pandas.core.resample.Resampler.pipe

Resampler.pipe(func, *args, **kwargs)[source]

Apply a function func with arguments to this Resampler object and return the function’s result.

New in version 0.23.0.

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.

Parameters:

func : callable or tuple of (callable, string)

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.

args : iterable, optional

positional arguments passed into func.

kwargs : dict, optional

a dictionary of keyword arguments passed into func.

Returns:
object : the return type of func.

See also

pandas.Series.pipe
Apply a function with arguments to a series.
pandas.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
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