BUIL_SA_51\n",
      "BUIL_SA_52\n",
      "BUIL_SA_53\n",
      "BUIL_SA_54\n",
      "BUIL_WS_20\n",
      "BUIL_WS_21\n",
      "BUIL_WS_22\n",
      "BUIL_WS_23\n",
      "BUIL_WS_24\n",
      "BUIL_WS_50\n",
      "BUIL_WS_51\n",
      "BUIL_WS_52\n",
      "BUIL_WS_53\n",
      "BUIL_WS_54\n",
      "BUIL_WS_55\n",
      "BUIL_WS_56\n",
      "BUIL_WS_57\n",
      "BUIL_WS_58\n",
      "BUIL_WS_5_\n",
      "DISTRICT\n",
      "DISTRICT.1\n",
      "DISTRICT.2\n",
      "DISTRICT.3\n",
      "DISTRICT.4\n",
      "DISTRICT.5\n",
      "DISTRICT.6\n",
      "FID\n",
      "FID.1\n",
      "FID.2\n",
      "FID.3\n",
      "FID.4\n",
      "FID.5\n",
      "FID.6\n",
      "JAMOAT\n",
      "JAMOAT.1\n",
      "JAMOAT.2\n",
      "JAMOAT.3\n",
      "JAMOAT.4\n",
      "JAMOAT.5\n",
      "JAMOAT.6\n",
      "JAMOAT_NAM\n",
      "JAMOAT_NAM.1\n",
      "JAMOAT_NAM.2\n",
      "JAMOAT_NAM.3\n",
      "JAMOAT_NAM.4\n",
      "JAMOAT_NAM.5\n",
      "JAMOAT_NAM.6\n",
      "NR_BUILDIN\n",
      "NR_BUILDIN.1\n",
      "NR_BUILDIN.2\n",
      "NR_BUILDIN.3\n",
      "NR_BUILDIN.4\n",
      "NR_BUILDIN.5\n",
      "NR_BUILDIN.6\n",
      "NR_PEOPLE\n",
      "NR_PEOPLE.1\n",
      "NR_PEOPLE.2\n",
      "NR_PEOPLE.3\n",
      "NR_PEOPLE.4\n",
      "NR_PEOPLE.5\n",
      "NR_PEOPLE.6\n",
      "OBJECTID\n",
      "OBJECTID.1\n",
      "OBJECTID.2\n",
      "OBJECTID.3\n",
      "OBJECTID.4\n",
      "OBJECTID.5\n",
      "OBJECTID.6\n",
      "PEOP_DR_10\n",
      "PEOP_DR_11\n",
      "PEOP_DR_12\n",
      "PEOP_DR_13\n",
      "PEOP_DR_14\n",
      "PEOP_DR_25\n",
      "PEOP_DR_26\n",
      "PEOP_DR_27\n",
      "PEOP_DR_28\n",
      "PEOP_DR_29\n",
      "PEOP_DR_50\n",
      "PEOP_DR_51\n",
      "PEOP_DR_52\n",
      "PEOP_DR_53\n",
      "PEOP_DR_54\n",
      "PEOP_EQ_10\n",
      "PEOP_EQ_11\n",
      "PEOP_EQ_12\n",
      "PEOP_EQ_13\n",
      "PEOP_EQ_14\n",
      "PEOP_EQ_15\n",
      "PEOP_EQ_16\n",
      "PEOP_EQ_17\n",
      "PEOP_EQ_18\n",
      "PEOP_EQ_19\n",
      "PEOP_EQ_47\n",
      "PEOP_EQ_48\n",
      "PEOP_EQ_49\n",
      "PEOP_EQ_50\n",
      "PEOP_EQ_51\n",
      "PEOP_FL_10\n",
      "PEOP_FL_11\n",
      "PEOP_FL_12\n",
      "PEOP_FL_13\n",
      "PEOP_FL_14\n",
      "PEOP_FL_20\n",
      "PEOP_FL_21\n",
      "PEOP_FL_22\n",
      "PEOP_FL_23\n",
      "PEOP_FL_24\n",
      "PEOP_FL_51\n",
      "PEOP_FL_52\n",
      "PEOP_FL_53\n",
      "PEOP_FL_54\n",
      "PEOP_FL_5_\n",
      "PEOP_LS_10\n",
      "PEOP_LS_11\n",
      "PEOP_LS_12\n",
      "PEOP_LS_13\n",
      "PEOP_LS_14\n",
      "PEOP_LS_20\n",
      "PEOP_LS_21\n",
      "PEOP_LS_22\n",
      "PEOP_LS_23\n",
      "PEOP_LS_24\n",
      "PEOP_LS_50\n",
      "PEOP_LS_51\n",
      "PEOP_LS_52\n",
      "PEOP_LS_53\n",
      "PEOP_LS_54\n",
      "PEOP_MF_10\n",
      "PEOP_MF_11\n",
      "PEOP_MF_12\n",
      "PEOP_MF_13\n",
      "PEOP_MF_14\n",
      "PEOP_MF_20\n",
      "PEOP_MF_21\n",
      "PEOP_MF_22\n",
      "PEOP_MF_23\n",
      "PEOP_MF_24\n",
      "PEOP_MF_50\n",
      "PEOP_MF_51\n",
      "PEOP_MF_52\n",
      "PEOP_MF_53\n",
      "PEOP_MF_54\n",
      "PEOP_SA_10\n",
      "PEOP_SA_11\n",
      "PEOP_SA_12\n",
      "PEOP_SA_13\n",
      "PEOP_SA_14\n",
      "PEOP_SA_20\n",
      "PEOP_SA_21\n",
      "PEOP_SA_22\n",
      "PEOP_SA_23\n",
      "PEOP_SA_24\n",
      "PEOP_SA_50\n",
      "PEOP_SA_51\n",
      "PEOP_SA_52\n",
      "PEOP_SA_53\n",
      "PEOP_SA_54\n",
      "PEOP_WS_20\n",
      "PEOP_WS_21\n",
      "PEOP_WS_22\n",
      "PEOP_WS_23\n",
      "PEOP_WS_24\n",
      "PEOP_WS_50\n",
      "PEOP_WS_51\n",
      "PEOP_WS_52\n",
      "PEOP_WS_53\n",
      "PEOP_WS_54\n",
      "PEOP_WS_55\n",
      "PEOP_WS_56\n",
      "PEOP_WS_57\n",
      "PEOP_WS_58\n",
      "PEOP_WS_5_\n",
      "ROAD_DR_10\n",
      "ROAD_DR_11\n",
      "ROAD_DR_12\n",
      "ROAD_DR_13\n",
      "ROAD_DR_14\n",
      "ROAD_DR_25\n",
      "ROAD_DR_26\n",
      "ROAD_DR_27\n",
      "ROAD_DR_28\n",
      "ROAD_DR_29\n",
      "ROAD_DR_50\n",
      "ROAD_DR_51\n",
      "ROAD_DR_52\n",
      "ROAD_DR_53\n",
      "ROAD_DR_54\n",
      "ROAD_EQ_10\n",
      "ROAD_EQ_11\n",
      "ROAD_EQ_12\n",
      "ROAD_EQ_13\n",
      "ROAD_EQ_14\n",
      "ROAD_EQ_15\n",
      "ROAD_EQ_16\n",
      "ROAD_EQ_47\n",
      "ROAD_EQ_48\n",
      "ROAD_EQ_49\n",
      "ROAD_EQ_50\n",
      "ROAD_EQ_51\n",
      "ROAD_FL_10\n",
      "ROAD_FL_11\n",
      "ROAD_FL_12\n",
      "ROAD_FL_13\n",
      "ROAD_FL_14\n",
      "ROAD_FL_20\n",
      "ROAD_FL_21\n",
      "ROAD_FL_22\n",
      "ROAD_FL_23\n",
      "ROAD_FL_24\n",
      "ROAD_FL_51\n",
      "ROAD_FL_52\n",
      "ROAD_FL_53\n",
      "ROAD_FL_54\n",
      "ROAD_FL_5_\n",
      "ROAD_LENGT\n",
      "ROAD_LENGT.1\n",
      "ROAD_LENGT.2\n",
      "ROAD_LENGT.3\n",
      "ROAD_LENGT.4\n",
      "ROAD_LENGT.5\n",
      "ROAD_LENGT.6\n",
      "ROAD_LS_10\n",
      "ROAD_LS_11\n",
      "ROAD_LS_12\n",
      "ROAD_LS_13\n",
      "ROAD_LS_14\n",
      "ROAD_LS_20\n",
      "ROAD_LS_21\n",
      "ROAD_LS_22\n",
      "ROAD_LS_23\n",
      "ROAD_LS_24\n",
      "ROAD_LS_50\n",
      "ROAD_LS_51\n",
      "ROAD_LS_52\n",
      "ROAD_LS_53\n",
      "ROAD_LS_54\n",
      "ROAD_MF_10\n",
      "ROAD_MF_11\n",
      "ROAD_MF_12\n",
      "ROAD_MF_13\n",
      "ROAD_MF_14\n",
      "ROAD_MF_20\n",
      "ROAD_MF_21\n",
      "ROAD_MF_22\n",
      "ROAD_MF_23\n",
      "ROAD_MF_24\n",
      "ROAD_MF_50\n",
      "ROAD_MF_51\n",
      "ROAD_MF_52\n",
      "ROAD_MF_53\n",
      "ROAD_MF_54\n",
      "ROAD_SA_10\n",
      "ROAD_SA_11\n",
      "ROAD_SA_12\n",
      "ROAD_SA_13\n",
      "ROAD_SA_14\n",
      "ROAD_SA_20\n",
      "ROAD_SA_21\n",
      "ROAD_SA_22\n",
      "ROAD_SA_23\n",
      "ROAD_SA_24\n",
      "ROAD_SA_50\n",
      "ROAD_SA_51\n",
      "ROAD_SA_52\n",
      "ROAD_SA_53\n",
      "ROAD_SA_54\n",
