Pandas Cookbook : Practical Recipes for Scientific Computing, Time Series, and Exploratory Data Analysis Using Python / William Ayd, Matthew Harrison, and Wes McKinney.

Format:

Book

Author/Creator:

Ayd, William, author.

Harrison, Matthew, author.

McKinney, Wes, author.

Language:

English

Subjects (All):

Data mining.

Programming languages (Electronic computers).

Python (Computer program language).

Physical Description:

1 online resource (0 pages)

Edition:

Third edition.

Place of Publication:

Birmingham, England : Packt Publishing, [2024]

Biography/History:

Ayd William: 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 itHarrison Matthew: 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

Summary:

"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. 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 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. With this latest edition unlock the full potential of pandas 2.x onwards. Whether you're a beginner or an experienced data analyst, this book offers a wealth of practical recipes to help you excel in your data analysis projects. This cookbook covers everything from fundamental data manipulation tasks to advanced techniques for handling big data, visualization, and more. Each recipe is designed to address common real-world challenges, providing clear explanations and step-by-step instructions to guide you through the process. Explore cutting-edge topics such as idiomatic pandas coding, efficient handling of large datasets, and advanced data visualization techniques. Whether you're looking to sharpen or expand your skills, the ""Pandas Cookbook"" is your essential companion for mastering data analysis and manipulation with pandas 2.x, and beyond. 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".

Contents:

Cover

Copyright

Foreword

Contributors

Table of Contents

Preface

Chapter 1: pandas Foundations

Importing pandas

Series

DataFrame

Index

Series attributes

DataFrame attributes

Chapter 2: Selection and Assignment

Basic selection from a Series

Basic selection from a DataFrame

Position-based selection of a Series

Position-based selection of a DataFrame

Label-based selection from a Series

Label-based selection from a DataFrame

Mixing position-based and label-based selection

DataFrame.filter

Selection by data type

Selection/filtering via Boolean arrays

Selection with a MultiIndex – A single level

Selection with a MultiIndex – Multiple levels

Selection with a MultiIndex – a DataFrame

Item assignment with .loc and .iloc

DataFrame column assignment

Chapter 3: Data Types Generated by AI.

Notes:

Description based on publisher supplied metadata and other sources.

Part of the metadata in this record was created by AI, based on the text of the resource.

Description based on print version record.

ISBN:

1-83620-586-4

OCLC:

1468098148