This content originally appeared on DEV Community and was authored by Labby
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.
VM Tips
After the VM startup is done, click the top left corner to switch to the Notebook tab to access Jupyter Notebook for practice.
Sometimes, you may need to wait a few seconds for Jupyter Notebook to finish loading. The validation of operations cannot be automated because of limitations in Jupyter Notebook.
If you face issues during learning, feel free to ask Labby. Provide feedback after the session, and we will promptly resolve the problem for you.
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
Want to Learn More?
- 🌳 Learn the latest Python Skill Trees
- đź“– Read More Python Tutorials
- đź’¬ Join our Discord or tweet us @WeAreLabEx
This content originally appeared on DEV Community and was authored by Labby
Labby | Sciencx (2024-07-20T03:24:26+00:00) Mastering NumPy Array Mean Calculation. Retrieved from https://www.scien.cx/2024/07/20/mastering-numpy-array-mean-calculation/
Please log in to upload a file.
There are no updates yet.
Click the Upload button above to add an update.