Since pandas is a large library with many different specialist features and functions, these excercises focus mainly on the fundamentals of manipulating data (indexing, grouping, aggregating, cleaning), making use of the core DataFrame and Series objects. Many of the excerises here are straightforward in that the solutions require no more than a few lines of code (in pandas or NumPy - don't go using pure Python!)
| Section Name | Description | Difficulty |
|---|---|---|
| Importing pandas | Getting started and checking your pandas setup | Easy |
| DataFrame basics | A few of the fundamental routines for selecting, sorting, adding, and aggregating data in DataFrames | Easy |
| DataFrames: beyond the basics | Slightly trickier: you may need to combine two or more methods to get the right answer | Medium |
| DataFrames: harder problems | These might require a bit of thinking outside the box... | Hard |
| Series and DatetimeIndex | Exercises for creating and manipulating Series with datetime data | Easy/Medium |
| Cleaning Data | Making a DataFrame easier to work with | Easy/Medium |
| Using MultiIndexes | Go beyond flat DataFrames with additional index levels | Medium |
| Advanced_Aggregation | Performing Agg functions on higher level | Hard |
| Plotting | Explore pandas' part of plotting functionality to see trends in data | Medium |