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Pandas Cookbook: Practical recipes for scientific computing, time series, and exploratory data analysis using Python 3rd ed. Edition

by William Ayd (Author), Matthew Harrison (Author)
5.0 out of 5 stars 31 ratings
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Purchase options and add-ons

From fundamental techniques to advanced strategies for handling big data, visualization, and more, this book equips you with skills to excel in real-world data analysis projects.

Bonus: Free PDF copy + join our Discord + code files

Key Features

  • This book targets features in pandas 2.x and beyond
  • Practical, easy to implement recipes for quick solutions to common problems in data using pandas
  • Master the fundamentals of pandas to quickly begin exploring any dataset

Book Description

Unlock the full power of pandas 2.x with this hands-on cookbook, designed for Python developers, data analysts, and data scientists who need fast, efficient solutions for real-world data challenges. This book provides practical, ready-to-use recipes to streamline your workflow. With step-by-step guidance, you'll master data wrangling, visualization, performance optimization, and scalable data analysis using pandas’ most powerful features.

From importing and merging large datasets to advanced time series analysis and SQL-like operations, this cookbook equips you with the tools to analyze, manipulate, and visualize data like a pro. Learn how to boost efficiency, optimize memory usage, and seamlessly integrate pandas with NumPy, PyArrow, and databases. This book will help you transform raw data into actionable insights with ease.

What you will learn

  • The pandas type system and how to best navigate it
  • Import/export DataFrames to/from common data formats
  • Data exploration in pandas through dozens of practice problems
  • Grouping, aggregation, transformation, reshaping, and filtering data
  • Merge data from different sources through pandas SQL-like operations
  • Leverage the robust pandas time series functionality in advanced analyses
  • Scale pandas operations to get the most out of your system
  • The large ecosystem that pandas can coordinate with and supplement

Who this book is for

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

Table of Contents

  1. pandas Foundations
  2. Selection and Assignment
  3. Data Types
  4. The pandas I/O System
  5. Algorithms and How to Apply Them
  6. Visualization
  7. Reshaping DataFrames
  8. Group By
  9. Temporal Data Types and Algorithms
  10. General Usage and Performance Tips
  11. The pandas Ecosystem
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  1. ISBN-10
    1836205872
  2. ISBN-13
    978-1836205876
  3. Edition
    3rd ed.
  4. Publisher
    Packt Publishing
  5. Publication date
    October 31, 2024
  6. Language
    English
  7. Dimensions
    1.04 x 7.5 x 9.25 inches
  8. Print length
    404 pages
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From the brand

From the Publisher

pandas cookbook
pandas will

You've been an active part of the Pandas core team. How has this community involvement influenced the new content in this edition?

When I first started contributing to pandas, I was biased toward my personal needs. The more I contributed to the project, my mind became more open to different ways of problem solving. Even after so many years, I’m developing new analytical techniques, which has made me appreciate the contributions from many people in the open source space.

In this third edition, I’ve gathered diverse techniques into one package. Our discussions in my book will cover algorithms, data types, data exploration, data cleansing, visualization, and more. They were enabled by contributors from different backgrounds, and the fact that they are all a part of pandas makes it a wonderfully useful tool for data practitioners everywhere.

pandas

What did you prioritize in this update to address the needs of the community?

The challenging aspect of learning pandas is its type system and handling of missing values, features that have become burdens as the open source ecosystem evolved. Though there's hope for future improvements, altering a library as popular as pandas is a complex task. Despite this, there are current methods that improve upon these aspects, which I’ve detailed in Chapter 3 of my book. At the end of the day, a library like pandas is beholden to the desires of its users, so I hope that educating users on the type system, missing value handling, and other areas for improvement will help inspire collective passion toward making the library even better over the next many years.

pandas

“I am excited to see the third edition of this book come together. It is an excellent resource full of practical solutions to problems you will encounter in your data analysis work in Python. It covers the essential features of pandas while delving into more advanced functionality and features that were only added to the library in the last few years.”

