Pandas 1.x Cookbook: Practical recipes for scientific computing, time series analysis, and exploratory data analysis using Python, 2nd Edition.

626 pages, published in 626

- This is the first book about pandas 1.x.
- Practical and easy-to-implement recipes for quickly solving common data problems with pandas
- Master the basics of pandas to quickly start exploring each record

The Panda library is huge, and it's common for frequent users to be unaware of many of its most impressive features. The official Panda documentation, while comprehensive, does not contain many useful examples of how to put multiple commands together like a real-world analysis. As if looking over the shoulder of an expert, this book guides you through the most likely situations.

This new updated and revised edition brings you unique, idiomatic, and fun recipes for basic and advanced data manipulation tasks with pandas. Some recipes focus on gaining a deeper understanding of the basic principles or on comparing and contrasting two similar operations. Other recipes will delve deep into a particular data set, revealing new and unexpected insights in the process. Many advanced recipes combine different functions in the Panda library to produce results.

- Examining key dates in pandas through dozens of practical problems
- Group, aggregate, transform, reshape and filter data
- Merge data from different sources using Panda-like SQL operations
- Create visualizations using panda hooks on Matplotlib and Seaborn
- Use panda and time series functions to perform powerful analysis
- Import, cleanse, and prepare real-world data sets for machine learning

This book is intended for Python developers, data scientists, engineers, and analysts. Pandas is the ideal tool for manipulating structured data using Python, and this book provides detailed instructions and examples. Not only does it cover the basics required to master it, it also goes into the details of idiomatic pandas.

- Panda foundations
- Basic DataFrame operations
- Creation and persistence of DataFrame
- Start of data analysis
- Exploratory data analysis
- Selection of subsets of data
- Filter the lines
- Index alignment
- Grouping by aggregation, filtering and transformation
- Restructuring data into an orderly form
- Combine the items of the pandas
- Time series analysis
- Visualization with Matplotlib, Pandas and Seaborn
- Panda debugging and testing

This is a huge book full of details on the basic and advanced uses of pandas. Each recipe is largely self-contained, so you can jump straight to any topic you want to know more about without searching all parts of the book. This means there are some repetitions as you read from start to finish, but I think it's a good compromise. The attached Jupyter notebook examples are also a nice addition.

I had the paper copy of the first edition, this time I bought the Kindle version. Because these books will be obsolete in 6 months. As for the book, there are many new things to learn such as the new Panda NA types that behave exactly like the R-NAs, the Pipe method, pd.NamedAgg, etc. These are some of the new methods recently introduced with pandas. I am following these two writers and there is a lot to learn. They both have their own companies and there is a lot to learn from their content. I'm glad you decided to work with us and you wrote this. Even if you have experience with pandas. You will still have many new things. I finished the book in 15 days doing examples side by side. This book is not heavy. It is a very applied book, not a nonsense book. Thanks to Ted and Matt for writing this book.

Being a machine learning engineer, I felt this book was a wealth of information for a large number of people. It covered a large number of topics. The ones I personally liked are the panda object and the panda test and debug. I would recommend it to many people who are using this book to learn about pandas.

The only learning resource you will ever need! Ignore the options of the popular online courses!

Excellent structure of the topic, supplemented by simplified explanations. I can quickly grasp the concepts in a short amount of time (compared to the popular Udemy courses, Datacamp and even Wes McKinney's book!). Thanks to the authors for creating such a wonderful and comprehensive learning guide and for giving me the confidence to work with pandas on my own. Also, a side note: if you don't have a Kindle reader / e-book, consider buying the print version if possible. The book is over 600 pages long and learning from a laptop / desktop is not a very effective approach! Personally, I believe that a printed book always works wonders for memory retention ... happy learning! :-)

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