Search results for "Python in Depth"
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Python Programming for Data Analysis
This textbook grew out of notes for the ECE143 Programming for Data Analysis class that the author has been teaching at University of California, San Diego, which is a requirement for both graduate and undergraduate degrees in Machine Learning and Data Science. This book is ideal for readers with some Python programming experience. The book covers key language concepts that must be understood to program effectively, especially for data analysis applications. Certain low-level language features are discussed in detail, especially Python memory management and data structures. Using Python effectively means taking advantage of its vast ecosystem. The book discusses Python package management and how to use third-party modules as well as how to structure your own Python modules. The section on object-oriented programming explains features of the language that facilitate common programming patterns.After developing the key Python language features, the book moves on to third-party modules that are foundational for effective data analysis, starting with Numpy. The book develops key Numpy concepts and discusses internal Numpy array data structures and memory usage. Then, the author moves onto Pandas and details its many features for data processing and alignment. Because strong visualizations are important for communicating data analysis, key modules such as Matplotlib are developed in detail, along with web-based options such as Bokeh, Holoviews, Altair, and Plotly.The text is sprinkled with many tricks-of-the-trade that help avoid common pitfalls. The author explains the internal logic embodied in the Python language so that readers can get into the Python mindset and make better design choices in their codes, which is especially helpful for newcomers to both Python and data analysis. To get the most out of this book, open a Python interpreter and type along with the many code samples.
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Python Data Science Handbook
Python is a first-class tool for many researchers, primarily 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 new edition of 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 the second edition of 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:IPython and Jupyter provide computational environments for scientists using PythonNumPy includes the ndarray for efficient storage and manipulation of dense data arraysPandas contains the DataFrame for efficient storage and manipulation of labeled/columnar dataMatplotlib includes capabilities for a flexible range of data visualizationsScikit-learn helps you build efficient and clean Python implementations of the most important and established machine learning algorithms
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Hands-On RESTful Python Web Services
Explore the best tools and techniques to create lightweight, maintainable, and scalable Python web servicesKey FeaturesCombine Python with different data sources to build complex RESTful APIs from scratchConfigure and fine-tune your APIs using the best tools and techniques availableUse command-line and GUI tools to test CRUD operations performed by RESTful Web Services or APIsBook DescriptionPython is the language of choice for millions of developers worldwide that builds great web services in RESTful architecture. This second edition of Hands-On RESTful Python Web Services will cover the best tools you can use to build engaging web services.This book shows you how to develop RESTful APIs using the most popular Python frameworks and all the necessary stacks with Python, combined with related libraries and tools. You’ll learn to incorporate all new features of Python 3.7, Flask 1.0.2, Django 2.1, Tornado 5.1, and also a new framework, Pyramid. As you advance through the chapters, you will get to grips with each of these frameworks to build various web services, and be shown use cases and best practices covering when to use a particular framework.You’ll then successfully develop RESTful APIs with all frameworks and understand how each framework processes HTTP requests and routes URLs. You’ll also discover best practices for validation, serialization, and deserialization. In the concluding chapters, you will take advantage of specific features available in certain frameworks such as integrated ORMs, built-in authorization and authentication, and work with asynchronous code. At the end of each framework, you will write tests for RESTful APIs and improve code coverage.By the end of the book, you will have gained a deep understanding of the stacks needed to build RESTful web services.What you will learnSelect the most appropriate framework based on requirementsDevelop complex RESTful APIs from scratch using PythonUse requests handlers, URL patterns, serialization, and validationsAdd authentication, authorization, and interaction with ORMs and databasesDebug, test, and improve RESTful APIs with four frameworksDesign RESTful APIs with frameworks and create automated testsWho this book is forThis book is for web developers who have a working knowledge of Python and would like to build amazing web services by taking advantage of the various frameworks of Python. You should have some knowledge of RESTful APIs.
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Python For Kids, 2nd Edition
Python for Kids brings Python to life and brings kids (and their parents) into the wonderful world of programming. Author Jason R. Briggs guides readers through the basics, experimenting with unique (and often hilarious) example programs that feature ravenous monsters, secret agents, thieving ravens, and more. Full-colour illustrations keep things fun and engaging throughout! This second edition has been completely updated and revised to reflect the latest Python version and programming practices, with new puzzles to inspire readers to take their code farther than ever before.
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Competitive Programming in Python
Want to kill it at your job interview in the tech industry? Want to win that coding competition? Learn all the algorithmic techniques and programming skills you need from two experienced coaches, problem setters, and jurors for coding competitions. The authors highlight the versatility of each algorithm by considering a variety of problems and show how to implement algorithms in simple and efficient code. Readers can expect to master 128 algorithms in Python and discover the right way to tackle a problem and quickly implement a solution of low complexity. Classic problems like Dijkstra's shortest path algorithm and Knuth-Morris-Pratt's string matching algorithm are featured alongside lesser known data structures like Fenwick trees and Knuth's dancing links. The book provides a framework to tackle algorithmic problem solving, including: Definition, Complexity, Applications, Algorithm, Key Information, Implementation, Variants, In Practice, and Problems. Python code included in the book and on the companion website.
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Python Recipes Handbook
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 LearnCode with the pandas (Python Data Analysis Library)Work with the various Python algorithms useful for today's big data analytics and cloud applicationsUse NumPy and other numerical Python packages and code for doing various kinds of analysisDiscover Python's new popular modules, packages, extensions and templates libraryWho This Book Is ForThis handy reference is for those with some experience with Python.
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Natural Language Processing with Python Quick Start Guide
Build and deploy intelligent applications for natural language processing with Python by using industry standard tools and recently popular methods in deep learningKey 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 DescriptionNLP 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 forProgrammers 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.
