Python for SAS Users: A SAS-Oriented Introduction to Python

"Python for SAS users" is a solid book on data analysis with Python. The book uses an interesting approach, which expands the reader's understanding of Python by making reference to SAS. Instead of studying Python in a vacuum, the text offers a hands-on journey through programming and data analysis through the use of clear coding examples tackled from the perspectives of Python and SAS. The core of the book focuses on the Pandas library and covers data analysis, data wrangling and data management using Python. I have certainly gained a deeper understanding and appreciation of Python and SAS through reading this book.

I have to tell you that this is one of the most useful books I’ve ever laid hands on. I spent more than a decade working with SAS, but have changed jobs and had to move to jupyter/colab notebooks. As someone who does primarily data analysis, without engineering training, this was perfect for me. The examples are clear and they pair very explicit, worked examples with a plain-language explanation of what the syntax does. Niche need (I know SAS but now have to learn python), but easily 5 stars in my book!

It’s useless book. I bought with lot of hope and I now greatly regret because is not worth the price.

The book is practical and helpful as a reference for both Python and SAS as I have to use both.

Well written which helps this Python novice become comfortable working in this new environment.Thanks for taking the time to provide the road map I needed!

This book covers key subjects in working with data using SAS and Python with clear examples. Very useful.

The book spends a lot of time talking on topics that are very minor - (nearly half the book is on Reading/Writing Data, Date Time, and SASPY) whilst omitting some really useful information which are pretty vital in the practical world.As an example, it does go into the generation of multiple "worked" variables using the .AGG (aggregate) command, however does not bother to explain how to then reduce the multi-index'd output back to single index so you can merge it back to your master data set. From a working professional perspective, it is crazy how this is not explained as this is something pretty fundamental to everyday work.There are some good chapters but still feels like too much filler and not enough practical real world discussion, for example both the LEFT and RIGHT join get their own section when it could be explained far more succinct, it just makes me believe that they were trying to hit "page" numbers than writing the best quality product.Honestly pretty disappointed, the free stuff online is much more succinct and offers a more "practical" view of playing with data.

Excelente libto

C'est livre que j'ai acheté pour mon cousin datascientiste, et il m'a l'air parfaitement satisfait.