Effective Pandas: Patterns for Data Manipulation (Treading on Python)

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I've learned and used pandas quite a bit mainly by googling problems, but never really knew what the best practices were. We will discuss optimized operations that get your code to run 10-20 times faster while still retaining that easy to read quality. The course is served on the Podia platform, which has a decent interface for presenting the course materials and a video player with standard controls.

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. It is the first work on pandas that seriously leverages the power of method chaining for running clean and efficient pandas code. Python can take you far but mastering the Pandas package is a goal every data scientist worth their weight/paycheck should have high in their priority list. As I said earlier, if days were 50 hours long, perhaps I could include the whole kitchen sink in the book.My conference "tour" this year was mostly focused on some ideas that can make your Pandas code better. 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. This can lead to some very messy code, in which the time-pressed data scientist ends up melding several different programming philosophies just to get their aggregation to work. Could you share your insights in reducing latency time with Pandas operations for real time ML applications especially computer vision. Hi Matt Harrison congratulations and thank you for deciding to answer our questions on this community.

I've struggled to understand how to use Pandas as nobody really taught me how and I had even read the booked published by the person who originally wrote the Pandas library.

However, they are fine for string operations (though you may want to consider conversion to categoricals) as strings are already on the “slow path”. To calculate the overall star rating and percentage breakdown by star, we don’t use a simple average. From doing SQL-like operations in-memory, to statistical analysis, to data visualization - this book covers pandas in its full breadth! However, I don't think that exhaustive treatment was appropriate for the book (even though it is not available anywhere, except for some hand-written notes on my desk).

I come from R and statistics background, so I like having a very detailed background explanation of why some things are structured the way they are. One possibility is to check out the standard version of the Effective Pandas course, which is about 3 hours of video organized into lecture and exercise videos. I skimmed through several chapters because I’m reasonably familiar with pandas, but I’d recommend the book to anyone who uses pandas a lot. There are so many Python-based libraries out there which can be used for a variety of data science tasks. This book has taught me new concepts such as chaining and other ways to improve performance such as the categories topic.We don’t share your credit card details with third-party sellers, and we don’t sell your information to others. I have started digging into your book, and it is one of the best technical books in the area of data handling and the best book in the PyData sphere I have owned and read. Clear explanations and usage examples of a boatload of pandas Series and DataFrame attributes and methods. Congratulations on the release of the book, I am sure it will cover many techniques to help the ML community.

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. Even with chatgpt majorly assisting with df manipulation these days, knowing these patterns and what’s available helps a lot. Used to commit all the ‘sins’ - lots of intermediate dataframes, lots of single cell calculations/transformations in the notebook. What I mean is that most authors try to get their word out, teachers go further and try to make the ideas get in readers' brains.Truly, it is one of the most straightforward and powerful data manipulation libraries, yet, because it is so easy to use, no one really spends much time trying to understand the best, most pythonic way to employ the library to its full extent.