Bøger af David Mertz

Move Beyond Python Code That "Mostly Works" to Code That Is Expressive, Robust, and Efficient Python is arguably the most-used programming language in the world, with applications from primary school education to workaday web development, to the most advanced scientific research institutes. While there are many ways to perform a task in Python, some are wrong, inelegant, or inefficient. Better Python Code is a guide to "Pythonic" programming, a collection of best practices, ways of working, and nuances that are easy to miss, especially when ingrained habits are borrowed from other programming languages. Author David Mertz presents concrete and concise examples of various misunderstandings, pitfalls, and bad habits in action. He explains why some practices are better than others, based on his 25+ years of experience as an acclaimed contributor to the Python community. Each chapter thoroughly covers related clusters of concepts, with chapters sequenced in ascending order of sophistication. Whether you are starting out with Python or are an experienced developer pushing through the limitations of your Python code, this book is for all who aspire to be more Pythonic when writing better Python code. Use the right kind of loops in Python Learn the ins and outs of mutable and immutable objects Get expert advice to avoid Python "gotchas" Examine advanced Python topics Navigate the "attractive nuisances" that exist in Python Learn the most useful data structures in Python and how to avoid misusing them Avoid security mistakes Understand the basics of numeric computation, including floating point numbers and numeric datatypes "My high expectations for this engaging Python book have been exceeded: it offers a great deal of insight for intermediate or advanced programmers to improve their Python skills, includes copious sharing of precious experience practicing and teaching the language, yet remains concise, easy to read, and conversational."--From the Foreword by Alex Martelli Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.

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 will 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 will have a clear understanding of where AI assistants can reliably write code for you and where you will still need a human touch. Plus, you will learn a lot about regular expressions! About the reader Code examples use simple Python and Regular Expressions. No experience with AI coding tools is required.

In Data Cleaning for Effective Data Science, leading data science trainer David Mertz provides the most systematic guide to cleaning data for any project, using any library or toolset. Mertz introduces many powerful techniques for analyzing, manipulating, and pre-processing data sources. He offers best practices for working with leading data formats such as JSON, CSV, SQL RDBMSes, HDF5, NoSQL databases, files in image formats, binary serialized data structures, and more. Mertz also focuses on crucial issues within the data itself, including missing data, outliers, biasing trends, class imbalance, value imputation, over/under-sampling, normalization and/or randomization, and anomalies. This guide is organized around downloadable datasets, each illuminating specific issues with data integrity or quality. Each chapter explores the best ways to diagnose, analyze, and remediate these issues, offering hands-on practice using tools such as Python, Pandas, sklearn.preprocessing, scipy.stats, R, and Tidyverse. While the examples are demonstrated with widely-used tools, Mertz's concepts are applicable with any toolset. Each chapter also links to additional datasets with more problems, exercises, and solutions. Ancillary resources include Instructor Notes and PowerPoint lecture slides, which will both be downloadable from Pearson.com/us.

Data in its raw state is rarely ready for productive analysis. This book not only teaches you data preparation, but also what questions you should ask of your data. It focuses on the thought processes necessary for successful data cleaning as much as on concise and precise code examples that express these thoughts.