notebook_air.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "H9wADwK78DCz"
   },
   "source": [
    "# Proyek Analisis Data: Air Quality Dataset\n",
    "- **Nama:** Dimas Firmansyah\n",
    "- **Email:** foreirn@gmail.com\n",
    "- **ID Dicoding:** dfirmansyah"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "eE0raob58DC0"
   },
   "source": [
    "## Menentukan Pertanyaan Bisnis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GmQeQ5YF8DC0"
   },
   "source": [
    "- Bagaimana angka Air Quality Index (AQI) tiap wilayah?\n",
    "- Apa wilayah yang paling buruk kualitas udaranya?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "H-z4QGlO8DC1"
   },
   "source": [
    "## Import Semua Packages/Library yang Digunakan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {
    "id": "FVYwaObI8DC1"
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import os\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "h_Sh51Xy8DC1"
   },
   "source": [
    "## Data Wrangling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sXU2GBYu8DC1"
   },
   "source": [
    "### Gathering Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {
    "id": "zjCBk1BI8DC1"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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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>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>47412</th>\n",
       "      <td>2014</td>\n",
       "      <td>7</td>\n",
       "      <td>28</td>\n",
       "      <td>12</td>\n",
       "      <td>131.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>225.0</td>\n",
       "      <td>34.5</td>\n",
       "      <td>996.0</td>\n",
       "      <td>20.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>SW</td>\n",
       "      <td>1.5</td>\n",
       "      <td>Gucheng</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237384</th>\n",
       "      <td>2016</td>\n",
       "      <td>3</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>14.5</td>\n",
       "      <td>1008.2</td>\n",
       "      <td>-10.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>N</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Changping</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243635</th>\n",
       "      <td>2016</td>\n",
       "      <td>12</td>\n",
       "      <td>15</td>\n",
       "      <td>11</td>\n",
       "      <td>11.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1025.7</td>\n",
       "      <td>-14.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>WNW</td>\n",
       "      <td>7.5</td>\n",
       "      <td>Changping</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223881</th>\n",
       "      <td>2014</td>\n",
       "      <td>9</td>\n",
       "      <td>14</td>\n",
       "      <td>9</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>22.3</td>\n",
       "      <td>1009.9</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NNW</td>\n",
       "      <td>2.9</td>\n",
       "      <td>Changping</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258053</th>\n",
       "      <td>2014</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>52.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>700.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>21.8</td>\n",
       "      <td>1004.0</td>\n",
       "      <td>19.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NNE</td>\n",
       "      <td>1.1</td>\n",
       "      <td>Wanliu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103972</th>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>7.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>-3.5</td>\n",
       "      <td>1018.6</td>\n",
       "      <td>-12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NNW</td>\n",
       "      <td>1.2</td>\n",
       "      <td>Huairou</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>373487</th>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>23</td>\n",
       "      <td>7.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>9.6</td>\n",
       "      <td>1006.1</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>ESE</td>\n",
       "      <td>1.1</td>\n",
       "      <td>Dingling</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>369872</th>\n",
       "      <td>2015</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>12.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>14.1</td>\n",
       "      <td>1000.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>W</td>\n",
       "      <td>3.2</td>\n",
       "      <td>Dingling</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>406260</th>\n",
       "      <td>2015</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>65.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>1006.0</td>\n",
       "      <td>12.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>N</td>\n",
       "      <td>1.2</td>\n",
       "      <td>Guanyuan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>282047</th>\n",
       "      <td>2013</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>23</td>\n",
       "      <td>95.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>17.3</td>\n",
       "      <td>1013.2</td>\n",
       "      <td>5.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>SW</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Dongsi</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        year  month  day  hour  PM2.5   PM10   SO2   NO2      CO     O3  TEMP  \\\n",
       "47412   2014      7   28    12  131.0  142.0   7.0  27.0  1300.0  225.0  34.5   \n",
       "237384  2016      3   30     0    5.0   19.0   2.0  17.0   300.0   80.0  14.5   \n",
       "243635  2016     12   15    11   11.0   69.0   7.0  19.0   400.0   40.0   2.0   \n",
       "223881  2014      9   14     9   13.0   13.0   2.0  11.0   300.0   54.0  22.3   \n",
       "258053  2014      8    8     5   52.0  148.0  11.0  64.0   700.0   29.0  21.8   \n",
       "103972  2017      1    9     4    7.0   11.0   2.0  12.0   300.0   42.0  -3.5   \n",
       "373487  2015     10    8    23    7.0   70.0   7.0  21.0   300.0   29.0   9.6   \n",
       "369872  2015      5   11     8   12.0   20.0   2.0  16.0   400.0   37.0  14.1   \n",
       "406260  2015      7    5    12   65.0   65.0  19.0  31.0  1400.0  142.0  26.6   \n",
       "282047  2013      5    3    23   95.0  135.0  22.0  51.0  1200.0  118.0  17.3   \n",
       "\n",
       "          PRES  DEWP  RAIN   wd  WSPM    station  \n",
       "47412    996.0  20.1   0.0   SW   1.5    Gucheng  \n",
       "237384  1008.2 -10.3   0.0    N   2.0  Changping  \n",
       "243635  1025.7 -14.2   0.0  WNW   7.5  Changping  \n",
       "223881  1009.9   1.3   0.0  NNW   2.9  Changping  \n",
       "258053  1004.0  19.7   0.0  NNE   1.1     Wanliu  \n",
       "103972  1018.6 -12.0   0.0  NNW   1.2    Huairou  \n",
       "373487  1006.1  -0.3   0.0  ESE   1.1   Dingling  \n",
       "369872  1000.7   0.0   0.0    W   3.2   Dingling  \n",
       "406260  1006.0  12.6   0.0    N   1.2   Guanyuan  \n",
       "282047  1013.2   5.6   0.0   SW   2.3     Dongsi  "
      ]
     },
     "execution_count": 262,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_dir = \"./data/PRSA_Data_20130301-20170228\"\n",
    "\n",
    "dfs: list[pd.DataFrame] = []\n",
    "for file in os.listdir(data_dir):\n",
    "    if not file.endswith(\".csv\"):\n",
    "        continue\n",
    "    df = pd.read_csv(os.path.join(data_dir, file))\n",
    "    df.drop(columns=[\"No\"], inplace=True)\n",
    "    dfs.append(df)\n",
    "\n",
    "df = pd.concat(dfs).reset_index(drop=True)\n",
    "df.sample(n=10, random_state=69)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MMi6xGaDkbCi"
   },
   "source": [
    "#### Insight\n",
    "- xxx\n",
    "- xxx"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FHSiqaZp8DC1"
   },
   "source": [
    "### Assessing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {
    "id": "ax-3tEjc9Cj1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 420768 entries, 0 to 420767\n",
      "Data columns (total 17 columns):\n",
      " #   Column   Non-Null Count   Dtype  \n",
      "---  ------   --------------   -----  \n",
      " 0   year     420768 non-null  int64  \n",
      " 1   month    420768 non-null  int64  \n",
      " 2   day      420768 non-null  int64  \n",
      " 3   hour     420768 non-null  int64  \n",
      " 4   PM2.5    412029 non-null  float64\n",
      " 5   PM10     414319 non-null  float64\n",
      " 6   SO2      411747 non-null  float64\n",
      " 7   NO2      408652 non-null  float64\n",
      " 8   CO       400067 non-null  float64\n",
      " 9   O3       407491 non-null  float64\n",
      " 10  TEMP     420370 non-null  float64\n",
      " 11  PRES     420375 non-null  float64\n",
      " 12  DEWP     420365 non-null  float64\n",
      " 13  RAIN     420378 non-null  float64\n",
      " 14  wd       418946 non-null  object \n",
      " 15  WSPM     420450 non-null  float64\n",
      " 16  station  420768 non-null  object \n",
      "dtypes: float64(11), int64(4), object(2)\n",
      "memory usage: 54.6+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Duplikasi:  0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "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"
      ]
     },
     "execution_count": 264,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Duplikasi: \", df.duplicated().sum())\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>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>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>1024.5</td>\n",
       "      <td>-21.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NNW</td>\n",
       "      <td>5.7</td>\n",
       "      <td>Tiantan</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>6.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>1025.1</td>\n",
       "      <td>-22.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NW</td>\n",
       "      <td>3.9</td>\n",
       "      <td>Tiantan</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>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>-1.2</td>\n",
       "      <td>1025.3</td>\n",
       "      <td>-24.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NNW</td>\n",
       "      <td>5.3</td>\n",
       "      <td>Tiantan</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>4.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>-1.4</td>\n",
       "      <td>1026.2</td>\n",
       "      <td>-25.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>N</td>\n",
       "      <td>4.9</td>\n",
       "      <td>Tiantan</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>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>-1.9</td>\n",
       "      <td>1027.1</td>\n",
