A map method to work with many columns

Hi,

I would like to map a function expecting N arguments to a DataFrame of N columns. Today to do that I do:

df = pd.DataFrame({"A": [1, 2, 3, 4], "B": [5, 6, 7, 8]})
def f(x,y):
     return x*y

df[['A', 'B']].apply(lambda x: f(*x), axis=1) 

So the map method for DataFrame would be:

def map(self, f):
    return self.apply(lambda x: f(*x), axis=1)

if I am right. Then df.map(f) would produce a Serie.

Note that with 1 colummn and a 1 argument function map would work as numpy.map does.

Thanks,

Olivier.

ps: I edited this post after the talk I had with Tom, so it must seem strange now 😃

[TomAugspurger]

Thanks for the clarification. Perhaps you could update your original post with your new proposal.

Since map does not exist for DataFrame, you are not going to break anything.

Indeed, this would be preferable. Then we’ll have to decide whether the additional method is worth implementing, & maintaining. Especially since your workaround is relatively straightforward.

About your example, apply would detect that f expect 1 argument and there it would behave as it does now.

In general, we try to avoid magic behavior like this. It often leads to surprising / difficult to debug behavior in unanticipated cases.

[oricou]

I want to apply a 2 arguments function to 2 columns of my DataFrame. As you said apply expect a vector (a column or a row) which means the function given to apply should have one argument. Imagine you have functions with N arguments and you want to map it on all the rows for the N columns you have selected. The best way I have found to do that is the use of a lambda function to call my function (as explained in my first post).

Now thinking about it, it may be more logic to use map instead of apply to do what I want. Map a function expecting N arguments to N columns would run row by row the function with the N values. It would behave the same than map with a 1 argument function applied to a Serie.

Example:

df = pd.DataFrame({'A':[1,2], 'B':[3,4]})
df.apply(lambda x,y: x+2*y, axis=1)   # does not work

# what about having?
df.map(lambda x,y: x+2*y)

Since map does not exist for DataFrame, you are not going to break anything.