In this course, you’ve learned how to start exploring a dataset with the pandas Python library. You saw how you could access specific rows and columns to manage even the largest of datasets. You’ve also seen multiple techniques to prepare and clean your data, by specifying the data type of columns, dealing with missing values, and more. You’ve even created queries, aggregations, and plots based on those.

Now you can:

Work with Series and DataFrame objects
Subset your data with .loc, .iloc, and the indexing operator
Answer questions with queries, grouping, and aggregation
Handle missing, invalid, and inconsistent data
Visualize your dataset in a Jupyter Notebook

You can further develop these skills with Fast, Flexible, Easy and Intuitive: How to Speed Up Your pandas Projects and Python pandas: Tricks & Features You May Not Know. With enough practice, you will be able to tackle any datasets you find interesting and share your insights and observations with your friends and colleagues!

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Course Slides (.pdf)

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Congratulations, you made it to the end of the course! What’s your #1 takeaway or favorite thing you learned? How are you going to put your newfound skills to use? Leave a comment in the discussion section and let us know.

00:00 This course was about exploratory data analysis using Python and Pandas. You learned about the building blocks of Pandas, the Series and DataFrame.

00:10 You learned how to access specific data values with .loc, .iloc, and indexing. You learned how to summarize data with grouping and aggregation.

00:21 You learned how to write queries to get precise results. You learned how to manage missing data. And you learned how to create visualizations. As you followed the course, you saw how these could be used together to tell the story of the data, and that’s where the real power is: knowing which features of Pandas to use at which time. Most important, the power of Pandas does not mean you can’t understand your data.

00:47 Pandas cannot read your mind!

00:50 Pandas is one of the many tools you can use for data science.

00:55 To learn more about Pandas, check out these posts on Real Python. Thank you for watching this course. I hope it was of use to you. If you’d like to continue the discussion, join the Real Python Slack or comment in the discussion forums.

Vijaya Kumar Marneni on June 5, 2021

I am beginner for pandas, it is quite useful to get flavour of it. I deep dive into library with this knowledge. Thank you Douglas Starnes

aniketbarphe on Nov. 25, 2021

Thank You!

pnmcdos on April 7, 2022

Very informative. I was able to follow along with your video. This was extremely helpful.

wrucknroll on Feb. 18, 2023

Hi thanks for the videos. Great content if a little fast with the typing speed ups. My brain needs some time to process what is happening!

The recommended reading tutorial that is linked to this course exactly the same and I found it to be great way to digest what I just watched.

Emmanuel Ozemhoya on June 1, 2023

This is the reason I subscribed for a second year; I can comprehend and understand Python at this level. Now, I can get similar datasets and test my knowledge henceforth.

JVargas on Sept. 5, 2023

It will be helpfull to read the Fedex csv files I get from Fedex portal.

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Explore Your Dataset With pandas (Summary)