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)?
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
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
map instead of applymapapplymap has been deprecated.
In [101]: df.map(lambda x: x > 1)
Out[101]:
A B C
0 False False False
1 False True False
2 False False True
3 False True True
4 False False False
In [102]: df > 1
Out[102]:
A B C
0 False False False
1 False True False
2 False False True
3 False True True
4 False False False
Also, you could use apply or transform.
If you don't need additional parameters, they will seem to be basically the same, except for using the comparison operator which is the recommended choice in OP's case.
Read the documentation for more details
