desa_miskin_ekstrim.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>nama_provinsi</th>\n",
       "      <th>nama_kab</th>\n",
       "      <th>nama_kec</th>\n",
       "      <th>nama_desa</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>JAWA TENGAH</td>\n",
       "      <td>BANYUMAS</td>\n",
       "      <td>LUMBIR</td>\n",
       "      <td>CINGEBUL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>JAWA TENGAH</td>\n",
       "      <td>BANYUMAS</td>\n",
       "      <td>LUMBIR</td>\n",
       "      <td>KEDUNGGEDE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>JAWA TENGAH</td>\n",
       "      <td>BANYUMAS</td>\n",
       "      <td>LUMBIR</td>\n",
       "      <td>CIDORA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  nama_provinsi  nama_kab nama_kec   nama_desa\n",
       "0   JAWA TENGAH  BANYUMAS   LUMBIR    CINGEBUL\n",
       "1   JAWA TENGAH  BANYUMAS   LUMBIR  KEDUNGGEDE\n",
       "2   JAWA TENGAH  BANYUMAS   LUMBIR      CIDORA"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "desa_miskin = pd.read_excel('data/Desa Miskin Ekstrim 2022 Jawa Tengah.xlsx', sheet_name='JAWA TENGAH', header=2,\n",
    "              names=['nama_provinsi', 'nama_kab', 'nama_kec', 'nama_desa'])\n",
    "desa_miskin.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\niam\\AppData\\Local\\Temp\\ipykernel_23072\\676098703.py:3: FutureWarning: The default value of regex will change from True to False in a future version.\n",
      "  desa_id['nama_kab'] = desa_id['nama_kab'].str.replace('KAB. ', '')\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>kode_prov</th>\n",
       "      <th>nama_provinsi</th>\n",
       "      <th>kode_kab</th>\n",
       "      <th>nama_kab</th>\n",
       "      <th>kode_kec</th>\n",
       "      <th>nama_kec</th>\n",
       "      <th>kdwilayah</th>\n",
       "      <th>nama_desa</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>31791</th>\n",
       "      <td>33</td>\n",
       "      <td>JAWA TENGAH</td>\n",
       "      <td>3301</td>\n",
       "      <td>CILACAP</td>\n",
       "      <td>3301010</td>\n",
       "      <td>DAYEUHLUHUR</td>\n",
       "      <td>3301010001</td>\n",
       "      <td>PANULISAN BARAT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31792</th>\n",
       "      <td>33</td>\n",
       "      <td>JAWA TENGAH</td>\n",
       "      <td>3301</td>\n",
       "      <td>CILACAP</td>\n",
       "      <td>3301010</td>\n",
       "      <td>DAYEUHLUHUR</td>\n",
       "      <td>3301010002</td>\n",
       "      <td>PANULISAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31793</th>\n",
       "      <td>33</td>\n",
       "      <td>JAWA TENGAH</td>\n",
       "      <td>3301</td>\n",
       "      <td>CILACAP</td>\n",
       "      <td>3301010</td>\n",
       "      <td>DAYEUHLUHUR</td>\n",
       "      <td>3301010003</td>\n",
       "      <td>PANULISAN TIMUR</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      kode_prov nama_provinsi kode_kab nama_kab kode_kec     nama_kec  \\\n",
       "31791        33   JAWA TENGAH     3301  CILACAP  3301010  DAYEUHLUHUR   \n",
       "31792        33   JAWA TENGAH     3301  CILACAP  3301010  DAYEUHLUHUR   \n",
       "31793        33   JAWA TENGAH     3301  CILACAP  3301010  DAYEUHLUHUR   \n",
       "\n",
       "        kdwilayah        nama_desa  \n",
       "31791  3301010001  PANULISAN BARAT  \n",
       "31792  3301010002        PANULISAN  \n",
       "31793  3301010003  PANULISAN TIMUR  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "desa_id = pd.read_csv('data/idwilayah.csv', dtype='str')\n",
    "desa_id = desa_id[desa_id['nama_provinsi']=='JAWA TENGAH']\n",
    "desa_id['nama_kab'] = desa_id['nama_kab'].str.replace('KAB. ', '')\n",
    "desa_id.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>nama_provinsi</th>\n",
       "      <th>nama_kab</th>\n",
       "      <th>nama_kec</th>\n",
       "      <th>nama_desa</th>\n",
       "      <th>kode_prov</th>\n",
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       "      <th>0</th>\n",
       "      <td>JAWA TENGAH</td>\n",
       "      <td>BANYUMAS</td>\n",
       "      <td>LUMBIR</td>\n",
       "      <td>CINGEBUL</td>\n",
       "      <td>33</td>\n",
       "      <td>3302</td>\n",
       "      <td>3302010</td>\n",
       "      <td>3302010001</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>JAWA TENGAH</td>\n",
       "      <td>BANYUMAS</td>\n",
