What's the advantage of preparing a matrix to return in python?

It's the code in the book 'Machine Learning in Action'. source code

And the what is passed to dataSet is a m * 3 array(datingTestSet2.txt which can be found in the superior directory. )

My question is:

What's the advantage of preparing a matrix to return? (conserve memory?)

If I don't prepare the matrix, will it go wrong? (It seems not.)

from numpy import *
def autoNorm(dataSet):
    minVals = dataSet.min(0)
    maxVals = dataSet.max(0)
    ranges = maxVals - minVals
    normDataSet = zeros(shape(dataSet)) # prepare matrix to return(It's my own comment, not in the source code. )
    # Because there is a similar code before it, 
    # I think it should be the same meaning. Or any means else?
    m = dataSet.shape[0]
    normDataSet = dataSet - tile(minVals, (m,1))
    normDataSet = normDataSet/tile(ranges, (m,1))   #element wise divide
    return normDataSet, ranges, minVals