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Pandas 1.x Cookbook: Practical recipes for scientific computing, time series analysis, and exploratory data analysis using Python, 2nd Edition
Use the power of pandas to solve most complex scientific computing problems with ease. Revised for pandas 1.x. The pandas library is massive, and it's common for frequent users to be unaware of many of its more impressive features. The official pandas documentation, while thorough, does not contain many useful examples of how to piece together multiple commands as one would do during an actual analysis. This book guides you, as if you were looking over the shoulder of an expert, through situations that you are highly likely to encounter. This new updated and revised edition provides you with unique, idiomatic, and fun recipes for both fundamental and advanced data manipulation tasks with pandas. Some recipes focus on achieving a deeper understanding of basic principles, or comparing and contrasting two similar operations. Other recipes will dive deep into a particular dataset, uncovering new and unexpected insights along the way. Many advanced recipes combine several different features across the pandas library to generate results. This book is for Python developers, data scientists, engineers, and analysts. Pandas is the ideal tool for manipulating structured data with Python and this book provides ample instruction and examples. Not only does it cover the basics required to be proficient, but it goes into the details of idiomatic pandas.
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Published February 27, 2020
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Displaying 1 - 2 of 2 reviews
January 14, 2021
This is a quite complete pandas cookbook with plenty of different recipes for major uses of pandas. Overall, it is a fantastic resource for major concepts, description of functions and methods, some tricks etc. In general the book introduces a major concept, accompanied by code and later explain the code and how it works. This strategy is ok and everything is well explained although it may lead to some problems. In some cases, long pieces of code with many functions/methods are used without a previous introduction. Although they are in most cases explained in the last part of the chapter, it could perhaps help to explain first and use later. The book dedicates a final section in each chapter to discussing why specific commands were used and comparing them with other alternatives that are also explanied in parallel. Given the enormous number of possibilities in pandas, it is very difficult to cover everything in a single manual and this book does a good job in selecting relevant functions and explaining them to the level required for reproducing them in your won work.
Why 4 stars? Despite the high quality of the book, in my opinion the book could be better organized with a better clustering of common ideas and methods. Some chapters are not focused to pandas functions but are focused (and entitled) to address very specific problems of a specific database. Obviously, this is a useful strategy since by doing so you learn pandas, but I thing it distracts from learning how pandas is organized. Some functions are used in early chapters before they are explained later in the book. This may also be ok but, in some cases, it is difficult to follow the current explanation by adding a piece of code that you don't understand at all and it is not explained. But perhaps the major criticism relates to the databases/dataframes/examples selected for analysis. In many cases, understanding the data selected for the analysis is more difficult than understanding the method explained in the chapter. Whereas this may be closer to the real world, it does not help to understand the basic functions and methods that would be much better explained first, with a simpler example, and later put together to solve a big problem. In many cases I preferred to type my own simpler dataframes to learn a new function instead of using the examples proposed.
In any case, and given the large number of possibilities with pandas, it is a very good book to get introduced to data analysis, time series, visualization and additional utilities. Many recipes are very rich in ideas and tricks and I am sure I will revisit them for solving specific problems.
Why 4 stars? Despite the high quality of the book, in my opinion the book could be better organized with a better clustering of common ideas and methods. Some chapters are not focused to pandas functions but are focused (and entitled) to address very specific problems of a specific database. Obviously, this is a useful strategy since by doing so you learn pandas, but I thing it distracts from learning how pandas is organized. Some functions are used in early chapters before they are explained later in the book. This may also be ok but, in some cases, it is difficult to follow the current explanation by adding a piece of code that you don't understand at all and it is not explained. But perhaps the major criticism relates to the databases/dataframes/examples selected for analysis. In many cases, understanding the data selected for the analysis is more difficult than understanding the method explained in the chapter. Whereas this may be closer to the real world, it does not help to understand the basic functions and methods that would be much better explained first, with a simpler example, and later put together to solve a big problem. In many cases I preferred to type my own simpler dataframes to learn a new function instead of using the examples proposed.
In any case, and given the large number of possibilities with pandas, it is a very good book to get introduced to data analysis, time series, visualization and additional utilities. Many recipes are very rich in ideas and tricks and I am sure I will revisit them for solving specific problems.
November 10, 2022
One of the only programming books that I read each and every page, but not because it was good. While I don't regret getting this book, if I were to do it over again, I would take a shot at a different pandas book.
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