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| python quick reference guide: Python in a Nutshell Alex Martelli, 2006-07-14 This volume offers Python programmers a straightforward guide to the important tools and modules of this open source language. It deals with the most frequently used parts of the standard library as well as the most popular and important third party extensions. |
| python quick reference guide: The Quick Python Book Vernon L. Ceder, Naomi R. Ceder, 2010 Introduces the programming language's syntax, control flow, and basic data structures and covers its interaction with applications and mangement of large collections of code. |
| python quick reference guide: Python All-in-One For Dummies John C. Shovic, Alan Simpson, 2019-04-18 Your one-stop resource on all things Python Thanks to its flexibility, Python has grown to become one of the most popular programming languages in the world. Developers use Python in app development, web development, data science, machine learning, and even in coding education classes. There's almost no type of project that Python can't make better. From creating apps to building complex websites to sorting big data, Python provides a way to get the work done. Python All-in-One For Dummies offers a starting point for those new to coding by explaining the basics of Python and demonstrating how it’s used in a variety of applications. Covers the basics of the language Explains its syntax through application in high-profile industries Shows how Python can be applied to projects in enterprise Delves into major undertakings including artificial intelligence, physical computing, machine learning, robotics and data analysis This book is perfect for anyone new to coding as well as experienced coders interested in adding Python to their toolbox. |
| python quick reference guide: The Python Quick Syntax Reference Gregory Walters, 2014-02-28 The Python Quick Syntax Reference is the go to book that contains an easy to read and use guide to Python programming and development. This condensed code and syntax reference presents the Python language in a well-organized format designed to be used time and again. You won't find jargon, bloated samples, case studies, or history of Hello World and computer theory in this handy reference. This Python syntax reference is packed with useful information and is a must-have for any Python developer. |
| python quick reference guide: Python for Data Mining Quick Syntax Reference Valentina Porcu, 2018-12-19 Learn how to use Python and its structures, how to install Python, and which tools are best suited for data analyst work. This book provides you with a handy reference and tutorial on topics ranging from basic Python concepts through to data mining, manipulating and importing datasets, and data analysis. Python for Data Mining Quick Syntax Reference covers each concept concisely, with many illustrative examples. You'll be introduced to several data mining packages, with examples of how to use each of them. The first part covers core Python including objects, lists, functions, modules, and error handling. The second part covers Python's most important data mining packages: NumPy and SciPy for mathematical functions and random data generation, pandas for dataframe management and data import, Matplotlib for drawing charts, and scikitlearn for machine learning. What You'll Learn Install Python and choose a development environment Understand the basic concepts of object-oriented programming Import, open, and edit files Review the differences between Python 2.x and 3.x Who This Book Is For Programmers new to Python's data mining packages or with experience in other languages, who want a quick guide to Pythonic tools and techniques. |
| python quick reference guide: Python Quick Reference Guide JORDAN. LOOPMAN, 2025-02-11 Python Quick Reference Guide: The Cheat Sheet for Fast Learning Learn Python's Key Concepts and Boost Your Coding Productivity Struggling to remember Python syntax? Wasting time searching for basic commands? Stop Googling and start coding faster with this ultimate Python cheat sheet! This book is designed for beginners, students, and busy professionals who want a quick, easy-to-use reference guide to Python's most essential features. No fluff, no unnecessary explanations-just straight-to-the-point information to help you code efficiently. What You'll Learn Inside: ✔ Python Essentials - Variables, data types, loops, functions, and more ✔ Built-in Libraries - Master os, random, math, and other must-know modules ✔ Error Handling & Debugging - Fix bugs like a pro ✔ Productivity Hacks - Write cleaner, faster, and more Pythonic code ✔ Advanced Concepts - File handling, object-oriented programming, and automation Why This Book? ✅ Instant Access to Key Python Concepts - Perfect for quick reference ✅ Beginner-Friendly Yet Powerful - Simple explanations with practical examples ✅ Boost Your Coding Productivity - Spend less time stuck, more time building Whether you're learning Python for the first time or need a handy reference for daily coding, this concise, easy-to-follow guide will help you master Python quickly and effectively. ⚡ Get your copy today and start coding smarter! ⚡ |
| python quick reference guide: Python Programming Language Berajah Jayne, 2019-05 Created for developers of all skill levels to find the essentials of common operations combined with the fastest reference guide for writing code. This handy 6 page laminated guide is a concise desktop reference to key concepts behind Python logic, syntax, and operation. Expertly written to concisely cover the planning of a program written in Python, assigning your first variables, importing other libraries, formatting output strings, and creating classes. Beginning students or seasoned programmers will find this tool a perfect go-to for reference to those core concepts. This unbeatable value makes it easy to add this reference to your programmer's toolbox. 6 page laminated guide includes: Working with Python Using Python Code Importing Modules Scope (Indentation) Naming Conventions Reserved Keywords Comments Writing Code Basics Making Variables Types Console Error Handling Saving & Loading Files Coding Structures Math Operators (int, float & complex) List Operations (list, tuple & dict) Strings Statements Functions Dictionaries Using Structures String Formatting String Methods Escape Sequences Bool Characters Writing Boolean Statements Recursion & Iteration Classes Coding Concepts Inheritance Generators Polymorphism Lambda Expressions |