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       "      <td>KEDUNGGEDE</td>\n",
       "      <td>33</td>\n",
       "      <td>3302</td>\n",
       "      <td>3302010</td>\n",
       "      <td>3302010002</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>JAWA TENGAH</td>\n",
       "      <td>BANYUMAS</td>\n",
       "      <td>LUMBIR</td>\n",
       "      <td>CIDORA</td>\n",
       "      <td>33</td>\n",
       "      <td>3302</td>\n",
       "      <td>3302010</td>\n",
       "      <td>3302010003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>JAWA TENGAH</td>\n",
       "      <td>BANYUMAS</td>\n",
       "      <td>LUMBIR</td>\n",
       "      <td>BESUKI</td>\n",
       "      <td>33</td>\n",
       "      <td>3302</td>\n",
       "      <td>3302010</td>\n",
       "      <td>3302010004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>JAWA TENGAH</td>\n",
       "      <td>BANYUMAS</td>\n",
       "      <td>LUMBIR</td>\n",
       "      <td>PARUNGKAMAL</td>\n",
       "      <td>33</td>\n",
       "      <td>3302</td>\n",
       "      <td>3302010</td>\n",
       "      <td>3302010005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5666</th>\n",
       "      <td>JAWA TENGAH</td>\n",
       "      <td>BREBES</td>\n",
       "      <td>BREBES</td>\n",
       "      <td>SIGAMBIR</td>\n",
       "      <td>33</td>\n",
       "      <td>3329</td>\n",
       "      <td>3329170</td>\n",
       "      <td>3329170019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5667</th>\n",
       "      <td>JAWA TENGAH</td>\n",
       "      <td>BREBES</td>\n",
       "      <td>BREBES</td>\n",
       "      <td>PAGEJUGAN</td>\n",
       "      <td>33</td>\n",
       "      <td>3329</td>\n",
       "      <td>3329170</td>\n",
       "      <td>3329170020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5668</th>\n",
       "      <td>JAWA TENGAH</td>\n",
       "      <td>BREBES</td>\n",
       "      <td>BREBES</td>\n",
       "      <td>KEDUNGUTER</td>\n",
       "      <td>33</td>\n",
       "      <td>3329</td>\n",
       "      <td>3329170</td>\n",
       "      <td>3329170021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5669</th>\n",
       "      <td>JAWA TENGAH</td>\n",
       "      <td>BREBES</td>\n",
       "      <td>BREBES</td>\n",
       "      <td>TENGKI</td>\n",
       "      <td>33</td>\n",
       "      <td>3329</td>\n",
       "      <td>3329170</td>\n",
       "      <td>3329170022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5670</th>\n",
       "      <td>JAWA TENGAH</td>\n",
       "      <td>BREBES</td>\n",
       "      <td>BREBES</td>\n",
       "      <td>KALIWLINGI</td>\n",
       "      <td>33</td>\n",
       "      <td>3329</td>\n",
       "      <td>3329170</td>\n",
       "      <td>3329170023</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5671 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     nama_provinsi  nama_kab nama_kec    nama_desa kode_prov kode_kab  \\\n",
       "0      JAWA TENGAH  BANYUMAS   LUMBIR     CINGEBUL        33     3302   \n",
       "1      JAWA TENGAH  BANYUMAS   LUMBIR   KEDUNGGEDE        33     3302   \n",
       "2      JAWA TENGAH  BANYUMAS   LUMBIR       CIDORA        33     3302   \n",
       "3      JAWA TENGAH  BANYUMAS   LUMBIR       BESUKI        33     3302   \n",
       "4      JAWA TENGAH  BANYUMAS   LUMBIR  PARUNGKAMAL        33     3302   \n",
       "...            ...       ...      ...          ...       ...      ...   \n",
       "5666   JAWA TENGAH    BREBES   BREBES     SIGAMBIR        33     3329   \n",
       "5667   JAWA TENGAH    BREBES   BREBES    PAGEJUGAN        33     3329   \n",
       "5668   JAWA TENGAH    BREBES   BREBES   KEDUNGUTER        33     3329   \n",
       "5669   JAWA TENGAH    BREBES   BREBES       TENGKI        33     3329   \n",
       "5670   JAWA TENGAH    BREBES   BREBES   KALIWLINGI        33     3329   \n",
       "\n",
       "     kode_kec   kdwilayah  \n",
       "0     3302010  3302010001  \n",
       "1     3302010  3302010002  \n",
       "2     3302010  3302010003  \n",
       "3     3302010  3302010004  \n",
       "4     3302010  3302010005  \n",
       "...       ...         ...  \n",
       "5666  3329170  3329170019  \n",
       "5667  3329170  3329170020  \n",
       "5668  3329170  3329170021  \n",
       "5669  3329170  3329170022  \n",
       "5670  3329170  3329170023  \n",
       "\n",
