Transforming+columns+features.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Extracting date and time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "air_quality = pd.read_csv('air_quality.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 95685 entries, 0 to 95684\n",
      "Data columns (total 14 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   date_time  95685 non-null  object \n",
      " 1   PM2.5      95685 non-null  float64\n",
      " 2   PM10       95685 non-null  float64\n",
      " 3   SO2        95685 non-null  float64\n",
      " 4   NO2        95685 non-null  float64\n",
      " 5   CO         95685 non-null  float64\n",
      " 6   O3         95685 non-null  float64\n",
      " 7   TEMP       95685 non-null  float64\n",
      " 8   PRES       95685 non-null  float64\n",
      " 9   DEWP       95685 non-null  float64\n",
      " 10  RAIN       95685 non-null  float64\n",
      " 11  wd         95685 non-null  object \n",
      " 12  WSPM       95685 non-null  float64\n",
      " 13  station    95685 non-null  object \n",
      "dtypes: float64(11), object(3)\n",
      "memory usage: 10.2+ MB\n"
     ]
    }
   ],
   "source": [
    "air_quality.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date_time</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>2013-03-01 00:00:00</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>89.0</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>Dongsi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013-03-01 01:00:00</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>88.0</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>Dongsi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013-03-01 05:00:00</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>78.0</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>Dongsi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013-03-01 06:00:00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>67.0</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>Dongsi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013-03-01 07:00:00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>52.0</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>Dongsi</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             date_time  PM2.5  PM10   SO2   NO2     CO    O3  TEMP    PRES  \\\n",
       "0  2013-03-01 00:00:00    9.0   9.0   3.0  17.0  300.0  89.0  -0.5  1024.5   \n",
       "1  2013-03-01 01:00:00    4.0   4.0   3.0  16.0  300.0  88.0  -0.7  1025.1   \n",
       "2  2013-03-01 05:00:00    4.0   4.0   9.0  25.0  300.0  78.0  -2.4  1027.5   \n",
       "3  2013-03-01 06:00:00    5.0   5.0  10.0  29.0  400.0  67.0  -2.5  1028.2   \n",
       "4  2013-03-01 07:00:00    3.0   6.0  12.0  40.0  400.0  52.0  -1.4  1029.5   \n",
       "\n",
       "   DEWP  RAIN   wd  WSPM station  \n",
       "0 -21.4   0.0  NNW   5.7  Dongsi  \n",
       "1 -22.1   0.0   NW   3.9  Dongsi  \n",
       "2 -21.3   0.0   NW   2.4  Dongsi  \n",
       "3 -20.4   0.0   NW   2.2  Dongsi  \n",
       "4 -20.4   0.0  NNW   3.0  Dongsi  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "air_quality.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# air_quality['date_time'].dt.year # AttributeError: Can only use .dt accessor with datetimelike values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       2013-03-01 00:00:00\n",
       "1       2013-03-01 01:00:00\n",
       "2       2013-03-01 05:00:00\n",
       "3       2013-03-01 06:00:00\n",
       "4       2013-03-01 07:00:00\n",
       "                ...        \n",
       "95680   2017-02-28 15:00:00\n",
       "95681   2017-02-28 16:00:00\n",
       "95682   2017-02-28 21:00:00\n",
       "95683   2017-02-28 22:00:00\n",
       "95684   2017-02-28 23:00:00\n",
       "Name: date_time, Length: 95685, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.to_datetime(air_quality['date_time'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "air_quality['date_time'] = pd.to_datetime(air_quality['date_time'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date_time    datetime64[ns]\n",
       "PM2.5               float64\n",
       "PM10                float64\n",
       "SO2                 float64\n",
       "NO2                 float64\n",
       "CO                  float64\n",
       "O3                  float64\n",
       "TEMP                float64\n",
       "PRES                float64\n",
       "DEWP                float64\n",
       "RAIN                float64\n",
       "wd                   object\n",
       "WSPM                float64\n",
       "station              object\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "air_quality.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>date_time</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>hour</th>\n",
       "      <th>quarter</th>\n",
       "      <th>day_of_week_num</th>\n",
       "      <th>day_of_week_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013-03-01 00:00:00</td>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Friday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013-03-01 01:00:00</td>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Friday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013-03-01 05:00:00</td>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Friday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013-03-01 06:00:00</td>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Friday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013-03-01 07:00:00</td>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Friday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>95680</th>\n",
       "      <td>2017-02-28 15:00:00</td>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>28</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Tuesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95681</th>\n",
