Python

 Practice makes perfect pandas!

 Work out your pandas skills against dozens of real-world challenges, each carefully designed to build an intuitive knowledge of essential pandas tasks.

In Pandas Workout you’ll learn how to:

• Clean your data for accurate analysis
• Work with rows and columns for retrieving and assigning data
• Handle indexes, including hierarchical indexes
• Read and write data with a number of common formats, such as CSV and JSON
• Process and manipulate textual data from within pandas
• Work with dates and times in pandas
• Perform aggregate calculations on selected subsets of data
• Produce attractive and useful visualizations that make your data come alive

 Pandas Workout hones your pandas skills to a professional-level through two hundred exercises, each designed to strengthen your pandas skills. You’ll test your abilities against common pandas challenges such as importing and exporting, data cleaning, visualization, and performance optimization. Each exercise utilizes a real-world scenario based on real-world data, from tracking the parking tickets in New York City, to working out which country makes the best wines. You’ll soon find your pandas skills becoming second nature—no more trips to StackOverflow for what is now a natural part of your skillset.

 Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

About the technology

 Python’s pandas library can massively reduce the time you spend analyzing, cleaning, exploring, and manipulating data. And the only path to pandas mastery is practice, practice, and, you guessed it, more practice. In this book, Python guru Reuven Lerner is your personal trainer and guide through over 200 exercises guaranteed to boost your pandas skills.

About the book

 Pandas Workout is a thoughtful collection of practice problems, challenges, and mini-projects designed to build your data analysis skills using Python and pandas. The workouts use realistic data from many sources: the New York taxi fleet, Olympic athletes, SAT scores, oil prices, and more. Each can be completed in ten minutes or less. You’ll explore pandas’ rich functionality for string and date/time handling, complex indexing, and visualization, along with practical tips for every stage of a data analysis project.

What's inside

• Clean data with less manual labor
• Retrieving and assigning data
• Process and manipulate text
• Calculations on selected data subsets

About the reader

 For Python programmers and data analysts.

«Программирование на Python с нуля» — это идеальная книга для тех, кто хочет познакомиться с основами одного из самых популярных языков программирования. С её помощью вы отправитесь в увлекательное путешествие от первых строк кода до создания полноценной игры.
В книге вы не только изучите ключевые элементы Python, но и попрактикуетесь в их использовании благодаря практическим упражнениям от автора. По мере чтения задачи будут становиться всё сложнее и интереснее.
К концу книги вы сможете не только создать свою первую научно-фантастическую игру, но и расшифровать секретное сообщение. Это руководство подойдёт даже тем, кто совсем не имеет опыта в программировании.
 

 В «Рецептах Python» используется простой, но эффективный метод освоения 63-х базовых навыков программирования на Python. Сначала формулируется вопрос, например «Как найти элементы в последовательности?» Затем приводится базовое решение на чистом понятном коде. Далее исследуются другие интересные подходы, такие как поиск подстрок или пользовательские классы. Перед переходом к следующему вопросу полученные навыки закрепляются с помощью решения задач.

 Автор рассматривает все языковые средства, необходимые для уверенного владения Python. По ходу знакомства с книгой вы изучите лучшие приемы написания питонического кода. В освоении каждого инструмента помогут конкретные рекомендации и рисунки. Многочисленные перекрестные ссылки указывают на возможность повторного использования рассматриваемых средств и концепций в различных контекстах.

 Organized with a strong focus on open data, Data Science Fundamentals with R, Python, and Open Data discusses concepts, techniques, tools, and first steps to carry out data science projects, with a focus on Python and RStudio, reflecting a clear industry trend emerging towards the integration of the two. The text examines intricacies and inconsistencies often found in real data, explaining how to recognize them and guiding readers through possible solutions, and enables readers to handle real data confidently and apply transformations to reorganize, indexing, aggregate, and elaborate.

 This book is full of reader interactivity, with a companion website hosting supplementary material including datasets used in the examples and complete running code (R scripts and Jupyter notebooks) of all examples. Exam-style questions are implemented and multiple choice questions to support the readers’ active learning. Each chapter presents one or more case studies.

