Normalization of 1D-Array
Suppose, we have an array = [1,2,3] and to normalize it in range [0,1] means that it will convert array [1,2,3] to [0, 0.5, 1] as 1, 2 and 3 are equidistant.
Array [1,2,4] -> [0, 0.3, 1]
This can also be done in a Range i.e. instead of [0,1], we will use [3,7].
Now,
Array [1,2,3] -> [3,5,7]
and
Array [1,2,4] -> [3,4.3,7]
Let’s see examples with code
Example 1:
Python3
# import moduleimport numpy as np# explicit function to normalize arraydef normalize(arr, t_min, t_max): norm_arr = [] diff = t_max - t_min diff_arr = max(arr) - min(arr) for i in arr: temp = (((i - min(arr))*diff)/diff_arr) + t_min norm_arr.append(temp) return norm_arr# gives range starting from 1 and ending at 3 array_1d = np.arange(1,4) range_to_normalize = (0,1)normalized_array_1d = normalize(array_1d, range_to_normalize[0], range_to_normalize[1])# display original and normalized arrayprint("Original Array = ",array_1d)print("Normalized Array = ",normalized_array_1d) |
Output:
Example 2:
Now, Lets input array is [1,2,4,8,10,15] and range is again [0,1]
Python3
# import moduleimport numpy as np# explicit function to normalize arraydef normalize(arr, t_min, t_max): norm_arr = [] diff = t_max - t_min diff_arr = max(arr) - min(arr) for i in arr: temp = (((i - min(arr))*diff)/diff_arr) + t_min norm_arr.append(temp) return norm_arr# assign array and rangearray_1d = [1, 2, 4, 8, 10, 15]range_to_normalize = (0, 1)normalized_array_1d = normalize( array_1d, range_to_normalize[0], range_to_normalize[1])# display original and normalized arrayprint("Original Array = ", array_1d)print("Normalized Array = ", normalized_array_1d) |
Output:
How to normalize an array in NumPy in Python?
In this article, we are going to discuss how to normalize 1D and 2D arrays in Python using NumPy. Normalization refers to scaling values of an array to the desired range.