dask_expr._collection.Index.pipe
dask_expr._collection.Index.pipe¶
- Index.pipe(func, *args, **kwargs)¶
Apply chainable functions that expect Series or DataFrames.
This docstring was copied from pandas.core.frame.DataFrame.pipe.
Some inconsistencies with the Dask version may exist.
- Parameters
- funcfunction
Function to apply to the Series/DataFrame.
args
, andkwargs
are passed intofunc
. Alternatively a(callable, data_keyword)
tuple wheredata_keyword
is a string indicating the keyword ofcallable
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 return type of
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 asdf
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