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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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Dancing with Python
Develop skills in Python and Quantum Computing by implementing exciting algorithms, mathematical functions, classical searching, data analysis, plotting data, machine learning techniques, and quantum circuits.Key FeaturesCreate quantum circuits and algorithms using Qiskit and run them on quantum computing hardware and simulatorsLearn the Pythonic way to write elegant and efficient codeDelve into Python's advanced features, including machine learning, analyzing data, and searchingBook DescriptionDancing with Python helps you learn Python and quantum computing in a practical way. It will help you explore how to work with numbers, strings, collections, iterators, and files.The book goes beyond functions and classes and teaches you to use Python and Qiskit to create gates and circuits for classical and quantum computing. Learn how quantum extends traditional techniques using the Grover Search Algorithm and the code that implements it. Dive into some advanced and widely used applications of Python and revisit strings with more sophisticated tools, such as regular expressions and basic natural language processing (NLP). The final chapters introduce you to data analysis, visualizations, and supervised and unsupervised machine learning.By the end of the book, you will be proficient in programming the latest and most powerful quantum computers, the Pythonic way.What you will learnExplore different quantum gates and build quantum circuits with Qiskit and PythonWrite succinct code the Pythonic way using magic methods, iterators, and generatorsAnalyze data, build basic machine learning models, and plot the resultsSearch for information using the quantum Grover Search AlgorithmOptimize and test your code to run efficientlyWho this book is forThe book will help you get started with coding for Python and Quantum Computing. Basic familiarity with algebra, geometry, trigonometry, and logarithms is required as the book does not cover the detailed mathematics and theory of quantum computing. You can check out the author’s Dancing with Qubits book, also published by Packt, for an approachable and comprehensive introduction to quantum computing.
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Optimizing Visual Studio Code for Python Development
Learn Visual Studio Code and implement its features in Python coding, debugging, linting, and overall project management. This book addresses custom scenarios for writing programs in Python frameworks, such as Django and Flask.The book starts with an introduction to Visual Studio Code followed by code editing in Python. Here, you will learn about the required extensions of Visual Studio Code to perform various functions such as linting and debugging in Python. Next, you will set up the environment and run your projects along with the support for Jupyter. You will also work with Python frameworks such as Django and go through data science specific-information and tutorials. Finally, you will learn how to integrate Azure for Python and how to use containers in Visual Studio Code.Optimizing Visual Studio Code for Python Development is your ticket to writing Python scripts with this versatile code editor.What You'll LearnExecute Flask development in Visual Studio Code for control over libraries used in an applicationOptimize Visual Studio Code to code faster and betterUnderstand linting and debugging Python code in Visual Studio CodeWork with Jupyter Notebooks in Visual Studio CodeWho This Book Is ForPython developers, beginners, and experts looking to master Visual Studio Code
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Python Packages
Python Packages introduces Python packaging at an introductory and practical level that’s suitable for those with no previous packaging experience. Despite this, the text builds up to advanced topics such as automated testing, creating documentation, versioning and updating a package, and implementing continuous integration and deployment. Covering the entire Python packaging life cycle, this essential guide takes readers from package creation all the way to effective maintenance and updating.Python Packages focuses on the use of current and best-practice packaging tools and services like poetry, cookiecutter, pytest, sphinx, GitHub, and GitHub Actions.Features:The book’s source code is available online as a GitHub repository where it is collaborated on, automatically tested, and built in real time as changes are made; demonstrating the use of good reproducible and clear project workflows.Covers not just the process of creating a package, but also how to document it, test it, publish it to the Python Package Index (PyPI), and how to properly version and update it.All concepts in the book are demonstrated using examples. Readers can follow along, creating their own Python packages using the reproducible code provided in the text.Focuses on a modern approach to Python packaging with emphasis on automating and streamlining the packaging process using new and emerging tools such as poetry and GitHub Actions.
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Hands-On Software Engineering with Python
Grow your software engineering discipline, incorporating and mastering design, development, testing, and deployment best practices examples in a realistic Python project structure.Key FeaturesUnderstand what makes Software Engineering a discipline, distinct from basic programmingGain practical insight into updating, refactoring, and scaling an existing Python systemImplement robust testing, CI/CD pipelines, and cloud-ready architecture decisionsBook DescriptionSoftware engineering is more than coding; it’s the strategic design and continuous improvement of systems that serve real-world needs. This newly updated second edition of Hands-On Software Engineering with Python expands on its foundational approach to help you grow into a senior or staff-level engineering role.Fully revised for today’s Python ecosystem, this edition includes updated tooling, practices, and architectural patterns. You’ll explore key changes across five minor Python versions, examine new features like dataclasses and type hinting, and evaluate modern tools such as Poetry, pytest, and GitHub Actions. A new chapter introduces high-performance computing in Python, and the entire development process is enhanced with cloud-readiness in mind.You’ll follow a complete redesign and refactor of a multi-tier system from the first edition, gaining insight into how software evolves—and what it takes to do that responsibly. From system modeling and SDLC phases to data persistence, testing, and CI/CD automation, each chapter builds your engineering mindset while updating your hands-on skills.By the end of this book, you'll have mastered modern Python software engineering practices and be equipped to revise and future-proof complex systems with confidence.What you will learnDistinguish software engineering from general programmingBreak down and apply each phase of the SDLC to Python systemsCreate system models to plan architecture before writing codeApply Agile, Scrum, and other modern development methodologiesUse dataclasses, pydantic, and schemas for robust data modelingSet up CI/CD pipelines with GitHub Actions and cloud build toolsWrite and structure unit, integration, and end-to-end testsEvaluate and integrate tools like Poetry, pytest, and DockerWho this book is forThis book is for Python developers with a basic grasp of software development who want to grow into senior or staff-level engineering roles. It’s ideal for professionals looking to deepen their understanding of software architecture, system modeling, testing strategies, and cloud-aware development. Familiarity with core Python programming is required, as the book focuses on applying engineering principles to maintain, extend, and modernize real-world systems.
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Introduction to Python in Earth Science Data Analysis
This textbook introduces the use of Python programming for exploring and modelling data in the field of Earth Sciences. It drives the reader from his very first steps with Python, like setting up the environment and starting writing the first lines of codes, to proficient use in visualizing, analyzing, and modelling data in the field of Earth Science. Each chapter contains explicative examples of code, and each script is commented in detail. The book is minded for very beginners in Python programming, and it can be used in teaching courses at master or PhD levels. Also, Early careers and experienced researchers who would like to start learning Python programming for the solution of geological problems will benefit the reading of the book.
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Natural Language Processing with Python
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.
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Python Quick Syntax Reference
The Python Quick Syntax Reference is the "go to" book that contains an easy to read and useguide to Python programming and development. This condensed code and syntaxreference presents the Python language in a well-organized format designed tobe used time and again.You won't find jargon, bloated samples, case studies, or history of Hello Worldand computer theory in this handy reference. This Python syntax reference ispacked with useful information and is a must-have for any Python developer.
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