| python quick reference guide: Python Essential Reference David M. Beazley, 2009 Python Essential Reference is the definitive reference guide to the Python programming language--the one authoritative handbook that reliably untangles and explains both the core Python library. Designed for the practicing programmer, the book is concise, to the point, and highly accessible. It also includes detailed information on the Python library and many advanced subjects that is not available in either the official Python documentation or any other single reference source. Thoroughly updated to reflect the significant new programming language features and library modules that have been introduced in Python 2.6 and Python 3, the fourth edition of Python Essential Reference is the complete guide for programmers who need to modernize existing Python code or who are planning an eventual migration to Python 3. |
| python quick reference guide: The Hitchhiker's Guide to Python Kenneth Reitz, Tanya Schlusser, 2016-08-30 The Hitchhiker's Guide to Python takes the journeyman Pythonista to true expertise. More than any other language, Python was created with the philosophy of simplicity and parsimony. Now 25 years old, Python has become the primary or secondary language (after SQL) for many business users. With popularity comes diversityâ??and possibly dilution. This guide, collaboratively written by over a hundred members of the Python community, describes best practices currently used by package and application developers. Unlike other books for this audience, The Hitchhikerâ??s Guide is light on reusable code and heavier on design philosophy, directing the reader to excellent sources that already exist. |
| python quick reference guide: Python Standard Library Berajah Jayne, 2020-05 An extremely handy programmer's standard library reference that is as durable as it is portable. This 6 page laminated guide includes essential script modules used by developers of all skill levels to simplify the process of programming in Python. This guide is all script and is organized to find needed script quickly. As with QuickStudy reference on any subject, with continued reference, the format lends itself to memorization. Beginning students or seasoned programmers will find this tool a perfect go-to for the at-a-glance script answer and memory jog you might need. At this price and for the bank of script included it's an easy add to your programmer's toolbox. 6 page laminated guide includes: General Functionality Date/Time Processing System and Computer Controls OS Module Classes of the OS Module Pathlib Module Threading Module Debugging Functionality PDB Module Debugging for the PDB Module Mathematic and Numeric Operations Math Module Random Module Iterable and Iterator Operations Collections Module Classes of the Collections Module Itertools Module Web and Data Transfer Operations HTML Parser Module HTML Module Audio Manipulation |
| python quick reference guide: Recurrent Neural Networks with Python Quick Start Guide Simeon Kostadinov, 2018-11-30 Learn how to develop intelligent applications with sequential learning and apply modern methods for language modeling with neural network architectures for deep learning with Python's most popular TensorFlow framework. Key FeaturesTrain and deploy Recurrent Neural Networks using the popular TensorFlow libraryApply long short-term memory unitsExpand your skills in complex neural network and deep learning topicsBook Description Developers struggle to find an easy-to-follow learning resource for implementing Recurrent Neural Network (RNN) models. RNNs are the state-of-the-art model in deep learning for dealing with sequential data. From language translation to generating captions for an image, RNNs are used to continuously improve results. This book will teach you the fundamentals of RNNs, with example applications in Python and the TensorFlow library. The examples are accompanied by the right combination of theoretical knowledge and real-world implementations of concepts to build a solid foundation of neural network modeling. Your journey starts with the simplest RNN model, where you can grasp the fundamentals. The book then builds on this by proposing more advanced and complex algorithms. We use them to explain how a typical state-of-the-art RNN model works. From generating text to building a language translator, we show how some of today's most powerful AI applications work under the hood. After reading the book, you will be confident with the fundamentals of RNNs, and be ready to pursue further study, along with developing skills in this exciting field. What you will learnUse TensorFlow to build RNN modelsUse the correct RNN architecture for a particular machine learning taskCollect and clear the training data for your modelsUse the correct Python libraries for any task during the building phase of your modelOptimize your model for higher accuracyIdentify the differences between multiple models and how you can substitute themLearn the core deep learning fundamentals applicable to any machine learning modelWho this book is for This book is for Machine Learning engineers and data scientists who want to learn about Recurrent Neural Network models with practical use-cases. Exposure to Python programming is required. Previous experience with TensorFlow will be helpful, but not mandatory. |
| python quick reference guide: Learning Python Mark Lutz, 2013-06-12 Get a comprehensive, in-depth introduction to the core Python language with this hands-on book. Based on author Mark Lutz’s popular training course, this updated fifth edition will help you quickly write efficient, high-quality code with Python. It’s an ideal way to begin, whether you’re new to programming or a professional developer versed in other languages. Complete with quizzes, exercises, and helpful illustrations, this easy-to-follow, self-paced tutorial gets you started with both Python 2.7 and 3.3— the latest releases in the 3.X and 2.X lines—plus all other releases in common use today. You’ll also learn some advanced language features that recently have become more common in Python code. Explore Python’s major built-in object types such as numbers, lists, and dictionaries Create and process objects with Python statements, and learn Python’s general syntax model Use functions to avoid code redundancy and package code for reuse Organize statements, functions, and other tools into larger components with modules Dive into classes: Python’s object-oriented programming tool for structuring code Write large programs with Python’s exception-handling model and development tools Learn advanced Python tools, including decorators, descriptors, metaclasses, and Unicode processing |
