pipe(self, func: 'Callable[..., T] | tuple[Callable[..., T], str]', *args, **kwargs) -> 'T'

Use :None:None:`.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)  # doctest: +SKIP

You can write

>>> (df.groupby('group')
...    .pipe(f)
...    .pipe(g, arg1=a)
...    .pipe(h, arg2=b, arg3=c))  # doctest: +SKIP

which is much more readable.

Notes

See more :None:None:`here <https://pandas.pydata.org/pandas-docs/stable/user_guide/groupby.html#piping-function-calls>`

Parameters

func : callable or tuple of (callable, str)

Function to apply to this GroupBy object or, alternatively, a :None:None:`(callable, data_keyword)` tuple where :None:None:`data_keyword` is a string indicating the keyword of :None:None:`callable` that expects the GroupBy 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`.

Apply a function func with arguments to this GroupBy object and return the function's result.

See Also

DataFrame.pipe

Apply a function with arguments to a dataframe.

Series.pipe

Apply a function with arguments to a series.

apply

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

Examples

This example is valid syntax, but we were not able to check execution

>>> df = pd.DataFrame({'A': 'a b a b'.split(), 'B': [1, 2, 3, 4]})
... df
   A  B
0  a  1
1  b  2
2  a  3
3  b  4

To get the difference between each groups maximum and minimum value in one pass, you can do

This example is valid syntax, but we were not able to check execution

>>> df.groupby('A').pipe(lambda x: x.max() - x.min())
   B
A
a  2
b  2

See :

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File: /pandas/core/groupby/groupby.py#691
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