The book is old (2016), so don’t expect things to necessarily be up to date with the most recent pandas releases I consider knowing Pandas an essential skill for anyone working with structured data (in Python). It has its problems, and I don't shy away from them in this book. At this point, I think Pandas the API is more important than Pandas the library. What does that even mean? Well, there are more than a dozen libraries that implement the Pandas API. The API has become the defacto Python interface for data (somewhat like SQL). Many SQL people would tell you that not all SQL is the same, and it is possible to write clean SQL and poor SQL. I feel similar about Pandas. Do I Submit Issues? Push and pull of speedups with sparse arrays: balance between losing the use of efficient caching and vectorization versus not having to do a lot of the calculations associated with the zero values of the matrix Folks who read my book, Effective Pandas, or follow my Pandas content know that I’m a big proponent of chaining.

Annotation option that output an HTML file -> more yellow = more calls into the Python virtual machine; more white = more non-Python C code I just can not say it enough, but I bought a lot of books the last 2 years regarding python but Effective Pandas is my most valued one. I have it almost always open and have learned so much from it and I'm still not finished with it. So thanks for the great work!! Hope to read more books from you in the future.I thought I could take a break during the holidays, but Matt Harrison's book on Effective Pandas was too good to put away.

Then you have to do edits. Tweak code, tweak examples, clarify. It takes a bit of time. There is a lot of bouncing around, which is necessary but the context switches slow down the process. I Use Google to Search for Pandas Recipes, Will this Help? Is it Better than the Pandas Docs? This series is about how to make effective use of pandas, a data analysis library for the Python programming language. If you don’t have a documented restart plan, you should assume you’ll have to write one at the worst possible time” The warning I silenced above with the context manager links to an explanation that’s quite helpful. I’ll summarize the high points here.

Why Self-publishing?

AOT (ahead of time): Cython -> you’ll have a library that can instantly be used -> best speedups, but requires the most manual effort

Many users create intermediate variables (and call .copyon the dataframe), thus removing the low overhead benefit Best book i have come across till date ☺. I am using pandas for quite a while but never knew i was doing it in dumb way until i started reading this book. I love the concept of chaining, avoiding apply & using vectors approach with numpy select/where, shift operation, assign, clip & many more. Still half way through this book but definitely recommend all analyst/applied scientists to read this 📙. Thanks Matt for writing such a wonderful 📙 ☺ Regarding the Pandas docs question. There is some good content in the Pandas documentation. But the official documentation has different goals. It needs to be a reference for every function and method. I don't need to be a reference, in fact, I skip (on purpose) some of the functionality because I strongly believe that your life will be better if you didn't know it existed. Single pass or online algorithms: at any point in our calculation with a generator, we have only the current value and cannot reference any other items in the sequence

What is Your Process for Writing a Book?

So far, so good. What if you wanted to select the rows whose origin was Chicago O’Hare ( ORD) or Des Moines International Airport (DSM). Asynchronous I/O helps utilize the wasted I/O wait time by allowing us to perform other operations while we are in that state Introduction to asynchronous programming Even if you are ultimately going to be working with terabytes of data, you’ll start out doing exploratory data analysis. The tool that you’ll use for that is most likely going to be Pandas. One of the best investments that you can make when becoming a data scientist is to become a Pandas expert, and there is no better book than Harrison’s to help you get there. Plus, many of the interview questions you will face during the hiring process will probably involve Pandas. Blow your interviewers out of the water by showing them corners of the Pandas library they didn’t even know! Still, I thought there was room for a guide that is up to date (as of March 2016) and emphasizes idiomatic pandas code (code that is pandorable).

Use hash tables to achieve O(1) lookups and insertions -> clever usage of a hash function to turn an arbitrary key (i.e., a string or object) into an index for a list Using a cloud-based cluster can mitigate a lot of these problems, and some cloud providers also offer a spot-priced market for cheap but temporary computing resources. memoryview: allows the same low-level access to any object that implements the buffer interface, including numpy arrays and Python arrays NumbaEffective Pandas is my book and recommendation for all who want to write good Pandas code. Who is the target audience for this book? The book displays practical knowledge and insights for solving real-world problems with Pandas, even for beginners to Python and data analytics. Key concepts are introduced through simple examples, and exercise complexity is built incrementally. Updated for Python 3.9, and has extended coverage of plotting with Seaborn, and online bonus materials on GeoPandas, Dask, and Altair. The book has dedicated sections on plotting and covers machine learning extensively, which is something you may or may not like. Other than that, you can expect coverage of all Pandas topics that are a must in data analysis.