final_project_template.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "53cfe1b6-22ce-44b8-be55-688f134c1582",
   "metadata": {},
   "source": [
    "# Import Library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "6b81d56b-cc25-4c51-8f3e-aabf377f1035",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b9d5b37-5179-44d9-b459-fd9fadb3ee2f",
   "metadata": {},
   "source": [
    "# Load Data, Eksplorasi Data Sederhana, dan Data Cleaning\n",
    "\n",
    "Boilerplate untuk membuka file excel dengan menggunakan pandas\n",
    "\n",
    "## Contoh : Sheet \"Kasus Aktif\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "8e6eae7c-62e3-46b8-a4ab-7b2c57aac0c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\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>Date</th>\n",
       "      <th>Aceh</th>\n",
       "      <th>Bali</th>\n",
       "      <th>Banten</th>\n",
       "      <th>Babel</th>\n",
       "      <th>Bengkulu</th>\n",
       "      <th>DIY</th>\n",
       "      <th>Jakarta</th>\n",
       "      <th>Jambi</th>\n",
       "      <th>Jabar</th>\n",
       "      <th>...</th>\n",
       "      <th>Sulteng</th>\n",
       "      <th>Lampung</th>\n",
       "      <th>Riau</th>\n",
       "      <th>Malut</th>\n",
       "      <th>Maluku</th>\n",
       "      <th>Papbar</th>\n",
       "      <th>Papua</th>\n",
       "      <th>Sulbar</th>\n",
       "      <th>NTT</th>\n",
       "      <th>Gorontalo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-03-15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-03-16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-03-17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-03-18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-03-19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date  Aceh  Bali  Banten  Babel  Bengkulu  DIY  Jakarta  Jambi  Jabar  \\\n",
       "0 2020-03-15   NaN   NaN     NaN    NaN       NaN  NaN     19.0    NaN    NaN   \n",
       "1 2020-03-16   NaN   NaN     1.0    NaN       NaN  NaN     14.0    NaN    1.0   \n",
       "2 2020-03-17   NaN   NaN     NaN    NaN       NaN  NaN      NaN    NaN    NaN   \n",
       "3 2020-03-18   NaN   NaN     4.0    NaN       NaN  1.0     30.0    NaN   12.0   \n",
       "4 2020-03-19   NaN   NaN    10.0    NaN       NaN  2.0     52.0    NaN    2.0   \n",
       "\n",
       "   ...  Sulteng  Lampung  Riau  Malut  Maluku  Papbar  Papua  Sulbar  NTT  \\\n",
       "0  ...      NaN      NaN   NaN    NaN     NaN     NaN    NaN     NaN  NaN   \n",
       "1  ...      NaN      NaN   NaN    NaN     NaN     NaN    NaN     NaN  NaN   \n",
       "2  ...      NaN      NaN   NaN    NaN     NaN     NaN    NaN     NaN  NaN   \n",
       "3  ...      NaN      1.0   1.0    NaN     NaN     NaN    NaN     NaN  NaN   \n",
       "4  ...      NaN      NaN   1.0    NaN     NaN     NaN    NaN     NaN  NaN   \n",
       "\n",
       "   Gorontalo  \n",
       "0        NaN  \n",
       "1        NaN  \n",
       "2        NaN  \n",
       "3        NaN  \n",
       "4        NaN  \n",
       "\n",
       "[5 rows x 35 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = 'data/COVID-19 Indonesia.xlsx'\n",
    "daily_cases=pd.read_excel(data,sheet_name='Kasus Aktif')\n",
    "\n",
    "daily_cases.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "9ea8974d-40d4-486c-8ccc-a802ca9f02be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    .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>Aceh</th>\n",
       "      <th>Bali</th>\n",
       "      <th>Banten</th>\n",
       "      <th>Babel</th>\n",
       "      <th>Bengkulu</th>\n",
       "      <th>DIY</th>\n",
       "      <th>Jakarta</th>\n",
       "      <th>Jambi</th>\n",
       "      <th>Jabar</th>\n",
       "      <th>Jateng</th>\n",
       "      <th>...</th>\n",
       "      <th>Sulteng</th>\n",
       "      <th>Lampung</th>\n",
       "      <th>Riau</th>\n",
       "      <th>Malut</th>\n",
       "      <th>Maluku</th>\n",
       "      <th>Papbar</th>\n",
       "      <th>Papua</th>\n",
       "      <th>Sulbar</th>\n",
       "      <th>NTT</th>\n",
       "      <th>Gorontalo</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-03-15</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-16</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-17</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-18</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-19</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Aceh  Bali  Banten  Babel  Bengkulu  DIY  Jakarta  Jambi  Jabar  \\\n",
       "Date                                                                          \n",
       "2020-03-15   0.0   0.0     0.0    0.0       0.0  0.0     19.0    0.0    0.0   \n",
       "2020-03-16   0.0   0.0     1.0    0.0       0.0  0.0     14.0    0.0    1.0   \n",
       "2020-03-17   0.0   0.0     0.0    0.0       0.0  0.0      0.0    0.0    0.0   \n",
       "2020-03-18   0.0   0.0     4.0    0.0       0.0  1.0     30.0    0.0   12.0   \n",
       "2020-03-19   0.0   0.0    10.0    0.0       0.0  2.0     52.0    0.0    2.0   \n",
       "\n",
       "            Jateng  ...  Sulteng  Lampung  Riau  Malut  Maluku  Papbar  Papua  \\\n",
       "Date                ...                                                         \n",
       "2020-03-15     2.0  ...      0.0      0.0   0.0    0.0     0.0     0.0    0.0   \n",
       "2020-03-16     1.0  ...      0.0      0.0   0.0    0.0     0.0     0.0    0.0   \n",
       "2020-03-17     0.0  ...      0.0      0.0   0.0    0.0     0.0     0.0    0.0   \n",
       "2020-03-18     2.0  ...      0.0      1.0   1.0    0.0     0.0     0.0    0.0   \n",
       "2020-03-19     4.0  ...      0.0      0.0   1.0    0.0     0.0     0.0    0.0   \n",
       "\n",
       "            Sulbar  NTT  Gorontalo  \n",
       "Date                                \n",
       "2020-03-15     0.0  0.0        0.0  \n",
       "2020-03-16     0.0  0.0        0.0  \n",
       "2020-03-17     0.0  0.0        0.0  \n",
       "2020-03-18     0.0  0.0        0.0  \n",
       "2020-03-19     0.0  0.0        0.0  \n",
       "\n",
       "[5 rows x 34 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Handling Missing Value\n",
    "daily_cases.fillna(0,inplace=True)\n",
    "daily_cases.set_index('Date',inplace=True)\n",
    "daily_cases.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "438e0c42-da8d-4a98-bff1-81df0e19500f",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# plot grafik pertumbuhan jakarta\n",
    "\n",
    "daily_cases['Jakarta'].plot();\n",
    "plt.title(\"Grafik kasus per hari DKI Jakarta\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "2376fc87",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 527 entries, 2020-03-15 to 2021-08-23\n",
      "Data columns (total 34 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   Aceh       527 non-null    float64\n",
      " 1   Bali       527 non-null    float64\n",
      " 2   Banten     527 non-null    float64\n",
      " 3   Babel      527 non-null    float64\n",
      " 4   Bengkulu   527 non-null    float64\n",
      " 5   DIY        527 non-null    float64\n",
      " 6   Jakarta    527 non-null    float64\n",
      " 7   Jambi      527 non-null    float64\n",
      " 8   Jabar      527 non-null    float64\n",
      " 9   Jateng     527 non-null    float64\n",
      " 10  Jatim      527 non-null    float64\n",
      " 11  Kalbar     527 non-null    float64\n",
      " 12  Kaltim     527 non-null    float64\n",
      " 13  Kalteng    527 non-null    float64\n",
      " 14  Kalsel     527 non-null    float64\n",
      " 15  Kaltara    527 non-null    float64\n",
      " 16  Kep Riau   527 non-null    float64\n",
      " 17  NTB        527 non-null    float64\n",
      " 18  Sumsel     527 non-null    float64\n",
      " 19  Sumbar     527 non-null    float64\n",
      " 20  Sulut      527 non-null    float64\n",
      " 21  Sumut      527 non-null    float64\n",
      " 22  Sultra     527 non-null    float64\n",
      " 23  Sulsel     527 non-null    float64\n",
      " 24  Sulteng    527 non-null    float64\n",
      " 25  Lampung    527 non-null    float64\n",
      " 26  Riau       527 non-null    float64\n",
      " 27  Malut      527 non-null    float64\n",
      " 28  Maluku     527 non-null    float64\n",
      " 29  Papbar     527 non-null    float64\n",
      " 30  Papua      527 non-null    float64\n",
      " 31  Sulbar     527 non-null    float64\n",
      " 32  NTT        527 non-null    float64\n",
      " 33  Gorontalo  527 non-null    float64\n",
      "dtypes: float64(34)\n",
      "memory usage: 144.1 KB\n",
      "None\n",
      "(527, 34)\n"
     ]
    }
   ],
   "source": [
    "print(daily_cases.info())\n",
    "print(daily_cases.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "a67c1800",
   "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>Aceh</th>\n",
       "      <th>Bali</th>\n",
       "      <th>Banten</th>\n",
       "      <th>Babel</th>\n",
       "      <th>Bengkulu</th>\n",
       "      <th>DIY</th>\n",
       "      <th>Jakarta</th>\n",
       "      <th>Jambi</th>\n",
       "      <th>Jabar</th>\n",
       "      <th>Jateng</th>\n",
       "      <th>...</th>\n",
       "      <th>Sulteng</th>\n",
       "      <th>Lampung</th>\n",
       "      <th>Riau</th>\n",
       "      <th>Malut</th>\n",
       "      <th>Maluku</th>\n",
       "      <th>Papbar</th>\n",
       "      <th>Papua</th>\n",
       "      <th>Sulbar</th>\n",
       "      <th>NTT</th>\n",
       "      <th>Gorontalo</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-03-15</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-16</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-17</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-18</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-19</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Aceh  Bali  Banten  Babel  Bengkulu  DIY  Jakarta  Jambi  Jabar  \\\n",
       "Date                                                                          \n",
       "2020-03-15   0.0   0.0     0.0    0.0       0.0  0.0     19.0    0.0    0.0   \n",
       "2020-03-16   0.0   0.0     1.0    0.0       0.0  0.0     14.0    0.0    1.0   \n",
       "2020-03-17   0.0   0.0     0.0    0.0       0.0  0.0      0.0    0.0    0.0   \n",
       "2020-03-18   0.0   0.0     4.0    0.0       0.0  1.0     30.0    0.0   12.0   \n",
       "2020-03-19   0.0   0.0    10.0    0.0       0.0  2.0     52.0    0.0    2.0   \n",
       "\n",
       "            Jateng  ...  Sulteng  Lampung  Riau  Malut  Maluku  Papbar  Papua  \\\n",
       "Date                ...                                                         \n",
       "2020-03-15     2.0  ...      0.0      0.0   0.0    0.0     0.0     0.0    0.0   \n",
       "2020-03-16     1.0  ...      0.0      0.0   0.0    0.0     0.0     0.0    0.0   \n",
       "2020-03-17     0.0  ...      0.0      0.0   0.0    0.0     0.0     0.0    0.0   \n",
       "2020-03-18     2.0  ...      0.0      1.0   1.0    0.0     0.0     0.0    0.0   \n",
       "2020-03-19     4.0  ...      0.0      0.0   1.0    0.0     0.0     0.0    0.0   \n",
       "\n",
       "            Sulbar  NTT  Gorontalo  \n",
       "Date                                \n",
       "2020-03-15     0.0  0.0        0.0  \n",
       "2020-03-16     0.0  0.0        0.0  \n",
       "2020-03-17     0.0  0.0        0.0  \n",
       "2020-03-18     0.0  0.0        0.0  \n",
       "2020-03-19     0.0  0.0        0.0  \n",
       "\n",
       "[5 rows x 34 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily_cases.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "523f1582",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in daily_cases.columns:\n",
    "    daily_cases[i] = daily_cases[i].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "c9bc586b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 527 entries, 2020-03-15 to 2021-08-23\n",
      "Data columns (total 34 columns):\n",
      " #   Column     Non-Null Count  Dtype\n",
      "---  ------     --------------  -----\n",
      " 0   Aceh       527 non-null    int32\n",
      " 1   Bali       527 non-null    int32\n",
      " 2   Banten     527 non-null    int32\n",
      " 3   Babel      527 non-null    int32\n",
      " 4   Bengkulu   527 non-null    int32\n",
      " 5   DIY        527 non-null    int32\n",
      " 6   Jakarta    527 non-null    int32\n",