       "[5671 rows x 8 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "desa_miskin_id = desa_miskin.merge(desa_id, how='left', on=['nama_provinsi', 'nama_kab', 'nama_kec', 'nama_desa'])\n",
    "desa_miskin_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BANJARNEGARA       278\n",
       "BANYUMAS           331\n",
       "BATANG             248\n",
       "BLORA              295\n",
       "BOYOLALI           300\n",
       "BREBES             297\n",
       "CILACAP            284\n",
       "DEMAK              249\n",
       "GROBOGAN           280\n",
       "JEPARA             195\n",
       "KARANGANYAR        177\n",
       "KEBUMEN            460\n",
       "KENDAL             286\n",
       "KLATEN             401\n",
       "KOTA MAGELANG       17\n",
       "KOTA PEKALONGAN     27\n",
       "KOTA SALATIGA       23\n",
       "KOTA SEMARANG      177\n",
       "KOTA SURAKARTA      51\n",
       "KOTA TEGAL          27\n",
       "KUDUS              132\n",
       "MAGELANG           372\n",
       "PATI               406\n",
       "PEKALONGAN         285\n",
       "PEMALANG           222\n",
       "PURBALINGGA        239\n",
       "PURWOREJO          494\n",
       "REMBANG            294\n",
       "SEMARANG           235\n",
       "SRAGEN             208\n",
       "SUKOHARJO          167\n",
       "TEGAL              287\n",
       "TEMANGGUNG         289\n",
       "WONOGIRI           294\n",
       "WONOSOBO           265\n",
       "Name: nama_kab, dtype: int64"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jml_base = desa_id['nama_kab'].value_counts().sort_index()\n",
    "jml_base"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BANJARNEGARA    217\n",
       "BANYUMAS        309\n",
       "BATANG          196\n",
       "BLORA           216\n",
       "BOYOLALI        192\n",
       "BREBES          242\n",
       "DEMAK           218\n",
       "GROBOGAN        234\n",
       "JEPARA          174\n",
       "KARANGANYAR     154\n",
       "KEBUMEN         277\n",
       "KENDAL          236\n",
       "KLATEN          304\n",
       "KUDUS           101\n",
       "MAGELANG        220\n",
       "PATI            336\n",
       "PEKALONGAN      229\n",
       "PEMALANG        201\n",
       "PURBALINGGA     227\n",
       "REMBANG         151\n",
       "SEMARANG        161\n",
       "SRAGEN          204\n",
       "SUKOHARJO       143\n",
       "TEGAL           276\n",
       "TEMANGGUNG      106\n",
       "WONOGIRI        144\n",
       "WONOSOBO        203\n",
       "Name: nama_kab, dtype: int64"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jml_miskin = desa_miskin_id['nama_kab'].value_counts().sort_index()\n",
    "jml_miskin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "desa_miskin_base = pd.concat([jml_base, jml_miskin], axis=1).reset_index()\n",
    "desa_miskin_base = desa_miskin_base.set_axis(['nama_kab', 'jml_desa', 'jml_desa_miskin'], axis='columns')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>nama_kab</th>\n",
       "      <th>jml_desa</th>\n",
       "      <th>jml_desa_miskin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BANJARNEGARA</td>\n",
       "      <td>278</td>\n",
       "      <td>217.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BANYUMAS</td>\n",
       "      <td>331</td>\n",
       "      <td>309.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BATANG</td>\n",
       "      <td>248</td>\n",
       "      <td>196.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BLORA</td>\n",
       "      <td>295</td>\n",
       "      <td>216.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BOYOLALI</td>\n",
       "      <td>300</td>\n",
       "      <td>192.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>BREBES</td>\n",
       "      <td>297</td>\n",
       "      <td>242.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>CILACAP</td>\n",
       "      <td>284</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>DEMAK</td>\n",
       "      <td>249</td>\n",
       "      <td>218.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>GROBOGAN</td>\n",
       "      <td>280</td>\n",
       "      <td>234.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>JEPARA</td>\n",
       "      <td>195</td>\n",
       "      <td>174.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>KARANGANYAR</td>\n",
       "      <td>177</td>\n",
       "      <td>154.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>KEBUMEN</td>\n",
       "      <td>460</td>\n",
