How to obtain the variance over a specified axis in pandas

Overview

The var() function in pandas obtains the variance of the values of a specified axis of a given DataFrame.

Mathematically, variance is defined as the measure of the spread between the values of a data set.

It takes the formula below:

S² =$\frac{Σ(xi -x)}{n-1}$

Where:

• S² = variance
• x_i = value of the dataset
• x = the number of values in the dataset

In another context, the variance of a dataset is given as √standard deviation. That is, the square root of the standard deviation.

Syntax

The var() function takes the following syntax:

Parameter values

The var() function takes the following optional parameter values:

• axis: This represents the name of the row (designated as 0 or 'index') or the column (designated as 1 or columns) axis.
• skipna: This takes a boolean value indicating whether NA or null values are to be excluded.
• ddof: This takes an int that represents the delta degrees of freedom.
• numeric_only: This takes a boolean value indicating whether to include only float, int, or boolean columns.
• **kwargs: This is an additional keyword argument that can be passed to the function.

Return value

The var() function returns a DataFrame object holding the results.

Example

Explanation

• Line 4: We import the pandas library.
• Lines 7–10: We create a DataFrame, df.
• Line 12: We print df.
• Line 15: Using the var() function, we obtain the variance of the values that run downwards across the rows (axis 0). We print the result to the console.
• Line 18: Using the var() function, we obtain the variance of values that run horizontally across columns (axis 1). We print the result to the console.