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# SOME DESCRIPTIVE TITLE.
# Copyright (C) 2001 Python Software Foundation
# This file is distributed under the same license as the Python package.
# FIRST AUTHOR <EMAIL@ADDRESS>, YEAR.
#
# Translators:
# python-doc bot, 2025
#
#, fuzzy
msgid ""
msgstr ""
"Project-Id-Version: Python 3.15\n"
"Report-Msgid-Bugs-To: \n"
"POT-Creation-Date: 2026-09-11 17:24+0000\n"
"PO-Revision-Date: 2025-09-16 00:00+0000\n"
"Last-Translator: python-doc bot, 2025\n"
"Language-Team: Russian (https://app.transifex.com/python-doc/teams/5390/"
"ru/)\n"
"MIME-Version: 1.0\n"
"Content-Type: text/plain; charset=UTF-8\n"
"Content-Transfer-Encoding: 8bit\n"
"Language: ru\n"
"Plural-Forms: nplurals=4; plural=(n%10==1 && n%100!=11 ? 0 : n%10>=2 && "
"n%10<=4 && (n%100<12 || n%100>14) ? 1 : n%10==0 || (n%10>=5 && n%10<=9) || "
"(n%100>=11 && n%100<=14)? 2 : 3);\n"
msgid "Functional Programming HOWTO"
msgstr ""
msgid ""
"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 :"
"term:`iterator`\\s and :term:`generator`\\s and relevant library modules "
"such as :mod:`itertools` and :mod:`functools`."
msgstr ""
msgid "Introduction"
msgstr ""
msgid ""
"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 :ref:`functional-howto-iterators`."
msgstr ""
msgid ""
"Programming languages support decomposing problems in several different ways:"
msgstr ""
msgid ""
"Most programming languages are **procedural**: programs are lists of "
"instructions that tell the computer what to do with the program's input. C, "
"Pascal, and even Unix shells are procedural languages."
msgstr ""
msgid ""
"In **declarative** languages, you write a specification that describes the "
"problem to be solved, and the language implementation figures out how to "
"perform the computation efficiently. SQL is the declarative language you're "
"most likely to be familiar with; a SQL query describes the data set you want "
"to retrieve, and the SQL engine decides whether to scan tables or use "
"indexes, which subclauses should be performed first, etc."
msgstr ""
msgid ""
"**Object-oriented** programs manipulate collections of objects. Objects "
"have internal state and support methods that query or modify this internal "
"state in some way. Smalltalk and Java are object-oriented languages. C++ "
"and Python are languages that support object-oriented programming, but don't "
"force the use of object-oriented features."
msgstr ""
msgid ""
"**Functional** programming decomposes a problem into a set of functions. "
"Ideally, functions only take inputs and produce outputs, and don't have any "
"internal state that affects the output produced for a given input. Well-"
"known functional languages include the ML family (Standard ML, OCaml, and "
"other variants) and Haskell."
msgstr ""
msgid ""
"The designers of some computer languages choose to emphasize one particular "
"approach to programming. This often makes it difficult to write programs "
"that use a different approach. Other languages are multi-paradigm languages "
"that support several different approaches. Lisp, C++, and Python are multi-"
"paradigm; you can write programs or libraries that are largely procedural, "
"object-oriented, or functional in all of these languages. In a large "
"program, different sections might be written using different approaches; the "
"GUI might be object-oriented while the processing logic is procedural or "
"functional, for example."
msgstr ""
msgid ""
"In a functional program, input flows through a set of functions. Each "
"function operates on its input and produces some output. Functional style "
"discourages functions with side effects that modify internal state or make "
"other changes that aren't visible in the function's return value. Functions "
"that have no side effects at all are called **purely functional**. Avoiding "
"side effects means not using data structures that get updated as a program "
"runs; every function's output must only depend on its input."
