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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)?
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Use apply() to Apply Functions to Columns in Pandas The apply() method allows to apply a function for a whole DataFrame, either across columns or rows. We set the parameter axis as 0 for rows and 1 for columns. The new appended e column is the sum of data in column a and b .
Just select the cell F2, place the cursor on the bottom right corner, hold and drag the Fill handle to apply the formula to the entire column in all adjacent cells.
The apply() method allows you to apply a function along one of the axis of the DataFrame, default 0, which is the index (row) axis.
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 If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
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