Handling Analytical Data

In this chapter, we take a deep dive into the DataFrames.jl package ecosystem, which allows you to conveniently perform the most common data transformation tasks. In particular, we cover the following topics:

• Converting between DataFrame and Matrix
• Investigating the contents of DataFrame
• Reading CSV data from the internet into DataFrame
• Working with categorical data
• Handling missing data
• Applying the split-apply-combine pattern to DataFrame
• Converting DataFrame between wide and narrow formats
• Comparing two data frames for identity
• Applying complex transformations to rows of DataFrame
• Creating pivot tables by chaining operations on data frames