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Normalization means converting the given data to another scale. We rescale the data so that it is between two values. Most of the time, the data is rescaled between 0 and 1. We rescale data for different purposes. For example, machine learning algorithms perform better when the data set has smaller values.

Suppose we have a list {1,2,3}, after normalizing to scale of 0 and 1, the list will be {0, 0.5, 1}. We can also normalize our data to some other scale. Suppose between 2 and 6. So, if we have the list {1,2,3} after normalization, it will be {2,4,6}.

Normalization formula

Understand how normalization works. We will look at its formula. We subtract the minimum value from each number and then divide it by the range ie: max-min. So, in the output, we get the normalized value of that particular number.

There are two methods we can use to normalize a list. We can use the inbuilt function which sklearnis available in the preprocessing module of the package. Or we can formulate the logic for it with the same formula as discussed above.

Normalize a list of numbers using the function sklearnin PythonMinMaxScaler

sklearnpreprocessingA built-in method called is available in the module of the package MinMaxScaler(). We will create a one-dimensional NumPyarray and pass it to the function. We have to install the NumPyand sklearnpackages to use this function.

Sample code:

# python 3.x
import numpy as np
from sklearn import preprocessing

list = np.array([6, 1, 0, 2, 7, 3, 8, 1, 5]).reshape(-1, 1)
print("Original List:", list)
scaler = preprocessing.MinMaxScaler()
normalizedlist = scaler.fit_transform(list)
print("Normalized List:", normalizedlist)

Output:

Original List: [[6]
 [1]
 [0]
 [2]
 [7]
 [3]
 [8]
 [1]
 [5]]
Normalized List: [[0.75 ]
 [0.125]
 [0.   ]
 [0.25 ]
 [0.875]
 [0.375]
 [1.   ]
 [0.125]
 [0.625]]

Now let's say we want to specify the range of our normalization. To do this, we'll MinMaxScaler()specify ours in range. By default, if we don't specify a range, it will be 0 and 1. But we can define our scaling range. In this example, we specify the range 0 and 3.

Sample code:

# python 3.x
import numpy as np
from sklearn import preprocessing

list = np.array([6, 1, 0, 2, 7, 3, 8, 1, 5]).reshape(-1, 1)
print("Original List:", list)
scaler = preprocessing.MinMaxScaler(feature_range=(0, 3))
normalizedlist = scaler.fit_transform(list)
print("Normalized List:", normalizedlist)

Output:

Original List: [[6]
 [1]
 [0]
 [2]
 [7]
 [3]
 [8]
 [1]
 [5]]
Normalized List: [[2.25 ]
 [0.375]
 [0.   ]
 [0.75 ]
 [2.625]
 [1.125]
 [3.   ]
 [0.375]
 [1.875]]

Manually normalizing a list of numbers in Python

We can also normalize a list of numbers manually by writing a complete code for it, which uses the same formula as the normalization discussed above.

Sample code:

list = [6, 1, 0, 2, 7, 3, 8, 1, 5]
print("Original List:", list)
xmin = min(list)
xmax = max(list)
for i, x in enumerate(list):
    list[i] = (x - xmin) / (xmax - xmin)
print("Normalized List:", list)

Output:

Original List: [6, 1, 0, 2, 7, 3, 8, 1, 5]
Normalized List: [0.75, 0.125, 0.0, 0.25, 0.875, 0.375, 1.0, 0.125, 0.625]