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函数式Python编程东南
- 出版社:读买天下图书专营店
- 出版时间:2019-05
- 热度:11771
- 上架时间:2024-06-30 09:38:03
- 价格:0.0
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内容介绍
| 商品基本信息，请以下列介绍为准 | |
| 商品名称： | 函数式Python编程 |
| 作者： | Steven F. Lott |
| 定价： | 99.0 |
| 出版社： | 东南大学出版社 |
| 出版日期： | 2019-05-01 |
| ISBN： | 9787564183257 |
| 印次： | |
| 版次： | |
| 装帧： | |
| 开本： | 小16开 |
| 内容简介 | |
| Python是一种计算机程序设计语言。是一种动态的、面向对象的脚本语言，*初被设计用于编写自动化脚本(shell)，随着版本的不断更新和语能的添加，越来越多地被用于独立的、大型项目的开发。本书讲解python知识。 |
| 目录 | |
| Copyright and Creditr>Preface Chapter 1： Understanding Functional Programming Identifying a paradigm Subdividing the procedural paradigm Using the functional paradigm Using a functional hybrid Lo at object creation The stack of turtler>A classic example of functional programming Exploratory data analysir>Summary Chapter 2： Introducing Essential Functional Conceptr>First-class functionr>Pure functionr>Higher-order functionr>Immutable data Strict and non-strict evaluation Recursion instead of an explicit loop state Functional type systemr>Familiar territory Learning some advanced conceptr>Summary Chapter 3： Functions， Iterators， and Generatorr>Writing pure functionr>Functions as first-clasjectr>Using stringr>Using tuples and named tupler>Using generator expressionr>Exploring the limitations of generatorr>Combining generator expressionr>Cleaning raw data with generator functionr>Using lists， dicts， and setr>Using stateful mappingr>Using the bisect module to create a mapping Using stateful setr>Summary Chapter 4： Wo with Collectionr>An overview of function varietier>Wo with iterabler>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 functionr>Using any（） and all（） as reductionr>Using lenO and sum（） Using sums and counts for statisticr>Using zip（） to structure and flatten sequencer>Unzipping a zipped sequence Flattening sequencer>Structuring flat sequencer>Structuring flat sequences - an alternative approach Using reversed（） to change the order Using enumerate（） to include a sequence number Summary Chapter 5： Higher-Order Functionr>Using max（） and min0 to find extrema Using Python lambda formr>Lambdas and the lambda calculur>Using the map（） function to apply a function to a collection Wo with lambda forms and map（） Using map（） with multiple sequencer>Using the filter（） function to pass or reject data Using filter（） to identify outlierr>The iter0 function with a sentinel value Using sorted（） to put data in order Writing higher-order functionr>Writing higher-order mappings and filterr>Unwrapping data while mapping Wrapping itional data while mapping Flattening data while mapping Structuring data while filtering Writing generator functionr>Building higher-order functions with callabler>Assuring good functional design Review of some design patternr>Summary Chapter 6： Recursions and Reductionr>Simple numerical recursionr>Implementing tail-call optimization Leaving recursion in place Handling difficult tail-call optimization Processing collections through recursion Tail-call optimization for collectionr>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 valuer>Writing more general group-by reductionr>Writing higher-order reductionr>Writing file parserr>Parsing CSV filer>Parsing plain text files with headerr>Summary Chapter 7： itional Tuple Techniquer>Using tuples to collect data Using named tuples to collect data Building named tuples with functional constructorr>Avoiding stateful classey using families of tupler>Assigning statistical rankr>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 Wo with the infinite iteratorr>Counting with count（） Counting with float argumentr>Re-iterating a cycle with cycle（） Repeating a single value with repeat（） Using the finite iteratorr>Assigning numbers with enumerate（） Running totals with accumulate（） Combining iterators with chain（） Partitioning an iterator with groupby0 Merging iterables with zip_longest0 and zip（） Filtering with compress（） Pi sets with islice（） Stateful filtering with dropwhile0 and takewhile0 Two approaches to filtering with filterfalse（） and filter（） Applying a function to data via starmap0 and map（） Cloning iterators with tee（） The itertools reciper>Summary Chapter 9： More Itertools Techniquer>Enumerating the Cartesian product Reducing a product Computing distancer>Getting all pixels and all colorr>Performance analysir>Rearranging the problem Combining two transformationr>Permuting a collection of valuer>Generating all combinationr>Reciper>Summary Chapter 10： The Functools Module Function toolr>Memoizing previous results with Iru_cache Defining classes with total ordering Defining number classer>Applying partial arguments with partial（） Reducing sets of data with the reduce（） function Combining map（） and reduce（） Using the reduce（） and partial（） functionr>Using the map（） and reduce（） functions to sanitize raw data Using the groupby0 and reduce（） functionr>Summary Chapter 11： Decorator Design Techniquer>Decorators as higher-order functionr>Using the functools update_wrapper0 functionr>Cross-cutting concernr>Composite design Preprocessing bad data ing a parameter to a decorator Implementing more complex decoratorr>CompLex design considerationr>Summary Chapter 12： The Multiprocessing and Threading Moduler>Functional programming and concurrency What concurrency really meanr>The boundary conditionr>Sharing resources with process or threadr>Where benefits will accrue Using multiprocessing pools and taskr>Processing many large filer>Parsing log files - gathering the rowr>Parsing log lines into namedtupler>Parsing itional fields of an Accesject Filtering the access detailr>Analyzing the access detailr>The complete analysis procer>Using a multiprocessing pool for concurrent processing Using apply（） to make a single requer>Using the map_async0， starmap_async（）， and apply_async0 functionr>More complex multiprocessing architecturer>Using the concurrent．futures module Using concurrent．futures thread poolr>Using the threading and queue moduler>Designing concurrent processing Summary Chapter 13： Conditional Expressions and the Operator Module Evaluating conditional expressionr>Exploiting non-strict dictionary ruler>Filtering true conditional expressionr>Finding a matching pattern Using the operator module instead of lambdar>Getting named attributes when using higher-order functionr>Starmapping with operatorr>Reducing with operator module functionr>Summary Chapter 14： The PyMonad Library Downloading and installing Functional composition and currying Using curried higher-order functionr>Currying the hard way Functional composition and the PyMonad * operator Functors and applicative functorr>Using the lazy List（） functor Monad bind（） function and the >> operator Implementing simulation with monadr>itional PyMonad featurer>Summary Chapter 15： A Functional Approach to Web Servicer>The HTTP request-response model Injecting state through cookier>Considering a server with a functional design Lo more deeply into the functional view Nesting the servicer>The WSGI standard Throwing exceptions during WSGI processing Pragmatic WSGI applicationr>Defining web services as functionr>Creating the WSGI application Getting raw data Applying a filter Serializing the resultr>Serializing data into JSON or CSV formatr>Serializing data into XML Serializing data into HTML Tra usage Summary Chapter 16： Optimizations and Improvementr>Memoization and caching Specializing memoization Tail recursion optimizationr>Optimizing storage Optimizing accuracy Reducing accuracy based on audience requirementr>Case study-m a chi-squared decision Filtering and reducing the raw data with a Counter object Reading summarized data Computing sums with a Counter object Computing probabilities from Counter objectr>Computing expected values and displaying a contingency table Computing the chi-squared value Computing the chi-squared threshold Computing the incomplete gamma function Computing the complete gamma function Computing the s of a distribution being random Functional programming design patternr>Summary Other Books You May Enjoy Index |