daily_std_dev

hydrostats.data.daily_std_dev(merged_data: DataFrame) → DataFrame

Calculate daily seasonal standard deviation of the timeseries data in a DataFrame.

Parameters:

merged_data (DataFrame) – A pandas DataFrame with a datetime index and columns containing float type values.

Returns:

A pandas dataframe with a string type index of date representations and the daily seasonal standard deviation 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_std_dev(merged_df)
       Streamflow Prediction Tool       GLOFAS
01/01                 3349.139373  2969.748253
01/02                 3273.308852  2617.851437
01/03                 3165.117397  2556.319898
01/04                 3043.888685  2501.999235
01/05                 2894.662206  2436.603046
01/06                 2741.049485  2372.487729
01/07                 2612.931931  2341.011275
                           ...          ...
12/25                 3631.744428  3352.464257
12/26                 3487.081980  3355.480036
12/27                 3448.825041  3300.770870
12/28                 3439.995086  3194.812751
12/29                 3395.528078  3061.536706
12/30                 3318.884936  2928.699478
12/31                 3235.528520  2808.611992
[366 rows x 2 columns]