notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Proyek Analis Data : Air Quality Dataset\n",
    "- Nama : Andi Fiqri Aulia Artsam\n",
    "- email : fiqartsam@gmail.com\n",
    "- Id Dicoding : andiartsam"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Menentukkan Pertanyaan Bisnis\n",
    "- Pertanyaan 1 : Bagaimana tingkat kelembapan, suhu, dan curah hujan bulanan di Kawasan Beijing dalam kurun waktu 5 tahun?\n",
    "- Pertanyaan 2 : Stasiun manakah yang menunjukkan tingkat polusi udara yang paling tinggi dalam kurun waktu 5 tahun?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Catatan Kaki\n",
    "\n",
    "| Variabel            | Satuan* |\n",
    "| ------------------- | :-----: |\n",
    "| PM 2.5*             | µg/m³   |\n",
    "| PM 10**             | µg/m³   |\n",
    "| SO<sub>2</sub>      | µg/m³   |\n",
    "| NO<sub>2</sub>      | µg/m³   |\n",
    "| CO                  | mg/m³   |\n",
    "| O<sub>3</sub>       | µg/m³   |\n",
    "| Temperature (TEMP)  | °C      |\n",
    "| Pressure (PRESS)    | hPa     |\n",
    "| Dew point (DEWP)    | °C      |\n",
    "| Rainfall (Rain)     | mm      |\n",
    "| Wind Speed (wspm)   | m/s     |\n",
    "\n",
    "\n",
    "###### *Particulate Matter dengan diameter berkisar 2.5 mm<br>\n",
    "###### **Particulate Matter dengan diameter berkisar 10 mm\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Persiapan Seluruh Library yang diperlukan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates\n",
    "import math"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data Wrangling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Gathering Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mengonversi Data CSV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Konversi seluruh data tipe CSV ke Data Frame\n",
    "\n",
    "aot_df = pd.read_csv('./data/Aotizhongxin.csv', delimiter=',')\n",
    "cha_df = pd.read_csv('./data/Changping.csv', delimiter=',')\n",
    "din_df = pd.read_csv('./data/Dingling.csv', delimiter=',')\n",
    "don_df = pd.read_csv('./data/Dongsi.csv', delimiter=',')\n",
    "gua_df = pd.read_csv('./data/Guanyuan.csv', delimiter=',')\n",
    "guc_df = pd.read_csv('./data/Gucheng.csv', delimiter=',')\n",
    "hua_df = pd.read_csv('./data/Huairou.csv', delimiter=',')\n",
    "non_df = pd.read_csv('./data/Nongzhanguan.csv', delimiter=',')\n",
    "shu_df = pd.read_csv('./data/Shuny.csv', delimiter=',')\n",
    "tia_df = pd.read_csv('./data/Tiantan.csv', delimiter=',')\n",
    "wanl_df = pd.read_csv('./data/Wanliu.csv', delimiter=',')\n",
    "wans_df = pd.read_csv('./data/Wanshouxigong.csv', delimiter=',')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Menggabungkan seluruh dataframe menjadi satu dataframe karena seluruh stasiun berada dalam satu kawasan Beijing, Tiongkok."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>No</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>hour</th>\n",
       "      <th>PM2.5</th>\n",
       "      <th>PM10</th>\n",
       "      <th>SO2</th>\n",
       "      <th>NO2</th>\n",
       "      <th>CO</th>\n",
       "      <th>O3</th>\n",
       "      <th>TEMP</th>\n",
       "      <th>PRES</th>\n",
       "      <th>DEWP</th>\n",
       "      <th>RAIN</th>\n",
       "      <th>wd</th>\n",
       "      <th>WSPM</th>\n",
       "      <th>station</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>1023.0</td>\n",
       "      <td>-18.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NNW</td>\n",
       "      <td>4.4</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>1023.2</td>\n",
       "      <td>-18.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>N</td>\n",
       "      <td>4.7</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>1023.5</td>\n",
       "      <td>-18.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NNW</td>\n",
       "      <td>5.6</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>-1.4</td>\n",
       "      <td>1024.5</td>\n",
       "      <td>-19.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NW</td>\n",
       "      <td>3.1</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>1025.2</td>\n",
       "      <td>-19.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>N</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   No  year  month  day  hour  PM2.5  PM10   SO2   NO2     CO    O3  TEMP  \\\n",
       "0   1  2013      3    1     0    4.0   4.0   4.0   7.0  300.0  77.0  -0.7   \n",
       "1   2  2013      3    1     1    8.0   8.0   4.0   7.0  300.0  77.0  -1.1   \n",
       "2   3  2013      3    1     2    7.0   7.0   5.0  10.0  300.0  73.0  -1.1   \n",
       "3   4  2013      3    1     3    6.0   6.0  11.0  11.0  300.0  72.0  -1.4   \n",
       "4   5  2013      3    1     4    3.0   3.0  12.0  12.0  300.0  72.0  -2.0   \n",
       "\n",
       "     PRES  DEWP  RAIN   wd  WSPM       station  \n",
       "0  1023.0 -18.8   0.0  NNW   4.4  Aotizhongxin  \n",
       "1  1023.2 -18.2   0.0    N   4.7  Aotizhongxin  \n",
       "2  1023.5 -18.2   0.0  NNW   5.6  Aotizhongxin  \n",
       "3  1024.5 -19.4   0.0   NW   3.1  Aotizhongxin  \n",
       "4  1025.2 -19.5   0.0    N   2.0  Aotizhongxin  "
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_df = pd.concat([aot_df, cha_df, din_df, don_df, gua_df, guc_df, hua_df, non_df, shu_df, tia_df, wanl_df, wans_df])\n",
    "all_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>No</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>hour</th>\n",
       "      <th>PM2.5</th>\n",
       "      <th>PM10</th>\n",
       "      <th>SO2</th>\n",
       "      <th>NO2</th>\n",
       "      <th>CO</th>\n",
       "      <th>O3</th>\n",
       "      <th>TEMP</th>\n",
       "      <th>PRES</th>\n",
       "      <th>DEWP</th>\n",
       "      <th>RAIN</th>\n",
       "      <th>wd</th>\n",
       "      <th>WSPM</th>\n",
       "      <th>station</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>35059</th>\n",
       "      <td>35060</td>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>28</td>\n",
       "      <td>19</td>\n",
       "      <td>11.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>12.5</td>\n",
       "      <td>1013.5</td>\n",
       "      <td>-16.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NW</td>\n",
       "      <td>2.4</td>\n",
       "      <td>Wanshouxigong</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35060</th>\n",
       "      <td>35061</td>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>28</td>\n",
       "      <td>20</td>\n",
       "      <td>13.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>11.6</td>\n",
       "      <td>1013.6</td>\n",
       "      <td>-15.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>WNW</td>\n",
       "      <td>0.9</td>\n",
       "      <td>Wanshouxigong</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35061</th>\n",
       "      <td>35062</td>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>28</td>\n",
       "      <td>21</td>\n",
       "      <td>14.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>10.8</td>\n",
       "      <td>1014.2</td>\n",
       "      <td>-13.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NW</td>\n",
       "      <td>1.1</td>\n",
       "      <td>Wanshouxigong</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35062</th>\n",
       "      <td>35063</td>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>28</td>\n",
       "      <td>22</td>\n",
       "      <td>12.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>10.5</td>\n",
       "      <td>1014.4</td>\n",
       "      <td>-12.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NNW</td>\n",
       "      <td>1.2</td>\n",
       "      <td>Wanshouxigong</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35063</th>\n",
       "      <td>35064</td>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>28</td>\n",
       "      <td>23</td>\n",
       "      <td>13.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>600.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>8.6</td>\n",
       "      <td>1014.1</td>\n",
       "      <td>-15.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NNE</td>\n",
       "      <td>1.3</td>\n",
       "      <td>Wanshouxigong</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          No  year  month  day  hour  PM2.5  PM10  SO2   NO2     CO    O3  \\\n",
       "35059  35060  2017      2   28    19   11.0  32.0  3.0  24.0  400.0  72.0   \n",
       "35060  35061  2017      2   28    20   13.0  32.0  3.0  41.0  500.0  50.0   \n",
       "35061  35062  2017      2   28    21   14.0  28.0  4.0  38.0  500.0  54.0   \n",
       "35062  35063  2017      2   28    22   12.0  23.0  4.0  30.0  400.0  59.0   \n",
       "35063  35064  2017      2   28    23   13.0  19.0  4.0  38.0  600.0  49.0   \n",
       "\n",
       "       TEMP    PRES  DEWP  RAIN   wd  WSPM        station  \n",
       "35059  12.5  1013.5 -16.2   0.0   NW   2.4  Wanshouxigong  \n",
       "35060  11.6  1013.6 -15.1   0.0  WNW   0.9  Wanshouxigong  \n",
       "35061  10.8  1014.2 -13.3   0.0   NW   1.1  Wanshouxigong  \n",
       "35062  10.5  1014.4 -12.9   0.0  NNW   1.2  Wanshouxigong  \n",
       "35063   8.6  1014.1 -15.9   0.0  NNE   1.3  Wanshouxigong  "
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data dihimpun dari 1/3/2013 s.d. 28/2/2017"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Assesing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 420768 entries, 0 to 35063\n",
      "Data columns (total 18 columns):\n",
      " #   Column   Non-Null Count   Dtype  \n",
      "---  ------   --------------   -----  \n",
      " 0   No       420768 non-null  int64  \n",
      " 1   year     420768 non-null  int64  \n",
      " 2   month    420768 non-null  int64  \n",
      " 3   day      420768 non-null  int64  \n",
      " 4   hour     420768 non-null  int64  \n",
      " 5   PM2.5    412029 non-null  float64\n",
      " 6   PM10     414319 non-null  float64\n",
      " 7   SO2      411747 non-null  float64\n",
      " 8   NO2      408652 non-null  float64\n",
      " 9   CO       400067 non-null  float64\n",
      " 10  O3       407491 non-null  float64\n",
      " 11  TEMP     420370 non-null  float64\n",
      " 12  PRES     420375 non-null  float64\n",
      " 13  DEWP     420365 non-null  float64\n",
      " 14  RAIN     420378 non-null  float64\n",
      " 15  wd       418946 non-null  object \n",
      " 16  WSPM     420450 non-null  float64\n",
      " 17  station  420768 non-null  object \n",
      "dtypes: float64(11), int64(5), object(2)\n",
      "memory usage: 61.0+ MB\n"
     ]
    },
    {
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       "      <th>PM2.5</th>\n",
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       "      <th>SO2</th>\n",
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       "      <th>CO</th>\n",
       "      <th>O3</th>\n",
       "      <th>TEMP</th>\n",
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       "      <th>DEWP</th>\n",
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       "      <th>wd</th>\n",
       "      <th>WSPM</th>\n",
       "      <th>station</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>420768.000000</td>\n",
       "      <td>420768.000000</td>\n",
       "      <td>420768.000000</td>\n",
       "      <td>420768.000000</td>\n",
       "      <td>420768.000000</td>\n",
       "      <td>412029.000000</td>\n",
       "      <td>414319.000000</td>\n",
       "      <td>411747.000000</td>\n",
       "      <td>408652.000000</td>\n",
       "      <td>400067.000000</td>\n",
       "      <td>407491.000000</td>\n",
       "      <td>420370.000000</td>\n",
       "      <td>420375.000000</td>\n",
       "      <td>420365.000000</td>\n",
       "      <td>420378.000000</td>\n",
       "      <td>418946</td>\n",
       "      <td>420450.000000</td>\n",
       "      <td>420768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\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",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\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",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\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",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>43335</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>17532.500000</td>\n",
       "      <td>2014.662560</td>\n",
       "      <td>6.522930</td>\n",
       "      <td>15.729637</td>\n",
       "      <td>11.500000</td>\n",
       "      <td>79.793428</td>\n",
       "      <td>104.602618</td>\n",
       "      <td>15.830835</td>\n",
       "      <td>50.638586</td>\n",
       "      <td>1230.766454</td>\n",
       "      <td>57.372271</td>\n",
       "      <td>13.538976</td>\n",
       "      <td>1010.746982</td>\n",
       "      <td>2.490822</td>\n",
       "      <td>0.064476</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.729711</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>10122.116943</td>\n",
       "      <td>1.177198</td>\n",
       "      <td>3.448707</td>\n",
       "      <td>8.800102</td>\n",
       "      <td>6.922195</td>\n",
       "      <td>80.822391</td>\n",
       "      <td>91.772426</td>\n",
       "      <td>21.650603</td>\n",
       "      <td>35.127912</td>\n",
       "      <td>1160.182716</td>\n",
       "      <td>56.661607</td>\n",
       "      <td>11.436139</td>\n",
       "      <td>10.474055</td>\n",
       "      <td>13.793847</td>\n",
       "      <td>0.821004</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.246386</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>2013.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.285600</td>\n",
       "      <td>1.026500</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>0.214200</td>\n",
       "      <td>-19.900000</td>\n",
       "      <td>982.400000</td>\n",
       "      <td>-43.400000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>8766.750000</td>\n",
       "      <td>2014.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>5.750000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>3.100000</td>\n",
       "      <td>1002.300000</td>\n",
       "      <td>-8.900000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>17532.500000</td>\n",
       "      <td>2015.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>11.500000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>900.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>14.500000</td>\n",
       "      <td>1010.400000</td>\n",
       "      <td>3.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>26298.250000</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>17.250000</td>\n",
       "      <td>111.000000</td>\n",
       "      <td>145.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>1500.000000</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>23.300000</td>\n",
       "      <td>1019.000000</td>\n",
       "      <td>15.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.200000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>35064.000000</td>\n",
       "      <td>2017.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>290.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>1071.000000</td>\n",
       "      <td>41.600000</td>\n",
       "      <td>1042.800000</td>\n",
       "      <td>29.100000</td>\n",
       "      <td>72.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.200000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   No           year          month            day  \\\n",
       "count   420768.000000  420768.000000  420768.000000  420768.000000   \n",
       "unique            NaN            NaN            NaN            NaN   \n",
       "top               NaN            NaN            NaN            NaN   \n",
       "freq              NaN            NaN            NaN            NaN   \n",
       "mean     17532.500000    2014.662560       6.522930      15.729637   \n",
       "std      10122.116943       1.177198       3.448707       8.800102   \n",
       "min          1.000000    2013.000000       1.000000       1.000000   \n",
       "25%       8766.750000    2014.000000       4.000000       8.000000   \n",
       "50%      17532.500000    2015.000000       7.000000      16.000000   \n",
       "75%      26298.250000    2016.000000      10.000000      23.000000   \n",
       "max      35064.000000    2017.000000      12.000000      31.000000   \n",
       "\n",
       "                 hour          PM2.5           PM10            SO2  \\\n",
       "count   420768.000000  412029.000000  414319.000000  411747.000000   \n",
       "unique            NaN            NaN            NaN            NaN   \n",
       "top               NaN            NaN            NaN            NaN   \n",
       "freq              NaN            NaN            NaN            NaN   \n",
       "mean        11.500000      79.793428     104.602618      15.830835   \n",
       "std          6.922195      80.822391      91.772426      21.650603   \n",
       "min          0.000000       2.000000       2.000000       0.285600   \n",
       "25%          5.750000      20.000000      36.000000       3.000000   \n",
       "50%         11.500000      55.000000      82.000000       7.000000   \n",
       "75%         17.250000     111.000000     145.000000      20.000000   \n",
       "max         23.000000     999.000000     999.000000     500.000000   \n",
       "\n",
       "                  NO2             CO             O3           TEMP  \\\n",
       "count   408652.000000  400067.000000  407491.000000  420370.000000   \n",
       "unique            NaN            NaN            NaN            NaN   \n",
       "top               NaN            NaN            NaN            NaN   \n",
       "freq              NaN            NaN            NaN            NaN   \n",
       "mean        50.638586    1230.766454      57.372271      13.538976   \n",
       "std         35.127912    1160.182716      56.661607      11.436139   \n",
       "min          1.026500     100.000000       0.214200     -19.900000   \n",
       "25%         23.000000     500.000000      11.000000       3.100000   \n",
       "50%         43.000000     900.000000      45.000000      14.500000   \n",
       "75%         71.000000    1500.000000      82.000000      23.300000   \n",
       "max        290.000000   10000.000000    1071.000000      41.600000   \n",
       "\n",
       "                 PRES           DEWP           RAIN      wd           WSPM  \\\n",
       "count   420375.000000  420365.000000  420378.000000  418946  420450.000000   \n",
       "unique            NaN            NaN            NaN      16            NaN   \n",
       "top               NaN            NaN            NaN      NE            NaN   \n",
       "freq              NaN            NaN            NaN   43335            NaN   \n",
       "mean      1010.746982       2.490822       0.064476     NaN       1.729711   \n",
       "std         10.474055      13.793847       0.821004     NaN       1.246386   \n",
       "min        982.400000     -43.400000       0.000000     NaN       0.000000   \n",
       "25%       1002.300000      -8.900000       0.000000     NaN       0.900000   \n",
       "50%       1010.400000       3.100000       0.000000     NaN       1.400000   \n",
       "75%       1019.000000      15.100000       0.000000     NaN       2.200000   \n",
       "max       1042.800000      29.100000      72.500000     NaN      13.200000   \n",
       "\n",
       "             station  \n",
       "count         420768  \n",
       "unique            12  \n",
       "top     Aotizhongxin  \n",
       "freq           35064  \n",
       "mean             NaN  \n",
       "std              NaN  \n",
       "min              NaN  \n",
       "25%              NaN  \n",
       "50%              NaN  \n",
       "75%              NaN  \n",
       "max              NaN  "
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_df.info()\n",
    "all_df.describe(include='all')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pemeriksaan kelengkapan data harian 24 jam tiap stasiun"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Jumlah data seharusnya: 35064\n"
     ]
    }
   ],
   "source": [
    "# Data ekspektasi yang dihimpun dimulai 2013/3/1 s.d. 2017/2/28 dengan bantuan library pandas\n",
    "isExpected_dates = pd.date_range(start='2013-03-01', end='2017-02-28 23:00:00', freq='H') #freq ='H' -> dalam 24 jam\n",
    "print('Jumlah data seharusnya:', len(isExpected_dates))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Selisih data: 0.0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "station\n",
       "Aotizhongxin     35064\n",
       "Changping        35064\n",
       "Dingling         35064\n",
       "Dongsi           35064\n",
       "Guanyuan         35064\n",
       "Gucheng          35064\n",
       "Huairou          35064\n",
       "Nongzhanguan     35064\n",
       "Shunyi           35064\n",
       "Tiantan          35064\n",
       "Wanliu           35064\n",
       "Wanshouxigong    35064\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Data aktual yang dihimpun oleh tiap stasiun\n",
    "difference_dates = all_df['station'].value_counts().mean() - len(isExpected_dates)\n",
    "print('Selisih data:', difference_dates)\n",
    "all_df['station'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pengecekan data duplikat, *missing value*, *innacurate*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Jumlah Duplikasi: 0\n",
      "Jumlah Missing Value:\n",
      "No             0\n",
      "year           0\n",
      "month          0\n",
      "day            0\n",
      "hour           0\n",
      "PM2.5       8739\n",
      "PM10        6449\n",
      "SO2         9021\n",
      "NO2        12116\n",
      "CO         20701\n",
      "O3         13277\n",
      "TEMP         398\n",
      "PRES         393\n",
      "DEWP         403\n",
      "RAIN         390\n",
      "wd          1822\n",
      "WSPM         318\n",
      "station        0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(\"Jumlah Duplikasi:\", all_df.duplicated().sum())\n",
    "print(f'Jumlah Missing Value:\\n{all_df.isna().sum()}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "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>No</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>hour</th>\n",
       "      <th>PM2.5</th>\n",
       "      <th>PM10</th>\n",
       "      <th>SO2</th>\n",
       "      <th>NO2</th>\n",
       "      <th>CO</th>\n",
       "      <th>O3</th>\n",
       "      <th>TEMP</th>\n",
       "      <th>PRES</th>\n",
       "      <th>DEWP</th>\n",
       "      <th>RAIN</th>\n",
       "      <th>wd</th>\n",
       "      <th>WSPM</th>\n",
       "      <th>station</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>75</td>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>34.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.0000</td>\n",
       "      <td>300.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>8.1</td>\n",
       "      <td>1016.7</td>\n",
       "      <td>-11.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>N</td>\n",
       "      <td>4.3</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>76</td>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>12.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>6.0000</td>\n",
       "      <td>12.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>77.0</td>\n",
       "      <td>7.2</td>\n",
       "      <td>1016.9</td>\n",
       "      <td>-11.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>N</td>\n",
       "      <td>2.8</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>77</td>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>7.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>14.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>400.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1018.0</td>\n",
       "      <td>-11.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NNW</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>125</td>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>192.0</td>\n",
       "      <td>203.0</td>\n",
       "      <td>83.6808</td>\n",
       "      <td>140.4252</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.0</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1007.3</td>\n",
       "      <td>-6.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NE</td>\n",
       "      <td>1.4</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>171</td>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>339.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>162.0000</td>\n",
       "      <td>3899.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>998.2</td>\n",
       "      <td>-1.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>SW</td>\n",
       "      <td>0.8</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35028</th>\n",
       "      <td>35029</td>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>27</td>\n",
       "      <td>12</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>12.1</td>\n",
       "      <td>1017.3</td>\n",
       "      <td>-5.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>SSW</td>\n",
       "      <td>1.2</td>\n",
       "      <td>Wanshouxigong</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35029</th>\n",
       "      <td>35030</td>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>27</td>\n",
       "      <td>13</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>14.0</td>\n",
       "      <td>1015.8</td>\n",
       "      <td>-5.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>SSE</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Wanshouxigong</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35054</th>\n",
       "      <td>35055</td>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>28</td>\n",
       "      <td>14</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>82.0</td>\n",
       "      <td>14.6</td>\n",
       "      <td>1013.3</td>\n",
       "      <td>-15.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>N</td>\n",
       "      <td>3.6</td>\n",
       "      <td>Wanshouxigong</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35055</th>\n",
       "      <td>35056</td>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>28</td>\n",
       "      <td>15</td>\n",
       "      <td>11.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>200.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.4</td>\n",
       "      <td>1013.0</td>\n",
       "      <td>-15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NNW</td>\n",
       "      <td>3.3</td>\n",
       "      <td>Wanshouxigong</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35056</th>\n",
       "      <td>35057</td>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>28</td>\n",
       "      <td>16</td>\n",
       "      <td>6.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>14.9</td>\n",
       "      <td>1012.6</td>\n",
       "      <td>-15.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NW</td>\n",
       "      <td>2.1</td>\n",
       "      <td>Wanshouxigong</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>38600 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          No  year  month  day  hour  PM2.5   PM10      SO2       NO2      CO  \\\n",
       "74        75  2013      3    4     2   34.0   62.0      NaN   14.0000   300.0   \n",
       "75        76  2013      3    4     3   12.0   34.0   6.0000   12.0000     NaN   \n",
       "76        77  2013      3    4     4    7.0   18.0  14.0000       NaN   400.0   \n",
       "124      125  2013      3    6     4  192.0  203.0  83.6808  140.4252     NaN   \n",
       "170      171  2013      3    8     2  339.0  400.0      NaN  162.0000  3899.0   \n",
       "...      ...   ...    ...  ...   ...    ...    ...      ...       ...     ...   \n",
       "35028  35029  2017      2   27    12    NaN    NaN      NaN       NaN     NaN   \n",
       "35029  35030  2017      2   27    13    NaN    NaN      NaN       NaN     NaN   \n",
       "35054  35055  2017      2   28    14    3.0    6.0      NaN    5.0000     NaN   \n",
       "35055  35056  2017      2   28    15   11.0   21.0   2.0000    5.0000   200.0   \n",
       "35056  35057  2017      2   28    16    6.0   20.0   3.0000       NaN   200.0   \n",
       "\n",
       "         O3  TEMP    PRES  DEWP  RAIN   wd  WSPM        station  \n",
       "74     68.0   8.1  1016.7 -11.8   0.0    N   4.3   Aotizhongxin  \n",
       "75     77.0   7.2  1016.9 -11.6   0.0    N   2.8   Aotizhongxin  \n",
       "76     42.0   6.0  1018.0 -11.6   0.0  NNW   1.0   Aotizhongxin  \n",
       "124    22.0   3.9  1007.3  -6.1   0.0   NE   1.4   Aotizhongxin  \n",
       "170    84.0   2.5   998.2  -1.9   0.0   SW   0.8   Aotizhongxin  \n",
       "...     ...   ...     ...   ...   ...  ...   ...            ...  \n",
       "35028   NaN  12.1  1017.3  -5.4   0.0  SSW   1.2  Wanshouxigong  \n",
       "35029   NaN  14.0  1015.8  -5.2   0.0  SSE   1.8  Wanshouxigong  \n",
       "35054  82.0  14.6  1013.3 -15.6   0.0    N   3.6  Wanshouxigong  \n",
       "35055   NaN  15.4  1013.0 -15.0   0.0  NNW   3.3  Wanshouxigong  \n",
       "35056  80.0  14.9  1012.6 -15.4   0.0   NW   2.1  Wanshouxigong  \n",
       "\n",
       "[38600 rows x 18 columns]"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_df[all_df.isna().any(axis=1)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cleaning Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Menghapus kolom **No** karena sudah terwakili oleh index, serta menghapus kolom **PRES**, **wd**, dan **WSPM** karena tidak diperlukan pada pertanyaan bisnis yang diajukan."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>hour</th>\n",
       "      <th>PM2.5</th>\n",
       "      <th>PM10</th>\n",
       "      <th>SO2</th>\n",
       "      <th>NO2</th>\n",
       "      <th>CO</th>\n",
       "      <th>O3</th>\n",
       "      <th>TEMP</th>\n",
       "      <th>DEWP</th>\n",
       "      <th>RAIN</th>\n",
       "      <th>station</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>-18.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>-18.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>-18.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>-1.4</td>\n",
       "      <td>-19.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>-19.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  month  day  hour  PM2.5  PM10   SO2   NO2     CO    O3  TEMP  DEWP  \\\n",
       "0  2013      3    1     0    4.0   4.0   4.0   7.0  300.0  77.0  -0.7 -18.8   \n",
       "1  2013      3    1     1    8.0   8.0   4.0   7.0  300.0  77.0  -1.1 -18.2   \n",
       "2  2013      3    1     2    7.0   7.0   5.0  10.0  300.0  73.0  -1.1 -18.2   \n",
       "3  2013      3    1     3    6.0   6.0  11.0  11.0  300.0  72.0  -1.4 -19.4   \n",
       "4  2013      3    1     4    3.0   3.0  12.0  12.0  300.0  72.0  -2.0 -19.5   \n",
       "\n",
       "   RAIN       station  \n",
       "0   0.0  Aotizhongxin  \n",
       "1   0.0  Aotizhongxin  \n",
       "2   0.0  Aotizhongxin  \n",
       "3   0.0  Aotizhongxin  \n",
       "4   0.0  Aotizhongxin  "
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_df.drop(columns= ['No', 'PRES', 'wd', 'WSPM'], inplace=True)\n",
    "all_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mengonversi kolom **year**, **month**, **day**, dan **hour** menjadi tipe data datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DateTime</th>\n",
       "      <th>PM2.5</th>\n",
       "      <th>PM10</th>\n",
       "      <th>SO2</th>\n",
       "      <th>NO2</th>\n",
       "      <th>CO</th>\n",
       "      <th>O3</th>\n",
       "      <th>TEMP</th>\n",
       "      <th>DEWP</th>\n",
       "      <th>RAIN</th>\n",
       "      <th>station</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013-03-01 00:00:00</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>-18.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013-03-01 01:00:00</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>-18.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013-03-01 02:00:00</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>-18.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013-03-01 03:00:00</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>-1.4</td>\n",
       "      <td>-19.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013-03-01 04:00:00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>-19.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             DateTime  PM2.5  PM10   SO2   NO2     CO    O3  TEMP  DEWP  RAIN  \\\n",
       "0 2013-03-01 00:00:00    4.0   4.0   4.0   7.0  300.0  77.0  -0.7 -18.8   0.0   \n",
       "1 2013-03-01 01:00:00    8.0   8.0   4.0   7.0  300.0  77.0  -1.1 -18.2   0.0   \n",
