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.

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 a 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