 Written by a highly qualified academic, Data Science Fundamentals with R, Python, and Open Data discuss sample topics such as:

• Data organization and operations on data frames, covering reading CSV dataset and common errors, and slicing, creating, and deleting columns in R
• Logical conditions and row selection, covering selection of rows with logical condition and operations on dates, strings, and missing values
• Pivoting operations and wide form-long form transformations, indexing by groups with multiple variables, and indexing by group and aggregations
• Conditional statements and iterations, multicolumn functions and operations, data frame joins, and handling data in list/dictionary format

 Data Science Fundamentals with R, Python, and Open Data is a highly accessible learning resource for students from heterogeneous disciplines where Data Science and quantitative, computational methods are gaining popularity, along with hard sciences not closely related to computer science, and medical fields using stochastic and quantitative models.

 Master effective navigation of neural networks, including convolutions and transformers, to tackle computer vision and NLP tasks using Python

Key Features

• Understand the theory, mathematical foundations and the structure of deep neural networks
• Become familiar with transformers, large language models, and convolutional networks
• Learn how to apply them on various computer vision and natural language processing problems Purchase of the print or Kindle book includes a free PDF eBook

Book Description

 The field of deep learning has developed rapidly in the past years and today covers broad range of applications. This makes it challenging to navigate and hard to understand without solid foundations. This book will guide you from the basics of neural networks to the state-of-the-art large language models in use today.

 The first part of the book introduces the main machine learning concepts and paradigms. It covers the mathematical foundations, the structure, and the training algorithms of neural networks and dives into the essence of deep learning.

 The second part of the book introduces convolutional networks for computer vision. We'll learn how to solve image classification, object detection, instance segmentation, and image generation tasks.

 The third part focuses on the attention mechanism and transformers - the core network architecture of large language models. We'll discuss new types of advanced tasks, they can solve, such as chat bots and text-to-image generation.

 By the end of this book, you'll have a thorough understanding of the inner workings of deep neural networks. You'll have the ability to develop new models or adapt existing ones to solve your tasks. You'll also have sufficient understanding to continue your research and stay up to date with the latest advancements in the field.

What you will learn

• Establish theoretical foundations of deep neural networks
• Understand convolutional networks and apply them in computer vision applications
• Become well versed with natural language processing and recurrent networks
• Explore the attention mechanism and transformers
• Apply transformers and large language models for natural language and computer vision
• Implement coding examples with PyTorch, Keras, and Hugging Face Transformers
• Use MLOps to develop and deploy neural network models

Who this book is for

 This book is for software developers/engineers, students, data scientists, data analysts, machine learning engineers, statisticians, and anyone interested in deep learning. Prior experience with Python programming is a prerequisite.

 A Simple Introduction to Python is aimed at pre-university students and complete novices to programming. The whole book has been created using Jupyter notebooks. After introducing Python as a powerful calculator, simple programming constructs are covered, and the NumPy, MatPlotLib and SymPy modules (libraries) are introduced. Python is then used for Mathematics, Cryptography, Artificial Intelligence, Data Science and Object Oriented Programming.
 The reader is shown how to program using the integrated development environments: Python IDLE, Spyder, Jupyter notebooks, and through cloud computing with Google Colab.

Features:

• No prior experience in programming is required.
• Demonstrates how to format Jupyter notebooks for publication on the Web.
• Full solutions to exercises are available as a Jupyter notebook on the Web.
• All Jupyter notebook solution files can be downloaded through GitHub.

 Django for Professionals takes your web development skills to the next level, teaching you how to build production-ready websites with Python and Django.

 Once you have learned the basics of Django there is a massive gap between building simple "toy apps" and what it takes to build a "production-ready" web application suitable for deployment to thousands or even millions of users.

 In the book you’ll learn how to:

• Build a Bookstore website from scratch
• Use Docker and PostgreSQL locally to mimic production settings
• Implement advanced user registration with email
• Customize permissions to control user access
• Write comprehensive tests
• Adopt advanced security and performance improvements
• Add search and file/image uploads
• Deploy with confidence

 If you want to take advantage of all that Django has to offer, Django for Professionals is a comprehensive best practices guide to building and deploying modern websites.