       "2 2013-03-01 02:00:00    7.0   7.0   5.0  10.0  300.0  73.0  -1.1 -18.2   0.0   \n",
       "3 2013-03-01 03:00:00    6.0   6.0  11.0  11.0  300.0  72.0  -1.4 -19.4   0.0   \n",
       "4 2013-03-01 04:00:00    3.0   3.0  12.0  12.0  300.0  72.0  -2.0 -19.5   0.0   \n",
       "\n",
       "        station  \n",
       "0  Aotizhongxin  \n",
       "1  Aotizhongxin  \n",
       "2  Aotizhongxin  \n",
       "3  Aotizhongxin  \n",
       "4  Aotizhongxin  "
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datetimeCol = ['year', 'month', 'day', 'hour']\n",
    "\n",
    "all_df['DateTime'] = pd.to_datetime(all_df[datetimeCol])\n",
    "all_df.insert(0, 'DateTime', all_df.pop('DateTime')) # Mengubah posisi kolom DateTime berada di paling kiri\n",
    "all_df.drop(columns= datetimeCol, inplace=True) # Menghapus kolom-kolom yang berkenaan waktu\n",
    "all_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mengatasi *Missing Value* dengan *Interpolation* karena *date series* yang digunakan memiliki data lengkap 24 jam."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DateTime        0\n",
       "PM2.5        8739\n",
       "PM10         6449\n",
       "SO2          9021\n",
       "NO2         12116\n",
       "CO          20701\n",
       "O3          13277\n",
       "TEMP          398\n",
       "DEWP          403\n",
       "RAIN          390\n",
       "station         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Metode interpolation yang digunakan ialah *time* dengan tujuan untuk mengisi nilai-nilai yang hilang antara dua titik waktu  alam suatu deret waktu."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\user1\\AppData\\Local\\Temp\\ipykernel_1868\\3476750346.py:6: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead.\n",
      "  interpolated_group = group.interpolate(method='time')\n",
      "C:\\Users\\user1\\AppData\\Local\\Temp\\ipykernel_1868\\3476750346.py:6: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead.\n",
      "  interpolated_group = group.interpolate(method='time')\n",
      "C:\\Users\\user1\\AppData\\Local\\Temp\\ipykernel_1868\\3476750346.py:6: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead.\n",
      "  interpolated_group = group.interpolate(method='time')\n",
      "C:\\Users\\user1\\AppData\\Local\\Temp\\ipykernel_1868\\3476750346.py:6: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead.\n",
      "  interpolated_group = group.interpolate(method='time')\n",
      "C:\\Users\\user1\\AppData\\Local\\Temp\\ipykernel_1868\\3476750346.py:6: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead.\n",
      "  interpolated_group = group.interpolate(method='time')\n",
      "C:\\Users\\user1\\AppData\\Local\\Temp\\ipykernel_1868\\3476750346.py:6: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead.\n",
      "  interpolated_group = group.interpolate(method='time')\n",
      "C:\\Users\\user1\\AppData\\Local\\Temp\\ipykernel_1868\\3476750346.py:6: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead.\n",
      "  interpolated_group = group.interpolate(method='time')\n",
      "C:\\Users\\user1\\AppData\\Local\\Temp\\ipykernel_1868\\3476750346.py:6: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead.\n",
      "  interpolated_group = group.interpolate(method='time')\n",
      "C:\\Users\\user1\\AppData\\Local\\Temp\\ipykernel_1868\\3476750346.py:6: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead.\n",
      "  interpolated_group = group.interpolate(method='time')\n",
      "C:\\Users\\user1\\AppData\\Local\\Temp\\ipykernel_1868\\3476750346.py:6: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead.\n",
      "  interpolated_group = group.interpolate(method='time')\n",
      "C:\\Users\\user1\\AppData\\Local\\Temp\\ipykernel_1868\\3476750346.py:6: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead.\n",
      "  interpolated_group = group.interpolate(method='time')\n",
      "C:\\Users\\user1\\AppData\\Local\\Temp\\ipykernel_1868\\3476750346.py:6: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead.\n",
      "  interpolated_group = group.interpolate(method='time')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "PM2.5       0\n",
       "PM10        0\n",
       "SO2         0\n",
       "NO2        22\n",
       "CO          0\n",
       "O3          0\n",
       "TEMP        0\n",
       "DEWP        0\n",
       "RAIN        0\n",
       "station     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_df.set_index('DateTime', inplace=True) # index menyesuaikan urutan kolom 'DateTime' supaya interpolasi dapat dilakukan\n",
    "bystation_df = all_df.groupby(by='station') # Melakukan interpolasi berdasarkan stasiun\n",
    "interpolated_data = pd.DataFrame()\n",
    "\n",
    "for name, group in bystation_df:\n",
    "    interpolated_group = group.interpolate(method='time')\n",
    "    interpolated_data = pd.concat([interpolated_data, interpolated_group])\n",
    "\n",
    "all_df = interpolated_data\n",
    "all_df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PM2.5</th>\n",
       "      <th>PM10</th>\n",
       "      <th>SO2</th>\n",
       "      <th>NO2</th>\n",
       "      <th>CO</th>\n",
       "      <th>O3</th>\n",
       "      <th>TEMP</th>\n",
       "      <th>DEWP</th>\n",
       "      <th>RAIN</th>\n",
       "      <th>station</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DateTime</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>2013-03-01 00:00:00</th>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>-2.3</td>\n",
       "      <td>-19.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Dingling</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01 01:00:00</th>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>-2.5</td>\n",
       "      <td>-19.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Dingling</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01 00:00:00</th>\n",
       "      <td>6.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>800.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>-18.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Gucheng</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01 01:00:00</th>\n",
       "      <td>6.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>800.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>-19.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Gucheng</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01 02:00:00</th>\n",
       "      <td>5.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>5.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>700.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>-19.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Gucheng</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01 03:00:00</th>\n",
       "      <td>6.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>650.0</td>\n",
       "      <td>62.5</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-21.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Gucheng</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01 04:00:00</th>\n",
       "      <td>5.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>600.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>-1.3</td>\n",
       "      <td>-21.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Gucheng</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01 05:00:00</th>\n",
       "      <td>4.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>700.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>-1.8</td>\n",
       "      <td>-21.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Gucheng</td>\n",
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       "    <tr>\n",
       "      <th>2013-03-01 06:00:00</th>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>700.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>-2.6</td>\n",
       "      <td>-20.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Gucheng</td>\n",
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       "    <tr>\n",
       "      <th>2013-03-01 07:00:00</th>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>-0.9</td>\n",
       "      <td>-20.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Gucheng</td>\n",
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       "    <tr>\n",
       "      <th>2013-03-01 08:00:00</th>\n",
       "      <td>5.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>900.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>-20.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Gucheng</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01 09:00:00</th>\n",
       "      <td>4.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>900.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>-20.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Gucheng</td>\n",
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       "    <tr>\n",
       "      <th>2013-03-01 10:00:00</th>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>900.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>-21.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Gucheng</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01 11:00:00</th>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>800.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>3.3</td>\n",
       "      <td>-20.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Gucheng</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01 12:00:00</th>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>800.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>4.1</td>\n",
       "      <td>-20.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Gucheng</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01 13:00:00</th>\n",
       "      <td>10.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>900.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>5.3</td>\n",
       "      <td>-20.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Gucheng</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01 14:00:00</th>\n",
       "      <td>13.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>800.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>5.5</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Gucheng</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01 15:00:00</th>\n",
       "      <td>13.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>800.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>6.3</td>\n",
       "      <td>-19.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Gucheng</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01 16:00:00</th>\n",
       "      <td>11.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>800.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>5.9</td>\n",
       "      <td>-18.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Gucheng</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01 17:00:00</th>\n",
       "      <td>10.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>800.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>-18.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Gucheng</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01 18:00:00</th>\n",
       "      <td>11.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>900.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>3.7</td>\n",
       "      <td>-19.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Gucheng</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01 19:00:00</th>\n",
       "      <td>11.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>2.7</td>\n",
       "      <td>-19.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Gucheng</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     PM2.5  PM10  SO2  NO2      CO    O3  TEMP  DEWP  RAIN  \\\n",
       "DateTime                                                                     \n",
       "2013-03-01 00:00:00    4.0   4.0  3.0  NaN   200.0  82.0  -2.3 -19.7   0.0   \n",
       "2013-03-01 01:00:00    7.0   7.0  3.0  NaN   200.0  80.0  -2.5 -19.0   0.0   \n",
       "2013-03-01 00:00:00    6.0  18.0  5.0  NaN   800.0  88.0   0.1 -18.6   0.0   \n",
       "2013-03-01 01:00:00    6.0  15.0  5.0  NaN   800.0  88.0  -0.3 -19.0   0.0   \n",
       "2013-03-01 02:00:00    5.0  18.0  5.5  NaN   700.0  52.0  -0.7 -19.8   0.0   \n",
       "2013-03-01 03:00:00    6.0  20.0  6.0  NaN   650.0  62.5  -1.0 -21.2   0.0   \n",
       "2013-03-01 04:00:00    5.0  17.0  5.0  NaN   600.0  73.0  -1.3 -21.4   0.0   \n",
       "2013-03-01 05:00:00    4.0  11.0  3.0  NaN   700.0  87.0  -1.8 -21.9   0.0   \n",
       "2013-03-01 06:00:00    3.0   6.0  3.0  NaN   700.0  92.0  -2.6 -20.4   0.0   \n",
       "2013-03-01 07:00:00    5.0   5.0  3.0  NaN   800.0  86.0  -0.9 -20.5   0.0   \n",
       "2013-03-01 08:00:00    5.0   9.0  5.0  NaN   900.0  81.0   0.1 -20.3   0.0   \n",
       "2013-03-01 09:00:00    4.0  10.0  6.0  NaN   900.0  82.0   1.1 -20.6   0.0   \n",
       "2013-03-01 10:00:00    7.0   8.0  8.0  NaN   900.0  82.0   1.9 -21.3   0.0   \n",
       "2013-03-01 11:00:00    3.0   6.0  8.0  NaN   800.0  85.0   3.3 -20.9   0.0   \n",
       "2013-03-01 12:00:00    9.0  10.0  8.0  NaN   800.0  86.0   4.1 -20.3   0.0   \n",
       "2013-03-01 13:00:00   10.0  19.0  9.0  NaN   900.0  86.0   5.3 -20.1   0.0   \n",
       "2013-03-01 14:00:00   13.0  20.0  8.0  NaN   800.0  88.0   5.5 -20.0   0.0   \n",
       "2013-03-01 15:00:00   13.0  16.0  8.0  NaN   800.0  90.0   6.3 -19.3   0.0   \n",
       "2013-03-01 16:00:00   11.0  22.0  6.0  NaN   800.0  90.0   5.9 -18.8   0.0   \n",
       "2013-03-01 17:00:00   10.0  20.0  4.0  NaN   800.0  87.0   5.1 -18.7   0.0   \n",
       "2013-03-01 18:00:00   11.0  20.0  6.0  NaN   900.0  80.0   3.7 -19.2   0.0   \n",
       "2013-03-01 19:00:00   11.0  31.0  8.0  NaN  1000.0  69.0   2.7 -19.3   0.0   \n",
       "\n",
       "                      station  \n",
       "DateTime                       \n",
       "2013-03-01 00:00:00  Dingling  \n",
       "2013-03-01 01:00:00  Dingling  \n",
       "2013-03-01 00:00:00   Gucheng  \n",
       "2013-03-01 01:00:00   Gucheng  \n",
       "2013-03-01 02:00:00   Gucheng  \n",
       "2013-03-01 03:00:00   Gucheng  \n",
       "2013-03-01 04:00:00   Gucheng  \n",
       "2013-03-01 05:00:00   Gucheng  \n",
       "2013-03-01 06:00:00   Gucheng  \n",
       "2013-03-01 07:00:00   Gucheng  \n",
       "2013-03-01 08:00:00   Gucheng  \n",
       "2013-03-01 09:00:00   Gucheng  \n",
       "2013-03-01 10:00:00   Gucheng  \n",
       "2013-03-01 11:00:00   Gucheng  \n",
       "2013-03-01 12:00:00   Gucheng  \n",
       "2013-03-01 13:00:00   Gucheng  \n",
       "2013-03-01 14:00:00   Gucheng  \n",
       "2013-03-01 15:00:00   Gucheng  \n",
       "2013-03-01 16:00:00   Gucheng  \n",
       "2013-03-01 17:00:00   Gucheng  \n",
       "2013-03-01 18:00:00   Gucheng  \n",
       "2013-03-01 19:00:00   Gucheng  "
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_df[all_df.isna().any(axis=1)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sebagaimana diketahui di atas, ternyata kolom **NO2** masih terdapat *Missing Value*, yakni pada stasiun Dingling dan Gucheng. Hal ini mungkin disebabkan adanya kelangkaan data pada suatu waktu dan/atau tidak adanya dua titik baris data diantara data hilang yang mana menyebabkan interpolasi tidak dapat dilakukan. Demikian, perlu dilakukan pemeriksaan pada waktu 24 jam di hari itu, yakni 2013/3/1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PM2.5</th>\n",
       "      <th>PM10</th>\n",
       "      <th>SO2</th>\n",
       "      <th>NO2</th>\n",
       "      <th>CO</th>\n",
       "      <th>O3</th>\n",
       "      <th>TEMP</th>\n",
       "      <th>DEWP</th>\n",
       "      <th>RAIN</th>\n",
       "      <th>station</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DateTime</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>2013-03-01 00:00:00</th>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>-2.3</td>\n",
       "      <td>-19.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Dingling</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01 01:00:00</th>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>-2.5</td>\n",
       "      <td>-19.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Dingling</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01 02:00:00</th>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>200.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>-19.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Dingling</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01 03:00:00</th>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.666667</td>\n",
       "      <td>200.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>-3.6</td>\n",
       "      <td>-19.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Dingling</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01 04:00:00</th>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.333333</td>\n",
       "      <td>200.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>-3.5</td>\n",
       "      <td>-19.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Dingling</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01 05:00:00</th>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>200.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>-4.5</td>\n",
       "      <td>-19.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Dingling</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01 06:00:00</th>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>200.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>-4.5</td>\n",
       "      <td>-19.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Dingling</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01 07:00:00</th>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>200.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>-2.1</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Dingling</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01 08:00:00</th>\n",
       "      <td>8.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>200.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>-20.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Dingling</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01 09:00:00</th>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>200.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>-20.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Dingling</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01 10:00:00</th>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>200.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-21.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Dingling</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01 11:00:00</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>200.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>-20.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Dingling</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01 12:00:00</th>\n",
       "      <td>6.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>200.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>-19.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Dingling</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01 13:00:00</th>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>200.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>-20.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Dingling</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01 14:00:00</th>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>200.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>5.9</td>\n",
       "      <td>-20.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Dingling</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01 15:00:00</th>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>200.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>-20.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Dingling</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01 16:00:00</th>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>200.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>5.4</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Dingling</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01 17:00:00</th>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>200.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>-20.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Dingling</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01 18:00:00</th>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>200.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>2.7</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Dingling</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01 19:00:00</th>\n",
       "      <td>14.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>200.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-19.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Dingling</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01 20:00:00</th>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>200.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>-18.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Dingling</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01 21:00:00</th>\n",
       "      <td>9.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>200.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>-17.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Dingling</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01 22:00:00</th>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>200.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>-17.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Dingling</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01 23:00:00</th>\n",
       "      <td>6.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>300.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>-16.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Dingling</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     PM2.5  PM10  SO2       NO2     CO    O3  TEMP  DEWP  \\\n",
       "DateTime                                                                   \n",
       "2013-03-01 00:00:00    4.0   4.0  3.0       NaN  200.0  82.0  -2.3 -19.7   \n",
       "2013-03-01 01:00:00    7.0   7.0  3.0       NaN  200.0  80.0  -2.5 -19.0   \n",
       "2013-03-01 02:00:00    5.0   5.0  3.0  2.000000  200.0  79.0  -3.0 -19.9   \n",
       "2013-03-01 03:00:00    6.0   6.0  3.0  2.666667  200.0  79.0  -3.6 -19.1   \n",
       "2013-03-01 04:00:00    5.0   5.0  3.0  3.333333  200.0  81.0  -3.5 -19.4   \n",
       "2013-03-01 05:00:00    6.0   6.0  3.0  4.000000  200.0  79.0  -4.5 -19.5   \n",
       "2013-03-01 06:00:00    5.0  10.0  3.0  4.000000  200.0  77.0  -4.5 -19.5   \n",
       "2013-03-01 07:00:00    5.0   6.0  3.0  2.000000  200.0  80.0  -2.1 -20.0   \n",
       "2013-03-01 08:00:00    8.0   7.0  3.0  3.000000  200.0  79.0  -0.2 -20.5   \n",
       "2013-03-01 09:00:00    8.0   8.0  3.0  2.000000  200.0  81.0   0.6 -20.4   \n",
       "2013-03-01 10:00:00    8.0   5.0  3.0  2.000000  200.0  83.0   2.0 -21.3   \n",
       "2013-03-01 11:00:00    3.0   2.0  3.0  2.000000  200.0  83.0   3.6 -20.7   \n",
       "2013-03-01 12:00:00    6.0   4.5  3.0  2.000000  200.0  85.0   4.8 -19.7   \n",
       "2013-03-01 13:00:00    7.0   7.0  3.0  2.000000  200.0  84.0   5.8 -20.6   \n",
       "2013-03-01 14:00:00    6.0   7.0  3.0  2.000000  200.0  89.0   5.9 -20.5   \n",
       "2013-03-01 15:00:00   10.0  10.0  3.0  2.000000  200.0  88.0   6.0 -20.4   \n",
       "2013-03-01 16:00:00    3.0   6.0  3.0  2.000000  200.0  87.0   5.4 -20.0   \n",
       "2013-03-01 17:00:00    5.0   5.0  3.0  2.000000  200.0  87.0   3.5 -20.8   \n",
       "2013-03-01 18:00:00    5.0   6.0  3.0  2.000000  200.0  85.0   2.7 -20.0   \n",
       "2013-03-01 19:00:00   14.0   6.0  3.0  2.000000  200.0  83.0   2.0 -19.3   \n",
       "2013-03-01 20:00:00    6.0   7.0  3.0  2.000000  200.0  83.0   1.1 -18.3   \n",
       "2013-03-01 21:00:00    9.0   7.0  3.0  2.000000  200.0  82.0   1.6 -17.4   \n",
       "2013-03-01 22:00:00    6.0   5.0  3.0  2.000000  200.0  79.0   0.4 -17.4   \n",
       "2013-03-01 23:00:00    6.0   9.0  3.0  2.000000  300.0  72.0   0.3 -16.6   \n",
       "\n",
       "                     RAIN   station  \n",
       "DateTime                             \n",
       "2013-03-01 00:00:00   0.0  Dingling  \n",
       "2013-03-01 01:00:00   0.0  Dingling  \n",
       "2013-03-01 02:00:00   0.0  Dingling  \n",
       "2013-03-01 03:00:00   0.0  Dingling  \n",
       "2013-03-01 04:00:00   0.0  Dingling  \n",
       "2013-03-01 05:00:00   0.0  Dingling  \n",
       "2013-03-01 06:00:00   0.0  Dingling  \n",
       "2013-03-01 07:00:00   0.0  Dingling  \n",
       "2013-03-01 08:00:00   0.0  Dingling  \n",
       "2013-03-01 09:00:00   0.0  Dingling  \n",
       "2013-03-01 10:00:00   0.0  Dingling  \n",
       "2013-03-01 11:00:00   0.0  Dingling  \n",
       "2013-03-01 12:00:00   0.0  Dingling  \n",
       "2013-03-01 13:00:00   0.0  Dingling  \n",
       "2013-03-01 14:00:00   0.0  Dingling  \n",
       "2013-03-01 15:00:00   0.0  Dingling  \n",
       "2013-03-01 16:00:00   0.0  Dingling  \n",
       "2013-03-01 17:00:00   0.0  Dingling  \n",
       "2013-03-01 18:00:00   0.0  Dingling  \n",
       "2013-03-01 19:00:00   0.0  Dingling  \n",
       "2013-03-01 20:00:00   0.0  Dingling  \n",
       "2013-03-01 21:00:00   0.0  Dingling  \n",
       "2013-03-01 22:00:00   0.0  Dingling  \n",
       "2013-03-01 23:00:00   0.0  Dingling  "
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dingling\n",
    "thatday = ('2013-03-01 00:00:00' <= all_df.index) & (all_df.index <= '2013-03-01 23:59:59')\n",
    "Dingling_in_thatday_df = all_df.loc[ thatday & (all_df['station'] == 'Dingling')]\n",
    "Dingling_in_thatday_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PM2.5</th>\n",
       "      <th>PM10</th>\n",
       "      <th>SO2</th>\n",
       "      <th>NO2</th>\n",
       "      <th>CO</th>\n",
       "      <th>O3</th>\n",
       "      <th>TEMP</th>\n",
       "      <th>DEWP</th>\n",
       "      <th>RAIN</th>\n",
       "      <th>station</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DateTime</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>2013-03-01 00:00:00</th>\n",
       "      <td>6.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>800.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>-18.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Gucheng</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01 01:00:00</th>\n",
       "      <td>6.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>800.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>-19.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Gucheng</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01 02:00:00</th>\n",
       "      <td>5.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>5.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>700.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>-19.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Gucheng</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01 03:00:00</th>\n",
       "      <td>6.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>650.0</td>\n",
       "      <td>62.5</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-21.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Gucheng</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01 04:00:00</th>\n",
       "      <td>5.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>600.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>-1.3</td>\n",
       "      <td>-21.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Gucheng</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01 05:00:00</th>\n",
       "      <td>4.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>700.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>-1.8</td>\n",
       "      <td>-21.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Gucheng</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01 06:00:00</th>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>700.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>-2.6</td>\n",
       "      <td>-20.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Gucheng</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01 07:00:00</th>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>800.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>-0.9</td>\n",
       "      <td>-20.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Gucheng</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01 08:00:00</th>\n",
       "      <td>5.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>900.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>-20.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Gucheng</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01 09:00:00</th>\n",
       "      <td>4.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>900.0</td>\n",
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       "      <td>1.1</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>Gucheng</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01 10:00:00</th>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>900.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>-21.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Gucheng</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01 11:00:00</th>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>800.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>3.3</td>\n",
       "      <td>-20.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Gucheng</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01 12:00:00</th>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>800.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>4.1</td>\n",
       "      <td>-20.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Gucheng</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01 13:00:00</th>\n",
       "      <td>10.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>900.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>5.3</td>\n",
       "      <td>-20.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Gucheng</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01 14:00:00</th>\n",
       "      <td>13.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>800.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>5.5</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Gucheng</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01 15:00:00</th>\n",
       "      <td>13.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>800.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>6.3</td>\n",
       "      <td>-19.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Gucheng</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01 16:00:00</th>\n",
       "      <td>11.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>800.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>5.9</td>\n",
       "      <td>-18.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Gucheng</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01 17:00:00</th>\n",
       "      <td>10.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>800.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>-18.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Gucheng</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01 18:00:00</th>\n",
       "      <td>11.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>900.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>3.7</td>\n",
       "      <td>-19.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Gucheng</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01 19:00:00</th>\n",
       "      <td>11.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>2.7</td>\n",
       "      <td>-19.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Gucheng</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01 20:00:00</th>\n",
       "      <td>13.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>-18.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Gucheng</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01 21:00:00</th>\n",
       "      <td>15.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-17.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Gucheng</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01 22:00:00</th>\n",
       "      <td>16.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>-17.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Gucheng</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01 23:00:00</th>\n",
       "      <td>16.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>-17.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Gucheng</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     PM2.5  PM10   SO2   NO2      CO    O3  TEMP  DEWP  RAIN  \\\n",