msgstr ""
msgid ""
"Some languages are very strict about purity and don't even have assignment "
"statements such as ``a=3`` or ``c = a + b``, but it's difficult to avoid all "
"side effects, such as printing to the screen or writing to a disk file. "
"Another example is a call to the :func:`print` or :func:`time.sleep` "
"function, neither of which returns a useful value. Both are called only for "
"their side effects of sending some text to the screen or pausing execution "
"for a second."
msgstr ""
msgid ""
"Python programs written in functional style usually won't go to the extreme "
"of avoiding all I/O or all assignments; instead, they'll provide a "
"functional-appearing interface but will use non-functional features "
"internally. For example, the implementation of a function will still use "
"assignments to local variables, but won't modify global variables or have "
"other side effects."
msgstr ""
msgid ""
"Functional programming can be considered the opposite of object-oriented "
"programming. Objects are little capsules containing some internal state "
"along with a collection of method calls that let you modify this state, and "
"programs consist of making the right set of state changes. Functional "
"programming wants to avoid state changes as much as possible and works with "
"data flowing between functions. In Python you might combine the two "
"approaches by writing functions that take and return instances representing "
"objects in your application (e-mail messages, transactions, etc.)."
msgstr ""
msgid ""
"Functional design may seem like an odd constraint to work under. Why should "
"you avoid objects and side effects? There are theoretical and practical "
"advantages to the functional style:"
msgstr ""
msgid "Formal provability."
msgstr ""
msgid "Modularity."
msgstr ""
msgid "Composability."
msgstr ""
msgid "Ease of debugging and testing."
msgstr ""
msgid "Formal provability"
msgstr ""
msgid ""
"A theoretical benefit is that it's easier to construct a mathematical proof "
"that a functional program is correct."
msgstr ""
msgid ""
"For a long time researchers have been interested in finding ways to "
"mathematically prove programs correct. This is different from testing a "
"program on numerous inputs and concluding that its output is usually "
"correct, or reading a program's source code and concluding that the code "
"looks right; the goal is instead a rigorous proof that a program produces "
"the right result for all possible inputs."
msgstr ""
msgid ""
"The technique used to prove programs correct is to write down "
"**invariants**, properties of the input data and of the program's variables "
"that are always true. For each line of code, you then show that if "
"invariants X and Y are true **before** the line is executed, the slightly "
"different invariants X' and Y' are true **after** the line is executed. "
"This continues until you reach the end of the program, at which point the "
"invariants should match the desired conditions on the program's output."
msgstr ""
msgid ""
"Functional programming's avoidance of assignments arose because assignments "
"are difficult to handle with this technique; assignments can break "
"invariants that were true before the assignment without producing any new "
"invariants that can be propagated onward."
msgstr ""
msgid ""
"Unfortunately, proving programs correct is largely impractical and not "
"relevant to Python software. Even trivial programs require proofs that are "
"several pages long; the proof of correctness for a moderately complicated "
"program would be enormous, and few or none of the programs you use daily "
"(the Python interpreter, your XML parser, your web browser) could be proven "
"correct. Even if you wrote down or generated a proof, there would then be "
"the question of verifying the proof; maybe there's an error in it, and you "
"wrongly believe you've proved the program correct."
msgstr ""
msgid "Modularity"
msgstr ""
msgid ""
"A more practical benefit of functional programming is that it forces you to "
"break apart your problem into small pieces. Programs are more modular as a "
"result. It's easier to specify and write a small function that does one "
"thing than a large function that performs a complicated transformation. "
"Small functions are also easier to read and to check for errors."
msgstr ""
msgid "Ease of debugging and testing"
msgstr ""
msgid "Testing and debugging a functional-style program is easier."
msgstr ""
msgid ""
"Debugging is simplified because functions are generally small and clearly "
"specified. When a program doesn't work, each function is an interface point "
"where you can check that the data are correct. You can look at the "
"intermediate inputs and outputs to quickly isolate the function that's "
"responsible for a bug."
