class_docstr: Returns datasets included in the library in `pandas.DataFrame` format.
  
  Attributes
  ----------
  available_datasets : `list` [`str`]
      The names of the available datasets.
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docstr: Loads the Beijing Particulate Matter (PM2.5) dataset.
  https://archive.ics.uci.edu/ml/datasets/Beijing+PM2.5+Data
  
  This hourly data set contains the PM2.5 data of US Embassy in Beijing. Meanwhile, meteorological data
  from Beijing Capital International Airport are also included.
  
  The dataset's time period is between Jan 1st, 2010 to Dec 31st, 2014. Missing data are denoted as NA.
  
  Below is the dataset attribute information:
  
      No : row number
      year : year of data in this row
      month : month of data in this row
      day : day of data in this row
      hour : hour of data in this row
      pm2.5: PM2.5 concentration (ug/m^3)
      DEWP : dew point (celsius)
      TEMP : temperature (celsius)
      PRES : pressure (hPa)
      cbwd : combined wind direction
      Iws : cumulated wind speed (m/s)
      Is : cumulated hours of snow
      Ir : cumulated hours of rain
  
  Parameters
  ----------
  Refer to the input of function `get_aggregated_data`.
  
  Returns
  -------
  df : `pandas.DataFrame` with Beijing PM2.5 data.
      Has the following columns:
  
          "ts" : hourly timestamp
          "year" : year of data in this row
          "month" : month of data in this row
          "day" : day of data in this row
          "hour" : hour of data in this row
          "pm" : PM2.5 concentration (ug/m^3)
          "dewp" : dew point (celsius)
          "temp" : temperature (celsius)
          "pres" : pressure (hPa)
          "cbwd" : combined wind direction
          "iws" : cumulated wind speed (m/s)
          "is" : cumulated hours of snow
          "ir" : cumulated hours of rain
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code: def load_beijing_pm(self, agg_freq=None, agg_func=None):
      """Loads the Beijing Particulate Matter (PM2.5) dataset.
      https://archive.ics.uci.edu/ml/datasets/Beijing+PM2.5+Data
  
      This hourly data set contains the PM2.5 data of US Embassy in Beijing. Meanwhile, meteorological data
      from Beijing Capital International Airport are also included.
  
      The dataset's time period is between Jan 1st, 2010 to Dec 31st, 2014. Missing data are denoted as NA.
  
      Below is the dataset attribute information:
  
          No : row number
          year : year of data in this row
          month : month of data in this row
          day : day of data in this row
          hour : hour of data in this row
          pm2.5: PM2.5 concentration (ug/m^3)
          DEWP : dew point (celsius)
          TEMP : temperature (celsius)
          PRES : pressure (hPa)
          cbwd : combined wind direction
          Iws : cumulated wind speed (m/s)
          Is : cumulated hours of snow
          Ir : cumulated hours of rain
  
      Parameters
      ----------
      Refer to the input of function `get_aggregated_data`.
  
      Returns
      -------
      df : `pandas.DataFrame` with Beijing PM2.5 data.
          Has the following columns:
  
              "ts" : hourly timestamp
              "year" : year of data in this row
              "month" : month of data in this row
              "day" : day of data in this row
              "hour" : hour of data in this row
              "pm" : PM2.5 concentration (ug/m^3)
              "dewp" : dew point (celsius)
              "temp" : temperature (celsius)
              "pres" : pressure (hPa)
              "cbwd" : combined wind direction
              "iws" : cumulated wind speed (m/s)
              "is" : cumulated hours of snow
              "ir" : cumulated hours of rain
      """
      data_path = self.get_data_home(
          data_dir=None, data_sub_dir="hourly"
      )
      df = self.get_df(
          data_path=data_path, data_name="hourly_beijing_pm"
      )
  
      df.drop("No", axis=1, inplace=True)
      df.columns = map(str.lower, df.columns)
      df.rename(columns={"pm2.5": "pm"}, inplace=True)
  
      hour_string = (
          df["year"].astype(str)
          + "-"
          + df["month"].astype(str)
          + "-"
          + df["day"].astype(str)
          + "-"
          + df["hour"].astype(str)
      )
      hour_datetime = hour_string.apply(
          lambda x: datetime.datetime.strptime(x, "%Y-%m-%d-%H")
      )
      df.insert(0, TIME_COL, hour_datetime)
  
      return self.get_aggregated_data(
          df=df, agg_freq=agg_freq, agg_func=agg_func
      )
  
imports: import datetime
  import os
  import warnings
  from pathlib import Path
  import pandas
  from greykite.common.constants import TIME_COL
label_desc: Use image node_id 17 for calling the DataLoader obj's underlying member method code with example usage: obj.load_beijing_pm(agg_freq, agg_func) and returns: self