| python quick reference guide: Python Language Reference Manual Guido Van Rossum, Fred L. Drake, 2003 Describes the syntax and datatypes of Python, an object-oriented interpreted programming language. |
| python quick reference guide: Python Martin C. Brown, 2001 Includes complete module guide and details on using Python for RAD--cover. |
| python quick reference guide: Perl to Python Migration Martin C. Brown, 2002 Perl programmers: Leverage your Perl skills with Python. Key issues developers face in migrating to Python--and practical solutions. Includes an in-depth introduction to Python's object-oriented facilities. Contains extensive sample code for data manipulation, networking, and Web programming. |
| python quick reference guide: Python Cookbook David Beazley, Brian K. Jones, 2013-05-10 If you need help writing programs in Python 3, or want to update older Python 2 code, this book is just the ticket. Packed with practical recipes written and tested with Python 3.3, this unique cookbook is for experienced Python programmers who want to focus on modern tools and idioms. Inside, youâ??ll find complete recipes for more than a dozen topics, covering the core Python language as well as tasks common to a wide variety of application domains. Each recipe contains code samples you can use in your projects right away, along with a discussion about how and why the solution works. Topics include: Data Structures and Algorithms Strings and Text Numbers, Dates, and Times Iterators and Generators Files and I/O Data Encoding and Processing Functions Classes and Objects Metaprogramming Modules and Packages Network and Web Programming Concurrency Utility Scripting and System Administration Testing, Debugging, and Exceptions C Extensions |
| python quick reference guide: Python Toby Donaldson, 2008-12-04 Python is a remarkably powerful dynamic programming language that is used in a wide variety of application domains such as Web, database access, desktop GUIs, game and software development, and network programming. Fans of Python use the phrase batteries included to describe the standard library, which covers everything from asynchronous processing to zip files. The language itself is a flexible powerhouse that can handle practically any application domain. This task-based tutorial is for students with no programming experience as well as those programmers who have some experience with the programming language and now want to take their skills to the next level. The book walks a reader through all the fundamentals and then moves on to more advanced topics. It's a complete end-to-end tutorial and reference. |
| python quick reference guide: Natural Language Processing with Python Quick Start Guide Nirant Kasliwal, 2018-11-30 Build and deploy intelligent applications for natural language processing with Python by using industry standard tools and recently popular methods in deep learning Key FeaturesA no-math, code-driven programmer’s guide to text processing and NLPGet state of the art results with modern tooling across linguistics, text vectors and machine learningFundamentals of NLP methods from spaCy, gensim, scikit-learn and PyTorchBook Description NLP in Python is among the most sought after skills among data scientists. With code and relevant case studies, this book will show how you can use industry-grade tools to implement NLP programs capable of learning from relevant data. We will explore many modern methods ranging from spaCy to word vectors that have reinvented NLP. The book takes you from the basics of NLP to building text processing applications. We start with an introduction to the basic vocabulary along with a workflow for building NLP applications. We use industry-grade NLP tools for cleaning and pre-processing text, automatic question and answer generation using linguistics, text embedding, text classifier, and building a chatbot. With each project, you will learn a new concept of NLP. You will learn about entity recognition, part of speech tagging and dependency parsing for Q and A. We use text embedding for both clustering documents and making chatbots, and then build classifiers using scikit-learn. We conclude by deploying these models as REST APIs with Flask. By the end, you will be confident building NLP applications, and know exactly what to look for when approaching new challenges. What you will learnUnderstand classical linguistics in using English grammar for automatically generating questions and answers from a free text corpusWork with text embedding models for dense number representations of words, subwords and characters in the English language for exploring document clusteringDeep Learning in NLP using PyTorch with a code-driven introduction to PyTorchUsing an NLP project management Framework for estimating timelines and organizing your project into stagesHack and build a simple chatbot application in 30 minutesDeploy an NLP or machine learning application using Flask as RESTFUL APIsWho this book is for Programmers who wish to build systems that can interpret language. Exposure to Python programming is required. Familiarity with NLP or machine learning vocabulary will be helpful, but not mandatory. |
