Numpy:zero mean data and standardization

Question

I saw in tutorial (there were no further explanation) that we can process data to zero mean with x -= np.mean(x, axis=0) and normalize data with x /= np.std(x

Answer 1

Follow the comments in the code below

import numpy as np

# create x
x = np.asarray([1,2,3,4], dtype=np.float64)

np.mean(x) # calculates the mean of the array x
x-np.mean(x) # this is euivalent to subtracting the mean of x from each value in x
x-=np.mean(x) # the -= means can be read as x = x- np.mean(x)

np.std(x) # this calcualtes the standard deviation of the array
x/=np.std(x) # the /= means can be read as x = x/np.std(x)