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Building AI Intensive Python Applications
Master retrieval-augmented generation architecture and fine-tune your AI stack, along with discovering real-world use cases and best practices to create powerful AI appsKey FeaturesGet to grips with the fundamentals of LLMs, vector databases, and Python frameworksImplement effective retrieval-augmented generation strategies with MongoDB AtlasOptimize AI models for performance and accuracy with model compression and deployment optimizationPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionThe era of generative AI is upon us, and this book serves as a roadmap to harness its full potential. With its help, you’ll learn the core components of the AI stack: large language models (LLMs), vector databases, and Python frameworks, and see how these technologies work together to create intelligent applications.The chapters will help you discover best practices for data preparation, model selection, and fine-tuning, and teach you advanced techniques such as retrieval-augmented generation (RAG) to overcome common challenges, such as hallucinations and data leakage. You’ll get a solid understanding of vector databases, implement effective vector search strategies, refine models for accuracy, and optimize performance to achieve impactful results. You’ll also identify and address AI failures to ensure your applications deliver reliable and valuable results. By evaluating and improving the output of LLMs, you’ll be able to enhance their performance and relevance.By the end of this book, you’ll be well-equipped to build sophisticated AI applications that deliver real-world value.What you will learnUnderstand the architecture and components of the generative AI stackExplore the role of vector databases in enhancing AI applicationsMaster Python frameworks for AI developmentImplement Vector Search in AI applicationsFind out how to effectively evaluate LLM outputOvercome common failures and challenges in AI developmentWho this book is forThis book is for software engineers and developers looking to build intelligent applications using generative AI. While the book is suitable for beginners, a basic understanding of Python programming is required to make the most of it.
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Foundational Python for Data Science
Data science and machine learning—two of the world’s hottest fields—are attracting talent from a wide variety of technical, business, and liberal arts disciplines. Python, the world's #1 programming language, is also the most popular language for data science and machine learning. This is the first guide specifically designed to help millions of people with widely diverse backgrounds learn Python so they can use it for data science and machine learning. Leading data science instructor and practitioner Kennedy Behrman first walks through the process of learning to code for the first time with Python and Jupyter notebook, then introduces key libraries every Python data science programmer needs to master. Once you've learned these foundations, Behrman introduces intermediate and applied Python techniques for real-world problem-solving. Throughout, Foundational Python for Data Science presents hands-on exercises, learning assessments, case studies, and more—all created with Colab (Jupyter compatible) notebooks, so you can execute all coding examples interactively without installing or configuring any software.
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Hands-On Reactive Programming with Python
A comprehensive guide to help you understand the principles of Reactive and asynchronous programming and its benefitsKey FeaturesExplore the advantages of Reactive programmingUse concurrency and parallelism in RxPY to build powerful reactive applicationsDeploy and scale your reactive applications using DockerBook DescriptionReactive programming is central to many concurrent systems, but it’s famous for its steep learning curve, which makes most developers feel like they're hitting a wall. With this book, you will get to grips with reactive programming by steadily exploring various conceptsThis hands-on guide gets you started with Reactive Programming (RP) in Python. You will learn abouta the principles and benefits of using RP, which can be leveraged to build powerful concurrent applications. As you progress through the chapters, you will be introduced to the paradigm of Functional and Reactive Programming (FaRP), observables and observers, and concurrency and parallelism. The book will then take you through the implementation of an audio transcoding server and introduce you to a library that helps in the writing of FaRP code. You will understand how to use third-party services and dynamically reconfigure an application.By the end of the book, you will also have learned how to deploy and scale your applications with Docker and Traefik and explore the significant potential behind the reactive streams concept, and you'll have got to grips with a comprehensive set of best practices.What you will learnStructure Python code for better readability, testing, and performanceExplore the world of event-based programmingGrasp the use of the most common operators in RxUnderstand reactive extensions beyond simple examplesMaster the art of writing reusable componentsDeploy an application on a cloud platform with Docker and TraefikWho this book is forIf you are a Python developer who wants to learn Reactive programming to build powerful concurrent and asynchronous applications, this book is for you. Basic understanding of the Python language is all you need to understand the concepts covered in this book.
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Python for the Life Sciences
Treat yourself to a lively, intuitive, and easy-to-follow introduction to computer programming in Python. The book was written specifically for biologists with little or no prior experience of writing code - with the goal of giving them not only a foundation in Python programming, but also the confidence and inspiration to start using Python in their own research. Virtually all of the examples in the book are drawn from across a wide spectrum of life science research, from simple biochemical calculations and sequence analysis, to modeling the dynamic interactions of genes and proteins in cells, or the drift of genes in an evolving population. Best of all, Python for the Life Sciences shows you how to implement all of these projects in Python, one of the most popular programming languages for scientific computing. If you are a life scientist interested in learning Python to jump-start your research, this is the book for you.What You'll Learn Write Python scripts to automate your lab calculationsSearch for important motifs in genome sequencesUse object-oriented programming with PythonStudy mining interaction network data for patternsReview dynamic modeling of biochemical switchesWho This Book Is ForLife scientists with little or no programming experience, including undergraduate and graduate students, postdoctoral researchers in academia and industry, medical professionals, and teachers/lecturers.“A comprehensive introduction to using Python for computational biology... A lovely book with humor and perspective” -- John Novembre, Associate Professor of Human Genetics, University of Chicago and MacArthur Fellow“Fun, entertaining, witty and darn useful. A magical portal to the big data revolution” -- Sandro Santagata, Assistant Professor in Pathology, Harvard Medical School“Alex and Gordon’s enthusiasm for Python is contagious” -- Glenys Thomson Professor of Integrative Biology, University of California, Berkeley
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