This week, I have reached a small personal milestone as the Journal of Statistical Software has just published the DataFrames.jl: Flexible and Fast Tabular Data in Julia paper that I co-authored with Milan Bouchet-Valat.
Therefore, in this post, I thought to summarize various resources I have been working on over the years that can help you master DataFrames.jl.
Types of documentation
It is hard to document a package properly. The reason is that different users have different expectations. In this post there is an excellent summary of four typical kinds of documentation:
- learning-oriented tutorials;
- goal-oriented how-to guides;
- understanding-oriented discussions;
- information-oriented reference material.
Each kind of documentation requires a slightly different approach, and having it all in a single place is hard. In the following sections, I will go through various materials I have prepared over the years and explain my intention behind them.
The Journal of Statistical Software paper
The objective of the DataFrames.jl: Flexible and Fast Tabular Data in Julia paper is understanding-oriented. Together with Milan we tried to explain in it how the DataFrames.jl package was designed, and what were the motivations behind these decisions.
For this reason, you most likely cannot learn how to work with DataFrames.jl after reading the paper. However, you will get an intuition what are the basic building blocks of the package.
It is similar to learning languages. I have recently decided to learn French. As a part of this process, I watched the ALL THE RULES OF FRENCH IN 20 MINUTES video on YouTube. I did not directly learn French from it, but it helped a lot in understanding the “design” of the French language.
Julia for Data Analysis book
Another major resource I have created is my Julia for Data Analysis book. This resource is learning-oriented. I start it from the basics of the Julia language and gradually add more complex elements so that eventually, the reader should be able to:
- read and write data in various formats;
- work with tabular data, including subsetting, grouping, and transforming;
- visualize data;
- build predictive models;
- create data processing pipelines;
As you can probably guess, the DataFrames.jl package is a backbone of this material. The book was prepared as a textbook that can be used in a 1-semester introductory course on data analysis using Julia and is accompanied by numerous extra materials that can be found here.
Over the years, I have created a lot of goal-oriented how-to guides. You can find their list here.
Also, my blog since year 2020 brings you each week some practical information on working with Julia (quite often DataFrames.jl oriented).
Since the volume of the tutorials is large, it might be sometimes a bit hard to navigate, but probably this is unavoidable, as there is a substantial variety of questions that users might have.
I strongly prefer implementing the functionality of DataFrames.jl following the contract specified in the documentation of provided functions. Therefore, I believe that we have quite a strong collection of reference materials that make it precise how DataFrames.jl functionality is implemented. It is divided into four major parts:
- specification of how types exposed by DataFrames.jl are designed is given here;
- reference on provided functions can be found here;
- a complete description of how indexing works in DataFrames.jl is available here;
- information on how data frames handle table and column metadata is given here.
It is essential to highlight that these materials aim to be complete and precise. Unfortunately, this means that they are verbose and sometimes hard to digest by new users. Unfortunately, I think this cannot be helped, and that is why we provide other kinds of documentation to make it easier to get started with DataFrames.jl.
I hope that this post can serve DataFrames.jl users as a helpful guide to different resources I have co-authored that are provided to make learning and using the package easy and fun. Enjoy!