       "DateTime                                                                       \n",
       "2013-03-01 00:00:00    6.0  18.0   5.0   NaN   800.0  88.0   0.1 -18.6   0.0   \n",
       "2013-03-01 01:00:00    6.0  15.0   5.0   NaN   800.0  88.0  -0.3 -19.0   0.0   \n",
       "2013-03-01 02:00:00    5.0  18.0   5.5   NaN   700.0  52.0  -0.7 -19.8   0.0   \n",
       "2013-03-01 03:00:00    6.0  20.0   6.0   NaN   650.0  62.5  -1.0 -21.2   0.0   \n",
       "2013-03-01 04:00:00    5.0  17.0   5.0   NaN   600.0  73.0  -1.3 -21.4   0.0   \n",
       "2013-03-01 05:00:00    4.0  11.0   3.0   NaN   700.0  87.0  -1.8 -21.9   0.0   \n",
       "2013-03-01 06:00:00    3.0   6.0   3.0   NaN   700.0  92.0  -2.6 -20.4   0.0   \n",
       "2013-03-01 07:00:00    5.0   5.0   3.0   NaN   800.0  86.0  -0.9 -20.5   0.0   \n",
       "2013-03-01 08:00:00    5.0   9.0   5.0   NaN   900.0  81.0   0.1 -20.3   0.0   \n",
       "2013-03-01 09:00:00    4.0  10.0   6.0   NaN   900.0  82.0   1.1 -20.6   0.0   \n",
       "2013-03-01 10:00:00    7.0   8.0   8.0   NaN   900.0  82.0   1.9 -21.3   0.0   \n",
       "2013-03-01 11:00:00    3.0   6.0   8.0   NaN   800.0  85.0   3.3 -20.9   0.0   \n",
       "2013-03-01 12:00:00    9.0  10.0   8.0   NaN   800.0  86.0   4.1 -20.3   0.0   \n",
       "2013-03-01 13:00:00   10.0  19.0   9.0   NaN   900.0  86.0   5.3 -20.1   0.0   \n",
       "2013-03-01 14:00:00   13.0  20.0   8.0   NaN   800.0  88.0   5.5 -20.0   0.0   \n",
       "2013-03-01 15:00:00   13.0  16.0   8.0   NaN   800.0  90.0   6.3 -19.3   0.0   \n",
       "2013-03-01 16:00:00   11.0  22.0   6.0   NaN   800.0  90.0   5.9 -18.8   0.0   \n",
       "2013-03-01 17:00:00   10.0  20.0   4.0   NaN   800.0  87.0   5.1 -18.7   0.0   \n",
       "2013-03-01 18:00:00   11.0  20.0   6.0   NaN   900.0  80.0   3.7 -19.2   0.0   \n",
       "2013-03-01 19:00:00   11.0  31.0   8.0   NaN  1000.0  69.0   2.7 -19.3   0.0   \n",
       "2013-03-01 20:00:00   13.0  25.0  12.0   5.0  1100.0  61.0   1.6 -18.4   0.0   \n",
       "2013-03-01 21:00:00   15.0  23.0  14.0  13.0  1200.0  52.0   1.0 -17.4   0.0   \n",
       "2013-03-01 22:00:00   16.0  28.0  16.0  19.0  1200.0  45.0   1.3 -17.6   0.0   \n",
       "2013-03-01 23:00:00   16.0  28.0  14.0  20.0  1100.0  44.0   0.2 -17.6   0.0   \n",
       "\n",
       "                     station  \n",
       "DateTime                      \n",
       "2013-03-01 00:00:00  Gucheng  \n",
       "2013-03-01 01:00:00  Gucheng  \n",
       "2013-03-01 02:00:00  Gucheng  \n",
       "2013-03-01 03:00:00  Gucheng  \n",
       "2013-03-01 04:00:00  Gucheng  \n",
       "2013-03-01 05:00:00  Gucheng  \n",
       "2013-03-01 06:00:00  Gucheng  \n",
       "2013-03-01 07:00:00  Gucheng  \n",
       "2013-03-01 08:00:00  Gucheng  \n",
       "2013-03-01 09:00:00  Gucheng  \n",
       "2013-03-01 10:00:00  Gucheng  \n",
       "2013-03-01 11:00:00  Gucheng  \n",
       "2013-03-01 12:00:00  Gucheng  \n",
       "2013-03-01 13:00:00  Gucheng  \n",
       "2013-03-01 14:00:00  Gucheng  \n",
       "2013-03-01 15:00:00  Gucheng  \n",
       "2013-03-01 16:00:00  Gucheng  \n",
       "2013-03-01 17:00:00  Gucheng  \n",
       "2013-03-01 18:00:00  Gucheng  \n",
       "2013-03-01 19:00:00  Gucheng  \n",
       "2013-03-01 20:00:00  Gucheng  \n",
       "2013-03-01 21:00:00  Gucheng  \n",
       "2013-03-01 22:00:00  Gucheng  \n",
       "2013-03-01 23:00:00  Gucheng  "
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Gucheng\n",
    "Gucheng_in_thatday_df = all_df.loc[ thatday & (all_df['station'] == 'Gucheng')]\n",
    "Gucheng_in_thatday_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nilai yang hilang akan digantikan dengan nilai rata-rata pada hari itu melalui metode Imputation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PM2.5</th>\n",
       "      <th>PM10</th>\n",
       "      <th>SO2</th>\n",
       "      <th>NO2</th>\n",
       "      <th>CO</th>\n",
       "      <th>O3</th>\n",
       "      <th>TEMP</th>\n",
       "      <th>DEWP</th>\n",
       "      <th>RAIN</th>\n",
       "      <th>station</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DateTime</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>2013-03-01 00:00:00</th>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.318182</td>\n",
       "      <td>200.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>-2.3</td>\n",
       "      <td>-19.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Dingling</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01 01:00:00</th>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.318182</td>\n",
       "      <td>200.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>-2.5</td>\n",
       "      <td>-19.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Dingling</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01 02:00:00</th>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>200.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>-19.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Dingling</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01 03:00:00</th>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.666667</td>\n",
       "      <td>200.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>-3.6</td>\n",
       "      <td>-19.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Dingling</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01 04:00:00</th>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.333333</td>\n",
       "      <td>200.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>-3.5</td>\n",
       "      <td>-19.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Dingling</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01 05:00:00</th>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>200.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>-4.5</td>\n",
       "      <td>-19.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Dingling</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01 06:00:00</th>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>200.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>-4.5</td>\n",
       "      <td>-19.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Dingling</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01 07:00:00</th>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>200.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>-2.1</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Dingling</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01 08:00:00</th>\n",
       "      <td>8.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>200.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>-20.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Dingling</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01 09:00:00</th>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>200.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>-20.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Dingling</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01 10:00:00</th>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>200.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-21.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Dingling</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01 11:00:00</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>200.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>-20.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Dingling</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01 12:00:00</th>\n",
       "      <td>6.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>200.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>-19.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Dingling</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01 13:00:00</th>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>200.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>-20.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Dingling</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01 14:00:00</th>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>200.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>5.9</td>\n",
       "      <td>-20.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Dingling</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01 15:00:00</th>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>200.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>-20.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Dingling</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01 16:00:00</th>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>200.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>5.4</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Dingling</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01 17:00:00</th>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>200.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>-20.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Dingling</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01 18:00:00</th>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>200.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>2.7</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Dingling</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01 19:00:00</th>\n",
       "      <td>14.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>200.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-19.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Dingling</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01 20:00:00</th>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>200.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>-18.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Dingling</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01 21:00:00</th>\n",
       "      <td>9.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>200.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>-17.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Dingling</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01 22:00:00</th>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>200.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>-17.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Dingling</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01 23:00:00</th>\n",
       "      <td>6.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>300.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>-16.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Dingling</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     PM2.5  PM10  SO2       NO2     CO    O3  TEMP  DEWP  \\\n",
       "DateTime                                                                   \n",
       "2013-03-01 00:00:00    4.0   4.0  3.0  2.318182  200.0  82.0  -2.3 -19.7   \n",
       "2013-03-01 01:00:00    7.0   7.0  3.0  2.318182  200.0  80.0  -2.5 -19.0   \n",
       "2013-03-01 02:00:00    5.0   5.0  3.0  2.000000  200.0  79.0  -3.0 -19.9   \n",
       "2013-03-01 03:00:00    6.0   6.0  3.0  2.666667  200.0  79.0  -3.6 -19.1   \n",
       "2013-03-01 04:00:00    5.0   5.0  3.0  3.333333  200.0  81.0  -3.5 -19.4   \n",
       "2013-03-01 05:00:00    6.0   6.0  3.0  4.000000  200.0  79.0  -4.5 -19.5   \n",
       "2013-03-01 06:00:00    5.0  10.0  3.0  4.000000  200.0  77.0  -4.5 -19.5   \n",
       "2013-03-01 07:00:00    5.0   6.0  3.0  2.000000  200.0  80.0  -2.1 -20.0   \n",
       "2013-03-01 08:00:00    8.0   7.0  3.0  3.000000  200.0  79.0  -0.2 -20.5   \n",
       "2013-03-01 09:00:00    8.0   8.0  3.0  2.000000  200.0  81.0   0.6 -20.4   \n",
       "2013-03-01 10:00:00    8.0   5.0  3.0  2.000000  200.0  83.0   2.0 -21.3   \n",
       "2013-03-01 11:00:00    3.0   2.0  3.0  2.000000  200.0  83.0   3.6 -20.7   \n",
       "2013-03-01 12:00:00    6.0   4.5  3.0  2.000000  200.0  85.0   4.8 -19.7   \n",
       "2013-03-01 13:00:00    7.0   7.0  3.0  2.000000  200.0  84.0   5.8 -20.6   \n",
       "2013-03-01 14:00:00    6.0   7.0  3.0  2.000000  200.0  89.0   5.9 -20.5   \n",
       "2013-03-01 15:00:00   10.0  10.0  3.0  2.000000  200.0  88.0   6.0 -20.4   \n",
       "2013-03-01 16:00:00    3.0   6.0  3.0  2.000000  200.0  87.0   5.4 -20.0   \n",
       "2013-03-01 17:00:00    5.0   5.0  3.0  2.000000  200.0  87.0   3.5 -20.8   \n",
       "2013-03-01 18:00:00    5.0   6.0  3.0  2.000000  200.0  85.0   2.7 -20.0   \n",
       "2013-03-01 19:00:00   14.0   6.0  3.0  2.000000  200.0  83.0   2.0 -19.3   \n",
       "2013-03-01 20:00:00    6.0   7.0  3.0  2.000000  200.0  83.0   1.1 -18.3   \n",
       "2013-03-01 21:00:00    9.0   7.0  3.0  2.000000  200.0  82.0   1.6 -17.4   \n",
       "2013-03-01 22:00:00    6.0   5.0  3.0  2.000000  200.0  79.0   0.4 -17.4   \n",
       "2013-03-01 23:00:00    6.0   9.0  3.0  2.000000  300.0  72.0   0.3 -16.6   \n",
       "\n",
       "                     RAIN   station  \n",
       "DateTime                             \n",
       "2013-03-01 00:00:00   0.0  Dingling  \n",
       "2013-03-01 01:00:00   0.0  Dingling  \n",
       "2013-03-01 02:00:00   0.0  Dingling  \n",
       "2013-03-01 03:00:00   0.0  Dingling  \n",
       "2013-03-01 04:00:00   0.0  Dingling  \n",
       "2013-03-01 05:00:00   0.0  Dingling  \n",
       "2013-03-01 06:00:00   0.0  Dingling  \n",
       "2013-03-01 07:00:00   0.0  Dingling  \n",
       "2013-03-01 08:00:00   0.0  Dingling  \n",
       "2013-03-01 09:00:00   0.0  Dingling  \n",
       "2013-03-01 10:00:00   0.0  Dingling  \n",
       "2013-03-01 11:00:00   0.0  Dingling  \n",
       "2013-03-01 12:00:00   0.0  Dingling  \n",
       "2013-03-01 13:00:00   0.0  Dingling  \n",
       "2013-03-01 14:00:00   0.0  Dingling  \n",
       "2013-03-01 15:00:00   0.0  Dingling  \n",
       "2013-03-01 16:00:00   0.0  Dingling  \n",
       "2013-03-01 17:00:00   0.0  Dingling  \n",
       "2013-03-01 18:00:00   0.0  Dingling  \n",
       "2013-03-01 19:00:00   0.0  Dingling  \n",
       "2013-03-01 20:00:00   0.0  Dingling  \n",
       "2013-03-01 21:00:00   0.0  Dingling  \n",
       "2013-03-01 22:00:00   0.0  Dingling  \n",
       "2013-03-01 23:00:00   0.0  Dingling  "
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_df.fillna(Dingling_in_thatday_df['NO2'].mean(), inplace= True)\n",
    "Dingling_in_thatday_df = all_df.loc[ thatday & (all_df['station'] == 'Dingling')]\n",
    "Dingling_in_thatday_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PM2.5</th>\n",
       "      <th>PM10</th>\n",
       "      <th>SO2</th>\n",
       "      <th>NO2</th>\n",
       "      <th>CO</th>\n",
       "      <th>O3</th>\n",
       "      <th>TEMP</th>\n",
       "      <th>DEWP</th>\n",
       "      <th>RAIN</th>\n",
       "      <th>station</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DateTime</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>2013-03-01 00:00:00</th>\n",
       "      <td>6.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.318182</td>\n",
       "      <td>800.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>-18.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Gucheng</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01 01:00:00</th>\n",
       "      <td>6.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.318182</td>\n",
       "      <td>800.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>-19.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Gucheng</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01 02:00:00</th>\n",
       "      <td>5.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>5.5</td>\n",
       "      <td>2.318182</td>\n",
       "      <td>700.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>-19.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Gucheng</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01 03:00:00</th>\n",
       "      <td>6.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.318182</td>\n",
       "      <td>650.0</td>\n",
       "      <td>62.5</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-21.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Gucheng</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01 04:00:00</th>\n",
       "      <td>5.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.318182</td>\n",
       "      <td>600.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>-1.3</td>\n",
       "      <td>-21.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Gucheng</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01 05:00:00</th>\n",
       "      <td>4.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.318182</td>\n",
       "      <td>700.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>-1.8</td>\n",
       "      <td>-21.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Gucheng</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01 06:00:00</th>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.318182</td>\n",
       "      <td>700.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>-2.6</td>\n",
       "      <td>-20.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Gucheng</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01 07:00:00</th>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.318182</td>\n",
       "      <td>800.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>-0.9</td>\n",
       "      <td>-20.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Gucheng</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01 08:00:00</th>\n",
       "      <td>5.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.318182</td>\n",
       "      <td>900.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>-20.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Gucheng</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01 09:00:00</th>\n",
       "      <td>4.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.318182</td>\n",
       "      <td>900.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>-20.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Gucheng</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01 10:00:00</th>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.318182</td>\n",
       "      <td>900.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>-21.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Gucheng</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01 11:00:00</th>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.318182</td>\n",
       "      <td>800.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>3.3</td>\n",
       "      <td>-20.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Gucheng</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01 12:00:00</th>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.318182</td>\n",
       "      <td>800.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>4.1</td>\n",
       "      <td>-20.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Gucheng</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01 13:00:00</th>\n",
       "      <td>10.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.318182</td>\n",
       "      <td>900.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>5.3</td>\n",
       "      <td>-20.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Gucheng</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01 14:00:00</th>\n",
       "      <td>13.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.318182</td>\n",
       "      <td>800.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>5.5</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Gucheng</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01 15:00:00</th>\n",
       "      <td>13.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.318182</td>\n",
       "      <td>800.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>6.3</td>\n",
       "      <td>-19.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Gucheng</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01 16:00:00</th>\n",
       "      <td>11.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.318182</td>\n",
       "      <td>800.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>5.9</td>\n",
       "      <td>-18.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Gucheng</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01 17:00:00</th>\n",
       "      <td>10.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.318182</td>\n",
       "      <td>800.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>-18.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Gucheng</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01 18:00:00</th>\n",
       "      <td>11.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.318182</td>\n",
       "      <td>900.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>3.7</td>\n",
       "      <td>-19.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Gucheng</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01 19:00:00</th>\n",
       "      <td>11.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.318182</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>2.7</td>\n",
       "      <td>-19.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Gucheng</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01 20:00:00</th>\n",
       "      <td>13.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>-18.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Gucheng</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01 21:00:00</th>\n",
       "      <td>15.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-17.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Gucheng</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01 22:00:00</th>\n",
       "      <td>16.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>-17.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Gucheng</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01 23:00:00</th>\n",
       "      <td>16.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>-17.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Gucheng</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     PM2.5  PM10   SO2        NO2      CO    O3  TEMP  DEWP  \\\n",
       "DateTime                                                                      \n",
       "2013-03-01 00:00:00    6.0  18.0   5.0   2.318182   800.0  88.0   0.1 -18.6   \n",
       "2013-03-01 01:00:00    6.0  15.0   5.0   2.318182   800.0  88.0  -0.3 -19.0   \n",
       "2013-03-01 02:00:00    5.0  18.0   5.5   2.318182   700.0  52.0  -0.7 -19.8   \n",
       "2013-03-01 03:00:00    6.0  20.0   6.0   2.318182   650.0  62.5  -1.0 -21.2   \n",
       "2013-03-01 04:00:00    5.0  17.0   5.0   2.318182   600.0  73.0  -1.3 -21.4   \n",
       "2013-03-01 05:00:00    4.0  11.0   3.0   2.318182   700.0  87.0  -1.8 -21.9   \n",
       "2013-03-01 06:00:00    3.0   6.0   3.0   2.318182   700.0  92.0  -2.6 -20.4   \n",
       "2013-03-01 07:00:00    5.0   5.0   3.0   2.318182   800.0  86.0  -0.9 -20.5   \n",
       "2013-03-01 08:00:00    5.0   9.0   5.0   2.318182   900.0  81.0   0.1 -20.3   \n",
       "2013-03-01 09:00:00    4.0  10.0   6.0   2.318182   900.0  82.0   1.1 -20.6   \n",
       "2013-03-01 10:00:00    7.0   8.0   8.0   2.318182   900.0  82.0   1.9 -21.3   \n",
       "2013-03-01 11:00:00    3.0   6.0   8.0   2.318182   800.0  85.0   3.3 -20.9   \n",
       "2013-03-01 12:00:00    9.0  10.0   8.0   2.318182   800.0  86.0   4.1 -20.3   \n",
       "2013-03-01 13:00:00   10.0  19.0   9.0   2.318182   900.0  86.0   5.3 -20.1   \n",
       "2013-03-01 14:00:00   13.0  20.0   8.0   2.318182   800.0  88.0   5.5 -20.0   \n",
       "2013-03-01 15:00:00   13.0  16.0   8.0   2.318182   800.0  90.0   6.3 -19.3   \n",
       "2013-03-01 16:00:00   11.0  22.0   6.0   2.318182   800.0  90.0   5.9 -18.8   \n",
       "2013-03-01 17:00:00   10.0  20.0   4.0   2.318182   800.0  87.0   5.1 -18.7   \n",
       "2013-03-01 18:00:00   11.0  20.0   6.0   2.318182   900.0  80.0   3.7 -19.2   \n",
       "2013-03-01 19:00:00   11.0  31.0   8.0   2.318182  1000.0  69.0   2.7 -19.3   \n",
       "2013-03-01 20:00:00   13.0  25.0  12.0   5.000000  1100.0  61.0   1.6 -18.4   \n",
       "2013-03-01 21:00:00   15.0  23.0  14.0  13.000000  1200.0  52.0   1.0 -17.4   \n",
       "2013-03-01 22:00:00   16.0  28.0  16.0  19.000000  1200.0  45.0   1.3 -17.6   \n",
       "2013-03-01 23:00:00   16.0  28.0  14.0  20.000000  1100.0  44.0   0.2 -17.6   \n",
       "\n",
       "                     RAIN  station  \n",
       "DateTime                            \n",
       "2013-03-01 00:00:00   0.0  Gucheng  \n",
       "2013-03-01 01:00:00   0.0  Gucheng  \n",
       "2013-03-01 02:00:00   0.0  Gucheng  \n",
       "2013-03-01 03:00:00   0.0  Gucheng  \n",
       "2013-03-01 04:00:00   0.0  Gucheng  \n",
       "2013-03-01 05:00:00   0.0  Gucheng  \n",
       "2013-03-01 06:00:00   0.0  Gucheng  \n",
       "2013-03-01 07:00:00   0.0  Gucheng  \n",
       "2013-03-01 08:00:00   0.0  Gucheng  \n",
       "2013-03-01 09:00:00   0.0  Gucheng  \n",
       "2013-03-01 10:00:00   0.0  Gucheng  \n",
       "2013-03-01 11:00:00   0.0  Gucheng  \n",
       "2013-03-01 12:00:00   0.0  Gucheng  \n",
       "2013-03-01 13:00:00   0.0  Gucheng  \n",
       "2013-03-01 14:00:00   0.0  Gucheng  \n",
       "2013-03-01 15:00:00   0.0  Gucheng  \n",
       "2013-03-01 16:00:00   0.0  Gucheng  \n",
       "2013-03-01 17:00:00   0.0  Gucheng  \n",
       "2013-03-01 18:00:00   0.0  Gucheng  \n",
       "2013-03-01 19:00:00   0.0  Gucheng  \n",
       "2013-03-01 20:00:00   0.0  Gucheng  \n",
       "2013-03-01 21:00:00   0.0  Gucheng  \n",
       "2013-03-01 22:00:00   0.0  Gucheng  \n",
       "2013-03-01 23:00:00   0.0  Gucheng  "
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_df.fillna(Gucheng_in_thatday_df['NO2'].mean(), inplace= True)\n",
    "Gucheng_in_thatday_df = all_df.loc[ thatday & (all_df['station'] == 'Gucheng')]\n",
    "Gucheng_in_thatday_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Seluruh data yang hilang telah teratasi."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PM2.5      0\n",
       "PM10       0\n",
       "SO2        0\n",
       "NO2        0\n",
       "CO         0\n",
       "O3         0\n",
       "TEMP       0\n",
       "DEWP       0\n",
       "RAIN       0\n",
       "station    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DateTime</th>\n",
       "      <th>PM2.5</th>\n",
       "      <th>PM10</th>\n",
       "      <th>SO2</th>\n",
       "      <th>NO2</th>\n",
       "      <th>CO</th>\n",
       "      <th>O3</th>\n",
       "      <th>TEMP</th>\n",
       "      <th>DEWP</th>\n",
       "      <th>RAIN</th>\n",
       "      <th>station</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013-03-01 00:00:00</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>-18.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013-03-01 01:00:00</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>-18.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013-03-01 02:00:00</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>-18.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013-03-01 03:00:00</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>-1.4</td>\n",
       "      <td>-19.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013-03-01 04:00:00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>-19.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             DateTime  PM2.5  PM10   SO2   NO2     CO    O3  TEMP  DEWP  RAIN  \\\n",
       "0 2013-03-01 00:00:00    4.0   4.0   4.0   7.0  300.0  77.0  -0.7 -18.8   0.0   \n",
       "1 2013-03-01 01:00:00    8.0   8.0   4.0   7.0  300.0  77.0  -1.1 -18.2   0.0   \n",
       "2 2013-03-01 02:00:00    7.0   7.0   5.0  10.0  300.0  73.0  -1.1 -18.2   0.0   \n",
       "3 2013-03-01 03:00:00    6.0   6.0  11.0  11.0  300.0  72.0  -1.4 -19.4   0.0   \n",
       "4 2013-03-01 04:00:00    3.0   3.0  12.0  12.0  300.0  72.0  -2.0 -19.5   0.0   \n",
       "\n",
       "        station  \n",
       "0  Aotizhongxin  \n",
       "1  Aotizhongxin  \n",
       "2  Aotizhongxin  \n",
       "3  Aotizhongxin  \n",
       "4  Aotizhongxin  "
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Me-reset index setelah interpolasi agar tampilan tanggal menjadi kolom\n",
    "all_df.reset_index(inplace=True)\n",
    "all_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 420768 entries, 0 to 420767\n",
      "Data columns (total 11 columns):\n",
      " #   Column    Non-Null Count   Dtype         \n",
      "---  ------    --------------   -----         \n",
      " 0   DateTime  420768 non-null  datetime64[ns]\n",
      " 1   PM2.5     420768 non-null  float64       \n",
      " 2   PM10      420768 non-null  float64       \n",
      " 3   SO2       420768 non-null  float64       \n",
      " 4   NO2       420768 non-null  float64       \n",
      " 5   CO        420768 non-null  float64       \n",
      " 6   O3        420768 non-null  float64       \n",
      " 7   TEMP      420768 non-null  float64       \n",
      " 8   DEWP      420768 non-null  float64       \n",
      " 9   RAIN      420768 non-null  float64       \n",
      " 10  station   420768 non-null  object        \n",
      "dtypes: datetime64[ns](1), float64(9), object(1)\n",
      "memory usage: 35.3+ MB\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DateTime</th>\n",
       "      <th>PM2.5</th>\n",
       "      <th>PM10</th>\n",
       "      <th>SO2</th>\n",
       "      <th>NO2</th>\n",
       "      <th>CO</th>\n",
       "      <th>O3</th>\n",
       "      <th>TEMP</th>\n",
       "      <th>DEWP</th>\n",
       "      <th>RAIN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>420768</td>\n",
       "      <td>420768.000000</td>\n",
       "      <td>420768.000000</td>\n",
       "      <td>420768.000000</td>\n",
       "      <td>420768.000000</td>\n",
       "      <td>420768.000000</td>\n",
       "      <td>420768.000000</td>\n",
       "      <td>420768.000000</td>\n",
       "      <td>420768.000000</td>\n",
       "      <td>420768.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2015-03-01 11:30:00.000001024</td>\n",
       "      <td>79.839718</td>\n",
       "      <td>104.910268</td>\n",
       "      <td>15.913090</td>\n",
       "      <td>50.598892</td>\n",
       "      <td>1235.682649</td>\n",
       "      <td>57.237872</td>\n",
       "      <td>13.531692</td>\n",
       "      <td>2.482421</td>\n",
       "      <td>0.064428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2013-03-01 00:00:00</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.285600</td>\n",
       "      <td>1.026500</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>0.214200</td>\n",
       "      <td>-19.900000</td>\n",
       "      <td>-43.400000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2014-03-01 05:45:00</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>3.100000</td>\n",
       "      <td>-8.900000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2015-03-01 11:30:00</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>900.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>14.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2016-02-29 17:15:00</td>\n",
       "      <td>111.000000</td>\n",
       "      <td>145.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>1500.000000</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>23.300000</td>\n",
       "      <td>15.100000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2017-02-28 23:00:00</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>290.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>1071.000000</td>\n",
       "      <td>41.600000</td>\n",
       "      <td>29.100000</td>\n",
       "      <td>72.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>80.950217</td>\n",
       "      <td>92.431422</td>\n",
       "      <td>21.896609</td>\n",
       "      <td>35.172089</td>\n",
       "      <td>1161.790893</td>\n",
       "      <td>57.135195</td>\n",
       "      <td>11.437867</td>\n",
       "      <td>13.797675</td>\n",
       "      <td>0.820638</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            DateTime          PM2.5           PM10  \\\n",
       "count                         420768  420768.000000  420768.000000   \n",
       "mean   2015-03-01 11:30:00.000001024      79.839718     104.910268   \n",
       "min              2013-03-01 00:00:00       2.000000       2.000000   \n",
       "25%              2014-03-01 05:45:00      20.000000      36.000000   \n",
       "50%              2015-03-01 11:30:00      55.000000      82.000000   \n",
       "75%              2016-02-29 17:15:00     111.000000     145.000000   \n",
       "max              2017-02-28 23:00:00     999.000000     999.000000   \n",
       "std                              NaN      80.950217      92.431422   \n",
       "\n",
       "                 SO2            NO2             CO             O3  \\\n",