| python quick reference guide: Julia Quick Syntax Reference Antonello Lobianco, 2019-11-11 This quick Julia programming language guide is a condensed code and syntax reference to the Julia 1.x programming language, updated with the latest features of the Julia APIs, libraries, and packages. It presents the essential Julia syntax in a well-organized format that can be used as a handy reference. This book provides an introduction that reveals basic Julia structures and syntax; discusses data types, control flow, functions, input/output, exceptions, metaprogramming, performance, and more. Additionally, you'll learn to interface Julia with other programming languages such as R for statistics or Python. You will learn how to use Julia packages for data analysis, numerical optimization and symbolic computation, and how to disseminate your results in dynamic documents or interactive web pages. In this book, the focus is on providing important information as quickly as possible. It is packed with useful information and is a must-have for any Julia programmer. What You Will Learn Set up the software needed to run Julia and your first Hello World example Work with types and the different containers that Julia makes available for rapid application development Use vectorized, classical loop-based code, logical operators, and blocks Explore Julia functions by looking at arguments, return values, polymorphism, parameters, anonymous functions, and broadcasts Build custom structures in Julia Interface Julia with other languages such as C/C++, Python, and R Program a richer API, modifying the code before it is executed using expressions, symbols, macros, quote blocks, and more Maximize your code’s performance Who This Book Is For Experienced programmers new to Julia, as well as existing Julia coders new tothe now stable Julia version 1.0 release. |
| python quick reference guide: Fluent Python Luciano Ramalho, 2015-07-30 Python’s simplicity lets you become productive quickly, but this often means you aren’t using everything it has to offer. With this hands-on guide, you’ll learn how to write effective, idiomatic Python code by leveraging its best—and possibly most neglected—features. Author Luciano Ramalho takes you through Python’s core language features and libraries, and shows you how to make your code shorter, faster, and more readable at the same time. Many experienced programmers try to bend Python to fit patterns they learned from other languages, and never discover Python features outside of their experience. With this book, those Python programmers will thoroughly learn how to become proficient in Python 3. This book covers: Python data model: understand how special methods are the key to the consistent behavior of objects Data structures: take full advantage of built-in types, and understand the text vs bytes duality in the Unicode age Functions as objects: view Python functions as first-class objects, and understand how this affects popular design patterns Object-oriented idioms: build classes by learning about references, mutability, interfaces, operator overloading, and multiple inheritance Control flow: leverage context managers, generators, coroutines, and concurrency with the concurrent.futures and asyncio packages Metaprogramming: understand how properties, attribute descriptors, class decorators, and metaclasses work |
| python quick reference guide: Text Processing in Python David Mertz, 2003 bull; Demonstrates how Python is the perfect language for text-processing functions. bull; Provides practical pointers and tips that emphasize efficient, flexible, and maintainable approaches to text-processing challenges. bull; Helps programmers develop solutions for dealing with the increasing amounts of data with which we are all inundated. |
| python quick reference guide: Python 101 Michael Driscoll, 2014-06-03 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! |
| python quick reference guide: Python Data Science Handbook Jake VanderPlas, 2016-11-21 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 quick reference guide: Head First Python Paul Barry, 2016-11-21 Want to learn the Python language without slogging your way through how-to manuals? With Head First Python, you’ll quickly grasp Python’s fundamentals, working with the built-in data structures and functions. Then you’ll move on to building your very own webapp, exploring database management, exception handling, and data wrangling. If you’re intrigued by what you can do with context managers, decorators, comprehensions, and generators, it’s all here. This second edition is a complete learning experience that will help you become a bonafide Python programmer in no time. Why does this book look so different? Based on the latest research in cognitive science and learning theory, Head First Pythonuses a visually rich format to engage your mind, rather than a text-heavy approach that puts you to sleep. Why waste your time struggling with new concepts? This multi-sensory learning experience is designed for the way your brain really works. |
| python quick reference guide: Python Tutorial 3.11.3 Guido Van Rossum, Python Development Team, 2023-05-12 |
| python quick reference guide: pytest Quick Start Guide Bruno Oliveira, 2018-08-29 Python's built-in unittest module is showing it's age; hard to extend, debug and track what's going on. The pytest framework overcomes these problems and simplifies testing your Python software. Many users love to use pytest and the improvement in their testing shows! This book is the ideal introduction to pytest, teaching you how to write ... |
| python quick reference guide: Rapid GUI Programming with Python and Qt Mark Summerfield, 2007-10-18 Whether you're building GUI prototypes or full-fledged cross-platform GUI applications with native look-and-feel, PyQt 4 is your fastest, easiest, most powerful solution. Qt expert Mark Summerfield has written the definitive best-practice guide to PyQt 4 development. With Rapid GUI Programming with Python and Qt you'll learn how to build efficient GUI applications that run on all major operating systems, including Windows, Mac OS X, Linux, and many versions of Unix, using the same source code for all of them. Summerfield systematically introduces every core GUI development technique: from dialogs and windows to data handling; from events to printing; and more. Through the book's realistic examples you'll discover a completely new PyQt 4-based programming approach, as well as coverage of many new topics, from PyQt 4's rich text engine to advanced model/view and graphics/view programming. Every key concept is illuminated with realistic, downloadable examples–all tested on Windows, Mac OS X, and Linux with Python 2.5, Qt 4.2, and PyQt 4.2, and on Windows and Linux with Qt 4.3 and PyQt 4.3. |