       "      <td>-24.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NNW</td>\n",
       "      <td>3.2</td>\n",
       "      <td>Tiantan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>-2.4</td>\n",
       "      <td>1027.5</td>\n",
       "      <td>-21.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NW</td>\n",
       "      <td>2.4</td>\n",
       "      <td>Tiantan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>8.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>-2.5</td>\n",
       "      <td>1028.2</td>\n",
       "      <td>-20.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NW</td>\n",
       "      <td>2.2</td>\n",
       "      <td>Tiantan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>-1.4</td>\n",
       "      <td>1029.5</td>\n",
       "      <td>-20.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NNW</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Tiantan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>1030.4</td>\n",
       "      <td>-21.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NW</td>\n",
       "      <td>4.6</td>\n",
       "      <td>Tiantan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1030.5</td>\n",
       "      <td>-23.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>N</td>\n",
       "      <td>5.5</td>\n",
       "      <td>Tiantan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1030.2</td>\n",
       "      <td>-22.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>N</td>\n",
       "      <td>5.2</td>\n",
       "      <td>Tiantan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>9.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1029.8</td>\n",
       "      <td>-22.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NNW</td>\n",
       "      <td>5.3</td>\n",
       "      <td>Tiantan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>9.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1028.6</td>\n",
       "      <td>-21.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>W</td>\n",
       "      <td>4.6</td>\n",
       "      <td>Tiantan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>10.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1027.8</td>\n",
       "      <td>-21.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NW</td>\n",
       "      <td>4.5</td>\n",
       "      <td>Tiantan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>10.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>6.2</td>\n",
       "      <td>1027.6</td>\n",
       "      <td>-22.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NW</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Tiantan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1027.7</td>\n",
       "      <td>-21.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NNW</td>\n",
       "      <td>2.1</td>\n",
       "      <td>Tiantan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>8.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>5.6</td>\n",
       "      <td>1027.7</td>\n",
       "      <td>-20.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>N</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Tiantan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>10.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1028.2</td>\n",
       "      <td>-20.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>N</td>\n",
       "      <td>3.5</td>\n",
       "      <td>Tiantan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>11.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1029.4</td>\n",
       "      <td>-20.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NNW</td>\n",
       "      <td>0.5</td>\n",
       "      <td>Tiantan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>12.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>600.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1030.1</td>\n",
       "      <td>-19.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>N</td>\n",
       "      <td>1.1</td>\n",
       "      <td>Tiantan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    year  month  day  hour  PM2.5  PM10   SO2   NO2     CO    O3  TEMP  \\\n",
       "0   2013      3    1     0    6.0   6.0   4.0   8.0  300.0  81.0  -0.5   \n",
       "1   2013      3    1     1    6.0  29.0   5.0   9.0  300.0  80.0  -0.7   \n",
       "2   2013      3    1     2    6.0   6.0   4.0  12.0  300.0  75.0  -1.2   \n",
       "3   2013      3    1     3    6.0   6.0   4.0  12.0  300.0  74.0  -1.4   \n",
       "4   2013      3    1     4    5.0   5.0   7.0  15.0  400.0  70.0  -1.9   \n",
       "5   2013      3    1     5   10.0  10.0  12.0  15.0  400.0  70.0  -2.4   \n",
       "6   2013      3    1     6    8.0  19.0  12.0  14.0  400.0  72.0  -2.5   \n",
       "7   2013      3    1     7    7.0   7.0  12.0  19.0  400.0  67.0  -1.4   \n",
       "8   2013      3    1     8    3.0   6.0  14.0  29.0  500.0  56.0  -0.3   \n",
       "9   2013      3    1     9    8.0   2.0  11.0  22.0  500.0  65.0   0.4   \n",
       "10  2013      3    1    10    9.0   9.0   8.0  17.0  400.0  72.0   1.4   \n",
       "11  2013      3    1    11    9.0  23.0   8.0  16.0  400.0  75.0   2.9   \n",
       "12  2013      3    1    12    9.0  19.0   7.0  16.0  400.0  76.0   4.0   \n",
       "13  2013      3    1    13   10.0  34.0   7.0  12.0  400.0  81.0   5.0   \n",
       "14  2013      3    1    14   10.0  19.0   6.0  12.0  400.0  83.0   6.2   \n",
       "15  2013      3    1    15    3.0   6.0   6.0  15.0  400.0  81.0   6.0   \n",
       "16  2013      3    1    16    8.0  22.0   7.0  14.0  300.0  82.0   5.6   \n",
       "17  2013      3    1    17   10.0  28.0   9.0  21.0  400.0  75.0   4.4   \n",
       "18  2013      3    1    18   11.0  21.0  10.0  28.0  500.0  66.0   3.2   \n",
       "19  2013      3    1    19   12.0   4.0   7.0  39.0  600.0  53.0   3.0   \n",
       "\n",
       "      PRES  DEWP  RAIN   wd  WSPM  station  \n",
       "0   1024.5 -21.4   0.0  NNW   5.7  Tiantan  \n",
       "1   1025.1 -22.1   0.0   NW   3.9  Tiantan  \n",
       "2   1025.3 -24.6   0.0  NNW   5.3  Tiantan  \n",
       "3   1026.2 -25.5   0.0    N   4.9  Tiantan  \n",
       "4   1027.1 -24.5   0.0  NNW   3.2  Tiantan  \n",
       "5   1027.5 -21.3   0.0   NW   2.4  Tiantan  \n",
       "6   1028.2 -20.4   0.0   NW   2.2  Tiantan  \n",
       "7   1029.5 -20.4   0.0  NNW   3.0  Tiantan  \n",
       "8   1030.4 -21.2   0.0   NW   4.6  Tiantan  \n",
       "9   1030.5 -23.3   0.0    N   5.5  Tiantan  \n",
       "10  1030.2 -22.5   0.0    N   5.2  Tiantan  \n",
       "11  1029.8 -22.9   0.0  NNW   5.3  Tiantan  \n",
       "12  1028.6 -21.2   0.0    W   4.6  Tiantan  \n",
       "13  1027.8 -21.2   0.0   NW   4.5  Tiantan  \n",
       "14  1027.6 -22.2   0.0   NW   4.0  Tiantan  \n",
       "15  1027.7 -21.3   0.0  NNW   2.1  Tiantan  \n",
       "16  1027.7 -20.7   0.0    N   1.8  Tiantan  \n",
       "17  1028.2 -20.9   0.0    N   3.5  Tiantan  \n",
       "18  1029.4 -20.3   0.0  NNW   0.5  Tiantan  \n",
       "19  1030.1 -19.7   0.0    N   1.1  Tiantan  "
      ]
     },
     "execution_count": 265,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(n=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "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>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>WSPM</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>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>420450.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\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>1.729711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\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>1.246386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\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>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\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>0.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\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>1.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\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>2.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\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>13.200000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                year          month            day           hour  \\\n",
       "count  420768.000000  420768.000000  420768.000000  420768.000000   \n",
       "mean     2014.662560       6.522930      15.729637      11.500000   \n",
       "std         1.177198       3.448707       8.800102       6.922195   \n",
       "min      2013.000000       1.000000       1.000000       0.000000   \n",
       "25%      2014.000000       4.000000       8.000000       5.750000   \n",
       "50%      2015.000000       7.000000      16.000000      11.500000   \n",
       "75%      2016.000000      10.000000      23.000000      17.250000   \n",
       "max      2017.000000      12.000000      31.000000      23.000000   \n",
       "\n",
       "               PM2.5           PM10            SO2            NO2  \\\n",
       "count  412029.000000  414319.000000  411747.000000  408652.000000   \n",
       "mean       79.793428     104.602618      15.830835      50.638586   \n",
       "std        80.822391      91.772426      21.650603      35.127912   \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           TEMP           PRES  \\\n",
       "count  400067.000000  407491.000000  420370.000000  420375.000000   \n",
       "mean     1230.766454      57.372271      13.538976    1010.746982   \n",
       "std      1160.182716      56.661607      11.436139      10.474055   \n",
       "min       100.000000       0.214200     -19.900000     982.400000   \n",
       "25%       500.000000      11.000000       3.100000    1002.300000   \n",
       "50%       900.000000      45.000000      14.500000    1010.400000   \n",
       "75%      1500.000000      82.000000      23.300000    1019.000000   \n",
       "max     10000.000000    1071.000000      41.600000    1042.800000   \n",
       "\n",
       "                DEWP           RAIN           WSPM  \n",
       "count  420365.000000  420378.000000  420450.000000  \n",
       "mean        2.490822       0.064476       1.729711  \n",
       "std        13.793847       0.821004       1.246386  \n",
       "min       -43.400000       0.000000       0.000000  \n",
       "25%        -8.900000       0.000000       0.900000  \n",
       "50%         3.100000       0.000000       1.400000  \n",
       "75%        15.100000       0.000000       2.200000  \n",
       "max        29.100000      72.500000      13.200000  "
      ]
     },
     "execution_count": 266,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        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>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>109</th>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>13</td>\n",