       "count  420768.000000  420768.000000  420768.000000  420768.000000   \n",
       "mean       15.913090      50.598892    1235.682649      57.237872   \n",
       "min         0.285600       1.026500     100.000000       0.214200   \n",
       "25%         3.000000      23.000000     500.000000      10.000000   \n",
       "50%         7.000000      43.000000     900.000000      44.000000   \n",
       "75%        20.000000      71.000000    1500.000000      82.000000   \n",
       "max       500.000000     290.000000   10000.000000    1071.000000   \n",
       "std        21.896609      35.172089    1161.790893      57.135195   \n",
       "\n",
       "                TEMP           DEWP           RAIN  \n",
       "count  420768.000000  420768.000000  420768.000000  \n",
       "mean       13.531692       2.482421       0.064428  \n",
       "min       -19.900000     -43.400000       0.000000  \n",
       "25%         3.100000      -8.900000       0.000000  \n",
       "50%        14.500000       3.000000       0.000000  \n",
       "75%        23.300000      15.100000       0.000000  \n",
       "max        41.600000      29.100000      72.500000  \n",
       "std        11.437867      13.797675       0.820638  "
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_df.info()\n",
    "all_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DateTime</th>\n",
       "      <th>PM2.5</th>\n",
       "      <th>PM10</th>\n",
       "      <th>SO2</th>\n",
       "      <th>NO2</th>\n",
       "      <th>CO</th>\n",
       "      <th>O3</th>\n",
       "      <th>TEMP</th>\n",
       "      <th>DEWP</th>\n",
       "      <th>RAIN</th>\n",
       "      <th>station</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>420768</td>\n",
       "      <td>420768.000000</td>\n",
       "      <td>420768.000000</td>\n",
       "      <td>420768.000000</td>\n",
       "      <td>420768.000000</td>\n",
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       "      <td>420768.000000</td>\n",
       "      <td>420768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\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",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\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",
       "      <td>Aotizhongxin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\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",
       "      <td>35064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2015-03-01 11:30:00.000001024</td>\n",
       "      <td>79.839718</td>\n",
       "      <td>104.910268</td>\n",
       "      <td>15.913090</td>\n",
       "      <td>50.598892</td>\n",
       "      <td>1235.682649</td>\n",
       "      <td>57.237872</td>\n",
       "      <td>13.531692</td>\n",
       "      <td>2.482421</td>\n",
       "      <td>0.064428</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2013-03-01 00:00:00</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.285600</td>\n",
       "      <td>1.026500</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>0.214200</td>\n",
       "      <td>-19.900000</td>\n",
       "      <td>-43.400000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2014-03-01 05:45:00</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>3.100000</td>\n",
       "      <td>-8.900000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2015-03-01 11:30:00</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>900.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>14.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2016-02-29 17:15:00</td>\n",
       "      <td>111.000000</td>\n",
       "      <td>145.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>1500.000000</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>23.300000</td>\n",
       "      <td>15.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2017-02-28 23:00:00</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>290.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>1071.000000</td>\n",
       "      <td>41.600000</td>\n",
       "      <td>29.100000</td>\n",
       "      <td>72.500000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>80.950217</td>\n",
       "      <td>92.431422</td>\n",
       "      <td>21.896609</td>\n",
       "      <td>35.172089</td>\n",
       "      <td>1161.790893</td>\n",
       "      <td>57.135195</td>\n",
       "      <td>11.437867</td>\n",
       "      <td>13.797675</td>\n",
       "      <td>0.820638</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             DateTime          PM2.5           PM10  \\\n",
       "count                          420768  420768.000000  420768.000000   \n",
       "unique                            NaN            NaN            NaN   \n",
       "top                               NaN            NaN            NaN   \n",
       "freq                              NaN            NaN            NaN   \n",
       "mean    2015-03-01 11:30:00.000001024      79.839718     104.910268   \n",
       "min               2013-03-01 00:00:00       2.000000       2.000000   \n",
       "25%               2014-03-01 05:45:00      20.000000      36.000000   \n",
       "50%               2015-03-01 11:30:00      55.000000      82.000000   \n",
       "75%               2016-02-29 17:15:00     111.000000     145.000000   \n",
       "max               2017-02-28 23:00:00     999.000000     999.000000   \n",
       "std                               NaN      80.950217      92.431422   \n",
       "\n",
       "                  SO2            NO2             CO             O3  \\\n",
       "count   420768.000000  420768.000000  420768.000000  420768.000000   \n",
       "unique            NaN            NaN            NaN            NaN   \n",
       "top               NaN            NaN            NaN            NaN   \n",
       "freq              NaN            NaN            NaN            NaN   \n",
       "mean        15.913090      50.598892    1235.682649      57.237872   \n",
       "min          0.285600       1.026500     100.000000       0.214200   \n",
       "25%          3.000000      23.000000     500.000000      10.000000   \n",
       "50%          7.000000      43.000000     900.000000      44.000000   \n",
       "75%         20.000000      71.000000    1500.000000      82.000000   \n",
       "max        500.000000     290.000000   10000.000000    1071.000000   \n",
       "std         21.896609      35.172089    1161.790893      57.135195   \n",
       "\n",
       "                 TEMP           DEWP           RAIN       station  \n",
       "count   420768.000000  420768.000000  420768.000000        420768  \n",
       "unique            NaN            NaN            NaN            12  \n",
       "top               NaN            NaN            NaN  Aotizhongxin  \n",
       "freq              NaN            NaN            NaN         35064  \n",
       "mean        13.531692       2.482421       0.064428           NaN  \n",
       "min        -19.900000     -43.400000       0.000000           NaN  \n",
       "25%          3.100000      -8.900000       0.000000           NaN  \n",
       "50%         14.500000       3.000000       0.000000           NaN  \n",
       "75%         23.300000      15.100000       0.000000           NaN  \n",
       "max         41.600000      29.100000      72.500000           NaN  \n",
       "std         11.437867      13.797675       0.820638           NaN  "
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_df.describe(include='all')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Perhitungan Kelembapan tiap Stasiun di Kawasan Beijing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Menghitung kelembapan relatif melalui pendekatan persamaan Clausius-Clapeyron.\n",
    "\n",
    "$$ \\text{Rh} = \\frac{E0 \\times exp[(L/Rv) \\times {(1/T0) - (1/Td)}]}{E0 \\times exp[(L/Rv) \\times {(1/T0) - (1/T)}]} \\times 100 \\% $$\n",
    "\n",
    "*Rh* = Kelembapan (%) <br>\n",
    "*E0* = 0.611 kPa<br>\n",
    "*(L/Rv)* = 5423 K<br>\n",
    "*T0* = 273 K<br>\n",
    "*T* = suhu (K)<br>\n",
    "*Td* = Titik embun\n",
    "\n",
    "Source : Lawrence, Mark G., 2005: The relationship between relative humidity and the dewpoint temperature in moist air: A simple conversion and applications. Bull. Amer. Meteor. Soc., 86, 225-233. doi: http;//dx.doi.org/10.1175/BAMS-86-2-225"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fungsi Konversi °C ke Kelvin\n",
    "def cel_to_kel(celsius):\n",
    "    kelvin = celsius + 273.15\n",
    "    return kelvin\n",
    "\n",
    "# Fungsi Perhitungan Rh\n",
    "def Rh(temp, dewpoint):\n",
    "    num = 0.611 * math.exp(5423 * ((1 / 273) - (1 / dewpoint)))\n",
    "    den = 0.611 * math.exp(5423 * ((1 / 273) - (1 / temp)))\n",
    "    Rh = num / den * 100\n",
    "    return Rh\n",
    "\n",
    "# Fungsi untuk memasukkan nilai pada rumus\n",
    "def calculate_Rh(row):\n",
    "    cv_t = cel_to_kel(row['TEMP'])\n",
    "    cv_d = cel_to_kel(row['DEWP'])\n",
    "    hasil = Rh(cv_t, cv_d)\n",
    "    return hasil"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Menerapkan fungsi ke setiap baris pada kolom **TEMP** dan **DEWP** dan menyimpan hasil pada kolom **Rh**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_df['Rh'] = all_df.apply(lambda row: calculate_Rh(row), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DateTime</th>\n",
       "      <th>PM2.5</th>\n",
       "      <th>PM10</th>\n",
       "      <th>SO2</th>\n",
       "      <th>NO2</th>\n",
       "      <th>CO</th>\n",
       "      <th>O3</th>\n",
       "      <th>TEMP</th>\n",
       "      <th>DEWP</th>\n",
       "      <th>RAIN</th>\n",
       "      <th>Rh</th>\n",
       "      <th>station</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013-03-01 00:00:00</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>-18.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.257491</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013-03-01 01:00:00</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>-18.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.263193</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013-03-01 02:00:00</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>-18.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.263193</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013-03-01 03:00:00</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>-1.4</td>\n",
       "      <td>-19.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.278311</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013-03-01 04:00:00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>-19.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.161525</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             DateTime  PM2.5  PM10   SO2   NO2     CO    O3  TEMP  DEWP  RAIN  \\\n",
       "0 2013-03-01 00:00:00    4.0   4.0   4.0   7.0  300.0  77.0  -0.7 -18.8   0.0   \n",
       "1 2013-03-01 01:00:00    8.0   8.0   4.0   7.0  300.0  77.0  -1.1 -18.2   0.0   \n",
       "2 2013-03-01 02:00:00    7.0   7.0   5.0  10.0  300.0  73.0  -1.1 -18.2   0.0   \n",
       "3 2013-03-01 03:00:00    6.0   6.0  11.0  11.0  300.0  72.0  -1.4 -19.4   0.0   \n",
       "4 2013-03-01 04:00:00    3.0   3.0  12.0  12.0  300.0  72.0  -2.0 -19.5   0.0   \n",
       "\n",
       "          Rh       station  \n",
       "0  24.257491  Aotizhongxin  \n",
       "1  26.263193  Aotizhongxin  \n",
       "2  26.263193  Aotizhongxin  \n",
       "3  24.278311  Aotizhongxin  \n",
       "4  25.161525  Aotizhongxin  "
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_df.insert(10, 'Rh', all_df.pop('Rh')) # Mengubah posisi kolom Rh agar station tetap berada di paling kanan (faktor estetika)\n",
    "all_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Adapun beberapa ketentuan data cuaca sebagai berikut:\n",
    "- Data Curah Hujan (mm) ditampilkan dalam bentuk akumulasi\n",
    "- Data Suhu (°C) menampilkan batasan suhu minimum dan maksimumnya\n",
    "- Data Rh (%) ditampilkan dalam bentuk rerata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Rincian Parameter Curah Hujan, Suhu, dan Tingkat Kelembapan (Rh) berdasarkan Stasiun dalam Kurun Waktu 5 Tahun"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Curah Hujan</th>\n",
       "      <th colspan=\"3\" halign=\"left\">Suhu</th>\n",
       "      <th>Kelembapan</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>sum</th>\n",
       "      <th>min</th>\n",
       "      <th>mean</th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tahun</th>\n",
       "      <th>station</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 rowspan=\"12\" valign=\"top\">2013</th>\n",
       "      <th>Aotizhongxin</th>\n",
       "      <td>525.70</td>\n",
       "      <td>-9.400000</td>\n",
       "      <td>15.497740</td>\n",
       "      <td>37.9</td>\n",
       "      <td>60.171318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Changping</th>\n",
       "      <td>485.20</td>\n",
       "      <td>-9.600000</td>\n",
       "      <td>15.961397</td>\n",
       "      <td>37.9</td>\n",
       "      <td>51.602171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dingling</th>\n",
       "      <td>485.20</td>\n",
       "      <td>-9.600000</td>\n",
       "      <td>15.961397</td>\n",
       "      <td>37.9</td>\n",
       "      <td>51.602171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dongsi</th>\n",
       "      <td>425.80</td>\n",
       "      <td>-8.500000</td>\n",
       "      <td>15.963657</td>\n",
       "      <td>37.5</td>\n",
       "      <td>54.741841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Guanyuan</th>\n",
       "      <td>525.70</td>\n",
       "      <td>-9.400000</td>\n",
       "      <td>15.497740</td>\n",
       "      <td>37.9</td>\n",
       "      <td>60.171318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gucheng</th>\n",
       "      <td>514.10</td>\n",
       "      <td>-8.000000</td>\n",
       "      <td>15.932823</td>\n",
       "      <td>37.7</td>\n",
       "      <td>55.111165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Huairou</th>\n",
       "      <td>531.20</td>\n",
       "      <td>-11.600000</td>\n",
       "      <td>14.660335</td>\n",
       "      <td>36.9</td>\n",
       "      <td>59.432276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nongzhanguan</th>\n",
       "      <td>425.80</td>\n",
       "      <td>-8.500000</td>\n",
       "      <td>15.963657</td>\n",
       "      <td>37.5</td>\n",
       "      <td>54.741841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shunyi</th>\n",
       "      <td>476.10</td>\n",
       "      <td>-9.900000</td>\n",
       "      <td>15.535103</td>\n",
       "      <td>37.8</td>\n",
       "      <td>56.755222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tiantan</th>\n",
       "      <td>425.80</td>\n",
       "      <td>-8.500000</td>\n",
       "      <td>15.963657</td>\n",
       "      <td>37.5</td>\n",
       "      <td>54.741841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wanliu</th>\n",
       "      <td>525.70</td>\n",
       "      <td>-9.400000</td>\n",
       "      <td>15.497740</td>\n",
       "      <td>37.9</td>\n",
       "      <td>60.171318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wanshouxigong</th>\n",
       "      <td>478.70</td>\n",
       "      <td>-10.100000</td>\n",
       "      <td>15.854153</td>\n",
       "      <td>38.0</td>\n",
       "      <td>56.826610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"12\" valign=\"top\">2014</th>\n",
       "      <th>Aotizhongxin</th>\n",
       "      <td>495.90</td>\n",
       "      <td>-12.200000</td>\n",
       "      <td>13.878128</td>\n",
       "      <td>40.5</td>\n",
       "      <td>57.777574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Changping</th>\n",
       "      <td>331.90</td>\n",
       "      <td>-10.900000</td>\n",
       "      <td>14.172511</td>\n",
       "      <td>41.4</td>\n",
       "      <td>45.949114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dingling</th>\n",
       "      <td>331.90</td>\n",
       "      <td>-10.900000</td>\n",
       "      <td>14.172511</td>\n",
       "      <td>41.4</td>\n",
       "      <td>45.949114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dongsi</th>\n",
       "      <td>474.90</td>\n",
       "      <td>-11.500000</td>\n",
       "      <td>13.881153</td>\n",
       "      <td>41.1</td>\n",
       "      <td>52.250906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Guanyuan</th>\n",
       "      <td>495.90</td>\n",
       "      <td>-12.200000</td>\n",
       "      <td>13.878128</td>\n",
       "      <td>40.5</td>\n",
       "      <td>57.777574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gucheng</th>\n",
       "      <td>323.20</td>\n",
       "      <td>-10.600000</td>\n",
       "      <td>14.431661</td>\n",
       "      <td>41.6</td>\n",
       "      <td>51.618127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Huairou</th>\n",
       "      <td>463.60</td>\n",
       "      <td>-14.500000</td>\n",
       "      <td>12.800468</td>\n",
       "      <td>40.3</td>\n",
       "      <td>54.313500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nongzhanguan</th>\n",
       "      <td>474.90</td>\n",
       "      <td>-11.500000</td>\n",
       "      <td>13.881153</td>\n",
       "      <td>41.1</td>\n",
       "      <td>52.250906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shunyi</th>\n",
       "      <td>445.80</td>\n",
       "      <td>-10.500000</td>\n",
       "      <td>13.838950</td>\n",
       "      <td>40.6</td>\n",
       "      <td>51.593891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tiantan</th>\n",
       "      <td>474.90</td>\n",
       "      <td>-11.500000</td>\n",
       "      <td>13.881153</td>\n",
       "      <td>41.1</td>\n",
       "      <td>52.250906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wanliu</th>\n",
       "      <td>495.60</td>\n",
       "      <td>-12.200000</td>\n",
       "      <td>13.878402</td>\n",
       "      <td>40.5</td>\n",
       "      <td>57.793326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wanshouxigong</th>\n",
       "      <td>429.80</td>\n",
       "      <td>-11.000000</td>\n",
       "      <td>14.292637</td>\n",
       "      <td>40.6</td>\n",
       "      <td>52.333947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"12\" valign=\"top\">2015</th>\n",
       "      <th>Aotizhongxin</th>\n",
       "      <td>636.60</td>\n",
       "      <td>-10.000000</td>\n",
       "      <td>13.474687</td>\n",
       "      <td>38.8</td>\n",
       "      <td>54.379833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Changping</th>\n",
       "      <td>721.60</td>\n",
       "      <td>-10.300000</td>\n",
       "      <td>13.567626</td>\n",
       "      <td>39.2</td>\n",
       "      <td>50.681674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dingling</th>\n",
       "      <td>721.60</td>\n",
       "      <td>-10.300000</td>\n",
       "      <td>13.567626</td>\n",
       "      <td>39.2</td>\n",
       "      <td>50.681674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dongsi</th>\n",
       "      <td>638.30</td>\n",
       "      <td>-10.000000</td>\n",
       "      <td>13.428637</td>\n",
       "      <td>38.8</td>\n",
       "      <td>54.331192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Guanyuan</th>\n",
       "      <td>636.60</td>\n",
       "      <td>-10.000000</td>\n",
       "      <td>13.474687</td>\n",
       "      <td>38.8</td>\n",
       "      <td>54.379833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gucheng</th>\n",
       "      <td>706.80</td>\n",
       "      <td>-9.300000</td>\n",
       "      <td>14.027483</td>\n",
       "      <td>39.3</td>\n",
       "      <td>53.798439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Huairou</th>\n",
       "      <td>707.70</td>\n",
       "      <td>-13.800000</td>\n",
       "      <td>12.408231</td>\n",
       "      <td>39.0</td>\n",
       "      <td>57.380594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nongzhanguan</th>\n",
       "      <td>638.30</td>\n",
       "      <td>-10.000000</td>\n",
       "      <td>13.428637</td>\n",
       "      <td>38.8</td>\n",
       "      <td>54.331192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shunyi</th>\n",
       "      <td>668.35</td>\n",
       "      <td>-11.200000</td>\n",
       "      <td>13.251330</td>\n",
       "      <td>39.0</td>\n",
       "      <td>54.677253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tiantan</th>\n",
       "      <td>638.30</td>\n",
       "      <td>-10.000000</td>\n",
       "      <td>13.428637</td>\n",
       "      <td>38.8</td>\n",
       "      <td>54.331192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wanliu</th>\n",
       "      <td>634.70</td>\n",
       "      <td>-10.900000</td>\n",
       "      <td>13.352189</td>\n",
       "      <td>39.0</td>\n",
       "      <td>55.933755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wanshouxigong</th>\n",
       "      <td>641.10</td>\n",
       "      <td>-9.700000</td>\n",
       "      <td>13.563500</td>\n",
       "      <td>38.8</td>\n",
       "      <td>54.415864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"12\" valign=\"top\">2016</th>\n",
       "      <th>Aotizhongxin</th>\n",
       "      <td>700.10</td>\n",
       "      <td>-16.800000</td>\n",
       "      <td>13.871962</td>\n",
       "      <td>37.3</td>\n",
       "      <td>51.102627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Changping</th>\n",
       "      <td>573.40</td>\n",
       "      <td>-16.600000</td>\n",
       "      <td>13.499471</td>\n",
       "      <td>38.1</td>\n",
       "      <td>49.824936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dingling</th>\n",
       "      <td>573.40</td>\n",
       "      <td>-16.600000</td>\n",
       "      <td>13.499471</td>\n",
       "      <td>38.1</td>\n",
       "      <td>49.824936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dongsi</th>\n",
       "      <td>700.10</td>\n",
       "      <td>-16.800000</td>\n",
       "      <td>13.871962</td>\n",
       "      <td>37.3</td>\n",
       "      <td>51.102627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Guanyuan</th>\n",
       "      <td>700.10</td>\n",
       "      <td>-16.800000</td>\n",
       "      <td>13.871962</td>\n",
       "      <td>37.3</td>\n",
       "      <td>51.102627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gucheng</th>\n",
       "      <td>709.20</td>\n",
       "      <td>-15.600000</td>\n",
       "      <td>13.547228</td>\n",
       "      <td>37.2</td>\n",
       "      <td>51.375981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Huairou</th>\n",
       "      <td>674.50</td>\n",
       "      <td>-19.900000</td>\n",
       "      <td>12.413948</td>\n",
       "      <td>36.5</td>\n",
       "      <td>56.019554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nongzhanguan</th>\n",
       "      <td>700.10</td>\n",
       "      <td>-16.800000</td>\n",
       "      <td>13.871962</td>\n",
       "      <td>37.3</td>\n",
       "      <td>51.102627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shunyi</th>\n",
       "      <td>545.30</td>\n",
       "      <td>-16.800000</td>\n",
       "      <td>13.390331</td>\n",
       "      <td>36.6</td>\n",
       "      <td>52.139438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tiantan</th>\n",
       "      <td>700.10</td>\n",
       "      <td>-16.800000</td>\n",
       "      <td>13.871962</td>\n",
       "      <td>37.3</td>\n",
       "      <td>51.102627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wanliu</th>\n",
       "      <td>732.00</td>\n",
       "      <td>-15.800000</td>\n",
       "      <td>13.413623</td>\n",
       "      <td>38.2</td>\n",
       "      <td>53.608384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wanshouxigong</th>\n",
       "      <td>700.10</td>\n",
       "      <td>-16.800000</td>\n",
       "      <td>13.871962</td>\n",
       "      <td>37.3</td>\n",
       "      <td>51.102627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"12\" valign=\"top\">2017</th>\n",
       "      <th>Aotizhongxin</th>\n",
       "      <td>4.40</td>\n",
       "      <td>-9.700000</td>\n",
       "      <td>0.664779</td>\n",
       "      <td>15.9</td>\n",
       "      <td>40.791485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Changping</th>\n",
       "      <td>2.00</td>\n",
       "      <td>-9.966667</td>\n",
       "      <td>0.407973</td>\n",
       "      <td>15.6</td>\n",
       "      <td>37.206496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dingling</th>\n",
       "      <td>2.00</td>\n",
       "      <td>-9.966667</td>\n",
       "      <td>0.407973</td>\n",
       "      <td>15.6</td>\n",
       "      <td>37.206496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dongsi</th>\n",
       "      <td>4.40</td>\n",
       "      <td>-9.700000</td>\n",
       "      <td>0.664779</td>\n",
       "      <td>15.9</td>\n",
       "      <td>40.791485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Guanyuan</th>\n",
       "      <td>4.40</td>\n",
       "      <td>-9.700000</td>\n",
       "      <td>0.664779</td>\n",
       "      <td>15.9</td>\n",
       "      <td>40.791485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gucheng</th>\n",
       "      <td>3.90</td>\n",
       "      <td>-9.900000</td>\n",
       "      <td>0.373600</td>\n",
       "      <td>15.5</td>\n",
       "      <td>38.648174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Huairou</th>\n",
       "      <td>5.60</td>\n",
       "      <td>-13.425000</td>\n",
       "      <td>-1.181355</td>\n",
       "      <td>15.8</td>\n",
       "      <td>43.813606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nongzhanguan</th>\n",
       "      <td>4.40</td>\n",
       "      <td>-9.700000</td>\n",
       "      <td>0.664779</td>\n",
       "      <td>15.9</td>\n",
       "      <td>40.791485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shunyi</th>\n",
       "      <td>3.70</td>\n",
       "      <td>-11.000000</td>\n",
       "      <td>-0.015328</td>\n",
       "      <td>15.4</td>\n",
       "      <td>41.943738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tiantan</th>\n",
       "      <td>4.40</td>\n",
       "      <td>-9.700000</td>\n",
       "      <td>0.664779</td>\n",
       "      <td>15.9</td>\n",
       "      <td>40.791485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wanliu</th>\n",
       "      <td>4.20</td>\n",
       "      <td>-10.200000</td>\n",
       "      <td>0.410612</td>\n",
       "      <td>16.2</td>\n",
       "      <td>41.572025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wanshouxigong</th>\n",
       "      <td>4.40</td>\n",
       "      <td>-9.700000</td>\n",
       "      <td>0.664779</td>\n",
       "      <td>15.9</td>\n",
       "      <td>40.791485</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Curah Hujan       Suhu                  Kelembapan\n",
       "                            sum        min       mean   max       mean\n",
       "Tahun station                                                         \n",
       "2013  Aotizhongxin       525.70  -9.400000  15.497740  37.9  60.171318\n",
       "      Changping          485.20  -9.600000  15.961397  37.9  51.602171\n",
       "      Dingling           485.20  -9.600000  15.961397  37.9  51.602171\n",
       "      Dongsi             425.80  -8.500000  15.963657  37.5  54.741841\n",
       "      Guanyuan           525.70  -9.400000  15.497740  37.9  60.171318\n",
       "      Gucheng            514.10  -8.000000  15.932823  37.7  55.111165\n",
       "      Huairou            531.20 -11.600000  14.660335  36.9  59.432276\n",
       "      Nongzhanguan       425.80  -8.500000  15.963657  37.5  54.741841\n",
       "      Shunyi             476.10  -9.900000  15.535103  37.8  56.755222\n",
       "      Tiantan            425.80  -8.500000  15.963657  37.5  54.741841\n",
       "      Wanliu             525.70  -9.400000  15.497740  37.9  60.171318\n",
       "      Wanshouxigong      478.70 -10.100000  15.854153  38.0  56.826610\n",
       "2014  Aotizhongxin       495.90 -12.200000  13.878128  40.5  57.777574\n",
       "      Changping          331.90 -10.900000  14.172511  41.4  45.949114\n",
       "      Dingling           331.90 -10.900000  14.172511  41.4  45.949114\n",
       "      Dongsi             474.90 -11.500000  13.881153  41.1  52.250906\n",
       "      Guanyuan           495.90 -12.200000  13.878128  40.5  57.777574\n",
       "      Gucheng            323.20 -10.600000  14.431661  41.6  51.618127\n",
       "      Huairou            463.60 -14.500000  12.800468  40.3  54.313500\n",
       "      Nongzhanguan       474.90 -11.500000  13.881153  41.1  52.250906\n",
       "      Shunyi             445.80 -10.500000  13.838950  40.6  51.593891\n",
       "      Tiantan            474.90 -11.500000  13.881153  41.1  52.250906\n",
       "      Wanliu             495.60 -12.200000  13.878402  40.5  57.793326\n",
       "      Wanshouxigong      429.80 -11.000000  14.292637  40.6  52.333947\n",
       "2015  Aotizhongxin       636.60 -10.000000  13.474687  38.8  54.379833\n",
       "      Changping          721.60 -10.300000  13.567626  39.2  50.681674\n",
       "      Dingling           721.60 -10.300000  13.567626  39.2  50.681674\n",
       "      Dongsi             638.30 -10.000000  13.428637  38.8  54.331192\n",
       "      Guanyuan           636.60 -10.000000  13.474687  38.8  54.379833\n",
       "      Gucheng            706.80  -9.300000  14.027483  39.3  53.798439\n",
       "      Huairou            707.70 -13.800000  12.408231  39.0  57.380594\n",
       "      Nongzhanguan       638.30 -10.000000  13.428637  38.8  54.331192\n",
       "      Shunyi             668.35 -11.200000  13.251330  39.0  54.677253\n",
       "      Tiantan            638.30 -10.000000  13.428637  38.8  54.331192\n",
       "      Wanliu             634.70 -10.900000  13.352189  39.0  55.933755\n",
       "      Wanshouxigong      641.10  -9.700000  13.563500  38.8  54.415864\n",
       "2016  Aotizhongxin       700.10 -16.800000  13.871962  37.3  51.102627\n",
       "      Changping          573.40 -16.600000  13.499471  38.1  49.824936\n",
       "      Dingling           573.40 -16.600000  13.499471  38.1  49.824936\n",
       "      Dongsi             700.10 -16.800000  13.871962  37.3  51.102627\n",
       "      Guanyuan           700.10 -16.800000  13.871962  37.3  51.102627\n",
       "      Gucheng            709.20 -15.600000  13.547228  37.2  51.375981\n",
       "      Huairou            674.50 -19.900000  12.413948  36.5  56.019554\n",
       "      Nongzhanguan       700.10 -16.800000  13.871962  37.3  51.102627\n",
       "      Shunyi             545.30 -16.800000  13.390331  36.6  52.139438\n",
       "      Tiantan            700.10 -16.800000  13.871962  37.3  51.102627\n",
       "      Wanliu             732.00 -15.800000  13.413623  38.2  53.608384\n",
       "      Wanshouxigong      700.10 -16.800000  13.871962  37.3  51.102627\n",
       "2017  Aotizhongxin         4.40  -9.700000   0.664779  15.9  40.791485\n",
       "      Changping            2.00  -9.966667   0.407973  15.6  37.206496\n",
       "      Dingling             2.00  -9.966667   0.407973  15.6  37.206496\n",
       "      Dongsi               4.40  -9.700000   0.664779  15.9  40.791485\n",
       "      Guanyuan             4.40  -9.700000   0.664779  15.9  40.791485\n",
       "      Gucheng              3.90  -9.900000   0.373600  15.5  38.648174\n",
       "      Huairou              5.60 -13.425000  -1.181355  15.8  43.813606\n",
       "      Nongzhanguan         4.40  -9.700000   0.664779  15.9  40.791485\n",
       "      Shunyi               3.70 -11.000000  -0.015328  15.4  41.943738\n",
       "      Tiantan              4.40  -9.700000   0.664779  15.9  40.791485\n",
       "      Wanliu               4.20 -10.200000   0.410612  16.2  41.572025\n",
       "      Wanshouxigong        4.40  -9.700000   0.664779  15.9  40.791485"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "climate_sumByStation = all_df.groupby(by=[all_df['DateTime'].dt.year.rename('Tahun'), 'station']).agg({\n",
    "                    'RAIN' : 'sum',\n",
    "                    'TEMP': ['min', 'mean', 'max'],\n",
    "                    'Rh' : 'mean'\n",
    "                    })\n",
    "\n",
    "\n",
    "climate_sumByStation.rename(columns={\n",
    "    'RAIN' : 'Curah Hujan',\n",
    "    'TEMP' : 'Suhu',\n",
    "    'Rh' : 'Kelembapan'\n",
    "    }, inplace=True)\n",
    "\n",