| python quick reference guide: Python For Kids For Dummies Brendan Scott, 2015-09-14 The kid-friendly way to learning coding with Python Calling all wanna-be coders! Experts point to Python as one of the best languages to start with when you're learning coding, and Python For Kids For Dummies makes it easier than ever. Packed with approachable, bite-sized projects that won't make you lose your cool, this fun and friendly guide teaches the basics of coding with Python in a language you can understand. In no time, you'll be installing Python tools, creating guessing games, building a geek speak translator, making a trivia game, constructing a Minecraft chat client, and so much more. Whether you don't have the opportunity to take coding classes at school or in camp—or just simply prefer to learn on your own—Python For Kids For Dummies makes getting acquainted with this popular coding language fast and easy. It walks you step-by-step through basic coding projects and provides lots of hands-on tasks that give you a sweet sense of accomplishment when you complete them. What's not to love about that? Navigate the basics of coding with the Python language Create your own applications and games Find help from other Python users Expand your technology skills with Python If you're a pre-to-early-teen looking to add coding skills to your creativity toolbox, Python For Kids For Dummies is your sure-fire weapon for getting up and running with one of the hottest programming languages around. |
| python quick reference guide: Machine Learning with scikit-learn Quick Start Guide Kevin Jolly, 2018-10-30 Deploy supervised and unsupervised machine learning algorithms using scikit-learn to perform classification, regression, and clustering. Key FeaturesBuild your first machine learning model using scikit-learnTrain supervised and unsupervised models using popular techniques such as classification, regression and clusteringUnderstand how scikit-learn can be applied to different types of machine learning problemsBook Description Scikit-learn is a robust machine learning library for the Python programming language. It provides a set of supervised and unsupervised learning algorithms. This book is the easiest way to learn how to deploy, optimize, and evaluate all of the important machine learning algorithms that scikit-learn provides. This book teaches you how to use scikit-learn for machine learning. You will start by setting up and configuring your machine learning environment with scikit-learn. To put scikit-learn to use, you will learn how to implement various supervised and unsupervised machine learning models. You will learn classification, regression, and clustering techniques to work with different types of datasets and train your models. Finally, you will learn about an effective pipeline to help you build a machine learning project from scratch. By the end of this book, you will be confident in building your own machine learning models for accurate predictions. What you will learnLearn how to work with all scikit-learn's machine learning algorithmsInstall and set up scikit-learn to build your first machine learning modelEmploy Unsupervised Machine Learning Algorithms to cluster unlabelled data into groupsPerform classification and regression machine learningUse an effective pipeline to build a machine learning project from scratchWho this book is for This book is for aspiring machine learning developers who want to get started with scikit-learn. Intermediate knowledge of Python programming and some fundamental knowledge of linear algebra and probability will help. |
| python quick reference guide: Natural Language Processing with Python Quick Start Guide Nirant Kasliwal, 2018-11-30 Build and deploy intelligent applications for natural language processing with Python by using industry standard tools and recently popular methods in deep learning Key Features A no-math, code-driven programmer's guide to text processing and NLP Get state of the art results with modern tooling across linguistics, text vectors and machine learning Fundamentals of NLP methods from spaCy, gensim, scikit-learn and PyTorch Book Description NLP in Python is among the most sought after skills among data scientists. With code and relevant case studies, this book will show how you can use industry-grade tools to implement NLP programs capable of learning from relevant data. We will explore many modern methods ranging from spaCy to word vectors that have reinvented NLP. The book takes you from the basics of NLP to building text processing applications. We start with an introduction to the basic vocabulary along with a workflow for building NLP applications. We use industry-grade NLP tools for cleaning and pre-processing text, automatic question and answer generation using linguistics, text embedding, text classifier, and building a chatbot. With each project, you will learn a new concept of NLP. You will learn about entity recognition, part of speech tagging and dependency parsing for Q and A. We use text embedding for both clustering documents and making chatbots, and then build classifiers using scikit-learn. We conclude by deploying these models as REST APIs with Flask. By the end, you will be confident building NLP applications, and know exactly what to look for when approaching new challenges. What you will learn Understand classical linguistics in using English grammar for automatically generating questions and answers from a free text corpus Work with text embedding models for dense number representations of words, subwords and characters in the English language for exploring document clustering Deep Learning in NLP using PyTorch with a code-driven introduction to PyTorch Using an NLP project management Framework for estimating timelines and organizing your project into stages Hack and build a simple chatbot application in 30 minutes Deploy an NLP or machine learning application using Flask as RESTFUL APIs Who this book is for Programmers who wish to build systems that can interpret language. Exposure to Python programming is required. Familiarity with NLP or machine learning vocabulary will be helpful, but not mandatory. |