       "      <td>277.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>KENDAL</td>\n",
       "      <td>286</td>\n",
       "      <td>236.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>KLATEN</td>\n",
       "      <td>401</td>\n",
       "      <td>304.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>KOTA MAGELANG</td>\n",
       "      <td>17</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>KOTA PEKALONGAN</td>\n",
       "      <td>27</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>KOTA SALATIGA</td>\n",
       "      <td>23</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>KOTA SEMARANG</td>\n",
       "      <td>177</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>KOTA SURAKARTA</td>\n",
       "      <td>51</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>KOTA TEGAL</td>\n",
       "      <td>27</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>KUDUS</td>\n",
       "      <td>132</td>\n",
       "      <td>101.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>MAGELANG</td>\n",
       "      <td>372</td>\n",
       "      <td>220.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>PATI</td>\n",
       "      <td>406</td>\n",
       "      <td>336.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>PEKALONGAN</td>\n",
       "      <td>285</td>\n",
       "      <td>229.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>PEMALANG</td>\n",
       "      <td>222</td>\n",
       "      <td>201.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>PURBALINGGA</td>\n",
       "      <td>239</td>\n",
       "      <td>227.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>PURWOREJO</td>\n",
       "      <td>494</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>REMBANG</td>\n",
       "      <td>294</td>\n",
       "      <td>151.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>SEMARANG</td>\n",
       "      <td>235</td>\n",
       "      <td>161.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>SRAGEN</td>\n",
       "      <td>208</td>\n",
       "      <td>204.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>SUKOHARJO</td>\n",
       "      <td>167</td>\n",
       "      <td>143.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>TEGAL</td>\n",
       "      <td>287</td>\n",
       "      <td>276.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>TEMANGGUNG</td>\n",
       "      <td>289</td>\n",
       "      <td>106.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>WONOGIRI</td>\n",
       "      <td>294</td>\n",
       "      <td>144.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>WONOSOBO</td>\n",
       "      <td>265</td>\n",
       "      <td>203.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           nama_kab  jml_desa  jml_desa_miskin\n",
       "0      BANJARNEGARA       278            217.0\n",
       "1          BANYUMAS       331            309.0\n",
       "2            BATANG       248            196.0\n",
       "3             BLORA       295            216.0\n",
       "4          BOYOLALI       300            192.0\n",
       "5            BREBES       297            242.0\n",
       "6           CILACAP       284              NaN\n",
       "7             DEMAK       249            218.0\n",
       "8          GROBOGAN       280            234.0\n",
       "9            JEPARA       195            174.0\n",
       "10      KARANGANYAR       177            154.0\n",
       "11          KEBUMEN       460            277.0\n",
       "12           KENDAL       286            236.0\n",
       "13           KLATEN       401            304.0\n",
       "14    KOTA MAGELANG        17              NaN\n",
       "15  KOTA PEKALONGAN        27              NaN\n",
       "16    KOTA SALATIGA        23              NaN\n",
       "17    KOTA SEMARANG       177              NaN\n",
       "18   KOTA SURAKARTA        51              NaN\n",
       "19       KOTA TEGAL        27              NaN\n",
       "20            KUDUS       132            101.0\n",
       "21         MAGELANG       372            220.0\n",
       "22             PATI       406            336.0\n",
       "23       PEKALONGAN       285            229.0\n",
       "24         PEMALANG       222            201.0\n",
       "25      PURBALINGGA       239            227.0\n",
       "26        PURWOREJO       494              NaN\n",
       "27          REMBANG       294            151.0\n",
       "28         SEMARANG       235            161.0\n",