    "climate_sumByStation\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Rincian Parameter Curah Hujan Wilayah, Suhu, dan Tingkat Kelembapan (Rh) Bulanan di Kawasan Beijing dalam Kurun Waktu 5 Tahun"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Curah Hujan</th>\n",
       "      <th>Suhu Minimum</th>\n",
       "      <th>Suhu Rerata</th>\n",
       "      <th>Suhu Maksimum</th>\n",
       "      <th>Kelembapan</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tahun</th>\n",
       "      <th>Bulan</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 rowspan=\"10\" valign=\"top\">2013</th>\n",
       "      <th>3</th>\n",
       "      <td>212.20</td>\n",
       "      <td>-7.200</td>\n",
       "      <td>5.992249</td>\n",
       "      <td>22.9</td>\n",
       "      <td>45.525133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>123.50</td>\n",
       "      <td>-1.600</td>\n",
       "      <td>12.380058</td>\n",
       "      <td>29.4</td>\n",
       "      <td>39.081310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>51.70</td>\n",
       "      <td>7.800</td>\n",
       "      <td>21.676691</td>\n",
       "      <td>35.4</td>\n",
       "      <td>47.024361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1093.20</td>\n",
       "      <td>13.100</td>\n",
       "      <td>23.583426</td>\n",
       "      <td>35.7</td>\n",
       "      <td>72.180721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2459.20</td>\n",
       "      <td>18.100</td>\n",
       "      <td>27.108826</td>\n",
       "      <td>38.0</td>\n",
       "      <td>71.965485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>972.00</td>\n",
       "      <td>12.900</td>\n",
       "      <td>26.904620</td>\n",
       "      <td>37.7</td>\n",
       "      <td>69.468357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>788.00</td>\n",
       "      <td>6.100</td>\n",
       "      <td>20.240683</td>\n",
       "      <td>31.8</td>\n",
       "      <td>72.627152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>124.40</td>\n",
       "      <td>-0.200</td>\n",
       "      <td>12.984017</td>\n",
       "      <td>26.4</td>\n",
       "      <td>63.629676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.80</td>\n",
       "      <td>-9.700</td>\n",
       "      <td>5.963900</td>\n",
       "      <td>18.9</td>\n",
       "      <td>43.580772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.00</td>\n",
       "      <td>-11.600</td>\n",
       "      <td>0.054167</td>\n",
       "      <td>15.4</td>\n",
       "      <td>38.376122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"12\" valign=\"top\">2014</th>\n",
       "      <th>1</th>\n",
       "      <td>0.00</td>\n",
       "      <td>-14.300</td>\n",
       "      <td>-0.283031</td>\n",
       "      <td>13.4</td>\n",
       "      <td>42.473667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>65.00</td>\n",
       "      <td>-14.500</td>\n",
       "      <td>-0.346850</td>\n",
       "      <td>9.9</td>\n",
       "      <td>54.551167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17.10</td>\n",
       "      <td>-7.300</td>\n",
       "      <td>9.901467</td>\n",
       "      <td>26.2</td>\n",
       "      <td>37.535102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>167.70</td>\n",
       "      <td>2.700</td>\n",
       "      <td>17.017998</td>\n",
       "      <td>30.6</td>\n",
       "      <td>45.142749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>700.30</td>\n",
       "      <td>4.300</td>\n",
       "      <td>21.616420</td>\n",
       "      <td>41.6</td>\n",
       "      <td>43.492232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1513.80</td>\n",
       "      <td>13.200</td>\n",
       "      <td>24.766296</td>\n",
       "      <td>37.5</td>\n",
       "      <td>62.112910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>822.10</td>\n",
       "      <td>18.500</td>\n",
       "      <td>28.030735</td>\n",
       "      <td>37.3</td>\n",
       "      <td>63.122255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>760.80</td>\n",
       "      <td>15.000</td>\n",
       "      <td>26.026019</td>\n",
       "      <td>35.7</td>\n",
       "      <td>64.454405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>981.30</td>\n",
       "      <td>8.300</td>\n",
       "      <td>20.701493</td>\n",
       "      <td>34.5</td>\n",
       "      <td>68.245951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>189.90</td>\n",
       "      <td>1.600</td>\n",
       "      <td>13.567260</td>\n",
       "      <td>25.5</td>\n",
       "      <td>66.814536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>6.00</td>\n",
       "      <td>-4.600</td>\n",
       "      <td>5.910110</td>\n",
       "      <td>21.5</td>\n",
       "      <td>50.895486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>14.30</td>\n",
       "      <td>-11.900</td>\n",
       "      <td>-0.890535</td>\n",
       "      <td>12.6</td>\n",
       "      <td>33.710884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"12\" valign=\"top\">2015</th>\n",
       "      <th>1</th>\n",
       "      <td>3.50</td>\n",
       "      <td>-13.000</td>\n",
       "      <td>-0.893806</td>\n",
       "      <td>14.0</td>\n",
       "      <td>39.673276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>93.05</td>\n",
       "      <td>-13.000</td>\n",
       "      <td>1.264342</td>\n",
       "      <td>14.0</td>\n",
       "      <td>38.473984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>41.60</td>\n",
       "      <td>-8.900</td>\n",
       "      <td>8.506220</td>\n",
       "      <td>24.1</td>\n",
       "      <td>31.724656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>422.40</td>\n",
       "      <td>-0.600</td>\n",
       "      <td>15.581226</td>\n",
       "      <td>32.3</td>\n",
       "      <td>43.464797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>444.40</td>\n",
       "      <td>7.000</td>\n",
       "      <td>21.351697</td>\n",
       "      <td>34.6</td>\n",
       "      <td>43.189849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1067.60</td>\n",
       "      <td>12.200</td>\n",
       "      <td>24.462847</td>\n",
       "      <td>34.1</td>\n",
       "      <td>56.827136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2676.70</td>\n",
       "      <td>14.800</td>\n",
       "      <td>26.291207</td>\n",
       "      <td>39.3</td>\n",
       "      <td>65.388768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1057.20</td>\n",
       "      <td>16.100</td>\n",
       "      <td>26.240715</td>\n",
       "      <td>37.0</td>\n",
       "      <td>64.968612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1443.10</td>\n",
       "      <td>8.700</td>\n",
       "      <td>20.424225</td>\n",
       "      <td>32.3</td>\n",
       "      <td>69.282162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>152.80</td>\n",
       "      <td>-1.400</td>\n",
       "      <td>14.192059</td>\n",
       "      <td>27.5</td>\n",
       "      <td>56.616157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>556.20</td>\n",
       "      <td>-13.800</td>\n",
       "      <td>3.009815</td>\n",
       "      <td>20.8</td>\n",
       "      <td>76.587934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>31.40</td>\n",
       "      <td>-11.800</td>\n",
       "      <td>-0.316308</td>\n",
       "      <td>10.0</td>\n",
       "      <td>62.570729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"12\" valign=\"top\">2016</th>\n",
       "      <th>1</th>\n",
       "      <td>6.20</td>\n",
       "      <td>-19.900</td>\n",
       "      <td>-4.576232</td>\n",
       "      <td>8.1</td>\n",
       "      <td>38.315701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>122.10</td>\n",
       "      <td>-13.300</td>\n",
       "      <td>1.234052</td>\n",
       "      <td>11.8</td>\n",
       "      <td>32.508131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.00</td>\n",
       "      <td>-6.600</td>\n",
       "      <td>8.993929</td>\n",
       "      <td>24.0</td>\n",
       "      <td>31.684669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>77.30</td>\n",
       "      <td>4.200</td>\n",
       "      <td>16.336354</td>\n",
       "      <td>31.5</td>\n",
       "      <td>37.248284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>441.80</td>\n",
       "      <td>5.100</td>\n",
       "      <td>21.282482</td>\n",
       "      <td>34.0</td>\n",
       "      <td>42.952814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>921.40</td>\n",
       "      <td>14.200</td>\n",
       "      <td>25.406875</td>\n",
       "      <td>38.2</td>\n",
       "      <td>55.805849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3601.60</td>\n",
       "      <td>17.800</td>\n",
       "      <td>27.345912</td>\n",
       "      <td>37.5</td>\n",
       "      <td>69.296284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>652.60</td>\n",
       "      <td>14.500</td>\n",
       "      <td>27.396685</td>\n",
       "      <td>36.2</td>\n",
       "      <td>64.408655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1086.00</td>\n",
       "      <td>6.050</td>\n",
       "      <td>21.727431</td>\n",
       "      <td>32.6</td>\n",
       "      <td>64.679713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1007.00</td>\n",
       "      <td>-2.600</td>\n",
       "      <td>13.057237</td>\n",
       "      <td>29.4</td>\n",
       "      <td>71.021352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>92.40</td>\n",
       "      <td>-9.000</td>\n",
       "      <td>4.198391</td>\n",
       "      <td>20.9</td>\n",
       "      <td>59.109728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.00</td>\n",
       "      <td>-11.500</td>\n",
       "      <td>0.226254</td>\n",
       "      <td>11.6</td>\n",
       "      <td>51.479598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2017</th>\n",
       "      <th>1</th>\n",
       "      <td>2.90</td>\n",
       "      <td>-13.425</td>\n",
       "      <td>-1.425101</td>\n",
       "      <td>9.8</td>\n",
       "      <td>46.045659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>44.90</td>\n",
       "      <td>-11.300</td>\n",
       "      <td>2.349030</td>\n",
       "      <td>16.2</td>\n",
       "      <td>34.209054</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Curah Hujan  Suhu Minimum  Suhu Rerata  Suhu Maksimum  Kelembapan\n",
       "Tahun Bulan                                                                   \n",
       "2013  3           212.20        -7.200     5.992249           22.9   45.525133\n",
       "      4           123.50        -1.600    12.380058           29.4   39.081310\n",
       "      5            51.70         7.800    21.676691           35.4   47.024361\n",
       "      6          1093.20        13.100    23.583426           35.7   72.180721\n",
       "      7          2459.20        18.100    27.108826           38.0   71.965485\n",
       "      8           972.00        12.900    26.904620           37.7   69.468357\n",
       "      9           788.00         6.100    20.240683           31.8   72.627152\n",
       "      10          124.40        -0.200    12.984017           26.4   63.629676\n",
       "      11            0.80        -9.700     5.963900           18.9   43.580772\n",
       "      12            0.00       -11.600     0.054167           15.4   38.376122\n",
       "2014  1             0.00       -14.300    -0.283031           13.4   42.473667\n",
       "      2            65.00       -14.500    -0.346850            9.9   54.551167\n",
       "      3            17.10        -7.300     9.901467           26.2   37.535102\n",
       "      4           167.70         2.700    17.017998           30.6   45.142749\n",
       "      5           700.30         4.300    21.616420           41.6   43.492232\n",
       "      6          1513.80        13.200    24.766296           37.5   62.112910\n",
       "      7           822.10        18.500    28.030735           37.3   63.122255\n",
       "      8           760.80        15.000    26.026019           35.7   64.454405\n",
       "      9           981.30         8.300    20.701493           34.5   68.245951\n",
       "      10          189.90         1.600    13.567260           25.5   66.814536\n",
       "      11            6.00        -4.600     5.910110           21.5   50.895486\n",
       "      12           14.30       -11.900    -0.890535           12.6   33.710884\n",
       "2015  1             3.50       -13.000    -0.893806           14.0   39.673276\n",
       "      2            93.05       -13.000     1.264342           14.0   38.473984\n",
       "      3            41.60        -8.900     8.506220           24.1   31.724656\n",
       "      4           422.40        -0.600    15.581226           32.3   43.464797\n",
       "      5           444.40         7.000    21.351697           34.6   43.189849\n",
       "      6          1067.60        12.200    24.462847           34.1   56.827136\n",
       "      7          2676.70        14.800    26.291207           39.3   65.388768\n",
       "      8          1057.20        16.100    26.240715           37.0   64.968612\n",
       "      9          1443.10         8.700    20.424225           32.3   69.282162\n",
       "      10          152.80        -1.400    14.192059           27.5   56.616157\n",
       "      11          556.20       -13.800     3.009815           20.8   76.587934\n",
       "      12           31.40       -11.800    -0.316308           10.0   62.570729\n",
       "2016  1             6.20       -19.900    -4.576232            8.1   38.315701\n",
       "      2           122.10       -13.300     1.234052           11.8   32.508131\n",
       "      3             0.00        -6.600     8.993929           24.0   31.684669\n",
       "      4            77.30         4.200    16.336354           31.5   37.248284\n",
       "      5           441.80         5.100    21.282482           34.0   42.952814\n",
       "      6           921.40        14.200    25.406875           38.2   55.805849\n",
       "      7          3601.60        17.800    27.345912           37.5   69.296284\n",
       "      8           652.60        14.500    27.396685           36.2   64.408655\n",
       "      9          1086.00         6.050    21.727431           32.6   64.679713\n",
       "      10         1007.00        -2.600    13.057237           29.4   71.021352\n",
       "      11           92.40        -9.000     4.198391           20.9   59.109728\n",
       "      12            0.00       -11.500     0.226254           11.6   51.479598\n",
       "2017  1             2.90       -13.425    -1.425101            9.8   46.045659\n",
       "      2            44.90       -11.300     2.349030           16.2   34.209054"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly_climateBeijing = all_df.groupby(by=[all_df['DateTime'].dt.year.rename('Tahun'), \n",
    "                                            all_df['DateTime'].dt.month.rename('Bulan')]).agg({\n",
    "                                            'RAIN': 'sum',\n",
    "                                            'TEMP': ['min', 'mean', 'max'],\n",
    "                                            'Rh' : 'mean'\n",
    "                                        })\n",
    "monthly_climateBeijing.columns = ['Curah Hujan', 'Suhu Minimum', 'Suhu Rerata', 'Suhu Maksimum', 'Kelembapan']\n",
    "monthly_climateBeijing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "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>Tanggal</th>\n",
       "      <th>Curah Hujan</th>\n",
       "      <th>Suhu Minimum</th>\n",
       "      <th>Suhu Rerata</th>\n",
       "      <th>Suhu Maksimum</th>\n",
       "      <th>Kelembapan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013-03-01</td>\n",
       "      <td>212.20</td>\n",
       "      <td>-7.200</td>\n",
       "      <td>5.992249</td>\n",
       "      <td>22.9</td>\n",
       "      <td>45.525133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013-04-01</td>\n",
       "      <td>123.50</td>\n",
       "      <td>-1.600</td>\n",
       "      <td>12.380058</td>\n",
       "      <td>29.4</td>\n",
       "      <td>39.081310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013-05-01</td>\n",
       "      <td>51.70</td>\n",
       "      <td>7.800</td>\n",
       "      <td>21.676691</td>\n",
       "      <td>35.4</td>\n",
       "      <td>47.024361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013-06-01</td>\n",
       "      <td>1093.20</td>\n",
       "      <td>13.100</td>\n",
       "      <td>23.583426</td>\n",
       "      <td>35.7</td>\n",
       "      <td>72.180721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013-07-01</td>\n",
       "      <td>2459.20</td>\n",
       "      <td>18.100</td>\n",
       "      <td>27.108826</td>\n",
       "      <td>38.0</td>\n",
       "      <td>71.965485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2013-08-01</td>\n",
       "      <td>972.00</td>\n",
       "      <td>12.900</td>\n",
       "      <td>26.904620</td>\n",
       "      <td>37.7</td>\n",
       "      <td>69.468357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2013-09-01</td>\n",
       "      <td>788.00</td>\n",
       "      <td>6.100</td>\n",
       "      <td>20.240683</td>\n",
       "      <td>31.8</td>\n",
       "      <td>72.627152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2013-10-01</td>\n",
       "      <td>124.40</td>\n",
       "      <td>-0.200</td>\n",
       "      <td>12.984017</td>\n",
       "      <td>26.4</td>\n",
       "      <td>63.629676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2013-11-01</td>\n",
       "      <td>0.80</td>\n",
       "      <td>-9.700</td>\n",
       "      <td>5.963900</td>\n",
       "      <td>18.9</td>\n",
       "      <td>43.580772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2013-12-01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-11.600</td>\n",
       "      <td>0.054167</td>\n",
       "      <td>15.4</td>\n",
       "      <td>38.376122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-14.300</td>\n",
       "      <td>-0.283031</td>\n",
       "      <td>13.4</td>\n",
       "      <td>42.473667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2014-02-01</td>\n",
       "      <td>65.00</td>\n",
       "      <td>-14.500</td>\n",
       "      <td>-0.346850</td>\n",
       "      <td>9.9</td>\n",
       "      <td>54.551167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2014-03-01</td>\n",
       "      <td>17.10</td>\n",
       "      <td>-7.300</td>\n",
       "      <td>9.901467</td>\n",
       "      <td>26.2</td>\n",
       "      <td>37.535102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2014-04-01</td>\n",
       "      <td>167.70</td>\n",
       "      <td>2.700</td>\n",
       "      <td>17.017998</td>\n",
       "      <td>30.6</td>\n",
       "      <td>45.142749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2014-05-01</td>\n",
       "      <td>700.30</td>\n",
       "      <td>4.300</td>\n",
       "      <td>21.616420</td>\n",
       "      <td>41.6</td>\n",
       "      <td>43.492232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2014-06-01</td>\n",
       "      <td>1513.80</td>\n",
       "      <td>13.200</td>\n",
       "      <td>24.766296</td>\n",
       "      <td>37.5</td>\n",
       "      <td>62.112910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2014-07-01</td>\n",
       "      <td>822.10</td>\n",
       "      <td>18.500</td>\n",
       "      <td>28.030735</td>\n",
       "      <td>37.3</td>\n",
       "      <td>63.122255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2014-08-01</td>\n",
       "      <td>760.80</td>\n",
       "      <td>15.000</td>\n",
       "      <td>26.026019</td>\n",
       "      <td>35.7</td>\n",
       "      <td>64.454405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2014-09-01</td>\n",
       "      <td>981.30</td>\n",
       "      <td>8.300</td>\n",
       "      <td>20.701493</td>\n",
       "      <td>34.5</td>\n",
       "      <td>68.245951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2014-10-01</td>\n",
       "      <td>189.90</td>\n",
       "      <td>1.600</td>\n",
       "      <td>13.567260</td>\n",
       "      <td>25.5</td>\n",
       "      <td>66.814536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2014-11-01</td>\n",
       "      <td>6.00</td>\n",
       "      <td>-4.600</td>\n",
       "      <td>5.910110</td>\n",
       "      <td>21.5</td>\n",
       "      <td>50.895486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2014-12-01</td>\n",
       "      <td>14.30</td>\n",
       "      <td>-11.900</td>\n",
       "      <td>-0.890535</td>\n",
       "      <td>12.6</td>\n",
       "      <td>33.710884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>3.50</td>\n",
       "      <td>-13.000</td>\n",
       "      <td>-0.893806</td>\n",
       "      <td>14.0</td>\n",
       "      <td>39.673276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2015-02-01</td>\n",
       "      <td>93.05</td>\n",
       "      <td>-13.000</td>\n",
       "      <td>1.264342</td>\n",
       "      <td>14.0</td>\n",
       "      <td>38.473984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2015-03-01</td>\n",
       "      <td>41.60</td>\n",
       "      <td>-8.900</td>\n",
       "      <td>8.506220</td>\n",
       "      <td>24.1</td>\n",
       "      <td>31.724656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2015-04-01</td>\n",
       "      <td>422.40</td>\n",
       "      <td>-0.600</td>\n",
       "      <td>15.581226</td>\n",
       "      <td>32.3</td>\n",
       "      <td>43.464797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2015-05-01</td>\n",
       "      <td>444.40</td>\n",
       "      <td>7.000</td>\n",
       "      <td>21.351697</td>\n",
       "      <td>34.6</td>\n",
       "      <td>43.189849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2015-06-01</td>\n",
       "      <td>1067.60</td>\n",
       "      <td>12.200</td>\n",
       "      <td>24.462847</td>\n",
       "      <td>34.1</td>\n",
       "      <td>56.827136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2015-07-01</td>\n",
       "      <td>2676.70</td>\n",
       "      <td>14.800</td>\n",
       "      <td>26.291207</td>\n",
       "      <td>39.3</td>\n",
       "      <td>65.388768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2015-08-01</td>\n",
       "      <td>1057.20</td>\n",
       "      <td>16.100</td>\n",
       "      <td>26.240715</td>\n",
       "      <td>37.0</td>\n",
       "      <td>64.968612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2015-09-01</td>\n",
       "      <td>1443.10</td>\n",
       "      <td>8.700</td>\n",
       "      <td>20.424225</td>\n",
       "      <td>32.3</td>\n",
       "      <td>69.282162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2015-10-01</td>\n",
       "      <td>152.80</td>\n",
       "      <td>-1.400</td>\n",
       "      <td>14.192059</td>\n",
       "      <td>27.5</td>\n",
       "      <td>56.616157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2015-11-01</td>\n",
       "      <td>556.20</td>\n",
       "      <td>-13.800</td>\n",
       "      <td>3.009815</td>\n",
       "      <td>20.8</td>\n",
       "      <td>76.587934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>2015-12-01</td>\n",
       "      <td>31.40</td>\n",
       "      <td>-11.800</td>\n",
       "      <td>-0.316308</td>\n",
       "      <td>10.0</td>\n",
       "      <td>62.570729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>6.20</td>\n",
       "      <td>-19.900</td>\n",
       "      <td>-4.576232</td>\n",
       "      <td>8.1</td>\n",
       "      <td>38.315701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>2016-02-01</td>\n",
       "      <td>122.10</td>\n",
       "      <td>-13.300</td>\n",
       "      <td>1.234052</td>\n",
       "      <td>11.8</td>\n",
       "      <td>32.508131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>2016-03-01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-6.600</td>\n",
       "      <td>8.993929</td>\n",
       "      <td>24.0</td>\n",
       "      <td>31.684669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>2016-04-01</td>\n",
       "      <td>77.30</td>\n",
       "      <td>4.200</td>\n",
       "      <td>16.336354</td>\n",
       "      <td>31.5</td>\n",
       "      <td>37.248284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>2016-05-01</td>\n",
       "      <td>441.80</td>\n",
       "      <td>5.100</td>\n",
       "      <td>21.282482</td>\n",
       "      <td>34.0</td>\n",
       "      <td>42.952814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>2016-06-01</td>\n",
       "      <td>921.40</td>\n",
       "      <td>14.200</td>\n",
       "      <td>25.406875</td>\n",
       "      <td>38.2</td>\n",
       "      <td>55.805849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>2016-07-01</td>\n",
       "      <td>3601.60</td>\n",
       "      <td>17.800</td>\n",
       "      <td>27.345912</td>\n",
       "      <td>37.5</td>\n",
       "      <td>69.296284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>2016-08-01</td>\n",
       "      <td>652.60</td>\n",
       "      <td>14.500</td>\n",
       "      <td>27.396685</td>\n",
       "      <td>36.2</td>\n",
       "      <td>64.408655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>2016-09-01</td>\n",
       "      <td>1086.00</td>\n",
       "      <td>6.050</td>\n",
       "      <td>21.727431</td>\n",
       "      <td>32.6</td>\n",
       "      <td>64.679713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>2016-10-01</td>\n",
       "      <td>1007.00</td>\n",
       "      <td>-2.600</td>\n",
       "      <td>13.057237</td>\n",
       "      <td>29.4</td>\n",
       "      <td>71.021352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>2016-11-01</td>\n",
       "      <td>92.40</td>\n",
       "      <td>-9.000</td>\n",
       "      <td>4.198391</td>\n",
       "      <td>20.9</td>\n",
       "      <td>59.109728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>2016-12-01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-11.500</td>\n",
       "      <td>0.226254</td>\n",
       "      <td>11.6</td>\n",
       "      <td>51.479598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>2.90</td>\n",
       "      <td>-13.425</td>\n",
       "      <td>-1.425101</td>\n",
       "      <td>9.8</td>\n",
       "      <td>46.045659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>2017-02-01</td>\n",
       "      <td>44.90</td>\n",
       "      <td>-11.300</td>\n",
       "      <td>2.349030</td>\n",
       "      <td>16.2</td>\n",
       "      <td>34.209054</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Tanggal  Curah Hujan  Suhu Minimum  Suhu Rerata  Suhu Maksimum  \\\n",
       "0  2013-03-01       212.20        -7.200     5.992249           22.9   \n",
       "1  2013-04-01       123.50        -1.600    12.380058           29.4   \n",
       "2  2013-05-01        51.70         7.800    21.676691           35.4   \n",
       "3  2013-06-01      1093.20        13.100    23.583426           35.7   \n",
       "4  2013-07-01      2459.20        18.100    27.108826           38.0   \n",
       "5  2013-08-01       972.00        12.900    26.904620           37.7   \n",
       "6  2013-09-01       788.00         6.100    20.240683           31.8   \n",
       "7  2013-10-01       124.40        -0.200    12.984017           26.4   \n",
       "8  2013-11-01         0.80        -9.700     5.963900           18.9   \n",
       "9  2013-12-01         0.00       -11.600     0.054167           15.4   \n",
       "10 2014-01-01         0.00       -14.300    -0.283031           13.4   \n",
       "11 2014-02-01        65.00       -14.500    -0.346850            9.9   \n",
       "12 2014-03-01        17.10        -7.300     9.901467           26.2   \n",
       "13 2014-04-01       167.70         2.700    17.017998           30.6   \n",
       "14 2014-05-01       700.30         4.300    21.616420           41.6   \n",
       "15 2014-06-01      1513.80        13.200    24.766296           37.5   \n",
       "16 2014-07-01       822.10        18.500    28.030735           37.3   \n",
       "17 2014-08-01       760.80        15.000    26.026019           35.7   \n",
       "18 2014-09-01       981.30         8.300    20.701493           34.5   \n",
       "19 2014-10-01       189.90         1.600    13.567260           25.5   \n",
       "20 2014-11-01         6.00        -4.600     5.910110           21.5   \n",
       "21 2014-12-01        14.30       -11.900    -0.890535           12.6   \n",
       "22 2015-01-01         3.50       -13.000    -0.893806           14.0   \n",
       "23 2015-02-01        93.05       -13.000     1.264342           14.0   \n",
       "24 2015-03-01        41.60        -8.900     8.506220           24.1   \n",
       "25 2015-04-01       422.40        -0.600    15.581226           32.3   \n",
       "26 2015-05-01       444.40         7.000    21.351697           34.6   \n",
       "27 2015-06-01      1067.60        12.200    24.462847           34.1   \n",
       "28 2015-07-01      2676.70        14.800    26.291207           39.3   \n",
       "29 2015-08-01      1057.20        16.100    26.240715           37.0   \n",
       "30 2015-09-01      1443.10         8.700    20.424225           32.3   \n",
       "31 2015-10-01       152.80        -1.400    14.192059           27.5   \n",
       "32 2015-11-01       556.20       -13.800     3.009815           20.8   \n",
       "33 2015-12-01        31.40       -11.800    -0.316308           10.0   \n",
       "34 2016-01-01         6.20       -19.900    -4.576232            8.1   \n",
       "35 2016-02-01       122.10       -13.300     1.234052           11.8   \n",
       "36 2016-03-01         0.00        -6.600     8.993929           24.0   \n",
       "37 2016-04-01        77.30         4.200    16.336354           31.5   \n",
       "38 2016-05-01       441.80         5.100    21.282482           34.0   \n",
       "39 2016-06-01       921.40        14.200    25.406875           38.2   \n",
       "40 2016-07-01      3601.60        17.800    27.345912           37.5   \n",
       "41 2016-08-01       652.60        14.500    27.396685           36.2   \n",
       "42 2016-09-01      1086.00         6.050    21.727431           32.6   \n",
       "43 2016-10-01      1007.00        -2.600    13.057237           29.4   \n",
       "44 2016-11-01        92.40        -9.000     4.198391           20.9   \n",
       "45 2016-12-01         0.00       -11.500     0.226254           11.6   \n",
       "46 2017-01-01         2.90       -13.425    -1.425101            9.8   \n",
       "47 2017-02-01        44.90       -11.300     2.349030           16.2   \n",
       "\n",
       "    Kelembapan  \n",
       "0    45.525133  \n",
       "1    39.081310  \n",
       "2    47.024361  \n",
       "3    72.180721  \n",
       "4    71.965485  \n",
       "5    69.468357  \n",
       "6    72.627152  \n",
       "7    63.629676  \n",
       "8    43.580772  \n",
       "9    38.376122  \n",
       "10   42.473667  \n",
       "11   54.551167  \n",
       "12   37.535102  \n",
       "13   45.142749  \n",
       "14   43.492232  \n",
       "15   62.112910  \n",
       "16   63.122255  \n",
       "17   64.454405  \n",
       "18   68.245951  \n",
       "19   66.814536  \n",
       "20   50.895486  \n",
       "21   33.710884  \n",
       "22   39.673276  \n",
       "23   38.473984  \n",
       "24   31.724656  \n",
       "25   43.464797  \n",
       "26   43.189849  \n",
       "27   56.827136  \n",
       "28   65.388768  \n",
       "29   64.968612  \n",
       "30   69.282162  \n",
       "31   56.616157  \n",
       "32   76.587934  \n",
       "33   62.570729  \n",
       "34   38.315701  \n",
       "35   32.508131  \n",
       "36   31.684669  \n",
       "37   37.248284  \n",
       "38   42.952814  \n",
       "39   55.805849  \n",
       "40   69.296284  \n",
       "41   64.408655  \n",
       "42   64.679713  \n",
       "43   71.021352  \n",
       "44   59.109728  \n",
       "45   51.479598  \n",
       "46   46.045659  \n",
       "47   34.209054  "
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Menyiapkan data agar dapat ditampilkan secara visual\n",
    "monthly_climateBeijing.reset_index(inplace=True)\n",
    "monthly_climateBeijing['Tanggal'] = pd.to_datetime(monthly_climateBeijing['Tahun'].astype(str) + '-' + \\\n",
    "                                                   monthly_climateBeijing['Bulan'].astype(str), format='%Y-%m')\n",
    "monthly_climateBeijing.drop(columns= ['Tahun', 'Bulan'], inplace=True)\n",
    "monthly_climateBeijing.insert(0, 'Tanggal', monthly_climateBeijing.pop('Tanggal'))\n",
