Knowing just enough Pandas can mean the difference between exploring and understanding data from a fundamental. Knowing how to transform data, use multi-indexes, and customize Pandas' visual aspects in Jupyter Lab gives you the power to approach everyday problems confidently. In this session, you will build on fundamentals you already know to handle a more comprehensive array of data problems.
This session is for anyone who already has a solid foundation of Pandas fundamentals who wants to extend their knowledge with more advanced features, including aggregation, multi-indexing, designing transformations, and customizing DataFrame output.
The majority of our session will be in Jupyter notebooks and writing code hands-on. Please review the environment setup ahead of time.
- Configure their Python environments
- Review the fundamentals of Pandas
- Perform simple aggregations
- Review: DataFrame Axis
- Understand when to use Built-in vs. ".apply()" for data transformation
- Describe when multi-indexing makes sense
- Be familiar with common multi-index use cases
- Understand how to select data with multi-indexes
- Describe which aspects of a DataFrame can be customized
- How to write callback functions on DataFrame aesthetics