Download This PDF Book: Thinking in Pandas: How to Use the Python Data Analysis Library the Right Way 1st ed by Hannah Stepanek
Introduction:
Using the pandas Python library requires a shift in thinking that is not always intuitive for those who use it. For beginners, pandas’ rich API can often be overwhelming and unclear when determining which solution is optimal.
This book aims to give you an intuition for using pandas correctly by explaining how its operations work underneath. We will establish a foundation of knowledge covering information such as Python and NumPy data structures, computer architecture, and performance differences between Python and C.
With this foundation, we will then be able to explain why certain pandas operations perform the way they do under certain circumstances.
We’ll learn when to use certain operations and when to use a more performant alternative. And near the end we’ll cover what improvements can be and are being made to make pandas even more performant.
Understand and implement big data analysis solutions in pandas with an emphasis on performance. This book strengthens your intuition for working with pandas, the Python data analysis library, by exploring its underlying implementation and data structures.
Thinking in Pandas introduces the topic of big data and demonstrates concepts by looking at exciting and impactful projects that pandas helped to solve.
From there, you will learn to assess your own projects by size and type to see if pandas is the appropriate library for your needs. Author Hannah Stepanek explains how to load and normalize data in pandas efficiently, and reviews some of the most commonly used loaders and several of their most powerful options.
You will then learn how to access and transform data efficiently, what methods to avoid, and when to employ more advanced performance techniques.
You will also go over basic data access and munging in pandas and the intuitive dictionary syntax. Choosing the right DataFrame format, working with multi-level DataFrames, and how pandas might be improved upon in the future are also covered.
By the end of the book, you will have a solid understanding of how the pandas library works under the hood. Get ready to make confident decisions in your own projects by utilizing pandas—the right way.
What You Will Learn
Understand the underlying data structure of pandas and why it performs the way it does under certain circumstances
Discover how to use pandas to extract, transform, and load data correctly with an emphasis on performance
Choose the right DataFrame so that the data analysis is simple and efficient.
Improve performance of pandas operations with other Python libraries
Who This Book Is ForSoftware engineers with basic programming skills in Python keen on using pandas for a big data analysis project. Python software developers interested in big data.
About the Author
Hannah Stepanek is a software developer with a passion for performance and is an open source advocate. She has over seven years of industry experience programming in Python and spent about two of those years implementing a data analysis project using pandas.
Hannah was born and raised in Corvallis, OR, and graduated from Oregon State University with a major in Electrical Computer Engineering. She enjoys engaging with the software community, often giving talks at local meetups as well as larger conferences.
In early 2019, she spoke at PyCon US about the pandas library and at OpenCon Cascadia about the benefits of open source software. In her spare time she enjoys riding her horse Sophie and playing board games.
Contents:
Chapter 1: Introduction1
Chapter 2: Basic Data Access and Merging
Chapter 3: How pandas Works Under the Hood
Chapter 4: Loading and Normalizing Data
Chapter 5: Basic Data Transformation in pandas
Chapter 6: The apply Method
Chapter 7: Groupby
Chapter 8: Performance Improvements Beyond pandas
Chapter 9: The Future of pandas
Appendix: Useful Reference Tables
About The Book:
Publisher : Apress; 1st ed. edition (June 5, 2020)
Language : English
File : PDF, 7 MB
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