      " 7   Jambi      527 non-null    int32\n",
      " 8   Jabar      527 non-null    int32\n",
      " 9   Jateng     527 non-null    int32\n",
      " 10  Jatim      527 non-null    int32\n",
      " 11  Kalbar     527 non-null    int32\n",
      " 12  Kaltim     527 non-null    int32\n",
      " 13  Kalteng    527 non-null    int32\n",
      " 14  Kalsel     527 non-null    int32\n",
      " 15  Kaltara    527 non-null    int32\n",
      " 16  Kep Riau   527 non-null    int32\n",
      " 17  NTB        527 non-null    int32\n",
      " 18  Sumsel     527 non-null    int32\n",
      " 19  Sumbar     527 non-null    int32\n",
      " 20  Sulut      527 non-null    int32\n",
      " 21  Sumut      527 non-null    int32\n",
      " 22  Sultra     527 non-null    int32\n",
      " 23  Sulsel     527 non-null    int32\n",
      " 24  Sulteng    527 non-null    int32\n",
      " 25  Lampung    527 non-null    int32\n",
      " 26  Riau       527 non-null    int32\n",
      " 27  Malut      527 non-null    int32\n",
      " 28  Maluku     527 non-null    int32\n",
      " 29  Papbar     527 non-null    int32\n",
      " 30  Papua      527 non-null    int32\n",
      " 31  Sulbar     527 non-null    int32\n",
      " 32  NTT        527 non-null    int32\n",
      " 33  Gorontalo  527 non-null    int32\n",
      "dtypes: int32(34)\n",
      "memory usage: 74.1 KB\n"
     ]
    }
   ],
   "source": [
    "daily_cases.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7162e193-8967-4369-b528-3c49ba1f2a68",
   "metadata": {},
   "source": [
    "### Jawablah Pertanyaan Berikut dan Ceritakan dalam Bentuk Grafik / Visualisasi"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "613b6700-8ba0-4764-be04-bf3b17c56f30",
   "metadata": {},
   "source": [
    "- Bagaimana perkembangan covid-19 di Indonesia?\n",
    "- Apakah kita sudah mengalami puncak kedua?\n",
    "- Provinsi mana yang paling banyak total kasusnya? (bandingkan hingga 15 provinsi teratas)\n",
    "- Apakah PPKM sudah cukup efektif mengurangi kasus Covid-19?\n",
    "- Bagaimana perkembangan vaksinasi di Indonesia baik dosis pertama atau kedua?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "4a5c6685",
   "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>Aceh</th>\n",
       "      <th>Bali</th>\n",
       "      <th>Banten</th>\n",
       "      <th>Babel</th>\n",
       "      <th>Bengkulu</th>\n",
       "      <th>DIY</th>\n",
       "      <th>Jakarta</th>\n",
       "      <th>Jambi</th>\n",
       "      <th>Jabar</th>\n",
       "      <th>Jateng</th>\n",
       "      <th>...</th>\n",
       "      <th>Sulteng</th>\n",
       "      <th>Lampung</th>\n",
       "      <th>Riau</th>\n",
       "      <th>Malut</th>\n",
       "      <th>Maluku</th>\n",
       "      <th>Papbar</th>\n",
       "      <th>Papua</th>\n",
       "      <th>Sulbar</th>\n",
       "      <th>NTT</th>\n",
       "      <th>Gorontalo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>527.000000</td>\n",
       "      <td>527.000000</td>\n",
       "      <td>527.000000</td>\n",
       "      <td>527.000000</td>\n",
       "      <td>527.000000</td>\n",
       "      <td>527.000000</td>\n",
       "      <td>527.000000</td>\n",
       "      <td>527.000000</td>\n",
       "      <td>527.000000</td>\n",
       "      <td>527.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>527.000000</td>\n",
       "      <td>527.000000</td>\n",
       "      <td>527.000000</td>\n",
       "      <td>527.000000</td>\n",
       "      <td>527.000000</td>\n",
       "      <td>527.000000</td>\n",
       "      <td>527.000000</td>\n",
       "      <td>527.000000</td>\n",
       "      <td>527.000000</td>\n",
       "      <td>527.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>57.504744</td>\n",
       "      <td>194.635674</td>\n",
       "      <td>240.937381</td>\n",
       "      <td>84.574953</td>\n",
       "      <td>41.821632</td>\n",
       "      <td>275.371917</td>\n",
       "      <td>1606.396584</td>\n",
       "      <td>51.212524</td>\n",
       "      <td>1271.901328</td>\n",
       "      <td>878.504744</td>\n",
       "      <td>...</td>\n",
       "      <td>75.032258</td>\n",
       "      <td>85.296015</td>\n",
       "      <td>227.582543</td>\n",
       "      <td>21.612903</td>\n",
       "      <td>26.988615</td>\n",
       "      <td>41.487666</td>\n",
       "      <td>59.561670</td>\n",
       "      <td>21.127135</td>\n",
       "      <td>107.979127</td>\n",
       "      <td>20.588235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>83.514141</td>\n",
       "      <td>302.023984</td>\n",
       "      <td>542.959302</td>\n",
       "      <td>144.649299</td>\n",
       "      <td>76.552664</td>\n",
       "      <td>494.263275</td>\n",
       "      <td>2332.912723</td>\n",
       "      <td>90.083255</td>\n",
       "      <td>1875.198679</td>\n",
       "      <td>1212.583373</td>\n",
       "      <td>...</td>\n",
       "      <td>175.146731</td>\n",
       "      <td>131.813108</td>\n",
       "      <td>307.297932</td>\n",
       "      <td>42.616760</td>\n",
       "      <td>46.582849</td>\n",
       "      <td>75.263336</td>\n",
       "      <td>102.924164</td>\n",
       "      <td>41.305237</td>\n",
       "      <td>260.653882</td>\n",
       "      <td>37.013348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>43.500000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>370.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>138.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>19.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>108.000000</td>\n",
       "      <td>965.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>673.000000</td>\n",
       "      <td>421.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>132.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>85.000000</td>\n",
       "      <td>189.000000</td>\n",
       "      <td>206.000000</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>42.500000</td>\n",
       "      <td>254.000000</td>\n",
       "      <td>1643.500000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>1420.500000</td>\n",
       "      <td>981.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>288.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>37.500000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>89.500000</td>\n",
       "      <td>22.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>459.000000</td>\n",
       "      <td>1910.000000</td>\n",
       "      <td>4016.000000</td>\n",
       "      <td>862.000000</td>\n",
       "      <td>520.000000</td>\n",
       "      <td>2732.000000</td>\n",
       "      <td>14619.000000</td>\n",
       "      <td>567.000000</td>\n",
       "      <td>11101.000000</td>\n",
       "      <td>5655.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1566.000000</td>\n",
       "      <td>717.000000</td>\n",
       "      <td>2205.000000</td>\n",
       "      <td>360.000000</td>\n",
       "      <td>349.000000</td>\n",
       "      <td>605.000000</td>\n",
       "      <td>1755.000000</td>\n",
       "      <td>241.000000</td>\n",
       "      <td>3598.000000</td>\n",
       "      <td>278.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Aceh         Bali       Banten       Babel    Bengkulu  \\\n",
       "count  527.000000   527.000000   527.000000  527.000000  527.000000   \n",
       "mean    57.504744   194.635674   240.937381   84.574953   41.821632   \n",
       "std     83.514141   302.023984   542.959302  144.649299   76.552664   \n",
       "min      0.000000     0.000000     0.000000    0.000000    0.000000   \n",
       "25%      2.000000    43.500000    22.000000    1.000000    2.000000   \n",
       "50%     19.000000    96.000000    90.000000   22.000000   13.000000   \n",
       "75%     85.000000   189.000000   206.000000   95.000000   42.500000   \n",
       "max    459.000000  1910.000000  4016.000000  862.000000  520.000000   \n",
       "\n",
       "               DIY       Jakarta       Jambi         Jabar       Jateng  ...  \\\n",
       "count   527.000000    527.000000  527.000000    527.000000   527.000000  ...   \n",
       "mean    275.371917   1606.396584   51.212524   1271.901328   878.504744  ...   \n",
       "std     494.263275   2332.912723   90.083255   1875.198679  1212.583373  ...   \n",
       "min      -1.000000      0.000000    0.000000      0.000000     0.000000  ...   \n",
       "25%      12.000000    370.500000    1.000000     82.000000   138.500000  ...   \n",
       "50%     108.000000    965.000000   21.000000    673.000000   421.000000  ...   \n",
       "75%     254.000000   1643.500000   55.000000   1420.500000   981.500000  ...   \n",
       "max    2732.000000  14619.000000  567.000000  11101.000000  5655.000000  ...   \n",
       "\n",
       "           Sulteng     Lampung         Riau       Malut      Maluku  \\\n",
       "count   527.000000  527.000000   527.000000  527.000000  527.000000   \n",
       "mean     75.032258   85.296015   227.582543   21.612903   26.988615   \n",
       "std     175.146731  131.813108   307.297932   42.616760   46.582849   \n",
       "min       0.000000    0.000000    -1.000000    0.000000    0.000000   \n",
       "25%       1.000000    3.000000    11.000000    1.000000    1.000000   \n",
       "50%      15.000000   47.000000   132.000000    7.000000   11.000000   \n",
       "75%      55.000000   94.000000   288.000000   20.000000   32.000000   \n",
       "max    1566.000000  717.000000  2205.000000  360.000000  349.000000   \n",
       "\n",
       "           Papbar        Papua      Sulbar          NTT   Gorontalo  \n",
       "count  527.000000   527.000000  527.000000   527.000000  527.000000  \n",
       "mean    41.487666    59.561670   21.127135   107.979127   20.588235  \n",
       "std     75.263336   102.924164   41.305237   260.653882   37.013348  \n",
       "min      0.000000     0.000000    0.000000     0.000000    0.000000  \n",
       "25%      4.000000     6.000000    1.000000     0.000000    0.000000  \n",
       "50%     16.000000    32.000000    4.000000    14.000000    6.000000  \n",
       "75%     37.500000    70.000000   16.000000    89.500000   22.000000  \n",
       "max    605.000000  1755.000000  241.000000  3598.000000  278.000000  \n",
       "\n",
       "[8 rows x 34 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily_cases.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "92e1d820",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
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       "        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>Aceh</th>\n",
       "      <th>Bali</th>\n",
       "      <th>Banten</th>\n",
       "      <th>Babel</th>\n",
       "      <th>Bengkulu</th>\n",
       "      <th>DIY</th>\n",
       "      <th>Jakarta</th>\n",
       "      <th>Jambi</th>\n",
       "      <th>Jabar</th>\n",
       "      <th>Jateng</th>\n",
       "      <th>...</th>\n",
       "      <th>Lampung</th>\n",
       "      <th>Riau</th>\n",
       "      <th>Malut</th>\n",
       "      <th>Maluku</th>\n",
       "      <th>Papbar</th>\n",
       "      <th>Papua</th>\n",
       "      <th>Sulbar</th>\n",
       "      <th>NTT</th>\n",
       "      <th>Gorontalo</th>\n",
       "      <th>Total_Harian</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-03-15</th>\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>19</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-16</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-17</th>\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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-18</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-19</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>52</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Aceh  Bali  Banten  Babel  Bengkulu  DIY  Jakarta  Jambi  Jabar  \\\n",
       "Date                                                                          \n",
       "2020-03-15     0     0       0      0         0    0       19      0      0   \n",
       "2020-03-16     0     0       1      0         0    0       14      0      1   \n",
       "2020-03-17     0     0       0      0         0    0        0      0      0   \n",
       "2020-03-18     0     0       4      0         0    1       30      0     12   \n",
       "2020-03-19     0     0      10      0         0    2       52      0      2   \n",
       "\n",