       "      <td>161.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>3200.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1010.1</td>\n",
       "      <td>-8.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>ENE</td>\n",
       "      <td>1.5</td>\n",
       "      <td>Tiantan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>14</td>\n",
       "      <td>182.0</td>\n",
       "      <td>184.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>3200.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>13.3</td>\n",
       "      <td>1009.5</td>\n",
       "      <td>-8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>ENE</td>\n",
       "      <td>1.6</td>\n",
       "      <td>Tiantan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "      <td>198.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>2299.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>13.8</td>\n",
       "      <td>1009.1</td>\n",
       "      <td>-7.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>E</td>\n",
       "      <td>1.9</td>\n",
       "      <td>Tiantan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>16</td>\n",
       "      <td>168.0</td>\n",
       "      <td>198.0</td>\n",
       "      <td>109.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>1800.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>13.2</td>\n",
       "      <td>1009.0</td>\n",
       "      <td>-7.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>ENE</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Tiantan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>19</td>\n",
       "      <td>162.0</td>\n",
       "      <td>179.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>155.0</td>\n",
       "      <td>2799.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>6.7</td>\n",
       "      <td>1010.4</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>ESE</td>\n",
       "      <td>0.1</td>\n",
       "      <td>Tiantan</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>420538</th>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>19</td>\n",
       "      <td>10</td>\n",
       "      <td>161.0</td>\n",
       "      <td>197.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1003.6</td>\n",
       "      <td>-7.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NE</td>\n",
       "      <td>1.2</td>\n",
       "      <td>Guanyuan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>420539</th>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>19</td>\n",
       "      <td>11</td>\n",
       "      <td>169.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>6.7</td>\n",
       "      <td>1003.1</td>\n",
       "      <td>-7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NE</td>\n",
       "      <td>2.9</td>\n",
       "      <td>Guanyuan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>420540</th>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>19</td>\n",
       "      <td>12</td>\n",
       "      <td>174.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>2200.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>8.6</td>\n",
       "      <td>1002.7</td>\n",
       "      <td>-6.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NNE</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Guanyuan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>420541</th>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>19</td>\n",
       "      <td>13</td>\n",
       "      <td>181.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>10.2</td>\n",
       "      <td>1002.1</td>\n",
       "      <td>-6.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NNE</td>\n",
       "      <td>1.5</td>\n",
       "      <td>Guanyuan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>420614</th>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>22</td>\n",
       "      <td>14</td>\n",
       "      <td>167.0</td>\n",
       "      <td>167.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1016.8</td>\n",
       "      <td>-3.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>SW</td>\n",
       "      <td>1.9</td>\n",
       "      <td>Guanyuan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>58265 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        year  month  day  hour  PM2.5   PM10    SO2    NO2      CO    O3  \\\n",
       "109     2013      3    5    13  161.0  180.0   88.0  156.0  3200.0  99.0   \n",
       "110     2013      3    5    14  182.0  184.0   92.0  168.0  3200.0  99.0   \n",
       "111     2013      3    5    15  198.0  200.0  108.0  135.0  2299.0  99.0   \n",
       "112     2013      3    5    16  168.0  198.0  109.0   94.0  1800.0  99.0   \n",
       "115     2013      3    5    19  162.0  179.0   90.0  155.0  2799.0  94.0   \n",
       "...      ...    ...  ...   ...    ...    ...    ...    ...     ...   ...   \n",
       "420538  2017      2   19    10  161.0  197.0   53.0   86.0  1000.0  14.0   \n",
       "420539  2017      2   19    11  169.0  187.0   54.0   80.0  2000.0  27.0   \n",
       "420540  2017      2   19    12  174.0  219.0   51.0   95.0  2200.0  70.0   \n",
       "420541  2017      2   19    13  181.0  190.0   49.0   98.0  2000.0  34.0   \n",
       "420614  2017      2   22    14  167.0  167.0   12.0   66.0  1300.0  41.0   \n",
       "\n",
       "        TEMP    PRES  DEWP  RAIN   wd  WSPM   station  \n",
       "109     13.0  1010.1  -8.3   0.0  ENE   1.5   Tiantan  \n",
       "110     13.3  1009.5  -8.0   0.0  ENE   1.6   Tiantan  \n",
       "111     13.8  1009.1  -7.6   0.0    E   1.9   Tiantan  \n",
       "112     13.2  1009.0  -7.5   0.0  ENE   1.8   Tiantan  \n",
       "115      6.7  1010.4  -5.0   0.0  ESE   0.1   Tiantan  \n",
       "...      ...     ...   ...   ...  ...   ...       ...  \n",
       "420538   5.1  1003.6  -7.4   0.0   NE   1.2  Guanyuan  \n",
       "420539   6.7  1003.1  -7.0   0.0   NE   2.9  Guanyuan  \n",
       "420540   8.6  1002.7  -6.8   0.0  NNE   2.3  Guanyuan  \n",
       "420541  10.2  1002.1  -6.6   0.0  NNE   1.5  Guanyuan  \n",
       "420614   4.2  1016.8  -3.6   0.0   SW   1.9  Guanyuan  \n",
       "\n",
       "[58265 rows x 17 columns]"
      ]
     },
     "execution_count": 267,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q1 = df[\"PM2.5\"].quantile(0.25)\n",
    "q3 = df[\"PM2.5\"].quantile(0.75)\n",
    "iqr = q3 - q1\n",
    "\n",
    "lower_bound = q1 - 1.5 * iqr\n",
    "upper_bound = q1 + 1.5 * iqr\n",
    "\n",
    "outliers = df[(df[\"PM2.5\"] < lower_bound) | (df[\"PM2.5\"] > upper_bound)]\n",
    "outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.boxplot()\n",
    "plt.title(\"Box Plot for Outliers\")\n",
    "plt.ylabel(\"Value\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "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>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>station</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Aotizhongxin</th>\n",
       "      <td>35064</td>\n",
       "      <td>35064</td>\n",
       "      <td>35064</td>\n",
       "      <td>35064</td>\n",
       "      <td>34139</td>\n",
       "      <td>34346</td>\n",
       "      <td>34129</td>\n",
       "      <td>34041</td>\n",
       "      <td>33288</td>\n",
       "      <td>33345</td>\n",
       "      <td>35044</td>\n",
       "      <td>35044</td>\n",
       "      <td>35044</td>\n",
       "      <td>35044</td>\n",
       "      <td>34983</td>\n",
       "      <td>35050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Changping</th>\n",
       "      <td>35064</td>\n",
       "      <td>35064</td>\n",
       "      <td>35064</td>\n",
       "      <td>35064</td>\n",
       "      <td>34290</td>\n",
       "      <td>34482</td>\n",
       "      <td>34436</td>\n",
       "      <td>34397</td>\n",
       "      <td>33543</td>\n",
       "      <td>34460</td>\n",
       "      <td>35011</td>\n",
       "      <td>35014</td>\n",
       "      <td>35011</td>\n",
       "      <td>35013</td>\n",
       "      <td>34924</td>\n",
       "      <td>35021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dingling</th>\n",
       "      <td>35064</td>\n",
       "      <td>35064</td>\n",
       "      <td>35064</td>\n",
       "      <td>35064</td>\n",
       "      <td>34285</td>\n",
       "      <td>34408</td>\n",
       "      <td>34334</td>\n",
       "      <td>33830</td>\n",
       "      <td>33052</td>\n",
       "      <td>33850</td>\n",
       "      <td>35011</td>\n",
       "      <td>35014</td>\n",
       "      <td>35011</td>\n",
       "      <td>35013</td>\n",
       "      <td>34924</td>\n",
       "      <td>35021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dongsi</th>\n",
       "      <td>35064</td>\n",
       "      <td>35064</td>\n",
       "      <td>35064</td>\n",
       "      <td>35064</td>\n",
       "      <td>34314</td>\n",
       "      <td>34511</td>\n",
       "      <td>34401</td>\n",
       "      <td>33463</td>\n",
       "      <td>31867</td>\n",
       "      <td>34400</td>\n",
       "      <td>35044</td>\n",
       "      <td>35044</td>\n",
       "      <td>35044</td>\n",
       "      <td>35044</td>\n",
       "      <td>34986</td>\n",
       "      <td>35050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Guanyuan</th>\n",
       "      <td>35064</td>\n",
       "      <td>35064</td>\n",
       "      <td>35064</td>\n",
       "      <td>35064</td>\n",
       "      <td>34448</td>\n",
       "      <td>34635</td>\n",
       "      <td>34590</td>\n",
       "      <td>34405</td>\n",
       "      <td>33311</td>\n",
       "      <td>33891</td>\n",
       "      <td>35044</td>\n",
       "      <td>35044</td>\n",
       "      <td>35044</td>\n",
       "      <td>35044</td>\n",
       "      <td>34983</td>\n",
       "      <td>35050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gucheng</th>\n",
       "      <td>35064</td>\n",
       "      <td>35064</td>\n",
       "      <td>35064</td>\n",
       "      <td>35064</td>\n",
       "      <td>34418</td>\n",
       "      <td>34683</td>\n",
       "      <td>34557</td>\n",
       "      <td>34396</td>\n",
       "      <td>33663</td>\n",
       "      <td>34335</td>\n",
       "      <td>35013</td>\n",
       "      <td>35014</td>\n",
       "      <td>35013</td>\n",
       "      <td>35021</td>\n",
       "      <td>34905</td>\n",
       "      <td>35022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Huairou</th>\n",
       "      <td>35064</td>\n",
       "      <td>35064</td>\n",
       "      <td>35064</td>\n",
       "      <td>35064</td>\n",
       "      <td>34111</td>\n",
       "      <td>34287</td>\n",
       "      <td>34084</td>\n",
       "      <td>33425</td>\n",