      "ROAD_WS_20\n",
      "ROAD_WS_21\n",
      "ROAD_WS_22\n",
      "ROAD_WS_23\n",
      "ROAD_WS_24\n",
      "ROAD_WS_50\n",
      "ROAD_WS_51\n",
      "ROAD_WS_52\n",
      "ROAD_WS_53\n",
      "ROAD_WS_54\n",
      "ROAD_WS_55\n",
      "ROAD_WS_56\n",
      "ROAD_WS_57\n",
      "ROAD_WS_58\n",
      "ROAD_WS_5_\n",
      "Shape_Area\n",
      "Shape_Area.1\n",
      "Shape_Area.2\n",
      "Shape_Area.3\n",
      "Shape_Area.4\n",
      "Shape_Area.5\n",
      "Shape_Area.6\n",
      "Shape_Length\n",
      "Shape_Length.1\n",
      "Shape_Length.2\n",
      "Shape_Length.3\n",
      "Shape_Length.4\n",
      "Shape_Length.5\n",
      "Shape_Length.6\n",
      "TEMP\n",
      "TEMP.1\n",
      "TEMP.2\n",
      "TEMP.3\n",
      "TEMP.4\n",
      "TEMP.5\n",
      "TEMP.6\n"
     ]
    }
   ],
   "source": [
    "for col in df.columns:\n",
    "    print(col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#to remove duplicated columns in datafreame\n",
    "df = df.loc[:,~df.columns.duplicated()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AGRI_AREA</th>\n",
       "      <th>AGRI_AREA.1</th>\n",
       "      <th>AGRI_AREA.2</th>\n",
       "      <th>AGRI_AREA.3</th>\n",
       "      <th>AGRI_AREA.4</th>\n",
       "      <th>AGRI_AREA.5</th>\n",
       "      <th>AGRI_AREA.6</th>\n",
       "      <th>AGRI_DR_10</th>\n",
       "      <th>AGRI_DR_11</th>\n",
       "      <th>AGRI_DR_12</th>\n",
       "      <th>...</th>\n",
       "      <th>Shape_Length.4</th>\n",
       "      <th>Shape_Length.5</th>\n",
       "      <th>Shape_Length.6</th>\n",
       "      <th>TEMP</th>\n",
       "      <th>TEMP.1</th>\n",
       "      <th>TEMP.2</th>\n",
       "      <th>TEMP.3</th>\n",
       "      <th>TEMP.4</th>\n",
       "      <th>TEMP.5</th>\n",
       "      <th>TEMP.6</th>\n",
       "    </tr>\n",
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       "      <td>31333.528856</td>\n",
       "      <td>31333.528856</td>\n",
       "      <td>95</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1064</td>\n",
       "      <td>1064</td>\n",
       "      <td>1064</td>\n",
       "      <td>1064</td>\n",
       "      <td>1064</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>11576</td>\n",
       "      <td>11576</td>\n",
       "      <td>11576</td>\n",
       "      <td>11576</td>\n",
       "      <td>11576</td>\n",
       "      <td>11576</td>\n",
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       "      <td>0</td>\n",
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       "      <td>10636</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>5857</td>\n",
       "      <td>5857</td>\n",
       "      <td>5857</td>\n",
       "      <td>5857</td>\n",
       "      <td>5857</td>\n",
       "      <td>5857</td>\n",
       "      <td>5857</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>32691.648718</td>\n",
       "      <td>32691.648718</td>\n",
       "      <td>32691.648718</td>\n",
       "      <td>63</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>6566</td>\n",
       "      <td>6566</td>\n",
       "      <td>6566</td>\n",
       "      <td>6566</td>\n",
       "      <td>6566</td>\n",
       "      <td>6566</td>\n",
       "      <td>6566</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4928</td>\n",
       "      <td>4928</td>\n",
       "      <td>4928</td>\n",
       "      <td>4928</td>\n",
       "      <td>4928</td>\n",
       "      <td>4928</td>\n",
       "      <td>4928</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>32388.667103</td>\n",
       "      <td>32388.667103</td>\n",
       "      <td>32388.667103</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>11905</td>\n",
       "      <td>11905</td>\n",
       "      <td>11905</td>\n",
       "      <td>11905</td>\n",
       "      <td>11905</td>\n",
       "      <td>11905</td>\n",
       "      <td>11905</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>48921.648995</td>\n",
       "      <td>48921.648995</td>\n",
       "      <td>48921.648995</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>148 rows × 508 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    AGRI_AREA  AGRI_AREA.1  AGRI_AREA.2  AGRI_AREA.3  AGRI_AREA.4  \\\n",
       "0        2992         2992         2992         2992         2992   \n",
       "1        1980         1980         1980         1980         1980   \n",
       "2        1980         1980         1980         1980         1980   \n",
       "3        1064         1064         1064         1064         1064   \n",
       "4       11576        11576        11576        11576        11576   \n",
       "..        ...          ...          ...          ...          ...   \n",
       "0       10636        10636        10636        10636        10636   \n",
       "1        5857         5857         5857         5857         5857   \n",
       "2        6566         6566         6566         6566         6566   \n",
       "3        4928         4928         4928         4928         4928   \n",
       "4       11905        11905        11905        11905        11905   \n",
       "\n",
       "    AGRI_AREA.5  AGRI_AREA.6  AGRI_DR_10  AGRI_DR_11  AGRI_DR_12  ...  \\\n",
       "0          2992         2992           0           0           0  ...   \n",
       "1          1980         1980           0           0           0  ...   \n",
       "2          1980         1980           0           0           0  ...   \n",
       "3          1064         1064           0           0           0  ...   \n",
       "4         11576        11576           0           0           0  ...   \n",
       "..          ...          ...         ...         ...         ...  ...   \n",
       "0         10636        10636           0           0           0  ...   \n",