       "      <td>2017-02-28 16:00:00</td>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>28</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Tuesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95682</th>\n",
       "      <td>2017-02-28 21:00:00</td>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>28</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Tuesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95683</th>\n",
       "      <td>2017-02-28 22:00:00</td>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>28</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Tuesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95684</th>\n",
       "      <td>2017-02-28 23:00:00</td>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>28</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Tuesday</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>95685 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                date_time  year  month  day  hour  quarter  day_of_week_num  \\\n",
       "0     2013-03-01 00:00:00  2013      3    1     0        1                4   \n",
       "1     2013-03-01 01:00:00  2013      3    1     1        1                4   \n",
       "2     2013-03-01 05:00:00  2013      3    1     5        1                4   \n",
       "3     2013-03-01 06:00:00  2013      3    1     6        1                4   \n",
       "4     2013-03-01 07:00:00  2013      3    1     7        1                4   \n",
       "...                   ...   ...    ...  ...   ...      ...              ...   \n",
       "95680 2017-02-28 15:00:00  2017      2   28    15        1                1   \n",
       "95681 2017-02-28 16:00:00  2017      2   28    16        1                1   \n",
       "95682 2017-02-28 21:00:00  2017      2   28    21        1                1   \n",
       "95683 2017-02-28 22:00:00  2017      2   28    22        1                1   \n",
       "95684 2017-02-28 23:00:00  2017      2   28    23        1                1   \n",
       "\n",
       "      day_of_week_name  \n",
       "0               Friday  \n",
       "1               Friday  \n",
       "2               Friday  \n",
       "3               Friday  \n",
       "4               Friday  \n",
       "...                ...  \n",
       "95680          Tuesday  \n",
       "95681          Tuesday  \n",
       "95682          Tuesday  \n",
       "95683          Tuesday  \n",
       "95684          Tuesday  \n",
       "\n",
       "[95685 rows x 8 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "air_quality['year'] = air_quality['date_time'].dt.year\n",
    "air_quality['month'] = air_quality['date_time'].dt.month\n",
    "air_quality['day'] = air_quality['date_time'].dt.day\n",
    "air_quality['hour'] = air_quality['date_time'].dt.hour\n",
    "air_quality['quarter'] = air_quality['date_time'].dt.quarter\n",
    "air_quality['day_of_week_num'] = air_quality['date_time'].dt.dayofweek\n",
    "air_quality['day_of_week_name'] = air_quality['date_time'].dt.day_name()\n",
    "\n",
    "air_quality[['date_time', 'year', 'month', 'day', 'hour', 'quarter', 'day_of_week_num', 'day_of_week_name']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "day_of_week_num  day_of_week_name\n",
       "6                Sunday              13931\n",
       "5                Saturday            13812\n",
       "4                Friday              13782\n",
       "3                Thursday            13633\n",
       "0                Monday              13536\n",
       "1                Tuesday             13523\n",
       "2                Wednesday           13468\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "air_quality[['day_of_week_num', 'day_of_week_name']].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2017-02-28 23:00:00')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "air_quality['date_time'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2013-03-01 00:00:00')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "air_quality['date_time'].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timedelta('1460 days 23:00:00')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "air_quality['date_time'].max() - air_quality['date_time'].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "date_time_range = air_quality['date_time'].max() - air_quality['date_time'].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timedelta('1 days 00:00:00')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Timedelta(days=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timedelta('365 days 00:00:00')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Timedelta(days=365)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1460.9583333333333"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "date_time_range/pd.Timedelta(days=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.002625570776256"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "date_time_range/pd.Timedelta(days=365)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.002625570776256"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1460.9583333333333/365"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2022-01-01 00:00:00')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Timestamp('2022/01/01')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       3228 days 00:00:00\n",
       "1       3227 days 23:00:00\n",
       "2       3227 days 19:00:00\n",
       "3       3227 days 18:00:00\n",
       "4       3227 days 17:00:00\n",
       "               ...        \n",
       "95680   1767 days 09:00:00\n",
       "95681   1767 days 08:00:00\n",
       "95682   1767 days 03:00:00\n",
       "95683   1767 days 02:00:00\n",
       "95684   1767 days 01:00:00\n",
       "Name: date_time, Length: 95685, dtype: timedelta64[ns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Timestamp('2022/01/01') - air_quality['date_time']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "air_quality['time_until_2022'] = pd.Timestamp('2022/01/01') - air_quality['date_time']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        3228.000000\n",