- Wes McKinney

Creator of the pandas project

Pandas Cookbook: Practical recipes for scientific computing, time series and ...
Pandas 1.x Cookbook - Second Edition: Practical recipes for scientific comput...
Customer Reviews
5.0 out of 5 stars 31
4.3 out of 5 stars 109
Price $49.99 $45.38
Version 2.x and beyond 1.x
How will you progress in your learnings Leverage pandas within a broader analytics ecosystem and optimize workflows Get started on learning essential pandas techniques and how to process big data
Key features Future-focused pandas best practices, evolving idioms, analytical thinking, and improved recipes Efficient data manipulation, method chaining, comprehensive feature summary, and row/column ops
Who are each of these editions for? Those who want to stay up-to-date with advanced techniques, large datasets, and new features Those seeking to deepen their understanding of core pandas concepts and build a strong foundation

Editorial Reviews

About the Author

Will Ayd is a core maintainer of the pandas project, serving in that role since 2018. For over a decade working as a consultant, Will has helped countless clients get the most value from their data using pandas and the open-source ecosystem surrounding it

Matt Harrison has been using Python since 2000. He runs MetaSnake, which provides corporate training for Python and Data Science. He is the author of Machine Learning Pocket Reference, the bestselling Illustrated Guide to Python 3, and Learning the Pandas Library, among other books

Product details

  • Publisher ‏ : ‎ Packt Publishing; 3rd ed. edition (October 31, 2024)
  • Language ‏ : ‎ English
  • Paperback ‏ : ‎ 404 pages
  • ISBN-10 ‏ : ‎ 1836205872
  • ISBN-13 ‏ : ‎ 978-1836205876
  • Item Weight ‏ : ‎ 1.9 pounds
  • Dimensions ‏ : ‎ 1.04 x 7.5 x 9.25 inches
  • Customer Reviews:
    5.0 out of 5 stars 31 ratings

About the author

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William Ayd

William Ayd

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Will Ayd is an independent consultant who leverages open source tools to make clients' data easy, actionable, and insightful. Will also contributes heavily to the open source ecosystem he champions, having served as a maintainer of the pandas library since 2018 and as a Committer to the Apache Arrow project since 2024.

Customer reviews

5 out of 5 stars
31 global ratings

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Easy data cooking with Pandas
5 out of 5 stars
Easy data cooking with Pandas
This book is a very useful tool for all those developers, engineers or data scientists who are starting out in data management with Python. It is a book that you should not leave on the shelf, but on your work table. It takes you through all the options that Pandas offers for data management: data selection, file access, visualization, transformations, date management, time series, etc. And all of this with a clear, direct and practical approach. Totally recommended.
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Top reviews from the United States

  • Mario Rodríguez Hernández
    5.0 out of 5 stars Easy data cooking with Pandas
    Reviewed in the United States on January 19, 2025
    Verified Purchase
    This book is a very useful tool for all those developers, engineers or data scientists who are starting out in data management with Python. It is a book that you should not leave on the shelf, but on your work table. It takes you through all the options that Pandas offers for data management: data selection, file access, visualization, transformations, date management, time series, etc. And all of this with a clear, direct and practical approach.

    Totally recommended.
    Customer image
    Mario Rodríguez Hernández
    5.0 out of 5 stars
    Easy data cooking with Pandas

    Reviewed in the United States on January 19, 2025
    This book is a very useful tool for all those developers, engineers or data scientists who are starting out in data management with Python. It is a book that you should not leave on the shelf, but on your work table. It takes you through all the options that Pandas offers for data management: data selection, file access, visualization, transformations, date management, time series, etc. And all of this with a clear, direct and practical approach.

    Totally recommended.
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  • Allen Wyma
    5.0 out of 5 stars Great Resource for Beginners
    Reviewed in the United States on December 4, 2024
    Verified Purchase
    I have been working with some financial clients who use a lot of python and Pandas. I havne't had much experience with it, so this book was a great guide to getting started.

