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# pandas.api.typing.Resampler.pipe

final Resampler.pipe(func, *args, **kwargs)

  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.

  Parameters:
    func callable 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.

    *args iterable, optional

      Positional arguments passed into func.

    **kwargs dict, optional

      A dictionary of keyword arguments passed into func.

  Returns:
    any

      The result of applying `func` to the Resampler object.

  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