msgstr ""
msgid ""
"Testing is easier because each function is a potential subject for a unit "
"test. Functions don't depend on system state that needs to be replicated "
"before running a test; instead you only have to synthesize the right input "
"and then check that the output matches expectations."
msgstr ""
msgid "Composability"
msgstr ""
msgid ""
"As you work on a functional-style program, you'll write a number of "
"functions with varying inputs and outputs. Some of these functions will be "
"unavoidably specialized to a particular application, but others will be "
"useful in a wide variety of programs. For example, a function that takes a "
"directory path and returns all the XML files in the directory, or a function "
"that takes a filename and returns its contents, can be applied to many "
"different situations."
msgstr ""
msgid ""
"Over time you'll form a personal library of utilities. Often you'll "
"assemble new programs by arranging existing functions in a new configuration "
"and writing a few functions specialized for the current task."
msgstr ""
msgid "Iterators"
msgstr ""
msgid ""
"I'll start by looking at a Python language feature that's an important "
"foundation for writing functional-style programs: iterators."
msgstr ""
msgid ""
"An iterator is an object representing a stream of data; this object returns "
"the data one element at a time. A Python iterator must support a method "
"called :meth:`~iterator.__next__` that takes no arguments and always returns "
"the next element of the stream. If there are no more elements in the "
"stream, :meth:`~iterator.__next__` must raise the :exc:`StopIteration` "
"exception. Iterators don't have to be finite, though; it's perfectly "
"reasonable to write an iterator that produces an infinite stream of data."
msgstr ""
msgid ""
"The built-in :func:`iter` function takes an arbitrary object and tries to "
"return an iterator that will return the object's contents or elements, "
"raising :exc:`TypeError` if the object doesn't support iteration. Several "
"of Python's built-in data types support iteration, the most common being "
"lists and dictionaries. An object is called :term:`iterable` if you can get "
"an iterator for it."
msgstr ""
msgid "You can experiment with the iteration interface manually:"
msgstr ""
msgid ""
"Python expects iterable objects in several different contexts, the most "
"important being the :keyword:`for` statement. In the statement ``for X in "
"Y``, Y must be an iterator or some object for which :func:`iter` can create "
"an iterator. These two statements are equivalent::"
msgstr ""
msgid ""
"for i in iter(obj):\n"
" print(i)\n"
"\n"
"for i in obj:\n"
" print(i)"
msgstr ""
msgid ""
"Iterators can be materialized as lists or tuples by using the :func:`list` "
"or :func:`tuple` constructor functions:"
msgstr ""
msgid ""
"Sequence unpacking also supports iterators: if you know an iterator will "
"return N elements, you can unpack them into an N-tuple:"
msgstr ""
msgid ""
"Built-in functions such as :func:`max` and :func:`min` can take a single "
"iterator argument and will return the largest or smallest element. The "
"``\"in\"`` and ``\"not in\"`` operators also support iterators: ``X in "
"iterator`` is true if X is found in the stream returned by the iterator. "
"You'll run into obvious problems if the iterator is infinite; :func:`max`, :"
"func:`min` will never return, and if the element X never appears in the "
"stream, the ``\"in\"`` and ``\"not in\"`` operators won't return either."
msgstr ""
msgid ""
"Note that you can only go forward in an iterator; there's no way to get the "
"previous element, reset the iterator, or make a copy of it. Iterator "
"objects can optionally provide these additional capabilities, but the "
"iterator protocol only specifies the :meth:`~iterator.__next__` method. "
"Functions may therefore consume all of the iterator's output, and if you "
"need to do something different with the same stream, you'll have to create a "
"new iterator."
msgstr ""
msgid "Data Types That Support Iterators"
msgstr ""
msgid ""
"We've already seen how lists and tuples support iterators. In fact, any "
"Python sequence type, such as strings, will automatically support creation "
"of an iterator."
