pandas.core.window.expanding.Expanding.pipe#

final Expanding.pipe(func, *args, **kwargs)[source]#

Apply a func with arguments to this Expanding object and return its result.

Use .pipe when you want to improve readability by chaining together functions that expect Series, DataFrames, GroupBy, Rolling, Expanding or Resampler objects. Instead of writing

>>> h = lambda x, arg2, arg3: x + 1 - arg2 * arg3
>>> g = lambda x, arg1: x * 5 / arg1
>>> f = lambda x: x ** 4
>>> df = pd.DataFrame({'A': [1, 2, 3, 4]}, index=pd.date_range('2012-08-02', periods=4))
>>> h(g(f(df.rolling('2D')), arg1=1), arg2=2, arg3=3)  

You can write

>>> (df.rolling('2D')
...    .pipe(f)
...    .pipe(g, arg1=1)
...    .pipe(h, arg2=2, arg3=3))  

which is much more readable.

Parameters:

funccallable or tuple of (callable, str): Function to apply to this Expanding object or, alternatively, a (callable, data_keyword) tuple where data_keyword is a string indicating the keyword of callable that expects the Expanding object.
*argsiterable, optional: Positional arguments passed into func.
**kwargsdict, optional: A dictionary of keyword arguments passed into func.

Returns:

Expanding: The original object with the function func applied.

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 Expanding 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 expanding window’s maximum and minimum value in one pass, you can do

>>> df.expanding().pipe(lambda x: x.max() - x.min())
              A
2012-08-02  0.0
2012-08-03  1.0
2012-08-04  2.0
2012-08-05  3.0

pandas.core.window.expanding.Expanding.pipe#

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