| python quick reference guide: Learn Python Quickly Code Quickly, 2020-03-10 Python has gone to be one of the most popular programming languages in the world, and you will be one of the few people left out if you don't add this knowledge to your arsenal. If you're looking to learn Python, now is an excellent time to do so. But where do you begin? You can start right here, right now, with this book. It makes learning Python simple, fast, and easy, taking away the confusion from learning a new language. When learning a new language, it's easy to be overwhelmed and not know where to start or what to focus on. You can spend a long time pursuing tutorials online only to find out you don't really understand any of the concepts they covered. That won't be a problem here! This book follows a step by step guide, walking you through everything you need to know about Python in an easy to follow fashion. It will teach you all the basics of Python, and even some of the more advanced Python concepts, taking you from beginner to intermediate Python programmer. This book will give you: A solid foundation in Python programming. Intermediate and advanced topics once you've mastered the basics. Simple explanations of code, broken down into easy to follow steps. Python programming exercises and solutions. Two projects at the end of the book designed to help you bring all the concepts you've learned together. Source code files you can refer to and run on your computer. |
| python quick reference guide: Powerful Python Aaron Maxwell, 2024-11-08 Once you've mastered the basics of Python, how do you skill up to the top 1%? How do you focus your learning time on topics that yield the most benefit for production engineering and data teams—without getting distracted by info of little real-world use? This book answers these questions and more. Based on author Aaron Maxwell's software engineering career in Silicon Valley, this unique book focuses on the Python first principles that act to accelerate everything else: the 5% of programming knowledge that makes the remaining 95% fall like dominos. It's also this knowledge that helps you become an exceptional Python programmer, fast. Learn how to think like a Pythonista: explore advanced Pythonic thinking Create lists, dicts, and other data structures using a high-level, readable, and maintainable syntax Explore higher-order function abstractions that form the basis of Python libraries Examine Python's metaprogramming tool for priceless patterns of code reuse Master Python's error model and learn how to leverage it in your own code Learn the more potent and advanced tools of Python's object system Take a deep dive into Python's automated testing and TDD Learn how Python logging helps you troubleshoot and debug more quickly |
| python quick reference guide: Natural Language Processing with Python Steven Bird, Ewan Klein, Edward Loper, 2009-06-12 This book offers a highly accessible introduction to natural language processing, the field that supports a variety of language technologies, from predictive text and email filtering to automatic summarization and translation. With it, you'll learn how to write Python programs that work with large collections of unstructured text. You'll access richly annotated datasets using a comprehensive range of linguistic data structures, and you'll understand the main algorithms for analyzing the content and structure of written communication. Packed with examples and exercises, Natural Language Processing with Python will help you: Extract information from unstructured text, either to guess the topic or identify named entities Analyze linguistic structure in text, including parsing and semantic analysis Access popular linguistic databases, including WordNet and treebanks Integrate techniques drawn from fields as diverse as linguistics and artificial intelligence This book will help you gain practical skills in natural language processing using the Python programming language and the Natural Language Toolkit (NLTK) open source library. If you're interested in developing web applications, analyzing multilingual news sources, or documenting endangered languages -- or if you're simply curious to have a programmer's perspective on how human language works -- you'll find Natural Language Processing with Python both fascinating and immensely useful. |
| python quick reference guide: Introducing Python Bill Lubanovic, 2019-11-06 Easy to understand and fun to read, this updated edition of Introducing Python is ideal for beginning programmers as well as those new to the language. Author Bill Lubanovic takes you from the basics to more involved and varied topics, mixing tutorials with cookbook-style code recipes to explain concepts in Python 3. End-of-chapter exercises help you practice what you’ve learned. You’ll gain a strong foundation in the language, including best practices for testing, debugging, code reuse, and other development tips. This book also shows you how to use Python for applications in business, science, and the arts, using various Python tools and open source packages. |
| python quick reference guide: Machine Learning Pocket Reference Matthew Harrison, 2019 With detailed notes, tables, and examples, this handy reference will help you navigate the basics of structured machine learning. Author Matt Harrison delivers a valuable guide that you can use for additional support during training and as a convenient resource when you dive into your next machine learning project. Ideal for programmers, data scientists, and AI engineers, this book includes an overview of the machine learning process and walks you through classification with structured data. You'll also learn methods for clustering, predicting a continuous value (regression), and reducing dimensionality, among other topics. This pocket reference includes sections that cover: Classification, using the Titanic dataset Cleaning data and dealing with missing data Exploratory data analysis Common preprocessing steps using sample data Selecting features useful to the model Model selection Metrics and classification evaluation Regression examples using k-nearest neighbor, decision trees, boosting, and more Metrics for regression evaluation Clustering Dimensionality reduction Scikit-learn pipelines. |
