pandas.DataFrame.pipe#

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

Apply chainable functions that expect Series or DataFrames.

Parameters:

funcfunction: Function to apply to the Series/DataFrame. args, and kwargs are passed into func. Alternatively a (callable, data_keyword) tuple where data_keyword is a string indicating the keyword of callable that expects the Series/DataFrame.
*argsiterable, optional: Positional arguments passed into func.
**kwargsmapping, optional: A dictionary of keyword arguments passed into func.

Returns:

The return type of func.: The result of applying func to the Series or DataFrame.

See also

DataFrame.apply: Apply a function along input axis of DataFrame.
DataFrame.map: Apply a function elementwise on a whole DataFrame.
Series.map: Apply a mapping correspondence on a Series.

Notes

Use .pipe when chaining together functions that expect Series, DataFrames or GroupBy objects.

Examples

Constructing an income DataFrame from a dictionary.

>>> data = [[8000, 1000], [9500, np.nan], [5000, 2000]]
>>> df = pd.DataFrame(data, columns=["Salary", "Others"])
>>> df
   Salary  Others
0    8000  1000.0
1    9500     NaN
2    5000  2000.0

Functions that perform tax reductions on an income DataFrame.

>>> def subtract_federal_tax(df):
...     return df * 0.9
>>> def subtract_state_tax(df, rate):
...     return df * (1 - rate)
>>> def subtract_national_insurance(df, rate, rate_increase):
...     new_rate = rate + rate_increase
...     return df * (1 - new_rate)

Instead of writing

>>> subtract_national_insurance(
...     subtract_state_tax(subtract_federal_tax(df), rate=0.12),
...     rate=0.05,
...     rate_increase=0.02,
... )  

You can write

>>> (
...     df.pipe(subtract_federal_tax)
...     .pipe(subtract_state_tax, rate=0.12)
...     .pipe(subtract_national_insurance, rate=0.05, rate_increase=0.02)
... )
    Salary   Others
0  5892.48   736.56
1  6997.32      NaN
2  3682.80  1473.12

If you have a function that takes the data as (say) the second argument, pass a tuple indicating which keyword expects the data. For example, suppose national_insurance takes its data as df in the second argument:

>>> def subtract_national_insurance(rate, df, rate_increase):
...     new_rate = rate + rate_increase
...     return df * (1 - new_rate)
>>> (
...     df.pipe(subtract_federal_tax)
...     .pipe(subtract_state_tax, rate=0.12)
...     .pipe(
...         (subtract_national_insurance, "df"), rate=0.05, rate_increase=0.02
...     )
... )
    Salary   Others
0  5892.48   736.56
1  6997.32      NaN
2  3682.80  1473.12