       "      <td>33642</td>\n",
       "      <td>33913</td>\n",
       "      <td>35013</td>\n",
       "      <td>35011</td>\n",
       "      <td>35011</td>\n",
       "      <td>35009</td>\n",
       "      <td>34762</td>\n",
       "      <td>35015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nongzhanguan</th>\n",
       "      <td>35064</td>\n",
       "      <td>35064</td>\n",
       "      <td>35064</td>\n",
       "      <td>35064</td>\n",
       "      <td>34436</td>\n",
       "      <td>34624</td>\n",
       "      <td>34618</td>\n",
       "      <td>34372</td>\n",
       "      <td>33858</td>\n",
       "      <td>34558</td>\n",
       "      <td>35044</td>\n",
       "      <td>35044</td>\n",
       "      <td>35044</td>\n",
       "      <td>35044</td>\n",
       "      <td>34986</td>\n",
       "      <td>35050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shunyi</th>\n",
       "      <td>35064</td>\n",
       "      <td>35064</td>\n",
       "      <td>35064</td>\n",
       "      <td>35064</td>\n",
       "      <td>34151</td>\n",
       "      <td>34516</td>\n",
       "      <td>33768</td>\n",
       "      <td>33699</td>\n",
       "      <td>32886</td>\n",
       "      <td>33575</td>\n",
       "      <td>35013</td>\n",
       "      <td>35013</td>\n",
       "      <td>35010</td>\n",
       "      <td>35013</td>\n",
       "      <td>34581</td>\n",
       "      <td>35020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tiantan</th>\n",
       "      <td>35064</td>\n",
       "      <td>35064</td>\n",
       "      <td>35064</td>\n",
       "      <td>35064</td>\n",
       "      <td>34387</td>\n",
       "      <td>34467</td>\n",
       "      <td>33946</td>\n",
       "      <td>34320</td>\n",
       "      <td>33938</td>\n",
       "      <td>34221</td>\n",
       "      <td>35044</td>\n",
       "      <td>35044</td>\n",
       "      <td>35044</td>\n",
       "      <td>35044</td>\n",
       "      <td>34986</td>\n",
       "      <td>35050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wanliu</th>\n",
       "      <td>35064</td>\n",
       "      <td>35064</td>\n",
       "      <td>35064</td>\n",
       "      <td>35064</td>\n",
       "      <td>34682</td>\n",
       "      <td>34780</td>\n",
       "      <td>34489</td>\n",
       "      <td>33994</td>\n",
       "      <td>33252</td>\n",
       "      <td>32957</td>\n",
       "      <td>35044</td>\n",
       "      <td>35044</td>\n",
       "      <td>35044</td>\n",
       "      <td>35044</td>\n",
       "      <td>34941</td>\n",
       "      <td>35050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wanshouxigong</th>\n",
       "      <td>35064</td>\n",
       "      <td>35064</td>\n",
       "      <td>35064</td>\n",
       "      <td>35064</td>\n",
       "      <td>34368</td>\n",
       "      <td>34580</td>\n",
       "      <td>34395</td>\n",
       "      <td>34310</td>\n",
       "      <td>33767</td>\n",
       "      <td>33986</td>\n",
       "      <td>35045</td>\n",
       "      <td>35045</td>\n",
       "      <td>35045</td>\n",
       "      <td>35045</td>\n",
       "      <td>34985</td>\n",
       "      <td>35051</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                year  month    day   hour  PM2.5   PM10    SO2    NO2     CO  \\\n",
       "station                                                                        \n",
       "Aotizhongxin   35064  35064  35064  35064  34139  34346  34129  34041  33288   \n",
       "Changping      35064  35064  35064  35064  34290  34482  34436  34397  33543   \n",
       "Dingling       35064  35064  35064  35064  34285  34408  34334  33830  33052   \n",
       "Dongsi         35064  35064  35064  35064  34314  34511  34401  33463  31867   \n",
       "Guanyuan       35064  35064  35064  35064  34448  34635  34590  34405  33311   \n",
       "Gucheng        35064  35064  35064  35064  34418  34683  34557  34396  33663   \n",
       "Huairou        35064  35064  35064  35064  34111  34287  34084  33425  33642   \n",
       "Nongzhanguan   35064  35064  35064  35064  34436  34624  34618  34372  33858   \n",
       "Shunyi         35064  35064  35064  35064  34151  34516  33768  33699  32886   \n",
       "Tiantan        35064  35064  35064  35064  34387  34467  33946  34320  33938   \n",
       "Wanliu         35064  35064  35064  35064  34682  34780  34489  33994  33252   \n",
       "Wanshouxigong  35064  35064  35064  35064  34368  34580  34395  34310  33767   \n",
       "\n",
       "                  O3   TEMP   PRES   DEWP   RAIN     wd   WSPM  \n",
       "station                                                         \n",
       "Aotizhongxin   33345  35044  35044  35044  35044  34983  35050  \n",
       "Changping      34460  35011  35014  35011  35013  34924  35021  \n",
       "Dingling       33850  35011  35014  35011  35013  34924  35021  \n",
       "Dongsi         34400  35044  35044  35044  35044  34986  35050  \n",
       "Guanyuan       33891  35044  35044  35044  35044  34983  35050  \n",
       "Gucheng        34335  35013  35014  35013  35021  34905  35022  \n",
       "Huairou        33913  35013  35011  35011  35009  34762  35015  \n",
       "Nongzhanguan   34558  35044  35044  35044  35044  34986  35050  \n",
       "Shunyi         33575  35013  35013  35010  35013  34581  35020  \n",
       "Tiantan        34221  35044  35044  35044  35044  34986  35050  \n",
       "Wanliu         32957  35044  35044  35044  35044  34941  35050  \n",
       "Wanshouxigong  33986  35045  35045  35045  35045  34985  35051  "
      ]
     },
     "execution_count": 269,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"station\").count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>year</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.4</td>\n",
       "      <td>1017.4</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NNW</td>\n",
       "      <td>3.2</td>\n",
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       "      <th>4981</th>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>2014</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.6</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>NNE</td>\n",
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       "    <tr>\n",
       "      <th>7757</th>\n",
       "      <td>2014</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>5</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>-2.2</td>\n",
       "      <td>1029.5</td>\n",
       "      <td>-16.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>ENE</td>\n",
       "      <td>1.6</td>\n",
       "      <td>Tiantan</td>\n",
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       "      <th>7758</th>\n",
       "      <td>2014</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>6</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>-2.7</td>\n",
       "      <td>1029.8</td>\n",
       "      <td>-16.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>E</td>\n",
       "      <td>2.2</td>\n",
       "      <td>Tiantan</td>\n",
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       "    <tr>\n",
       "      <th>7759</th>\n",
       "      <td>2014</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>7</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>-3.2</td>\n",
       "      <td>1030.1</td>\n",
       "      <td>-14.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>WNW</td>\n",
       "      <td>0.9</td>\n",
       "      <td>Tiantan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7760</th>\n",
       "      <td>2014</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>8</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>0.8</td>\n",
       "      <td>1030.9</td>\n",
       "      <td>-14.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NW</td>\n",
       "      <td>0.6</td>\n",
       "      <td>Tiantan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7761</th>\n",
       "      <td>2014</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>9</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>2.4</td>\n",
       "      <td>1031.1</td>\n",
       "      <td>-15.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NE</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Tiantan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7882</th>\n",
       "      <td>2014</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>10</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>-0.3</td>\n",
       "      <td>1013.6</td>\n",
       "      <td>-13.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>ENE</td>\n",
       "      <td>1.6</td>\n",
       "      <td>Tiantan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7939</th>\n",
       "      <td>2014</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>19</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>0.9</td>\n",
       "      <td>1027.0</td>\n",
       "      <td>-18.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NW</td>\n",
       "      <td>0.7</td>\n",
       "      <td>Tiantan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      year  month  day  hour  PM2.5  PM10  SO2  NO2  CO  O3  TEMP    PRES  \\\n",
       "1282  2013      4   23    10    NaN   NaN  NaN  NaN NaN NaN  12.6  1007.1   \n",
       "1283  2013      4   23    11    NaN   NaN  NaN  NaN NaN NaN  13.8  1006.6   \n",
       "4979  2013      9   24    11    NaN   NaN  NaN  NaN NaN NaN  18.9  1018.4   \n",
       "4980  2013      9   24    12    NaN   NaN  NaN  NaN NaN NaN  19.4  1017.4   \n",
       "4981  2013      9   24    13    NaN   NaN  NaN  NaN NaN NaN  20.6  1016.5   \n",
       "7749  2014      1   17    21    NaN   NaN  NaN  NaN NaN NaN  -1.6  1028.1   \n",
       "7750  2014      1   17    22    NaN   NaN  NaN  NaN NaN NaN  -3.1  1028.2   \n",
       "7751  2014      1   17    23    NaN   NaN  NaN  NaN NaN NaN  -0.8  1028.2   \n",
       "7752  2014      1   18     0    NaN   NaN  NaN  NaN NaN NaN  -0.7  1028.3   \n",
       "7753  2014      1   18     1    NaN   NaN  NaN  NaN NaN NaN   1.0  1028.7   \n",
       "7754  2014      1   18     2    NaN   NaN  NaN  NaN NaN NaN   1.4  1029.2   \n",
       "7755  2014      1   18     3    NaN   NaN  NaN  NaN NaN NaN   0.4  1029.2   \n",
       "7756  2014      1   18     4    NaN   NaN  NaN  NaN NaN NaN   0.6  1029.1   \n",
       "7757  2014      1   18     5    NaN   NaN  NaN  NaN NaN NaN  -2.2  1029.5   \n",
       "7758  2014      1   18     6    NaN   NaN  NaN  NaN NaN NaN  -2.7  1029.8   \n",
       "7759  2014      1   18     7    NaN   NaN  NaN  NaN NaN NaN  -3.2  1030.1   \n",
       "7760  2014      1   18     8    NaN   NaN  NaN  NaN NaN NaN   0.8  1030.9   \n",
       "7761  2014      1   18     9    NaN   NaN  NaN  NaN NaN NaN   2.4  1031.1   \n",