FastAPI is a young yet solid framework that takes advantage of newer Python features in a clean design. As its name implies, FastAPI is indeed fast, rivaling similar frameworks in languages such as Golang. With this practical book, developers familiar with Python will learn how FastAPI lets you accomplish more in less time with less code.

Author Bill Lubanovic covers the nuts and bolts of FastAPI development with how-to guides on various topics such as forms, database access, graphics, maps, and more that will take you beyond the basics. This book also includes how-to guides that will get you up to speed on RESTful APIs, data validation, authorization, and performance. With its similarities to frameworks like Flask and Django, you'll find it easy to get started with FastAPI.

Through the course of this book, you will:

• Learn how to build web applications with FastAPI
• Understand the differences between FastAPI, Starlette, and pydantic
• Learn two features that set FastAPI apart: asynchronous functions and data type checking and validation
• Examine new features of Python 3.8+, especially type annotations
• Understand the differences between sync and async Python
• Learn how to connect with external APIs and services

 This book shows how to use Python’s built-in graphics primitives - points, lines, and arrows – to create complex graphics for the visualization of two- and three-dimensional objects, data sets, and technical illustrations.

 This updated edition provides more detailed explanations where required, especially regarding Python code, and explores scientific applications to topics of contemporary importance. You’ll learn how to create any 2D or 3D object or illustration, as well as how to display images, use color, translate, rotate, shade, add shadows that are cast on other objects, remove hidden lines, plot 2D and 3D data, fit lines and curves to data sets, display points of intersection between 2D and 3D objects, and create digital art. Demonstrations are included which illustrate graphics programming techniques by example, the best way to learn a language.

 Also brand new to this edition are demonstrations on how to visualize electron probability clouds around a nucleus, climate change, ecological diversity, population dynamics, and resource management. Python source code, including detailed explanations, is included for all applications, making the book more accessible to novice Python programmers.

 After completing this book, you will be able to create compelling graphic images without being limited to functions available in existing Python libraries.

What You Will Learn

• Create 2D and 3D graphic images
• Add text and symbols to images
• Shade 3D objects
• Display cast shadows
• Use color for maximum effect
• View 2D and 3D data sets
• Fit lines and curves to data sets

Who This Book Is For

 Python developers, scientists, engineers, and students who use Python to produce technical illustrations and display and analyze data sets. Assumes familiarity with vectors, matrices, geometry and trigonometry.

 Minimalist Data Wrangling with Python is envisaged as a student's first introduction to data science, providing a high-level overview as well as discussing key concepts in detail. We explore methods for cleaning data gathered from different sources, transforming, selecting, and extracting features, performing exploratory data analysis and dimensionality reduction, identifying naturally occurring data clusters, modelling patterns in data, comparing data between groups, and reporting the results.

 Foundations of Data Science with Python introduces readers to the fundamentals of data science, including data manipulation and visualization, probability, statistics, and dimensionality reduction. This book is targeted toward engineers and scientists, but it should be readily understandable to anyone who knows basic calculus and the essentials of computer programming. It uses a computational-first approach to data science: the reader will learn how to use Python and the associated data-science libraries to visualize, transform, and model data, as well as how to conduct statistical tests using real data sets. Rather than relying on obscure formulas that only apply to very specific statistical tests, this book teaches readers how to perform statistical tests via resampling; this is a simple and general approach to conducting statistical tests using simulations that draw samples from the data being analyzed. The statistical techniques and tools are explained and demonstrated using a diverse collection of data sets to conduct statistical tests related to contemporary topics, from the effects of socioeconomic factors on the spread of the COVID-19 virus to the impact of state laws on firearms mortality.

 This book can be used as an undergraduate textbook for an Introduction to Data Science course or to provide a more contemporary approach in courses like Engineering Statistics. However, it is also intended to be accessible to practicing engineers and scientists who need to gain foundational knowledge of data science.

