pandas.DataFrame.pipe#

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

Apply chainable functions that expect Series or DataFrames.

Passes the object as the first argument to each function, so that df.pipe(f).pipe(g) is equivalent to g(f(df)). Improves readability when chaining several transformations.

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