    "monthly_climateBeijing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tingkat Polusi Udara Wilayah Beijing pada Tiap Stasiun"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Perhitungan tingkat polusi udara dilakukan dengan pendekatan *Air Quality Index* (AQI). Mengutip dari situs https://www.kaggle.com/code/rohanrao/calculating-aqi-air-quality-index-tutorial, diperlukan syarat-syarat sebagai berikut:\n",
    "\n",
    "- Perhitungan AQI menggunakan 7 ukuran: **PM2.5, PM10, SO2, NOx, NH3, CO dan O3**.\n",
    "- Untuk **PM2.5, PM10, SO2, NOx dan NH3** digunakan *nilai rata-rata* dalam 24 jam terakhir dengan syarat minimal memiliki 16 nilai.\n",
    "- Untuk **CO** dan **O3** digunakan *nilai maksimum* dalam 8 jam terakhir.\n",
    "- Setiap ukuran diubah menjadi Sub-Indeks berdasarkan kelompok yang telah ditentukan sebelumnya.\n",
    "- Terkadang pengukuran tidak tersedia (NaN) karena kurangnya pengukuran atau kurangnya titik data yang diperlukan.\n",
    "- AQI Final adalah Sub-Indeks maksimum dengan ketentuan harus tersedia paling sedikit satu dari PM2.5 dan PM10 dan paling sedikit tiga dari tujuh harus tersedia.\n",
    "\n",
    "Pada data yang dimiliki, terdapat 6 saja (**PM2.5, PM10, SO2, NO2, CO dan O3**) dimana semua data tersedia kecuali **NH3**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "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>PM2.5</th>\n",
       "      <th>PM10</th>\n",
       "      <th>SO2</th>\n",
       "      <th>NO2</th>\n",
       "      <th>CO</th>\n",
       "      <th>O3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>420768.000000</td>\n",
       "      <td>420768.000000</td>\n",
       "      <td>420768.000000</td>\n",
       "      <td>420768.000000</td>\n",
       "      <td>420768.000000</td>\n",
       "      <td>420768.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>79.839718</td>\n",
       "      <td>104.910268</td>\n",
       "      <td>15.913090</td>\n",
       "      <td>50.598892</td>\n",
       "      <td>1235.682649</td>\n",
       "      <td>57.237872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>80.950217</td>\n",
       "      <td>92.431422</td>\n",
       "      <td>21.896609</td>\n",
       "      <td>35.172089</td>\n",
       "      <td>1161.790893</td>\n",
       "      <td>57.135195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.285600</td>\n",
       "      <td>1.026500</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>0.214200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>20.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>55.000000</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>900.000000</td>\n",
       "      <td>44.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>111.000000</td>\n",
       "      <td>145.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>1500.000000</td>\n",
       "      <td>82.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>999.000000</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>290.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>1071.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               PM2.5           PM10            SO2            NO2  \\\n",
       "count  420768.000000  420768.000000  420768.000000  420768.000000   \n",
       "mean       79.839718     104.910268      15.913090      50.598892   \n",
       "std        80.950217      92.431422      21.896609      35.172089   \n",
       "min         2.000000       2.000000       0.285600       1.026500   \n",
       "25%        20.000000      36.000000       3.000000      23.000000   \n",
       "50%        55.000000      82.000000       7.000000      43.000000   \n",
       "75%       111.000000     145.000000      20.000000      71.000000   \n",
       "max       999.000000     999.000000     500.000000     290.000000   \n",
       "\n",
       "                  CO             O3  \n",
       "count  420768.000000  420768.000000  \n",
       "mean     1235.682649      57.237872  \n",
       "std      1161.790893      57.135195  \n",
       "min       100.000000       0.214200  \n",
       "25%       500.000000      10.000000  \n",
       "50%       900.000000      44.000000  \n",
       "75%      1500.000000      82.000000  \n",
       "max     10000.000000    1071.000000  "
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aqiCol = ['PM2.5','PM10', 'SO2', 'NO2', 'CO', 'O3']\n",
    "all_df[aqiCol].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DateTime    0\n",
       "PM2.5       0\n",
       "PM10        0\n",
       "SO2         0\n",
       "NO2         0\n",
       "CO          0\n",
       "O3          0\n",
       "TEMP        0\n",
       "DEWP        0\n",
       "RAIN        0\n",
       "Rh          0\n",
       "station     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fungsi **.rolling()** akan menghitung data secara berurutan sepanjang 24 data *(window=24)* dengan syarat minimal data tersedia sebanyak 16 *(min_periods=16)*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_df[\"PM2.5_24hr_avg\"] = all_df.groupby(\"station\")[\"PM2.5\"].rolling(window = 24, min_periods = 16).mean().values\n",
    "all_df[\"PM10_24hr_avg\"] = all_df.groupby(\"station\")[\"PM10\"].rolling(window = 24, min_periods = 16).mean().values\n",
    "all_df[\"SO2_24hr_avg\"] = all_df.groupby(\"station\")[\"SO2\"].rolling(window = 24, min_periods = 16).mean().values\n",
    "all_df[\"NO2_24hr_avg\"] = all_df.groupby(\"station\")[\"NO2\"].rolling(window = 24, min_periods = 16).mean().values\n",
    "all_df[\"CO_8hr_max\"] = all_df.groupby(\"station\")[\"CO\"].rolling(window = 8, min_periods = 1).max().values\n",
    "all_df[\"O3_8hr_max\"] = all_df.groupby(\"station\")[\"O3\"].rolling(window = 8, min_periods = 1).max().values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_df.insert(17, 'station', all_df.pop('station')) # Mengubah posisi kolom station berada di paling kanan (faktor estetika)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "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>DateTime</th>\n",
       "      <th>PM2.5</th>\n",
       "      <th>PM10</th>\n",
       "      <th>SO2</th>\n",
       "      <th>NO2</th>\n",
       "      <th>CO</th>\n",
       "      <th>O3</th>\n",
       "      <th>TEMP</th>\n",
       "      <th>DEWP</th>\n",
       "      <th>RAIN</th>\n",
       "      <th>Rh</th>\n",
       "      <th>PM2.5_24hr_avg</th>\n",
       "      <th>PM10_24hr_avg</th>\n",
       "      <th>SO2_24hr_avg</th>\n",
       "      <th>NO2_24hr_avg</th>\n",
       "      <th>CO_8hr_max</th>\n",
       "      <th>O3_8hr_max</th>\n",
       "      <th>station</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013-03-01 00:00:00</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>-18.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.257491</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>300.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013-03-01 01:00:00</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>-18.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.263193</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>300.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013-03-01 02:00:00</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>-18.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.263193</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>300.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013-03-01 03:00:00</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>-1.4</td>\n",
       "      <td>-19.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.278311</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>300.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013-03-01 04:00:00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>-19.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.161525</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>300.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             DateTime  PM2.5  PM10   SO2   NO2     CO    O3  TEMP  DEWP  RAIN  \\\n",
       "0 2013-03-01 00:00:00    4.0   4.0   4.0   7.0  300.0  77.0  -0.7 -18.8   0.0   \n",
       "1 2013-03-01 01:00:00    8.0   8.0   4.0   7.0  300.0  77.0  -1.1 -18.2   0.0   \n",
       "2 2013-03-01 02:00:00    7.0   7.0   5.0  10.0  300.0  73.0  -1.1 -18.2   0.0   \n",
       "3 2013-03-01 03:00:00    6.0   6.0  11.0  11.0  300.0  72.0  -1.4 -19.4   0.0   \n",
       "4 2013-03-01 04:00:00    3.0   3.0  12.0  12.0  300.0  72.0  -2.0 -19.5   0.0   \n",
       "\n",
       "          Rh  PM2.5_24hr_avg  PM10_24hr_avg  SO2_24hr_avg  NO2_24hr_avg  \\\n",
       "0  24.257491             NaN            NaN           NaN           NaN   \n",
       "1  26.263193             NaN            NaN           NaN           NaN   \n",
       "2  26.263193             NaN            NaN           NaN           NaN   \n",
       "3  24.278311             NaN            NaN           NaN           NaN   \n",
       "4  25.161525             NaN            NaN           NaN           NaN   \n",
       "\n",
       "   CO_8hr_max  O3_8hr_max       station  \n",
       "0       300.0        77.0  Aotizhongxin  \n",
       "1       300.0        77.0  Aotizhongxin  \n",
       "2       300.0        77.0  Aotizhongxin  \n",
       "3       300.0        77.0  Aotizhongxin  \n",
       "4       300.0        77.0  Aotizhongxin  "
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Membuat fungsi kategorisasi subindex dan mengelompokkan hasil kalkulasi rerata tiap parameter polutan\n",
    "PM2.5 (µg/m³)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "## PM2.5 Sub-Index calculation\n",
    "def get_PM25_subindex(x):\n",
    "    if x <= 30:\n",
    "        return x * 50 / 30\n",
    "    elif x <= 60:\n",
    "        return 50 + (x - 30) * 50 / 30\n",
    "    elif x <= 90:\n",
    "        return 100 + (x - 60) * 100 / 30\n",
    "    elif x <= 120:\n",
    "        return 200 + (x - 90) * 100 / 30\n",
    "    elif x <= 250:\n",
    "        return 300 + (x - 120) * 100 / 130\n",
    "    elif x > 250:\n",
    "        return 400 + (x - 250) * 100 / 130\n",
    "    else:\n",
    "        return 0\n",
    "\n",
    "all_df[\"PM2.5_SubIndex\"] = all_df[\"PM2.5_24hr_avg\"].apply(lambda x: get_PM25_subindex(x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "PM10 (µg/m³)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "## PM10 Sub-Index calculation\n",
    "def get_PM10_subindex(x):\n",
    "    if x <= 50:\n",
    "        return x\n",
    "    elif x <= 100:\n",
    "        return x\n",
    "    elif x <= 250:\n",
    "        return 100 + (x - 100) * 100 / 150\n",
    "    elif x <= 350:\n",
    "        return 200 + (x - 250)\n",
    "    elif x <= 430:\n",
    "        return 300 + (x - 350) * 100 / 80\n",
    "    elif x > 430:\n",
    "        return 400 + (x - 430) * 100 / 80\n",
    "    else:\n",
    "        return 0\n",
    "\n",
    "all_df[\"PM10_SubIndex\"] = all_df[\"PM10_24hr_avg\"].apply(lambda x: get_PM10_subindex(x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "SO<sub>2</sub>  (µg/m³)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "## SO2 Sub-Index calculation\n",
    "def get_SO2_subindex(x):\n",
    "    if x <= 40:\n",
    "        return x * 50 / 40\n",
    "    elif x <= 80:\n",
    "        return 50 + (x - 40) * 50 / 40\n",
    "    elif x <= 380:\n",
    "        return 100 + (x - 80) * 100 / 300\n",
    "    elif x <= 800:\n",
    "        return 200 + (x - 380) * 100 / 420\n",
    "    elif x <= 1600:\n",
    "        return 300 + (x - 800) * 100 / 800\n",
    "    elif x > 1600:\n",
    "        return 400 + (x - 1600) * 100 / 800\n",
    "    else:\n",
    "        return 0\n",
    "\n",
    "all_df[\"SO2_SubIndex\"] = all_df[\"SO2_24hr_avg\"].apply(lambda x: get_SO2_subindex(x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "NO<sub>2</sub>  (µg/m³)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "## NO2 Sub-Index calculation\n",
    "def get_NO2_subindex(x):\n",
    "    if x <= 40:\n",
    "        return x * 50 / 40\n",
    "    elif x <= 80:\n",
    "        return 50 + (x - 40) * 50 / 40\n",
    "    elif x <= 180:\n",
    "        return 100 + (x - 80) * 100 / 100\n",
    "    elif x <= 280:\n",
    "        return 200 + (x - 180) * 100 / 100\n",
    "    elif x <= 400:\n",
    "        return 300 + (x - 280) * 100 / 120\n",
    "    elif x > 400:\n",
    "        return 400 + (x - 400) * 100 / 120\n",
    "    else:\n",
    "        return 0\n",
    "\n",
    "all_df[\"NO2_SubIndex\"] = all_df[\"NO2_24hr_avg\"].apply(lambda x: get_NO2_subindex(x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "CO (mg/m³)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "## CO Sub-Index calculation\n",
    "def get_CO_subindex(x):\n",
    "    if x <= 1:\n",
    "        return x * 50 / 1\n",
    "    elif x <= 2:\n",
    "        return 50 + (x - 1) * 50 / 1\n",
    "    elif x <= 10:\n",
    "        return 100 + (x - 2) * 100 / 8\n",
    "    elif x <= 17:\n",
    "        return 200 + (x - 10) * 100 / 7\n",
    "    elif x <= 34:\n",
    "        return 300 + (x - 17) * 100 / 17\n",
    "    elif x > 34:\n",
    "        return 400 + (x - 34) * 100 / 17\n",
    "    else:\n",
    "        return 0\n",
    "\n",
    "all_df[\"CO_SubIndex\"] = all_df[\"CO_8hr_max\"].apply(lambda x: get_CO_subindex(x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "O<sub>3</sub>  (µg/m³)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "## O3 Sub-Index calculation\n",
    "def get_O3_subindex(x):\n",
    "    if x <= 50:\n",
    "        return x * 50 / 50\n",
    "    elif x <= 100:\n",
    "        return 50 + (x - 50) * 50 / 50\n",
    "    elif x <= 168:\n",
    "        return 100 + (x - 100) * 100 / 68\n",
    "    elif x <= 208:\n",
    "        return 200 + (x - 168) * 100 / 40\n",
    "    elif x <= 748:\n",
    "        return 300 + (x - 208) * 100 / 539\n",
    "    elif x > 748:\n",
    "        return 400 + (x - 400) * 100 / 539\n",
    "    else:\n",
    "        return 0\n",
    "\n",
    "all_df[\"O3_SubIndex\"] = all_df[\"O3_8hr_max\"].apply(lambda x: get_O3_subindex(x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Kategorisasi AQI\n",
    "![alt](https://i.imgur.com/XmnE0rT.png)\n",
    "###### source: https://www.kaggle.com/code/rohanrao/calculating-aqi-air-quality-index-tutorial/notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "# AQI bucketing\n",
    "def get_AQI_bucket(x):\n",
    "    if x <= 50:\n",
    "        return \"Good\"\n",
    "    elif x <= 100:\n",
    "        return \"Satisfactory\"\n",
    "    elif x <= 200:\n",
    "        return \"Moderate\"\n",
    "    elif x <= 300:\n",
    "        return \"Poor\"\n",
    "    elif x <= 400:\n",
    "        return \"Very Poor\"\n",
    "    elif x > 400:\n",
    "        return \"Severe\"\n",
    "    else:\n",
    "        return np.NaN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pemeriksaan kelengkapan sub-indeks melalui penyaringan hasil boolean kedalam bilangan biner terhadap seluruh kolom sub-indeks<br>(True = 1; False = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_df[\"Checks\"] = (all_df[\"PM2.5_SubIndex\"] > 0).astype(int) + \\\n",
    "                (all_df[\"PM10_SubIndex\"] > 0).astype(int) + \\\n",
    "                (all_df[\"SO2_SubIndex\"] > 0).astype(int) + \\\n",
    "                (all_df[\"NO2_SubIndex\"] > 0).astype(int) + \\\n",
    "                (all_df[\"CO_SubIndex\"] > 0).astype(int) + \\\n",
    "                (all_df[\"O3_SubIndex\"] > 0).astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "AQI final adalah sub-indeks maksimum di antara sub-indeks yang tersedia dengan ketentuan paling sedikit terdapat satu PM2.5 dan PM10 dan paling sedikit 3 dari 6 sub-indeks tersebut"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Mengambil nilai maksimum \n",
    "all_df[\"AQI_calculated\"] = round(all_df[[\"PM2.5_SubIndex\", \"PM10_SubIndex\", \"SO2_SubIndex\", \"NO2_SubIndex\", \"CO_SubIndex\", \"O3_SubIndex\"]].max(axis = 1))\n",
    "\n",
    "# Penanganan apabila data kurang lengkap (invalid) diubah menjadi none atau NaN\n",
    "all_df.loc[all_df[\"PM2.5_SubIndex\"] + all_df[\"PM10_SubIndex\"] <= 0, \"AQI_calculated\"] = np.NaN\n",
    "all_df.loc[all_df.Checks < 3, \"AQI_calculated\"] = np.NaN "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Menggelompokkan hasil Final AQI (*AQI_calculated*) sesuai kategori yang ditentukan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "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>DateTime</th>\n",
       "      <th>PM2.5</th>\n",
       "      <th>PM10</th>\n",
       "      <th>SO2</th>\n",
       "      <th>NO2</th>\n",
       "      <th>CO</th>\n",
       "      <th>O3</th>\n",
       "      <th>TEMP</th>\n",
       "      <th>DEWP</th>\n",
       "      <th>RAIN</th>\n",
       "      <th>...</th>\n",
       "      <th>station</th>\n",
       "      <th>PM2.5_SubIndex</th>\n",
       "      <th>PM10_SubIndex</th>\n",
       "      <th>SO2_SubIndex</th>\n",
       "      <th>NO2_SubIndex</th>\n",
       "      <th>CO_SubIndex</th>\n",
       "      <th>O3_SubIndex</th>\n",
       "      <th>Checks</th>\n",
       "      <th>AQI_calculated</th>\n",
       "      <th>AQI_category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2013-03-01 15:00:00</td>\n",
       "      <td>8.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>6.2</td>\n",
       "      <td>-18.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "      <td>7.395833</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>12.890625</td>\n",
       "      <td>22.265625</td>\n",
       "      <td>3141.176471</td>\n",
       "      <td>77.0</td>\n",
       "      <td>6</td>\n",
       "      <td>3141.0</td>\n",
       "      <td>Severe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2013-03-01 16:00:00</td>\n",
       "      <td>9.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>5.9</td>\n",
       "      <td>-18.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "      <td>7.843137</td>\n",
       "      <td>7.235294</td>\n",
       "      <td>12.794118</td>\n",
       "      <td>21.911765</td>\n",
       "      <td>2552.941176</td>\n",
       "      <td>77.0</td>\n",
       "      <td>6</td>\n",
       "      <td>2553.0</td>\n",
       "      <td>Severe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2013-03-01 17:00:00</td>\n",
       "      <td>10.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>4.3</td>\n",
       "      <td>-18.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "      <td>8.333333</td>\n",
       "      <td>8.111111</td>\n",
       "      <td>12.847222</td>\n",
       "      <td>21.736111</td>\n",
       "      <td>2552.941176</td>\n",
       "      <td>77.0</td>\n",
       "      <td>6</td>\n",
       "      <td>2553.0</td>\n",
       "      <td>Severe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2013-03-01 18:00:00</td>\n",
       "      <td>11.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>3.1</td>\n",
       "      <td>-18.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "      <td>8.859649</td>\n",
       "      <td>8.736842</td>\n",
       "      <td>12.697368</td>\n",
       "      <td>21.907895</td>\n",
       "      <td>3141.176471</td>\n",
       "      <td>77.0</td>\n",
       "      <td>6</td>\n",
       "      <td>3141.0</td>\n",
       "      <td>Severe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2013-03-01 19:00:00</td>\n",
       "      <td>8.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>-18.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "      <td>9.083333</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>12.812500</td>\n",
       "      <td>22.687500</td>\n",
       "      <td>3141.176471</td>\n",
       "      <td>77.0</td>\n",
       "      <td>6</td>\n",
       "      <td>3141.0</td>\n",
       "      <td>Severe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2013-03-01 20:00:00</td>\n",
       "      <td>11.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>600.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>-17.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "      <td>9.523810</td>\n",
       "      <td>9.380952</td>\n",
       "      <td>12.976190</td>\n",
       "      <td>23.571429</td>\n",
       "      <td>3729.411765</td>\n",
       "      <td>77.0</td>\n",
       "      <td>6</td>\n",
       "      <td>3729.0</td>\n",
       "      <td>Severe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2013-03-01 21:00:00</td>\n",
       "      <td>12.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>-16.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>9.772727</td>\n",
       "      <td>13.295455</td>\n",
       "      <td>24.488636</td>\n",
       "      <td>3729.411765</td>\n",
       "      <td>77.0</td>\n",
       "      <td>6</td>\n",
       "      <td>3729.0</td>\n",
       "      <td>Severe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2013-03-01 22:00:00</td>\n",
       "      <td>15.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>700.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>-17.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "      <td>10.652174</td>\n",
       "      <td>10.173913</td>\n",
       "      <td>13.858696</td>\n",
       "      <td>26.521739</td>\n",
       "      <td>4317.647059</td>\n",
       "      <td>76.0</td>\n",
       "      <td>6</td>\n",
       "      <td>4318.0</td>\n",
       "      <td>Severe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2013-03-01 23:00:00</td>\n",
       "      <td>24.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>600.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>-17.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "      <td>11.875000</td>\n",
       "      <td>10.750000</td>\n",
       "      <td>14.635417</td>\n",
       "      <td>28.229167</td>\n",
       "      <td>4317.647059</td>\n",
       "      <td>76.0</td>\n",
       "      <td>6</td>\n",
       "      <td>4318.0</td>\n",
       "      <td>Severe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2013-03-02 00:00:00</td>\n",
       "      <td>22.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>-17.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "      <td>13.125000</td>\n",
       "      <td>11.583333</td>\n",
       "      <td>15.677083</td>\n",
       "      <td>30.156250</td>\n",
       "      <td>4317.647059</td>\n",
       "      <td>74.0</td>\n",
       "      <td>6</td>\n",
       "      <td>4318.0</td>\n",
       "      <td>Severe</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              DateTime  PM2.5  PM10   SO2   NO2     CO    O3  TEMP  DEWP  \\\n",
       "15 2013-03-01 15:00:00    8.0  15.0   7.0  14.0  400.0  76.0   6.2 -18.6   \n",
       "16 2013-03-01 16:00:00    9.0  19.0   9.0  13.0  400.0  76.0   5.9 -18.1   \n",
       "17 2013-03-01 17:00:00   10.0  23.0  11.0  15.0  400.0  74.0   4.3 -18.7   \n",
       "18 2013-03-01 18:00:00   11.0  20.0   8.0  20.0  500.0  70.0   3.1 -18.4   \n",
       "19 2013-03-01 19:00:00    8.0  14.0  12.0  30.0  500.0  60.0   2.3 -18.4   \n",
       "20 2013-03-01 20:00:00   11.0  17.0  13.0  33.0  600.0  55.0   1.7 -17.3   \n",
       "21 2013-03-01 21:00:00   12.0  18.0  16.0  35.0  500.0  50.0   0.6 -16.7   \n",
       "22 2013-03-01 22:00:00   15.0  19.0  21.0  57.0  700.0  32.0   0.9 -17.4   \n",
       "23 2013-03-01 23:00:00   24.0  24.0  26.0  54.0  600.0  36.0  -0.2 -17.4   \n",
       "24 2013-03-02 00:00:00   22.0  24.0  24.0  44.0  500.0  44.0  -0.4 -17.6   \n",
       "\n",
       "    RAIN  ...       station  PM2.5_SubIndex  PM10_SubIndex  SO2_SubIndex  \\\n",
       "15   0.0  ...  Aotizhongxin        7.395833       6.500000     12.890625   \n",
       "16   0.0  ...  Aotizhongxin        7.843137       7.235294     12.794118   \n",
       "17   0.0  ...  Aotizhongxin        8.333333       8.111111     12.847222   \n",
       "18   0.0  ...  Aotizhongxin        8.859649       8.736842     12.697368   \n",
       "19   0.0  ...  Aotizhongxin        9.083333       9.000000     12.812500   \n",
       "20   0.0  ...  Aotizhongxin        9.523810       9.380952     12.976190   \n",
       "21   0.0  ...  Aotizhongxin       10.000000       9.772727     13.295455   \n",
       "22   0.0  ...  Aotizhongxin       10.652174      10.173913     13.858696   \n",
       "23   0.0  ...  Aotizhongxin       11.875000      10.750000     14.635417   \n",
       "24   0.0  ...  Aotizhongxin       13.125000      11.583333     15.677083   \n",
       "\n",
       "    NO2_SubIndex  CO_SubIndex  O3_SubIndex Checks  AQI_calculated  \\\n",
       "15     22.265625  3141.176471         77.0      6          3141.0   \n",
       "16     21.911765  2552.941176         77.0      6          2553.0   \n",
       "17     21.736111  2552.941176         77.0      6          2553.0   \n",
       "18     21.907895  3141.176471         77.0      6          3141.0   \n",
       "19     22.687500  3141.176471         77.0      6          3141.0   \n",
       "20     23.571429  3729.411765         77.0      6          3729.0   \n",
       "21     24.488636  3729.411765         77.0      6          3729.0   \n",
       "22     26.521739  4317.647059         76.0      6          4318.0   \n",
       "23     28.229167  4317.647059         76.0      6          4318.0   \n",
       "24     30.156250  4317.647059         74.0      6          4318.0   \n",
       "\n",
       "    AQI_category  \n",
       "15        Severe  \n",
       "16        Severe  \n",
       "17        Severe  \n",
       "18        Severe  \n",
       "19        Severe  \n",
       "20        Severe  \n",
       "21        Severe  \n",
       "22        Severe  \n",
       "23        Severe  \n",
       "24        Severe  \n",
       "\n",
       "[10 rows x 27 columns]"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_df[\"AQI_category\"] = all_df[\"AQI_calculated\"].apply(lambda x: get_AQI_bucket(x))\n",
    "all_df[~all_df.AQI_calculated.isna()].head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Penanganan Missing Value atas Perhitungan AQI\n",
    "Ternyata masih ada data yang tidak valid pada perhitungan **PM2.5, PM10, SO2, dan NO2**. Sebagaimana disampaikan di atas, data *NaN* disebabkan karena kurangnya pengukuran atau kurangnya titik data yang diperlukan. Namun, perlu diketahui data sumber sebelum proses kalkulasi menyatakan tidak terdapat *NaN* alias lengkap pasca Data Cleaning sebagaimana ditunjukkan pada rangkuman dibawah."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DateTime            0\n",
       "PM2.5               0\n",
       "PM10                0\n",
       "SO2                 0\n",
       "NO2                 0\n",
       "CO                  0\n",
       "O3                  0\n",
       "TEMP                0\n",
       "DEWP                0\n",
       "RAIN                0\n",
       "Rh                  0\n",
       "PM2.5_24hr_avg    180\n",
       "PM10_24hr_avg     180\n",
       "SO2_24hr_avg      180\n",
       "NO2_24hr_avg      180\n",
       "CO_8hr_max          0\n",
       "O3_8hr_max          0\n",
       "station             0\n",
       "PM2.5_SubIndex      0\n",
       "PM10_SubIndex       0\n",
       "SO2_SubIndex        0\n",
       "NO2_SubIndex        0\n",
       "CO_SubIndex         0\n",
       "O3_SubIndex         0\n",
       "Checks              0\n",
       "AQI_calculated    180\n",
       "AQI_category      180\n",
       "dtype: int64"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Memisahkan tabel dari pusat data guna mengetahui sebaran data *NaN*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "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>NO2_24hr_avg</th>\n",
       "      <th>PM10_24hr_avg</th>\n",
       "      <th>PM2.5_24hr_avg</th>\n",
       "      <th>SO2_24hr_avg</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>station</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Aotizhongxin</th>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Changping</th>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dingling</th>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dongsi</th>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Guanyuan</th>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gucheng</th>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Huairou</th>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nongzhanguan</th>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shunyi</th>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tiantan</th>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wanliu</th>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wanshouxigong</th>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               NO2_24hr_avg  PM10_24hr_avg  PM2.5_24hr_avg  SO2_24hr_avg\n",
       "station                                                                 \n",
       "Aotizhongxin             15             15              15            15\n",
       "Changping                15             15              15            15\n",
       "Dingling                 15             15              15            15\n",
       "Dongsi                   15             15              15            15\n",
       "Guanyuan                 15             15              15            15\n",
       "Gucheng                  15             15              15            15\n",
       "Huairou                  15             15              15            15\n",
       "Nongzhanguan             15             15              15            15\n",
       "Shunyi                   15             15              15            15\n",
       "Tiantan                  15             15              15            15\n",
       "Wanliu                   15             15              15            15\n",
       "Wanshouxigong            15             15              15            15"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AQIParam_df = all_df[['DateTime','PM2.5_24hr_avg','PM10_24hr_avg', 'SO2_24hr_avg', 'NO2_24hr_avg', 'station']]\n",
    "NaN_rows = AQIParam_df[AQIParam_df.isna().any(axis=1)]\n",
    "NaN_rows.groupby(by='station').agg({\n",
    "    'NO2_24hr_avg': lambda x: x.isna().sum(),\n",
    "    'PM10_24hr_avg': lambda x: x.isna().sum(),\n",
    "    'PM2.5_24hr_avg': lambda x: x.isna().sum(),\n",
    "    'SO2_24hr_avg': lambda x: x.isna().sum()\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "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></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>NO2_24hr_avg</th>\n",
       "      <th>PM10_24hr_avg</th>\n",
       "      <th>PM2.5_24hr_avg</th>\n",
       "      <th>SO2_24hr_avg</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DateTime</th>\n",
       "      <th>DateTime</th>\n",
       "      <th>DateTime</th>\n",
       "      <th>DateTime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"15\" valign=\"top\">2013</th>\n",
       "      <th rowspan=\"15\" valign=\"top\">3</th>\n",
       "      <th rowspan=\"15\" valign=\"top\">1</th>\n",
       "      <th>0</th>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     NO2_24hr_avg  PM10_24hr_avg  \\\n",
       "DateTime DateTime DateTime DateTime                                \n",
       "2013     3        1        0                   12             12   \n",
       "                           1                   12             12   \n",
       "                           2                   12             12   \n",
       "                           3                   12             12   \n",
       "                           4                   12             12   \n",
       "                           5                   12             12   \n",
       "                           6                   12             12   \n",
       "                           7                   12             12   \n",
       "                           8                   12             12   \n",
       "                           9                   12             12   \n",
       "                           10                  12             12   \n",
       "                           11                  12             12   \n",
       "                           12                  12             12   \n",
       "                           13                  12             12   \n",
       "                           14                  12             12   \n",
       "\n",
       "                                     PM2.5_24hr_avg  SO2_24hr_avg  \n",
       "DateTime DateTime DateTime DateTime                                \n",
       "2013     3        1        0                     12            12  \n",
       "                           1                     12            12  \n",
       "                           2                     12            12  \n",
