Normalize numpy array columns in python

If I understand correctly, what you want to do is divide by the maximum value in each column. You can do this easily using broadcasting.

Starting with your example array:

import numpy as npx = np.array([[1000,  10,   0.5],              [ 765,   5,  0.35],              [ 800,   7,  0.09]])x_normed = x / x.max(axis=0)print(x_normed)# [[ 1.     1.     1.   ]#  [ 0.765  0.5    0.7  ]#  [ 0.8    0.7    0.18 ]]

x.max(0) takes the maximum over the 0th dimension (i.e. rows). This gives you a vector of size (ncols,) containing the maximum value in each column. You can then divide x by this vector in order to normalize your values such that the maximum value in each column will be scaled to 1.

If x contains negative values you would need to subtract the minimum first:

x_normed = (x - x.min(0)) / x.ptp(0)

Here, x.ptp(0) returns the "peak-to-peak" (i.e. the range, max - min) along axis 0. This normalization also guarantees that the minimum value in each column will be 0.

You can use sklearn.preprocessing:

from sklearn.preprocessing import normalizedata = np.array([    [1000, 10, 0.5],    [765, 5, 0.35],    [800, 7, 0.09], ])data = normalize(data, axis=0, norm='max')print(data)>>[[ 1.     1.     1.   ][ 0.765  0.5    0.7  ][ 0.8    0.7    0.18 ]]