Generate Summary Statistics Using the pandas Python Package

30 minutes
  • 3 Learning Objectives

About this Hands-on Lab

In this lab, we will load a CSV file into a pandas DataFrame. Once loaded, we will use the `mean()` function to determine the average age of men versus women workers from the provided dataset.

Basic Python programming skills will be required for this lab. If you need a refresher, check out the following course:
– [Certified Associate in Python Programming Certification](

Learning Objectives

Successfully complete this lab by achieving the following learning objectives:

Load the CSV File

Load the provided CSV file into a pandas DataFrame object.

Confirm the Data

Confirm the data was loaded by viewing general information about the data.

Find the Average Age of Male vs. Female Workers

Using the mean() function find the average age and group by the workers’ sex.

Additional Resources

The Scenario

You are working as a database admin and overhear some coworkers arguing in the breakroom. Needing a coffee refill, you venture closer and learn there is a disagreement over the average age of men versus women in the workforce.

You recall a friend in web development previously shared a copy of the US census data with you. This data — combined with the pandas skills you acquired from the ACG courses you've been watching — can resolve this breakroom bickering. You quickly return to your workstation to complete the following tasks:

  • Load the CSV file.
  • Confirm the data.
  • Find the average age of men versus women workers.

Work quickly and bring your findings back to the breakroom, before things get out of hand!

Log in to the server over SSH using the credentials provided.

The data.csv file is already available in your workstation (the lab instance), but if you'd like to follow along on another machine, you can download it from here.

The data was sourced from the Center for Machine Learning and Intelligent Systems. Learn more here.

What are Hands-on Labs

Hands-on Labs are real environments created by industry experts to help you learn. These environments help you gain knowledge and experience, practice without compromising your system, test without risk, destroy without fear, and let you learn from your mistakes. Hands-on Labs: practice your skills before delivering in the real world.

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