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 Hourly Electricity dataset.
  
  This dataset contains the hourly consumption (in Kilowatt)
  of 321 clients from 2012 to 2014 published by Monash.
  We aggregated them by taking the average across the 321 clients.
  Source: https://zenodo.org/record/4656140#.YrpKtezMJqs
  
  Below is the dataset attribute information:
  
      ts : hourly timestamp
      y : average electricity consumption in Kilowatt
  
  Parameters
  ----------
  agg_freq : `str` or None, default None
      Possible values: "daily", "weekly", or "monthly".
      If None, data will not be aggregated and will include all columns.
  
  agg_func : `Dict` [`str`, `str`], default None
      A dictionary of the columns to be aggregated and the corresponding aggregating functions.
      Possible aggregating functions include "sum", "mean", "median", "max", "min", etc.
      An example input can be {"col1":"mean", "col2":"sum"}
      If None, data will not be aggregated and will include all columns.
  
  Returns
  -------
  df : `pandas.DataFrame` object with Electricity data.
      Has the following columns:
  
          "ts" : hourly timestamp
          "y" : average electricity consumption in Kilowatt
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code: def load_electricity(self, agg_freq=None, agg_func=None):
      """Loads the Hourly Electricity dataset.
  
      This dataset contains the hourly consumption (in Kilowatt)
      of 321 clients from 2012 to 2014 published by Monash.
      We aggregated them by taking the average across the 321 clients.
      Source: https://zenodo.org/record/4656140#.YrpKtezMJqs
  
      Below is the dataset attribute information:
  
          ts : hourly timestamp
          y : average electricity consumption in Kilowatt
  
      Parameters
      ----------
      agg_freq : `str` or None, default None
          Possible values: "daily", "weekly", or "monthly".
          If None, data will not be aggregated and will include all columns.
  
      agg_func : `Dict` [`str`, `str`], default None
          A dictionary of the columns to be aggregated and the corresponding aggregating functions.
          Possible aggregating functions include "sum", "mean", "median", "max", "min", etc.
          An example input can be {"col1":"mean", "col2":"sum"}
          If None, data will not be aggregated and will include all columns.
  
      Returns
      -------
      df : `pandas.DataFrame` object with Electricity data.
          Has the following columns:
  
              "ts" : hourly timestamp
              "y" : average electricity consumption in Kilowatt
      """
      data_path = self.get_data_home(
          data_dir=None, data_sub_dir="hourly"
      )
      df = self.get_df(
          data_path=data_path, data_name="hourly_electricity"
      )
      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 12 for calling the DataLoader obj's underlying member method code with example usage: obj.load_electricity(agg_freq, agg_func) and returns: self