Key Features:

• Applies a modern, computational approach to working with data
• Uses real data sets to conduct statistical tests that address a diverse set of contemporary issues
• Teaches the fundamentals of some of the most important tools in the Python data-science stack
• Provides a basic, but rigorous, introduction to Probability and its application to Statistics
• Offers an accompanying website that provides a unique set of online, interactive tools to help the reader learn the material

 Данное руководство по программированию на одном из широко распространенных языков – Python – основано на практических примерах кодов, написанных специалистами и экспертами сообщества Stack Overflow, в котором лучшие разработчики программного обеспечения со всего мира делятся своими знаниями и опытом, отвечая на многие технические вопросы. Опытные Python-программисты найдут в книге множество примеров кода с подробными комментариями, что поможет им усовершенствовать свои навыки и достичь новых высот в отрасли. Однако данное издание будет полезно и начинающим специалистам с минимальным опытом и уровнем знаний, так как содержит исчерпывающее объяснение важнейших концепций Python с примерами, которые позволят избежать погружения в сухую теорию и помогут быстро повысить уровень своих компетенций. Читатели найдут здесь мощный и универсальный инструментарий для профессиональной работы в самых разных областях применения: с базами данных, веб-фреймворком Flask, XML и JSON, звуковыми данными, синтаксическим анализатором Lex-Yacc, а также при сетевом программировании, визуализации данных, многопоточности и многопроцессорности, программировании «интернета вещей». Кроме того, в книге представлена информация о применении Python в сфере науки, например, в математике, химии и криптографии. Отдельные главы посвящены секретам повышения скорости работы Python-кода и оптимизирования его производительности.

 Добро пожаловать в увлекательное космическое путешествие по просторам бескрайней Вселенной! У каждого космонавта есть своя миссия, а у юного читателя этой книги миссия особенная – создать свою первую игру на языке Python. В этом ему помогут подробные инструкции от автора, пошаговые иллюстрации и пул полезных советов. В конце каждой главы вас ждут практические упражнения для закрепления материала, а в конце книги – образцовые фрагменты кода и алгоритмы удаления самых распространенных багов. И все это в формате больших космических приключений, где главный герой – это вы!

 Look no further! "Mastering Python for Artificial Intelligence" is your gateway to learning the essential coding skills that will empower you to build cutting-edge AI applications.

 Whether you're a beginner or an experienced programmer, this book will guide you through Python's intricacies and equip you with the knowledge to unleash the true potential of AI.

 Mastering Python for Artificial Intelligence" offers an innovative approach encompassing three well-defined principles, ensuring an empowering learning journey for readers.
 1. Practicality: The book strongly believes in the value of learning by doing. Unlike many other resources, "Mastering Python for Artificial Intelligence" immediately provides the outputs of ALL the examples. Readers won't have to wait to test the code on their computers or wonder if they are on the right track. This practical approach ensures hands-on experience, reinforcing knowledge and boosting confidence.
 2. Simplicity: Learning complex subjects should be approached step by step, and "Mastering Python for Artificial Intelligence" embraces this principle. Each concept is broken down into simple and easily digestible steps. The book aims to make learning efficient and enjoyable, allowing readers to grasp a multitude of topics in the shortest possible time. Clear explanations and examples accompany the content, ensuring rapid progress and understanding.
 3. Synthesis: Recognizing that starting with Python can be overwhelming, this book takes a thoughtful approach. Carefully selected topics provide a comprehensive introduction to Python, offering a solid foundation without overwhelming the reader. By presenting essential concepts in a structured manner, the book ensures broad exposure to Python and its applications in Artificial Intelligence.

 Here's a sneak peek into what you'll discover:

• Gain a solid understanding of Python's notable features and why it is the preferred language for AI development.
• Learn the step-by-step process of Python IDE installation, ensuring you have the optimal environment for AI programming.
• Explore Python programming fundamentals, including variables, statements, operators, and flow control, laying the groundwork for AI development.
• Dive into the world of data types, such as numeric, sequence, string, list, tuple, set, and dictionary, and understand how they play a crucial role in AI applications.
• Unleash the potential of Python classes and objects and understand how they form the building blocks of AI models and algorithms.
• Discover the wealth of Python libraries and frameworks available for AI development, such as TensorFlow, Keras, scikit-learn, and more.
• Learn how to preprocess data, train AI models, and evaluate their performance using Python's powerful AI libraries.
• Get hands-on experience with practical coding examples and exercises, allowing you to apply your newfound knowledge and solidify your skills.
• The SOLUTIONS to the exercises (but be sure to look at them only after first trying to solve the exercises on your own)
• BONUS: EMPOWERING YOUR LIFE: Harnessing the Power of Chat GPT and Python to Create Your Personal Assistant (scan the QR code inside the book)
• …and much, much more!

