根据列值的条件应用条件 function

Question

I have a data-frame where I would like to apply a simple function to every column except the first one. Take below as an example - although in reality my dataframe comprises hundreds of columns:

vals = [(0, 12, 0),
     (33, 0, 11),
     (44, 16, 21),
     (0, 32, 1),
     (66, 33, 27),
     (77, 0, 0)
     ]

df = pd.DataFrame(vals, columns=list('ABC'))

I would like to find a way I can instigate a rule whereby each value greater than 0 is replaced with a 1. Crucially, I do not want to apply this rule to the first column, which should remain as it is.

The closest I have got is a lambda function, which isn't working at all:

df = df.apply(lambda x: 1 if x > 0 else 0 if x.name != 'A' else x)

Answer 1

When using the apply method, the applied function will either receive all the entire column (by default and if axis=0 ), or it will receive the entire row ( axis=1 ). In your case, the lambda function is an element-wise function, this is why you need to use the applymap method.

df[['B', 'C']] = df[['B', 'C']].applymap(lambda x: 1 if x > 0 else 0)

Answer 2

You can do it this way:

>>> cols = ['B','C']
>>> df[cols] = df[cols].gt(0).astype(int)
>>> df
    A  B  C
0   0  1  0
1  33  0  1
2  44  1  1
3   0  1  1
4  66  1  1
5  77  0  0

You can change cols for something like df.columns[1:] for a more general case.