Pandas Cookbook
Practical recipes for scientific computing, time series, and exploratory data analysis using Python
3rd Edition
Published on 31. October 2024
Book
Paperback/Softback
404 pages
978-1-83620-587-6 (ISBN)
Description
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.
Get With Your Book: PDF Copy, AI Assistant, and Next-Gen Reader Free
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 DescriptionUnlock 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 forThis 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
Get With Your Book: PDF Copy, AI Assistant, and Next-Gen Reader Free
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 DescriptionUnlock 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 forThis 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
More details
Edition
3rd Revised edition
Language
English
Place of publication
Birmingham
United Kingdom
Edition type
Revised edition
Dimensions
Height: 235 mm
Width: 191 mm
Thickness: 22 mm
Weight
751 gr
ISBN-13
978-1-83620-587-6 (9781836205876)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
William Ayd Ayd, William | Matthew Harrison Harrison, Matthew | Wes McKinney McKinney, Wes
Pandas Cookbook
Practical recipes for scientific computing, time series, and exploratory data analysis using Python
E-Book
01/2025
3rd Edition
Packt Publishing
from
€29.99
Available for download
Persons
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
Content
Table of Contents
pandas Foundations
Selection and Assignment
Data Types
The pandas I/O System
Algorithms and How to Apply Them
Visualization
Reshaping DataFrames
Group By
Temporal Data Types and Algorithms
General Usage and Performance Tips
The pandas Ecosystem
pandas Foundations
Selection and Assignment
Data Types
The pandas I/O System
Algorithms and How to Apply Them
Visualization
Reshaping DataFrames
Group By
Temporal Data Types and Algorithms
General Usage and Performance Tips
The pandas Ecosystem