函数式Python编程(第2版影印版)(英文版)

如果你是一名想要了解如何利用函数式编程的强 大功能并将其应用于自己的程序中的Python开发人员 ，那么这是一本你不容错过的书，哪怕你对这种编程 范式几乎一无所知。 本书一开始先概述了函数式概念，你会了解到一 些常见的函数式特性，例如头等函数、高阶函数、纯 函数等。接下来，你将看到如何在Python中运用这些 特性，以此为你提供立足的核心基础知识。在这之后 ，你将学习用于Python的常见函数式优化，以帮助你 的应用程序达到更高的运行速度。 你会学习到函数式编程的概念，例如使用Python 的生成器函数和表达式实现惰性求值，然后学习如何 设计和实现装饰器来创建复合函数。此外你还将深入 探索数据预备技术和数据探查，了解Python标准库如 何适应函数式编程模型。 最后，为了结束Python函 数式编程世界的探索旅程，本书会向你展示PyMonad项 目和一些更大规模的示例，以开阔你的视野。

Copyright and Credits Preface Chapter 1: Understanding Functional Programming Identifying a paradigm Subdividing the procedural paradigm Using the functional paradigm Using a functional hybrid Looking at object creation The stack of turtles A classic example of functional programming Exploratory data analysis Summary Chapter 2: Introducing Essential Functional Concepts First-class functions Pure functions Higher-order functions Immutable data Strict and non-strict evaluation Recursion instead of an explicit loop state Functional type systems Familiar territory Learning some advanced concepts Summary Chapter 3: Functions, Iterators, and Generators Writing pure functions Functions as first-class objects Using strings Using tuples and named tuples Using generator expressions Exploring the limitations of generators Combining generator expressions Cleaning raw data with generator functions Using lists, dicts, and sets Using stateful mappings Using the bisect module to create a mapping Using stateful sets Summary Chapter 4: Working with Collections An overview of function varieties Working with iterables Parsing an XML file Parsing a file at a higher level Pairing up items from a sequence Using the iterO function explicitly Extending a simple loop Applying generator expressions to scalar functions Using any() and all() as reductions Using lenO and sum() Using sums and counts for statistics Using zip() to structure and flatten sequences Unzipping a zipped sequence Flattening sequences Structuring flat sequences Structuring flat sequences - an alternative approach Using reversed() to change the order Using enumerate() to include a sequence number Summary Chapter 5: Higher-Order Functions Using max() and min0 to find extrema Using Python lambda forms Lambdas and the lambda calculus Using the map() function to apply a function to a collection Working with lambda forms and map() Using map() with multiple sequences Using the filter() function to pass or reject data Using filter() to identify outliers The iter0 function with a sentinel value Using sorted() to put data in order Writing higher-order functions Writing higher-order mappings and filters Unwrapping data while mapping Wrapping additional data while mapping Flattening data while mapping Structuring data while filtering Writing generator functions Building higher-order functions with callables Assuring good functional design Review of some design patterns Summary Chapter 6: Recursions and Reductions Simple numerical recursions Implementing tail-call optimization Leaving recursion in place Handling difficult tail-call optimization Processing collections through recursion Tail-call optimization for collections Reductions and folding a collection from many items to one Group-by reduction from many items to fewer Building a mapping with Counter Building a mapping by sorting Grouping or partitioning data by key values Writing more general group-by reductions Writing higher-order reductions Writing file parsers Parsing CSV files Parsing plain text files with headers Summary Chapter 7: Additional Tuple Techniques Using tuples to collect data Using named tuples to collect data Building named tuples with functional constructors Avoiding stateful classes by using families of tuples Assigning statistical ranks Wrapping instead of state changing Rewrapping instead of state changing Computing Spearman rank-order correlation Polymorphism and type-pattern matching Summary Chapter 8: The Itertools Module Working with the infinite iterators Counting with count() Counting with float ar