daily_average

hydrostats.data.daily_average(df, rolling=False, **kwargs)

Calculates daily seasonal averages of the timeseries data in a DataFrame

Parameters:
  • df (DataFrame) – A pandas DataFrame with a datetime index and columns containing float type values.
  • rolling (bool) – If True, will calculate the rolling seasonal average.
  • **kwargs (pandas.DataFrame.rolling() properties, optional) – Options for how to compute the rolling averages. If not provided, the default is to use the following parameters: {window=6, min_periods=1, center=True, closed=”right”}. Specifying **kwargs will clear the defaults, however.
Returns:

A pandas dataframe with a string type index of date representations and the daily seasonal averages as float values in the columns.
Return type:

DataFrame

Examples

>>> import hydrostats.data as hd
>>> import pandas as pd
>>> pd.options.display.max_rows = 15

The data URLs contain streamflow data from two different models, and are provided from the Hydrostats Github page

>>> sfpt_url = r'https://github.com/waderoberts123/Hydrostats/raw/master/Sample_data/sfpt_data/magdalena-calamar_interim_data.csv'
>>> glofas_url = r'https://github.com/waderoberts123/Hydrostats/raw/master/Sample_data/GLOFAS_Data/magdalena-calamar_ECMWF_data.csv'
>>> merged_df = hd.merge_data(sfpt_url, glofas_url, column_names=('Streamflow Prediction Tool', 'GLOFAS'))

>>> hd.daily_average(merged_df)
       Streamflow Prediction Tool       GLOFAS
01/01                 7331.305278  7676.792460
01/02                 7108.640746  7753.671798
01/03                 6927.147740  7631.900453
01/04                 6738.162886  7483.029897
01/05                 6552.914171  7316.004227
01/06                 6388.213829  7154.650963
01/07                 6258.418600  7012.279722
                           ...          ...
12/25                 8321.367143  8948.101821
12/26                 8149.313143  8903.544978
12/27                 7994.357429  8807.690639
12/28                 7872.819143  8642.877365
12/29                 7791.741143  8435.175677
12/30                 7729.451143  8225.315074
12/31                 7656.042286  8041.918136
[366 rows x 2 columns]