Import data as a Matrix and normalize its values by finding the mean of each column

normalize.py

#!/usr/bin/env python

import numpy as np

# Know the delimiter used(Useful to differentiate between a space and tab).
# f = open('test.txt')
# lines = f.readlines()
# print lines

# Uncomment below line to import data from a file.
# x = np.loadtxt('test.txt', delimiter="\t")

# print x
# print x.shape
# print x.shape[0]


# Test Matrix. Comment variable 'x' below if you are importing data from a file.
x = np.array([[1,2,3,4],
              [3,4,2,1],
              [3,4,6,1]])

# print x
# print "========="
# mean = np.mean(x[:,0])

# print mean

newlist = []
nl = []
# first row
for a in range(0, x.shape[1]):
    mean = np.mean(x[:, a])
    for i in range(0, x.shape[0]):
        val = x[i, a] - mean
        # print val
        newlist.append(val)
        # print x[i, a]
    # print newlist
    # print "------"
    mean = np.mean(x[:, a])
    nl.append(newlist)
    newlist = []

print x
print " ------- Normalized values ---------"
# print nl
newarray = np.asarray(nl)

# Finally reverse the Matrix
print newarray.transpose()