Pandas DataFrame: apply function to all columns

Question

I can use .map(func) on any column in a df, like:

df=DataFrame({'a':[1,2,3,4,5,6],'b':[2,3,4,5,6,7]})  df['a']=df['a'].map(lambda x: x > 1) 

I could also:

df['a'],df['b']=df['a'].map(lambda x: x > 1),df['b'].map(lambda x: x > 1) 

Is there a more pythonic way to apply a function to all columns or the entire frame (without a loop)?

Answer 1

If I understand you right, you're looking for the applymap method.

>>> print df    A  B  C 0 -1  0  0 1 -4  3 -1 2 -1  0  2 3  0  3  2 4  1 -1  0 >>> print df.applymap(lambda x: x>1)        A      B      C 0  False  False  False 1  False   True  False 2  False  False   True 3  False   True   True 4  False  False  False 

Answer 2

From 0.20.0 onwards, you can use transform

In [578]: df.transform(lambda x: x > 1) Out[578]:        A      B      C 0  False  False  False 1  False   True  False 2  False  False   True 3  False   True   True 4  False  False  False  In [579]: df Out[579]:    A  B  C 0 -1  0  0 1 -4  3 -1 2 -1  0  2 3  0  3  2 4  1 -1  0 

And, for this simplistic case, why not just use df > 1 ?

In [582]: df > 1 Out[582]:        A      B      C 0  False  False  False 1  False   True  False 2  False  False   True 3  False   True   True 4  False  False  False