       "7882  2014      1   23    10    NaN   NaN  NaN  NaN NaN NaN  -0.3  1013.6   \n",
       "7939  2014      1   25    19    NaN   NaN  NaN  NaN NaN NaN   0.9  1027.0   \n",
       "\n",
       "      DEWP  RAIN   wd  WSPM  station  \n",
       "1282   8.3   0.0  SSE   1.1  Tiantan  \n",
       "1283   8.2   0.0   SE   1.5  Tiantan  \n",
       "4979   0.0   0.0   NE   1.5  Tiantan  \n",
       "4980   0.9   0.0  NNW   3.2  Tiantan  \n",
       "4981  -0.7   0.0   NW   3.3  Tiantan  \n",
       "7749  -6.5   0.0    W   1.8  Tiantan  \n",
       "7750  -8.2   0.0  NNW   0.6  Tiantan  \n",
       "7751 -14.4   0.0    E   1.0  Tiantan  \n",
       "7752 -15.8   0.0   NW   1.1  Tiantan  \n",
       "7753 -16.9   0.0   NW   1.9  Tiantan  \n",
       "7754 -16.5   0.0    N   3.4  Tiantan  \n",
       "7755 -16.5   0.0    N   1.1  Tiantan  \n",
       "7756 -16.7   0.0  NNE   2.4  Tiantan  \n",
       "7757 -16.3   0.0  ENE   1.6  Tiantan  \n",
       "7758 -16.1   0.0    E   2.2  Tiantan  \n",
       "7759 -14.9   0.0  WNW   0.9  Tiantan  \n",
       "7760 -14.8   0.0   NW   0.6  Tiantan  \n",
       "7761 -15.7   0.0   NE   2.0  Tiantan  \n",
       "7882 -13.6   0.0  ENE   1.6  Tiantan  \n",
       "7939 -18.0   0.0   NW   0.7  Tiantan  "
      ]
     },
     "execution_count": 270,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df[[\"PM2.5\", \"PM10\", \"SO2\", \"NO2\", \"CO\", \"O3\"]].isna().all(axis=1)].head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7dtxhAPrkhPL"
   },
   "source": [
    "#### Insight\n",
    "- Data tidak memiliki duplikat\n",
    "- Data berisi data yang diambil setiap jam\n",
    "- Cukup banyak kolom yang kosong, namun karena data merupakan time-series, dapat menggunakan interpolasi untuk mengisi bagian `NaN`\n",
    "- Nilai maksimal PM2.5 dan PM10 cukup mencurigakan"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EhN5R4hr8DC1"
   },
   "source": [
    "### Cleaning Data\n",
    "\n",
    "#### Drop data yang tidak dibutuhkan\n",
    "\n",
    "Kita hanya tertarik untuk perhitungan AQI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>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>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>1024.5</td>\n",
       "      <td>Tiantan</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>6.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>1025.1</td>\n",
       "      <td>Tiantan</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>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>-1.2</td>\n",
       "      <td>1025.3</td>\n",
       "      <td>Tiantan</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>4.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>-1.4</td>\n",
       "      <td>1026.2</td>\n",
       "      <td>Tiantan</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>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>-1.9</td>\n",
       "      <td>1027.1</td>\n",
       "      <td>Tiantan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  month  day  hour  PM2.5  PM10  SO2   NO2     CO    O3  TEMP    PRES  \\\n",
       "0  2013      3    1     0    6.0   6.0  4.0   8.0  300.0  81.0  -0.5  1024.5   \n",
       "1  2013      3    1     1    6.0  29.0  5.0   9.0  300.0  80.0  -0.7  1025.1   \n",
       "2  2013      3    1     2    6.0   6.0  4.0  12.0  300.0  75.0  -1.2  1025.3   \n",
       "3  2013      3    1     3    6.0   6.0  4.0  12.0  300.0  74.0  -1.4  1026.2   \n",
       "4  2013      3    1     4    5.0   5.0  7.0  15.0  400.0  70.0  -1.9  1027.1   \n",
       "\n",
       "   station  \n",
       "0  Tiantan  \n",
       "1  Tiantan  \n",
       "2  Tiantan  \n",
       "3  Tiantan  \n",
       "4  Tiantan  "
      ]
     },
     "execution_count": 271,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(columns=[\"DEWP\", \"RAIN\", \"wd\", \"WSPM\"], inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Mengubah kolom `year`, `month`, `day`, `hour` menjadi `datetime`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "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>PRES</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>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>1024.5</td>\n",
       "      <td>Tiantan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013-03-01 01:00:00</td>\n",
       "      <td>6.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>1025.1</td>\n",
       "      <td>Tiantan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013-03-01 02:00:00</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>-1.2</td>\n",
       "      <td>1025.3</td>\n",
       "      <td>Tiantan</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>4.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>-1.4</td>\n",
       "      <td>1026.2</td>\n",
       "      <td>Tiantan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013-03-01 04:00:00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>-1.9</td>\n",
       "      <td>1027.1</td>\n",
       "      <td>Tiantan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             datetime  PM2.5  PM10  SO2   NO2     CO    O3  TEMP    PRES  \\\n",
       "0 2013-03-01 00:00:00    6.0   6.0  4.0   8.0  300.0  81.0  -0.5  1024.5   \n",
       "1 2013-03-01 01:00:00    6.0  29.0  5.0   9.0  300.0  80.0  -0.7  1025.1   \n",
       "2 2013-03-01 02:00:00    6.0   6.0  4.0  12.0  300.0  75.0  -1.2  1025.3   \n",
       "3 2013-03-01 03:00:00    6.0   6.0  4.0  12.0  300.0  74.0  -1.4  1026.2   \n",
       "4 2013-03-01 04:00:00    5.0   5.0  7.0  15.0  400.0  70.0  -1.9  1027.1   \n",
       "\n",
       "   station  \n",
       "0  Tiantan  \n",
       "1  Tiantan  \n",
       "2  Tiantan  \n",
       "3  Tiantan  \n",
       "4  Tiantan  "
      ]
     },
     "execution_count": 272,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt_cols = [\"year\", \"month\", \"day\", \"hour\"]\n",
    "\n",
    "df.insert(0, \"datetime\", pd.to_datetime(df[dt_cols]))\n",
    "df.drop(columns=dt_cols, inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Interpolasi untuk mengisi nilai `NaN`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime     0\n",
       "PM2.5        0\n",
       "PM10         0\n",
       "SO2          0\n",
       "NO2         22\n",
       "CO           0\n",
       "O3           0\n",
       "TEMP         0\n",
       "PRES         0\n",
       "station      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 273,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pol_cols = [\"PM2.5\", \"PM10\", \"SO2\", \"NO2\", \"CO\", \"O3\", \"TEMP\", \"PRES\"]\n",
    "grouped_df = df.groupby(\"station\").apply(\n",
    "    lambda x: x.interpolate(), include_groups=False\n",
    ")\n",
    "\n",
    "for col in pol_cols:\n",
    "    df[col] = grouped_df[col].values\n",
    "\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr>\n",
       "      <th>70128</th>\n",
       "      <td>2013-03-01 00:00:00</td>\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>1020.8</td>\n",
       "      <td>Huairou</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70129</th>\n",
       "      <td>2013-03-01 01:00:00</td>\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>1021.3</td>\n",
       "      <td>Huairou</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175320</th>\n",
       "      <td>2013-03-01 00:00:00</td>\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>1021.1</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175321</th>\n",
       "      <td>2013-03-01 01:00:00</td>\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>1021.5</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175322</th>\n",
       "      <td>2013-03-01 02:00:00</td>\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>1021.5</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175323</th>\n",
       "      <td>2013-03-01 03:00:00</td>\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>1022.7</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175324</th>\n",
       "      <td>2013-03-01 04:00:00</td>\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>1023.0</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175325</th>\n",
       "      <td>2013-03-01 05:00:00</td>\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>1023.6</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175326</th>\n",
       "      <td>2013-03-01 06:00:00</td>\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>1024.3</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175327</th>\n",
       "      <td>2013-03-01 07:00:00</td>\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>1025.6</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175328</th>\n",
       "      <td>2013-03-01 08:00:00</td>\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>1026.1</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175329</th>\n",
       "      <td>2013-03-01 09:00:00</td>\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>1026.1</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175330</th>\n",
       "      <td>2013-03-01 10:00:00</td>\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>1026.1</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175331</th>\n",
       "      <td>2013-03-01 11:00:00</td>\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>1026.1</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175332</th>\n",
       "      <td>2013-03-01 12:00:00</td>\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>1025.1</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175333</th>\n",
       "      <td>2013-03-01 13:00:00</td>\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>1024.1</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175334</th>\n",
       "      <td>2013-03-01 14:00:00</td>\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>1023.8</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175335</th>\n",
       "      <td>2013-03-01 15:00:00</td>\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>1023.5</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175336</th>\n",
       "      <td>2013-03-01 16:00:00</td>\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>1023.6</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175337</th>\n",
       "      <td>2013-03-01 17:00:00</td>\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>1024.2</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175338</th>\n",
       "      <td>2013-03-01 18:00:00</td>\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>1025.3</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175339</th>\n",
       "      <td>2013-03-01 19:00:00</td>\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>1026.1</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  datetime  PM2.5  PM10  SO2  NO2      CO    O3  TEMP    PRES  \\\n",