       "            Jateng  ...  Lampung  Riau  Malut  Maluku  Papbar  Papua  Sulbar  \\\n",
       "Date                ...                                                        \n",
       "2020-03-15       2  ...        0     0      0       0       0      0       0   \n",
       "2020-03-16       1  ...        0     0      0       0       0      0       0   \n",
       "2020-03-17       0  ...        0     0      0       0       0      0       0   \n",
       "2020-03-18       2  ...        1     1      0       0       0      0       0   \n",
       "2020-03-19       4  ...        0     1      0       0       0      0       0   \n",
       "\n",
       "            NTT  Gorontalo  Total_Harian  \n",
       "Date                                      \n",
       "2020-03-15    0          0            21  \n",
       "2020-03-16    0          0            17  \n",
       "2020-03-17    0          0             0  \n",
       "2020-03-18    0          0            53  \n",
       "2020-03-19    0          0            82  \n",
       "\n",
       "[5 rows x 35 columns]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily_cases['Total_Harian']=daily_cases.sum(axis=1)\n",
    "#daily_cases.plot(title='Jumlah Kasus Harian Covid-19', x='Date', y='Total_Harian')\n",
    "daily_cases.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ce10046",
   "metadata": {},
   "source": [
    "## Bagaimana perkembangan covid-19 di Indonesia?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "ad3dd87a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Date', 'Kasus harian', 'Kasus Impor', 'Kasus Lokal', 'Total kasus',\n",
       "       'Kasus aktif', '% kasus aktif', 'Sembuh\\n(baru)', 'Sembuh',\n",
       "       'Tingkat kesembuhan (seluruh kasus)',\n",
       "       'Tingkat kesembuhan (closed cases)', 'Meninggal\\n(baru)',\n",
       "       'Meninggal\\nDunia', 'Tingkat kematian (seluruh kasus)',\n",
       "       'Tingkat kematian (closed cases)', 'PDP', 'ODP', 'Suspek aktif',\n",
       "       'Jumlah spesimen diperiksa (dari 1 April)', 'Jumlah orang diperiksa',\n",
       "       'Negatif', 'Positif \\n(=C)', 'Dalam Proses', 'Spesimen',\n",
       "       'Spesimen (PCR-TCM)', 'Spesimen (Antigen)', 'Orang yang dites',\n",
       "       'Orang yang dites (PCR-TCM)', 'Orang yang dites (Antigen)',\n",
       "       'Vaksinasi (ineligible)', 'Jumlah test/juta penduduk', 'Test/Positif',\n",
       "       'Jumlah org yg dites / positif', 'Jumlah Test/Positif harian',\n",
       "       'Positif / Jumlah Tes', 'Positive rate harian', 'Case Growth Rate',\n",
       "       'Tingkat positivitas mingguan',\n",
       "       'Jumlah spesimen diperiksa (rata-rata 7 hari)',\n",
       "       'Jumlah orang diperiksa (rata-rata 7 hari)',\n",
       "       'Rasio spesimen/orang diperiksa (rata-rata 7 hari)', 'Unnamed: 41',\n",
       "       'Dosis pertama', 'Dosis kedua', 'Dosis ketiga', 'Dosis pertama (%)',\n",
       "       'Dosis kedua (%)', 'Dosis pertama (harian)', 'Dosis kedua (harian)',\n",
       "       'Dosis harian', 'Dosis pertama (mingguan)', 'Dosis kedua (mingguan)',\n",
       "       'Rata2 dosis harian (mingguan)', 'Dosis pertama (SDM kesehatan)',\n",
       "       'Dosis kedua (SDM kesehatan)', 'Dosis ketiga (SDM kesehatan)',\n",
       "       'Dosis pertama (SDM kesehatan) %', 'Dosis kedua (SDM kesehatan) %',\n",
       "       'Dosis pertama (petugas publik)', 'Dosis kedua (petugas publik)',\n",
       "       'Dosis pertama (petugas publik) %', 'Dosis kedua (petugas publik) %',\n",
       "       'Dosis pertama (lansia)', 'Dosis kedua (lansia)',\n",
       "       'Dosis pertama (lansia) %', 'Dosis kedua (lansia) %',\n",
       "       'Dosis pertama (warga umum)', 'Dosis kedua (warga umum)',\n",
       "       'Dosis pertama (remaja)', 'Dosis kedua (remaja)',\n",
       "       'Dosis pertama (Vakgor)', 'Dosis kedua (Vakgor)',\n",
       "       'Test PCR Orang Nasional Rata-rata 7 hari',\n",
       "       'Pos Rate Nasional - rata-rata 7 hari', 'Test Orang DKI', 'Positif DKI',\n",
       "       'Unnamed: 76', 'Pos Rate DKI', 'Pos Rate DKI - 7 harian', 'NON DKI',\n",
       "       'Positif Non DKI', 'Unnamed: 81', 'Pos Rate Non DKI',\n",
       "       'Pos Rate Non DKI - 7 harian', 'CFR', 'Unnamed: 85', 'Unnamed: 86',\n",
       "       'Unnamed: 87', 'Unnamed: 88', 'Unnamed: 89', 'Unnamed: 90',\n",
       "       'Unnamed: 91', 'Unnamed: 92', 'Unnamed: 93'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "statistik_harian=pd.read_excel(data,sheet_name=\"Statistik Harian\")\n",
    "statistik_harian.head()\n",
    "statistik_harian.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "c034c6b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>date</th>\n",
       "      <th>kasus_harian</th>\n",
       "      <th>sembuh</th>\n",
       "      <th>meninggal</th>\n",
       "      <th>jumlah_test</th>\n",
       "      <th>dosis_pertama</th>\n",
       "      <th>dosis_kedua</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-03-02</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-03-03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-03-04</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-03-05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-03-06</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date  kasus_harian  sembuh  meninggal  jumlah_test  dosis_pertama  \\\n",
       "0 2020-03-02           2.0     0.0        0.0          NaN            NaN   \n",
       "1 2020-03-03           0.0     0.0        0.0          NaN            NaN   \n",
       "2 2020-03-04           0.0     0.0        0.0          NaN            NaN   \n",
       "3 2020-03-05           0.0     0.0        0.0          NaN            NaN   \n",
       "4 2020-03-06           2.0     0.0        0.0          NaN            NaN   \n",
       "\n",
       "   dosis_kedua  \n",
       "0          NaN  \n",
       "1          NaN  \n",
       "2          NaN  \n",
       "3          NaN  \n",
       "4          NaN  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "statistik_harian = pd.DataFrame({\n",
    "    'date': statistik_harian['Date'],\n",
    "    'kasus_harian': statistik_harian['Kasus harian'],\n",
    "    'sembuh': statistik_harian['Sembuh\\n(baru)'],\n",
    "    'meninggal': statistik_harian['Meninggal\\n(baru)'],\n",
    "    'jumlah_test': statistik_harian['Jumlah test/juta penduduk'],\n",
    "    'dosis_pertama': statistik_harian['Dosis pertama'],\n",
    "    'dosis_kedua': statistik_harian['Dosis kedua']\n",
    "})\n",
    "\n",
    "statistik_harian.head()\n",
    "                              \n",
    "                               \n",
    "                               "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "c9a883f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "statistik_harian.fillna(0,inplace=True)\n",
    "statistik_harian.set_index('date',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "71ea731d",
   "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>kasus_harian</th>\n",
       "      <th>sembuh</th>\n",
       "      <th>meninggal</th>\n",
       "      <th>jumlah_test</th>\n",
       "      <th>dosis_pertama</th>\n",
       "      <th>dosis_kedua</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-03-02 00:00:00</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-03 00:00:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-04 00:00:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-05 00:00:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-06 00:00:00</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     kasus_harian  sembuh  meninggal  jumlah_test  \\\n",
       "date                                                                \n",
       "2020-03-02 00:00:00           2.0     0.0        0.0          0.0   \n",
       "2020-03-03 00:00:00           0.0     0.0        0.0          0.0   \n",
       "2020-03-04 00:00:00           0.0     0.0        0.0          0.0   \n",
       "2020-03-05 00:00:00           0.0     0.0        0.0          0.0   \n",
       "2020-03-06 00:00:00           2.0     0.0        0.0          0.0   \n",
       "\n",
       "                     dosis_pertama  dosis_kedua  \n",
       "date                                             \n",
       "2020-03-02 00:00:00            0.0          0.0  \n",
       "2020-03-03 00:00:00            0.0          0.0  \n",
       "2020-03-04 00:00:00            0.0          0.0  \n",
       "2020-03-05 00:00:00            0.0          0.0  \n",
       "2020-03-06 00:00:00            0.0          0.0  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "statistik_harian.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "6f15bb37",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Jumlah Kasus Covid-19 di Indonesia'}, xlabel='date'>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "statistik_harian.reset_index().plot(title=\"Jumlah Kasus Covid-19 di Indonesia\", x='date', y=['kasus_harian', 'sembuh', 'meninggal'], figsize=(15, 8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "dc5b357d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "127212.0"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "statistik_harian['meninggal'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "fdf0db57",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Jumlah Kasus Covid-19 di Indonesia'}, xlabel='date'>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "statistik_harian.reset_index().plot(title=\"Jumlah Kasus Covid-19 di Indonesia\", x='date', y=['kasus_harian', 'sembuh', 'meninggal'], figsize=(15, 8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "7db572a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Jumlah Kasus Covid-19 di Indonesia'}, xlabel='Date'>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "daily_cases.reset_index().plot(title=\"Jumlah Kasus Covid-19 di Indonesia\", x='Date', y='Total_Harian', figsize=(15,8))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7f62831",
   "metadata": {},
   "source": [
    "## Provinsi mana yang paling banyak total kasusnya? (bandingkan hingga 15 provinsi teratas)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "1f6aac79",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Jumlah Kasus Aktif')"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "daily_cases.iloc[:, 0:15].sum().sort_values(ascending=False).plot(kind=\"bar\", figsize=(15, 8))\n",
    "plt.xlabel(\"Daerah Provinsi\")\n",
    "plt.ylabel(\"Jumlah Kasus Aktif\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "6673766a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "daily_cases['Jakarta'].plot(figsize=(15, 8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "442d4c02",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Aceh',\n",
       " 'Bali',\n",
       " 'Banten',\n",
       " 'Babel',\n",
       " 'Bengkulu',\n",
       " 'DIY',\n",
       " 'Jakarta',\n",
       " 'Jambi',\n",
       " 'Jabar',\n",
       " 'Jateng',\n",
       " 'Jatim',\n",
       " 'Kalbar',\n",
       " 'Kaltim',\n",
       " 'Kalteng',\n",
       " 'Kalsel',\n",
       " 'Kaltara',\n",
       " 'Kep Riau',\n",
       " 'NTB',\n",
       " 'Sumsel',\n",
       " 'Sumbar',\n",
       " 'Sulut',\n",
       " 'Sumut',\n",
       " 'Sultra',\n",
       " 'Sulsel',\n",
       " 'Sulteng',\n",
       " 'Lampung',\n",
       " 'Riau',\n",
       " 'Malut',\n",
       " 'Maluku',\n",
       " 'Papbar',\n",
       " 'Papua',\n",
       " 'Sulbar',\n",
       " 'NTT',\n",
       " 'Gorontalo',\n",
       " 'Total_Harian']"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "semua_provinsi = []\n",
    "\n",
    "for nama_provinsi in daily_cases.columns:\n",
    "    semua_provinsi.append(nama_provinsi)\n",
    "\n",
    "semua_provinsi\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "dd5c21c7",
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'DataFrame' object has no attribute 'unique'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-88-d58badaf3970>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdaily_cases\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0msemua_provinsi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munique\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m__getattr__\u001b[1;34m(self, name)\u001b[0m\n\u001b[0;32m   5463\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_can_hold_identifiers_and_holds_name\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5464\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 5465\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   5466\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5467\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'unique'"
