BPt.Dataset.pipe#

Dataset.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

objectthe return type of func.

See also

DataFrame.apply: Apply a function along input axis of DataFrame.
DataFrame.applymap: 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. Instead of writing

>>> func(g(h(df), arg1=a), arg2=b, arg3=c)  

You can write

>>> (df.pipe(h)
...    .pipe(g, arg1=a)
...    .pipe(func, arg2=b, arg3=c)
... )  

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 f takes its data as arg2:

>>> (df.pipe(h)
...    .pipe(g, arg1=a)
...    .pipe((func, 'arg2'), arg1=a, arg3=c)
...  )  