| python quick reference guide: The Definitive Guide to Jython Josh Juneau, Jim Baker, Frank Wierzbicki, Leo Soto Muoz, Victor Ng, Alex Ng, Donna L. Baker, 2010-12-28 Jython is an open source implementation of the high-level, dynamic, object-oriented scripting language Python seamlessly integrated with the Java platform. The predecessor to Jython, JPython, is certified as 100% Pure Java. Jython is freely available for both commercial and noncommercial use and is distributed with source code. Jython is complementary to Java. The Definitive Guide to Jython, written by the official Jython team leads, covers Jython 2.5 (or 2.5.x)—from the basics to more advanced features. This book begins with a brief introduction to the language and then journeys through Jython’s different features and uses. The Definitive Guide to Jython is organized for beginners as well as advanced users of the language. The book provides a general overview of the Jython language itself, but it also includes intermediate and advanced topics regarding database, web, and graphical user interface (GUI) applications; Web services/SOA; and integration, concurrency, and parallelism, to name a few. |
| python quick reference guide: Python for Geeks Muhammad Asif, 2021-10-20 Take your Python skills to the next level to develop scalable, real-world applications for local as well as cloud deployment Key FeaturesAll code examples have been tested with Python 3.7 and Python 3.8 and are expected to work with any future 3.x releaseLearn how to build modular and object-oriented applications in PythonDiscover how to use advanced Python techniques for the cloud and clustersBook Description Python is a multipurpose language that can be used for multiple use cases. Python for Geeks will teach you how to advance in your career with the help of expert tips and tricks. You'll start by exploring the different ways of using Python optimally, both from the design and implementation point of view. Next, you'll understand the life cycle of a large-scale Python project. As you advance, you'll focus on different ways of creating an elegant design by modularizing a Python project and learn best practices and design patterns for using Python. You'll also discover how to scale out Python beyond a single thread and how to implement multiprocessing and multithreading in Python. In addition to this, you'll understand how you can not only use Python to deploy on a single machine but also use clusters in private as well as in public cloud computing environments. You'll then explore data processing techniques, focus on reusable, scalable data pipelines, and learn how to use these advanced techniques for network automation, serverless functions, and machine learning. Finally, you'll focus on strategizing web development design using the techniques and best practices covered in the book. By the end of this Python book, you'll be able to do some serious Python programming for large-scale complex projects. What you will learnUnderstand how to design and manage complex Python projectsStrategize test-driven development (TDD) in PythonExplore multithreading and multiprogramming in PythonUse Python for data processing with Apache Spark and Google Cloud Platform (GCP)Deploy serverless programs on public clouds such as GCPUse Python to build web applications and application programming interfacesApply Python for network automation and serverless functionsGet to grips with Python for data analysis and machine learningWho this book is for This book is for intermediate-level Python developers in any field who are looking to build their skills to develop and manage large-scale complex projects. Developers who want to create reusable modules and Python libraries and cloud developers building applications for cloud deployment will also find this book useful. Prior experience with Python will help you get the most out of this book. |
| python quick reference guide: Python Pocket Reference Mark Lutz, 1998 This handy reference guide summarizes Python statements, built-in functions, escape and formatting codes, and other prominent Python language features. |
| python quick reference guide: The Python Library Reference Guido van Rossum, Python Development Team, 2018-02-03 This book is the first half of The Python Library Reference for Release 3.6.4, and covers chapters 1-18. The second book may be found with ISBN 9781680921090. The original Python Library Reference book is 1920 pages long. This book contains the original page numbers and index, along with the back sections fully intact. While reference-index describes the exact syntax and semantics of the Python language, this library reference manual describes the standard library that is distributed with Python. It also describes some of the optional components that are commonly included in Python distributions. Python's standard library is very extensive, offering a wide range of facilities as indicated by the long table of contents listed below. The library contains built-in modules (written in C) that provide access to system functionality such as file I/O that would otherwise be inaccessible to Python programmers, as well as modules written in Python that provide standardized solutions for many problems that occur in everyday programming. Some of these modules are explicitly designed to encourage and enhance the portability of Python programs by abstracting away platform-specifics into platform-neutral APIs. This book is available for free as a PDF at python.org. |
| python quick reference guide: How To Code in Python 3 Lisa Tagliaferri, 2018-02-01 This educational book introduces emerging developers to computer programming through the Python software development language, and serves as a reference book for experienced developers looking to learn a new language or re-familiarize themselves with computational logic and syntax. |
Is there a "not equal" operator in Python? - Stack Overflow
Jun 16, 2012 · Python is dynamically, but strongly typed, and other statically typed languages would complain about comparing different types. There's also the else clause: # This will …
What does colon equal (:=) in Python mean? - Stack Overflow
In Python this is simply =. To translate this pseudocode into Python you would need to know the data structures being referenced, and a bit more of the algorithm implementation. Some notes …
What is Python's equivalent of && (logical-and) in an if-statement?