       "29           SRAGEN       208            204.0\n",
       "30        SUKOHARJO       167            143.0\n",
       "31            TEGAL       287            276.0\n",
       "32       TEMANGGUNG       289            106.0\n",
       "33         WONOGIRI       294            144.0\n",
       "34         WONOSOBO       265            203.0"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "desa_miskin_base"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "desa_miskin_base = desa_miskin_base[desa_miskin_base['jml_desa_miskin'].notna()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\niam\\AppData\\Local\\Temp\\ipykernel_23072\\807785107.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  desa_miskin_base['pct_desa_miskin'] = desa_miskin_base['jml_desa_miskin']/desa_miskin_base['jml_desa']\n"
     ]
    }
   ],
   "source": [
    "desa_miskin_base['pct_desa_miskin'] = desa_miskin_base['jml_desa_miskin']/desa_miskin_base['jml_desa'] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>nama_kab</th>\n",
       "      <th>jml_desa</th>\n",
       "      <th>jml_desa_miskin</th>\n",
       "      <th>pct_desa_miskin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>SRAGEN</td>\n",
       "      <td>208</td>\n",
       "      <td>204.0</td>\n",
       "      <td>0.980769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>TEGAL</td>\n",
       "      <td>287</td>\n",
       "      <td>276.0</td>\n",
       "      <td>0.961672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>PURBALINGGA</td>\n",
       "      <td>239</td>\n",
       "      <td>227.0</td>\n",
       "      <td>0.949791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BANYUMAS</td>\n",
       "      <td>331</td>\n",
       "      <td>309.0</td>\n",
       "      <td>0.933535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>PEMALANG</td>\n",
       "      <td>222</td>\n",
       "      <td>201.0</td>\n",
       "      <td>0.905405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>JEPARA</td>\n",
       "      <td>195</td>\n",
       "      <td>174.0</td>\n",
       "      <td>0.892308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>DEMAK</td>\n",
       "      <td>249</td>\n",
       "      <td>218.0</td>\n",
       "      <td>0.875502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>KARANGANYAR</td>\n",
       "      <td>177</td>\n",
       "      <td>154.0</td>\n",
       "      <td>0.870056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>SUKOHARJO</td>\n",
       "      <td>167</td>\n",
       "      <td>143.0</td>\n",
       "      <td>0.856287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>GROBOGAN</td>\n",
       "      <td>280</td>\n",
       "      <td>234.0</td>\n",
       "      <td>0.835714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>PATI</td>\n",
       "      <td>406</td>\n",
       "      <td>336.0</td>\n",
       "      <td>0.827586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>KENDAL</td>\n",
       "      <td>286</td>\n",
       "      <td>236.0</td>\n",
       "      <td>0.825175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>BREBES</td>\n",
       "      <td>297</td>\n",
       "      <td>242.0</td>\n",
       "      <td>0.814815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>PEKALONGAN</td>\n",
       "      <td>285</td>\n",
       "      <td>229.0</td>\n",
       "      <td>0.803509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BATANG</td>\n",
       "      <td>248</td>\n",
       "      <td>196.0</td>\n",
       "      <td>0.790323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BANJARNEGARA</td>\n",
       "      <td>278</td>\n",
       "      <td>217.0</td>\n",
       "      <td>0.780576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>WONOSOBO</td>\n",
       "      <td>265</td>\n",
       "      <td>203.0</td>\n",
       "      <td>0.766038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>KUDUS</td>\n",
       "      <td>132</td>\n",
       "      <td>101.0</td>\n",
       "      <td>0.765152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>KLATEN</td>\n",
       "      <td>401</td>\n",
       "      <td>304.0</td>\n",
       "      <td>0.758105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BLORA</td>\n",
       "      <td>295</td>\n",
       "      <td>216.0</td>\n",
       "      <td>0.732203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>SEMARANG</td>\n",
       "      <td>235</td>\n",
       "      <td>161.0</td>\n",
