Build a Data Analysis Library from Scratch in Python

Build a fully-functioning Python library that is able to complete many common data analysis tasks.

Level : 
Starting on Nov 8
Format : 
6 live workshops (2 hours each) with recordings

Teddy Petrou

Teddy Petrou is founder of Dunder Data, a company that specializes in helping students become experts at data science using Python. Teddy is the author of multiple highly rated texts such as Pandas Cookbook, Master the Fundamentals of Python, and Master Data Analysis with Python. Teddy has taught hundreds of students Python and data science during in-person classroom settings. He sees first hand exactly where students struggle and continually upgrades his material to minimize these struggles by providing simple and direct paths forward. Teddy holds a master's degree in statistics from Rice University.

What you'll learn

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Module 1: Project Genesis

We will download and install VS Code, an excellent free interactive development environment, then set up the environment and learn about test-driven development. We begin by inspecting the init file which will begin as the sole location for our library code. We’ll then learn how to import our library into a Jupyter Notebook. DataFrame construction is begun by checking input types.

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Module 2: Properties and Subset Selection

We will implement several basic DataFrame properties such as access to the columns, values, and data types. A nice visual representation of the DataFrame will be displayed. We’ll then learn how to select subsets of data with the square brackets.

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Module 3: Methods

We’ll create several methods, adding powerful features to our DataFrame. It will have the ability to aggregate, determine if a value is missing or not, and find unique values.

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Module 4: Advanced Methods

Several more powerful methods will be added to our DataFrame. It will have the ability to rename and drop columns, sort column values, take random samples, create pivot tables and more.

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Module 5: Reading Data

We’ll add the ability to read text data from files. This can be quite a challenge as data can be formatted in a wide variety of ways.

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Module 6: Conclusion

You’ll now have a complete data analysis library with many of the same capabilities as the pandas library. Your code will have passed at least 100 tests through this process. We’ll discuss possible additional features and next steps you can take to improve the library.


This is an intermediate/advanced course. Participants must feel comfortable with the fundamentals of Python.

This course is for:

Python software engineers, data scientists, those that are interested in building a complete Python library.

What you will be able to do after this course:

By the end of the course, you will have built a fully function data analysis library.

Frequently Asked Questions

Are all sessions live?

Yes, all sessions during the cohort will be live with the instructor. However, we will record each session and make them available for everyone in the cohort.

What is the time commitment?

Our courses typically have 2-4 modules, with each module lasting for approximately 2h per week which you can block out on your calendar during your work day. You also get some take home projects that you can complete at your own pace.

Do I earn a certificate for this course?

Of course! Once you’ve completed the course modules, you will get a certificate of completion that you can showcase to the world.

Can I expense this course?

Yes. Most of our cohort participants have expensed this course through their Learning & Development budget, similar to how you expense conferences. You can use this email template to request expense approval from your manager.

Do you offer discounts for students?

Yes. Email us at and we'll be happy to give you a discount.

I have more questions. Get in touch with us!

If you have more questions, email us at

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