class_docstr: Returns datasets included in the library in `pandas.DataFrame` or
  `~greykite.framework.input.univariate_time_series.UnivariateTimeSeries` format.
  
  Extends `~greykite.common.data_loader.DataLoader`
class_docstr_tok: ["Returns","datasets","included","in","the","library","in","`","pandas.DataFrame","`","or","`","~greykite.framework.input.univariate_time_series.UnivariateTimeSeries","`","format",".","Extends","`","~greykite.common.data_loader.DataLoader","`"]
docstr: Loads the Hourly Bike Sharing Count dataset.
  
  This dataset contains aggregated hourly count of the number of rented bikes.
  The data also includes weather data: Maximum Daily temperature (tmax);
  Minimum Daily Temperature (tmin); Precipitation (pn)
  The raw bike-sharing data is provided by Capital Bikeshare.
  Source: https://www.capitalbikeshare.com/system-data
  The raw weather data (Baltimore-Washington INTL Airport)
  https://www.ncdc.noaa.gov/data-access/land-based-station-data
  
  Below is the dataset attribute information:
  
      ts : hour and date
      count : number of shared bikes
      tmin : minimum daily temperature
      tmax : maximum daily temperature
      pn : precipitation
  
  Returns
  -------
  ts : `~greykite.framework.input.univariate_time_series.UnivariateTimeSeries`
      Bike Sharing Count data. Time and value column:
  
          ``time_col`` : "ts"
              Hour and Date.
          ``value_col`` : "y"
               Number of rented bikes across Washington DC.
  
      Additional regressors:
  
          "tmin" : minimum daily temperature
          "tmax" : maximum daily temperature
          "pn" : precipitation
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code: def load_bikesharing_ts(self):
      """Loads the Hourly Bike Sharing Count dataset.
  
      This dataset contains aggregated hourly count of the number of rented bikes.
      The data also includes weather data: Maximum Daily temperature (tmax);
      Minimum Daily Temperature (tmin); Precipitation (pn)
      The raw bike-sharing data is provided by Capital Bikeshare.
      Source: https://www.capitalbikeshare.com/system-data
      The raw weather data (Baltimore-Washington INTL Airport)
      https://www.ncdc.noaa.gov/data-access/land-based-station-data
  
      Below is the dataset attribute information:
  
          ts : hour and date
          count : number of shared bikes
          tmin : minimum daily temperature
          tmax : maximum daily temperature
          pn : precipitation
  
      Returns
      -------
      ts : `~greykite.framework.input.univariate_time_series.UnivariateTimeSeries`
          Bike Sharing Count data. Time and value column:
  
              ``time_col`` : "ts"
                  Hour and Date.
              ``value_col`` : "y"
                   Number of rented bikes across Washington DC.
  
          Additional regressors:
  
              "tmin" : minimum daily temperature
              "tmax" : maximum daily temperature
              "pn" : precipitation
      """
      df = self.load_bikesharing()
      ts = UnivariateTimeSeries()
      ts.load_data(
          df=df,
          time_col="ts",
          value_col="count",
          freq="H",
          regressor_cols=["tmin", "tmax", "pn"],
      )
      return ts
  
file_path: greykite/greykite/framework/benchmark/data_loader_ts.py
imports: from greykite.common.constants import TIME_COL
  from greykite.common.constants import VALUE_COL
  from greykite.common.data_loader import DataLoader
  from greykite.framework.input.univariate_time_series import UnivariateTimeSeries
label_desc: Use image node_id 4 for calling the DataLoaderTS obj's underlying member method code with example usage: obj.load_bikesharing_ts() and returns: ts