Pandas 1.x Cookbook Second Edition

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

Matt Harrison , Theodore Petrou 지음

Packt(GCO Science) 2020년 02월 27일 출간

작품소개

책소개

Use the power of pandas to solve most complex scientific computing problems with ease. Revised for pandas 1.x.

▶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.

This new updated and revised edition provides you with unique, idiomatic, and fun recipes for both fundamental and advanced data manipulation tasks with pandas. Some recipes focus on achieving a deeper understanding of basic principles, or comparing and contrasting two similar operations. Other recipes will dive deep into a particular dataset, uncovering new and unexpected insights along the way. Many advanced recipes combine several different features across the pandas library to generate results.

▶What You Will Learn
-Master data exploration in pandas through dozens of practice problems
-Group, aggregate, transform, reshape, and filter data
-Merge data from different sources through pandas SQL-like operations
-Create visualizations via pandas hooks to matplotlib and seaborn
-Use pandas, time series functionality to perform powerful analyses
-Import, clean, and prepare real-world datasets for machine learning
-Create workflows for processing big data that doesn't fit in memory

▶Key Features
-This is the first book on pandas 1.x
-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

▶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.

목차

▶TABLE of CONTENTS
-Chapter 1: Pandas Foundations
-Chapter 2: Essential DataFrame Operations
-Chapter 3: Creating and Persisting DataFrames
-Chapter 4: Beginning Data Analysis
-Chapter 5: Exploratory Data Analysis
-Chapter 6: Selecting Subsets of Data
-Chapter 7: Filtering Rows
-Chapter 8: Index Alignment
-Chapter 9: Grouping for Aggregation, Filtration, and Transformation
-Chapter 10: Restructuring Data into a Tidy Form
-Chapter 11: Combining Pandas Objects
-Chapter 12: Time Series Analysis
-Chapter 13: Visualization with Matplotlib, Pandas, and Seaborn
-Chapter 14: Debugging and Testing Pandas

책 속으로

▶What this book covers
- Chapter 1, Pandas Foundations, covers the anatomy and vocabulary used to identify the components of the two main pandas data structures, the Series and the DataFrame. Each column must have exactly one type of data, and each of these data types is covered. You will learn how to unleash the power of the Series and the DataFrame by calling and chaining together their methods.

- Chapter 2, Essential DataFrame Operations, focuses on the most crucial and typical operations that you will perform during data analysis.

- Chapter 3, Creating and Persisting DataFrames, discusses the various ways to ingest data and create DataFrames.

- Chapter 4, Beginning Data Analysis, helps you develop a routine to get started after reading in your data.

- Chapter 5, Exploratory Data Analysis, covers basic analysis techniques for comparing numeric and categorical data. This chapter will also demonstrate common visualization techniques.

- Chapter 6, Selecting Subsets of Data, covers the many varied and potentially confusing ways of selecting different subsets of data.

- Chapter 7, Filtering Rows, covers the process of querying your data to select subsets of it based on Boolean conditions.

- Chapter 8, Index Alignment, targets the very important and often misunderstood index object. Misuse of the Index is responsible for lots of erroneous results, and these recipes show you how to use it correctly to deliver powerful results.

- Chapter 9, Grouping for Aggregation, Filtration, and Transformation, covers the powerful grouping capabilities that are almost always necessary during data analysis. You will build customized functions to apply to your groups.

- Chapter 10, Restructuring Data into a Tidy Form, explains what tidy data is and why it's so important, and then it shows you how to transform many different forms of messy datasets into tidy ones.

- Chapter 11, Combining Pandas Objects, covers the many available methods to combine DataFrames and Series vertically or horizontally. We will also do some web-scraping and connect to a SQL relational database.

- Chapter 12, Time Series Analysis, covers advanced and powerful time series capabilities to dissect by any dimension of time possible.

- Chapter 13, Visualization with Matplotlib, Pandas, and Seaborn, introduces the matplotlib library, which is responsible for all of the plotting in pandas. We will then shift focus to the pandas plot method and, finally, to the seaborn library, which is capable of producing aesthetically pleasing visualizations not directly available in pandas.

- Chapter 14, Debugging and Testing Pandas, explores mechanisms of testing our DataFrames and pandas code. If you are planning on deploying pandas in production, this chapter will help you have confidence in your code.

출판사 서평

▶ Preface
pandas is a library for creating and manipulating structured data with Python. What do I mean by structured? I mean tabular data in rows and columns like what you would find in a spreadsheet or database. Data scientists, analysts, programmers, engineers, and more are leveraging it to mold their data.

pandas is limited to "small data" (data that can fit in memory on a single machine). However, the syntax and operations have been adopted or inspired other projects: PySpark, Dask, Modin, cuDF, Baloo, Dexplo, Tabel, StaticFrame, among others. These projects have different goals, but some of them will scale out to big data. So there is a value in understanding how pandas works as the features are becoming the defacto API for interacting with structured data.

I, Matt Harrison, run a company, MetaSnake, that does corporate training. My bread and butter is training large companies that want to level up on Python and data skills. As such, I've taught thousands of Python and pandas users over the years. My goal in producing the second version of this book is to highlight and help with the aspects that many find confusing when coming to pandas. For all of its benefits, there are some rough edges or confusing aspects of pandas. I intend to navigate you to these and then guide you through them, so you will be able to deal with them in the real world.

If your company is interested in such live training, feel free to reach out ([e-mail]).

작가정보

저자(글) Matt Harrison

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.

저자(글) Theodore Petrou

Theodore Petrou is the founder of Dunder Data, a training company dedicated to helping teach the Python data science ecosystem effectively to individuals and corporations. Read his tutorials and attempt his data science challenges at the Dunder Data website.