       "1          5857         5857           0           0           0  ...   \n",
       "2          6566         6566           0           0           0  ...   \n",
       "3          4928         4928           0           0           0  ...   \n",
       "4         11905        11905           0           0           0  ...   \n",
       "\n",
       "    Shape_Length.4  Shape_Length.5  Shape_Length.6  TEMP  TEMP.1  TEMP.2  \\\n",
       "0    143639.872484   143639.872484   143639.872484   100       0       0   \n",
       "1     76782.018110    76782.018110    76782.018110    95       0       0   \n",
       "2     31333.528856    31333.528856    31333.528856    95       0       0   \n",
       "3     73993.806147    73993.806147    73993.806147   100       0       0   \n",
       "4    160412.185629   160412.185629   160412.185629   100       0       0   \n",
       "..             ...             ...             ...   ...     ...     ...   \n",
       "0     61089.242522    61089.242522    61089.242522    99       0       0   \n",
       "1     32691.648718    32691.648718    32691.648718    63       0       0   \n",
       "2     37212.766270    37212.766270    37212.766270     0       0       0   \n",
       "3     32388.667103    32388.667103    32388.667103     0       0       0   \n",
       "4     48921.648995    48921.648995    48921.648995     0       0       0   \n",
       "\n",
       "    TEMP.3  TEMP.4  TEMP.5  TEMP.6  \n",
       "0        0       0       0       0  \n",
       "1        0       0       0       0  \n",
       "2        0       0       0       0  \n",
       "3        0       0       0       0  \n",
       "4        0       0       0       0  \n",
       "..     ...     ...     ...     ...  \n",
       "0        0       0       0       0  \n",
       "1        0       0       0       0  \n",
       "2        0       0       0       0  \n",
       "3        0       0       0       0  \n",
       "4        0       0       0       0  \n",
       "\n",
       "[148 rows x 508 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop_duplicates(subset='JAMOAT_NAM', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>TEMP.2</th>\n",
       "      <th>TEMP.3</th>\n",
       "      <th>TEMP.4</th>\n",
       "      <th>TEMP.5</th>\n",
       "      <th>TEMP.6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2992</td>\n",
       "      <td>2992</td>\n",
       "      <td>2992</td>\n",
       "      <td>2992</td>\n",
       "      <td>2992</td>\n",
       "      <td>2992</td>\n",
       "      <td>2992</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>143639.872484</td>\n",
       "      <td>143639.872484</td>\n",
       "      <td>143639.872484</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1980</td>\n",
       "      <td>1980</td>\n",
       "      <td>1980</td>\n",
       "      <td>1980</td>\n",
       "      <td>1980</td>\n",
       "      <td>1980</td>\n",
       "      <td>1980</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>76782.018110</td>\n",
       "      <td>76782.018110</td>\n",
       "      <td>76782.018110</td>\n",
       "      <td>95</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1064</td>\n",
       "      <td>1064</td>\n",
       "      <td>1064</td>\n",
       "      <td>1064</td>\n",
       "      <td>1064</td>\n",
       "      <td>1064</td>\n",
       "      <td>1064</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>73993.806147</td>\n",
       "      <td>73993.806147</td>\n",
       "      <td>73993.806147</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>11576</td>\n",
       "      <td>11576</td>\n",
       "      <td>11576</td>\n",
       "      <td>11576</td>\n",
       "      <td>11576</td>\n",
       "      <td>11576</td>\n",
       "      <td>11576</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>160412.185629</td>\n",
       "      <td>160412.185629</td>\n",
       "      <td>160412.185629</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>7985</td>\n",
       "      <td>7985</td>\n",
       "      <td>7985</td>\n",
       "      <td>7985</td>\n",
       "      <td>7985</td>\n",
       "      <td>7985</td>\n",
       "      <td>7985</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>182506.616447</td>\n",
       "      <td>182506.616447</td>\n",
       "      <td>182506.616447</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>10636</td>\n",
       "      <td>10636</td>\n",
       "      <td>10636</td>\n",
       "      <td>10636</td>\n",
       "      <td>10636</td>\n",
       "      <td>10636</td>\n",
       "      <td>10636</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>61089.242522</td>\n",
       "      <td>61089.242522</td>\n",
       "      <td>61089.242522</td>\n",
       "      <td>99</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>5857</td>\n",
       "      <td>5857</td>\n",
       "      <td>5857</td>\n",
       "      <td>5857</td>\n",
       "      <td>5857</td>\n",
       "      <td>5857</td>\n",
       "      <td>5857</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>32691.648718</td>\n",
       "      <td>32691.648718</td>\n",
       "      <td>32691.648718</td>\n",
       "      <td>63</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>6566</td>\n",
       "      <td>6566</td>\n",
       "      <td>6566</td>\n",
       "      <td>6566</td>\n",
       "      <td>6566</td>\n",
       "      <td>6566</td>\n",
       "      <td>6566</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>37212.766270</td>\n",
       "      <td>37212.766270</td>\n",
       "      <td>37212.766270</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4928</td>\n",
       "      <td>4928</td>\n",
       "      <td>4928</td>\n",
       "      <td>4928</td>\n",
       "      <td>4928</td>\n",