     ]
    }
   ],
   "source": [
    " daily_cases[semua_provinsi].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "b1b3af54",
   "metadata": {},
   "outputs": [],
   "source": [
    "provinsi_select = daily_cases[np.unique(semua_provinsi)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "2c43ce54",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Can only compare identically-labeled DataFrame objects",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-96-f4f790092894>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdaily_cases\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0msemua_provinsi\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mprovinsi_select\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\ops\\common.py\u001b[0m in \u001b[0;36mnew_method\u001b[1;34m(self, other)\u001b[0m\n\u001b[0;32m     63\u001b[0m         \u001b[0mother\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mitem_from_zerodim\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mother\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     64\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 65\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mother\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     66\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     67\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mnew_method\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\arraylike.py\u001b[0m in \u001b[0;36m__eq__\u001b[1;34m(self, other)\u001b[0m\n\u001b[0;32m     27\u001b[0m     \u001b[1;33m@\u001b[0m\u001b[0munpack_zerodim_and_defer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"__eq__\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     28\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__eq__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mother\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 29\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_cmp_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mother\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moperator\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0meq\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     30\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     31\u001b[0m     \u001b[1;33m@\u001b[0m\u001b[0munpack_zerodim_and_defer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"__ne__\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m_cmp_method\u001b[1;34m(self, other, op)\u001b[0m\n\u001b[0;32m   5966\u001b[0m         \u001b[0maxis\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m1\u001b[0m  \u001b[1;31m# only relevant for Series other case\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5967\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 5968\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mother\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mops\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0malign_method_FRAME\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mother\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mflex\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   5969\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5970\u001b[0m         \u001b[1;31m# See GH#4537 for discussion of scalar op behavior\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\ops\\__init__.py\u001b[0m in \u001b[0;36malign_method_FRAME\u001b[1;34m(left, right, axis, flex, level)\u001b[0m\n\u001b[0;32m    267\u001b[0m                 \u001b[0mleft\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mright\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mleft\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0malign\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mright\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mjoin\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"outer\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    268\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 269\u001b[1;33m                 raise ValueError(\n\u001b[0m\u001b[0;32m    270\u001b[0m                     \u001b[1;34m\"Can only compare identically-labeled DataFrame objects\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    271\u001b[0m                 )\n",
      "\u001b[1;31mValueError\u001b[0m: Can only compare identically-labeled DataFrame objects"
     ]
    }
   ],
   "source": [
    "daily_cases[semua_provinsi] == provinsi_select"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "a37ccb3e",
   "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>Date</th>\n",
       "      <th>Kasus harian</th>\n",
       "      <th>Kasus Impor</th>\n",
       "      <th>Kasus Lokal</th>\n",
       "      <th>Total kasus</th>\n",
       "      <th>Kasus aktif</th>\n",
       "      <th>% kasus aktif</th>\n",
       "      <th>Sembuh\\n(baru)</th>\n",
       "      <th>Sembuh</th>\n",
       "      <th>Tingkat kesembuhan (seluruh kasus)</th>\n",
       "      <th>...</th>\n",
       "      <th>CFR</th>\n",
       "      <th>Unnamed: 85</th>\n",
       "      <th>Unnamed: 86</th>\n",
       "      <th>Unnamed: 87</th>\n",
       "      <th>Unnamed: 88</th>\n",
       "      <th>Unnamed: 89</th>\n",
       "      <th>Unnamed: 90</th>\n",
       "      <th>Unnamed: 91</th>\n",
       "      <th>Unnamed: 92</th>\n",
       "      <th>Unnamed: 93</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-03-02</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-03-03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-03-04</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-03-05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-03-06</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 94 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date  Kasus harian  Kasus Impor  Kasus Lokal  Total kasus  \\\n",
       "0 2020-03-02           2.0          0.0          2.0          2.0   \n",
       "1 2020-03-03           0.0          0.0          0.0          2.0   \n",
       "2 2020-03-04           0.0          0.0          0.0          2.0   \n",
       "3 2020-03-05           0.0          0.0          0.0          2.0   \n",
       "4 2020-03-06           2.0          0.0          2.0          4.0   \n",
       "\n",
       "   Kasus aktif  % kasus aktif  Sembuh\\n(baru)  Sembuh  \\\n",
       "0          2.0            1.0             0.0     0.0   \n",
       "1          2.0            1.0             0.0     0.0   \n",
       "2          2.0            1.0             0.0     0.0   \n",
       "3          2.0            1.0             0.0     0.0   \n",
       "4          4.0            1.0             0.0     0.0   \n",
       "\n",
       "   Tingkat kesembuhan (seluruh kasus)  ...  CFR  Unnamed: 85  Unnamed: 86  \\\n",
       "0                                 0.0  ...  NaN          NaN          NaN   \n",
       "1                                 0.0  ...  NaN          NaN          NaN   \n",
       "2                                 0.0  ...  NaN          NaN          NaN   \n",
       "3                                 0.0  ...  NaN          NaN          NaN   \n",
       "4                                 0.0  ...  NaN          NaN          NaN   \n",
       "\n",
       "   Unnamed: 87  Unnamed: 88  Unnamed: 89  Unnamed: 90  Unnamed: 91  \\\n",
       "0          NaN          NaN          NaN          NaN          NaN   \n",
       "1          NaN          NaN          NaN          NaN          NaN   \n",
       "2          NaN          NaN          NaN          NaN          NaN   \n",
       "3          NaN          NaN          NaN          NaN          NaN   \n",
       "4          NaN          NaN          NaN          NaN          NaN   \n",
       "\n",
       "   Unnamed: 92  Unnamed: 93  \n",
       "0          NaN          NaN  \n",
       "1          NaN          NaN  \n",
       "2          NaN          NaN  \n",
       "3          NaN          NaN  \n",
       "4          NaN          NaN  \n",
       "\n",
       "[5 rows x 94 columns]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "statistik_harian=pd.read_excel(data,sheet_name=\"Statistik Harian\")\n",
    "statistik_harian.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "8385b58d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 568 entries, 0 to 567\n",
      "Data columns (total 94 columns):\n",
      " #   Column                                             Non-Null Count  Dtype         \n",
      "---  ------                                             --------------  -----         \n",
      " 0   Date                                               539 non-null    datetime64[ns]\n",
      " 1   Kasus harian                                       539 non-null    float64       \n",
      " 2   Kasus Impor                                        10 non-null     float64       \n",
      " 3   Kasus Lokal                                        10 non-null     float64       \n",
      " 4   Total kasus                                        539 non-null    float64       \n",
      " 5   Kasus aktif                                        539 non-null    float64       \n",
      " 6   % kasus aktif                                      539 non-null    float64       \n",
      " 7   Sembuh\n",
      "(baru)                                      539 non-null    float64       \n",
      " 8   Sembuh                                             539 non-null    float64       \n",
      " 9   Tingkat kesembuhan (seluruh kasus)                 539 non-null    float64       \n",
      " 10  Tingkat kesembuhan (closed cases)                  531 non-null    float64       \n",
      " 11  Meninggal\n",
      "(baru)                                   539 non-null    float64       \n",
      " 12  Meninggal\n",
      "Dunia                                    539 non-null    float64       \n",
      " 13  Tingkat kematian (seluruh kasus)                   539 non-null    float64       \n",
      " 14  Tingkat kematian (closed cases)                    530 non-null    float64       \n",
      " 15  PDP                                                91 non-null     float64       \n",
      " 16  ODP                                                91 non-null     float64       \n",
      " 17  Suspek aktif                                       463 non-null    float64       \n",
      " 18  Jumlah spesimen diperiksa (dari 1 April)           539 non-null    float64       \n",
      " 19  Jumlah orang diperiksa                             539 non-null    float64       \n",
      " 20  Negatif                                            539 non-null    float64       \n",
      " 21  Positif \n",
      "(=C)                                      186 non-null    float64       \n",
      " 22  Dalam Proses                                       171 non-null    float64       \n",
      " 23  Spesimen                                           495 non-null    float64       \n",
      " 24  Spesimen (PCR-TCM)                                 167 non-null    float64       \n",
      " 25  Spesimen (Antigen)                                 167 non-null    float64       \n",
      " 26  Orang yang dites                                   537 non-null    float64       \n",
      " 27  Orang yang dites (PCR-TCM)                         169 non-null    float64       \n",
      " 28  Orang yang dites (Antigen)                         169 non-null    float64       \n",
      " 29  Vaksinasi (ineligible)                             3 non-null      float64       \n",
      " 30  Jumlah test/juta penduduk                          529 non-null    float64       \n",
      " 31  Test/Positif                                       539 non-null    float64       \n",
      " 32  Jumlah org yg dites / positif                      539 non-null    float64       \n",
      " 33  Jumlah Test/Positif harian                         534 non-null    float64       \n",
      " 34  Positif / Jumlah Tes                               539 non-null    float64       \n",
      " 35  Positive rate harian                               536 non-null    float64       \n",
      " 36  Case Growth Rate                                   537 non-null    float64       \n",
      " 37  Tingkat positivitas mingguan                       529 non-null    float64       \n",
