How to normalize a 2-dimensional numpy array in python less verbose?

Scikit-learn offers a function normalize() that lets you apply various normalizations. The "make it sum to 1" is called L1-norm. Therefore:

from sklearn.preprocessing import normalize

matrix = numpy.arange(0,27,3).reshape(3,3).astype(numpy.float64)
# array([[  0.,   3.,   6.],
#        [  9.,  12.,  15.],
#        [ 18.,  21.,  24.]])

normed_matrix = normalize(matrix, axis=1, norm='l1')
# [[ 0.          0.33333333  0.66666667]
#  [ 0.25        0.33333333  0.41666667]
#  [ 0.28571429  0.33333333  0.38095238]]

Now your rows will sum to 1.

I think this should work,

a = numpy.arange(0,27.,3).reshape(3,3)

a /=  a.sum(axis=1)[:,numpy.newaxis]

Broadcasting is really good for this:

row_sums = a.sum(axis=1)
new_matrix = a / row_sums[:, numpy.newaxis]

row_sums[:, numpy.newaxis] reshapes row_sums from being (3,) to being (3, 1). When you do a / b, a and b are broadcast against each other.

You can learn more about broadcasting here or even better here.