I'm only a few pages into the Effective Pandas book and so far I've found it to be very enjoyable even though the material is mostly known to me.

I have been using Pandas for a long time, so I picked this up thinking I already knew everything about Pandas but I was very wrong. We use Pandas extensively for various things from experimentation, modeling, ML etc, to measure incremental benefits of our marketing campaigns. It is also a legit hands-on material, and even the session on debugging chains that starts with commenting commands before applying more robust debugging methods mimics what data scientists are really doing in the wild. If you're a data specialist working with Pandas, I highly recommend checking out "Effective Pandas".

Clear explanations and usage examples of a boatload of pandas Series and DataFrame attributes and methods. It is a collection of tricks, wherein fluency only arises from many hours spent pulling one’s hair out. Every ensuing chapter builds incrementally on that foundation, with clear and pertinent examples throughout.

Effective coverage of all pandas table-steaks topics: loading data, filtering, inspecting dataframes, typing, sorting, merging/joining, grouping, aggregating, shaping, melting, plotting, exporting, debugging (and even more). Here is a snapshot of a (small) subset of all of my Coding, Data Science and Machine Learning books. The book is laid out in such a way that topics flow naturally one to the next and are conducive to a read-through for learning the pandas library piece by piece.

Even if you are ultimately going to be working with terabytes of data, you’ll start out doing exploratory data analysis.

The course has good productivity tips; for working in Jupyter notebooks, for working with Pandas documentation, and for data cleaning and wrangling. While not outdated, I have taught and used Pandas extensively since then, and have developed strong opinions on the correct way to use it.

This book is full of excellent tips for using Pandas both effectively and efficiently, and the author provides great examples to follow along with. It is by far, the best book you can get yourself if you want to take your data science skills to the next level, after all, they say modern data science is 90% data cleaning.

I added a few chapters, re-wrote much of the code (using some of the ideas found in my new book), edited some parts, and released the book. What makes this book particularly invaluable are the learnings from Matt's teaching and consulting experience. I realized that I never had an in-depth, rigorous, formal course that covers these topics and maybe was not fully using the capabilities or writing the most optimal code that can be written.It doesn't take into account that I have used tools for a long time, and thing that are difficult for beginners become second nature when you do it often. And you will have insight into your data that you wouldn't have even thought about until you see it. Code is written in Jupyter notebooks during the lectures and exercises, and the exercises are available for download in a Jupyter notebook in the standard course. Used to commit all the ‘sins’ - lots of intermediate dataframes, lots of single cell calculations/transformations in the notebook.