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Python variance() Example | Python Statistics variance() Function

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Python variance() Example | Python Statistics variance() Function is today’s topic. In this blog, we have already seen the Python Statistics mean(), median(), and mode() function. Python statistics module provides potent tools, which can be used to compute anything related to Statistics. The variance() is one such function. The variance() function helps to calculate a variance from the sample of data (sample is a subset of populated data).

Python variance() Example

There are mainly two ways for defining the variance. You have the variance n that you can use when you have the full set, and a variance n-1 that you use when you have the sample.

In the pure statistics, the variance is the squared deviation of the variable from its mean. It measures the spread of the random data in the set from its mean or median value.

A low value for variance indicates that the data are clustered together and are not spread apart widely, whereas the high value would suggest that the data in the given set are much more spread apart from an average value.

A variance is an essential tool in the sciences, where statistical analysis of data is common. It is the square of the standard deviation of the given data-set and is also known as the second central moment of a distribution.

The following formula calculates variance.

 

Python variance() Example

#Steps to Finding Variance

So let’s break this down into some more understandable steps.

  1. Find a mean of the set of data.
  2. Subtract each number from a mean.
  3. Square the result.
  4. Add the results together.
  5. Divide a result by the total number of numbers in the data set.

#Syntax of Python variance() Function

The syntax of the variance() function in Python is following.

statistics.variance(data, xbar=None)

If the data has fewer then two values StatisticsError raises.

#data:

Where data is an array of valid Python numbers, including Decimal and Fraction values. This parameter is required.

#xbar:

Where xbar is the mean of data. This parameter is optional. If this parameter is not given(none), then the mean is automatically calculated.

The variance() function is only available and compatible with Python 3.x.

See the following example.

# app.py

import statistics

dataset = [21, 19, 11, 21, 19, 46, 29]
output = statistics.variance(dataset) 

print(output)

See the following output.

➜  pyt python3 app.py
124.23809523809524
➜  pyt

#Python variance() with both Arguments

Calculate the mean first and pass it as an argument to the variance() method. See the following code.

# app.py

import statistics

dataset = [21, 19, 11, 21, 19, 46, 29]
meanValue = statistics.mean(dataset)
output = statistics.variance(dataset, meanValue) 

print(output)

See the following output.

➜  pyt python3 app.py
124.23809523809524
➜  pyt

#Python variance() Function Example of Fraction

Use Fraction array as an argument.

# app.py

from decimal import Decimal as D
from statistics import variance
 
print(variance([D("21.11"), D("19.21"), D("46.21"), D("18.21"), D("29.21"), D("21.06")]))

See the following output.

➜  pyt python3 app.py
114.73775
➜  pyt

#Compute the Variance in Python using Numpy

In this example, we use the numpy module.

Variance measures how far the set of (random) numbers are spread out from their average value.

In Python language, we can calculate a variance using the numpy module.

With the numpy module, the var() function calculates a variance for the given data set. See the following example.

# app.py

import numpy as np

dataset= [21, 11, 19, 18, 29, 46, 20]

variance= np.var(dataset)

print(variance)

See the output.

➜  pyt python3 app.py
108.81632653061224
➜  pyt

So let’s break down the above code.

We import the numpy module as np. This means that we reference the numpy module with the keyword, np.

We then create the variable, dataset, which is equal to, [21, 11, 19, 18, 29, 46, 20]

We then get a variance of the dataset by using an np.var() function. So instead of the np.var() function, we specify the variable, which is the dataset.

We then print out the variance, which in this case is 108.81632653061224.

So let’s go over the formula for a variance to see if this value calculated is correct.

The formula for variance is, variance= (x-mu)2/n

And this is how you can compute the variance of a data set in Python using the numpy module.

Finally, Python variance() Example | Python Statistics variance() Function article is over.

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