Resampler.
pipe
Apply a function func with arguments to this Resampler object and return the function’s result.
Use .pipe when you want to improve readability by chaining together functions that expect Series, DataFrames, GroupBy or Resampler objects. Instead of writing
>>> h(g(f(df.groupby('group')), arg1=a), arg2=b, arg3=c)
You can write
>>> (df.groupby('group') ... .pipe(f) ... .pipe(g, arg1=a) ... .pipe(h, arg2=b, arg3=c))
which is much more readable.
Function to apply to this Resampler object or, alternatively, a (callable, data_keyword) tuple where data_keyword is a string indicating the keyword of callable that expects the Resampler object.
Positional arguments passed into func.
A dictionary of keyword arguments passed into func.
See also
Series.pipe
Apply a function with arguments to a series.
DataFrame.pipe
Apply a function with arguments to a dataframe.
apply
Apply function to each group instead of to the full Resampler object.
Notes
See more here
Examples
>>> df = pd.DataFrame({'A': [1, 2, 3, 4]}, ... index=pd.date_range('2012-08-02', periods=4)) >>> df A 2012-08-02 1 2012-08-03 2 2012-08-04 3 2012-08-05 4
To get the difference between each 2-day period’s maximum and minimum value in one pass, you can do
>>> df.resample('2D').pipe(lambda x: x.max() - x.min()) A 2012-08-02 1 2012-08-04 1