       "                           3                     12            12  \n",
       "                           4                     12            12  \n",
       "                           5                     12            12  \n",
       "                           6                     12            12  \n",
       "                           7                     12            12  \n",
       "                           8                     12            12  \n",
       "                           9                     12            12  \n",
       "                           10                    12            12  \n",
       "                           11                    12            12  \n",
       "                           12                    12            12  \n",
       "                           13                    12            12  \n",
       "                           14                    12            12  "
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NaN_rows.groupby(by=[NaN_rows['DateTime'].dt.year, NaN_rows['DateTime'].dt.month, NaN_rows['DateTime'].dt.day, NaN_rows['DateTime'].dt.hour]).agg({\n",
    "    'NO2_24hr_avg': lambda x: x.isna().sum(),\n",
    "    'PM10_24hr_avg': lambda x: x.isna().sum(),\n",
    "    'PM2.5_24hr_avg': lambda x: x.isna().sum(),\n",
    "    'SO2_24hr_avg': lambda x: x.isna().sum()\n",
    "})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nilai NaN pada tiap stasiun bernilai sama dan merujuk pada tanggal yang sama yakni **2013-03-01 jam ke-0 s.d ke-14**.<br>\n",
    "Penanganan nilai NaN terhadap parameter tersebut dilakukan dengan menyeleksi 24 jam di hari tersebut pada tiap stasiun kemudian dilakukan rerata secara manual menggunakan *.transform()* alih-alih fungsi *.rolling()*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Menyeleksi tanggal 2013-03-01 dalam 24 jam\n",
    "thatDate = ('2013-03-01 00:00:00' <= all_df['DateTime']) & (all_df['DateTime'] <= '2013-03-01 23:59:59')\n",
    "\n",
    "# Menghitung rerata 24 jam pada masing-masing kolom\n",
    "PM25_24hr_newAvg = all_df[thatDate].groupby('station')['PM2.5'].transform('mean')\n",
    "PM10_24hr_newAvg = all_df[thatDate].groupby('station')['PM10'].transform('mean')\n",
    "SO2_24hr_newAvg = all_df[thatDate].groupby('station')['SO2'].transform('mean')\n",
    "NO2_24hr_newAvg = all_df[thatDate].groupby('station')['NO2'].transform('mean')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mengganti nilai NaN pada tiap kolom dengan perhitungan rerata baru"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_df[\"PM2.5_24hr_avg\"].fillna(PM25_24hr_newAvg,inplace=True)\n",
    "all_df[\"PM10_24hr_avg\"].fillna(PM10_24hr_newAvg,inplace=True)\n",
    "all_df[\"SO2_24hr_avg\"].fillna(SO2_24hr_newAvg,inplace=True)\n",
    "all_df[\"NO2_24hr_avg\"].fillna(NO2_24hr_newAvg,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DateTime            0\n",
       "PM2.5               0\n",
       "PM10                0\n",
       "SO2                 0\n",
       "NO2                 0\n",
       "CO                  0\n",
       "O3                  0\n",
       "TEMP                0\n",
       "DEWP                0\n",
       "RAIN                0\n",
       "Rh                  0\n",
       "PM2.5_24hr_avg      0\n",
       "PM10_24hr_avg       0\n",
       "SO2_24hr_avg        0\n",
       "NO2_24hr_avg        0\n",
       "CO_8hr_max          0\n",
       "O3_8hr_max          0\n",
       "station             0\n",
       "PM2.5_SubIndex      0\n",
       "PM10_SubIndex       0\n",
       "SO2_SubIndex        0\n",
       "NO2_SubIndex        0\n",
       "CO_SubIndex         0\n",
       "O3_SubIndex         0\n",
       "Checks              0\n",
       "AQI_calculated    180\n",
       "AQI_category      180\n",
       "dtype: int64"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mengulangi dari tahap pembuatan sub indeks untuk kategorisasi AQI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Menerapkan nilai rerata baru pada masing-masing fungsi sub indeks\n",
    "all_df[\"PM2.5_SubIndex\"] = all_df[\"PM2.5_24hr_avg\"].apply(lambda x: get_PM25_subindex(x))\n",
    "all_df[\"PM10_SubIndex\"] = all_df[\"PM10_24hr_avg\"].apply(lambda x: get_PM10_subindex(x))\n",
    "all_df[\"SO2_SubIndex\"] = all_df[\"SO2_24hr_avg\"].apply(lambda x: get_SO2_subindex(x))\n",
    "all_df[\"NO2_SubIndex\"] = all_df[\"NO2_24hr_avg\"].apply(lambda x: get_NO2_subindex(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "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>DateTime</th>\n",
       "      <th>PM2.5</th>\n",
       "      <th>PM10</th>\n",
       "      <th>SO2</th>\n",
       "      <th>NO2</th>\n",
       "      <th>CO</th>\n",
       "      <th>O3</th>\n",
       "      <th>TEMP</th>\n",
       "      <th>DEWP</th>\n",
       "      <th>RAIN</th>\n",
       "      <th>...</th>\n",
       "      <th>station</th>\n",
       "      <th>PM2.5_SubIndex</th>\n",
       "      <th>PM10_SubIndex</th>\n",
       "      <th>SO2_SubIndex</th>\n",
       "      <th>NO2_SubIndex</th>\n",
       "      <th>CO_SubIndex</th>\n",
       "      <th>O3_SubIndex</th>\n",
       "      <th>Checks</th>\n",
       "      <th>AQI_calculated</th>\n",
       "      <th>AQI_category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013-03-01 00:00:00</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>-18.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "      <td>11.875</td>\n",
       "      <td>10.75</td>\n",
       "      <td>14.635417</td>\n",
       "      <td>28.229167</td>\n",
       "      <td>1964.705882</td>\n",
       "      <td>77.0</td>\n",
       "      <td>6</td>\n",
       "      <td>1965.0</td>\n",
       "      <td>Severe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013-03-01 01:00:00</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>-18.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "      <td>11.875</td>\n",
       "      <td>10.75</td>\n",
       "      <td>14.635417</td>\n",
       "      <td>28.229167</td>\n",
       "      <td>1964.705882</td>\n",
       "      <td>77.0</td>\n",
       "      <td>6</td>\n",
       "      <td>1965.0</td>\n",
       "      <td>Severe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013-03-01 02:00:00</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>-18.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "      <td>11.875</td>\n",
       "      <td>10.75</td>\n",
       "      <td>14.635417</td>\n",
       "      <td>28.229167</td>\n",
       "      <td>1964.705882</td>\n",
       "      <td>77.0</td>\n",
       "      <td>6</td>\n",
       "      <td>1965.0</td>\n",
       "      <td>Severe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013-03-01 03:00:00</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>-1.4</td>\n",
       "      <td>-19.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "      <td>11.875</td>\n",
       "      <td>10.75</td>\n",
       "      <td>14.635417</td>\n",
       "      <td>28.229167</td>\n",
       "      <td>1964.705882</td>\n",
       "      <td>77.0</td>\n",
       "      <td>6</td>\n",
       "      <td>1965.0</td>\n",
       "      <td>Severe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013-03-01 04:00:00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>-19.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "      <td>11.875</td>\n",
       "      <td>10.75</td>\n",
       "      <td>14.635417</td>\n",
       "      <td>28.229167</td>\n",
       "      <td>1964.705882</td>\n",
       "      <td>77.0</td>\n",
       "      <td>6</td>\n",
       "      <td>1965.0</td>\n",
       "      <td>Severe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2013-03-01 05:00:00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>-2.2</td>\n",
       "      <td>-19.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "      <td>11.875</td>\n",
       "      <td>10.75</td>\n",
       "      <td>14.635417</td>\n",
       "      <td>28.229167</td>\n",
       "      <td>2552.941176</td>\n",
       "      <td>77.0</td>\n",
       "      <td>6</td>\n",
       "      <td>2553.0</td>\n",
       "      <td>Severe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2013-03-01 06:00:00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>-2.6</td>\n",
       "      <td>-19.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "      <td>11.875</td>\n",
       "      <td>10.75</td>\n",
       "      <td>14.635417</td>\n",
       "      <td>28.229167</td>\n",
       "      <td>3141.176471</td>\n",
       "      <td>77.0</td>\n",
       "      <td>6</td>\n",
       "      <td>3141.0</td>\n",
       "      <td>Severe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2013-03-01 07:00:00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>-1.6</td>\n",
       "      <td>-19.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "      <td>11.875</td>\n",
       "      <td>10.75</td>\n",
       "      <td>14.635417</td>\n",
       "      <td>28.229167</td>\n",
       "      <td>3141.176471</td>\n",
       "      <td>77.0</td>\n",
       "      <td>6</td>\n",
       "      <td>3141.0</td>\n",
       "      <td>Severe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2013-03-01 08:00:00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>-19.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "      <td>11.875</td>\n",
       "      <td>10.75</td>\n",
       "      <td>14.635417</td>\n",
       "      <td>28.229167</td>\n",
       "      <td>3141.176471</td>\n",
       "      <td>77.0</td>\n",
       "      <td>6</td>\n",
       "      <td>3141.0</td>\n",
       "      <td>Severe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2013-03-01 09:00:00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>-19.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "      <td>11.875</td>\n",
       "      <td>10.75</td>\n",
       "      <td>14.635417</td>\n",
       "      <td>28.229167</td>\n",
       "      <td>3141.176471</td>\n",
       "      <td>73.0</td>\n",
       "      <td>6</td>\n",
       "      <td>3141.0</td>\n",
       "      <td>Severe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2013-03-01 10:00:00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>-19.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "      <td>11.875</td>\n",
       "      <td>10.75</td>\n",
       "      <td>14.635417</td>\n",
       "      <td>28.229167</td>\n",
       "      <td>3141.176471</td>\n",
       "      <td>72.0</td>\n",
       "      <td>6</td>\n",
       "      <td>3141.0</td>\n",
       "      <td>Severe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2013-03-01 11:00:00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>2.9</td>\n",
       "      <td>-20.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "      <td>11.875</td>\n",
       "      <td>10.75</td>\n",
       "      <td>14.635417</td>\n",
       "      <td>28.229167</td>\n",
       "      <td>3141.176471</td>\n",
       "      <td>72.0</td>\n",
       "      <td>6</td>\n",
       "      <td>3141.0</td>\n",
       "      <td>Severe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2013-03-01 12:00:00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>3.9</td>\n",
       "      <td>-19.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "      <td>11.875</td>\n",
       "      <td>10.75</td>\n",
       "      <td>14.635417</td>\n",
       "      <td>28.229167</td>\n",
       "      <td>3141.176471</td>\n",
       "      <td>74.0</td>\n",
       "      <td>6</td>\n",
       "      <td>3141.0</td>\n",
       "      <td>Severe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2013-03-01 13:00:00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>5.3</td>\n",
       "      <td>-19.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "      <td>11.875</td>\n",
       "      <td>10.75</td>\n",
       "      <td>14.635417</td>\n",
       "      <td>28.229167</td>\n",
       "      <td>3141.176471</td>\n",
       "      <td>76.0</td>\n",
       "      <td>6</td>\n",
       "      <td>3141.0</td>\n",
       "      <td>Severe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2013-03-01 14:00:00</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>-19.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "      <td>11.875</td>\n",
       "      <td>10.75</td>\n",
       "      <td>14.635417</td>\n",
       "      <td>28.229167</td>\n",
       "      <td>3141.176471</td>\n",
       "      <td>77.0</td>\n",
       "      <td>6</td>\n",
       "      <td>3141.0</td>\n",
       "      <td>Severe</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              DateTime  PM2.5  PM10   SO2   NO2     CO    O3  TEMP  DEWP  \\\n",
       "0  2013-03-01 00:00:00    4.0   4.0   4.0   7.0  300.0  77.0  -0.7 -18.8   \n",
       "1  2013-03-01 01:00:00    8.0   8.0   4.0   7.0  300.0  77.0  -1.1 -18.2   \n",
       "2  2013-03-01 02:00:00    7.0   7.0   5.0  10.0  300.0  73.0  -1.1 -18.2   \n",
       "3  2013-03-01 03:00:00    6.0   6.0  11.0  11.0  300.0  72.0  -1.4 -19.4   \n",
       "4  2013-03-01 04:00:00    3.0   3.0  12.0  12.0  300.0  72.0  -2.0 -19.5   \n",
       "5  2013-03-01 05:00:00    5.0   5.0  18.0  18.0  400.0  66.0  -2.2 -19.6   \n",
       "6  2013-03-01 06:00:00    3.0   3.0  18.0  32.0  500.0  50.0  -2.6 -19.1   \n",
       "7  2013-03-01 07:00:00    3.0   6.0  19.0  41.0  500.0  43.0  -1.6 -19.1   \n",
       "8  2013-03-01 08:00:00    3.0   6.0  16.0  43.0  500.0  45.0   0.1 -19.2   \n",
       "9  2013-03-01 09:00:00    3.0   8.0  12.0  28.0  400.0  59.0   1.2 -19.3   \n",
       "10 2013-03-01 10:00:00    3.0   6.0   9.0  12.0  400.0  72.0   1.9 -19.4   \n",
       "11 2013-03-01 11:00:00    3.0   6.0   9.0  14.0  400.0  71.0   2.9 -20.5   \n",
       "12 2013-03-01 12:00:00    3.0   6.0   7.0  13.0  300.0  74.0   3.9 -19.7   \n",
       "13 2013-03-01 13:00:00    3.0   6.0   7.0  12.0  400.0  76.0   5.3 -19.3   \n",
       "14 2013-03-01 14:00:00    6.0   9.0   7.0  11.0  400.0  77.0   6.0 -19.6   \n",
       "\n",
       "    RAIN  ...       station  PM2.5_SubIndex  PM10_SubIndex  SO2_SubIndex  \\\n",
       "0    0.0  ...  Aotizhongxin          11.875          10.75     14.635417   \n",
       "1    0.0  ...  Aotizhongxin          11.875          10.75     14.635417   \n",
       "2    0.0  ...  Aotizhongxin          11.875          10.75     14.635417   \n",
       "3    0.0  ...  Aotizhongxin          11.875          10.75     14.635417   \n",
       "4    0.0  ...  Aotizhongxin          11.875          10.75     14.635417   \n",
       "5    0.0  ...  Aotizhongxin          11.875          10.75     14.635417   \n",
       "6    0.0  ...  Aotizhongxin          11.875          10.75     14.635417   \n",
       "7    0.0  ...  Aotizhongxin          11.875          10.75     14.635417   \n",
       "8    0.0  ...  Aotizhongxin          11.875          10.75     14.635417   \n",
       "9    0.0  ...  Aotizhongxin          11.875          10.75     14.635417   \n",
       "10   0.0  ...  Aotizhongxin          11.875          10.75     14.635417   \n",
       "11   0.0  ...  Aotizhongxin          11.875          10.75     14.635417   \n",
       "12   0.0  ...  Aotizhongxin          11.875          10.75     14.635417   \n",
       "13   0.0  ...  Aotizhongxin          11.875          10.75     14.635417   \n",
       "14   0.0  ...  Aotizhongxin          11.875          10.75     14.635417   \n",
       "\n",
       "    NO2_SubIndex  CO_SubIndex  O3_SubIndex Checks  AQI_calculated  \\\n",
       "0      28.229167  1964.705882         77.0      6          1965.0   \n",
       "1      28.229167  1964.705882         77.0      6          1965.0   \n",
       "2      28.229167  1964.705882         77.0      6          1965.0   \n",
       "3      28.229167  1964.705882         77.0      6          1965.0   \n",
       "4      28.229167  1964.705882         77.0      6          1965.0   \n",
       "5      28.229167  2552.941176         77.0      6          2553.0   \n",
       "6      28.229167  3141.176471         77.0      6          3141.0   \n",
       "7      28.229167  3141.176471         77.0      6          3141.0   \n",
       "8      28.229167  3141.176471         77.0      6          3141.0   \n",
       "9      28.229167  3141.176471         73.0      6          3141.0   \n",
       "10     28.229167  3141.176471         72.0      6          3141.0   \n",
       "11     28.229167  3141.176471         72.0      6          3141.0   \n",
       "12     28.229167  3141.176471         74.0      6          3141.0   \n",
       "13     28.229167  3141.176471         76.0      6          3141.0   \n",
       "14     28.229167  3141.176471         77.0      6          3141.0   \n",
       "\n",
       "    AQI_category  \n",
       "0         Severe  \n",
       "1         Severe  \n",
       "2         Severe  \n",
       "3         Severe  \n",
       "4         Severe  \n",
       "5         Severe  \n",
       "6         Severe  \n",
       "7         Severe  \n",
       "8         Severe  \n",
       "9         Severe  \n",
       "10        Severe  \n",
       "11        Severe  \n",
       "12        Severe  \n",
       "13        Severe  \n",
       "14        Severe  \n",
       "\n",
       "[15 rows x 27 columns]"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pembuatan modul untuk pemeriksaan kelengkapan parameter sub indeks\n",
    "all_df[\"Checks\"] = (all_df[\"PM2.5_SubIndex\"] > 0).astype(int) + \\\n",
    "                (all_df[\"PM10_SubIndex\"] > 0).astype(int) + \\\n",
    "                (all_df[\"SO2_SubIndex\"] > 0).astype(int) + \\\n",
    "                (all_df[\"NO2_SubIndex\"] > 0).astype(int) + \\\n",
    "                (all_df[\"CO_SubIndex\"] > 0).astype(int) + \\\n",
    "                (all_df[\"O3_SubIndex\"] > 0).astype(int)\n",
    "\n",
    "# Mengambil nilai maksimum\n",
    "all_df[\"AQI_calculated\"] = round(all_df[[\"PM2.5_SubIndex\", \"PM10_SubIndex\", \"SO2_SubIndex\", \"NO2_SubIndex\", \"CO_SubIndex\", \"O3_SubIndex\"]].max(axis = 1))\n",
    "\n",
    "# Penanganan apabila masih terdapat data kurang lengkap (invalid) diubah menjadi none atau NaN\n",
    "all_df.loc[all_df[\"PM2.5_SubIndex\"] + all_df[\"PM10_SubIndex\"] <= 0, \"AQI_calculated\"] = np.NaN\n",
    "all_df.loc[all_df.Checks < 3, \"AQI_calculated\"] = np.NaN\n",
    "\n",
    "# Pengelompokan AQI sesuai kategori\n",
    "all_df[\"AQI_category\"] = all_df[\"AQI_calculated\"].apply(lambda x: get_AQI_bucket(x))\n",
    "all_df[~all_df.AQI_calculated.isna()].head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DateTime          0\n",
       "PM2.5             0\n",
       "PM10              0\n",
       "SO2               0\n",
       "NO2               0\n",
       "CO                0\n",
       "O3                0\n",
       "TEMP              0\n",
       "DEWP              0\n",
       "RAIN              0\n",
       "Rh                0\n",
       "PM2.5_24hr_avg    0\n",
       "PM10_24hr_avg     0\n",
       "SO2_24hr_avg      0\n",
       "NO2_24hr_avg      0\n",
       "CO_8hr_max        0\n",
       "O3_8hr_max        0\n",
       "station           0\n",
       "PM2.5_SubIndex    0\n",
       "PM10_SubIndex     0\n",
       "SO2_SubIndex      0\n",
       "NO2_SubIndex      0\n",
       "CO_SubIndex       0\n",
       "O3_SubIndex       0\n",
       "Checks            0\n",
       "AQI_calculated    0\n",
       "AQI_category      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "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>station</th>\n",
       "      <th>Tahun</th>\n",
       "      <th>PM2.5</th>\n",
       "      <th>PM10</th>\n",
       "      <th>SO2</th>\n",
       "      <th>NO2</th>\n",
       "      <th>CO</th>\n",
       "      <th>O3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Aotizhongxin</td>\n",
       "      <td>2013</td>\n",
       "      <td>82.398859</td>\n",
       "      <td>112.687278</td>\n",
       "      <td>23.593281</td>\n",
       "      <td>63.338461</td>\n",
       "      <td>8900.0</td>\n",
       "      <td>358.9992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Aotizhongxin</td>\n",
       "      <td>2014</td>\n",
       "      <td>89.781602</td>\n",
       "      <td>122.446646</td>\n",
       "      <td>21.522597</td>\n",
       "      <td>63.188152</td>\n",
       "      <td>8100.0</td>\n",
       "      <td>338.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Aotizhongxin</td>\n",
       "      <td>2015</td>\n",
       "      <td>81.738832</td>\n",
       "      <td>112.080646</td>\n",
       "      <td>14.336274</td>\n",
       "      <td>60.833579</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>423.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Aotizhongxin</td>\n",
       "      <td>2016</td>\n",
       "      <td>73.905626</td>\n",
       "      <td>94.142382</td>\n",
       "      <td>10.843703</td>\n",
       "      <td>48.331351</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>350.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Aotizhongxin</td>\n",
       "      <td>2017</td>\n",
       "      <td>96.972105</td>\n",
       "      <td>109.663459</td>\n",
       "      <td>20.902101</td>\n",
       "      <td>67.160075</td>\n",
       "      <td>9900.0</td>\n",
       "      <td>133.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Changping</td>\n",
       "      <td>2013</td>\n",
       "      <td>72.354725</td>\n",
       "      <td>90.950255</td>\n",
       "      <td>19.133814</td>\n",
       "      <td>40.943908</td>\n",
       "      <td>8100.0</td>\n",
       "      <td>429.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Changping</td>\n",
       "      <td>2014</td>\n",
       "      <td>79.908615</td>\n",
       "      <td>107.759827</td>\n",
       "      <td>21.267623</td>\n",
       "      <td>45.978785</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>413.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Changping</td>\n",
       "      <td>2015</td>\n",
       "      <td>68.754331</td>\n",
       "      <td>93.223470</td>\n",
       "      <td>11.681481</td>\n",
       "      <td>42.623610</td>\n",
       "      <td>9900.0</td>\n",
       "      <td>359.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Changping</td>\n",
       "      <td>2016</td>\n",
       "      <td>61.445422</td>\n",
       "      <td>86.418232</td>\n",
       "      <td>8.128556</td>\n",
       "      <td>45.495386</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>327.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Changping</td>\n",
       "      <td>2017</td>\n",
       "      <td>81.614436</td>\n",
       "      <td>96.005738</td>\n",
       "      <td>16.619939</td>\n",
       "      <td>51.889507</td>\n",
       "      <td>9900.0</td>\n",
       "      <td>148.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Dingling</td>\n",
       "      <td>2013</td>\n",
       "      <td>65.169066</td>\n",
       "      <td>78.714695</td>\n",
       "      <td>13.944026</td>\n",
       "      <td>28.761469</td>\n",
       "      <td>5100.0</td>\n",
       "      <td>382.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Dingling</td>\n",
       "      <td>2014</td>\n",
       "      <td>75.669146</td>\n",
       "      <td>91.663119</td>\n",
       "      <td>14.882737</td>\n",
       "      <td>27.371543</td>\n",
       "      <td>6900.0</td>\n",
       "      <td>411.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Dingling</td>\n",
       "      <td>2015</td>\n",
       "      <td>64.278763</td>\n",
       "      <td>85.318309</td>\n",
       "      <td>11.298822</td>\n",
       "      <td>26.746088</td>\n",
       "      <td>9400.0</td>\n",
       "      <td>432.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Dingling</td>\n",
       "      <td>2016</td>\n",
       "      <td>61.482995</td>\n",
       "      <td>80.490615</td>\n",
       "      <td>7.155681</td>\n",
       "      <td>25.285538</td>\n",
       "      <td>9400.0</td>\n",
       "      <td>500.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Dingling</td>\n",
       "      <td>2017</td>\n",
       "      <td>70.050641</td>\n",
       "      <td>80.314501</td>\n",
       "      <td>13.453596</td>\n",
       "      <td>35.246851</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>104.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Dongsi</td>\n",
       "      <td>2013</td>\n",
       "      <td>86.773997</td>\n",
       "      <td>104.457241</td>\n",
       "      <td>23.696259</td>\n",
       "      <td>57.315807</td>\n",
       "      <td>9400.0</td>\n",
       "      <td>1071.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Dongsi</td>\n",
       "      <td>2014</td>\n",
       "      <td>88.018093</td>\n",
       "      <td>117.547439</td>\n",
       "      <td>24.217264</td>\n",
       "      <td>57.478800</td>\n",
       "      <td>7700.0</td>\n",
       "      <td>320.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Dongsi</td>\n",
       "      <td>2015</td>\n",
       "      <td>87.013348</td>\n",
       "      <td>112.036177</td>\n",
       "      <td>15.188659</td>\n",
       "      <td>50.244239</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>308.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Dongsi</td>\n",
       "      <td>2016</td>\n",
       "      <td>79.789632</td>\n",
       "      <td>102.323438</td>\n",
       "      <td>11.464395</td>\n",
       "      <td>50.552610</td>\n",
       "      <td>9900.0</td>\n",
       "      <td>342.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Dongsi</td>\n",
       "      <td>2017</td>\n",
       "      <td>105.266214</td>\n",
       "      <td>135.462070</td>\n",
       "      <td>20.492585</td>\n",
       "      <td>58.700948</td>\n",
       "      <td>9500.0</td>\n",
       "      <td>500.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Guanyuan</td>\n",
       "      <td>2013</td>\n",
       "      <td>81.611199</td>\n",
       "      <td>108.769584</td>\n",
       "      <td>21.852221</td>\n",
       "      <td>58.828863</td>\n",
       "      <td>7100.0</td>\n",
       "      <td>356.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Guanyuan</td>\n",
       "      <td>2014</td>\n",
       "      <td>88.561849</td>\n",
       "      <td>116.258738</td>\n",
       "      <td>23.138347</td>\n",
       "      <td>64.391334</td>\n",
       "      <td>7600.0</td>\n",
       "      <td>352.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Guanyuan</td>\n",
       "      <td>2015</td>\n",
       "      <td>82.037883</td>\n",
       "      <td>106.234053</td>\n",
       "      <td>14.050106</td>\n",
       "      <td>54.835618</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>352.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Guanyuan</td>\n",
       "      <td>2016</td>\n",
       "      <td>76.748141</td>\n",
       "      <td>104.488397</td>\n",
       "      <td>11.489128</td>\n",
       "      <td>53.375887</td>\n",
       "      <td>9300.0</td>\n",
       "      <td>335.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Guanyuan</td>\n",
       "      <td>2017</td>\n",
       "      <td>97.971325</td>\n",
       "      <td>119.443871</td>\n",
       "      <td>21.416718</td>\n",
       "      <td>65.852173</td>\n",
       "      <td>9800.0</td>\n",
       "      <td>415.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Gucheng</td>\n",
       "      <td>2013</td>\n",
       "      <td>81.465007</td>\n",
       "      <td>112.401484</td>\n",
       "      <td>17.495272</td>\n",
       "      <td>61.297134</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>325.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Gucheng</td>\n",
       "      <td>2014</td>\n",
       "      <td>90.581365</td>\n",
       "      <td>134.906812</td>\n",
       "      <td>21.301557</td>\n",
       "      <td>62.467297</td>\n",
       "      <td>8800.0</td>\n",
       "      <td>374.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Gucheng</td>\n",
       "      <td>2015</td>\n",
       "      <td>83.557369</td>\n",
       "      <td>116.218851</td>\n",
       "      <td>13.315590</td>\n",
       "      <td>49.499609</td>\n",
       "      <td>9900.0</td>\n",
       "      <td>450.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Gucheng</td>\n",
       "      <td>2016</td>\n",
       "      <td>77.088964</td>\n",
       "      <td>110.720320</td>\n",
       "      <td>10.208931</td>\n",
       "      <td>49.687415</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>348.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Gucheng</td>\n",
       "      <td>2017</td>\n",
       "      <td>103.876030</td>\n",
       "      <td>129.724782</td>\n",
       "      <td>20.724665</td>\n",
       "      <td>63.311558</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>74.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Huairou</td>\n",
       "      <td>2013</td>\n",
       "      <td>68.576876</td>\n",
       "      <td>98.348597</td>\n",
       "      <td>18.300219</td>\n",
       "      <td>35.023627</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>444.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Huairou</td>\n",
       "      <td>2014</td>\n",
       "      <td>77.183970</td>\n",
       "      <td>100.001878</td>\n",
       "      <td>17.598414</td>\n",
       "      <td>37.095883</td>\n",
       "      <td>6700.0</td>\n",
       "      <td>391.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Huairou</td>\n",
       "      <td>2015</td>\n",
       "      <td>70.424806</td>\n",
       "      <td>90.115697</td>\n",
       "      <td>8.657157</td>\n",
       "      <td>28.261598</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>413.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Huairou</td>\n",
       "      <td>2016</td>\n",
       "      <td>60.804341</td>\n",
       "      <td>82.107705</td>\n",
       "      <td>6.626082</td>\n",
       "      <td>27.222526</td>\n",
       "      <td>9300.0</td>\n",
       "      <td>356.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Huairou</td>\n",
       "      <td>2017</td>\n",
       "      <td>74.953154</td>\n",
       "      <td>92.941443</td>\n",
       "      <td>9.882857</td>\n",
       "      <td>39.366143</td>\n",
       "      <td>9800.0</td>\n",
       "      <td>119.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Nongzhanguan</td>\n",
       "      <td>2013</td>\n",
       "      <td>84.336327</td>\n",
       "      <td>103.539099</td>\n",
       "      <td>23.236353</td>\n",
       "      <td>59.496534</td>\n",
       "      <td>9800.0</td>\n",
       "      <td>310.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Nongzhanguan</td>\n",
       "      <td>2014</td>\n",
       "      <td>92.033332</td>\n",
       "      <td>127.335449</td>\n",
       "      <td>24.884344</td>\n",
       "      <td>62.328454</td>\n",
       "      <td>9400.0</td>\n",
       "      <td>367.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Nongzhanguan</td>\n",
       "      <td>2015</td>\n",
       "      <td>85.071069</td>\n",
       "      <td>106.931020</td>\n",
       "      <td>15.797029</td>\n",
       "      <td>58.150770</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>364.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Nongzhanguan</td>\n",
       "      <td>2016</td>\n",
       "      <td>75.750142</td>\n",
       "      <td>96.646037</td>\n",
       "      <td>11.552814</td>\n",
       "      <td>52.485148</td>\n",
       "      <td>9400.0</td>\n",
       "      <td>390.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Nongzhanguan</td>\n",
       "      <td>2017</td>\n",
       "      <td>103.806978</td>\n",
       "      <td>122.762682</td>\n",
       "      <td>20.734316</td>\n",
       "      <td>59.091425</td>\n",
       "      <td>9700.0</td>\n",
       "      <td>94.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Shunyi</td>\n",
       "      <td>2013</td>\n",
       "      <td>75.703572</td>\n",
       "      <td>94.936991</td>\n",
       "      <td>15.058772</td>\n",
       "      <td>41.610800</td>\n",
       "      <td>8700.0</td>\n",
       "      <td>351.7164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Shunyi</td>\n",
       "      <td>2014</td>\n",
       "      <td>85.231100</td>\n",
       "      <td>111.753362</td>\n",
       "      <td>17.718152</td>\n",
       "      <td>46.877078</td>\n",
       "      <td>7600.0</td>\n",
       "      <td>333.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Shunyi</td>\n",
       "      <td>2015</td>\n",
       "      <td>81.347976</td>\n",
       "      <td>100.351374</td>\n",
       "      <td>10.438345</td>\n",
       "      <td>43.023552</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>340.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Shunyi</td>\n",
       "      <td>2016</td>\n",
       "      <td>72.015431</td>\n",