       "1        3227.958333\n",
       "2        3227.791667\n",
       "3        3227.750000\n",
       "4        3227.708333\n",
       "            ...     \n",
       "95680    1767.375000\n",
       "95681    1767.333333\n",
       "95682    1767.125000\n",
       "95683    1767.083333\n",
       "95684    1767.041667\n",
       "Name: time_until_2022, Length: 95685, dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "air_quality['time_until_2022']/pd.Timedelta(days=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "air_quality['time_until_2022_days'] = air_quality['time_until_2022']/pd.Timedelta(days=1)\n",
    "air_quality['time_until_2022_weeks'] = air_quality['time_until_2022']/pd.Timedelta(weeks=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date_time</th>\n",
       "      <th>time_until_2022</th>\n",
       "      <th>time_until_2022_days</th>\n",
       "      <th>time_until_2022_weeks</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013-03-01 00:00:00</td>\n",
       "      <td>3228 days 00:00:00</td>\n",
       "      <td>3228.000000</td>\n",
       "      <td>461.142857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013-03-01 01:00:00</td>\n",
       "      <td>3227 days 23:00:00</td>\n",
       "      <td>3227.958333</td>\n",
       "      <td>461.136905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013-03-01 05:00:00</td>\n",
       "      <td>3227 days 19:00:00</td>\n",
       "      <td>3227.791667</td>\n",
       "      <td>461.113095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013-03-01 06:00:00</td>\n",
       "      <td>3227 days 18:00:00</td>\n",
       "      <td>3227.750000</td>\n",
       "      <td>461.107143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013-03-01 07:00:00</td>\n",
       "      <td>3227 days 17:00:00</td>\n",
       "      <td>3227.708333</td>\n",
       "      <td>461.101190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95680</th>\n",
       "      <td>2017-02-28 15:00:00</td>\n",
       "      <td>1767 days 09:00:00</td>\n",
       "      <td>1767.375000</td>\n",
       "      <td>252.482143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95681</th>\n",
       "      <td>2017-02-28 16:00:00</td>\n",
       "      <td>1767 days 08:00:00</td>\n",
       "      <td>1767.333333</td>\n",
       "      <td>252.476190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95682</th>\n",
       "      <td>2017-02-28 21:00:00</td>\n",
       "      <td>1767 days 03:00:00</td>\n",
       "      <td>1767.125000</td>\n",
       "      <td>252.446429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95683</th>\n",
       "      <td>2017-02-28 22:00:00</td>\n",
       "      <td>1767 days 02:00:00</td>\n",
       "      <td>1767.083333</td>\n",
       "      <td>252.440476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95684</th>\n",
       "      <td>2017-02-28 23:00:00</td>\n",
       "      <td>1767 days 01:00:00</td>\n",
       "      <td>1767.041667</td>\n",
       "      <td>252.434524</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>95685 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                date_time    time_until_2022  time_until_2022_days  \\\n",
       "0     2013-03-01 00:00:00 3228 days 00:00:00           3228.000000   \n",
       "1     2013-03-01 01:00:00 3227 days 23:00:00           3227.958333   \n",
       "2     2013-03-01 05:00:00 3227 days 19:00:00           3227.791667   \n",
       "3     2013-03-01 06:00:00 3227 days 18:00:00           3227.750000   \n",
       "4     2013-03-01 07:00:00 3227 days 17:00:00           3227.708333   \n",
       "...                   ...                ...                   ...   \n",
       "95680 2017-02-28 15:00:00 1767 days 09:00:00           1767.375000   \n",
       "95681 2017-02-28 16:00:00 1767 days 08:00:00           1767.333333   \n",
       "95682 2017-02-28 21:00:00 1767 days 03:00:00           1767.125000   \n",
       "95683 2017-02-28 22:00:00 1767 days 02:00:00           1767.083333   \n",
       "95684 2017-02-28 23:00:00 1767 days 01:00:00           1767.041667   \n",
       "\n",
       "       time_until_2022_weeks  \n",
       "0                 461.142857  \n",
       "1                 461.136905  \n",
       "2                 461.113095  \n",
       "3                 461.107143  \n",
       "4                 461.101190  \n",
       "...                      ...  \n",
       "95680             252.482143  \n",
       "95681             252.476190  \n",
       "95682             252.446429  \n",
       "95683             252.440476  \n",
       "95684             252.434524  \n",
       "\n",
       "[95685 rows x 4 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "air_quality[['date_time', 'time_until_2022', 'time_until_2022_days', 'time_until_2022_weeks']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "461.14285714285717"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "3228/7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         True\n",
       "1         True\n",
       "2         True\n",
       "3         True\n",
       "4         True\n",
       "         ...  \n",
       "95680    False\n",
       "95681    False\n",
       "95682    False\n",
       "95683    False\n",
       "95684    False\n",
       "Name: date_time, Length: 95685, dtype: bool"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "air_quality['date_time'] < pd.Timestamp('2016/01/01')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "air_quality['prior_2016_ind'] = (air_quality['date_time'] < pd.Timestamp('2016/01/01'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date_time</th>\n",
       "      <th>prior_2016_ind</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013-03-01 00:00:00</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013-03-01 01:00:00</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013-03-01 05:00:00</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013-03-01 06:00:00</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013-03-01 07:00:00</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95680</th>\n",
