CODEWITHSUNDEEP
pythonpandasscikit-learnnormalize
Juan
Juan

Reputation: 133

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

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

Upvotes: 2

Views: 3311

Answers (4)

ALollz
ALollz

Reputation: 59519

sklearn defaults to normalize rows with the L2 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 ]])

Upvotes: 1

razdi
razdi

Reputation: 1440

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.

Upvotes: 0

BENY
BENY

Reputation: 323226

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

Upvotes: 2

U13-Forward
U13-Forward

Reputation: 71560

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
>>>

Upvotes: 3

Related Questions