       "70128  2013-03-01 00:00:00    4.0   4.0  3.0  NaN   200.0  82.0  -2.3  1020.8   \n",
       "70129  2013-03-01 01:00:00    7.0   7.0  3.0  NaN   200.0  80.0  -2.5  1021.3   \n",
       "175320 2013-03-01 00:00:00    6.0  18.0  5.0  NaN   800.0  88.0   0.1  1021.1   \n",
       "175321 2013-03-01 01:00:00    6.0  15.0  5.0  NaN   800.0  88.0  -0.3  1021.5   \n",
       "175322 2013-03-01 02:00:00    5.0  18.0  5.5  NaN   700.0  52.0  -0.7  1021.5   \n",
       "175323 2013-03-01 03:00:00    6.0  20.0  6.0  NaN   650.0  62.5  -1.0  1022.7   \n",
       "175324 2013-03-01 04:00:00    5.0  17.0  5.0  NaN   600.0  73.0  -1.3  1023.0   \n",
       "175325 2013-03-01 05:00:00    4.0  11.0  3.0  NaN   700.0  87.0  -1.8  1023.6   \n",
       "175326 2013-03-01 06:00:00    3.0   6.0  3.0  NaN   700.0  92.0  -2.6  1024.3   \n",
       "175327 2013-03-01 07:00:00    5.0   5.0  3.0  NaN   800.0  86.0  -0.9  1025.6   \n",
       "175328 2013-03-01 08:00:00    5.0   9.0  5.0  NaN   900.0  81.0   0.1  1026.1   \n",
       "175329 2013-03-01 09:00:00    4.0  10.0  6.0  NaN   900.0  82.0   1.1  1026.1   \n",
       "175330 2013-03-01 10:00:00    7.0   8.0  8.0  NaN   900.0  82.0   1.9  1026.1   \n",
       "175331 2013-03-01 11:00:00    3.0   6.0  8.0  NaN   800.0  85.0   3.3  1026.1   \n",
       "175332 2013-03-01 12:00:00    9.0  10.0  8.0  NaN   800.0  86.0   4.1  1025.1   \n",
       "175333 2013-03-01 13:00:00   10.0  19.0  9.0  NaN   900.0  86.0   5.3  1024.1   \n",
       "175334 2013-03-01 14:00:00   13.0  20.0  8.0  NaN   800.0  88.0   5.5  1023.8   \n",
       "175335 2013-03-01 15:00:00   13.0  16.0  8.0  NaN   800.0  90.0   6.3  1023.5   \n",
       "175336 2013-03-01 16:00:00   11.0  22.0  6.0  NaN   800.0  90.0   5.9  1023.6   \n",
       "175337 2013-03-01 17:00:00   10.0  20.0  4.0  NaN   800.0  87.0   5.1  1024.2   \n",
       "175338 2013-03-01 18:00:00   11.0  20.0  6.0  NaN   900.0  80.0   3.7  1025.3   \n",
       "175339 2013-03-01 19:00:00   11.0  31.0  8.0  NaN  1000.0  69.0   2.7  1026.1   \n",
       "\n",
       "             station  \n",
       "70128        Huairou  \n",
       "70129        Huairou  \n",
       "175320  Aotizhongxin  \n",
       "175321  Aotizhongxin  \n",
       "175322  Aotizhongxin  \n",
       "175323  Aotizhongxin  \n",
       "175324  Aotizhongxin  \n",
       "175325  Aotizhongxin  \n",
       "175326  Aotizhongxin  \n",
       "175327  Aotizhongxin  \n",
       "175328  Aotizhongxin  \n",
       "175329  Aotizhongxin  \n",
       "175330  Aotizhongxin  \n",
       "175331  Aotizhongxin  \n",
       "175332  Aotizhongxin  \n",
       "175333  Aotizhongxin  \n",
       "175334  Aotizhongxin  \n",
       "175335  Aotizhongxin  \n",
       "175336  Aotizhongxin  \n",
       "175337  Aotizhongxin  \n",
       "175338  Aotizhongxin  \n",
       "175339  Aotizhongxin  "
      ]
     },
     "execution_count": 274,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df[\"NO2\"].isna()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Q_5ejIqckiSP"
   },
   "source": [
    "#### Insight\n",
    "- Setelah cleaning, masih ada beberapa nilai `NaN`, namun tidak masalah karena perhitungan AQI hanya perlu salah satu nilai polutan"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gp-Y6wU38DC1"
   },
   "source": [
    "## Exploratory Data Analysis (EDA)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Menghitung Air Quality Index\n",
    "Referensi: \n",
    "- https://document.airnow.gov/technical-assistance-document-for-the-reporting-of-daily-air-quailty.pdf\n",
    "- https://www.apis.ac.uk/unit-conversion\n",
    "\n",
    "#### Normalisasi satuan polutan\n",
    "Mengubah satuan dari ug/m3 ke satuan yang digunakan dalam perhitungan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [],
   "source": [
    "def to_ppb(col: str, weight: float):\n",
    "    t = df[\"TEMP\"] + 273\n",
    "    p = df[\"PRES\"]\n",
    "\n",
    "    vol = 22.41 * (t / 273.0) * (1013.0 / p)\n",
    "    return df[col] / (weight / vol)\n",
    "\n",
    "\n",
    "df[\"O3\"] = to_ppb(\"O3\", 48) / 1000.0\n",
    "df[\"CO\"] = to_ppb(\"CO\", 28.01) / 1000.0\n",
    "df[\"SO2\"] = to_ppb(\"SO2\", 64.066)\n",
    "df[\"NO2\"] = to_ppb(\"NO2\", 46.0055)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Menghitung rata-rata polutan\n",
    "Setiap polutan dihitung menggunakan rata-rata pada rentang waktu tertentu.\n",
    "- O3: 8 jam\n",
    "- PM2.5: 24 jam\n",
    "- PM10: 24 jam\n",
    "- CO: 8 jam\n",
    "- SO: 1 jam\n",
    "- NO2: 1 jam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {
    "id": "e9CQCZjk8DC2"
   },
   "outputs": [
    {
     "data": {
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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",
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       "      <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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.381952</td>\n",
       "      <td>3.367821</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>1023.0</td>\n",
       "      <td>Tiantan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013-03-01 01:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.379653</td>\n",
       "      <td>3.362216</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>1023.2</td>\n",
       "      <td>Tiantan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013-03-01 02:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.724060</td>\n",
       "      <td>4.801758</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>1023.5</td>\n",
       "      <td>Tiantan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013-03-01 03:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.785049</td>\n",
       "      <td>5.270956</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.4</td>\n",
       "      <td>1024.5</td>\n",
       "      <td>Tiantan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013-03-01 04:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.117210</td>\n",
       "      <td>5.733513</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>1025.2</td>\n",
       "      <td>Tiantan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2013-03-01 05:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.168850</td>\n",
       "      <td>8.590571</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.2</td>\n",
       "      <td>1025.6</td>\n",
       "      <td>Tiantan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2013-03-01 06:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.154337</td>\n",
       "      <td>15.236198</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.6</td>\n",
       "      <td>1026.5</td>\n",
       "      <td>Tiantan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2013-03-01 07:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.514558</td>\n",
       "      <td>19.576409</td>\n",
       "      <td>0.567353</td>\n",
       "      <td>0.036176</td>\n",
       "      <td>-1.6</td>\n",
       "      <td>1027.4</td>\n",
       "      <td>Tiantan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2013-03-01 08:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.515475</td>\n",
       "      <td>20.641879</td>\n",
       "      <td>0.576843</td>\n",
       "      <td>0.035747</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1028.3</td>\n",
       "      <td>Tiantan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2013-03-01 09:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.152460</td>\n",
       "      <td>13.492738</td>\n",
       "      <td>0.586806</td>\n",
       "      <td>0.035403</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1028.5</td>\n",
       "      <td>Tiantan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2013-03-01 10:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.123206</td>\n",
       "      <td>5.799056</td>\n",
       "      <td>0.607038</td>\n",
       "      <td>0.037159</td>\n",
       "      <td>1.9</td>\n",
       "      <td>1028.2</td>\n",
       "      <td>Tiantan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2013-03-01 11:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.134568</td>\n",
       "      <td>6.790176</td>\n",
       "      <td>0.622825</td>\n",
       "      <td>0.038516</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1028.2</td>\n",
       "      <td>Tiantan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2013-03-01 12:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.448977</td>\n",
       "      <td>6.333560</td>\n",
       "      <td>0.643939</td>\n",
       "      <td>0.039351</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1027.3</td>\n",
       "      <td>Tiantan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2013-03-01 13:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.463998</td>\n",
       "      <td>5.882221</td>\n",
       "      <td>0.665876</td>\n",
       "      <td>0.039421</td>\n",
       "      <td>5.3</td>\n",
       "      <td>1026.2</td>\n",
       "      <td>Tiantan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2013-03-01 14:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.470918</td>\n",
       "      <td>5.407179</td>\n",
       "      <td>0.678059</td>\n",
       "      <td>0.039345</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1025.9</td>\n",
       "      <td>Tiantan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2013-03-01 15:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.473171</td>\n",
       "      <td>6.888140</td>\n",
       "      <td>0.680309</td>\n",
       "      <td>0.039723</td>\n",
       "      <td>6.2</td>\n",
       "      <td>1025.7</td>\n",
       "      <td>Tiantan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2013-03-01 16:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.176684</td>\n",
       "      <td>6.389881</td>\n",
       "      <td>0.672307</td>\n",
       "      <td>0.040365</td>\n",
       "      <td>5.9</td>\n",
       "      <td>1025.6</td>\n",
       "      <td>Tiantan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2013-03-01 17:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.857707</td>\n",
       "      <td>7.325642</td>\n",
       "      <td>0.663701</td>\n",
       "      <td>0.040737</td>\n",
       "      <td>4.3</td>\n",
       "      <td>1026.3</td>\n",
       "      <td>Tiantan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2013-03-01 18:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.790473</td>\n",
       "      <td>9.714842</td>\n",
       "      <td>0.664357</td>\n",
       "      <td>0.040655</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1027.4</td>\n",
       "      <td>Tiantan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2013-03-01 19:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.169929</td>\n",
       "      <td>14.517323</td>\n",