       "      <td>2017-02-28 15:00:00</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95681</th>\n",
       "      <td>2017-02-28 16:00:00</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95682</th>\n",
       "      <td>2017-02-28 21:00:00</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95683</th>\n",
       "      <td>2017-02-28 22:00:00</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95684</th>\n",
       "      <td>2017-02-28 23:00:00</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>95685 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                date_time  prior_2016_ind\n",
       "0     2013-03-01 00:00:00            True\n",
       "1     2013-03-01 01:00:00            True\n",
       "2     2013-03-01 05:00:00            True\n",
       "3     2013-03-01 06:00:00            True\n",
       "4     2013-03-01 07:00:00            True\n",
       "...                   ...             ...\n",
       "95680 2017-02-28 15:00:00           False\n",
       "95681 2017-02-28 16:00:00           False\n",
       "95682 2017-02-28 21:00:00           False\n",
       "95683 2017-02-28 22:00:00           False\n",
       "95684 2017-02-28 23:00:00           False\n",
       "\n",
       "[95685 rows x 2 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "air_quality[['date_time', 'prior_2016_ind']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['date_time', 'PM2.5', 'PM10', 'SO2', 'NO2', 'CO', 'O3', 'TEMP', 'PRES',\n",
       "       'DEWP', 'RAIN', 'wd', 'WSPM', 'station', 'year', 'month', 'day', 'hour',\n",
       "       'quarter', 'day_of_week_num', 'day_of_week_name', 'time_until_2022',\n",
       "       'time_until_2022_days', 'time_until_2022_weeks', 'prior_2016_ind'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "air_quality.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Binning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    9.0\n",
       "1    4.0\n",
       "2    4.0\n",
       "3    5.0\n",
       "4    3.0\n",
       "Name: PM2.5, dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "air_quality['PM2.5'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "air_quality['PM2.5'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        (1.181, 83.9]\n",
       "1        (1.181, 83.9]\n",
       "2        (1.181, 83.9]\n",
       "3        (1.181, 83.9]\n",
       "4        (1.181, 83.9]\n",
       "             ...      \n",
       "95680    (1.181, 83.9]\n",
       "95681    (1.181, 83.9]\n",
       "95682    (1.181, 83.9]\n",
       "95683    (1.181, 83.9]\n",
       "95684    (1.181, 83.9]\n",
       "Name: PM2.5, Length: 95685, dtype: category\n",
       "Categories (10, interval[float64]): [(1.181, 83.9] < (83.9, 165.8] < (165.8, 247.7] < (247.7, 329.6] ... (493.4, 575.3] < (575.3, 657.2] < (657.2, 739.1] < (739.1, 821.0]]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.cut(air_quality['PM2.5'], bins=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1.181, 83.9]     59923\n",
       "(83.9, 165.8]     22741\n",
       "(165.8, 247.7]     8132\n",
       "(247.7, 329.6]     3225\n",
       "(329.6, 411.5]     1011\n",
       "(411.5, 493.4]      384\n",
       "(493.4, 575.3]      171\n",
       "(575.3, 657.2]       70\n",
       "(657.2, 739.1]       22\n",
       "(739.1, 821.0]        6\n",
       "Name: PM2.5, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.cut(air_quality['PM2.5'], bins=10).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "82.71900000000001"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "83.9 - 1.181"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "81.89999999999998"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "247.7 - 165.8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         (0.0, 12.0]\n",
       "1         (0.0, 12.0]\n",
       "2         (0.0, 12.0]\n",
       "3         (0.0, 12.0]\n",
       "4         (0.0, 12.0]\n",
       "             ...     \n",
       "95680     (0.0, 12.0]\n",
       "95681     (0.0, 12.0]\n",
       "95682    (12.0, 35.4]\n",
       "95683    (12.0, 35.4]\n",
       "95684    (12.0, 35.4]\n",
       "Name: PM2.5, Length: 95685, dtype: category\n",
       "Categories (6, interval[float64]): [(0.0, 12.0] < (12.0, 35.4] < (35.4, 55.4] < (55.4, 150.4] < (150.4, 250.4] < (250.4, 999.0]]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.cut(air_quality['PM2.5'], bins=[0, 12, 35.4, 55.4, 150.4, 250.4, 999])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0            Good\n",
       "1            Good\n",
       "2            Good\n",
       "3            Good\n",
       "4            Good\n",
       "           ...   \n",
       "95680        Good\n",
       "95681        Good\n",
       "95682    Moderate\n",
       "95683    Moderate\n",
       "95684    Moderate\n",
       "Name: PM2.5, Length: 95685, dtype: category\n",
       "Categories (6, object): ['Good' < 'Moderate' < 'Unhealthy for sensitive groups' < 'Unhealthy' < 'Very unhealthy' < 'Hazardous']"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.cut(air_quality['PM2.5'],\n",
    "       bins=[0, 12, 35.4, 55.4, 150.4, 250.4, 999],\n",
    "       labels=['Good', \n",
    "               'Moderate', \n",
    "               'Unhealthy for sensitive groups', \n",
    "               'Unhealthy',\n",
    "               'Very unhealthy',\n",
    "               'Hazardous'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "air_quality['PM2.5_category'] = pd.cut(air_quality['PM2.5'],\n",
    "                                       bins=[0, 12, 35.4, 55.4, 150.4, 250.4, 999],\n",
    "                                       labels=['Good', \n",
    "                                               'Moderate', \n",
    "                                               'Unhealthy for sensitive groups', \n",
    "                                               'Unhealthy',\n",
