In this article, we will see the program for creating an array of elements in which every element is the average of every consecutive subarrays of size k of a given numpy array of size n such that k is a factor of n i.e. (n%k==0). This task can be done by using numpy.mean() and numpy.reshape() functions together.
Syntax: numpy.mean(arr, axis = None)
Return: Arithmetic mean of the array (a scalar value if axis is none) or array with mean values along specified axis.
Syntax: numpy_array.reshape(shape)
Return: It returns numpy.ndarray
Example :
Arr = [1,2,3,4,5,6
7,8,9,10,11
12,13,14,15,16]
and K = 2 then
Output is [ 1.5, 3.5, 5.5, 7.5,
9.5, 11.5, 13.5, 15.5].
Here, subarray of size k and there average are calculated as :
[1 2] avg = ( 1 + 2 ) / 2 = 1.5
[3 4] avg = ( 3 + 4 ) / 2 = 3.5
[5 6] avg = ( 5 + 6 ) / 2 = 5.5
[7 8] avg = ( 7 + 8 ) / 2 = 7.5
[9 10] avg = ( 9 + 10 ) / 2 = 9.5
[11 12] avg = ( 11 + 12 ) / 2 = 11.5
[13 14] avg = ( 13 + 14 ) / 2 = 13.5
[15 16] avg = ( 15 + 16 ) / 2 = 15.5
Below is the implementation:
Python3
# importing library import numpy # create numpy array arr = numpy.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]) # view array print("Given Array:\n", arr) # declare k k = 2 # find the mean output = numpy.mean(arr.reshape(-1, k), axis=1) # view output print("Output Array:\n", output) |
Output:
Given Array: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16] Output Array: [ 1.5 3.5 5.5 7.5 9.5 11.5 13.5 15.5]