       "      <td>4928</td>\n",
       "      <td>4928</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>32388.667103</td>\n",
       "      <td>32388.667103</td>\n",
       "      <td>32388.667103</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>11905</td>\n",
       "      <td>11905</td>\n",
       "      <td>11905</td>\n",
       "      <td>11905</td>\n",
       "      <td>11905</td>\n",
       "      <td>11905</td>\n",
       "      <td>11905</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>48921.648995</td>\n",
       "      <td>48921.648995</td>\n",
       "      <td>48921.648995</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>113 rows × 508 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    AGRI_AREA  AGRI_AREA.1  AGRI_AREA.2  AGRI_AREA.3  AGRI_AREA.4  \\\n",
       "0        2992         2992         2992         2992         2992   \n",
       "1        1980         1980         1980         1980         1980   \n",
       "3        1064         1064         1064         1064         1064   \n",
       "4       11576        11576        11576        11576        11576   \n",
       "5        7985         7985         7985         7985         7985   \n",
       "..        ...          ...          ...          ...          ...   \n",
       "0       10636        10636        10636        10636        10636   \n",
       "1        5857         5857         5857         5857         5857   \n",
       "2        6566         6566         6566         6566         6566   \n",
       "3        4928         4928         4928         4928         4928   \n",
       "4       11905        11905        11905        11905        11905   \n",
       "\n",
       "    AGRI_AREA.5  AGRI_AREA.6  AGRI_DR_10  AGRI_DR_11  AGRI_DR_12  ...  \\\n",
       "0          2992         2992           0           0           0  ...   \n",
       "1          1980         1980           0           0           0  ...   \n",
       "3          1064         1064           0           0           0  ...   \n",
       "4         11576        11576           0           0           0  ...   \n",
       "5          7985         7985           0           0           0  ...   \n",
       "..          ...          ...         ...         ...         ...  ...   \n",
       "0         10636        10636           0           0           0  ...   \n",
       "1          5857         5857           0           0           0  ...   \n",
       "2          6566         6566           0           0           0  ...   \n",
       "3          4928         4928           0           0           0  ...   \n",
       "4         11905        11905           0           0           0  ...   \n",
       "\n",
       "    Shape_Length.4  Shape_Length.5  Shape_Length.6  TEMP  TEMP.1  TEMP.2  \\\n",
       "0    143639.872484   143639.872484   143639.872484   100       0       0   \n",
       "1     76782.018110    76782.018110    76782.018110    95       0       0   \n",
       "3     73993.806147    73993.806147    73993.806147   100       0       0   \n",
       "4    160412.185629   160412.185629   160412.185629   100       0       0   \n",
       "5    182506.616447   182506.616447   182506.616447   100       0       0   \n",
       "..             ...             ...             ...   ...     ...     ...   \n",
       "0     61089.242522    61089.242522    61089.242522    99       0       0   \n",
       "1     32691.648718    32691.648718    32691.648718    63       0       0   \n",
       "2     37212.766270    37212.766270    37212.766270     0       0       0   \n",
       "3     32388.667103    32388.667103    32388.667103     0       0       0   \n",
       "4     48921.648995    48921.648995    48921.648995     0       0       0   \n",
       "\n",
       "    TEMP.3  TEMP.4  TEMP.5  TEMP.6  \n",
       "0        0       0       0       0  \n",
       "1        0       0       0       0  \n",
       "3        0       0       0       0  \n",
       "4        0       0       0       0  \n",
       "5        0       0       0       0  \n",
       "..     ...     ...     ...     ...  \n",
       "0        0       0       0       0  \n",
       "1        0       0       0       0  \n",
       "2        0       0       0       0  \n",
       "3        0       0       0       0  \n",
       "4        0       0       0       0  \n",
       "\n",
       "[113 rows x 508 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AGRI_AREA\n",
      "AGRI_AREA.1\n",
      "AGRI_AREA.2\n",
      "AGRI_AREA.3\n",
      "AGRI_AREA.4\n",
      "AGRI_AREA.5\n",
      "AGRI_AREA.6\n",
      "AGRI_DR_10\n",
      "AGRI_DR_11\n",
      "AGRI_DR_12\n",
      "AGRI_DR_13\n",
      "AGRI_DR_14\n",
      "AGRI_DR_25\n",
      "AGRI_DR_26\n",
      "AGRI_DR_27\n",
      "AGRI_DR_28\n",
      "AGRI_DR_29\n",
      "AGRI_DR_50\n",
      "AGRI_DR_51\n",
      "AGRI_DR_52\n",
      "AGRI_DR_53\n",
      "AGRI_DR_54\n",
      "AGRI_EQ_10\n",
      "AGRI_EQ_11\n",
      "AGRI_EQ_12\n",
      "AGRI_EQ_13\n",
      "AGRI_EQ_14\n",
      "AGRI_EQ_15\n",
      "AGRI_EQ_16\n",
      "AGRI_EQ_17\n",
      "AGRI_EQ_18\n",
      "AGRI_EQ_19\n",
      "AGRI_EQ_47\n",
      "AGRI_EQ_48\n",
      "AGRI_EQ_49\n",
      "AGRI_EQ_50\n",
      "AGRI_EQ_51\n",
      "AGRI_FL_10\n",
      "AGRI_FL_11\n",
      "AGRI_FL_12\n",
      "AGRI_FL_13\n",
      "AGRI_FL_14\n",
      "AGRI_FL_20\n",
      "AGRI_FL_21\n",
      "AGRI_FL_22\n",
      "AGRI_FL_23\n",
      "AGRI_FL_24\n",
      "AGRI_FL_51\n",
      "AGRI_FL_52\n",
      "AGRI_FL_53\n",
      "AGRI_FL_54\n",
      "AGRI_FL_5_\n",
      "AGRI_LS_10\n",
      "AGRI_LS_11\n",
      "AGRI_LS_12\n",
      "AGRI_LS_13\n",
      "AGRI_LS_14\n",
      "AGRI_LS_20\n",
      "AGRI_LS_21\n",
      "AGRI_LS_22\n",
      "AGRI_LS_23\n",
      "AGRI_LS_24\n",