    "                                               'Very unhealthy',\n",
    "                                               'Hazardous'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PM2.5</th>\n",
       "      <th>PM2.5_category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9.0</td>\n",
       "      <td>Good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.0</td>\n",
       "      <td>Good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.0</td>\n",
       "      <td>Good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.0</td>\n",
       "      <td>Good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.0</td>\n",
       "      <td>Good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95680</th>\n",
       "      <td>9.0</td>\n",
       "      <td>Good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95681</th>\n",
       "      <td>10.0</td>\n",
       "      <td>Good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95682</th>\n",
       "      <td>18.0</td>\n",
       "      <td>Moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95683</th>\n",
       "      <td>15.0</td>\n",
       "      <td>Moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95684</th>\n",
       "      <td>15.0</td>\n",
       "      <td>Moderate</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>95685 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       PM2.5 PM2.5_category\n",
       "0        9.0           Good\n",
       "1        4.0           Good\n",
       "2        4.0           Good\n",
       "3        5.0           Good\n",
       "4        3.0           Good\n",
       "...      ...            ...\n",
       "95680    9.0           Good\n",
       "95681   10.0           Good\n",
       "95682   18.0       Moderate\n",
       "95683   15.0       Moderate\n",
       "95684   15.0       Moderate\n",
       "\n",
       "[95685 rows x 2 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "air_quality[['PM2.5', 'PM2.5_category']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unhealthy                         34257\n",
       "Moderate                          19853\n",
       "Good                              13576\n",
       "Unhealthy for sensitive groups    12457\n",
       "Very unhealthy                    10821\n",
       "Hazardous                          4721\n",
       "Name: PM2.5_category, dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "air_quality['PM2.5_category'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unhealthy                         0.358018\n",
       "Moderate                          0.207483\n",
       "Good                              0.141882\n",
       "Unhealthy for sensitive groups    0.130188\n",
       "Very unhealthy                    0.113090\n",
       "Hazardous                         0.049339\n",
       "Name: PM2.5_category, dtype: float64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "air_quality['PM2.5_category'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        (1.999, 23.0]\n",
       "1        (1.999, 23.0]\n",
       "2        (1.999, 23.0]\n",
       "3        (1.999, 23.0]\n",
       "4        (1.999, 23.0]\n",
       "             ...      \n",
       "95680    (1.999, 23.0]\n",
       "95681    (1.999, 23.0]\n",
       "95682    (1.999, 23.0]\n",
       "95683    (1.999, 23.0]\n",
       "95684    (1.999, 23.0]\n",
       "Name: PM2.5, Length: 95685, dtype: category\n",
       "Categories (4, interval[float64]): [(1.999, 23.0] < (23.0, 59.0] < (59.0, 116.0] < (116.0, 821.0]]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.qcut(air_quality['PM2.5'], q=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1.999, 23.0]     0.258222\n",
       "(59.0, 116.0]     0.249862\n",
       "(116.0, 821.0]    0.247113\n",
       "(23.0, 59.0]      0.244803\n",
       "Name: PM2.5, dtype: float64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.qcut(air_quality['PM2.5'], q=4).value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1.999, 23.0]     0.258222\n",
       "(23.0, 59.0]      0.244803\n",
       "(59.0, 116.0]     0.249862\n",
       "(116.0, 821.0]    0.247113\n",
       "Name: PM2.5, dtype: float64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.qcut(air_quality['PM2.5'], q=4).value_counts(normalize=True, sort=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    95685.000000\n",
       "mean        83.477884\n",
       "std         82.678134\n",
       "min          2.000000\n",
       "25%         23.000000\n",
       "50%         59.000000\n",
       "75%        116.000000\n",
       "max        821.000000\n",
       "Name: PM2.5, dtype: float64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "air_quality['PM2.5'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        Very Cold\n",
       "1        Very Cold\n",
       "2        Very Cold\n",
       "3        Very Cold\n",
       "4        Very Cold\n",
       "           ...    \n",
       "95680         Warm\n",
       "95681         Warm\n",
       "95682         Warm\n",
       "95683         Warm\n",
       "95684         Cold\n",
       "Name: TEMP, Length: 95685, dtype: category\n",
       "Categories (5, object): ['Very Cold' < 'Cold' < 'Warm' < 'Hot' < 'Very Hot']"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.qcut(air_quality['TEMP'], \n",
    "        q=5,\n",
    "        labels=['Very Cold', 'Cold', 'Warm', 'Hot', 'Very Hot'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Hot          19189\n",
       "Very Cold    19156\n",
       "Cold         19154\n",
       "Warm         19143\n",
       "Very Hot     19043\n",
       "Name: TEMP, dtype: int64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.qcut(air_quality['TEMP'], \n",
    "        q=5,\n",
    "        labels=['Very Cold', 'Cold', 'Warm', 'Hot', 'Very Hot']).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "air_quality['TEMP_category'] = pd.qcut(air_quality['TEMP'], \n",
    "                                       q=5,\n",