 Discrete mathematics deals with studying countable, distinct elements, and its principles are widely used in building algorithms for computer science and data science. The knowledge of discrete math concepts will help you understand the algorithms, binary, and general mathematics that sit at the core of data-driven tasks.

 Practical Discrete Mathematics is a comprehensive introduction for those who are new to the mathematics of countable objects. This book will help you get up to speed with using discrete math principles to take your computer science skills to a more advanced level.

 As you learn the language of discrete mathematics, you'll also cover methods crucial to studying and describing computer science and machine learning objects and algorithms. The chapters that follow will guide you through how memory and CPUs work. In addition to this, you'll understand how to analyze data for useful patterns, before finally exploring how to apply math concepts in network routing, web searching, and data science.

 By the end of this book, you'll have a deeper understanding of discrete math and its applications in computer science, and be ready to work on real-world algorithm development and machine learning.

What you will learn

• Understand the terminology and methods in discrete math and their usage in algorithms and data problems
• Use Boolean algebra in formal logic and elementary control structures
• Implement combinatorics to measure computational complexity and manage memory allocation
• Use random variables, calculate descriptive statistics, and find average-case computational complexity
• Solve graph problems involved in routing, pathfinding, and graph searches, such as depth-first search
• Perform ML tasks such as data visualization, regression, and dimensionality reduction

Who this book is for

 This book is for computer scientists looking to expand their knowledge of discrete math, the core topic of their field. University students looking to get hands-on with computer science, mathematics, statistics, engineering, or related disciplines will also find this book useful. Basic Python programming skills and knowledge of elementary real-number algebra are required to get started with this book.

Immerse yourself in the intricate details of Python with 50 specific tips and techniques that will help you write cleaner, more efficient, and easy-to-maintain code. Using practical examples and exercises that illustrate each technique and make them understandable, you'll gain an in-depth knowledge of Python's inner workings, data structures, and best practices. This book will serve as a reference you can turn to anytime you need to optimize your Python code, as well as learn how to utilize popular Python libraries such as NumPy, Pandas, Flask, and Django to tackle specific tasks.

 Добро пожаловать в увлекательный мир программирования на языке Python! Независимо от того, начинающий вы или опытный программист, вы вооружитесь знаниями и навыками, необходимыми для успешного освоения языка. Python, известный своей простотой и универсальностью, завоевал огромную популярность среди разработчиков во всем мире. Благодаря удобному синтаксису и широкой библиотечной поддержке он идеально подходит для решения широкого спектра задач – от веб-разработки и анализа данных до программирования графических интерфейсов. Книга представляет собой комплексное руководство по изучению языка Python с нуля.

 Candlesticks have become a key component of platforms and charting programs for financial trading. With these charts, traders can learn underlying patterns for interpreting price action history and forecasts. This A-Z guide shows portfolio managers, quants, strategists, and analysts how to use Python to recognize, scan, trade, and back-test the profitability of candlestick patterns.

 Financial author, trading consultant, and institutional market strategist Sofien Kaabar shows you how to create a candlestick scanner and indicator so you can compare the profitability of these patterns. With this hands-on book, you'll also explore a new type of charting system similar to candlesticks, as well as new patterns that have never been presented before.

With this book, you will:

• Create and understand the conditions required for classic and modern candlestick patterns
• Learn the market psychology behind them
• Use a framework to learn how back-testing trading strategies are conducted
• Explore different charting systems and understand their limitations
• Import OHLC historical FX data in Python in different time frames
• Use algorithms to scan for and reproduce patterns
• Learn a pattern's potential by evaluating its profitability and predictability

 Опираясь на богатый соревновательный и эвристический опыт, автор предлагает оригинальные реализации классических алгоритмов Computer Science на языках Python и C++. Особое внимание уделено математическим и геометрическим алгоритмам, графовым алгоритмам, структурам данных (в особенности различным деревьям), комбинаторике и работе со строками. Книга поможет заложить и расширить алгоритмическую подготовку, познакомит с эффективными решениями вычислительных задач, а для обучающихся станет настольной. Поможет подготовиться к экзаменам, сертификации, олимпиадам по программированию.

