The Numpy library in Python comes with a number of useful built-in functions for computing common descriptive statistics like mean, median, standard deviation, variance, etc. In this tutorial, we will look at how to get the variance of a Numpy array with the help of some examples.
How do you get the variance of an array in Numpy?
You can use the Numpy var() function to get the variance of the values in a Numpy array. Pass the array as an argument.
The following is the syntax –
# variance of all values in array numpy.var(ar)
It returns the variance taking into account all the values in the array. For multi-dimensional arrays, you can specify the axis along which you want to compute the variance (see the examples below).
Examples
Let’s now look at some examples of using the above syntax on single and multi-dimensional arrays.
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Example 1 – Variance of a one-dimensional Numpy array
Let’s first create a one-dimensional Numpy array.
import numpy as np # create numpy array ar = np.array([1, 2, 3, 4, 5]) # display the array print(ar)
Output:
[1 2 3 4 5]
Here, we used the numpy.array() function to create a one-dimensional array containing some numeric values.
Let’s now get the variance of all the values in the above array.
# variance of array print(np.var(ar))
Output:
2.0
We get the variance as 2.
Example 2 – Variance of multi-dimensional Numpy array
First, let’s create a 2-D Numpy array.
# create 2-D numpy array
ar = np.array([[1, 2, 3],
[2, 1, 1]])
# display the array
print(ar)
Output:
[[1 2 3] [2 1 1]]
Here, we used the numpy.array() function to create a Numpy array with two rows and three columns.
If you use the Numpy var() function on an array without specifying the axis, it will return the variance taking into account all the values inside the array.
# variance of array print(np.var(ar))
Output:
0.5555555555555556
We get the variance of all the values inside the 2-D array.
Use the numpy.var() function with axis=1 to get the variance for each row in the array.
# variance of each row in array print(np.var(ar, axis=1))
Output:
[0.66666667 0.22222222]
We get the variance of each row in the above 2-D array. The variance of the values in the first row (1, 2, 3) is 0.66 and the variance of the values in the second row (2, 1, 1) is 0.22.
Use the numpy.var() function with axis=0 to get the variance of each column in the array.
# variance of each column in array print(np.var(ar, axis=0))
Output:
[0.25 0.25 1. ]
We get the variance of each column in the above 2-D array. The variance of the values – in the first column (1, 2) is 0.25, in the second column (2, 1) is 0.25, and in the third column (3, 1) is 1.
Summary
In this tutorial, we looked at how to use the numpy.var() function to get the variance of values in an array. The following are the key takeaways from this tutorial.
- Use the
numpy.var()function without any arguments to get the variance of all the values inside the array. - For multi-dimensional arrays, use the
axisparameter to specify the axis along which to compute the variance. For example, for a 2-D array –- Pass
axis=1to get the variance of each row. - Pass
axis=0to get the variance of each column.
- Pass
You might also be interested in –
- Numpy – Get Max Value in Array
- Get the Mean of NumPy Array – (With Examples)
- Python – Find Average of values in a List
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