    "                                       labels=['Very Cold', 'Cold', 'Warm', 'Hot', 'Very Hot'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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>TEMP</th>\n",
       "      <th>TEMP_category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.5</td>\n",
       "      <td>Very Cold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.7</td>\n",
       "      <td>Very Cold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-2.4</td>\n",
       "      <td>Very Cold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-2.5</td>\n",
       "      <td>Very Cold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.4</td>\n",
       "      <td>Very Cold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95680</th>\n",
       "      <td>15.4</td>\n",
       "      <td>Warm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95681</th>\n",
       "      <td>14.9</td>\n",
       "      <td>Warm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95682</th>\n",
       "      <td>10.8</td>\n",
       "      <td>Warm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95683</th>\n",
       "      <td>10.5</td>\n",
       "      <td>Warm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95684</th>\n",
       "      <td>8.6</td>\n",
       "      <td>Cold</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>95685 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       TEMP TEMP_category\n",
       "0      -0.5     Very Cold\n",
       "1      -0.7     Very Cold\n",
       "2      -2.4     Very Cold\n",
       "3      -2.5     Very Cold\n",
       "4      -1.4     Very Cold\n",
       "...     ...           ...\n",
       "95680  15.4          Warm\n",
       "95681  14.9          Warm\n",
       "95682  10.8          Warm\n",
       "95683  10.5          Warm\n",
       "95684   8.6          Cold\n",
       "\n",
       "[95685 rows x 2 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "air_quality[['TEMP', 'TEMP_category']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "air_quality.to_pickle('air_quality.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Mapping to new values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "air_quality = pd.read_pickle('air_quality.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         Friday\n",
       "1         Friday\n",
       "2         Friday\n",
       "3         Friday\n",
       "4         Friday\n",
       "          ...   \n",
       "95680    Tuesday\n",
       "95681    Tuesday\n",
       "95682    Tuesday\n",
       "95683    Tuesday\n",
       "95684    Tuesday\n",
       "Name: day_of_week_name, Length: 95685, dtype: object"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "air_quality['day_of_week_name']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sunday       13931\n",
       "Saturday     13812\n",
       "Friday       13782\n",
       "Thursday     13633\n",
       "Monday       13536\n",
       "Tuesday      13523\n",
       "Wednesday    13468\n",
       "Name: day_of_week_name, dtype: int64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "air_quality['day_of_week_name'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        Weekday\n",
       "1        Weekday\n",
       "2        Weekday\n",
       "3        Weekday\n",
       "4        Weekday\n",
       "          ...   \n",
       "95680    Weekday\n",
       "95681    Weekday\n",
       "95682    Weekday\n",
       "95683    Weekday\n",
       "95684    Weekday\n",
       "Name: day_of_week_name, Length: 95685, dtype: object"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "day_of_week_mapping = {\n",
    "    'Monday': 'Weekday', \n",
    "    'Tuesday': 'Weekday',\n",
    "    'Wednesday': 'Weekday',\n",
    "    'Thursday': 'Weekday',\n",
    "    'Friday': 'Weekday',\n",
    "    'Saturday': 'Weekend',\n",
    "    'Sunday': 'Weekend'\n",
    "}\n",
    "air_quality['day_of_week_name'].map(arg=day_of_week_mapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Weekday    67942\n",
       "Weekend    27743\n",
       "Name: day_of_week_name, dtype: int64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "air_quality['day_of_week_name'].map(day_of_week_mapping).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sunday       13931\n",
       "Saturday     13812\n",
       "Friday       13782\n",
       "Thursday     13633\n",
       "Monday       13536\n",
       "Tuesday      13523\n",
       "Wednesday    13468\n",
       "Name: day_of_week_name, dtype: int64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "air_quality['day_of_week_name'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "# air_quality['day_of_week_name']=air_quality['day_of_week_name'].map(day_of_week_mapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        6\n",
       "1        6\n",
       "2        6\n",
       "3        6\n",
       "4        6\n",
       "        ..\n",
       "95680    7\n",
       "95681    7\n",
       "95682    7\n",
       "95683    7\n",
       "95684    7\n",
       "Name: day_of_week_name, Length: 95685, dtype: int64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "air_quality['day_of_week_name'].map(len)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        3228.000000\n",
       "1        3227.958333\n",
       "2        3227.791667\n",
       "3        3227.750000\n",
       "4        3227.708333\n",
       "            ...     \n",
       "95680    1767.375000\n",
       "95681    1767.333333\n",
       "95682    1767.125000\n",
       "95683    1767.083333\n",
       "95684    1767.041667\n",
       "Name: time_until_2022_days, Length: 95685, dtype: float64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "air_quality['time_until_2022_days']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        3228\n",
       "1        3227\n",
       "2        3227\n",
       "3        3227\n",
       "4        3227\n",
       "         ... \n",
       "95680    1767\n",