      "AGRI_LS_50\n",
      "AGRI_LS_51\n",
      "AGRI_LS_52\n",
      "AGRI_LS_53\n",
      "AGRI_LS_54\n",
      "AGRI_MF_10\n",
      "AGRI_MF_11\n",
      "AGRI_MF_12\n",
      "AGRI_MF_13\n",
      "AGRI_MF_14\n",
      "AGRI_MF_20\n",
      "AGRI_MF_21\n",
      "AGRI_MF_22\n",
      "AGRI_MF_23\n",
      "AGRI_MF_24\n",
      "AGRI_MF_50\n",
      "AGRI_MF_51\n",
      "AGRI_MF_52\n",
      "AGRI_MF_53\n",
      "AGRI_MF_54\n",
      "AGRI_SA_10\n",
      "AGRI_SA_11\n",
      "AGRI_SA_12\n",
      "AGRI_SA_13\n",
      "AGRI_SA_14\n",
      "AGRI_SA_20\n",
      "AGRI_SA_21\n",
      "AGRI_SA_22\n",
      "AGRI_SA_23\n",
      "AGRI_SA_24\n",
      "AGRI_SA_50\n",
      "AGRI_SA_51\n",
      "AGRI_SA_52\n",
      "AGRI_SA_53\n",
      "AGRI_SA_54\n",
      "AGRI_WS_20\n",
      "AGRI_WS_21\n",
      "AGRI_WS_22\n",
      "AGRI_WS_23\n",
      "AGRI_WS_24\n",
      "AGRI_WS_50\n",
      "AGRI_WS_51\n",
      "AGRI_WS_52\n",
      "AGRI_WS_53\n",
      "AGRI_WS_54\n",
      "AGRI_WS_55\n",
      "AGRI_WS_56\n",
      "AGRI_WS_57\n",
      "AGRI_WS_58\n",
      "AGRI_WS_5_\n",
      "BUAT_AREA\n",
      "BUAT_AREA.1\n",
      "BUAT_AREA.2\n",
      "BUAT_AREA.3\n",
      "BUAT_AREA.4\n",
      "BUAT_AREA.5\n",
      "BUAT_AREA.6\n",
      "BUIL_DR_10\n",
      "BUIL_DR_11\n",
      "BUIL_DR_12\n",
      "BUIL_DR_13\n",
      "BUIL_DR_14\n",
      "BUIL_DR_25\n",
      "BUIL_DR_26\n",
      "BUIL_DR_27\n",
      "BUIL_DR_28\n",
      "BUIL_DR_29\n",
      "BUIL_DR_50\n",
      "BUIL_DR_51\n",
      "BUIL_DR_52\n",
      "BUIL_DR_53\n",
      "BUIL_DR_54\n",
      "BUIL_EQ_10\n",
      "BUIL_EQ_11\n",
      "BUIL_EQ_12\n",
      "BUIL_EQ_13\n",
      "BUIL_EQ_14\n",
      "BUIL_EQ_15\n",
      "BUIL_EQ_16\n",
      "BUIL_EQ_17\n",
      "BUIL_EQ_18\n",
      "BUIL_EQ_19\n",
      "BUIL_EQ_47\n",
      "BUIL_EQ_48\n",
      "BUIL_EQ_49\n",
      "BUIL_EQ_50\n",
      "BUIL_EQ_51\n",
      "BUIL_FL_10\n",
      "BUIL_FL_11\n",
      "BUIL_FL_12\n",
      "BUIL_FL_13\n",
      "BUIL_FL_14\n",
      "BUIL_FL_20\n",
      "BUIL_FL_21\n",
      "BUIL_FL_22\n",
      "BUIL_FL_23\n",
      "BUIL_FL_24\n",
      "BUIL_FL_51\n",
      "BUIL_FL_52\n",
      "BUIL_FL_53\n",
      "BUIL_FL_54\n",
      "BUIL_FL_5_\n",
      "BUIL_LS_10\n",
      "BUIL_LS_11\n",
      "BUIL_LS_12\n",
      "BUIL_LS_13\n",
      "BUIL_LS_14\n",
      "BUIL_LS_20\n",
      "BUIL_LS_21\n",
      "BUIL_LS_22\n",
      "BUIL_LS_23\n",
      "BUIL_LS_24\n",
      "BUIL_LS_50\n",
      "BUIL_LS_51\n",
      "BUIL_LS_52\n",
      "BUIL_LS_53\n",
      "BUIL_LS_54\n",
      "BUIL_MF_10\n",
      "BUIL_MF_11\n",
      "BUIL_MF_12\n",
      "BUIL_MF_13\n",
      "BUIL_MF_14\n",
      "BUIL_MF_20\n",
      "BUIL_MF_21\n",
      "BUIL_MF_22\n",
      "BUIL_MF_23\n",
      "BUIL_MF_24\n",
      "BUIL_MF_50\n",
      "BUIL_MF_51\n",
      "BUIL_MF_52\n",
      "BUIL_MF_53\n",
      "BUIL_MF_54\n",
      "BUIL_SA_10\n",
      "BUIL_SA_11\n",
      "BUIL_SA_12\n",
      "BUIL_SA_13\n",
      "BUIL_SA_14\n",
      "BUIL_SA_20\n",
      "BUIL_SA_21\n",
      "BUIL_SA_22\n",
      "BUIL_SA_23\n",
      "BUIL_SA_24\n",
      "BUIL_SA_50\n",
      "BUIL_SA_51\n",
      "BUIL_SA_52\n",
      "BUIL_SA_53\n",
      "BUIL_SA_54\n",
      "BUIL_WS_20\n",
      "BUIL_WS_21\n",
      "BUIL_WS_22\n",
      "BUIL_WS_23\n",
      "BUIL_WS_24\n",
      "BUIL_WS_50\n",
      "BUIL_WS_51\n",
      "BUIL_WS_52\n",
      "BUIL_WS_53\n",
      "BUIL_WS_54\n",
      "BUIL_WS_55\n",
      "BUIL_WS_56\n",
      "BUIL_WS_57\n",
      "BUIL_WS_58\n",
      "BUIL_WS_5_\n",
      "DISTRICT\n",
      "DISTRICT.1\n",
      "DISTRICT.2\n",
      "DISTRICT.3\n",
      "DISTRICT.4\n",
      "DISTRICT.5\n",
      "DISTRICT.6\n",
      "FID\n",
      "FID.1\n",
      "FID.2\n",
      "FID.3\n",
      "FID.4\n",
      "FID.5\n",
      "FID.6\n",
      "JAMOAT\n",
      "JAMOAT.1\n",
      "JAMOAT.2\n",
      "JAMOAT.3\n",
      "JAMOAT.4\n",
      "JAMOAT.5\n",
      "JAMOAT.6\n",
      "JAMOAT_NAM\n",
      "JAMOAT_NAM.1\n",
      "JAMOAT_NAM.2\n",
      "JAMOAT_NAM.3\n",
      "JAMOAT_NAM.4\n",
      "JAMOAT_NAM.5\n",
      "JAMOAT_NAM.6\n",
      "NR_BUILDIN\n",
      "NR_BUILDIN.1\n",
      "NR_BUILDIN.2\n",
      "NR_BUILDIN.3\n",
      "NR_BUILDIN.4\n",
      "NR_BUILDIN.5\n",
      "NR_BUILDIN.6\n",
      "NR_PEOPLE\n",
      "NR_PEOPLE.1\n",
      "NR_PEOPLE.2\n",
      "NR_PEOPLE.3\n",
      "NR_PEOPLE.4\n",
      "NR_PEOPLE.5\n",
      "NR_PEOPLE.6\n",
      "OBJECTID\n",
      "OBJECTID.1\n",
      "OBJECTID.2\n",
      "OBJECTID.3\n",
      "OBJECTID.4\n",
      "OBJECTID.5\n",
      "OBJECTID.6\n",
      "PEOP_DR_10\n",
      "PEOP_DR_11\n",
      "PEOP_DR_12\n",
      "PEOP_DR_13\n",
      "PEOP_DR_14\n",
      "PEOP_DR_25\n",
      "PEOP_DR_26\n",
      "PEOP_DR_27\n",
      "PEOP_DR_28\n",
      "PEOP_DR_29\n",
      "PEOP_DR_50\n",
      "PEOP_DR_51\n",
      "PEOP_DR_52\n",
      "PEOP_DR_53\n",
      "PEOP_DR_54\n",
      "PEOP_EQ_10\n",
      "PEOP_EQ_11\n",
      "PEOP_EQ_12\n",
      "PEOP_EQ_13\n",
      "PEOP_EQ_14\n",
      "PEOP_EQ_15\n",
      "PEOP_EQ_16\n",
      "PEOP_EQ_17\n",
      "PEOP_EQ_18\n",
      "PEOP_EQ_19\n",
      "PEOP_EQ_47\n",
      "PEOP_EQ_48\n",
      "PEOP_EQ_49\n",
      "PEOP_EQ_50\n",
      "PEOP_EQ_51\n",
      "PEOP_FL_10\n",
      "PEOP_FL_11\n",
      "PEOP_FL_12\n",
      "PEOP_FL_13\n",
      "PEOP_FL_14\n",
      "PEOP_FL_20\n",
      "PEOP_FL_21\n",
      "PEOP_FL_22\n",
      "PEOP_FL_23\n",
      "PEOP_FL_24\n",
      "PEOP_FL_51\n",
      "PEOP_FL_52\n",
      "PEOP_FL_53\n",
      "PEOP_FL_54\n",
      "PEOP_FL_5_\n",
      "PEOP_LS_10\n",
      "PEOP_LS_11\n",
      "PEOP_LS_12\n",
      "PEOP_LS_13\n",
      "PEOP_LS_14\n",
      "PEOP_LS_20\n",
      "PEOP_LS_21\n",
      "PEOP_LS_22\n",
      "PEOP_LS_23\n",
      "PEOP_LS_24\n",
      "PEOP_LS_50\n",
