Matt Harrison - Pandas 1.x Cookbook: Practical recipes for scientific computing, time series analysis, and exploratory data analysis using Python, 2nd Edition
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- Book:Pandas 1.x Cookbook: Practical recipes for scientific computing, time series analysis, and exploratory data analysis using Python, 2nd Edition
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Use the power of pandas to solve most complex scientific computing problems with ease. Revised for pandas 1.x.
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
The pandas library is massive, and its 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 doesnt fit in memory
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.
Table of Contents- Pandas Foundations
- Essential DataFrame Operations
- Creating and Persisting DataFrames
- Beginning Data Analysis
- Exploratory Data Analysis
- Selecting Subsets of Data
- Filtering Rows
- Index Alignment
- Grouping for Aggregation, Filtration and Transformation
- Restructuring Data into a Tidy Form
- Combining Pandas Objects
- Time Series Analysis
- Visualization with Matplotlib, Pandas, and Seaborn
- Debugging and Testing Pandas
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Pandas 1.x Cookbook
Second Edition
Practical recipes for scientific computing, time series analysis, and exploratory data analysis using Python
Matt Harrison
Theodore Petrou
BIRMINGHAM - MUMBAI
Pandas 1.x Cookbook
Second Edition
Copyright 2020 Packt Publishing
All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.
Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the authors, nor Packt Publishing or its dealers and distributors, will be held liable for any damages caused or alleged to have been caused directly or indirectly by this book.
Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.
Producer: Tushar Gupta
Acquisition Editor Peer Reviews: Suresh Jain
Content Development Editor: Kate Blackham
Technical Editor: Gaurav Gavas
Project Editor: Kishor Rit
Proofreader: Safis Editing
Indexer: Pratik Shirodkar
Presentation Designer: Sandip Tadge
First published: October 2017
Second edition: February 2020
Production reference: 1260220
Published by Packt Publishing Ltd.
Livery Place
35 Livery Street
Birmingham B3 2PB, UK.
ISBN 978-1-83921-310-6
www.packt.com
Packt.com
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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 best-selling Illustrated Guide to Python 3, and Learning the Pandas Library, as well as other books.
Theodore Petrou is a data scientist and the founder of Dunder Data, a professional educational company focusing on exploratory data analysis. He is also the head of Houston Data Science, a meetup group with more than 2,000 members that has the primary goal of getting local data enthusiasts together in the same room to practice data science. Before founding Dunder Data, Ted was a data scientist at Schlumberger, a large oil services company, where he spent the vast majority of his time exploring data.
Some of his projects included using targeted sentiment analysis to discover the root cause of part failure from engineer text, developing customized client/server dashboarding applications, and real-time web services to avoid the mispricing of sales items. Ted received his masters degree in statistics from Rice University, and used his analytical skills to play poker professionally and teach math before becoming a data scientist. Ted is a strong supporter of learning through practice and can often be found answering questions about pandas on Stack Overflow.
Simon Hawkins holds a master's degree in aeronautical engineering from Imperial College London. During the early part of his career, he worked exclusively in the defense and nuclear sectors as a technology analyst focusing on various modelling capabilities and simulation techniques for high-integrity equipment. He then transitioned into the world of e-commerce and the focus shifted toward data analysis. Today, he is interested in all things data science and is a member of the pandas core development team.
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 (matt@metasnake.com).
This book contains nearly 100 recipes, ranging from very simple to advanced. All recipes strive to be written in clear, concise, and modern idiomatic pandas code. The How it works... sections contain extremely detailed descriptions of the intricacies of each step of the recipe. Often, in the There's more... section, you will get what may seem like an entirely new recipe. This book is densely packed with an extraordinary amount of pandas code.
As a generalization, the recipes in the first seven chapters tend to be simpler and more focused on the fundamental and essential operations of pandas than the later chapters, which focus on more advanced operations and are more project-driven. Due to the wide range of complexity, this book can be useful to both novice and everyday users alike. It has been my experience that even those who use pandas regularly will not master it without being exposed to idiomatic pandas code. This is somewhat fostered by the breadth that pandas offers. There are almost always multiple ways of completing the same operation, which can have users get the result they want but in a very inefficient manner. It is not uncommon to see an order of magnitude or more in performance difference between two sets of pandas solutions to the same problem.
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