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新書推薦： 《 无冕之王：齐桓公与齐国崛起 》 售價：NT$ 290.0 《 中国涉外法治蓝皮书（2024） 》 售價：NT$ 484.0 《 黄庭经详解（全2册） 》 售價：NT$ 660.0 《 台北故宫博物院典藏大系·绘画卷（全十册） 》 售價：NT$ 47400.0 《 姑苏繁华图 》 售價：NT$ 3190.0 《 人的消逝：从原子弹、互联网到人工智能 》 售價：NT$ 484.0 《 想象的共同体--民族主义的起源与散布（增订版） 》 售價：NT$ 352.0 《 医学图形图像处理基于Python VTK的实现 》 售價：NT$ 760.0

建議一齊購買： + NT$ 1210 《 深度学习 》 + NT$ 569 《 TensorFlow技术解析与实战 》 + NT$ 569 《 深入浅出深度学习：原理剖析与Python实践 》 + NT$ 900 《 金融数学中的带跳随机微分方程数值解 》 + NT$ 1001 《 流畅的Python 》 + NT$ 778 《 现代C++探秘：编码、工程与科研必修（基于C++ 14）（英文版） 》 內容簡介： 你的Python代码也许运行正确，但是你需要运行得更快速。通过探讨隐藏在设计备选方案中的基础理论，戈雷利克和欧日沃尔德编著的《高性能Python》将帮助你更深入地理解Python的实现。你将了解如何定位性能瓶颈，从而显著提升高数据流量程序中的代码执行效率。 你该如何利用多核架构和集群？或者你该如何搭建一个可以自由伸缩而不会影响可靠性的系统？有经验的Python程序员将会学习到这类问题的具体解决方案，以及来自于各个公司的如何把高性能Python用于社交媒体分析、产品机器学习和其他场景中去的曲折故事。 目錄： Preface 1. Understanding Performant Python 　The Fundamental Computer System 　Computing Units 　Memory Units 　Communications Layers 　Putting the Fundamental Elements Together 　Idealized Computing Versus the Python Virtual Machine 　So Why Use Python? 2. Profiling to Find Bottlenecks 　Profiling Efficiently 　Introducing the Julia Set 　Calculating the Full Julia Set 　Simple Approaches to Timing--print and a Decorator 　Simple Timing Using the Unix time Command 　Using the cProfile Module 　Using runsnakerun to Visualize cProfile Output 　Using line_profiler for Line-by-Line Measurements 　Using memory_profiler to Diagnose Memory Usage 　Inspecting Objects on the Heap with heapy 　Using dowser for Live Graphing of Instantiated Variables 　Using the dis Module to Examine CPython Bytecode 　Different Approaches, Different Complexity 　Unit Testing During Optimization to Maintain Correctness 　No-op @profile Decorator 　Strategies to Profile Your Code Successfully 　Wrap-Up 3. Lists and Tuples 　A More Efficient Search 　Lists Versus Tuples 　Lists as Dynamic Arrays 　Tuples As Static Arrays 　Wrap-Up 4. Dictionaries and Sets 　How Do Dictionaries and Sets Work? 　Inserting and Retrieving 　Deletion 　Resizing 　Hash Functions and Entropy 　Dictionaries and Namespaces 　Wrap-Up 5. Iterators and Generators 　Iterators for Infinite Series 　Lazy Generator Evaluation 　Wrap-Up 6. Matrix and Vector Computation 　Introduction to the Problem 　Aren''t Python Lists Good Enough? 　Problems with Allocating Too Much 　Memory Fragmentation 　Understanding perf 　Making Decisions with perf''s Output 　Enter numpy 　Applying numpy to the Diffusion Problem 　Memory Allocations and In-Place Operations 　Selective Optimizations: Finding What Needs to Be Fixed 　numexpr: Making In-Place Operations Faster and Easier 　A Cautionary Tale: Verify "Optimizations" scipy 　Wrap-Up 7. Compiling to C 　What Sort of Speed Gains Are Possible? 　JIT Versus AOT Compilers 　Why Does Type Information Help the Code Run Faster? 　