Does ddf.pipe() make sense?

Often times, i end up writing a function that takes in a dask.dataframe. pandas implements pd.pipe(func) that i find pretty convenient.

This is a pretty easy to implement but i think pipe may be pretty confusing in dask.dataframe world especially if someone tries to do columnar reductions. Thoughts on if it makes sense to implement?

[mrocklin]

I think that dask.dataframe should almost always follow pandas semantics

[shoyer]

I would literally copy the implementation of pipe from pandas, e.g.,

    def pipe(self, func, *args, **kwargs):
        if isinstance(func, tuple):
            func, target = func
            if target in kwargs:
                raise ValueError('%s is both the pipe target and a keyword '
                                 'argument' % target)
            kwargs[target] = self
            return func(*args, **kwargs)
        else:
            return func(self, *args, **kwargs)

df.pipe(func) should not separately map func over partitions no more than calling a func(df) does.