Python Debugging Handbook

Python Debugging Handbook
Author: R. Zimmerman
Publisher:
Total Pages: 206
Release: 2020-02-07
Genre:
ISBN:

Color Edition.This book is a systematic plan to debug your programs. The focus is not on the Python language, although Chapter 3 does cover the basics. Instead, my focus is always on debugging. If you're new to Python debugging, I think this book is a good starting point. Experienced Python programmers might want to review the table of contents, to see if there's anything special that catches their interest.The sample code demonstrates lists, tuples, loops, or classes, but in the simplest form I could imagine. Chapters 1-2 outline how to set up your Python debugging environment, and establish a debugging plan as you write your code.1.Work on small chunks of code, test, and then move on to the next piece.2.Keep multiple backup versions of your files.3.Have a clear idea of what you want your program to do.4.Use small data file samples that you know have clean data to develop your code. When you've tested your code and are confident there are no bugs, use live data connections or real data files. 5.Keep notes of where you stopped programming and the next steps.6.Divide and concur. Divide the code in half and test each half to see which half has the error. Repeat to drill down to the location with the error.7.When debugging, keep a record of experiments, so you know what you've already tried.Chapter 4 has simple suggestions for debugging your code, with specific examples of the code and results. Even if you've never seen Spyder or Python before, at the end of this chapter, I hope you'll feel confident debugging most of the issues you'll encounter.*With the Debugging Overview, you'll learn about the Editor, Variable Explorer, and Debug Mode and Interactive Mode in the Console. We'll look at those times when you don't see your object in Variable Explorer, and explore why the Console traceback says "NameError."*Delve into Debug Mode, including basic commands, how to set a breakpoint, and examples of stepping through the code.*Add Print Statements (and visual clues for the depth of loop statements).*Logging for those times when print statements roll off the screen.*Use Interactive Mode with several common Console commands and two magic commands. [object name]?dir(object)help(object)%debug%timeitChapter 5 briefly looks at the types of errors you may encounter, and then in Chapter 6, you can try out your debugging knowledge. We'll look at the syntax for retrieving object values, type information, the length of objects or data structures, arguments, and return values. Because syntax varies based on the type and length of objects, there are numerous examples for strings, numbers, tuples, lists, and dictionaries. We'll also look at the special "None" value, unique to Python.Chapter 7 is chock full of examples. The format for each example is the same: Description, Intended Outcome, Actual Result, Incorrect Code, Debugging Steps, How to Resolve the Issue, Good Code, and a Reference to earlier related topics.Finally, the Appendix-Reference chapter includes links to the Python.org docs and the iPython.readthedocs websites for more detailed information.

Python Data Science Handbook

Python Data Science Handbook
Author: Jake VanderPlas
Publisher: "O'Reilly Media, Inc."
Total Pages: 743
Release: 2016-11-21
Genre: Computers
ISBN: 1491912138

For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools. Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python. With this handbook, you’ll learn how to use: IPython and Jupyter: provide computational environments for data scientists using Python NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python Matplotlib: includes capabilities for a flexible range of data visualizations in Python Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms

Pro Python Best Practices

Pro Python Best Practices
Author: Kristian Rother
Publisher: Apress
Total Pages: 280
Release: 2017-03-15
Genre: Computers
ISBN: 1484222415

Learn software engineering and coding best practices to write Python code right and error free. In this book you’ll see how to properly debug, organize, test, and maintain your code, all of which leads to better, more efficient coding. Software engineering is difficult. Programs of any substantial length are inherently prone to errors of all kinds. The development cycle is full of traps unknown to the apprentice developer. Yet, in Python textbooks little attention is paid to this aspect of getting your code to run. At most, there is a chapter on debugging or unit testing in your average basic Python book. However, the proportion of time spent on getting your code to run is much higher in the real world. Pro Python Best Practices aims to solve this problem. What You'll Learn Learn common debugging techniques that help you find and eliminate errors Gain techniques to detect bugs more easily discover best="" practices="" to="" prevent="" bugscarry="" out="" automated="" testing="" discover="" problems="" fasteruse="" maintain="" a="" project="" over="" long="" timeLearn techniques to keep your project under controlbr/uldivbWho This Book Is For/bbr/divdivbr/divdivExperienced Python coders from web development, big data, and more./divdivbr/divdivdiv/div

Python Handbook

Python Handbook
Author: James Pattrick
Publisher: Createspace Independent Publishing Platform
Total Pages: 422
Release: 2017-05-29
Genre:
ISBN: 9781547124060

Python Handbook has a dynamic and varied nature. It reads easily and lays a good foundation for those who are interested in digging deeper. It has a practical and example-oriented approach through which both the introductory and the advanced topics are explained. Starting with the fundamentals of programming and Python, it ends by exploring very different topics, like GUIs, web apps and data science. The book takes you all the way to creating a fully fledged application. The book begins by exploring the essentials of programming, data structures and teaches you how to manipulate them. It then moves on to controlling the flow of a program and writing reusable and error proof code. You will then explore different programming paradigms that will allow you to find the best approach to any situation, and also learn how to perform performance optimization as well as effective debugging. Throughout, the book steers you through the various types of applications, and it concludes with a complete mini website built upon all the concepts that you learned.

