In this tutorial, we will look at how to get the variance of one or more columns in a pandas dataframe with the help of some examples.

## How to calculate the variance of pandas column?

You can use the pandas series `var()`

function to get the variance of a single column or the pandas dataframe `var()`

function to get the variance of all numerical columns in the dataframe. The following is the syntax:

# variance of single column df['Col'].var() # variance of all numerical columns in dataframe df.var()

Let’s create a sample dataframe that we will be using throughout this tutorial to demonstrate the usage of the methods and syntax mentioned.

import pandas as pd # create a dataframe df = pd.DataFrame({ 'sepal_length': [5.1, 4.9, 4.7, 4.6, 5.0, 5.4, 4.6, 5.0], 'sepal_width': [3.5, 3.0, 3.2, 3.1, 3.6, 3.9, 3.4, 3.4], 'petal_length': [1.4, 1.4, 1.3, 1.5, 1.4, 1.7, 1.4, 1.5], 'petal_width': [0.2, 0.2, 0.2, 0.2, 0.2, 0.4, 0.3, 0.2], 'sepices': ['setosa']*8 }) # display the dataframe print(df)

Output:

sepal_length sepal_width petal_length petal_width sepices 0 5.1 3.5 1.4 0.2 setosa 1 4.9 3.0 1.4 0.2 setosa 2 4.7 3.2 1.3 0.2 setosa 3 4.6 3.1 1.5 0.2 setosa 4 5.0 3.6 1.4 0.2 setosa 5 5.4 3.9 1.7 0.4 setosa 6 4.6 3.4 1.4 0.3 setosa 7 5.0 3.4 1.5 0.2 setosa

The sample dataframe is taken form a section of the Iris dataset. This sample has petal and sepal dimensions of eight data points of the “Setosa” species.

### Variance of a single column

You can use the pandas series `var()`

function to get the variance of individual columns (which essentially are pandas series). For example, let’s get the variance of the “sepal_length” column in the above dataframe.

# variance of sepal_length column print(df['sepal_length'].var())

Output:

0.07553571428571436

You see that we get the variance of the values in the “sepal_length” column as a scaler value.

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### Variance of more than one columns

For this, first, create a dataframe with the columns that you want to calculate the variance for and then apply the pandas dataframe `var()`

function. For example, let’s get the variance of the columns “sepal_length” and “sepal_width”.

# variance of more than one columns print(df[['sepal_length', 'sepal_width']].var())

Output:

sepal_length 0.075536 sepal_width 0.084107 dtype: float64

We get the result as a pandas series. Here, we first created a subset of the dataframe “df” with only the columns “sepal_length” and “sepal_width” and then applied the `var()`

function.

### Variance of all the columns

To get the variance of all the columns, use the same method as above but this time on the entire dataframe. Let’s use this function on the dataframe “df” created above.

# variance of all the columns print(df.var())

Output:

sepal_length 0.075536 sepal_width 0.084107 petal_length 0.014286 petal_width 0.005536 dtype: float64

You can see that we get the variance of all the numerical columns present in the dataframe.

For more on the pandas series var() function, refer to its documentation.

Variance is a measure of spread in the data but standard deviation, the square root of variance is more generally used (as a measure of spread) since it is in the same units as the data. You can use methods similar to the ones described in this tutorial to find the standard deviation of pandas columns.

With this, we come to the end of this tutorial. The code examples and results presented in this tutorial have been implemented in a Jupyter Notebook with a python (version 3.8.3) kernel having pandas version 1.0.5

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