Sep 13, 2023 · There is no bitwise negation in Python (just the bitwise inverse operator ~ - but that is not equivalent to not). See also 6.6. Unary arithmetic and bitwise/binary operations and …
What does the "at" (@) symbol do in Python? - Stack Overflow
Jun 17, 2011 · Functions, in Python, are first class objects - which means you can pass a function as an argument to another function, and return functions. Decorators do both of these things. If …
python - What is the purpose of the -m switch? - Stack Overflow
You must run python my_script.py from the directory where the file is located. Alternatively - python path/to/my_script.py. However, you can run python -m my_script (ie refer to the script …
What does [:-1] mean/do in python? - Stack Overflow
Mar 20, 2013 · Working on a python assignment and was curious as to what [:-1] means in the context of the following code: instructions = f.readline()[:-1] Have searched on here on S.O. and …
python - Errno 13 Permission denied - Stack Overflow
Jul 16, 2020 · The problem here is your user doesn't have proper rights/permissions to open the file this means that you'd need to grant some administrative privileges to your python ide …
python - Iterating over dictionaries using 'for' loops - Stack Overflow
Jul 21, 2010 · In Python 3.x, iteritems() was replaced with simply items(), which returns a set-like view backed by the dict, like iteritems() but even better. This is also available in 2.7 as …
python - What exactly do "u" and "r" string prefixes do, and what …
There are two types of string in Python 2: the traditional str type and the newer unicode type. If you type a string literal without the u in front you get the old str type which stores 8-bit …
python - How do I execute a program or call a system command?
Note on Python version: If you are still using Python 2, subprocess.call works in a similar way. ProTip: shlex.split can help you to parse the command for run, call, and other subprocess …
Is there a "not equal" operator in Python? - Stack Overflow
Jun 16, 2012 · Python is dynamically, but strongly typed, and other statically typed languages would complain about comparing different types. There's also the else clause: # This will …
What does colon equal (:=) in Python mean? - Stack Overflow
In Python this is simply =. To translate this pseudocode into Python you would need to know the data structures being referenced, and a bit more of the algorithm implementation. Some notes …
What is Python's equivalent of && (logical-and) in an if-statement?
Sep 13, 2023 · There is no bitwise negation in Python (just the bitwise inverse operator ~ - but that is not equivalent to not). See also 6.6. Unary arithmetic and bitwise/binary operations and …
What does the "at" (@) symbol do in Python? - Stack Overflow
Jun 17, 2011 · Functions, in Python, are first class objects - which means you can pass a function as an argument to another function, and return functions. Decorators do both of these things. If …
python - What is the purpose of the -m switch? - Stack Overflow
You must run python my_script.py from the directory where the file is located. Alternatively - python path/to/my_script.py. However, you can run python -m my_script (ie refer to the script …
What does [:-1] mean/do in python? - Stack Overflow
Mar 20, 2013 · Working on a python assignment and was curious as to what [:-1] means in the context of the following code: instructions = f.readline()[:-1] Have searched on here on S.O. and …
python - Errno 13 Permission denied - Stack Overflow
Jul 16, 2020 · The problem here is your user doesn't have proper rights/permissions to open the file this means that you'd need to grant some administrative privileges to your python ide …
python - Iterating over dictionaries using 'for' loops - Stack Overflow
Jul 21, 2010 · In Python 3.x, iteritems() was replaced with simply items(), which returns a set-like view backed by the dict, like iteritems() but even better. This is also available in 2.7 as …
python - What exactly do "u" and "r" string prefixes do, and what …
There are two types of string in Python 2: the traditional str type and the newer unicode type. If you type a string literal without the u in front you get the old str type which stores 8-bit …
python - How do I execute a program or call a system command?
Note on Python version: If you are still using Python 2, subprocess.call works in a similar way. ProTip: shlex.split can help you to parse the command for run, call, and other subprocess …
