NumPy: Compute the weighted of a given array
Write a NumPy program to compute the weighted of a given array.
Sample Solution:
Python Code:
# Importing the NumPy library
import numpy as np
# Creating an array 'x' using arange with 5 elements
x = np.arange(5)
# Displaying the original array 'x'
print("\nOriginal array:")
print(x)
# Creating weights from 1 to 5 using arange
weights = np.arange(1, 6)
# Calculating the weighted average of the array 'x' using np.average() and 'weights'
r1 = np.average(x, weights=weights)
# Calculating the weighted average manually
r2 = (x * (weights / weights.sum())).sum()
# Asserting if the results from np.average() and manual calculation are close
assert np.allclose(r1, r2)
# Displaying the calculated weighted average of the array 'x'
print("\nWeighted average of the said array:")
print(r1)
Sample Output:
Original array: [0 1 2 3 4] Weighted average of the said array: 2.6666666666666665
Explanation:
In the above code –
x = np.arange(5): An array x is created using the numpy.arange function to generate the values [0, 1, 2, 3, 4].
weights = np.arange(1, 6): Here numpy.arange generates the values [1, 2, 3, 4, 5], which will be used as the weights for the weighted average calculation.
r1 = np.average(x, weights=weights): The numpy.average function is then used to calculate the weighted average of x using the weights array. The resulting value is assigned to r1.
r2 = (x*(weights/weights.sum())).sum(): This code calculates the weighted average manually using the formula r2 = (x*(weights/weights.sum())).sum().
assert np.allclose(r1, r2): Finally, the code uses the numpy.allclose function to assert that r1 and r2 are equal within a tolerance. It returns true as r1 and r2 are equal.
Python-Numpy Code Editor: