class_docstr: Returns datasets included in the library in `pandas.DataFrame` format.
  
  Attributes
  ----------
  available_datasets : `list` [`str`]
      The names of the available datasets.
class_docstr_tok: ["Returns","datasets","included","in","the","library","in","`","pandas.DataFrame","`","format",".","Attributes","--","--","--","--","--","available_datasets",":","`","list","`","[","`","str","`","]","The","names","of","the","available","datasets","."]
docstr: Loads the Hourly San Francisco Bay Area Traffic dataset.
  
  This dataset contains the road occupancy rates (between 0 and 1)
  measured by different sensors on San Francisco
  Bay area freeways from 2015 to 2016.
  Source: https://zenodo.org/record/4656132#.YrpMxuzMLGp
  
  Below is the dataset attribute information:
  
      ts : hourly timestamp
      y : average occupancy rate
  
  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 San Francisco Bay Area Traffic data.
      Has the following columns:
  
          "ts" : hourly timestamp
          "y" : average occupancy rate
docstr_tok: ["Loads","the","Hourly","San","Francisco","Bay","Area","Traffic","dataset",".","This","dataset","contains","the","road","occupancy","rates","(","between","0","and","1",")","measured","by","different","sensors","on","San","Francisco","Bay","area","freeways","from","2015","to","2016",".","Source",":","https",":","\/\/zenodo.org\/record\/4656132","#",".YrpMxuzMLGp","Below","is","the","dataset","attribute","information",":","ts",":","hourly","timestamp","y",":","average","occupancy","rate","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","San","Francisco","Bay","Area","Traffic","data",".","Has","the","following","columns",":","``","ts","''",":","hourly","timestamp","``","y","''",":","average","occupancy","rate"]
code: def load_sf_traffic(self, agg_freq=None, agg_func=None):
      """Loads the Hourly San Francisco Bay Area Traffic dataset.
  
      This dataset contains the road occupancy rates (between 0 and 1)
      measured by different sensors on San Francisco
      Bay area freeways from 2015 to 2016.
      Source: https://zenodo.org/record/4656132#.YrpMxuzMLGp
  
      Below is the dataset attribute information:
  
          ts : hourly timestamp
          y : average occupancy rate
  
      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 San Francisco Bay Area Traffic data.
          Has the following columns:
  
              "ts" : hourly timestamp
              "y" : average occupancy rate
      """
      data_path = self.get_data_home(
          data_dir=None, data_sub_dir="hourly"
      )
      df = self.get_df(
          data_path=data_path, data_name="hourly_sf_traffic"
      )
      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 13 for calling the DataLoader obj's underlying member method code with example usage: obj.load_sf_traffic(agg_freq, agg_func) and returns: self