       "      <td>0.685106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BOYOLALI</td>\n",
       "      <td>300</td>\n",
       "      <td>192.0</td>\n",
       "      <td>0.640000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>KEBUMEN</td>\n",
       "      <td>460</td>\n",
       "      <td>277.0</td>\n",
       "      <td>0.602174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>MAGELANG</td>\n",
       "      <td>372</td>\n",
       "      <td>220.0</td>\n",
       "      <td>0.591398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>REMBANG</td>\n",
       "      <td>294</td>\n",
       "      <td>151.0</td>\n",
       "      <td>0.513605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>WONOGIRI</td>\n",
       "      <td>294</td>\n",
       "      <td>144.0</td>\n",
       "      <td>0.489796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>TEMANGGUNG</td>\n",
       "      <td>289</td>\n",
       "      <td>106.0</td>\n",
       "      <td>0.366782</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        nama_kab  jml_desa  jml_desa_miskin  pct_desa_miskin\n",
       "29        SRAGEN       208            204.0         0.980769\n",
       "31         TEGAL       287            276.0         0.961672\n",
       "25   PURBALINGGA       239            227.0         0.949791\n",
       "1       BANYUMAS       331            309.0         0.933535\n",
       "24      PEMALANG       222            201.0         0.905405\n",
       "9         JEPARA       195            174.0         0.892308\n",
       "7          DEMAK       249            218.0         0.875502\n",
       "10   KARANGANYAR       177            154.0         0.870056\n",
       "30     SUKOHARJO       167            143.0         0.856287\n",
       "8       GROBOGAN       280            234.0         0.835714\n",
       "22          PATI       406            336.0         0.827586\n",
       "12        KENDAL       286            236.0         0.825175\n",
       "5         BREBES       297            242.0         0.814815\n",
       "23    PEKALONGAN       285            229.0         0.803509\n",
       "2         BATANG       248            196.0         0.790323\n",
       "0   BANJARNEGARA       278            217.0         0.780576\n",
       "34      WONOSOBO       265            203.0         0.766038\n",
       "20         KUDUS       132            101.0         0.765152\n",
       "13        KLATEN       401            304.0         0.758105\n",
       "3          BLORA       295            216.0         0.732203\n",
       "28      SEMARANG       235            161.0         0.685106\n",
       "4       BOYOLALI       300            192.0         0.640000\n",
       "11       KEBUMEN       460            277.0         0.602174\n",
       "21      MAGELANG       372            220.0         0.591398\n",
       "27       REMBANG       294            151.0         0.513605\n",
       "33      WONOGIRI       294            144.0         0.489796\n",
       "32    TEMANGGUNG       289            106.0         0.366782"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "desa_miskin_base.sort_values('pct_desa_miskin', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    27.000000\n",
       "mean      0.770866\n",
       "std       0.153052\n",
       "min       0.366782\n",
       "25%       0.708655\n",
       "50%       0.803509\n",
       "75%       0.872779\n",
       "max       0.980769\n",
       "Name: pct_desa_miskin, dtype: float64"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "desa_miskin_base['pct_desa_miskin'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "desa_miskin_base.sort_values('pct_desa_miskin', ascending=False).to_csv('data/jumlah desa miskin by kab.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='nama_kab'>"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "desa_miskin_base.sort_values('pct_desa_miskin', ascending=True).plot.barh(x='nama_kab', y='pct_desa_miskin')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='jml_desa', ylabel='jml_desa_miskin'>"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "desa_miskin_base.plot.scatter(x='jml_desa', y='jml_desa_miskin')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.8.3 64-bit",
   "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": "3.8.3"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "9650cb4e16cdd4a8e8e2d128bf38d875813998db22a3c986335f89e0cb4d7bb2"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}