Average of NumPy Array - Examples

NumPy Array Average

Using NumPy, you can calculate the average of elements for an entire NumPy array, along a specific axis, or as a weighted average of elements.

To find the average of a NumPy array, you can use the numpy.average() statistical function.

Syntax

The syntax of the numpy.average() function is:

numpy.average(a, axis=None, weights=None, returned=False)

We will explain the parameters in this syntax using examples below.

Examples

1. Average of 2D NumPy Array

In this example, we calculate the average of a 2D NumPy array using the numpy.average() function.

Python Program

import numpy as np

arr = np.array([[4, 5], [3, 7]])
avg = np.average(arr)

print('array\n', arr)
print('average\n', avg)

Output

array
 [[4 5]
  [3 7]]
average
 4.75

Explanation

  1. The input array is [[4, 5], [3, 7]].
  2. All elements are summed: 4 + 5 + 3 + 7 = 19.
  3. The average is calculated as 19 / 4 = 4.75.
  4. The result, 4.75, is printed as the output.

2. Average of NumPy Array Along an Axis

You can calculate the average along a specific axis using the axis parameter.

Python Program

import numpy as np

arr = np.array([[4, 5], [3, 7]])
avg = np.average(arr, axis=1)

print('array\n', arr)
print('average along axis=1\n', avg)

Output

array
 [[4 5]
  [3 7]]
average along axis=1
 [4.5 5. ]

Explanation

  1. The input array is [[4, 5], [3, 7]].
  2. The averages along axis=1 (rows) are calculated:
    • Row 1: (4 + 5) / 2 = 4.5
    • Row 2: (3 + 7) / 2 = 5.0
  3. The result, [4.5, 5.0], is printed as the output.

3. Weighted Average of NumPy Array

You can specify weights to calculate a weighted average of elements in the array. The weights are multiplied with the elements, and their sum is divided by the total of the weights.

Python Program

import numpy as np

arr = np.array([[4, 5], [3, 7]])
avg = np.average(arr, axis=1, weights=[0.2, 0.8])

print('array\n', arr)
print('average along axis=1 with weights\n', avg)

Output

array
 [[4 5]
  [3 7]]
average along axis=1 with weights
 [4.8 6.2]

Explanation

  1. The input array is [[4, 5], [3, 7]].
  2. Weights are [0.2, 0.8].
  3. Weighted averages are calculated for each row:
    • Row 1: (4*0.2 + 5*0.8) = 4.8
    • Row 2: (3*0.2 + 7*0.8) = 6.2
  4. The result, [4.8, 6.2], is printed as the output.

4. Returned Parameter in NumPy Average

You can set returned=True to return a tuple of the average and the sum of weights.

Python Program

import numpy as np

arr = np.array([4, 5, 6, 7])
avg, sum_weights = np.average(arr, weights=[1, 2, 3, 4], returned=True)

print('array\n', arr)
print('average\n', avg)
print('sum of weights\n', sum_weights)

Output

array
 [4 5 6 7]
average
 6.1
sum of weights
 10

Explanation

  1. The input array is [4, 5, 6, 7], and weights are [1, 2, 3, 4].
  2. The weighted sum is (4*1 + 5*2 + 6*3 + 7*4) = 60.
  3. The total sum of weights is 1 + 2 + 3 + 4 = 10.
  4. The average is 60 / 10 = 6.0, and the sum of weights is 10.

Summary

In this NumPy Tutorial, we learned how to calculate the average of NumPy array elements using numpy.average(), along an axis, with weights, and using the returned parameter. These examples demonstrate the flexibility and utility of the numpy.average() function.

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