      " 38  Jumlah spesimen diperiksa (rata-rata 7 hari)       489 non-null    float64       \n",
      " 39  Jumlah orang diperiksa (rata-rata 7 hari)          489 non-null    float64       \n",
      " 40  Rasio spesimen/orang diperiksa (rata-rata 7 hari)  489 non-null    float64       \n",
      " 41  Unnamed: 41                                        162 non-null    float64       \n",
      " 42  Dosis pertama                                      223 non-null    float64       \n",
      " 43  Dosis kedua                                        209 non-null    float64       \n",
      " 44  Dosis ketiga                                       6 non-null      float64       \n",
      " 45  Dosis pertama (%)                                  223 non-null    float64       \n",
      " 46  Dosis kedua (%)                                    209 non-null    float64       \n",
      " 47  Dosis pertama (harian)                             223 non-null    float64       \n",
      " 48  Dosis kedua (harian)                               209 non-null    float64       \n",
      " 49  Dosis harian                                       223 non-null    float64       \n",
      " 50  Dosis pertama (mingguan)                           217 non-null    float64       \n",
      " 51  Dosis kedua (mingguan)                             203 non-null    float64       \n",
      " 52  Rata2 dosis harian (mingguan)                      217 non-null    float64       \n",
      " 53  Dosis pertama (SDM kesehatan)                      175 non-null    float64       \n",
      " 54  Dosis kedua (SDM kesehatan)                        175 non-null    float64       \n",
      " 55  Dosis ketiga (SDM kesehatan)                       6 non-null      float64       \n",
      " 56  Dosis pertama (SDM kesehatan) %                    168 non-null    float64       \n",
      " 57  Dosis kedua (SDM kesehatan) %                      168 non-null    float64       \n",
      " 58  Dosis pertama (petugas publik)                     175 non-null    float64       \n",
      " 59  Dosis kedua (petugas publik)                       175 non-null    float64       \n",
      " 60  Dosis pertama (petugas publik) %                   168 non-null    float64       \n",
      " 61  Dosis kedua (petugas publik) %                     168 non-null    float64       \n",
      " 62  Dosis pertama (lansia)                             175 non-null    float64       \n",
      " 63  Dosis kedua (lansia)                               175 non-null    float64       \n",
      " 64  Dosis pertama (lansia) %                           168 non-null    float64       \n",
      " 65  Dosis kedua (lansia) %                             168 non-null    float64       \n",
      " 66  Dosis pertama (warga umum)                         43 non-null     float64       \n",
      " 67  Dosis kedua (warga umum)                           43 non-null     float64       \n",
      " 68  Dosis pertama (remaja)                             43 non-null     float64       \n",
      " 69  Dosis kedua (remaja)                               43 non-null     float64       \n",
      " 70  Dosis pertama (Vakgor)                             3 non-null      float64       \n",
      " 71  Dosis kedua (Vakgor)                               3 non-null      float64       \n",
      " 72  Test PCR Orang Nasional Rata-rata 7 hari           149 non-null    float64       \n",
      " 73  Pos Rate Nasional - rata-rata 7 hari               149 non-null    float64       \n",
      " 74  Test Orang DKI                                     141 non-null    float64       \n",
      " 75  Positif DKI                                        141 non-null    float64       \n",
      " 76  Unnamed: 76                                        141 non-null    float64       \n",
      " 77  Pos Rate DKI                                       141 non-null    float64       \n",
      " 78  Pos Rate DKI - 7 harian                            141 non-null    float64       \n",
      " 79  NON DKI                                            141 non-null    float64       \n",
      " 80  Positif Non DKI                                    141 non-null    float64       \n",
      " 81  Unnamed: 81                                        141 non-null    float64       \n",
      " 82  Pos Rate Non DKI                                   141 non-null    float64       \n",
      " 83  Pos Rate Non DKI - 7 harian                        141 non-null    float64       \n",
      " 84  CFR                                                361 non-null    float64       \n",
      " 85  Unnamed: 85                                        0 non-null      float64       \n",
      " 86  Unnamed: 86                                        0 non-null      float64       \n",
      " 87  Unnamed: 87                                        0 non-null      float64       \n",
      " 88  Unnamed: 88                                        0 non-null      float64       \n",
      " 89  Unnamed: 89                                        0 non-null      float64       \n",
      " 90  Unnamed: 90                                        0 non-null      float64       \n",
      " 91  Unnamed: 91                                        0 non-null      float64       \n",
      " 92  Unnamed: 92                                        0 non-null      float64       \n",
      " 93  Unnamed: 93                                        0 non-null      float64       \n",
      "dtypes: datetime64[ns](1), float64(93)\n",
      "memory usage: 417.2 KB\n"
     ]
    }
   ],
   "source": [
    "statistik_harian.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "a94a368f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Kasus harian</th>\n",
       "      <th>Kasus Impor</th>\n",
       "      <th>Kasus Lokal</th>\n",
       "      <th>Total kasus</th>\n",
       "      <th>Kasus aktif</th>\n",
       "      <th>% kasus aktif</th>\n",
       "      <th>Sembuh\\n(baru)</th>\n",
       "      <th>Sembuh</th>\n",
       "      <th>Tingkat kesembuhan (seluruh kasus)</th>\n",
       "      <th>...</th>\n",
       "      <th>CFR</th>\n",
       "      <th>Unnamed: 85</th>\n",
       "      <th>Unnamed: 86</th>\n",
       "      <th>Unnamed: 87</th>\n",
       "      <th>Unnamed: 88</th>\n",
       "      <th>Unnamed: 89</th>\n",
       "      <th>Unnamed: 90</th>\n",
       "      <th>Unnamed: 91</th>\n",
       "      <th>Unnamed: 92</th>\n",
       "      <th>Unnamed: 93</th>\n",
       "    </tr>\n",
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       "<p>5 rows × 94 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Date  Kasus harian  Kasus Impor  Kasus Lokal  Total kasus  \\\n",
       "0  2020-03-02 00:00:00           2.0          0.0          2.0          2.0   \n",
       "1  2020-03-03 00:00:00           0.0          0.0          0.0          2.0   \n",
       "2  2020-03-04 00:00:00           0.0          0.0          0.0          2.0   \n",
       "3  2020-03-05 00:00:00           0.0          0.0          0.0          2.0   \n",
       "4  2020-03-06 00:00:00           2.0          0.0          2.0          4.0   \n",
       "\n",
       "   Kasus aktif  % kasus aktif  Sembuh\\n(baru)  Sembuh  \\\n",
       "0          2.0            1.0             0.0     0.0   \n",
       "1          2.0            1.0             0.0     0.0   \n",
       "2          2.0            1.0             0.0     0.0   \n",
       "3          2.0            1.0             0.0     0.0   \n",
       "4          4.0            1.0             0.0     0.0   \n",
       "\n",
       "   Tingkat kesembuhan (seluruh kasus)  ...  CFR  Unnamed: 85  Unnamed: 86  \\\n",
       "0                                 0.0  ...  0.0          0.0          0.0   \n",
       "1                                 0.0  ...  0.0          0.0          0.0   \n",
       "2                                 0.0  ...  0.0          0.0          0.0   \n",
       "3                                 0.0  ...  0.0          0.0          0.0   \n",
       "4                                 0.0  ...  0.0          0.0          0.0   \n",
       "\n",
       "   Unnamed: 87  Unnamed: 88  Unnamed: 89  Unnamed: 90  Unnamed: 91  \\\n",
       "0          0.0          0.0          0.0          0.0          0.0   \n",
       "1          0.0          0.0          0.0          0.0          0.0   \n",
       "2          0.0          0.0          0.0          0.0          0.0   \n",
       "3          0.0          0.0          0.0          0.0          0.0   \n",
       "4          0.0          0.0          0.0          0.0          0.0   \n",
       "\n",
       "   Unnamed: 92  Unnamed: 93  \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",
       "[5 rows x 94 columns]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "statistik_harian.fillna(0,inplace=True)\n",
    "statistik_harian.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "4f77e959",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Date', 'Kasus harian', 'Kasus Impor', 'Kasus Lokal', 'Total kasus',\n",
       "       'Kasus aktif', '% kasus aktif', 'Sembuh\\n(baru)', 'Sembuh',\n",
       "       'Tingkat kesembuhan (seluruh kasus)',\n",
       "       'Tingkat kesembuhan (closed cases)', 'Meninggal\\n(baru)',\n",
       "       'Meninggal\\nDunia', 'Tingkat kematian (seluruh kasus)',\n",
       "       'Tingkat kematian (closed cases)', 'PDP', 'ODP', 'Suspek aktif',\n",
       "       'Jumlah spesimen diperiksa (dari 1 April)', 'Jumlah orang diperiksa',\n",
       "       'Negatif', 'Positif \\n(=C)', 'Dalam Proses', 'Spesimen',\n",
       "       'Spesimen (PCR-TCM)', 'Spesimen (Antigen)', 'Orang yang dites',\n",
       "       'Orang yang dites (PCR-TCM)', 'Orang yang dites (Antigen)',\n",
       "       'Vaksinasi (ineligible)', 'Jumlah test/juta penduduk', 'Test/Positif',\n",
       "       'Jumlah org yg dites / positif', 'Jumlah Test/Positif harian',\n",
       "       'Positif / Jumlah Tes', 'Positive rate harian', 'Case Growth Rate',\n",
       "       'Tingkat positivitas mingguan',\n",
       "       'Jumlah spesimen diperiksa (rata-rata 7 hari)',\n",
       "       'Jumlah orang diperiksa (rata-rata 7 hari)',\n",
       "       'Rasio spesimen/orang diperiksa (rata-rata 7 hari)', 'Unnamed: 41',\n",
       "       'Dosis pertama', 'Dosis kedua', 'Dosis ketiga', 'Dosis pertama (%)',\n",
       "       'Dosis kedua (%)', 'Dosis pertama (harian)', 'Dosis kedua (harian)',\n",
       "       'Dosis harian', 'Dosis pertama (mingguan)', 'Dosis kedua (mingguan)',\n",
       "       'Rata2 dosis harian (mingguan)', 'Dosis pertama (SDM kesehatan)',\n",
       "       'Dosis kedua (SDM kesehatan)', 'Dosis ketiga (SDM kesehatan)',\n",
       "       'Dosis pertama (SDM kesehatan) %', 'Dosis kedua (SDM kesehatan) %',\n",
       "       'Dosis pertama (petugas publik)', 'Dosis kedua (petugas publik)',\n",
       "       'Dosis pertama (petugas publik) %', 'Dosis kedua (petugas publik) %',\n",
       "       'Dosis pertama (lansia)', 'Dosis kedua (lansia)',\n",
       "       'Dosis pertama (lansia) %', 'Dosis kedua (lansia) %',\n",
       "       'Dosis pertama (warga umum)', 'Dosis kedua (warga umum)',\n",
       "       'Dosis pertama (remaja)', 'Dosis kedua (remaja)',\n",
       "       'Dosis pertama (Vakgor)', 'Dosis kedua (Vakgor)',\n",
       "       'Test PCR Orang Nasional Rata-rata 7 hari',\n",
       "       'Pos Rate Nasional - rata-rata 7 hari', 'Test Orang DKI', 'Positif DKI',\n",
       "       'Unnamed: 76', 'Pos Rate DKI', 'Pos Rate DKI - 7 harian', 'NON DKI',\n",
       "       'Positif Non DKI', 'Unnamed: 81', 'Pos Rate Non DKI',\n",
       "       'Pos Rate Non DKI - 7 harian', 'CFR', 'Unnamed: 85', 'Unnamed: 86',\n",
       "       'Unnamed: 87', 'Unnamed: 88', 'Unnamed: 89', 'Unnamed: 90',\n",
       "       'Unnamed: 91', 'Unnamed: 92', 'Unnamed: 93'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "statistik_harian.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "d9d728cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_kasus_vaksin = pd.DataFrame({\n",
    "    'date': statistik_harian['Date'],\n",
    "    'dosis_pertama': statistik_harian['Dosis pertama'],\n",
    "    'dosis_kedua': statistik_harian['Dosis kedua']\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "eeb8b4e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "58014403"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_kasus_vaksin['dosis_pertama'] = df_kasus_vaksin['dosis_pertama'].astype(int)\n",
    "\n",
    "df_kasus_vaksin.iloc[538]['dosis_pertama']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "8e9b8926",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 568 entries, 0 to 567\n",
      "Data columns (total 3 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   date           568 non-null    object \n",
      " 1   dosis_pertama  568 non-null    float64\n",
      " 2   dosis_kedua    568 non-null    float64\n",
      "dtypes: float64(2), object(1)\n",
      "memory usage: 13.4+ KB\n"
     ]
    }
   ],
   "source": [
    "df_kasus_vaksin.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "cde2de7b",
   "metadata": {},
   "outputs": [
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       "                  date  dosis_pertama  dosis_kedua\n",
       "0  2020-03-02 00:00:00            0.0          0.0\n",
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   ],
   "source": [
    "df_kasus_vaksin.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "4815ec05",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(568, 3)"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_kasus_vaksin.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "dd0478ae",
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "                  date  dosis_pertama  dosis_kedua\n",
       "0  2020-03-02 00:00:00              0            0\n",
       "1  2020-03-03 00:00:00              0            0\n",
       "2  2020-03-04 00:00:00              0            0\n",
       "3  2020-03-05 00:00:00              0            0\n",
       "4  2020-03-06 00:00:00              0            0"
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     "execution_count": 155,
     "metadata": {},
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   ],
   "source": [
    "df_kasus_vaksin.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "3981deaa",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "      <th>dosis_pertama</th>\n",
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      "text/plain": [
       "    date  dosis_pertama  dosis_kedua\n",
       "563    0              0            0\n",
       "564    0              0            0\n",
       "565    0              0            0\n",
       "566    0              0            0\n",
       "567    0              0            0"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_kasus_vaksin.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "65b5a435",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 568 entries, 0 to 567\n",
      "Data columns (total 3 columns):\n",
      " #   Column         Non-Null Count  Dtype \n",
      "---  ------         --------------  ----- \n",
      " 0   date           568 non-null    object\n",
      " 1   dosis_pertama  568 non-null    int32 \n",
      " 2   dosis_kedua    568 non-null    int32 \n",
      "dtypes: int32(2), object(1)\n",
      "memory usage: 9.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df_kasus_vaksin.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "800d3612",
   "metadata": {},
   "outputs": [],
   "source": [
    "#df_kasus_vaksin.set_index('date',inplace=True)\n",
    "#df_kasus_vaksin.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "9183881d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "58014403"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dosis_pertama = df_kasus_vaksin.at[df_kasus_vaksin.index[538], 'dosis_pertama']\n",
    "dosis_pertama"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "a660423e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32239869.0"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dosis_kedua = df_kasus_vaksin.iloc[538]['dosis_kedua']\n",
    "dosis_kedua"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "e12d0e40",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Penyebaran vaksin'}>"
      ]
     },
     "execution_count": 178,
     "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": [
    "df_kasus_vaksin.iloc[538][['dosis_pertama', 'dosis_kedua']].plot(kind='bar', title='Penyebaran vaksin')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "d0301126",
   "metadata": {},
   "outputs": [],
   "source": [
    "kasus_per_provinsi=pd.read_excel(data,sheet_name=\"Kasus per Provinsi\")\n",
    "\n",
    "#kasus_per_provinsi.drop(index=kasus_per_provinsi.index[0], axis=0, inplace=False)\n",
    "# kasus_per_provinsi = pd.DataFrame({\n",
    "#     'provinsi': kasus_per_provinsi['Provinsi Asal'],\n",
    "#     'kasus': kasus_per_provinsi['Kasus'],\n",
    "#     'sembuh': kasus_per_provinsi['sembuh'],\n",
    "#     'meninggal': kasus_per_provinsi['Kematian']\n",
    "# })\n",
    "\n",
    "\n",
    "#kasus_per_provinsi.drop(index=kasus_per_provinsi.index[0], axis=0, inplace=False)\n",
    "kasus_per_provinsi = pd.DataFrame({\n",
    "    'provinsi': kasus_per_provinsi['Unnamed: 1'],\n",
    "    'kasus_aktif': kasus_per_provinsi['Unnamed: 2'],\n",
    "    'sembuh': kasus_per_provinsi['Unnamed: 4'],\n",
    "    'meninggal': kasus_per_provinsi['Unnamed: 6']\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "c6cd1bbc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['provinsi', 'kasus_aktif', 'sembuh', 'meninggal'], dtype='object')"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kasus_per_provinsi.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "06f3c7eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "kasus_per_provinsi.drop(index=kasus_per_provinsi.index[0], axis=0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "7d0ad922",
   "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>provinsi</th>\n",
       "      <th>kasus_aktif</th>\n",
       "      <th>sembuh</th>\n",
       "      <th>meninggal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Aceh</td>\n",
       "      <td>30305</td>\n",
       "      <td>22726</td>\n",
       "      <td>1321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bali</td>\n",
       "      <td>102574</td>\n",
       "      <td>90579</td>\n",
       "      <td>3181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Banten</td>\n",
       "      <td>127037</td>\n",
       "      <td>118919</td>\n",
       "      <td>2567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bangka Belitung</td>\n",
       "      <td>44571</td>\n",
       "      <td>38967</td>\n",
       "      <td>1117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Bengkulu</td>\n",
       "      <td>22040</td>\n",
       "      <td>18721</td>\n",
       "      <td>396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>147 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            provinsi kasus_aktif  sembuh meninggal\n",
       "1               Aceh       30305   22726      1321\n",
       "2               Bali      102574   90579      3181\n",
       "3             Banten      127037  118919      2567\n",
       "4    Bangka Belitung       44571   38967      1117\n",
       "5           Bengkulu       22040   18721       396\n",
       "..               ...         ...     ...       ...\n",
       "143              NaN         NaN     NaN       NaN\n",
       "144              NaN         NaN     NaN       NaN\n",
       "145              NaN         NaN     NaN       NaN\n",
       "146              NaN         NaN     NaN       NaN\n",
       "147              NaN         NaN     NaN       NaN\n",
       "\n",
       "[147 rows x 4 columns]"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kasus_per_provinsi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "a8402e87",
   "metadata": {},
   "outputs": [],
   "source": [
    "kasus_per_provinsi.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "0824c291",
   "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>provinsi</th>\n",
       "      <th>kasus_aktif</th>\n",
       "      <th>sembuh</th>\n",
       "      <th>meninggal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Aceh</td>\n",
       "      <td>30305</td>\n",
       "      <td>22726</td>\n",
       "      <td>1321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bali</td>\n",
       "      <td>102574</td>\n",
       "      <td>90579</td>\n",
       "      <td>3181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Banten</td>\n",
       "      <td>127037</td>\n",
       "      <td>118919</td>\n",
       "      <td>2567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bangka Belitung</td>\n",
       "      <td>44571</td>\n",
       "      <td>38967</td>\n",
       "      <td>1117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Bengkulu</td>\n",
       "      <td>22040</td>\n",
       "      <td>18721</td>\n",
       "      <td>396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>DI Yogyakarta</td>\n",
       "      <td>145115</td>\n",
       "      <td>123459</td>\n",
       "      <td>4614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>DKI Jakarta</td>\n",
       "      <td>846416</td>\n",
       "      <td>825029</td>\n",
       "      <td>13221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Jambi</td>\n",
       "      <td>26989</td>\n",
       "      <td>22677</td>\n",
       "      <td>635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Jawa Barat</td>\n",
       "      <td>670585</td>\n",
       "      <td>607095</td>\n",
       "      <td>12506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Jawa Tengah</td>\n",
       "      <td>462977</td>\n",
       "      <td>407542</td>\n",
       "      <td>27808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Jawa Timur</td>\n",
       "      <td>373465</td>\n",
       "      <td>326149</td>\n",
       "      <td>26882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Kalimantan Barat</td>\n",
       "      <td>34063</td>\n",
       "      <td>31259</td>\n",
       "      <td>903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Kalimantan Timur</td>\n",
       "      <td>146198</td>\n",
       "      <td>131563</td>\n",
       "      <td>4860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Kalimantan Tengah</td>\n",
       "      <td>42068</td>\n",
       "      <td>35923</td>\n",
       "      <td>1222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Kalimantan Selatan</td>\n",
       "      <td>63224</td>\n",
       "      <td>50846</td>\n",
       "      <td>1994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Kalimantan Utara</td>\n",
       "      <td>30255</td>\n",
       "      <td>23724</td>\n",
       "      <td>634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Kepulauan Riau</td>\n",
       "      <td>51402</td>\n",
       "      <td>47977</td>\n",
       "      <td>1572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Nusa Tenggara Barat</td>\n",
       "      <td>24756</td>\n",
       "      <td>22679</td>\n",
       "      <td>682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Sumatera Selatan</td>\n",
       "      <td>56802</td>\n",
       "      <td>49072</td>\n",
       "      <td>2758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Sumatera Barat</td>\n",
       "      <td>84360</td>\n",
       "      <td>75421</td>\n",
       "      <td>1906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Sulawesi Utara</td>\n",
       "      <td>31151</td>\n",
       "      <td>26086</td>\n",
       "      <td>901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Sumatera Utara</td>\n",
       "      <td>89582</td>\n",
       "      <td>61808</td>\n",
       "      <td>2131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Sulawesi Tenggara</td>\n",
       "      <td>19245</td>\n",
       "      <td>16502</td>\n",
       "      <td>470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Sulawesi Selatan</td>\n",
       "      <td>101617</td>\n",
       "      <td>90783</td>\n",
       "      <td>1888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Sulawesi Tengah</td>\n",
       "      <td>39541</td>\n",
       "      <td>29587</td>\n",
       "      <td>1235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Lampung</td>\n",
       "      <td>44955</td>\n",
       "      <td>36384</td>\n",
       "      <td>3317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Riau</td>\n",
       "      <td>119928</td>\n",
       "      <td>108088</td>\n",
       "      <td>3502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Maluku Utara</td>\n",
       "      <td>11468</td>\n",
       "      <td>10299</td>\n",