       "95681    1767\n",
       "95682    1767\n",
       "95683    1767\n",
       "95684    1767\n",
       "Name: time_until_2022_days, Length: 95685, dtype: int64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "air_quality['time_until_2022_days'].map(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        Weekday\n",
       "1        Weekday\n",
       "2        Weekday\n",
       "3        Weekday\n",
       "4        Weekday\n",
       "          ...   \n",
       "95680    Weekday\n",
       "95681    Weekday\n",
       "95682    Weekday\n",
       "95683    Weekday\n",
       "95684    Weekday\n",
       "Name: day_of_week_name, Length: 95685, dtype: object"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "air_quality['day_of_week_name'].map(lambda day: 'Weekend' if day in ['Saturday', 'Sunday'] else 'Weekday')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "def weekday_weekend(day):\n",
    "    if day in ['Saturday', 'Sunday']:\n",
    "        return 'Weekend'\n",
    "    else:\n",
    "        return 'Weekday'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        Weekday\n",
       "1        Weekday\n",
       "2        Weekday\n",
       "3        Weekday\n",
       "4        Weekday\n",
       "          ...   \n",
       "95680    Weekday\n",
       "95681    Weekday\n",
       "95682    Weekday\n",
       "95683    Weekday\n",
       "95684    Weekday\n",
       "Name: day_of_week_name, Length: 95685, dtype: object"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "air_quality['day_of_week_name'].map(weekday_weekend)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NE     9447\n",
       "ENE    8744\n",
       "E      7848\n",
       "NW     7124\n",
       "N      6962\n",
       "SW     6410\n",
       "ESE    6107\n",
       "SSW    6103\n",
       "NNE    5930\n",
       "S      4939\n",
       "WSW    4686\n",
       "SE     4651\n",
       "NNW    4605\n",
       "WNW    4324\n",
       "SSE    3951\n",
       "W      3854\n",
       "Name: wd, dtype: int64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "air_quality['wd'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "def wd_direction(wd):\n",
    "    if wd[0] == 'N':\n",
    "        return 'North'\n",
    "    elif wd[0] == 'S':\n",
    "        return 'South'\n",
    "    elif wd[0] == 'E':\n",
    "        return 'East'\n",
    "    else:\n",
    "        return 'West'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        North\n",
       "1        North\n",
       "2        North\n",
       "3        North\n",
       "4        North\n",
       "         ...  \n",
       "95680    North\n",
       "95681    North\n",
       "95682    North\n",
       "95683    North\n",
       "95684    North\n",
       "Name: wd, Length: 95685, dtype: object"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "air_quality['wd'].map(wd_direction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "North    34068\n",
       "South    26054\n",
       "East     22699\n",
       "West     12864\n",
       "Name: wd, dtype: int64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "air_quality['wd'].map(wd_direction).value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Applying functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "air_quality = pd.read_pickle('air_quality.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PM2.5</th>\n",
       "      <th>PM10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>9.0</td>\n",
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       "    <tr>\n",
       "      <th>95681</th>\n",
       "      <td>10.0</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95682</th>\n",
       "      <td>18.0</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95683</th>\n",
       "      <td>15.0</td>\n",
       "      <td>42.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95684</th>\n",
       "      <td>15.0</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>95685 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       PM2.5  PM10\n",
       "0        9.0   9.0\n",
       "1        4.0   4.0\n",
       "2        4.0   4.0\n",
       "3        5.0   5.0\n",
       "4        3.0   6.0\n",
       "...      ...   ...\n",
       "95680    9.0   9.0\n",
       "95681   10.0  29.0\n",
       "95682   18.0  32.0\n",
       "95683   15.0  42.0\n",
       "95684   15.0  50.0\n",
       "\n",
       "[95685 rows x 2 columns]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "air_quality[['PM2.5', 'PM10']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PM2.5     83.477884\n",
       "PM10     111.899959\n",
       "dtype: float64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "air_quality[['PM2.5', 'PM10']].apply('mean')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PM2.5     83.477884\n",
       "PM10     111.899959\n",
       "dtype: float64"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "air_quality[['PM2.5', 'PM10']].apply('mean', axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         9.0\n",
       "1         4.0\n",
       "2         4.0\n",
       "3         5.0\n",
       "4         4.5\n",
       "         ... \n",
       "95680     9.0\n",
       "95681    19.5\n",
       "95682    25.0\n",
       "95683    28.5\n",
       "95684    32.5\n",
       "Length: 95685, dtype: float64"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "air_quality[['PM2.5', 'PM10']].apply('mean', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PM2.5     83.477884\n",
       "PM10     111.899959\n",
       "dtype: float64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "air_quality[['PM2.5', 'PM10']].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         9.0\n",
       "1         4.0\n",
       "2         4.0\n",
       "3         5.0\n",
       "4         4.5\n",
       "         ... \n",
       "95680     9.0\n",
       "95681    19.5\n",
       "95682    25.0\n",
       "95683    28.5\n",
       "95684    32.5\n",
