Откуда идут «функциональные» корни Python / Python Я никогда не полагал, что Python попадет под влияние функциональных языков, независимо от того что люди говорят или думают. Я знаком с императивными языками, такими как C и Algol68 и хотя я сделал функции объектами «первого класса», я не рассматривал Python как язык функционального программирования. Однако, было ясно, что пользователи хотят больше от списков и функций. Операции над списками применялись к каждому элементу списка и создавали новый список. Например: def square(x): return x*x vals = [1, 2, 3, 4] newvals = [] for v in vals: newvals.append(square(v)) На функциональных языках, таких как Lisp и Scheme, операции, такие как эта были разработаны как встроенные функции языка. def map(f, s): result = [] for x in s: result.append(f(x)) return result def square(x): return x*x vals = [1, 2, 3, 4] newvals = map(square,vals) Тонкость вышеупомянутого кода в том, что многим людям не нравился факт, что функция применяется к элементам списка как набор отдельных функций.
Probably Overthinking It: Regression with Python, pandas and StatsModels I was at Boston Data-Con 2014 this morning, which was a great event. The organizer, John Verostek, seems to have created this three-day event single-handedly, so I am hugely impressed. Imran Malek started the day with a very nice iPython tutorial. The description is here, and his slides are here. And Imran very kindly let me use his laptop to project slides for my talk, which was next. Regression is a powerful tool for fitting data and making predictions. As an example, I will use data from the National Survey of Family Growth to generate predictions for the date of birth, weight, and sex of an expected baby. This talk is appropriate for people with no prior experience with regression. And here are my slides: The material for this talk is from the second edition of Think Stats, which is in production now and scheduled for release in early November. As I expected, I prepared way more material than I could present. I believe video of the talk will be available soon.
Twisted Twisted is an event-driven networking engine written in Python and licensed under the open source MIT license. Twisted runs on Python 2 and an ever growing subset also works with Python 3. Twisted makes it easy to implement custom network applications. Here's a TCP server that echoes back everything that's written to it: from twisted.internet import protocol, reactor, endpoints class Echo(protocol.Protocol): def dataReceived(self, data): self.transport.write(data) class EchoFactory(protocol.Factory): def buildProtocol(self, addr): return Echo() endpoints.serverFromString(reactor, "tcp:1234").listen(EchoFactory()) reactor.run() Learn more about writing servers, writing clients and the core networking libraries , including support for SSL, UDP, scheduled events, unit testing infrastructure, and much more. Twisted includes an event-driven web server. Learn more about web application development, templates and Twisted's HTTP client. Twisted includes a sophisticated IMAP4 client library.
The Python I Would Like To See This post is surprisingly confused, it is phrased as a complaint about the language, then immediately degrades into CPython implementation specifics that have little bearing on the usability of the language itself. Ronacher should also know better than to post microbenchmarks like the one provided here, especially without corresponding (C) profiler output. At the C level, slots allow the implementation constant-time access to the most common code paths for an object, and especially when you have C code calling other C code via the type system (IMHO the primary use for Python, and still its strongest use case), "interpreter overhead" is reduced to a few extra memory indirection operations. In the alternative world, sure, perhaps some microbenchmark may behave faster, but now systemically, and for e.g. "reduce(operator.add, range(1000))" requires more hash table lookups than I can count.
Устранение Хвостовой рекурсии / Python Я недавно написал в своем блоге Python History пост «The origins of Python's functional features» (перевод). Упоминание о том, что Python не поддерживает хвостовую рекурсию (TRE) сразу спровоцировало несколько комментариев о том, как жаль, что Python не поддерживает данную оптимизацию. Появились ссылки на недавние записи в других блогах о том, что TRE может быть добавлена в Python легко. Вот длинный ответ.Во-первых, как кто-то заметил в комментариях, TRE является несовместимой с нормальной трассировкой стека: когда устранена хвостовая рекурсия, нет никакого стекового фрейма, оставленного, чтобы вывести traceback, если что-то пойдет не так. Во-вторых, идея, что TRE — просто оптимизация, которую отдельная реализация Python может реализовывать или нет, является неправильной. В-третьих, я не верю в рекурсию как базис всего программирования. Наконец, давайте посмотрим на то, как мы могли бы реализовать устранение хвостовой рекурсии. def f(x): if x > 0: return f(x-1) return 0
The Python I Would Like To See written on Saturday, August 16, 2014 It's no secret that I'm not a fan of Python 3 or where the language is currently going. This has led to a bunch of emails flying my way over the last few months about questions about what exactly I would prefer Python would do. Python is definitely a language that is not perfect. I want to take you on a journey that starts with a small oddity in the interpreter (slots) and ends up with the biggest mistake in the language design. In general though these posts will be an exploration about design decisions in the interpreter and what consequences they have on both the interpreter and the resulting language. Language vs Implementation I added this particular paragraph after I wrote the initial version of this article because I think it has been largely missed that Python as a language and CPython as the interpreter are not nearly as separate as developers might believe. Slots By far my biggest problem with the language is the stupid slot system. Yes. $ .
Functional Programming HOWTO In this document, we’ll take a tour of Python’s features suitable for implementing programs in a functional style. After an introduction to the concepts of functional programming, we’ll look at language features such as iterators and generators and relevant library modules such as itertools and functools. Introduction This section explains the basic concept of functional programming; if you’re just interested in learning about Python language features, skip to the next section on Iterators. Programming languages support decomposing problems in several different ways: Most programming languages are procedural: programs are lists of instructions that tell the computer what to do with the program’s input. The designers of some computer languages choose to emphasize one particular approach to programming. In a functional program, input flows through a set of functions. Functional programming can be considered the opposite of object-oriented programming. Formal provability Modularity Iterators
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