       "      <td>291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Maluku</td>\n",
       "      <td>14222</td>\n",
       "      <td>12466</td>\n",
       "      <td>245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Papua Barat</td>\n",
       "      <td>21859</td>\n",
       "      <td>20752</td>\n",
       "      <td>335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Papua</td>\n",
       "      <td>31392</td>\n",
       "      <td>18976</td>\n",
       "      <td>351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Sulawesi Barat</td>\n",
       "      <td>11140</td>\n",
       "      <td>9389</td>\n",
       "      <td>264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Nusa Tenggara Timur</td>\n",
       "      <td>56909</td>\n",
       "      <td>50423</td>\n",
       "      <td>1115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Gorontalo</td>\n",
       "      <td>10849</td>\n",
       "      <td>9212</td>\n",
       "      <td>390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Total</td>\n",
       "      <td>3989060</td>\n",
       "      <td>3571082</td>\n",
       "      <td>127214</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               provinsi kasus_aktif   sembuh meninggal\n",
       "1                  Aceh       30305    22726      1321\n",
       "2                  Bali      102574    90579      3181\n",
       "3                Banten      127037   118919      2567\n",
       "4       Bangka Belitung       44571    38967      1117\n",
       "5              Bengkulu       22040    18721       396\n",
       "6         DI Yogyakarta      145115   123459      4614\n",
       "7           DKI Jakarta      846416   825029     13221\n",
       "8                 Jambi       26989    22677       635\n",
       "9            Jawa Barat      670585   607095     12506\n",
       "10          Jawa Tengah      462977   407542     27808\n",
       "11           Jawa Timur      373465   326149     26882\n",
       "12     Kalimantan Barat       34063    31259       903\n",
       "13     Kalimantan Timur      146198   131563      4860\n",
       "14    Kalimantan Tengah       42068    35923      1222\n",
       "15   Kalimantan Selatan       63224    50846      1994\n",
       "16     Kalimantan Utara       30255    23724       634\n",
       "17       Kepulauan Riau       51402    47977      1572\n",
       "18  Nusa Tenggara Barat       24756    22679       682\n",
       "19     Sumatera Selatan       56802    49072      2758\n",
       "20       Sumatera Barat       84360    75421      1906\n",
       "21       Sulawesi Utara       31151    26086       901\n",
       "22       Sumatera Utara       89582    61808      2131\n",
       "23    Sulawesi Tenggara       19245    16502       470\n",
       "24     Sulawesi Selatan      101617    90783      1888\n",
       "25      Sulawesi Tengah       39541    29587      1235\n",
       "26              Lampung       44955    36384      3317\n",
       "27                 Riau      119928   108088      3502\n",
       "28         Maluku Utara       11468    10299       291\n",
       "29               Maluku       14222    12466       245\n",
       "30          Papua Barat       21859    20752       335\n",
       "31                Papua       31392    18976       351\n",
       "32       Sulawesi Barat       11140     9389       264\n",
       "33  Nusa Tenggara Timur       56909    50423      1115\n",
       "34            Gorontalo       10849     9212       390\n",
       "36                Total     3989060  3571082    127214"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kasus_per_provinsi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "d334930c",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_provinsi = kasus_per_provinsi.drop(labels=36, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "06172292",
   "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",
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       "    }\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>provinsi</th>\n",
       "      <th>kasus_aktif</th>\n",
       "      <th>sembuh</th>\n",
       "      <th>meninggal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Aceh</td>\n",
       "      <td>30305</td>\n",
       "      <td>22726</td>\n",
       "      <td>1321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bali</td>\n",
       "      <td>102574</td>\n",
       "      <td>90579</td>\n",
       "      <td>3181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Banten</td>\n",
       "      <td>127037</td>\n",
       "      <td>118919</td>\n",
       "      <td>2567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bangka Belitung</td>\n",
       "      <td>44571</td>\n",
       "      <td>38967</td>\n",
       "      <td>1117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Bengkulu</td>\n",
       "      <td>22040</td>\n",
       "      <td>18721</td>\n",
       "      <td>396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>DI Yogyakarta</td>\n",
       "      <td>145115</td>\n",
       "      <td>123459</td>\n",
       "      <td>4614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>DKI Jakarta</td>\n",
       "      <td>846416</td>\n",
       "      <td>825029</td>\n",
       "      <td>13221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Jambi</td>\n",
       "      <td>26989</td>\n",
       "      <td>22677</td>\n",
       "      <td>635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Jawa Barat</td>\n",
       "      <td>670585</td>\n",
       "      <td>607095</td>\n",
       "      <td>12506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Jawa Tengah</td>\n",
       "      <td>462977</td>\n",
       "      <td>407542</td>\n",
       "      <td>27808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Jawa Timur</td>\n",
       "      <td>373465</td>\n",
       "      <td>326149</td>\n",
       "      <td>26882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Kalimantan Barat</td>\n",
       "      <td>34063</td>\n",
       "      <td>31259</td>\n",
       "      <td>903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Kalimantan Timur</td>\n",
       "      <td>146198</td>\n",
       "      <td>131563</td>\n",
       "      <td>4860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Kalimantan Tengah</td>\n",
       "      <td>42068</td>\n",
       "      <td>35923</td>\n",
       "      <td>1222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Kalimantan Selatan</td>\n",
       "      <td>63224</td>\n",
       "      <td>50846</td>\n",
       "      <td>1994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Kalimantan Utara</td>\n",
       "      <td>30255</td>\n",
       "      <td>23724</td>\n",
       "      <td>634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Kepulauan Riau</td>\n",
       "      <td>51402</td>\n",
       "      <td>47977</td>\n",
       "      <td>1572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Nusa Tenggara Barat</td>\n",
       "      <td>24756</td>\n",
       "      <td>22679</td>\n",
       "      <td>682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Sumatera Selatan</td>\n",
       "      <td>56802</td>\n",
       "      <td>49072</td>\n",
       "      <td>2758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Sumatera Barat</td>\n",
       "      <td>84360</td>\n",
       "      <td>75421</td>\n",
       "      <td>1906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Sulawesi Utara</td>\n",
       "      <td>31151</td>\n",
       "      <td>26086</td>\n",
       "      <td>901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Sumatera Utara</td>\n",
       "      <td>89582</td>\n",
       "      <td>61808</td>\n",
       "      <td>2131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Sulawesi Tenggara</td>\n",
       "      <td>19245</td>\n",
       "      <td>16502</td>\n",
       "      <td>470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Sulawesi Selatan</td>\n",
       "      <td>101617</td>\n",
       "      <td>90783</td>\n",
       "      <td>1888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Sulawesi Tengah</td>\n",
       "      <td>39541</td>\n",
       "      <td>29587</td>\n",
       "      <td>1235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Lampung</td>\n",
       "      <td>44955</td>\n",
       "      <td>36384</td>\n",
       "      <td>3317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Riau</td>\n",
       "      <td>119928</td>\n",
       "      <td>108088</td>\n",
       "      <td>3502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Maluku Utara</td>\n",
       "      <td>11468</td>\n",
       "      <td>10299</td>\n",
       "      <td>291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Maluku</td>\n",
       "      <td>14222</td>\n",
       "      <td>12466</td>\n",
       "      <td>245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Papua Barat</td>\n",
       "      <td>21859</td>\n",
       "      <td>20752</td>\n",
       "      <td>335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Papua</td>\n",
       "      <td>31392</td>\n",
       "      <td>18976</td>\n",
       "      <td>351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Sulawesi Barat</td>\n",
       "      <td>11140</td>\n",
       "      <td>9389</td>\n",
       "      <td>264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Nusa Tenggara Timur</td>\n",
       "      <td>56909</td>\n",
       "      <td>50423</td>\n",
       "      <td>1115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Gorontalo</td>\n",
       "      <td>10849</td>\n",
       "      <td>9212</td>\n",
       "      <td>390</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               provinsi kasus_aktif  sembuh meninggal\n",
       "1                  Aceh       30305   22726      1321\n",
       "2                  Bali      102574   90579      3181\n",
       "3                Banten      127037  118919      2567\n",
       "4       Bangka Belitung       44571   38967      1117\n",
       "5              Bengkulu       22040   18721       396\n",
       "6         DI Yogyakarta      145115  123459      4614\n",
       "7           DKI Jakarta      846416  825029     13221\n",
       "8                 Jambi       26989   22677       635\n",
       "9            Jawa Barat      670585  607095     12506\n",
       "10          Jawa Tengah      462977  407542     27808\n",
       "11           Jawa Timur      373465  326149     26882\n",
       "12     Kalimantan Barat       34063   31259       903\n",
       "13     Kalimantan Timur      146198  131563      4860\n",
       "14    Kalimantan Tengah       42068   35923      1222\n",
       "15   Kalimantan Selatan       63224   50846      1994\n",
       "16     Kalimantan Utara       30255   23724       634\n",
       "17       Kepulauan Riau       51402   47977      1572\n",
       "18  Nusa Tenggara Barat       24756   22679       682\n",
       "19     Sumatera Selatan       56802   49072      2758\n",
       "20       Sumatera Barat       84360   75421      1906\n",
       "21       Sulawesi Utara       31151   26086       901\n",
       "22       Sumatera Utara       89582   61808      2131\n",
       "23    Sulawesi Tenggara       19245   16502       470\n",
       "24     Sulawesi Selatan      101617   90783      1888\n",
       "25      Sulawesi Tengah       39541   29587      1235\n",
       "26              Lampung       44955   36384      3317\n",
       "27                 Riau      119928  108088      3502\n",
       "28         Maluku Utara       11468   10299       291\n",
       "29               Maluku       14222   12466       245\n",
       "30          Papua Barat       21859   20752       335\n",
       "31                Papua       31392   18976       351\n",
       "32       Sulawesi Barat       11140    9389       264\n",
       "33  Nusa Tenggara Timur       56909   50423      1115\n",
       "34            Gorontalo       10849    9212       390"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_provinsi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "81c2c954",
   "metadata": {},
   "outputs": [],
   "source": [
    "total_dataframe = pd.DataFrame({\n",
    "    'status': ['kasus_aktif', 'sembuh', 'meninggal'],\n",
    "    'jumlah_kasus_per_provinsi': (data_provinsi.iloc[0]['kasus_aktif'],\n",
    "                                  data_provinsi.iloc[0]['sembuh'],\n",