       "Length: 95685, dtype: float64"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "air_quality[['PM2.5', 'PM10']].mean(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        1.000000\n",
       "1        1.000000\n",
       "2        1.000000\n",
       "3        1.000000\n",
       "4        0.500000\n",
       "           ...   \n",
       "95680    1.000000\n",
       "95681    0.344828\n",
       "95682    0.562500\n",
       "95683    0.357143\n",
       "95684    0.300000\n",
       "Length: 95685, dtype: float64"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def pm_ratio(row):\n",
    "    return row['PM2.5']/row['PM10']\n",
    "\n",
    "air_quality.apply(pm_ratio, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        1.000000\n",
       "1        1.000000\n",
       "2        1.000000\n",
       "3        1.000000\n",
       "4        0.500000\n",
       "           ...   \n",
       "95680    1.000000\n",
       "95681    0.344828\n",
       "95682    0.562500\n",
       "95683    0.357143\n",
       "95684    0.300000\n",
       "Length: 95685, dtype: float64"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "air_quality.apply(lambda row: row['PM2.5']/row['PM10'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        1.000000\n",
       "1        1.000000\n",
       "2        1.000000\n",
       "3        1.000000\n",
       "4        0.500000\n",
       "           ...   \n",
       "95680    1.000000\n",
       "95681    0.344828\n",
       "95682    0.562500\n",
       "95683    0.357143\n",
       "95684    0.300000\n",
       "Length: 95685, dtype: float64"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "air_quality['PM2.5']/air_quality['PM10']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add new column to air_quality: 'Go outside', 'Stay inside'\n",
    "# based on PM2.5_category, TEMP_category, RAIN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        Stay inside\n",
       "1        Stay inside\n",
       "2        Stay inside\n",
       "3        Stay inside\n",
       "4        Stay inside\n",
       "            ...     \n",
       "95680     Go outside\n",
       "95681     Go outside\n",
       "95682     Go outside\n",
       "95683     Go outside\n",
       "95684    Stay inside\n",
       "Length: 95685, dtype: object"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def activity_decision(pm25_category, temp_category, rain):\n",
    "    if pm25_category in ['Good', 'Moderate'] and temp_category in ['Warm', 'Hot'] and rain == 0:\n",
    "        return 'Go outside'\n",
    "    else:\n",
    "        return 'Stay inside'\n",
    "\n",
    "air_quality.apply(lambda row: activity_decision(row['PM2.5_category'], row['TEMP_category'], row['RAIN']), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "air_quality['activity'] = air_quality.apply(lambda row: activity_decision(row['PM2.5_category'], row['TEMP_category'], row['RAIN']), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "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>activity</th>\n",
       "      <th>PM2.5_category</th>\n",
       "      <th>TEMP_category</th>\n",
       "      <th>RAIN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Stay inside</td>\n",
       "      <td>Good</td>\n",
       "      <td>Very Cold</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Stay inside</td>\n",
       "      <td>Good</td>\n",
       "      <td>Very Cold</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Stay inside</td>\n",
       "      <td>Good</td>\n",
       "      <td>Very Cold</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Stay inside</td>\n",
       "      <td>Good</td>\n",
       "      <td>Very Cold</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Stay inside</td>\n",
       "      <td>Good</td>\n",
       "      <td>Very Cold</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>95680</th>\n",
       "      <td>Go outside</td>\n",
       "      <td>Good</td>\n",
       "      <td>Warm</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95681</th>\n",
       "      <td>Go outside</td>\n",
       "      <td>Good</td>\n",
       "      <td>Warm</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95682</th>\n",
       "      <td>Go outside</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>Warm</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95683</th>\n",
       "      <td>Go outside</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>Warm</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95684</th>\n",
       "      <td>Stay inside</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>Cold</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>95685 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          activity PM2.5_category TEMP_category  RAIN\n",
       "0      Stay inside           Good     Very Cold   0.0\n",
       "1      Stay inside           Good     Very Cold   0.0\n",
       "2      Stay inside           Good     Very Cold   0.0\n",
       "3      Stay inside           Good     Very Cold   0.0\n",
       "4      Stay inside           Good     Very Cold   0.0\n",
       "...            ...            ...           ...   ...\n",
       "95680   Go outside           Good          Warm   0.0\n",
       "95681   Go outside           Good          Warm   0.0\n",
       "95682   Go outside       Moderate          Warm   0.0\n",
       "95683   Go outside       Moderate          Warm   0.0\n",
       "95684  Stay inside       Moderate          Cold   0.0\n",
       "\n",
       "[95685 rows x 4 columns]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "air_quality[['activity', 'PM2.5_category', 'TEMP_category', 'RAIN']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Stay inside    0.874777\n",
       "Go outside     0.125223\n",
       "Name: activity, dtype: float64"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "air_quality['activity'].value_counts(normalize=True)"
   ]
  }
 ],
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