 Язык Python помогает упростить анализ данных. Если вы научились пользоваться электронными таблицами, то сможете освоить и pandas! Несмотря на сходство с табличной компоновкой Excel, pandas обладает большей гибкостью и более широкими возможностями. Эта библиотека для Python быстро выполняет операции с миллионами строк и способна взаимодействовать с другими инструментами. Она дает идеальную возможность выйти на новый уровень анализа данных.

This practical guide provides more than 200 self-contained recipes to help you solve machine learning challenges you may encounter in your work. If you're comfortable with Python and its libraries, including pandas and scikit-learn, you'll be able to address specific problems, from loading data to training models and leveraging neural networks.

Each recipe in this updated edition includes code that you can copy, paste, and run with a toy dataset to ensure that it works. From there, you can adapt these recipes according to your use case or application. Recipes include a discussion that explains the solution and provides meaningful context.

Go beyond theory and concepts by learning the nuts and bolts you need to construct working machine learning applications.

You'll find recipes for:

• Vectors, matrices, and arrays
• Working with data from CSV, JSON, SQL, databases, cloud storage, and other sources
• Handling numerical and categorical data, text, images, and dates and times
• Dimensionality reduction using feature extraction or feature selection
• Model evaluation and selection
• Linear and logical regression, trees and forests, and k-nearest neighbors
• Supporting vector machines (SVM), naäve Bayes, clustering, and tree-based models
• Saving, loading, and serving trained models from multiple frameworks

 This textbook introduces Python and its programming through a multitude of clearly presented examples and worked-out exercises.

 Based on a course taught to undergraduate students of mathematics, science, engineering and finance, the book includes chapters on handling data, calculus, solving equations, and graphics, thus covering all of the basic topics in Python. Each section starts with a description of a new topic and some basic examples. The author then demonstrates the new concepts through worked out exercises. The intention is to enable the reader to learn from the codes, thus avoiding lengthy, exhausting explanations. With its strong focus on programming and problem solving, and an emphasis on numerical problems that do not require advanced mathematics, this textbook is also ideal for self-study, for instance for researchers who wish to use Python as a computational tool.

 The course includes Certificate of Certification if the students submit the Final Exam with the project that accompanies this book. A Master's Course in Python course is designed to provide students with a solid foundation in Python programming, as well as an introduction to web development, data science, and machine learning. The course covers the fundamental concepts of Python, including data types, variables, control structures, functions, and modules. It also covers advanced essential concepts and best practices of Python. It includes real world case studies, a wealth of Research reports on Python programming concepts, Reference for additional books, website, and other study material as well as a Glossary of Terms. It provides hands-on experience with real-world projects, and prepares students for future opportunities in the field of software development. By the end of the course, students will be able to have a University level of Python programming.

Стандартные алгоритмы и структуры при применении к крупным распределенным наборам данных могут становиться медленными — или вообще не работать. Правильный подбор алгоритмов, предназначенных для работы с большими данными, экономит время, повышает точность и снижает стоимость обработки.  Книга знакомит с методами обработки и анализа больших распределенных данных. Насыщенное отраслевыми историями и занимательными иллюстрациями, это удобное руководство позволяет легко понять даже сложные концепции. Вы научитесь применять на реальных примерах такие мощные алгоритмы, как фильтры Блума, набросок count-min, HyperLogLog и LSM-деревья, в своих собственных проектах.

Приведены примеры на Python, R и в псевдокоде.

Основные темы:

• вероятностные структуры данных в виде набросков;
• выбор правильного движка базы данных;
• конструирование эффективных дисковых структур данных и алгоритмов;
• понимание алгоритмических компромиссов в крупно-масштабных системах;
• правильное формирование выборок из потоковых данных;
• вычисление процентилей при ограниченных пространственных ресурсах.