Calculate Variance in Pandas DataFrame

In this article, you are going to learn about how to calculate Variance in Pandas DataFrame

Pandas is a Python library that is widely used to perform data analysis and machine learning tasks. It is open-source and very powerful, fast, and easy to use. Basically, while working with big data we need to analyze, manipulate and update them and the pandas’ library plays a lead role there. Sometimes, we need to calculate the variance in a Pandas DataFrame.

Variance is a statistical term that refers to the measurement of dispersion that calculates the spread of all data points in a data set. It helps us to measure how much the data are separated from each other. Pandas Provide a function named var() to calculate the variance. In this article, we are going to explore this function and see how we can calculate Variance in Pandas DataFrame. Before doing so, let’s create a Pandas DataFrame first that consists of different data columns.

import pandas as pd

student_df = pd.DataFrame({'Age' : [20, 21, 24, 23, 19],
                           'CT_Marks1' : [71, 83, 64, 61, 83],
                           'CT_Marks2' : [81, 91, 74, 81, 73],
                          })

print(student_df)

# Output:
#    Age  CT_Marks1  CT_Marks2
# 0   20         71         81
# 1   21         83         91
# 2   24         64         74
# 3   23         61         81
# 4   19         83         73

Here, you can see that we have created a simple Pandas DataFrame that represents the student’s age, and CT marks. We will perform the variance based on this DataFrame’s information.

Calculate Variance: Single Column

We can calculate the Variance of a single column in Pandas DataFrame. The var() function will return the result and we need to store it in a variable. Let’s calculate the variance of the Age column in the below section:

import pandas as pd

student_df = pd.DataFrame({'Age' : [20, 21, 24, 23, 19],
                           'CT_Marks1' : [71, 83, 64, 61, 83],
                           'CT_Marks2' : [81, 91, 74, 81, 73],
                          })

age_var = student_df['Age'].var()

print('Age Variance: ',age_var)

# Output:
# Age Variance:  4.3

Here, you can see that we have calculated the variance of a single column which is Age. The result of the Age column variance is 4.3.

Calculate Variance: Multiple Columns

We can also calculate the Variance in multiple columns. The process of performing this action is almost the same. All we need to do is to add the multiple columns’ names inside a list and separate them with a comma. See the below code example:

import pandas as pd

student_df = pd.DataFrame({'Age' : [20, 21, 24, 23, 19],
                           'CT_Marks1' : [71, 83, 64, 61, 83],
                           'CT_Marks2' : [81, 91, 74, 81, 73],
                          })

multiple_cols_var = student_df[['CT_Marks1', 'CT_Marks2']].var()

print(multiple_cols_var)

# Output:
# CT_Marks1    106.8
# CT_Marks2     52.0
# dtype: float64

Here, you can see that we have calculated the variance for CT_Marks1 and CT_Marks2 and you can see the result in the output as 106.8 and 52.0. Here, we have shown two column variance but you can add as many columns as you want by following this process.

Calculate Variance: Whole DataFrame

We can also calculate the variance of a Pandas DataFrame as a whole. That means all the column’s variance. To perform this action see the below code example:

import pandas as pd

student_df = pd.DataFrame({'Age' : [20, 21, 24, 23, 19],
                           'CT_Marks1' : [71, 83, 64, 61, 83],
                           'CT_Marks2' : [81, 91, 74, 81, 73],
                          })

df_var = student_df.var()

print(df_var)

# Output:
# Age            4.3
# CT_Marks1    106.8
# CT_Marks2     52.0
# dtype: float64

This is all about calculating the Variance in Pandas DataFrame. You may follow these approaches to calculate the Variance in Pandas DataFrame.

Original article source at: https://codesource.io/

#pandas #dataframe #calculate

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If you want the full course, click here to sign up.

It’s now time for some practice problems! See below for details on how to proceed.

Course Repository & Practice Problems

All of the code for this course’s practice problems can be found in this GitHub repository.

There are two options that you can use to complete the practice problems:

Open them in your browser with a platform called Binder using this link (recommended)
Download the repository to your local computer and open them in a Jupyter Notebook using Anaconda (a bit more tedious)

Note that binder can take up to a minute to load the repository, so please be patient.

Within that repository, there is a folder called starter-files and a folder called finished-files. You should open the appropriate practice problems within the starter-files folder and only consult the corresponding file in the finished-files folder if you get stuck.

The repository is public, which means that you can suggest changes using a pull request later in this course if you’d like.

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Table of Contents

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