       "      <td>87.801339</td>\n",
       "      <td>9.547420</td>\n",
       "      <td>42.471022</td>\n",
       "      <td>9900.0</td>\n",
       "      <td>339.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Shunyi</td>\n",
       "      <td>2017</td>\n",
       "      <td>97.115554</td>\n",
       "      <td>108.867408</td>\n",
       "      <td>21.411694</td>\n",
       "      <td>56.278337</td>\n",
       "      <td>9600.0</td>\n",
       "      <td>173.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Tiantan</td>\n",
       "      <td>2013</td>\n",
       "      <td>83.178793</td>\n",
       "      <td>108.171368</td>\n",
       "      <td>18.201687</td>\n",
       "      <td>53.134734</td>\n",
       "      <td>8199.0</td>\n",
       "      <td>674.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Tiantan</td>\n",
       "      <td>2014</td>\n",
       "      <td>86.720244</td>\n",
       "      <td>114.962024</td>\n",
       "      <td>19.774014</td>\n",
       "      <td>56.201787</td>\n",
       "      <td>9300.0</td>\n",
       "      <td>369.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>Tiantan</td>\n",
       "      <td>2015</td>\n",
       "      <td>81.972854</td>\n",
       "      <td>102.786307</td>\n",
       "      <td>11.802392</td>\n",
       "      <td>51.932863</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>360.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>Tiantan</td>\n",
       "      <td>2016</td>\n",
       "      <td>73.411719</td>\n",
       "      <td>96.795495</td>\n",
       "      <td>8.948206</td>\n",
       "      <td>49.863241</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>347.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Tiantan</td>\n",
       "      <td>2017</td>\n",
       "      <td>100.921169</td>\n",
       "      <td>129.437677</td>\n",
       "      <td>14.092181</td>\n",
       "      <td>64.861832</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>104.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>Wanliu</td>\n",
       "      <td>2013</td>\n",
       "      <td>91.432421</td>\n",
       "      <td>112.158508</td>\n",
       "      <td>23.610133</td>\n",
       "      <td>68.477949</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>344.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>Wanliu</td>\n",
       "      <td>2014</td>\n",
       "      <td>90.421478</td>\n",
       "      <td>131.419271</td>\n",
       "      <td>25.071762</td>\n",
       "      <td>75.822255</td>\n",
       "      <td>9600.0</td>\n",
       "      <td>342.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>Wanliu</td>\n",
       "      <td>2015</td>\n",
       "      <td>80.113560</td>\n",
       "      <td>106.503571</td>\n",
       "      <td>14.819202</td>\n",
       "      <td>59.959757</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>364.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>Wanliu</td>\n",
       "      <td>2016</td>\n",
       "      <td>71.105049</td>\n",
       "      <td>92.564848</td>\n",
       "      <td>10.669944</td>\n",
       "      <td>58.155297</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>318.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>Wanliu</td>\n",
       "      <td>2017</td>\n",
       "      <td>96.579508</td>\n",
       "      <td>113.539460</td>\n",
       "      <td>20.499735</td>\n",
       "      <td>70.210040</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>102.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>Wanshouxigong</td>\n",
       "      <td>2013</td>\n",
       "      <td>83.739571</td>\n",
       "      <td>111.018252</td>\n",
       "      <td>21.769774</td>\n",
       "      <td>55.906100</td>\n",
       "      <td>8600.0</td>\n",
       "      <td>358.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>Wanshouxigong</td>\n",
       "      <td>2014</td>\n",
       "      <td>90.525489</td>\n",
       "      <td>127.267641</td>\n",
       "      <td>23.195529</td>\n",
       "      <td>61.837789</td>\n",
       "      <td>9600.0</td>\n",
       "      <td>347.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>Wanshouxigong</td>\n",
       "      <td>2015</td>\n",
       "      <td>84.165380</td>\n",
       "      <td>111.098238</td>\n",
       "      <td>14.369341</td>\n",
       "      <td>52.647028</td>\n",
       "      <td>9800.0</td>\n",
       "      <td>355.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>Wanshouxigong</td>\n",
       "      <td>2016</td>\n",
       "      <td>77.676906</td>\n",
       "      <td>98.375135</td>\n",
       "      <td>10.642755</td>\n",
       "      <td>50.081602</td>\n",
       "      <td>9800.0</td>\n",
       "      <td>340.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>Wanshouxigong</td>\n",
       "      <td>2017</td>\n",
       "      <td>109.605432</td>\n",
       "      <td>125.181321</td>\n",
       "      <td>18.674670</td>\n",
       "      <td>65.285370</td>\n",
       "      <td>9300.0</td>\n",
       "      <td>88.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          station  Tahun       PM2.5        PM10        SO2        NO2  \\\n",
       "0    Aotizhongxin   2013   82.398859  112.687278  23.593281  63.338461   \n",
       "1    Aotizhongxin   2014   89.781602  122.446646  21.522597  63.188152   \n",
       "2    Aotizhongxin   2015   81.738832  112.080646  14.336274  60.833579   \n",
       "3    Aotizhongxin   2016   73.905626   94.142382  10.843703  48.331351   \n",
       "4    Aotizhongxin   2017   96.972105  109.663459  20.902101  67.160075   \n",
       "5       Changping   2013   72.354725   90.950255  19.133814  40.943908   \n",
       "6       Changping   2014   79.908615  107.759827  21.267623  45.978785   \n",
       "7       Changping   2015   68.754331   93.223470  11.681481  42.623610   \n",
       "8       Changping   2016   61.445422   86.418232   8.128556  45.495386   \n",
       "9       Changping   2017   81.614436   96.005738  16.619939  51.889507   \n",
       "10       Dingling   2013   65.169066   78.714695  13.944026  28.761469   \n",
       "11       Dingling   2014   75.669146   91.663119  14.882737  27.371543   \n",
       "12       Dingling   2015   64.278763   85.318309  11.298822  26.746088   \n",
       "13       Dingling   2016   61.482995   80.490615   7.155681  25.285538   \n",
       "14       Dingling   2017   70.050641   80.314501  13.453596  35.246851   \n",
       "15         Dongsi   2013   86.773997  104.457241  23.696259  57.315807   \n",
       "16         Dongsi   2014   88.018093  117.547439  24.217264  57.478800   \n",
       "17         Dongsi   2015   87.013348  112.036177  15.188659  50.244239   \n",
       "18         Dongsi   2016   79.789632  102.323438  11.464395  50.552610   \n",
       "19         Dongsi   2017  105.266214  135.462070  20.492585  58.700948   \n",
       "20       Guanyuan   2013   81.611199  108.769584  21.852221  58.828863   \n",
       "21       Guanyuan   2014   88.561849  116.258738  23.138347  64.391334   \n",
       "22       Guanyuan   2015   82.037883  106.234053  14.050106  54.835618   \n",
       "23       Guanyuan   2016   76.748141  104.488397  11.489128  53.375887   \n",
       "24       Guanyuan   2017   97.971325  119.443871  21.416718  65.852173   \n",
       "25        Gucheng   2013   81.465007  112.401484  17.495272  61.297134   \n",
       "26        Gucheng   2014   90.581365  134.906812  21.301557  62.467297   \n",
       "27        Gucheng   2015   83.557369  116.218851  13.315590  49.499609   \n",
       "28        Gucheng   2016   77.088964  110.720320  10.208931  49.687415   \n",
       "29        Gucheng   2017  103.876030  129.724782  20.724665  63.311558   \n",
       "30        Huairou   2013   68.576876   98.348597  18.300219  35.023627   \n",
       "31        Huairou   2014   77.183970  100.001878  17.598414  37.095883   \n",
       "32        Huairou   2015   70.424806   90.115697   8.657157  28.261598   \n",
       "33        Huairou   2016   60.804341   82.107705   6.626082  27.222526   \n",
       "34        Huairou   2017   74.953154   92.941443   9.882857  39.366143   \n",
       "35   Nongzhanguan   2013   84.336327  103.539099  23.236353  59.496534   \n",
       "36   Nongzhanguan   2014   92.033332  127.335449  24.884344  62.328454   \n",
       "37   Nongzhanguan   2015   85.071069  106.931020  15.797029  58.150770   \n",
       "38   Nongzhanguan   2016   75.750142   96.646037  11.552814  52.485148   \n",
       "39   Nongzhanguan   2017  103.806978  122.762682  20.734316  59.091425   \n",
       "40         Shunyi   2013   75.703572   94.936991  15.058772  41.610800   \n",
       "41         Shunyi   2014   85.231100  111.753362  17.718152  46.877078   \n",
       "42         Shunyi   2015   81.347976  100.351374  10.438345  43.023552   \n",
       "43         Shunyi   2016   72.015431   87.801339   9.547420  42.471022   \n",
       "44         Shunyi   2017   97.115554  108.867408  21.411694  56.278337   \n",
       "45        Tiantan   2013   83.178793  108.171368  18.201687  53.134734   \n",
       "46        Tiantan   2014   86.720244  114.962024  19.774014  56.201787   \n",
       "47        Tiantan   2015   81.972854  102.786307  11.802392  51.932863   \n",
       "48        Tiantan   2016   73.411719   96.795495   8.948206  49.863241   \n",
       "49        Tiantan   2017  100.921169  129.437677  14.092181  64.861832   \n",
       "50         Wanliu   2013   91.432421  112.158508  23.610133  68.477949   \n",
       "51         Wanliu   2014   90.421478  131.419271  25.071762  75.822255   \n",
       "52         Wanliu   2015   80.113560  106.503571  14.819202  59.959757   \n",
       "53         Wanliu   2016   71.105049   92.564848  10.669944  58.155297   \n",
       "54         Wanliu   2017   96.579508  113.539460  20.499735  70.210040   \n",
       "55  Wanshouxigong   2013   83.739571  111.018252  21.769774  55.906100   \n",
       "56  Wanshouxigong   2014   90.525489  127.267641  23.195529  61.837789   \n",
       "57  Wanshouxigong   2015   84.165380  111.098238  14.369341  52.647028   \n",
       "58  Wanshouxigong   2016   77.676906   98.375135  10.642755  50.081602   \n",
       "59  Wanshouxigong   2017  109.605432  125.181321  18.674670  65.285370   \n",
       "\n",
       "         CO         O3  \n",
       "0    8900.0   358.9992  \n",
       "1    8100.0   338.0000  \n",
       "2   10000.0   423.0000  \n",
       "3   10000.0   350.0000  \n",
       "4    9900.0   133.0000  \n",
       "5    8100.0   429.0000  \n",
       "6   10000.0   413.0000  \n",
       "7    9900.0   359.0000  \n",
       "8   10000.0   327.0000  \n",
       "9    9900.0   148.0000  \n",
       "10   5100.0   382.0000  \n",
       "11   6900.0   411.0000  \n",
       "12   9400.0   432.0000  \n",
       "13   9400.0   500.0000  \n",
       "14  10000.0   104.0000  \n",
       "15   9400.0  1071.0000  \n",
       "16   7700.0   320.0000  \n",
       "17  10000.0   308.0000  \n",
       "18   9900.0   342.0000  \n",
       "19   9500.0   500.0000  \n",
       "20   7100.0   356.0000  \n",
       "21   7600.0   352.0000  \n",
       "22  10000.0   352.0000  \n",
       "23   9300.0   335.0000  \n",
       "24   9800.0   415.0000  \n",
       "25   8000.0   325.0000  \n",
       "26   8800.0   374.0000  \n",
       "27   9900.0   450.0000  \n",
       "28  10000.0   348.0000  \n",
       "29  10000.0    74.0000  \n",
       "30   8000.0   444.0000  \n",
       "31   6700.0   391.0000  \n",
       "32  10000.0   413.0000  \n",
       "33   9300.0   356.0000  \n",
       "34   9800.0   119.0000  \n",
       "35   9800.0   310.0000  \n",
       "36   9400.0   367.0000  \n",
       "37  10000.0   364.0000  \n",
       "38   9400.0   390.0000  \n",
       "39   9700.0    94.0000  \n",
       "40   8700.0   351.7164  \n",
       "41   7600.0   333.0000  \n",
       "42  10000.0   340.0000  \n",
       "43   9900.0   339.0000  \n",
       "44   9600.0   173.0000  \n",
       "45   8199.0   674.0000  \n",
       "46   9300.0   369.0000  \n",
       "47  10000.0   360.0000  \n",
       "48  10000.0   347.0000  \n",
       "49  10000.0   104.0000  \n",
       "50  10000.0   344.0000  \n",
       "51   9600.0   342.0000  \n",
       "52  10000.0   364.0000  \n",
       "53  10000.0   318.0000  \n",
       "54  10000.0   102.0000  \n",
       "55   8600.0   358.0000  \n",
       "56   9600.0   347.0000  \n",
       "57   9800.0   355.0000  \n",
       "58   9800.0   340.0000  \n",
       "59   9300.0    88.0000  "
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pollutantParam_Avg = ['PM2.5_24hr_avg','PM10_24hr_avg', 'SO2_24hr_avg', 'NO2_24hr_avg']\n",
    "pollutantParam_Max =['CO_8hr_max', 'O3_8hr_max']\n",
    "\n",
    "pollutantSum_byYear_df = all_df.groupby(by=['station', all_df['DateTime'].dt.year.rename('Tahun')]).agg({\n",
    "    **{param: 'mean' for param in pollutantParam_Avg},\n",
    "    **{param: 'max' for param in pollutantParam_Max}\n",
    "})\n",
    "pollutantSum_byYear_df.columns = ['PM2.5','PM10', 'SO2', 'NO2', 'CO', 'O3'] # Meringkas nama, namun tetap merupakan kalkulasi hasil (bukan rawdata)\n",
    "pollutantSum_byYear_df.reset_index(inplace=True)\n",
    "pollutantSum_byYear_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "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></th>\n",
       "      <th>AQI Value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AQI_category</th>\n",
       "      <th>station</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"12\" valign=\"top\">Severe</th>\n",
       "      <th>Aotizhongxin</th>\n",
       "      <td>10186.633099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Changping</th>\n",
       "      <td>9565.661761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dingling</th>\n",
       "      <td>7225.706251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dongsi</th>\n",
       "      <td>10584.239362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Guanyuan</th>\n",
       "      <td>10015.049738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gucheng</th>\n",
       "      <td>10638.536105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Huairou</th>\n",
       "      <td>8183.765172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nongzhanguan</th>\n",
       "      <td>10729.369809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shunyi</th>\n",
       "      <td>9960.520021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tiantan</th>\n",
       "      <td>10454.663672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wanliu</th>\n",
       "      <td>10593.675023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wanshouxigong</th>\n",
       "      <td>10939.144564</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               AQI Value\n",
       "AQI_category station                    \n",
       "Severe       Aotizhongxin   10186.633099\n",
       "             Changping       9565.661761\n",
       "             Dingling        7225.706251\n",
       "             Dongsi         10584.239362\n",
       "             Guanyuan       10015.049738\n",
       "             Gucheng        10638.536105\n",
       "             Huairou         8183.765172\n",
       "             Nongzhanguan   10729.369809\n",
       "             Shunyi          9960.520021\n",
       "             Tiantan        10454.663672\n",
       "             Wanliu         10593.675023\n",
       "             Wanshouxigong  10939.144564"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Beijing_in_AQIValue = all_df.groupby(by=['AQI_category', 'station']).agg({\n",
    "                              'AQI_calculated' : 'mean'\n",
    "                        })\n",
    "Beijing_in_AQIValue.columns=['AQI Value']\n",
    "Beijing_in_AQIValue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        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>station</th>\n",
       "      <th>Kategori AQI</th>\n",
       "      <th>Nilai AQI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Dingling</td>\n",
       "      <td>[Severe]</td>\n",
       "      <td>7225.706251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Huairou</td>\n",
       "      <td>[Severe]</td>\n",
       "      <td>8183.765172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Changping</td>\n",
       "      <td>[Severe]</td>\n",
       "      <td>9565.661761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Shunyi</td>\n",
       "      <td>[Severe]</td>\n",
       "      <td>9960.520021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Guanyuan</td>\n",
       "      <td>[Severe]</td>\n",
       "      <td>10015.049738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Aotizhongxin</td>\n",
       "      <td>[Severe]</td>\n",
       "      <td>10186.633099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Tiantan</td>\n",
       "      <td>[Severe]</td>\n",
       "      <td>10454.663672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Dongsi</td>\n",
       "      <td>[Severe]</td>\n",
       "      <td>10584.239362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Wanliu</td>\n",
       "      <td>[Severe]</td>\n",
       "      <td>10593.675023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Gucheng</td>\n",
       "      <td>[Severe]</td>\n",
       "      <td>10638.536105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Nongzhanguan</td>\n",
       "      <td>[Severe]</td>\n",
       "      <td>10729.369809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Wanshouxigong</td>\n",
       "      <td>[Severe]</td>\n",
       "      <td>10939.144564</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          station Kategori AQI     Nilai AQI\n",
       "0        Dingling     [Severe]   7225.706251\n",
       "1         Huairou     [Severe]   8183.765172\n",
       "2       Changping     [Severe]   9565.661761\n",
       "3          Shunyi     [Severe]   9960.520021\n",
       "4        Guanyuan     [Severe]  10015.049738\n",
       "5    Aotizhongxin     [Severe]  10186.633099\n",
       "6         Tiantan     [Severe]  10454.663672\n",
       "7          Dongsi     [Severe]  10584.239362\n",
       "8          Wanliu     [Severe]  10593.675023\n",
       "9         Gucheng     [Severe]  10638.536105\n",
       "10   Nongzhanguan     [Severe]  10729.369809\n",
       "11  Wanshouxigong     [Severe]  10939.144564"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Beijing_in_AQICategorySum = all_df.groupby(by=['station']).agg({\n",
    "    'AQI_category': lambda x: x.unique().tolist(),\n",
    "    'AQI_calculated' : 'mean'\n",
    "})\n",
    "Beijing_in_AQICategorySum.columns = ['Kategori AQI', 'Nilai AQI']\n",
    "Beijing_in_AQICategorySum.sort_values(by='Nilai AQI', inplace=True)\n",
    "Beijing_in_AQICategorySum.reset_index(inplace=True)\n",
    "Beijing_in_AQICategorySum"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mengeluarkan dari array pada kolom **Kategori AQI** karena seluruhnya menhasilkan satu nilai."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "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>station</th>\n",
       "      <th>Kategori AQI</th>\n",
       "      <th>Nilai AQI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Dingling</td>\n",
       "      <td>Severe</td>\n",
       "      <td>7225.706251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Huairou</td>\n",
       "      <td>Severe</td>\n",
       "      <td>8183.765172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Changping</td>\n",
       "      <td>Severe</td>\n",
       "      <td>9565.661761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Shunyi</td>\n",
       "      <td>Severe</td>\n",
       "      <td>9960.520021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Guanyuan</td>\n",
       "      <td>Severe</td>\n",
       "      <td>10015.049738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Aotizhongxin</td>\n",
       "      <td>Severe</td>\n",
       "      <td>10186.633099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Tiantan</td>\n",
       "      <td>Severe</td>\n",
       "      <td>10454.663672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Dongsi</td>\n",
       "      <td>Severe</td>\n",
       "      <td>10584.239362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Wanliu</td>\n",
       "      <td>Severe</td>\n",
       "      <td>10593.675023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Gucheng</td>\n",
       "      <td>Severe</td>\n",
       "      <td>10638.536105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Nongzhanguan</td>\n",
       "      <td>Severe</td>\n",
       "      <td>10729.369809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Wanshouxigong</td>\n",
       "      <td>Severe</td>\n",
       "      <td>10939.144564</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          station Kategori AQI     Nilai AQI\n",
       "0        Dingling       Severe   7225.706251\n",
       "1         Huairou       Severe   8183.765172\n",
       "2       Changping       Severe   9565.661761\n",
       "3          Shunyi       Severe   9960.520021\n",
       "4        Guanyuan       Severe  10015.049738\n",
       "5    Aotizhongxin       Severe  10186.633099\n",
       "6         Tiantan       Severe  10454.663672\n",
       "7          Dongsi       Severe  10584.239362\n",
       "8          Wanliu       Severe  10593.675023\n",
       "9         Gucheng       Severe  10638.536105\n",
       "10   Nongzhanguan       Severe  10729.369809\n",
       "11  Wanshouxigong       Severe  10939.144564"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Beijing_in_AQICategorySum['Kategori AQI'] = Beijing_in_AQICategorySum['Kategori AQI'].apply(lambda x: x[0])\n",
    "Beijing_in_AQICategorySum"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Seluruh stasiun menunjukkan bahwa kawasan Beijing memiliki AQI Level *Severe*."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualisasi Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Perolehan Curah Hujan, Suhu, dan Kelembapan di Kawasan Beijing dalam Kurun Waktu 5 Tahun"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Curah Hujan Bulanan di Kawasan Beijing dalam Kurun Waktu 5 Tahun"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Tingkat Curah Hujan\n",
    "- Rendah (0-100 mm/bulan)\n",
    "- Sedang (100-300 mm/bulan)\n",
    "- Tinggi (300-500 mm/bulan)\n",
    "- Sangat Tinggi (>500 mm/bulan)\n",
    "\n",
    "source : https://bmkgsampali.net/normal-hujan-bulanan/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Menginisiasi sumbu x dan sumbu y data cuaca\n",
    "x = monthly_climateBeijing['Tanggal']\n",
    "y1 = monthly_climateBeijing['Curah Hujan']\n",
    "y2 = monthly_climateBeijing['Suhu Minimum']\n",
    "y3 = monthly_climateBeijing['Suhu Rerata']\n",
    "y4 = monthly_climateBeijing['Suhu Maksimum']\n",
    "y5 = monthly_climateBeijing['Kelembapan']\n",
    "bt1, bt2, bt3 = 100, 300, 500"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(x, y1, marker='o', linewidth=2, label='Curah Hujan', color='blue')\n",
    "\n",
    "plt.axhline(y=bt1, color='green', linestyle='--', label='Batas Atas Rendah')\n",
    "plt.axhline(y=bt2, color='orange', linestyle='--', label='Batas Atas Sedang')\n",
    "plt.axhline(y=bt3, color='red', linestyle='--', label='Batas Atas Tinggi')\n",
    "\n",
    "plt.title(\"Curah Hujan Bulanan di Kawasan Beijing selama 5 Tahun\", fontsize=20)\n",
    "plt.xlabel(\"Tanggal\", fontsize=12)\n",
    "plt.ylabel(\"Curah Hujan (mm/bulan)\", fontsize=12)\n",
    "plt.grid(True)\n",
    "plt.xticks(rotation=45)\n",
    "plt.tight_layout()\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Suhu Bulanan di Kawasan Beijing dalam Kurun Waktu 5 Tahun"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(x, y2, marker='o', linewidth=2, label='Suhu Minimum', color='yellow')\n",
    "plt.plot(x, y3, marker='o', linewidth=2, label='Suhu Rerata', color='green')\n",
    "plt.plot(x, y4, marker='o', linewidth=2, label='Suhu Maksimum', color='red')\n",
    "\n",
    "# Menginisiasi Garis Tren Suhu\n",
    "x_numeric = mdates.date2num(x)\n",
    "coefficients = np.polyfit(x_numeric, y3, 1) # 1 = metode linear\n",
    "trend = np.polyval(coefficients, x_numeric)\n",
    "plt.plot(x, trend, linestyle='--', color='black', label='Garis Tren')\n",
    "\n",
    "plt.title(\"Rerata Suhu Bulanan di Kawasan Beijing selama 5 Tahun\", fontsize=20)\n",
    "plt.xlabel(\"Tanggal\", fontsize=12)\n",
    "plt.ylabel(\"Suhu (°C)\", fontsize=12)\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.xticks(rotation=45)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Kelembapan Bulanan di Kawasan Beijing dalam Kurun Waktu 5 Tahun"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![alt](https://www.higienis.com/media/wysiwyg/AAA.webp) <br>\n",
    "source : https://www.higienis.com/blog/humidity-guide/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ideal1, ideal2 = 45, 65\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(x, y5, marker='o', linewidth=2, label='Kelembapan', color='gray')\n",
    "\n",
    "plt.axhline(y=ideal1, color='green', linestyle='--', label='Batas Atas Ideal')\n",
    "plt.axhline(y=ideal2, color='green', linestyle='--', label='Batas Bawah Ideal')\n",
    "\n",
    "plt.title(\"Rerata Kelembapan di Kawasan Beijing selama 5 Tahun\", fontsize=20)\n",
    "plt.xlabel(\"Tanggal\", fontsize=12)\n",
    "plt.ylabel(\"Kelembapan (%)\", fontsize=12)\n",
    "plt.grid(True)\n",
    "plt.legend()\n",
    "plt.xticks(rotation=45)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Tingkat Polusi Udara di Berbagai Stasiun"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1200 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Inisasi Sumbu Tingkat Polusi Udara\n",
    "stationList = pollutantSum_byYear_df['station'].drop_duplicates().to_list() # sumbu x\n",
    "labels = pollutantSum_byYear_df.columns[2:8].to_list() # sumbu y\n",
    "barWidth = 0.15\n",
    "xPos= np.arange(len(stationList))\n",
    "\n",
    "# Visualisasi Data Polusi Udara\n",
    "fig, ax = plt.subplots(nrows=6, ncols=1, figsize=(15, 12))\n",
    "\n",
    "for i, label in enumerate(labels):\n",
    "    for year in range(2013, 2018):\n",
    "        ax[i].bar(xPos + (barWidth * (year - 2013)), \n",
    "                  pollutantSum_byYear_df.loc[pollutantSum_byYear_df['Tahun'] == year, label].tolist(), \n",
    "                  width=barWidth,\n",
    "                  label=f'{year}')\n",
    "    ax[i].set_xticks(xPos + (barWidth * 2))\n",
    "    ax[i].set_xticklabels(stationList)\n",
    "    ax[i].set_xlabel('Stasiun')\n",
    "    if label == 'CO':\n",
    "        ax[i].set_ylabel('mg/m³')\n",
    "    else:\n",
    "        ax[i].set_ylabel('µg/m³')\n",
    "    ax[i].set_title(label, pad=10)\n",
    "    ax[i].grid(visible=True, axis= 'y')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.legend(title='Tahun', loc='right', bbox_to_anchor=(1.1, 6))\n",
    "plt.suptitle(\"Tingkat Polusi Udara di Kawasan Beijing selama 5 Tahun \", fontsize=20)\n",
    "plt.subplots_adjust(top=0.9)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Perolehan Tingkat Polusi Berdasarkan *Air Quality Index* (AQI)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Inisiasi Sumbu AQI\n",
    "station = Beijing_in_AQICategorySum['station']\n",
    "nilaiAQI = Beijing_in_AQICategorySum['Nilai AQI']\n",
    "\n",
    "# Visualisasi Data AQI\n",
    "plt.figure(figsize=(10,5))\n",
    "plt.barh(station, nilaiAQI, color= 'r')\n",
    "plt.ylabel('Stasiun')\n",
    "plt.xlabel('Nilai Air Quality Index (AQI)')\n",
    "plt.title('Indeks Kualitas Udara Berdasarkan Stasiun di Kawasan Beijing selama 5 Tahun')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusion\n",
    "\n",
    "- Pertanyaan 1 : Kawasan Beijing memiliki curah hujan yang cenderung sangat tinggi, tren suhu cenderung turun, dan tingkat kelembapan yang cenderung ideal dalam kurun waktu 5 tahun.\n",
    "- Pertanyaan 2 : Kawasan Beijing memiliki indeks kualitas udara (*Air Quality Index*) pada tingkat *severe*."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Download Dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "unnecesarryCol = ['PM2.5', 'PM10', 'SO2', 'NO2', 'CO', 'O3','PM2.5_SubIndex', 'PM10_SubIndex', 'SO2_SubIndex', 'NO2_SubIndex', 'CO_SubIndex', 'O3_SubIndex', 'Checks']\n",
    "all_df.drop(columns= unnecesarryCol, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "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>DateTime</th>\n",
       "      <th>TEMP</th>\n",
       "      <th>DEWP</th>\n",
       "      <th>RAIN</th>\n",
       "      <th>Rh</th>\n",
       "      <th>PM2.5_24hr_avg</th>\n",
       "      <th>PM10_24hr_avg</th>\n",
       "      <th>SO2_24hr_avg</th>\n",
       "      <th>NO2_24hr_avg</th>\n",
       "      <th>CO_8hr_max</th>\n",
       "      <th>O3_8hr_max</th>\n",
       "      <th>station</th>\n",
       "      <th>AQI_calculated</th>\n",
       "      <th>AQI_category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013-03-01 00:00:00</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>-18.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.257491</td>\n",
       "      <td>7.125</td>\n",
       "      <td>10.75</td>\n",
       "      <td>11.708333</td>\n",
       "      <td>22.583333</td>\n",
       "      <td>300.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "      <td>1965.0</td>\n",
       "      <td>Severe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013-03-01 01:00:00</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>-18.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.263193</td>\n",
       "      <td>7.125</td>\n",
       "      <td>10.75</td>\n",
       "      <td>11.708333</td>\n",
       "      <td>22.583333</td>\n",
       "      <td>300.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "      <td>1965.0</td>\n",
       "      <td>Severe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013-03-01 02:00:00</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>-18.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.263193</td>\n",
       "      <td>7.125</td>\n",
       "      <td>10.75</td>\n",
       "      <td>11.708333</td>\n",
       "      <td>22.583333</td>\n",
       "      <td>300.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "      <td>1965.0</td>\n",
       "      <td>Severe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013-03-01 03:00:00</td>\n",
       "      <td>-1.4</td>\n",
       "      <td>-19.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.278311</td>\n",
       "      <td>7.125</td>\n",
       "      <td>10.75</td>\n",
       "      <td>11.708333</td>\n",
       "      <td>22.583333</td>\n",
       "      <td>300.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "      <td>1965.0</td>\n",
       "      <td>Severe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013-03-01 04:00:00</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>-19.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.161525</td>\n",
       "      <td>7.125</td>\n",
       "      <td>10.75</td>\n",
       "      <td>11.708333</td>\n",
       "      <td>22.583333</td>\n",
       "      <td>300.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "      <td>1965.0</td>\n",
       "      <td>Severe</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             DateTime  TEMP  DEWP  RAIN         Rh  PM2.5_24hr_avg  \\\n",
       "0 2013-03-01 00:00:00  -0.7 -18.8   0.0  24.257491           7.125   \n",
       "1 2013-03-01 01:00:00  -1.1 -18.2   0.0  26.263193           7.125   \n",
       "2 2013-03-01 02:00:00  -1.1 -18.2   0.0  26.263193           7.125   \n",
       "3 2013-03-01 03:00:00  -1.4 -19.4   0.0  24.278311           7.125   \n",
       "4 2013-03-01 04:00:00  -2.0 -19.5   0.0  25.161525           7.125   \n",
       "\n",
       "   PM10_24hr_avg  SO2_24hr_avg  NO2_24hr_avg  CO_8hr_max  O3_8hr_max  \\\n",
       "0          10.75     11.708333     22.583333       300.0        77.0   \n",
       "1          10.75     11.708333     22.583333       300.0        77.0   \n",
       "2          10.75     11.708333     22.583333       300.0        77.0   \n",
       "3          10.75     11.708333     22.583333       300.0        77.0   \n",
       "4          10.75     11.708333     22.583333       300.0        77.0   \n",
       "\n",
       "        station  AQI_calculated AQI_category  \n",
       "0  Aotizhongxin          1965.0       Severe  \n",
       "1  Aotizhongxin          1965.0       Severe  \n",
       "2  Aotizhongxin          1965.0       Severe  \n",
       "3  Aotizhongxin          1965.0       Severe  \n",
       "4  Aotizhongxin          1965.0       Severe  "
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_df.to_csv('./dashboard/newbeijing_climateData.csv', index= False)"
   ]
  }
 ],
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