Using a C Compiler 　Reviewing the Julia Set Example 　Cvthon 　Compiling a Pure-Python Version Using Cython 　Cython Annotations to Analyze a Block of Code 　Adding Some Type Annotations 　Shed Skin 　Building an Extension Module 　The Cost of the Memory Copies 　Cython and numpy 　ParaUelizing the Solution with OpenMP on One Machine 　Numba 　Pythran 　PyPy 　Garbage Collection Differences 　Running PyPy and Installing Modules 　When to Use Each Technology 　Other Upcoming Projects 　A Note on Graphics Processing Units GPUs 　A Wish for a Future Compiler Project 　Foreign Function Interfaces 　ctypes 　cffi 　f2py 　CPython Module 　Wrap-Up 8. Concurrency 　Introduction to Asynchronous Programming 　Serial Crawler 　gevent 　tornado 　AsyncIO 　Database Example 　Wrap-Up 9. lhe multiprocessing Module 　An Overview of the Multiprocessing Module 　Estimating Pi Using the Monte Carlo Method 　Estimating Pi Using Processes and Threads 　Using Python Objects 　Random Numbers in Parallel Systems 　Using numpy 　Finding Prime Numbers 　Queues of Work 　Verifying Primes Using Interprocess Communication 　Serial Solution 　Naive Pool Solution 　A Less Naive Pool Solution 　Using Manager.Value as a Flag 　Using Redis as a Flag 　Using RawValue as a Flag 　Using mmap as a Flag 　Using mmap as a Flag Redux 　Sharing numpy Data with multiprocessing 　Synchronizing File and Variable Access 　File Locking 　Locking a Value 　Wrap-Up 10. Clusters and Job Queues 　Benefits of Clustering 　Drawbacks of Clustering 　$462 Million Wall Street Loss Through Poor Cluster Upgrade Strategy 　Skype''s 24-Hour Global Outage 　Common Cluster Designs 　How to Start a Clustered Solution 　Ways to Avoid Pain When Using Clusters 　Three Clustering Solutions 　Using the Parallel Python Module for Simple Local Clusters 　Using IPython Parallel to Support Research 　NSQ for Robust Production Clustering 　Queues 　Pubsub 　Distributed Prime Calculation 　Other Clustering Tools to Look At 　Wrap-Up 11. Using Less RAM 　Objects for Primitives Are Expensive 　The Array Module Stores Many Primitive Objects Cheaply 　Understanding the RAM Used in a Collection 　Bytes Versus Unicode 　Efficiently Storing Lots of Text in RAM 　Trying These Approaches on 8 Million Tokens 　Tips for Using Less RAM 　Probabilistic Data Structures 　Very Approximate Counting with a 1-byte Morris Counter 　K-Minimum Values 　Bloom Filters 　LogLog Counter 　Real-World Example 12. Lessons from the Field 　Adaptive Lab''s Social Media Analytics SOMA 　Python at Adaptive Lab 　SoMA''s Design 　Our Development Methodology 　Maintaining SoMA 　Advice for Fellow Engineers 　Making Deep Learning Fly with RadimRehurek.com 　The Sweet Spot 　Lessons in Optimizing 　Wrap-Up 　Large-Scale Productionized Machine Learning at Lyst.com 　Pythons Place at Lyst 　Cluster Design 　Code Evolution in a Fast-Moving Start-Up 　Building the Recommendation Engine 　Reporting and Monitoring 　Some Advice 　Large-Scale Social Media Analysis at Smesh 　Pythons Role at Smesh 　The Platform 　High Performance Real-Time String Matching 　Reporting, Monitoring, Debugging, and Deployment 　PyPy for Successful Web and Data Processing Systems 　Prerequisites 　The Database 　The Web Application 　OCR and Translation 　Task Distribution and Workers 　Conclusion 　Task Queues at Lanyrd.com 　Python''s Role at Lanyrd 　Making the Task Queue Performant 　Reporting, Monitoring, Debugging, and Deployment 　Advice to a Fellow Developer 　Index

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