Better Python Code: A Guide for Aspiring Experts

Computers & Technology

Mertz, David

Programming Languages

Addison-Wesley Professional

<p>Text Processing with Python, written by Python authority David Mertz, teaches programmers how to accomplish numerous text processing tasks using the Python language. Filled with concrete examples, this book provides effective solutions to specific text processing problems and practical strategies for dealing with all types of text processing challenges. It provides the answers to questions such as: What is the best way to convert from binary to ASCII?; How do I work with full text indexing?; How do I find a URL or an email address in text?; What are the different levels of pattern matching?; How do I process a report with a concrete state machine?; How do I parse, create and manipulate HTML documents?; How do I handle a lossless and lossy compression?; And what is the most efficient way to find codepoints in Unicode?</p>

Text Processing in Python

Mike Hendrickson

<p>Learn how AI-assisted coding using ChatGPT and GitHub Copilot can dramatically increase your productivity (and fun) writing regular expressions and other programs.\nRegular Expression Puzzles and AI Coding Assistants is the story of two competitors. On one side is David Mertz, an expert programmer and the author of the Web's most popular Regex tutorial. On the other are the AI powerhouse coding assistants, GitHub Copilot and OpenAI ChatGPT.\nHere's how the contest works: David invents 24 Regex problems he calls puzzles and shows you how to tackle each one. When he's done, he has Copilot and ChatGPT work the same puzzles. What they produce intrigues him. Which side is likelier to get it right? Which will write simple and elegant code? Which makes smarter use of lesser known Regex library features? Read the book to find out.\nDavid also offers AI best practices, showing how smart prompts return better results. By the end, you'll be a master at solving your own Regex puzzles, whether you use AI or not.\nPurchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.\nAbout the technology<br />
Groundbreaking large language model research from OpenAI, Google, Amazon, and others have transformed expectations of machine-generated software. But how do these AI assistants, like ChatGPT and GitHub Copilot, measure up against regular expressions—a workhorse technology for developers used to describe, find, and manipulate patterns in text. Regular expressions are compact, complex, and subtle. Will AI assistants handle the challenge?\nAbout the book<br />
Regular Expression Puzzles and AI Coding Assistants is the perfect starting point for programmers of any experience level who want to understand the capabilities—and the limitations—of these exciting new tools. Author David Mertz presents 24 challenging regex puzzles, their traditional human-made solutions, and the fascinating answers given by popular AI assistants. Alongside these eye-opening puzzles you’ll learn how to write prompts, integrate AI-generated coding suggestions, and interact with the assistant to get the results you want. By the end of the book, you'll have a clear understanding of where AI assistants can reliably write code for you and where you’ll still need a human touch. Plus, you’ll learn a lot about regular expressions!\nAbout the reader<br />
Code examples use simple Python and Regular Expressions. No experience with AI coding tools required.\nAbout the author<br />
David Mertz is the founder of KDM Training and an acclaimed contributor to the Python community. He is also the author of The Puzzling Quirks of Regular Expressions, Cleaning Data for Effective Data Science: Doing the Other 80% of the Work, and other books.\nTable of Contents<br />
1 The map and the territory<br />
2 Quantifiers and special sub-patterns<br />
3 Pitfalls and sand in the gears<br />
4 Creating functions using regex<br />
5 Easy, difficult, and impossible tasks<br />
6 Conclusions<br />
Appendix A: Learning to use regular expressions</p>

Regular Expression Puzzles and AI Coding Assistants: 24 puzzles solved by the author, with and without assistance from Copilot, ChatGPT and more

Computer Science

Manning

<p><p><b>Think about your data intelligently and ask the right questions</b></p> <h4>Key Features</h4> <ul> <li>Master data cleaning techniques necessary to perform real-world data science and machine learning tasks</li> <li>Spot common problems with dirty data and develop flexible solutions from first principles</li> <li>Test and refine your newly acquired skills through detailed exercises at the end of each chapter</li> </ul> <h4>Book Description</h4> <p>Data cleaning is the all-important first step to successful data science, data analysis, and machine learning. If you work with any kind of data, this book is your go-to resource, arming you with the insights and heuristics experienced data scientists had to learn the hard way.</p> <p>In a light-hearted and engaging exploration of different tools, techniques, and datasets real and fictitious, Python veteran David Mertz teaches you the ins and outs of data preparation and the essential questions you should be asking of every piece of data you work with.</p> <p>Using a mixture of Python, R, and common command-line tools, Cleaning Data for Effective Data Science follows the data cleaning pipeline from start to end, focusing on helping you understand the principles underlying each step of the process. You'll look at data ingestion of a vast range of tabular, hierarchical, and other data formats, impute missing values, detect unreliable data and statistical anomalies, and generate synthetic features. The long-form exercises at the end of each chapter let you get hands-on with the skills you've acquired along the way, also providing a valuable resource for academic courses.</p> <h4>What you will learn</h4> <ul> <li>Ingest and work with common data formats like JSON, CSV, SQL and NoSQL databases, PDF, and binary serialized data structures</li> <li>Understand how and why we use tools such as pandas, SciPy, scikit-learn, Tidyverse, and Bash</li> <li>Apply useful rules and heuristics for assessing data quality and detecting bias, like Benford's law and the 68-95-99.7 rule</li> <li>Identify and handle unreliable data and outliers, examining z-score and other statistical properties</li> <li>Impute sensible values into missing data and use sampling to fix imbalances</li> <li>Use dimensionality reduction, quantization, one-hot encoding, and other feature engineering techniques to draw out patterns in your data</li> <li>Work carefully with time series data, performing de-trending and interpolation</li> </ul> <h4>Who this book is for</h4> <p>This book is designed to benefit software developers, data scientists, aspiring data scientists, teachers, and students who work with data. If you want to improve your rigor in data hygiene or are looking for a refresher, this book is for you.</p> <p>Basic familiarity with statistics, general concepts in machine learning, knowledge of a programming language (Python or R), and some exposure to data science are helpful.</p></p>

Cleaning Data for Effective Data Science: Doing the other 80% of the work with Python, R, and command-line tools

Packt Publishing

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Books by Mertz, David