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Synopsis
Python is one of the world's most versatile programming languages, but building software that performs efficiently at scale requires far more than writing code that simply works. Python Performance Engineering: Optimization Techniques Explained is a practical, professional guide that teaches developers how to identify bottlenecks, optimize execution speed, reduce memory consumption, and build high-performance Python applications for real-world environments.
From profiling and benchmarking to concurrency, multiprocessing, asynchronous programming, and advanced optimization techniques, this book provides a structured approach to improving application performance without sacrificing code quality or maintainability. Modern performance engineering relies on measuring first, optimizing second, and this book shows you exactly how to do both. Performance-focused Python resources consistently emphasize profiling, bottleneck analysis, efficient data structures, concurrency, and acceleration techniques such as Cython, Numba, and vectorization.
Inside, you'll learn how to:
- Profile CPU, memory, and I/O performance effectively
- Identify and eliminate performance bottlenecks
- Optimize algorithms, data structures, and execution paths
- Master Python's memory management and garbage collection
- Improve application responsiveness with asynchronous programming
- Leverage multiprocessing and parallel execution techniques
- Accelerate workloads using NumPy, Numba, and Cython
- Optimize web applications, APIs, and data-processing pipelines
- Build scalable systems that perform reliably under load
- Apply professional performance-engineering practices used in production environments
Through practical examples, performance case studies, and hands-on optimization exercises, you'll develop the skills needed to transform slow, resource-intensive programs into efficient, production-ready software.
Whether you're a software engineer, backend developer, data engineer, DevOps professional, or technical lead, this book will help you move beyond basic Python programming and master the engineering techniques required to build applications that are not only correct—but exceptionally fast.
"synopsis" may belong to another edition of this title.
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Python Performance Engineering: Optimization Techniques Explained
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Paperback. Condition: new. Paperback. Python is one of the world's most versatile programming languages, but building software that performs efficiently at scale requires far more than writing code that simply works. Python Performance Engineering: Optimization Techniques Explained is a practical, professional guide that teaches developers how to identify bottlenecks, optimize execution speed, reduce memory consumption, and build high-performance Python applications for real-world environments.From profiling and benchmarking to concurrency, multiprocessing, asynchronous programming, and advanced optimization techniques, this book provides a structured approach to improving application performance without sacrificing code quality or maintainability. Modern performance engineering relies on measuring first, optimizing second, and this book shows you exactly how to do both. Performance-focused Python resources consistently emphasize profiling, bottleneck analysis, efficient data structures, concurrency, and acceleration techniques such as Cython, Numba, and vectorization.Inside, you'll learn how to: Profile CPU, memory, and I/O performance effectivelyIdentify and eliminate performance bottlenecksOptimize algorithms, data structures, and execution pathsMaster Python's memory management and garbage collectionImprove application responsiveness with asynchronous programmingLeverage multiprocessing and parallel execution techniquesAccelerate workloads using NumPy, Numba, and CythonOptimize web applications, APIs, and data-processing pipelinesBuild scalable systems that perform reliably under loadApply professional performance-engineering practices used in production environmentsThrough practical examples, performance case studies, and hands-on optimization exercises, you'll develop the skills needed to transform slow, resource-intensive programs into efficient, production-ready software.Whether you're a software engineer, backend developer, data engineer, DevOps professional, or technical lead, this book will help you move beyond basic Python programming and master the engineering techniques required to build applications that are not only correct-but exceptionally fast. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Seller Inventory # 9798198337718
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Python Performance Engineering : Optimization Techniques Explained
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Taschenbuch. Condition: Neu. Neuware - Python is one of the world's most versatile programming languages, but building software that performs efficiently at scale requires far more than writing code that simply works. Python Performance Engineering: Optimization Techniques Explained is a practical, professional guide that teaches developers how to identify bottlenecks, optimize execution speed, reduce memory consumption, and build high-performance Python applications for real-world environments.From profiling and benchmarking to concurrency, multiprocessing, asynchronous programming, and advanced optimization techniques, this book provides a structured approach to improving application performance without sacrificing code quality or maintainability. Modern performance engineering relies on measuring first, optimizing second, and this book shows you exactly how to do both. Performance-focused Python resources consistently emphasize profiling, bottleneck analysis, efficient data structures, concurrency, and acceleration techniques such as Cython, Numba, and vectorization.Inside, you'll learn how to: - Profile CPU, memory, and I/O performance effectively- Identify and eliminate performance bottlenecks- Optimize algorithms, data structures, and execution paths- Master Python's memory management and garbage collection- Improve application responsiveness with asynchronous programming- Leverage multiprocessing and parallel execution techniques- Accelerate workloads using NumPy, Numba, and Cython- Optimize web applications, APIs, and data-processing pipelines- Build scalable systems that perform reliably under load- Apply professional performance-engineering practices used in production environmentsThrough practical examples, performance case studies, and hands-on optimization exercises, you'll develop the skills needed to transform slow, resource-intensive programs into efficient, production-ready software.Whether you're a software engineer, backend developer, data engineer, DevOps professional, or technical lead, this book will help you move beyond basic Python programming and master the engineering techniques required to build applications that are not only correct-but exceptionally fast. Seller Inventory # 9798198337718