Most of the material in this chapter has been geared at an understanding of the core data science tool for data analysis - the dataframe - and how to work with data in it (functions, tasks, manipulating and shaping data)
But we also covered good habits for data analysis, which will pay dividends as we dive into more (and new) datasets in the future
The Finance Applications chapter contains examples of how we can use pandas to compound returns, easily calculate betas, and more.
This covers method chaining so that Python can approximate R’s elegance at data wrangling and plotting
This also has nice and quick examples showing Python matching R functions
The most popular questions about
pandason Stackoverflow. This will give you an idea of common places others get stuck, and slicing and indexing issues are high on the list.