    "                                  data_provinsi.iloc[0]['meninggal'])\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "513cec5d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Aceh', 'Bali', 'Banten', 'Bangka Belitung', 'Bengkulu',\n",
       "       'DI Yogyakarta', 'DKI Jakarta', 'Jambi', 'Jawa Barat',\n",
       "       'Jawa Tengah', 'Jawa Timur', 'Kalimantan Barat',\n",
       "       'Kalimantan Timur', 'Kalimantan Tengah', 'Kalimantan Selatan',\n",
       "       'Kalimantan Utara', 'Kepulauan Riau', 'Nusa Tenggara Barat',\n",
       "       'Sumatera Selatan', 'Sumatera Barat', 'Sulawesi Utara',\n",
       "       'Sumatera Utara', 'Sulawesi Tenggara', 'Sulawesi Selatan',\n",
       "       'Sulawesi Tengah', 'Lampung', 'Riau', 'Maluku Utara', 'Maluku',\n",
       "       'Papua Barat', 'Papua', 'Sulawesi Barat', 'Nusa Tenggara Timur',\n",
       "       'Gorontalo'], dtype=object)"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_provinsi['provinsi'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "a08fce4c",
   "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>status</th>\n",
       "      <th>jumlah_kasus_per_provinsi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>kasus_aktif</td>\n",
       "      <td>30305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sembuh</td>\n",
       "      <td>22726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>meninggal</td>\n",
       "      <td>1321</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        status  jumlah_kasus_per_provinsi\n",
       "0  kasus_aktif                      30305\n",
       "1       sembuh                      22726\n",
       "2    meninggal                       1321"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "3802de02",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               provinsi kasus_aktif  sembuh meninggal\n",
      "1                  Aceh       30305   22726      1321\n",
      "2                  Bali      102574   90579      3181\n",
      "3                Banten      127037  118919      2567\n",
      "4       Bangka Belitung       44571   38967      1117\n",
      "5              Bengkulu       22040   18721       396\n",
      "6         DI Yogyakarta      145115  123459      4614\n",
      "7           DKI Jakarta      846416  825029     13221\n",
      "8                 Jambi       26989   22677       635\n",
      "9            Jawa Barat      670585  607095     12506\n",
      "10          Jawa Tengah      462977  407542     27808\n",
      "11           Jawa Timur      373465  326149     26882\n",
      "12     Kalimantan Barat       34063   31259       903\n",
      "13     Kalimantan Timur      146198  131563      4860\n",
      "14    Kalimantan Tengah       42068   35923      1222\n",
      "15   Kalimantan Selatan       63224   50846      1994\n",
      "16     Kalimantan Utara       30255   23724       634\n",
      "17       Kepulauan Riau       51402   47977      1572\n",
      "18  Nusa Tenggara Barat       24756   22679       682\n",
      "19     Sumatera Selatan       56802   49072      2758\n",
      "20       Sumatera Barat       84360   75421      1906\n",
      "21       Sulawesi Utara       31151   26086       901\n",
      "22       Sumatera Utara       89582   61808      2131\n",
      "23    Sulawesi Tenggara       19245   16502       470\n",
      "24     Sulawesi Selatan      101617   90783      1888\n",
      "25      Sulawesi Tengah       39541   29587      1235\n",
      "26              Lampung       44955   36384      3317\n",
      "27                 Riau      119928  108088      3502\n",
      "28         Maluku Utara       11468   10299       291\n",
      "29               Maluku       14222   12466       245\n",
      "30          Papua Barat       21859   20752       335\n",
      "31                Papua       31392   18976       351\n",
      "32       Sulawesi Barat       11140    9389       264\n",
      "33  Nusa Tenggara Timur       56909   50423      1115\n",
      "34            Gorontalo       10849    9212       390\n"
     ]
    }
   ],
   "source": [
    "print(data_provinsi[data_provinsi['provinsi'] == data_provinsi['provinsi'].unique()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "ff307895",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            Aceh  Babel  Bali  Banten  Bengkulu   DIY  Gorontalo  Jabar  \\\n",
      "Date                                                                      \n",
      "2020-03-15     0      0     0       0         0     0          0      0   \n",
      "2020-03-16     0      0     0       1         0     0          0      1   \n",
      "2020-03-17     0      0     0       0         0     0          0      0   \n",
      "2020-03-18     0      0     0       4         0     1          0     12   \n",
      "2020-03-19     0      0     0      10         0     2          0      2   \n",
      "...          ...    ...   ...     ...       ...   ...        ...    ...   \n",
      "2021-08-19   355    452  1032     395       129  1175         99   3266   \n",
      "2021-08-20   459    440  1039     449        98   820        118   2742   \n",
      "2021-08-21   382    393   849     374        71   930         47   1747   \n",
      "2021-08-22   334    301   583     214       113   783         87   1192   \n",
      "2021-08-23   229    191   434     124        55   507         39   1483   \n",
      "\n",
      "            Jakarta  Jambi  ...  Riau  Sulbar  Sulsel  Sulteng  Sultra  Sulut  \\\n",
      "Date                        ...                                                 \n",
      "2020-03-15       19      0  ...     0       0       0        0       0      0   \n",
      "2020-03-16       14      0  ...     0       0       0        0       0      0   \n",
      "2020-03-17        0      0  ...     0       0       0        0       0      0   \n",
      "2020-03-18       30      0  ...     1       0       0        0       0      0   \n",
      "2020-03-19       52      0  ...     1       0       2        0       3      0   \n",
      "...             ...    ...  ...   ...     ...     ...      ...     ...    ...   \n",
      "2021-08-19      731    357  ...   835      99     892      833     156    168   \n",
      "2021-08-20      968    325  ...   764      89     685      595      62    231   \n",
      "2021-08-21      891    321  ...   610     109     551      546      58    215   \n",
      "2021-08-22      702    198  ...   568      83     411      318      53    253   \n",
      "2021-08-23      485     62  ...   357      14     179      331      70     58   \n",
      "\n",
      "            Sumbar  Sumsel  Sumut  Total_Harian  \n",
      "Date                                             \n",
      "2020-03-15       0       0      0            21  \n",
      "2020-03-16       0       0      0            17  \n",
      "2020-03-17       0       0      0             0  \n",
      "2020-03-18       0       0      1            53  \n",
      "2020-03-19       0       0      1            82  \n",
      "...            ...     ...    ...           ...  \n",
      "2021-08-19     291     305   1129         22053  \n",
      "2021-08-20     390     263   1481         22004  \n",
      "2021-08-21     532     236   1189         16744  \n",
      "2021-08-22     330     175   1002         12408  \n",
      "2021-08-23      80     278    514          9604  \n",
      "\n",
      "[527 rows x 35 columns]\n"
     ]
    }
   ],
   "source": [
    "provinsi_selected = data_provinsi[data_provinsi['provinsi'] == data_provinsi['provinsi'].unique()]\n",
    "print(provinsi_select)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "431c56b4",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Boolean array expected for the condition, not object",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-153-451be4fe7b6e>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      7\u001b[0m     })\n\u001b[0;32m      8\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mtotal_dataframe\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 9\u001b[1;33m \u001b[0mtotal_provinsi\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_total_dataframe\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mprovinsi_selected\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     10\u001b[0m \u001b[0mtotal_provinsi\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-153-451be4fe7b6e>\u001b[0m in \u001b[0;36mget_total_dataframe\u001b[1;34m(df)\u001b[0m\n\u001b[0;32m      2\u001b[0m     total_dataframe = pd.DataFrame({\n\u001b[0;32m      3\u001b[0m         \u001b[1;34m'status'\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m'kasus_aktif'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'sembuh'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'meninggal'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m         'jumlah_kasus_per_provinsi': (data_provinsi[data_provinsi[df]]['kasus_aktif'],\n\u001b[0m\u001b[0;32m      5\u001b[0m                                       \u001b[0mdata_provinsi\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mdata_provinsi\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'sembuh'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m                                       data_provinsi[data_provinsi[df]]['meninggal'])\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   3009\u001b[0m         \u001b[1;31m# Do we have a (boolean) DataFrame?\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3010\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3011\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwhere\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3012\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3013\u001b[0m         \u001b[1;31m# Do we have a (boolean) 1d indexer?\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36mwhere\u001b[1;34m(self, cond, other, inplace, axis, level, errors, try_cast)\u001b[0m\n\u001b[0;32m   9284\u001b[0m         \"\"\"\n\u001b[0;32m   9285\u001b[0m         \u001b[0mother\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply_if_callable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mother\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 9286\u001b[1;33m         return self._where(\n\u001b[0m\u001b[0;32m   9287\u001b[0m             \u001b[0mcond\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mother\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minplace\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtry_cast\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtry_cast\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   9288\u001b[0m         )\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m_where\u001b[1;34m(self, cond, other, inplace, axis, level, errors, try_cast)\u001b[0m\n\u001b[0;32m   9041\u001b[0m                 \u001b[1;32mfor\u001b[0m \u001b[0mdt\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mcond\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtypes\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   9042\u001b[0m                     \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mis_bool_dtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdt\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 9043\u001b[1;33m                         \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdt\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   9044\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   9045\u001b[0m             \u001b[1;31m# GH#21947 we have an empty DataFrame/Series, could be object-dtype\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: Boolean array expected for the condition, not object"
     ]
    }
   ],
   "source": [
    "def get_total_dataframe(df):\n",
    "    total_dataframe = pd.DataFrame({\n",
    "        'status': ['kasus_aktif', 'sembuh', 'meninggal'],\n",
    "        'jumlah_kasus_per_provinsi': (data_provinsi[data_provinsi[df]]['kasus_aktif'],\n",
    "                                      data_provinsi[data_provinsi[df]]['sembuh'],\n",
    "                                      data_provinsi[data_provinsi[df]]['meninggal'])\n",
    "    })\n",
    "    return total_dataframe\n",
    "total_provinsi = get_total_dataframe(provinsi_selected)\n",
    "total_provinsi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ad5fcc3d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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