      "PEOP_LS_51\n",
      "PEOP_LS_52\n",
      "PEOP_LS_53\n",
      "PEOP_LS_54\n",
      "PEOP_MF_10\n",
      "PEOP_MF_11\n",
      "PEOP_MF_12\n",
      "PEOP_MF_13\n",
      "PEOP_MF_14\n",
      "PEOP_MF_20\n",
      "PEOP_MF_21\n",
      "PEOP_MF_22\n",
      "PEOP_MF_23\n",
      "PEOP_MF_24\n",
      "PEOP_MF_50\n",
      "PEOP_MF_51\n",
      "PEOP_MF_52\n",
      "PEOP_MF_53\n",
      "PEOP_MF_54\n",
      "PEOP_SA_10\n",
      "PEOP_SA_11\n",
      "PEOP_SA_12\n",
      "PEOP_SA_13\n",
      "PEOP_SA_14\n",
      "PEOP_SA_20\n",
      "PEOP_SA_21\n",
      "PEOP_SA_22\n",
      "PEOP_SA_23\n",
      "PEOP_SA_24\n",
      "PEOP_SA_50\n",
      "PEOP_SA_51\n",
      "PEOP_SA_52\n",
      "PEOP_SA_53\n",
      "PEOP_SA_54\n",
      "PEOP_WS_20\n",
      "PEOP_WS_21\n",
      "PEOP_WS_22\n",
      "PEOP_WS_23\n",
      "PEOP_WS_24\n",
      "PEOP_WS_50\n",
      "PEOP_WS_51\n",
      "PEOP_WS_52\n",
      "PEOP_WS_53\n",
      "PEOP_WS_54\n",
      "PEOP_WS_55\n",
      "PEOP_WS_56\n",
      "PEOP_WS_57\n",
      "PEOP_WS_58\n",
      "PEOP_WS_5_\n",
      "ROAD_DR_10\n",
      "ROAD_DR_11\n",
      "ROAD_DR_12\n",
      "ROAD_DR_13\n",
      "ROAD_DR_14\n",
      "ROAD_DR_25\n",
      "ROAD_DR_26\n",
      "ROAD_DR_27\n",
      "ROAD_DR_28\n",
      "ROAD_DR_29\n",
      "ROAD_DR_50\n",
      "ROAD_DR_51\n",
      "ROAD_DR_52\n",
      "ROAD_DR_53\n",
      "ROAD_DR_54\n",
      "ROAD_EQ_10\n",
      "ROAD_EQ_11\n",
      "ROAD_EQ_12\n",
      "ROAD_EQ_13\n",
      "ROAD_EQ_14\n",
      "ROAD_EQ_15\n",
      "ROAD_EQ_16\n",
      "ROAD_EQ_47\n",
      "ROAD_EQ_48\n",
      "ROAD_EQ_49\n",
      "ROAD_EQ_50\n",
      "ROAD_EQ_51\n",
      "ROAD_FL_10\n",
      "ROAD_FL_11\n",
      "ROAD_FL_12\n",
      "ROAD_FL_13\n",
      "ROAD_FL_14\n",
      "ROAD_FL_20\n",
      "ROAD_FL_21\n",
      "ROAD_FL_22\n",
      "ROAD_FL_23\n",
      "ROAD_FL_24\n",
      "ROAD_FL_51\n",
      "ROAD_FL_52\n",
      "ROAD_FL_53\n",
      "ROAD_FL_54\n",
      "ROAD_FL_5_\n",
      "ROAD_LENGT\n",
      "ROAD_LENGT.1\n",
      "ROAD_LENGT.2\n",
      "ROAD_LENGT.3\n",
      "ROAD_LENGT.4\n",
      "ROAD_LENGT.5\n",
      "ROAD_LENGT.6\n",
      "ROAD_LS_10\n",
      "ROAD_LS_11\n",
      "ROAD_LS_12\n",
      "ROAD_LS_13\n",
      "ROAD_LS_14\n",
      "ROAD_LS_20\n",
      "ROAD_LS_21\n",
      "ROAD_LS_22\n",
      "ROAD_LS_23\n",
      "ROAD_LS_24\n",
      "ROAD_LS_50\n",
      "ROAD_LS_51\n",
      "ROAD_LS_52\n",
      "ROAD_LS_53\n",
      "ROAD_LS_54\n",
      "ROAD_MF_10\n",
      "ROAD_MF_11\n",
      "ROAD_MF_12\n",
      "ROAD_MF_13\n",
      "ROAD_MF_14\n",
      "ROAD_MF_20\n",
      "ROAD_MF_21\n",
      "ROAD_MF_22\n",
      "ROAD_MF_23\n",
      "ROAD_MF_24\n",
      "ROAD_MF_50\n",
      "ROAD_MF_51\n",
      "ROAD_MF_52\n",
      "ROAD_MF_53\n",
      "ROAD_MF_54\n",
      "ROAD_SA_10\n",
      "ROAD_SA_11\n",
      "ROAD_SA_12\n",
      "ROAD_SA_13\n",
      "ROAD_SA_14\n",
      "ROAD_SA_20\n",
      "ROAD_SA_21\n",
      "ROAD_SA_22\n",
      "ROAD_SA_23\n",
      "ROAD_SA_24\n",
      "ROAD_SA_50\n",
      "ROAD_SA_51\n",
      "ROAD_SA_52\n",
      "ROAD_SA_53\n",
      "ROAD_SA_54\n",
      "ROAD_WS_20\n",
      "ROAD_WS_21\n",
      "ROAD_WS_22\n",
      "ROAD_WS_23\n",
      "ROAD_WS_24\n",
      "ROAD_WS_50\n",
      "ROAD_WS_51\n",
      "ROAD_WS_52\n",
      "ROAD_WS_53\n",
      "ROAD_WS_54\n",
      "ROAD_WS_55\n",
      "ROAD_WS_56\n",
      "ROAD_WS_57\n",
      "ROAD_WS_58\n",
      "ROAD_WS_5_\n",
      "Shape_Area\n",
      "Shape_Area.1\n",
      "Shape_Area.2\n",
      "Shape_Area.3\n",
      "Shape_Area.4\n",
      "Shape_Area.5\n",
      "Shape_Area.6\n",
      "Shape_Length\n",
      "Shape_Length.1\n",
      "Shape_Length.2\n",
      "Shape_Length.3\n",
      "Shape_Length.4\n",
      "Shape_Length.5\n",
      "Shape_Length.6\n",
      "TEMP\n",
      "TEMP.1\n",
      "TEMP.2\n",
      "TEMP.3\n",
      "TEMP.4\n",
      "TEMP.5\n",
      "TEMP.6\n"
     ]
    }
   ],
   "source": [
    "for col in df.columns:\n",
    "    print(col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df= df.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#defining variables\n",
    "\"\"\"\n",
    "convention for variables\n",
    "earthquake = e\n",
    "windstrome = w\n",
    "flood = f\n",
    "landslide = l\n",
    "mudflow = m\n",
    "drough = d\n",
    "snow avalanche = s\n",
    "\"\"\"\n",
    "jamoat = df['JAMOAT']\n",
    "district = df['DISTRICT']\n",
    "\n",
    "#EARTHQUAKE\n",
    "e1 = df['BUIL_EQ_48']\n",
    "e2 = df['BUIL_EQ_49']\n",
    "e3 = df['BUIL_EQ_50']\n",
    "e4 = df['PEOP_EQ_48']\n",
    "e5 = df['PEOP_EQ_49']\n",
    "e6 = df['PEOP_EQ_50']\n",
    "e7 = df['AGRI_EQ_48']\n",
    "e8 = df['AGRI_EQ_49']\n",
    "e9 = df['AGRI_EQ_50']\n",
    "e10 = df['ROAD_EQ_48']\n",
    "e11 = df['ROAD_EQ_49']\n",
    "e12 = df['ROAD_EQ_50']\n",
    "\n",
    "#WINDSTROME\n",
    "w1 = df['BUIL_WS_21']\n",
    "w2 = df['BUIL_WS_22']\n",
    "w3 = df['BUIL_WS_23']\n",
    "w4 = df['PEOP_WS_21']\n",
    "w5 = df['PEOP_WS_22']\n",
    "w6 = df['PEOP_WS_23']\n",
    "w7 = df['AGRI_WS_21']\n",
    "w8 = df['AGRI_WS_22']\n",
    "w9 = df['AGRI_WS_23']\n",
    "w10 = df['ROAD_WS_21']\n",
    "w11 = df['ROAD_WS_22']\n",
    "w12 = df['ROAD_WS_23']\n",
    "\n",
    "#FLOOD\n",
    "f1 = df['BUIL_FL_21']\n",
    "f2 = df['BUIL_FL_22']\n",
    "f3 = df['BUIL_FL_23']\n",
    "f4 = df['PEOP_FL_21']\n",
    "f5 = df['PEOP_FL_22']\n",
    "f6 = df['PEOP_FL_23']\n",
    "f7 = df['AGRI_FL_21']\n",
    "f8 = df['AGRI_FL_22']\n",
    "f9 = df['AGRI_FL_23']\n",
    "f10 = df['ROAD_FL_21']\n",
    "f11 = df['ROAD_FL_22']\n",
    "f12 = df['ROAD_FL_23']\n",
    "\n",
    "#LANDSLIDE\n",
    "l1 = df['BUIL_LS_21']\n",
    "l2 = df['BUIL_LS_22']\n",
    "l3 = df['BUIL_LS_23']\n",
    "l4 = df['PEOP_LS_21']\n",
    "l5 = df['PEOP_LS_22']\n",
