Mastering NumPy Array Mean Calculation

Introduction

NumPy is a Python package for scientific computing that provides a high-performance array object, which is the fundamental building block for mathematical operations. The mean can easily be calculated by adding all the items of an array and dividing them by the total number of array elements. The numpy.mean() function in the NumPy library is used to compute the arithmetic mean across the specified axis of a numpy array. By default, the average is calculated over the flattened array unless the user specifies an axis.

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Import the NumPy library

The first step is to import the NumPy library.

import numpy as np

Create a one-dimensional array

Create a one-dimensional array x with values [80, 23, 17, 1, 39].

x = np.array([80, 23, 17, 1, 39])

Calculate the mean of the array

Use the numpy.mean() function to calculate the mean of the one-dimensional x array.

array_mean = np.mean(x)
print("The mean of the input array is: ", array_mean)

Create a two-dimensional array

Create a two-dimensional array p with values [[14, 19, 12, 34, 43], [16, 8, 28, 8, 20], [25, 5, 55, 1, 2]].

p = np.array([[14, 19, 12, 34, 43], [16, 8, 28, 8, 20], [25, 5, 55, 1, 2]])

Calculate the mean of the flattened array

Use the numpy.mean() function to calculate the mean of the flattened p array.

mean_flattened = np.mean(p)
print("The mean of the array when axis = None : ", mean_flattened)

Calculate the mean along axis 0

Use the numpy.mean() function to calculate the mean of the p array along the axis 0.

mean_axis_0 = np.mean(p, axis = 0)
print("The mean of the array when axis = 0 : ", mean_axis_0)

Calculate the mean along axis 1

Use the numpy.mean() function to calculate the mean of the p array along the axis 1.

mean_axis_1 = np.mean(p, axis = 1)
print("The mean of the array when axis = 1 : ", mean_axis_1)

Out parameter

Use the numpy.mean() function with the out parameter to place the result in an alternative array.

out_arr = np.arange(3)
print("out_arr : ", out_arr)
print("Mean of arr, axis = 1: ", np.mean(p, axis = 1, out = out_arr))

Summary

In this tutorial, we covered the numpy.mean() function from the NumPy library. We explained what mean is, the syntax of the mean() function, and its parameters. We also provided step-by-step examples of using this function on both one-dimensional and two-dimensional arrays.

🚀 Practice Now: NumPy Array Mean Calculation

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