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Advanced Python Programming: Accelerate your Python programs using proven techniques and design patterns, 2nd Edition
Write fast, robust, and highly reusable applications using Python's internal optimization, state-of-the-art performance-benchmarking tools, and cutting-edge libraries
Key FeaturesBenchmark, profile, and accelerate Python programs using optimization toolsScale applications to multiple processors with concurrent programmingMake applications robust and reusable using effective design patternsBook DescriptionPython's powerful capabilities for implementing robust and efficient programs make it one of the most sought-after programming languages.
In this book, you'll explore the tools that allow you to improve performance and take your Python programs to the next level.
This book starts by examining the built-in as well as external libraries that streamline tasks in the development cycle, such as benchmarking, profiling, and optimizing. You'll then get to grips with using specialized tools such as dedicated libraries and compilers to increase your performance at number-crunching tasks, including training machine learning models.
The book covers concurrency, a major solution to making programs more efficient and scalable, and various concurrent programming techniques such as multithreading, multiprocessing, and asynchronous programming.
You'll also understand the common problems that cause undesirable behavior in concurrent programs.
Finally, you'll work with a wide range of design patterns, including creational, structural, and behavioral patterns that enable you to tackle complex design and architecture challenges, making your programs more robust and maintainable.
By the end of the book, you'll be exposed to a wide range of advanced functionalities in Python and be equipped with the practical knowledge needed to apply them to your use cases.
What you will learnWrite efficient numerical code with NumPy, pandas, and XarrayUse Cython and Numba to achieve native performanceFind bottlenecks in your Python code using profilersOptimize your machine learning models with JAXImplement multithreaded, multiprocessing, and asynchronous programsSolve common problems in concurrent programming, such as deadlocksTackle architecture challenges with design patternsWho this book is forThis book is for intermediate to experienced Python programmers who are looking to scale up their applications in a systematic and robust manner. Programmers from a range of backgrounds will find this book useful, including software engineers, scientific programmers, and software architects.
Table of ContentsBenchmarking and ProfilingPure Python OptimizationsFast Array Operations with NumPy and PandasC Performance with CythonExploring CompilersAutomatic Differentiation and Accelerated Linear Algebra for Machine LearningImplementing ConcurrencyParallel ProcessingConcurrent Web RequestsConcurrent Image ProcessingBuilding Communication Channels with asyncioDeadlocksStarvationRace ConditionsThe Global Interpreter LockThe Factory PatternThe Builder PatternOther Creational PatternsThe Adapter Patt
Key FeaturesBenchmark, profile, and accelerate Python programs using optimization toolsScale applications to multiple processors with concurrent programmingMake applications robust and reusable using effective design patternsBook DescriptionPython's powerful capabilities for implementing robust and efficient programs make it one of the most sought-after programming languages.
In this book, you'll explore the tools that allow you to improve performance and take your Python programs to the next level.
This book starts by examining the built-in as well as external libraries that streamline tasks in the development cycle, such as benchmarking, profiling, and optimizing. You'll then get to grips with using specialized tools such as dedicated libraries and compilers to increase your performance at number-crunching tasks, including training machine learning models.
The book covers concurrency, a major solution to making programs more efficient and scalable, and various concurrent programming techniques such as multithreading, multiprocessing, and asynchronous programming.
You'll also understand the common problems that cause undesirable behavior in concurrent programs.
Finally, you'll work with a wide range of design patterns, including creational, structural, and behavioral patterns that enable you to tackle complex design and architecture challenges, making your programs more robust and maintainable.
By the end of the book, you'll be exposed to a wide range of advanced functionalities in Python and be equipped with the practical knowledge needed to apply them to your use cases.
What you will learnWrite efficient numerical code with NumPy, pandas, and XarrayUse Cython and Numba to achieve native performanceFind bottlenecks in your Python code using profilersOptimize your machine learning models with JAXImplement multithreaded, multiprocessing, and asynchronous programsSolve common problems in concurrent programming, such as deadlocksTackle architecture challenges with design patternsWho this book is forThis book is for intermediate to experienced Python programmers who are looking to scale up their applications in a systematic and robust manner. Programmers from a range of backgrounds will find this book useful, including software engineers, scientific programmers, and software architects.
Table of ContentsBenchmarking and ProfilingPure Python OptimizationsFast Array Operations with NumPy and PandasC Performance with CythonExploring CompilersAutomatic Differentiation and Accelerated Linear Algebra for Machine LearningImplementing ConcurrencyParallel ProcessingConcurrent Web RequestsConcurrent Image ProcessingBuilding Communication Channels with asyncioDeadlocksStarvationRace ConditionsThe Global Interpreter LockThe Factory PatternThe Builder PatternOther Creational PatternsThe Adapter Patt
606 pages, Kindle Edition
Published March 25, 2022
9 people are currently reading
15 people want to read
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August 2, 2023
Книга, на мой взгляд, неоднородная. Наиболее понравилась вторая часть про параллелизм и асинхронное программирование. Материал достаточно неплох, но в основном поверхностен. Автор показывает общие подходы, не вдаваясь в мелкие детали. Третья часть (паттерны дизайна) весьма познавательна, но примеры какие-то слишком уж упрощенные, оторванные от реальной жизни. В первой части автор попытался объединить несколько далеких друг от друга тем (профайлинг, мемоизация, numpy, Cython и др). В итоге получилась непонятная солянка с чересчур сложными примерами.
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