How to normalize the columns of a DataFrame using sklearn.preprocessing.normalize?

Question

is there a way to normalize the columns of a DataFrame using sklearn's normalize? I think that by default it normalizes rows

For example, if I had df:
A     B
1000  10
234   3
500   1.5

I would want to get the following:

A       B
1       1
0.234   0.3
0.5     0.15

Answer 1

Why do you need sklearn ?

Just use pandas:

>>> df / df.max()
       A     B
0  1.000  1.00
1  0.234  0.30
2  0.500  0.15
>>> 

Answer 2

You can using div after get the max

df.div(df.max(),1)
Out[456]: 
       A     B
0  1.000  1.00
1  0.234  0.30
2  0.500  0.15

Answer 3

sklearn defaults to normalize rows with theL2 normalization . Both of these arguments need to be changed for your desired normalization by the maximum value along columns:

from sklearn import preprocessing 

preprocessing.normalize(df, axis=0, norm='max')
#array([[1.   , 1.   ],
#       [0.234, 0.3  ],
#       [0.5  , 0.15 ]])

Answer 4

From the documentation

axis : 0 or 1, optional (1 by default) axis used to normalize the data along. If 1, independently normalize each sample, otherwise (if 0) normalize each feature.

So just change the axis. Having said that, sklearn is an overkill for this task. It can be achieved easily using pandas.