msgstr ""
msgid ""
"Calling :func:`iter` on a dictionary returns an iterator that will loop over "
"the dictionary's keys::"
msgstr ""
msgid ""
">>> m = {'Jan': 1, 'Feb': 2, 'Mar': 3, 'Apr': 4, 'May': 5, 'Jun': 6,\n"
"... 'Jul': 7, 'Aug': 8, 'Sep': 9, 'Oct': 10, 'Nov': 11, 'Dec': 12}\n"
">>> for key in m:\n"
"... print(key, m[key])\n"
"Jan 1\n"
"Feb 2\n"
"Mar 3\n"
"Apr 4\n"
"May 5\n"
"Jun 6\n"
"Jul 7\n"
"Aug 8\n"
"Sep 9\n"
"Oct 10\n"
"Nov 11\n"
"Dec 12"
msgstr ""
msgid ""
"Note that starting with Python 3.7, dictionary iteration order is guaranteed "
"to be the same as the insertion order. In earlier versions, the behaviour "
"was unspecified and could vary between implementations."
msgstr ""
msgid ""
"Applying :func:`iter` to a dictionary always loops over the keys, but "
"dictionaries have methods that return other iterators. If you want to "
"iterate over values or key/value pairs, you can explicitly call the :meth:"
"`~dict.values` or :meth:`~dict.items` methods to get an appropriate iterator."
msgstr ""
msgid ""
"The :func:`dict` constructor can accept an iterator that returns a finite "
"stream of ``(key, value)`` tuples:"
msgstr ""
msgid ""
"Files also support iteration by calling the :meth:`~io.TextIOBase.readline` "
"method until there are no more lines in the file. This means you can read "
"each line of a file like this::"
msgstr ""
msgid ""
"for line in file:\n"
" # do something for each line\n"
" ..."
msgstr ""
msgid ""
"Sets can take their contents from an iterable and let you iterate over the "
"set's elements::"
msgstr ""
msgid ""
">>> S = {2, 3, 5, 7, 11, 13}\n"
">>> for i in S:\n"
"... print(i)\n"
"2\n"
"3\n"
"5\n"
"7\n"
"11\n"
"13"
msgstr ""
msgid "Generator expressions and list comprehensions"
msgstr ""
msgid ""
"Two common operations on an iterator's output are 1) performing some "
"operation for every element, 2) selecting a subset of elements that meet "
"some condition. For example, given a list of strings, you might want to "
"strip off trailing whitespace from each line or extract all the strings "
"containing a given substring."
msgstr ""
msgid ""
"List comprehensions and generator expressions (short form: \"listcomps\" and "
"\"genexps\") are a concise notation for such operations, borrowed from the "
"functional programming language Haskell (https://www.haskell.org/). You can "
"strip all the whitespace from a stream of strings with the following code::"
msgstr ""
msgid ""
">>> line_list = [' line 1\\n', 'line 2 \\n', ' \\n', '']\n"
"\n"
">>> # Generator expression -- returns iterator\n"
">>> stripped_iter = (line.strip() for line in line_list)\n"
"\n"
">>> # List comprehension -- returns list\n"
">>> stripped_list = [line.strip() for line in line_list]"
msgstr ""
msgid ""
"You can select only certain elements by adding an ``\"if\"`` condition::"
msgstr ""
msgid ""
">>> stripped_list = [line.strip() for line in line_list\n"
"... if line != \"\"]"
msgstr ""
msgid ""
"With a list comprehension, you get back a Python list; ``stripped_list`` is "
"a list containing the resulting lines, not an iterator. Generator "
"expressions return an iterator that computes the values as necessary, not "
"needing to materialize all the values at once. This means that list "
"comprehensions aren't useful if you're working with iterators that return an "
"infinite stream or a very large amount of data. Generator expressions are "
"preferable in these situations."
