Hands-On Data Analysis with Pandas - Second Edition: A Python data science handbook for data collection, wrangling, analysis, and visualization
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- Автор: Stefanie Molin
- ISBN-10: 1800563450
- ISBN-13: 978-1800563452
- Edition: 2nd ed.
- Publisher: Packt Publishing
- Publication date: April 29, 2021
- Language: English
- Dimensions: 9.25 x 7.5 x 1.62 inches
- Print length: 788 pages
From the Publisher
What makes this second edition of Hands-On Data Analysis with Pandas stand out from other pandas titles?
Hands-On Data Analysis with Pandas is not your typical data science book. Say goodbye to the stereotypical datasets that most tutorials and books use and say hello to real-world data with real-world issues; after all, the data you will work with in real life won’t be perfect either.
This book shows you how to work with realistic datasets, so you can master the use of pandas for data analysis. Elements of software engineering are also included throughout the chapters, which will strengthen your programming skills—you’ll learn how to build scripts with command-line arguments, package analysis code in classes, and build Python packages for modular and reusable analysis code.
What's new in the second edition of Hands-On Data Analysis with Pandas?
In this edition, the code examples have been updated for newer versions of the libraries used. The book also features new and revised examples highlighting new features in pandas 1.2. In addition, there are significant changes to the content of some chapters, while others have new examples and/or datasets.
What are the key takeaways for the readers buying this book?
Working with data doesn’t preclude good programming skills. This book will instill confidence and teach the concepts needed to write quality data science code using pandas and other Python data science libraries. You'll be able to apply new data wrangling and visualization skills to a variety of real-world datasets and have the confidence to search for solutions to common problems in both the documentation and resources like Stack Overflow with a solid foundation in pandas.