- 高性能Python(影印版第2版)(英文版)
- - 作者：(美)麦克·戈雷利克//(英)伊恩·奥兹瓦尔德|责编:张烨
  - 出版社：东南大学
  - ISBN：9787564194932
  - 出版日期：2021/05/01
  - 页数：444
- 售价：59.2

内容大纲
你的Python代码也许运行正确，但是你需要运行得更快。本书经过拓展后的新版向你展示了如何在高数据量程序中定位性能瓶颈，显著提高代码速度。通过探索设计选择背后的基础理论，《高性能Python》将帮助你更深入地理解Python的实现。该如何利用多核架构和集群？或者该如何构建能够自由伸缩同时又不失可靠性的系统？有经验的Python程序员会学到很多问题的具体解决方案，了解到各个公司如何将高性能Python应用于社交媒体分析、产品机器学习等场景的曲折故事。
作者介绍
目录
Foreword
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?
  How to Be a Highly Performant Programmer
  Good Working Practices
  Some Thoughts on Good Notebook Practice
  Getting the Joy Back into Your Work
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
  Visualizing cProfile Output with SnakeViz
  Using line_profiler for Line-by-Line Measurements
  Using memory_profiler to Diagnose Memory Usage
  Introspecting an Existing Process with PySpy
  Bytecode: Under the Hood
  Using the dis Module to Examine CPython Bytecode
  Different Approaches, Different Co mplexity
  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)
  Lessons from Matrix Optimizations
  Pandas
  Pandas's Internal Model
  Applying a Function to Many Rows of Data
  Building DataFrames and Series from Partial Results Rather than
  Concatenating
  There's More Than One (and Possibly a Faster) Way to Do a Job
  Advice for Effective Pandas Development asu
  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
  Cython
  Compiling a Pure Python Version Using Cython
  pyximport
  Cython Annotations to Analyze a Block of Code
  Adding Some Type Annotations
  Cython and numpy
  Parallelizing the Solution with OpenMP on One Machine
  Numba
  Numba to Compile NumPy for Pandas
  PyPy
  Garbage Collection Differences
  Running PyPy and Installing Modules
  A Summary of Speed Improvements
  When to Use Each Technology
  Other Upcoming Projects
  Graphics Processing Units (GPUs)
  Dynamic Graphs: PyTorch
  Basic GPU Profiling
  Performance Considerations of GPUs
  When to Use GPUs
  Foreign Function Interfaces
  ctypes

  cffi
  f2py
  CPython Module
  Wrap-Up
8.Asynchronous l/0.
9.The multiprocessing Module.
10.Clusters and Job Queues
11.Using Less RAM.
12.Lessons from the Field.
Index

内容大纲

作者介绍

目录

同类热销排行榜

推荐书目