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

Apply a func with arguments to this Resampler object and return its 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 = lambda x, arg2, arg3: x + 1 - arg2 * arg3
>>> g = lambda x, arg1: x * 5 / arg1
>>> f = lambda x: x ** 4
>>> df = pd.DataFrame([["a", 4], ["b", 5]], columns=["group", "value"])
>>> h(g(f(df.groupby('group')), arg1=1), arg2=2, arg3=3)  

You can write

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

which is much more readable.

funccallable or tuple of (callable, str)

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.

argsiterable, optional

Positional arguments passed into func.

kwargsdict, optional

A dictionary of keyword arguments passed into func.

the return type of func.

See also


Apply a function with arguments to a series.


Apply a function with arguments to a dataframe.


Apply function to each group instead of to the full Resampler object.


See more here


>>> df = pd.DataFrame({'A': [1, 2, 3, 4]},
...                   index=pd.date_range('2012-08-02', periods=4))
>>> df
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())
2012-08-02  1
2012-08-04  1