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Pandas Cookbook: Recipes for Scientific Computing, Time Series Analysis and Data Visualization using Python
Publisher's A new second edition, updated completely for pandas 1.x with additional chapters, has now been published. This edition from 2017 is outdated and is based on pandas 0.20.
Key FeaturesUse the power of pandas 0.20 to solve most complex scientific computing problems with easeLeverage fast, robust data structures in pandas 0.20 to gain useful insights from your dataPractical, easy to implement recipes for quick solutions to common problems in data using pandas 0.20Book DescriptionThis book will provide you with unique, idiomatic, and fun recipes for both fundamental and advanced data manipulation tasks with pandas 0.20. 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.
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 like one would do during an actual analysis. This book guides you, as if you were looking over the shoulder of an expert, through practical situations that you are highly likely to encounter.
Many advanced recipes combine several different features across the pandas 0.20 library to generate results.
What you will learnMaster the fundamentals of pandas 0.20 to quickly begin exploring any datasetIsolate any subset of data by properly selecting and querying the dataSplit data into independent groups before applying aggregations and transformations to each groupRestructure data into tidy form to make data analysis and visualization easierPrepare real-world messy datasets for machine learningCombine and merge data from different sources through pandas SQL-like operationsUtilize pandas unparalleled time series functionalityCreate beautiful and insightful visualizations through pandas 0.20 direct hooks to Matplotlib and Seaborn
Key FeaturesUse the power of pandas 0.20 to solve most complex scientific computing problems with easeLeverage fast, robust data structures in pandas 0.20 to gain useful insights from your dataPractical, easy to implement recipes for quick solutions to common problems in data using pandas 0.20Book DescriptionThis book will provide you with unique, idiomatic, and fun recipes for both fundamental and advanced data manipulation tasks with pandas 0.20. 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.
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 like one would do during an actual analysis. This book guides you, as if you were looking over the shoulder of an expert, through practical situations that you are highly likely to encounter.
Many advanced recipes combine several different features across the pandas 0.20 library to generate results.
What you will learnMaster the fundamentals of pandas 0.20 to quickly begin exploring any datasetIsolate any subset of data by properly selecting and querying the dataSplit data into independent groups before applying aggregations and transformations to each groupRestructure data into tidy form to make data analysis and visualization easierPrepare real-world messy datasets for machine learningCombine and merge data from different sources through pandas SQL-like operationsUtilize pandas unparalleled time series functionalityCreate beautiful and insightful visualizations through pandas 0.20 direct hooks to Matplotlib and Seaborn
534 pages, Kindle Edition
Published October 23, 2017
55 people are currently reading
69 people want to read
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Displaying 1 - 3 of 3 reviews
March 4, 2019
Just revisited the book after a while. Still one of the better books available on the market. Cleaning/transforming data remains a huge issue in data mining and machine learning. Until we see something comparable pipe-like toolkit as in R, this book remains a valuable reference for those leaned towards Python/pandas.
February 25, 2018
I found this to be a good book, providing a good clear grounding in Pandas with lots of good examples. A worthwhile read (especially taking the time to work through the examples before you get the explanations of why they work is worth the effort) and worth keeping around if you are working on building your knowledge.
May 13, 2025
This book is well written. The author spends a lot of time explaining how things work and his depth of understanding shows up as assorted nuggets of knowledge. For example, I didn't realize that the round() function differs in Python 2.x and Python 3.x.
Since this is a cookbook, it is a collection of recipes for doing specific things. Where I think it really shines, though, is in the case studies in which the author pulls together a bunch of tasks and uses them to perform data science.
I think the book is best suited to people with limited programming experience since it moves too slowly for more advanced users, especially if they have much experience processing massaging data.
Note that I believe (and follow) the meanings Goodreads gives for what each number of stars means. Therefore, the majority of my ratings are 3 stars ("liked it").
Since this is a cookbook, it is a collection of recipes for doing specific things. Where I think it really shines, though, is in the case studies in which the author pulls together a bunch of tasks and uses them to perform data science.
I think the book is best suited to people with limited programming experience since it moves too slowly for more advanced users, especially if they have much experience processing massaging data.
Note that I believe (and follow) the meanings Goodreads gives for what each number of stars means. Therefore, the majority of my ratings are 3 stars ("liked it").
Displaying 1 - 3 of 3 reviews