Normalise 2D Numpy Array: Zero Mean Unit Variance

Question

I have a 2D Numpy array, in which I want to normalise each column to zero mean and unit variance. Since I'm primarily used to C++, the method in which I'm doing is to use loops to iterate over elements in a column and do the necessary operations, followed by repeating this for all columns. I wanted to know about a pythonic way to do so.

Let class_input_data be my 2D array. I can get the column mean as:

column_mean = numpy.sum(class_input_data, axis = 0)/class_input_data.shape[0]

I then subtract the mean from all columns by:

class_input_data = class_input_data - column_mean

By now, the data should be zero mean. However, the value of:

numpy.sum(class_input_data, axis = 0)

isn't equal to 0, implying that I have done something wrong in my normalisation. By isn't equal to 0, I don't mean very small numbers which can be attributed to floating point inaccuracies.

Answer 1

Something like:

import numpy as np

eg_array = 5 + (np.random.randn(10, 10) * 2)
normed = (eg_array - eg_array.mean(axis=0)) / eg_array.std(axis=0)

normed.mean(axis=0)
Out[14]: 
array([  1.16573418e-16,  -7.77156117e-17,  -1.77635684e-16,
         9.43689571e-17,  -2.22044605e-17,  -6.09234885e-16,
        -2.22044605e-16,  -4.44089210e-17,  -7.10542736e-16,
         4.21884749e-16])

normed.std(axis=0)
Out[15]: array([ 1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.])