Allow DataFrame.apply() to accept dict of funcs

[mattayes]

When using DataFrame.apply(), it would be helpful to be able to apply different functions to different columns. There's a nice workaround on StackOverflow:

import numpy as np
import pandas as pd

def multi_func(functions):
    def f(col):
        return functions[col.name](col)
    return f

df = pd.DataFrame(np.random.random((10, 2)), columns=['A', 'B'])
results = df.apply(multi_func({'A': np.mean, 'B': np.sum}))

results
A    0.401456
B    6.845529
dtype: float64

My guess is that the changes would be made in pandas.core.frame.DataFrame, potentially adding a new _apply_X method. Any thoughts/advice?

[jreback]

This is just a part of the API that .aggreate should do. see the original issue here. This should be consistent with the .groupby and .rolling, .aggregate methods. Most of the machinery is already in place to do this.

So I'll leave this open even those its a duplicate as it shows another usecase.