In NumPy, we can compute the weighted of a given array by two approaches first approaches is with the help of numpy.average() function in which we pass the weight array in the parameter. And the second approach is by the mathematical computation first we divide the weight array sum from weight array then multiply with the given array to compute the sum of that array.
Method 1: Using numpy.average() method
Example 1:
Python
import numpy as np # Original array array = np.arange(5) print(array) weights = np.arange(10, 15) print(weights) # Weighted average of the given array res1 = np.average(array, weights=weights) print(res1) |
Output:
[0 1 2 3 4] [10 11 12 13 14] 2.1666666666666665
Example 2:
Python
import numpy as np # Original array array = np.arange(2, 7) print(array) weights = np.arange(2, 7) print(weights) # Weighted average of the given array res1 = np.average(array, weights=weights) print(res1) |
Output:
[2 3 4 5 6] [2 3 4 5 6] 4.5
Method 2: Using mathematical operation
Example 1:
Python
import numpy as np # Original array array = np.arange(2, 7) print(array) weights = np.arange(2, 7) print(weights) # Weighted average of the given array res2 = (array*(weights/weights.sum())).sum() print(res2) |
Output:
[2 3 4 5 6] [2 3 4 5 6] 4.5
Example 2:
Python
import numpy as np # Original array array = np.arange(5) print(array) weights = np.arange(10, 15) print(weights) # Weighted average of the given array res2 = (array*(weights/weights.sum())).sum() print(res2) |
Output:
[0 1 2 3 4] [10 11 12 13 14] 2.166666666666667