    I highly recommend if youre not familiar with Pandas to pick this book up.
    One person found this helpful
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  • HamRadioGuy
    5.0 out of 5 stars One of my best buy this year.
    Reviewed in the United States on December 26, 2024
    Verified Purchase
    Great book. The only book you need if you want to learn how to use pandas.
    One person found this helpful
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  • JR
    5.0 out of 5 stars Essential Reading for Pandas
    Reviewed in the United States on December 5, 2024
    Verified Purchase
    This book is great and exactly what I needed to get over a technological hump. Highly recommended
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  • Cheryl
    5.0 out of 5 stars Very comprehensive
    Reviewed in the United States on November 17, 2024
    Verified Purchase
    This book is ideal for anyone who work with data regularly.
    One person found this helpful
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  • Darnell Grantham Jr.
    5.0 out of 5 stars Concise Examples
    Reviewed in the United States on March 2, 2025
    Pandas Cookbook Third Edition is a step by step instructional resource for the novice data scientist and developer seeking to perform single computing, in-line memory
    Analysis of multidimensional structured data using the pandas library in Python. Authors, William Ayd and Matthew Harrison joined forces to develop a masterful resource of 11 chapters which snowball from simple pandas series, dataframes and indexing of small arrays or matrices to advanced statistical algorithms and complex groupings of data. Ayd and Harrison cut right to the chase of providing simple easy to follow examples to follow along for the novice with easy to understand code no longer than 10 lines; driving home each concept or available panda tool for accomplishing the function at hand. The authors do not reveal all things concerning pandas origins or background in the beginning of the book but rather jumps into the examples, slowly revealing more concerning pandas with the progression of each chapter.

    Chapter 1 through Chapter 5 start with the introduction of panda series, dataframes, indexes, masking and slicing of array values. Series are just a single set or array of numbers, dates or string values which can be indexed, searched or manipulated. DataFrames build upon the series concept and contain a multiple dimensional array of values in a matrix format that can be assigned to a variable and referenced in memory for analysis, manipulating and plotting. Ayd and Harrison introduce pandas data types, selections and assignments, I/O functionality of pandas and eventually algorithms in chapter 5. If the reader is following along using the pandas library in Python, it helps to follow the recipes in order to understand the more advanced concepts in chapter 5 and beyond. I was pleased to see that pandas can read and write to and from multiple formats like CSV, Excel, SQL, Apache, JSON, HTML to name a few.

    Chapter 6 through 9 present advanced pandas functionality such as plotting data to visualizations, Reshaping dataframes, Grouping dataframes, and temporal data types and algorithms. I was glad to see the clarity of the built in pandas plot libraries in producing standard visuals such as lines, bar, pie, scatter and density graphs produced from series and dataframe plots. These plots are produced from aggregated and unaggregated data. Greater visualizations require installing the PyQt5 library which the backend method Matplotlib uses. Python library, scipy can be installed when desiring to smooth out data with more advance graphing with a Kernel Density Estimate plot.Adding specialized arguments to the python plot commands can produce the series, labels, titles and graphic colors for visualizations. The Group By and Concatenation features introduced allow for complex data manipulation of dataframes.