       "      <td>0.684125</td>\n",
       "      <td>0.039713</td>\n",
       "      <td>2.3</td>\n",
       "      <td>1028.3</td>\n",
       "      <td>Tiantan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2013-03-01 20:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.504074</td>\n",
       "      <td>15.921864</td>\n",
       "      <td>0.713011</td>\n",
       "      <td>0.038211</td>\n",
       "      <td>1.7</td>\n",
       "      <td>1029.1</td>\n",
       "      <td>Tiantan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2013-03-01 21:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.515917</td>\n",
       "      <td>16.802877</td>\n",
       "      <td>0.740931</td>\n",
       "      <td>0.036151</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1030.1</td>\n",
       "      <td>Tiantan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2013-03-01 22:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.244766</td>\n",
       "      <td>27.384057</td>\n",
       "      <td>0.778949</td>\n",
       "      <td>0.033566</td>\n",
       "      <td>0.9</td>\n",
       "      <td>1030.5</td>\n",
       "      <td>Tiantan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2013-03-01 23:00:00</td>\n",
       "      <td>8.625</td>\n",
       "      <td>16.875</td>\n",
       "      <td>8.933688</td>\n",
       "      <td>25.838603</td>\n",
       "      <td>0.806356</td>\n",
       "      <td>0.030778</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>1030.5</td>\n",
       "      <td>Tiantan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              datetime  PM2.5    PM10       SO2        NO2        CO  \\\n",
       "0  2013-03-01 00:00:00    NaN     NaN  1.381952   3.367821       NaN   \n",
       "1  2013-03-01 01:00:00    NaN     NaN  1.379653   3.362216       NaN   \n",
       "2  2013-03-01 02:00:00    NaN     NaN  1.724060   4.801758       NaN   \n",
       "3  2013-03-01 03:00:00    NaN     NaN  3.785049   5.270956       NaN   \n",
       "4  2013-03-01 04:00:00    NaN     NaN  4.117210   5.733513       NaN   \n",
       "5  2013-03-01 05:00:00    NaN     NaN  6.168850   8.590571       NaN   \n",
       "6  2013-03-01 06:00:00    NaN     NaN  6.154337  15.236198       NaN   \n",
       "7  2013-03-01 07:00:00    NaN     NaN  6.514558  19.576409  0.567353   \n",
       "8  2013-03-01 08:00:00    NaN     NaN  5.515475  20.641879  0.576843   \n",
       "9  2013-03-01 09:00:00    NaN     NaN  4.152460  13.492738  0.586806   \n",
       "10 2013-03-01 10:00:00    NaN     NaN  3.123206   5.799056  0.607038   \n",
       "11 2013-03-01 11:00:00    NaN     NaN  3.134568   6.790176  0.622825   \n",
       "12 2013-03-01 12:00:00    NaN     NaN  2.448977   6.333560  0.643939   \n",
       "13 2013-03-01 13:00:00    NaN     NaN  2.463998   5.882221  0.665876   \n",
       "14 2013-03-01 14:00:00    NaN     NaN  2.470918   5.407179  0.678059   \n",
       "15 2013-03-01 15:00:00    NaN     NaN  2.473171   6.888140  0.680309   \n",
       "16 2013-03-01 16:00:00    NaN     NaN  3.176684   6.389881  0.672307   \n",
       "17 2013-03-01 17:00:00    NaN     NaN  3.857707   7.325642  0.663701   \n",
       "18 2013-03-01 18:00:00    NaN     NaN  2.790473   9.714842  0.664357   \n",
       "19 2013-03-01 19:00:00    NaN     NaN  4.169929  14.517323  0.684125   \n",
       "20 2013-03-01 20:00:00    NaN     NaN  4.504074  15.921864  0.713011   \n",
       "21 2013-03-01 21:00:00    NaN     NaN  5.515917  16.802877  0.740931   \n",
       "22 2013-03-01 22:00:00    NaN     NaN  7.244766  27.384057  0.778949   \n",
       "23 2013-03-01 23:00:00  8.625  16.875  8.933688  25.838603  0.806356   \n",
       "\n",
       "          O3  TEMP    PRES  station  \n",
       "0        NaN  -0.7  1023.0  Tiantan  \n",
       "1        NaN  -1.1  1023.2  Tiantan  \n",
       "2        NaN  -1.1  1023.5  Tiantan  \n",
       "3        NaN  -1.4  1024.5  Tiantan  \n",
       "4        NaN  -2.0  1025.2  Tiantan  \n",
       "5        NaN  -2.2  1025.6  Tiantan  \n",
       "6        NaN  -2.6  1026.5  Tiantan  \n",
       "7   0.036176  -1.6  1027.4  Tiantan  \n",
       "8   0.035747   0.1  1028.3  Tiantan  \n",
       "9   0.035403   1.2  1028.5  Tiantan  \n",
       "10  0.037159   1.9  1028.2  Tiantan  \n",
       "11  0.038516   2.9  1028.2  Tiantan  \n",
       "12  0.039351   3.9  1027.3  Tiantan  \n",
       "13  0.039421   5.3  1026.2  Tiantan  \n",
       "14  0.039345   6.0  1025.9  Tiantan  \n",
       "15  0.039723   6.2  1025.7  Tiantan  \n",
       "16  0.040365   5.9  1025.6  Tiantan  \n",
       "17  0.040737   4.3  1026.3  Tiantan  \n",
       "18  0.040655   3.1  1027.4  Tiantan  \n",
       "19  0.039713   2.3  1028.3  Tiantan  \n",
       "20  0.038211   1.7  1029.1  Tiantan  \n",
       "21  0.036151   0.6  1030.1  Tiantan  \n",
       "22  0.033566   0.9  1030.5  Tiantan  \n",
       "23  0.030778  -0.2  1030.5  Tiantan  "
      ]
     },
     "execution_count": 276,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def roll_avg(col: str, window: int):\n",
    "    df[col] = df.groupby(\"station\")[col].rolling(window=window).mean().values\n",
    "\n",
    "\n",
    "roll_avg(\"PM2.5\", 24)\n",
    "roll_avg(\"PM10\", 24)\n",
    "roll_avg(\"CO\", 8)\n",
    "roll_avg(\"O3\", 8)\n",
    "\n",
    "df.head(24)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Truncate nilai"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "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>PRES</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>1023.0</td>\n",
       "      <td>Tiantan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013-03-01 01:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>1023.2</td>\n",
       "      <td>Tiantan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013-03-01 02:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>1023.5</td>\n",
       "      <td>Tiantan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013-03-01 03:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.4</td>\n",
       "      <td>1024.5</td>\n",
       "      <td>Tiantan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013-03-01 04:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>1025.2</td>\n",
       "      <td>Tiantan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2013-03-01 05:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.2</td>\n",
       "      <td>1025.6</td>\n",
       "      <td>Tiantan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2013-03-01 06:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.6</td>\n",
       "      <td>1026.5</td>\n",
       "      <td>Tiantan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2013-03-01 07:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.036</td>\n",
       "      <td>-1.6</td>\n",
       "      <td>1027.4</td>\n",
       "      <td>Tiantan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2013-03-01 08:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.036</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1028.3</td>\n",
       "      <td>Tiantan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2013-03-01 09:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.035</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1028.5</td>\n",
       "      <td>Tiantan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2013-03-01 10:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.037</td>\n",
       "      <td>1.9</td>\n",
       "      <td>1028.2</td>\n",
       "      <td>Tiantan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2013-03-01 11:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.039</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1028.2</td>\n",
       "      <td>Tiantan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2013-03-01 12:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.039</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1027.3</td>\n",
       "      <td>Tiantan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2013-03-01 13:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.039</td>\n",
       "      <td>5.3</td>\n",
       "      <td>1026.2</td>\n",
       "      <td>Tiantan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2013-03-01 14:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.039</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1025.9</td>\n",
       "      <td>Tiantan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2013-03-01 15:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.040</td>\n",
       "      <td>6.2</td>\n",
       "      <td>1025.7</td>\n",
       "      <td>Tiantan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2013-03-01 16:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.040</td>\n",
       "      <td>5.9</td>\n",
       "      <td>1025.6</td>\n",
       "      <td>Tiantan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2013-03-01 17:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.041</td>\n",
       "      <td>4.3</td>\n",
       "      <td>1026.3</td>\n",
       "      <td>Tiantan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2013-03-01 18:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.041</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1027.4</td>\n",
       "      <td>Tiantan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2013-03-01 19:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.040</td>\n",
       "      <td>2.3</td>\n",
       "      <td>1028.3</td>\n",
       "      <td>Tiantan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2013-03-01 20:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.038</td>\n",
       "      <td>1.7</td>\n",
       "      <td>1029.1</td>\n",
       "      <td>Tiantan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2013-03-01 21:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.036</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1030.1</td>\n",
       "      <td>Tiantan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2013-03-01 22:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.034</td>\n",
       "      <td>0.9</td>\n",
       "      <td>1030.5</td>\n",
       "      <td>Tiantan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2013-03-01 23:00:00</td>\n",
       "      <td>8.6</td>\n",
       "      <td>17.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.031</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>1030.5</td>\n",
       "      <td>Tiantan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              datetime  PM2.5  PM10  SO2   NO2   CO     O3  TEMP    PRES  \\\n",
       "0  2013-03-01 00:00:00    NaN   NaN  1.0   3.0  NaN    NaN  -0.7  1023.0   \n",
       "1  2013-03-01 01:00:00    NaN   NaN  1.0   3.0  NaN    NaN  -1.1  1023.2   \n",
