Pandas Cookbook: Practical recipes for scientific computing, time series, and exploratory data analysis using Python

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.

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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

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