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This course takes a look at some of the lesser-known but highly useful methods that can be used in Pandas for advanced data analytics. We'll explore the methods available to you in Pandas to make your code more efficient through evaluating expressions and conditional iterative statements.

We'll also look at methods for time series and windows operations and how these can be used for analyzing datetime objects.

This is a hands-on course that is full of real-world demonstrations in Pandas. If you want to follow along with the course, you can find everything you need in the GitHub repo below.

If you have any feedback on this course, please write to us at support@cloudacademy.com.

Learning Objectives

  • Perform iterative operations in Pandas to make your code more efficient
  • Learn about evaluation expressions and how to use them
  • Perform time series data analysis using a variety of methods

Intended Audience

  • Data scientists
  • Anyone looking to enhance their knowledge of Pandas for data analytics


To the most out of this course, you should already have a good understanding of handling data using Pandas. We recommend taking our Data Wrangling with Pandas course before embarking on this one.


The GitHub repository for this course can be found here: https://github.com/cloudacademy/advanced-pandas-for-data-analytics



Congratulations. You’ve reached the end of this course on advanced methods in Pandas.

We’ve covered many functions that are still not very popular among data scientists and analysts. You will now be able to start using them and you will be able to surprise and perhaps shock (!)  your audience with these new tools.

In summary, we have covered the eval family, which is useful for evaluating string expressions in Pandas, as well as different ways to perform a for loop in Pandas.

In the last lecture, we covered a series of methods that are used in time series analysis.


I hope you have enjoyed this course. Please don’t forget to rate it, and if you have any feedback at all, please reach out to us at support@cloudacademy.com. Thank you!


About the Author
Learning paths1

Andrea is a Data Scientist at Cloud Academy. He is passionate about statistical modeling and machine learning algorithms, especially for solving business tasks.

He holds a PhD in Statistics, and he has published in several peer-reviewed academic journals. He is also the author of the book Applied Machine Learning with Python.