    "l6 = df['PEOP_LS_23']\n",
    "l7 = df['AGRI_LS_21']\n",
    "l8 = df['AGRI_LS_22']\n",
    "l9 = df['AGRI_LS_23']\n",
    "l10 = df['ROAD_LS_21']\n",
    "l11 = df['ROAD_LS_22']\n",
    "l12 = df['ROAD_LS_23']\n",
    "\n",
    "#MUDFLOW\n",
    "m1 = df['BUIL_MF_21']\n",
    "m2 = df['BUIL_MF_22']\n",
    "m3 = df['BUIL_MF_23']\n",
    "m4 = df['PEOP_MF_21']\n",
    "m5 = df['PEOP_MF_22']\n",
    "m6 = df['PEOP_MF_23']\n",
    "m7 = df['AGRI_MF_21']\n",
    "m8 = df['AGRI_MF_22']\n",
    "m9 = df['AGRI_MF_23']\n",
    "m10 = df['ROAD_MF_21']\n",
    "m11 = df['ROAD_MF_22']\n",
    "m12 = df['ROAD_MF_23']\n",
    "\n",
    "#SNOW AVALANCHE\n",
    "s1 = df['BUIL_SA_21']\n",
    "s2 = df['BUIL_SA_22']\n",
    "s3 = df['BUIL_SA_23']\n",
    "s4 = df['PEOP_SA_21']\n",
    "s5 = df['PEOP_SA_22']\n",
    "s6 = df['PEOP_SA_23']\n",
    "s7 = df['AGRI_SA_21']\n",
    "s8 = df['AGRI_SA_22']\n",
    "s9 = df['AGRI_SA_23']\n",
    "s10 = df['ROAD_SA_21']\n",
    "s11 = df['ROAD_SA_22']\n",
    "s12 = df['ROAD_SA_23']\n",
    "\n",
    "#DROUGHT\n",
    "d1 = df['BUIL_DR_25']\n",
    "d2 = df['BUIL_DR_26']\n",
    "d3 = df['BUIL_DR_27']\n",
    "d4 = df['PEOP_DR_25']\n",
    "d5 = df['PEOP_DR_26']\n",
    "d6 = df['PEOP_DR_27']\n",
    "d7 = df['AGRI_DR_25']\n",
    "d8 = df['AGRI_DR_26']\n",
    "d9 = df['AGRI_DR_27']\n",
    "d10 = df['ROAD_DR_25']\n",
    "d11 = df['ROAD_DR_26']\n",
    "d12 = df['ROAD_DR_27']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = df.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5352"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['BUIL_SA_21'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x283b0ffe088>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# set width of bar\n",
    "barWidth = 0.25\n",
    " \n",
    "# set height of bar\n",
    "bars1 = [e1[0], w1[0], f1[0], l1[0], m1[0], s1[0]]\n",
    "bars2 = [e2[0], w2[0], f2[0], l2[0], m2[0], s2[0]]\n",
    "bars3 = [e3[0], w3[0], f3[0], l3[0], m3[0], s3[0]]\n",
    "\n",
    " \n",
    "# Set position of bar on X axis\n",
    "r1 = np.arange(len(bars1))\n",
    "r2 = [x + barWidth for x in r1]\n",
    "r3 = [x + barWidth for x in r2]\n",
    " \n",
    "# Make the plot\n",
    "plt.bar(r1, bars1, color='g', width=barWidth, edgecolor='white', label='low')\n",
    "plt.bar(r2, bars2, color='y', width=barWidth, edgecolor='white', label='medium')\n",
    "plt.bar(r3, bars3, color='r', width=barWidth, edgecolor='white', label='high')\n",
    " \n",
    "# Add xticks on the middle of the group bars\n",
    "plt.xlabel('group', fontweight='bold')\n",
    "plt.xticks([r + barWidth for r in range(len(bars1))], ['FL', 'EQ', 'WS', 'LS', 'MF'])\n",
    " \n",
    "# Create legend & Show graphic\n",
    "plt.legend()\n",
    "# plt.savefig('bar1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 841.68x595.44 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f = plt.figure(figsize=(11.69,8.27))\n",
    "\n",
    "# set width of bar\n",
    "barWidth = 0.25\n",
    " \n",
    "# for first graphset height of bar\n",
    "bars1 = [e1[0], w1[0], f1[0], l1[0], m1[0], s1[0], d1[0]]\n",
    "bars2 = [e2[0], w2[0], f2[0], l2[0], m2[0], s2[0], d2[0]]\n",
    "bars3 = [e3[0], w3[0], f3[0], l3[0], m3[0], s3[0], d3[0]]\n",
    "\n",
    "# for second graphset height of bar\n",
    "bars4 = [e4[0], w4[0], f4[0], l4[0], m4[0], s4[0], d4[0]]\n",
    "bars5 = [e5[0], w5[0], f5[0], l5[0], m5[0], s5[0], d5[0]]\n",
    "bars6 = [e6[0], w6[0], f6[0], l6[0], m6[0], s6[0], d6[0]]\n",
    "\n",
    "# for third graphset height of bar\n",
    "bars7 = [e7[0], w7[0], f7[0], l7[0], m7[0], s7[0], d7[0]]\n",
    "bars8 = [e8[0], w8[0], f8[0], l8[0], m8[0], s8[0], d8[0]]\n",
    "bars9 = [e9[0], w9[0], f9[0], l9[0], m9[0], s9[0], d9[0]]\n",
    "\n",
    "# for third graphset height of bar\n",
    "bars10 = [e10[0], w10[0], f10[0], l10[0], m10[0], s10[0], d10[0]]\n",
    "bars11 = [e11[0], w11[0], f11[0], l11[0], m11[0], s11[0], d11[0]]\n",
    "bars12 = [e12[0], w12[0], f12[0], l12[0], m12[0], s12[0], d12[0]]\n",
    "\n",
    "# Set position of bar on X axis\n",
    "r1 = np.arange(len(bars1))\n",
    "r2 = [x + barWidth for x in r1]\n",
    "r3 = [x + barWidth for x in r2]\n",
    " \n",
    "# Make the plot\n",
    "plt.subplot(221)\n",
    "plt.title('Buiding exposure(number)')\n",
    "plt.bar(r1, bars1, color='g', width=barWidth, edgecolor='white', label='low')\n",
    "plt.bar(r2, bars2, color='y', width=barWidth, edgecolor='white', label='medium')\n",
    "plt.bar(r3, bars3, color='r', width=barWidth, edgecolor='white', label='high')\n",
    " \n",
    "# Add xticks on the middle of the group bars\n",
    "# plt.xlabel('group', fontweight='bold')\n",
    "plt.xticks([r + barWidth for r in range(len(bars1))], ['EQ', 'WS', 'FL', 'LS', 'MF', 'SA', 'DR'])\n",
    " \n",
    "    \n",
    "    # Make the plot\n",
    "plt.subplot(222)\n",
    "plt.title('Population(number)')\n",
    "plt.bar(r1, bars4, color='g', width=barWidth, edgecolor='white', label='low')\n",
    "plt.bar(r2, bars5, color='y', width=barWidth, edgecolor='white', label='medium')\n",
    "plt.bar(r3, bars6, color='r', width=barWidth, edgecolor='white', label='high')\n",
    " \n",
    "# Add xticks on the middle of the group bars\n",
    "# plt.xlabel('group', fontweight='bold')\n",
    "plt.xticks([r + barWidth for r in range(len(bars1))], ['EQ', 'WS', 'FL', 'LS', 'MF', 'SA', 'DR'])\n",
    "\n",
    "# Make the plot\n",
    "plt.subplot(223)\n",
    "plt.title('Agricultural land exposed(hectares)')\n",
    "plt.bar(r1, bars7, color='g', width=barWidth, edgecolor='white', label='low')\n",
    "plt.bar(r2, bars8, color='y', width=barWidth, edgecolor='white', label='medium')\n",
    "plt.bar(r3, bars9, color='r', width=barWidth, edgecolor='white', label='high')\n",
    " \n",
