The Numpy average function is a statistical tool that can be used to calculate the mean of an Numpy array. Numpy provides many methods for calculating averages, but this blog post will focus on using numpy.average() .
Table of Contents
Numpy Average Functions
Numpy has quite a few useful statistical functions for finding minimum, maximum, percentile standard deviation and variance, etc. from the given elements in the array.
One of these is called numpy average which calculates the arithmetic mean of all values in an array using np.mean() function.
It’s important to note that this method will take into account any missing data points as well as negative or zero values when calculating the mean value.
Numpy is a library for scientific computing in Python. Syntax is an important aspect of any programming language, and Numpy has its own syntax that can be used to perform mathematical operations on data arrays.
The average() function takes up the last parameter in this list, which specifies what should happen with the result once it’s calculated. With no arguments specified for this parameter, the default setting is returned=False.
This means that if you want to return a value from your code but don’t care about saving it somewhere else first, you would use average().
numpy.average(a, axis=None, weights=None, returned=False)
Example 01: Code
import numpy as np array = np.array([1, 1, 2, 3, 5, 8, 13, 21]).reshape(2,4) print('input\n',array) avg = np.average(array) print('average\n',avg)
Example 02: Code
import numpy as np array = np.array([1, 1, 2, 3, 5, 8, 13, 21]).reshape(2,4) print('input\n',array) avg = np.average(array, axis = 0) print('average\n',avg) avg = np.average(array, axis = 1) print('average\n',avg)
The numpy.average() function is a statistical tool that can be used to calculate the mean of an Numpy array. This blog post has demonstrated how you might use this method, as well as some other more advanced methods for calculating averages such as weighted means and medians.