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
- Subset your data with
.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!
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.