    "# Add xticks on the middle of the group bars\n",
    "# plt.xlabel('group', fontweight='bold')\n",
    "plt.xticks([r + barWidth for r in range(len(bars1))], ['EQ', 'WS', 'FL', 'LS', 'MF', 'SA', 'DR'])\n",
    "\n",
    "\n",
    "# Make the plot\n",
    "plt.subplot(224)\n",
    "plt.title('Road exposure(km)')\n",
    "plt.bar(r1, bars10, color='g', width=barWidth, edgecolor='white', label='low')\n",
    "plt.bar(r2, bars11, color='y', width=barWidth, edgecolor='white', label='medium')\n",
    "plt.bar(r3, bars12, color='r', width=barWidth, edgecolor='white', label='high')\n",
    " \n",
    "# Add xticks on the middle of the group bars\n",
    "# plt.xlabel('group', fontweight='bold')\n",
    "plt.xticks([r + barWidth for r in range(len(bars1))], ['EQ', 'WS', 'FL', 'LS', 'MF', 'SA', 'DR'])\n",
    "\n",
    "\n",
    "# Create legend & Show graphic\n",
    "plt.tight_layout()\n",
    "plt.savefig('figure', figsize=(11.69,8.27))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generateGraph(index):\n",
    "    f = plt.figure(figsize=(11.69,8.27))\n",
    "\n",
    "    # set width of bar\n",
    "    barWidth = 0.25\n",
    "\n",
    "    # for first graphset height of bar\n",
    "    bars1 = [e1[index], w1[index], f1[index], l1[index], m1[index], s1[index], d1[index]]\n",
    "    bars2 = [e2[index], w2[index], f2[index], l2[index], m2[index], s2[index], d2[index]]\n",
    "    bars3 = [e3[index], w3[index], f3[index], l3[index], m3[index], s3[index], d3[index]]\n",
    "\n",
    "    # for second graphset height of bar\n",
    "    bars4 = [e4[index], w4[index], f4[index], l4[index], m4[index], s4[index], d4[index]]\n",
    "    bars5 = [e5[index], w5[index], f5[index], l5[index], m5[index], s5[index], d5[index]]\n",
    "    bars6 = [e6[index], w6[index], f6[index], l6[index], m6[index], s6[index], d6[index]]\n",
    "\n",
    "    # for third graphset height of bar\n",
    "    bars7 = [e7[index], w7[index], f7[index], l7[index], m7[index], s7[index], d7[index]]\n",
    "    bars8 = [e8[index], w8[index], f8[index], l8[index], m8[index], s8[index], d8[index]]\n",
    "    bars9 = [e9[index], w9[index], f9[index], l9[index], m9[index], s9[index], d9[index]]\n",
    "\n",
    "    # for third graphset height of bar\n",
    "    bars10 = [e10[index], w10[index], f10[index], l10[index], m10[index], s10[index], d10[index]]\n",
    "    bars11 = [e11[index], w11[index], f11[index], l11[index], m11[index], s11[index], d11[index]]\n",
    "    bars12 = [e12[index], w12[index], f12[index], l12[index], m12[index], s12[index], d12[index]]\n",
    "\n",
    "    # Set position of bar on X axis\n",
    "    r1 = np.arange(len(bars1))\n",
    "    r2 = [x + barWidth for x in r1]\n",
    "    r3 = [x + barWidth for x in r2]\n",
    "\n",
    "    # Make the plot\n",
    "    plt.subplot(221)\n",
    "    plt.title('Buiding exposure(number)')\n",
    "    plt.bar(r1, bars1, color='#00b04f', width=barWidth, edgecolor='white', label='low')\n",
    "    plt.bar(r2, bars2, color='#ffbf00', width=barWidth, edgecolor='white', label='medium')\n",
    "    plt.bar(r3, bars3, color='#ff0000', width=barWidth, edgecolor='white', label='high')\n",
    "\n",
    "    # Add xticks on the middle of the group bars\n",
    "    # plt.xlabel('group', fontweight='bold')\n",
    "    plt.xticks([r + barWidth for r in range(len(bars1))], ['EQ', 'WS', 'FL', 'LS', 'MF', 'SA', 'DR'])\n",
    "\n",
    "\n",
    "        # Make the plot\n",
    "    plt.subplot(222)\n",
    "    plt.title('Population(number)')\n",
    "    plt.bar(r1, bars4, color='#00b04f', width=barWidth, edgecolor='white', label='low')\n",
    "    plt.bar(r2, bars5, color='#ffbf00', width=barWidth, edgecolor='white', label='medium')\n",
    "    plt.bar(r3, bars6, color='#ff0000', width=barWidth, edgecolor='white', label='high')\n",
    "\n",
    "    # Add xticks on the middle of the group bars\n",
    "    # plt.xlabel('group', fontweight='bold')\n",
    "    plt.xticks([r + barWidth for r in range(len(bars1))], ['EQ', 'WS', 'FL', 'LS', 'MF', 'SA', 'DR'])\n",
    "\n",
    "    # Make the plot\n",
    "    plt.subplot(223)\n",
    "    plt.title('Agricultural land exposed(hectares)')\n",
    "    plt.bar(r1, bars7, color='#00b04f', width=barWidth, edgecolor='white', label='low')\n",
    "    plt.bar(r2, bars8, color='#ffbf00', width=barWidth, edgecolor='white', label='medium')\n",
    "    plt.bar(r3, bars9, color='#ff0000', width=barWidth, edgecolor='white', label='high')\n",
    "\n",
    "    # Add xticks on the middle of the group bars\n",
    "    # plt.xlabel('group', fontweight='bold')\n",
    "    plt.xticks([r + barWidth for r in range(len(bars1))], ['EQ', 'WS', 'FL', 'LS', 'MF', 'SA', 'DR'])\n",
    "\n",
    "\n",
    "    # Make the plot\n",
    "    plt.subplot(224)\n",
    "    plt.title('Road exposure(km)')\n",
    "    plt.bar(r1, bars10, color='#00b04f', width=barWidth, edgecolor='white', label='low')\n",
    "    plt.bar(r2, bars11, color='#ffbf00', width=barWidth, edgecolor='white', label='medium')\n",
    "    plt.bar(r3, bars12, color='#ff0000', width=barWidth, edgecolor='white', label='high')\n",
    "\n",
    "    # Add xticks on the middle of the group bars\n",
    "    # plt.xlabel('group', fontweight='bold')\n",
    "    plt.xticks([r + barWidth for r in range(len(bars1))], ['EQ', 'WS', 'FL', 'LS', 'MF', 'SA', 'DR'])\n",
    "\n",
    "\n",
    "    # Create legend & Show graphic\n",
    "    plt.tight_layout()\n",
    "    plt.savefig('figure {}'.format(index), figsize=(11.69,8.27))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 841.68x595.44 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "generateGraph(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Asht_Punuk'"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jamoat[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}