Python Data Science Handbook

Python Data Science Handbook
Author: Jake VanderPlas
Publisher: "O'Reilly Media, Inc."
Total Pages: 548
Release: 2016-11-21
Genre: Computers
ISBN: 1491912146

For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools. Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python. With this handbook, you’ll learn how to use: IPython and Jupyter: provide computational environments for data scientists using Python NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python Matplotlib: includes capabilities for a flexible range of data visualizations in Python Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms

Python For Dummies

Python For Dummies
Author: Stef Maruch
Publisher: John Wiley & Sons
Total Pages: 443
Release: 2011-05-09
Genre: Computers
ISBN: 1118084845

Python is one of the most powerful, easy-to-read programming languages around, but it does have its limitations. This general purpose, high-level language that can be extended and embedded is a smart option for many programming problems, but a poor solution to others. Python For Dummies is the quick-and-easy guide to getting the most out of this robust program. This hands-on book will show you everything you need to know about building programs, debugging code, and simplifying development, as well as defining what actions it can perform. You’ll wrap yourself around all of its advanced features and become an expert Python user in no time. This guide gives you the tools you need to: Master basic elements and syntax Document, design, and debug programs Work with strings like a pro Direct a program with control structures Integrate integers, complex numbers, and modules Build lists, stacks, and queues Create an organized dictionary Handle functions, data, and namespace Construct applications with modules and packages Call, create, extend, and override classes Access the Internet to enhance your library Understand the new features of Python 2.5 Packed with critical idioms and great resources to maximize your productivity, Python For Dummies is the ultimate one-stop information guide. In a matter of minutes you’ll be familiar with Python’s building blocks, strings, dictionaries, and sets; and be on your way to writing the program that you’ve dreamed about!

Effective Debugging

Effective Debugging
Author: Diomidis Spinellis
Publisher: Addison-Wesley Professional
Total Pages: 512
Release: 2016-06-29
Genre: Computers
ISBN: 0134394887

Every software developer and IT professional understands the crucial importance of effective debugging. Often, debugging consumes most of a developer’s workday, and mastering the required techniques and skills can take a lifetime. In Effective Debugging, Diomidis Spinellis helps experienced programmers accelerate their journey to mastery, by systematically categorizing, explaining, and illustrating the most useful debugging methods, strategies, techniques, and tools. Drawing on more than thirty-five years of experience, Spinellis expands your arsenal of debugging techniques, helping you choose the best approaches for each challenge. He presents vendor-neutral, example-rich advice on general principles, high-level strategies, concrete techniques, high-efficiency tools, creative tricks, and the behavioral traits associated with effective debugging. Spinellis’s 66 expert techniques address every facet of debugging and are illustrated with step-by-step instructions and actual code. He addresses the full spectrum of problems that can arise in modern software systems, especially problems caused by complex interactions among components and services running on hosts scattered around the planet. Whether you’re debugging isolated runtime errors or catastrophic enterprise system failures, this guide will help you get the job done—more quickly, and with less pain. Key features include High-level strategies and methods for addressing diverse software failures Specific techniques to apply when programming, compiling, and running code Better ways to make the most of your debugger General-purpose skills and tools worth investing in Advanced ideas and techniques for escaping dead-ends and the maze of complexity Advice for making programs easier to debug Specialized approaches for debugging multithreaded, asynchronous, and embedded code Bug avoidance through improved software design, construction, and management

Python Recipes Handbook

Python Recipes Handbook
Author: Joey Bernard
Publisher: Apress
Total Pages: 148
Release: 2016-11-08
Genre: Computers
ISBN: 1484202414

Learn the code to write algorithms, numerical computations, data analysis and much more using the Python language: look up and re-use the recipes for your own Python coding. This book is your handy code cookbook reference. Whether you're a maker, game developer, cloud computing programmer and more, this is a must-have reference for your library. Python Recipes Handbook gives you the most common and contemporary code snippets, using pandas (Python Data Analysis Library), NumPy, and other numerical Python packages. What You'll Learn Code with the pandas (Python Data Analysis Library) Work with the various Python algorithms useful for today's big data analytics and cloud applications Use NumPy and other numerical Python packages and code for doing various kinds of analysis Discover Python's new popular modules, packages, extensions and templates library Who This Book Is For This handy reference is for those with some experience with Python.

Debugging C++

Debugging C++
Author: Chris H. Pappas
Publisher: McGraw-Hill Companies
Total Pages: 552
Release: 2000
Genre: Computers
ISBN:

This troubleshooting reference for the C++ programmer is filled with solutions to common and rare bugs. It helps developers identify bad coding habits and build clean code.

Python 101

Python 101
Author: Michael Driscoll
Publisher: Lulu.com
Total Pages: 296
Release: 2014-06-03
Genre:
ISBN: 0996062815

Learn how to program with Python from beginning to end. This book is for beginners who want to get up to speed quickly and become intermediate programmers fast!