How to do it...

Let's see how to normalize data in Python:

1. As we said, to normalize data, the preprocessing.normalize() function can be used as follows (we will use the same data as in the previous recipe):

>> data_normalized = preprocessing.normalize(data, norm='l1', axis=0)

1. To display the normalized array, we will use the following code:

>> print(data_normalized)

The following output is returned:

[[ 0.75 -0.17045455  0.47619048  -0.45762712]
 [ 0.    0.45454545 -0.07142857   0.1779661 ]
 [ 0.25  0.375      -0.45238095  -0.36440678]]

This is used a lot to make sure that datasets don't get boosted artificially due to the fundamental nature of their features. 

1. As already mentioned, the normalized array along the columns (features) must return a sum equal to 1. Let's check this for each column:

>> data_norm_abs = np.abs(data_normalized)
>> print(data_norm_abs.sum(axis=0))

In the first line of code, we used the np.abs() function to evaluate the absolute value of each element in the array. In the second row of code, we used the sum() function to calculate the sum of each column (axis=0). The following results are returned:

[1. 1. 1. 1.]

Therefore, the sum of the absolute value of the elements of each column is equal to 1, so the data is normalized.