    Chapter 10 and 11 uncover common mistakes that developers make when applying pandas code into a production environment and drive home the use of built in methods and functionality that will aid in performance for example, using a vectorized function to calculate a sum rather than a loop which can utilize more performance resources. Chapter 11 gives a simplified list of the diverse Python libraries and tools like NumPy, DuckDB, Apache Arrow, XGBoost and a host of others to accommodate more advanced algorithms. Each chapter ends with a discussion link and QR code for developers to learn more and converse more in depth with fellow pandas developers on Discord. I found this material clear and concise in addition to being easy to follow.
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  • ojo123
    5.0 out of 5 stars Review of the "Pandas Cookbook, Third Edition"
    Reviewed in the United States on April 19, 2025
    This cookbook delivers a thorough, recipe-style guide to mastering data analysis, time series, and scientific computing using Python's pandas library. Building on previous editions, it now includes extensive coverage of new features introduced in pandas 2.x, including enhanced integration with PyArrow.
    Key Strengths
    1. Comprehensive Coverage of Core pandas Features
    The book effectively guides readers through the fundamentals of Pandas, such as creating and manipulating DataFrame and Series objects, before progressing to advanced techniques like multi- indexing, reshaping (via melt, pivot, stack), and time- series analysis. Each concept is supported with clear, executable code snippets that aid step- by- step learning.
    2. Practical, Recipe- Based Format
    Organized around specific tasks—reading CSVs, merging tables, grouping, and summarizing—the cookbook' s modular, self- contained recipes allow readers to jump directly to the topics they need. This makes it a convenient tool for busy developers who prefer focused, actionable guidance over a cover- to- cover read.
    3. Integration with the Modern Pandas Ecosystem
    The authors thoughtfully position Pandas within the wider Python data landscape, showcasing how it integrates with tools like NumPy, PyArrow, DuckDB, Ibis, and Polars. This context is especially useful for those looking to scale workflows beyond single- machine analysis while retaining pandas’ familiar interface.
    4. Real- World, Practical Examples
    Examples are grounded in realistic datasets—stock prices, sports stats, crime reports, Excel files—and demonstrate how to clean and structure messy data into analysis- ready formats. The inclusion of performance tips (e. g., avoiding object dtypes, using efficient grouping patterns) makes the content particularly valuable for production scenarios.
    Who Should Read This Book
    • Intermediate to advanced Python users looking to deepen their understanding of pandas, particularly in areas like grouping, reshaping, and time- series analytics.
    • Data scientists and analysts who regularly handle large, complex datasets and want to adopt best practices for performance and scalability.
    • Engineers exploring how Pandas integrates with larger data processing frameworks like Arrow, Ibis, Dask, and Polars.
    Conclusion
    Pandas Cookbook, Third Edition is a must- have resource for Python developers aiming to elevate their data manipulation skills. Its task- oriented structure, modern tooling coverage, and practical examples make it both a powerful learning resource and a dependable reference. Whether you' re refining daily workflows or diving into the latest pandas capabilities, this book deserves a place on your shelf.
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Top reviews from other countries

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  • Stephen L.
    5.0 out of 5 stars Great Book Tutorial
    Reviewed in the United Kingdom on April 17, 2025
    Verified Purchase
    I'm making my way through this great cookbook made very easy to understand as I go along. Everything is broken down well and explained.
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  • Amazon Kunde
    5.0 out of 5 stars Sehr guter Rundumblick in die Pandas Bibliothek
    Reviewed in Germany on February 16, 2025
    Verified Purchase
    Dieses Buch erklärt sehr ausführlich und anschaulich alle relevanten Tipps und Hinweise, die bei der Benutzung der Pandas Bibliothek entscheidend sind.
    Nach dem Lesen dieses Buchs ist man gerüstet für den täglichen Einsatz der Pandas Bibliothek im Beruf und Alltag.
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  • sivakumar
    5.0 out of 5 stars A complete data analysis Bible on python
    Reviewed in India on January 25, 2025
    Verified Purchase
    Being a python developer, data wrangling using pandas is always a fun and this book offers to view all the hidden gems of pandas 2.x and enlightening readers with all the best ways to use the feat available. And and is an expert bringing all with his precise writing.
    Customer image
    sivakumar
    5.0 out of 5 stars
    A complete data analysis Bible on python

    Reviewed in India on January 25, 2025
    Being a python developer, data wrangling using pandas is always a fun and this book offers to view all the hidden gems of pandas 2.x and enlightening readers with all the best ways to use the feat available. And and is an expert bringing all with his precise writing.
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  • Corneliu
    5.0 out of 5 stars Great source on data manipulation and visualization with pandas, Matplotlib and seaborn libraries.
    Reviewed in Canada on March 27, 2025
    Verified Purchase
    I love this book because it is very well organized and has a lot of examples. It describes in great details how to use the Pandas library for manipulating my data including grouping, reshaping, aggregating, etc. It also provides examples of importing and exporting to various file formats and SQL databases. I liked the most the section about the possibilities to work with timeseries data sets and I am using it for my algorithmic trading profession. Also, this book describes very well methods of data visualization. I would recommend this book for intermediate beginners and those who need a nice overview on timeseries capabilities of pandas library.
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