       "2  2013-03-01 02:00:00    NaN   NaN  2.0   5.0  NaN    NaN  -1.1  1023.5   \n",
       "3  2013-03-01 03:00:00    NaN   NaN  4.0   5.0  NaN    NaN  -1.4  1024.5   \n",
       "4  2013-03-01 04:00:00    NaN   NaN  4.0   6.0  NaN    NaN  -2.0  1025.2   \n",
       "5  2013-03-01 05:00:00    NaN   NaN  6.0   9.0  NaN    NaN  -2.2  1025.6   \n",
       "6  2013-03-01 06:00:00    NaN   NaN  6.0  15.0  NaN    NaN  -2.6  1026.5   \n",
       "7  2013-03-01 07:00:00    NaN   NaN  7.0  20.0  0.6  0.036  -1.6  1027.4   \n",
       "8  2013-03-01 08:00:00    NaN   NaN  6.0  21.0  0.6  0.036   0.1  1028.3   \n",
       "9  2013-03-01 09:00:00    NaN   NaN  4.0  13.0  0.6  0.035   1.2  1028.5   \n",
       "10 2013-03-01 10:00:00    NaN   NaN  3.0   6.0  0.6  0.037   1.9  1028.2   \n",
       "11 2013-03-01 11:00:00    NaN   NaN  3.0   7.0  0.6  0.039   2.9  1028.2   \n",
       "12 2013-03-01 12:00:00    NaN   NaN  2.0   6.0  0.6  0.039   3.9  1027.3   \n",
       "13 2013-03-01 13:00:00    NaN   NaN  2.0   6.0  0.7  0.039   5.3  1026.2   \n",
       "14 2013-03-01 14:00:00    NaN   NaN  2.0   5.0  0.7  0.039   6.0  1025.9   \n",
       "15 2013-03-01 15:00:00    NaN   NaN  2.0   7.0  0.7  0.040   6.2  1025.7   \n",
       "16 2013-03-01 16:00:00    NaN   NaN  3.0   6.0  0.7  0.040   5.9  1025.6   \n",
       "17 2013-03-01 17:00:00    NaN   NaN  4.0   7.0  0.7  0.041   4.3  1026.3   \n",
       "18 2013-03-01 18:00:00    NaN   NaN  3.0  10.0  0.7  0.041   3.1  1027.4   \n",
       "19 2013-03-01 19:00:00    NaN   NaN  4.0  15.0  0.7  0.040   2.3  1028.3   \n",
       "20 2013-03-01 20:00:00    NaN   NaN  5.0  16.0  0.7  0.038   1.7  1029.1   \n",
       "21 2013-03-01 21:00:00    NaN   NaN  6.0  17.0  0.7  0.036   0.6  1030.1   \n",
       "22 2013-03-01 22:00:00    NaN   NaN  7.0  27.0  0.8  0.034   0.9  1030.5   \n",
       "23 2013-03-01 23:00:00    8.6  17.0  9.0  26.0  0.8  0.031  -0.2  1030.5   \n",
       "\n",
       "    station  \n",
       "0   Tiantan  \n",
       "1   Tiantan  \n",
       "2   Tiantan  \n",
       "3   Tiantan  \n",
       "4   Tiantan  \n",
       "5   Tiantan  \n",
       "6   Tiantan  \n",
       "7   Tiantan  \n",
       "8   Tiantan  \n",
       "9   Tiantan  \n",
       "10  Tiantan  \n",
       "11  Tiantan  \n",
       "12  Tiantan  \n",
       "13  Tiantan  \n",
       "14  Tiantan  \n",
       "15  Tiantan  \n",
       "16  Tiantan  \n",
       "17  Tiantan  \n",
       "18  Tiantan  \n",
       "19  Tiantan  \n",
       "20  Tiantan  \n",
       "21  Tiantan  \n",
       "22  Tiantan  \n",
       "23  Tiantan  "
      ]
     },
     "execution_count": 277,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.round(\n",
    "    {\n",
    "        \"O3\": 3,\n",
    "        \"PM2.5\": 1,\n",
    "        \"PM10\": 0,\n",
    "        \"CO\": 1,\n",
    "        \"SO2\": 0,\n",
    "        \"NO2\": 0,\n",
    "    }\n",
    ")\n",
    "\n",
    "df.head(24)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Menghitung AQI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {},
   "outputs": [],
   "source": [
    "bp_df = pd.DataFrame(\n",
    "    {\n",
    "        \"category\": [\n",
    "            \"Good\",\n",
    "            \"Moderate\",\n",
    "            \"Unhealty for Sensitive Groups\",\n",
    "            \"Unhealty\",\n",
    "            \"Very Unhealty\",\n",
    "            \"Hazardous\",\n",
    "        ],\n",
    "        \"AQI_min\": [0, 51, 101, 151, 201, 301],\n",
    "        \"AQI_max\": [50, 100, 150, 200, 300, 500],\n",
    "        \"O3_min\": [0, 0.055, 0.071, 0.086, 0.106, 0.201],\n",
    "        \"O3_max\": [0.054, 0.070, 0.085, 0.105, 0.200, 999999],\n",
    "        \"PM2.5_min\": [0, 9.1, 35.5, 55.5, 125.5, 225.5],\n",
    "        \"PM2.5_max\": [9.0, 35.4, 55.4, 125.4, 225.4, 999999],\n",
    "        \"PM10_min\": [0, 55, 155, 255, 355, 425],\n",
    "        \"PM10_max\": [54, 154, 254, 354, 424, 999999],\n",
    "        \"CO_min\": [0, 4.5, 9.5, 12.4, 15.5, 30.5],\n",
    "        \"CO_max\": [4.4, 9.4, 12.4, 15.4, 30.4, 999999],\n",
    "        \"SO2_min\": [0, 36, 76, 186, 305, 605],\n",
    "        \"SO2_max\": [35, 75, 185, 304, 604, 999999],\n",
    "        \"NO2_min\": [0, 54, 101, 361, 650, 1250],\n",
    "        \"NO2_max\": [53, 100, 360, 649, 1259, 999999],\n",
    "    }\n",
    ")\n",
    "\n",
    "\n",
    "def count_aqi(col: str, c: float):\n",
    "    if pd.isna(c):\n",
    "        return np.nan\n",
    "\n",
    "    hi, lo = f\"{col}_max\", f\"{col}_min\"\n",
    "\n",
    "    cond = (bp_df[lo] <= c) & (bp_df[hi] >= c)\n",
    "    row = bp_df[cond].iloc[0]\n",
    "\n",
    "    i_hi, i_lo = row[\"AQI_max\"], row[\"AQI_min\"]\n",
    "    b_hi, b_lo = row[hi], row[lo]\n",
    "\n",
    "    i = ((i_hi - i_lo) / (b_hi - b_lo)) * (c - b_lo) + i_lo\n",
    "    return np.round(i)\n",
    "\n",
    "\n",
    "# pol_cols = [\"O3\", \"PM2.5\", \"PM10\", \"CO\", \"SO2\", \"NO2\"]\n",
    "# values = df[pol_cols].values\n",
    "\n",
    "# aqis = []\n",
    "# for val in values:\n",
    "#     aqi = np.nanmax([count_aqi(pol_cols[i], c) for i, c in enumerate(val)])\n",
    "#     aqis.append(aqi)\n",
    "\n",
    "# df[\"AQI\"] = aqis\n",
    "\n",
    "# df.head(50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "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>PRES</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>420768</td>\n",
       "      <td>420492.000000</td>\n",
       "      <td>420492.000000</td>\n",
       "      <td>420768.000000</td>\n",
       "      <td>420746.000000</td>\n",
       "      <td>420684.000000</td>\n",
       "      <td>420684.000000</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>79.885914</td>\n",
       "      <td>104.966596</td>\n",
       "      <td>5.781740</td>\n",
       "      <td>25.704475</td>\n",
       "      <td>1.026257</td>\n",
       "      <td>0.028916</td>\n",
       "      <td>13.531692</td>\n",
       "      <td>1010.753337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2013-03-01 00:00:00</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-19.900000</td>\n",
       "      <td>982.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2014-03-01 05:45:00</td>\n",
       "      <td>29.800000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.008000</td>\n",
       "      <td>3.100000</td>\n",
       "      <td>1002.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2015-03-01 11:30:00</td>\n",
       "      <td>61.200000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.022000</td>\n",
       "      <td>14.500000</td>\n",
       "      <td>1010.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2016-02-29 17:15:00</td>\n",
       "      <td>106.900000</td>\n",
       "      <td>138.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>1.300000</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>23.300000</td>\n",
       "      <td>1019.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2017-02-28 23:00:00</td>\n",
       "      <td>580.700000</td>\n",
       "      <td>639.000000</td>\n",
       "      <td>192.000000</td>\n",
       "      <td>149.000000</td>\n",
       "      <td>8.300000</td>\n",
       "      <td>0.564000</td>\n",
       "      <td>41.600000</td>\n",
       "      <td>1042.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>69.463495</td>\n",
       "      <td>75.109005</td>\n",
       "      <td>7.743061</td>\n",
       "      <td>17.510356</td>\n",
       "      <td>0.868818</td>\n",
       "      <td>0.026866</td>\n",
       "      <td>11.437867</td>\n",
       "      <td>10.474302</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            datetime          PM2.5           PM10  \\\n",
       "count                         420768  420492.000000  420492.000000   \n",
       "mean   2015-03-01 11:30:00.000001024      79.885914     104.966596   \n",
       "min              2013-03-01 00:00:00       3.000000       3.000000   \n",
       "25%              2014-03-01 05:45:00      29.800000      50.000000   \n",
       "50%              2015-03-01 11:30:00      61.200000      88.000000   \n",
       "75%              2016-02-29 17:15:00     106.900000     138.000000   \n",
       "max              2017-02-28 23:00:00     580.700000     639.000000   \n",
       "std                              NaN      69.463495      75.109005   \n",
       "\n",
       "                 SO2            NO2             CO             O3  \\\n",
       "count  420768.000000  420746.000000  420684.000000  420684.000000   \n",
       "mean        5.781740      25.704475       1.026257       0.028916   \n",
       "min         0.000000       0.000000       0.100000       0.000000   \n",
       "25%         1.000000      12.000000       0.500000       0.008000   \n",
       "50%         3.000000      22.000000       0.800000       0.022000   \n",
       "75%         7.000000      36.000000       1.300000       0.040000   \n",
       "max       192.000000     149.000000       8.300000       0.564000   \n",
       "std         7.743061      17.510356       0.868818       0.026866   \n",
       "\n",
       "                TEMP           PRES  \n",
       "count  420768.000000  420768.000000  \n",
       "mean       13.531692    1010.753337  \n",
       "min       -19.900000     982.400000  \n",
       "25%         3.100000    1002.300000  \n",
       "50%        14.500000    1010.400000  \n",
       "75%        23.300000    1019.000000  \n",
       "max        41.600000    1042.800000  \n",
       "std        11.437867      10.474302  "
      ]
     },
     "execution_count": 279,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "th_Lzl2Fkj9O"
   },
   "source": [
    "**Insight:**\n",
    "- xxx\n",
    "- xxx"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zsyZjqak8DC2"
   },
   "source": [
    "## Visualization & Explanatory Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HZxOiQ6n8DC2"
   },
   "source": [
    "### Pertanyaan 1:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "1swJUdAD8DC2"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DgHI7CiU8DC2"
   },
   "source": [
    "### Pertanyaan 2:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Go0lCsvO8DC2"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_0-36BDLklRg"
   },
   "source": [
    "**Insight:**\n",
    "- xxx\n",
    "- xxx"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9y4VUsmcYNZ5"
   },
   "source": [
    "## Analisis Lanjutan (Opsional)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "iWhnzsJGYUCO"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_WeHlCeX8DC2"
   },
   "source": [
    "## Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZTcyR48Y8DC2"
   },
   "source": [
    "- Conclution pertanyaan 1\n",
    "- Conclution pertanyaan 2"
   ]
  }
 ],
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