msgstr ""
msgid ""
"Generator expressions are surrounded by parentheses (\"()\") and list "
"comprehensions are surrounded by square brackets (\"[]\"). Generator "
"expressions have the form::"
msgstr ""
msgid ""
"( expression for expr in sequence1\n"
" if condition1\n"
" for expr2 in sequence2\n"
" if condition2\n"
" for expr3 in sequence3\n"
" if condition3\n"
" ...\n"
" for exprN in sequenceN\n"
" if conditionN )"
msgstr ""
msgid ""
"Again, for a list comprehension only the outside brackets are different "
"(square brackets instead of parentheses)."
msgstr ""
msgid ""
"The elements of the generated output will be the successive values of "
"``expression``. The ``if`` clauses are all optional; if present, "
"``expression`` is only evaluated and added to the result when ``condition`` "
"is true."
msgstr ""
msgid ""
"Generator expressions always have to be written inside parentheses, but the "
"parentheses signalling a function call also count. If you want to create an "
"iterator that will be immediately passed to a function you can write::"
msgstr ""
msgid "obj_total = sum(obj.count for obj in list_all_objects())"
msgstr ""
msgid ""
"The ``for...in`` clauses contain the sequences to be iterated over. The "
"sequences do not have to be the same length, because they are iterated over "
"from left to right, **not** in parallel. For each element in ``sequence1``, "
"``sequence2`` is looped over from the beginning. ``sequence3`` is then "
"looped over for each resulting pair of elements from ``sequence1`` and "
"``sequence2``."
msgstr ""
msgid ""
"To put it another way, a list comprehension or generator expression is "
"equivalent to the following Python code::"
msgstr ""
msgid ""
"for expr1 in sequence1:\n"
" if not (condition1):\n"
" continue # Skip this element\n"
" for expr2 in sequence2:\n"
" if not (condition2):\n"
" continue # Skip this element\n"
" ...\n"
" for exprN in sequenceN:\n"
" if not (conditionN):\n"
" continue # Skip this element\n"
"\n"
" # Output the value of\n"
" # the expression."
msgstr ""
msgid ""
"This means that when there are multiple ``for...in`` clauses but no ``if`` "
"clauses, the length of the resulting output will be equal to the product of "
"the lengths of all the sequences. If you have two lists of length 3, the "
"output list is 9 elements long:"
msgstr ""
msgid ""
"To avoid introducing an ambiguity into Python's grammar, if ``expression`` "
"is creating a tuple, it must be surrounded with parentheses. The first list "
"comprehension below is a syntax error, while the second one is correct::"
msgstr ""
msgid ""
"# Syntax error\n"
"[x, y for x in seq1 for y in seq2]\n"
"# Correct\n"
"[(x, y) for x in seq1 for y in seq2]"
msgstr ""
msgid "Generators"
msgstr ""
msgid ""
"Generators are a special class of functions that simplify the task of "
"writing iterators. Regular functions compute a value and return it, but "
"generators return an iterator that returns a stream of values."
msgstr ""
msgid ""
"You're doubtless familiar with how regular function calls work in Python or "
"C. When you call a function, it gets a private namespace where its local "
"variables are created. When the function reaches a ``return`` statement, "
"the local variables are destroyed and the value is returned to the caller. "
"A later call to the same function creates a new private namespace and a "
"fresh set of local variables. But, what if the local variables weren't "
"thrown away on exiting a function? What if you could later resume the "
"function where it left off? This is what generators provide; they can be "
"thought of as resumable functions."
msgstr ""
msgid "Here's the simplest example of a generator function:"
msgstr ""
msgid ""
"Any function containing a :keyword:`yield` keyword is a generator function; "
"this is detected by Python's :term:`bytecode` compiler which compiles the "
"function specially as a result."
msgstr ""
msgid ""
"When you call a generator function, it doesn't return a single value; "
"instead it returns a generator object that supports the iterator protocol. "
"On executing the ``yield`` expression, the generator outputs the value of "
"``i``, similar to a ``return`` statement. The big difference between "
"``yield`` and a ``return`` statement is that on reaching a ``yield`` the "
"generator's state of execution is suspended and local variables are "
"preserved. On the next call to the generator's :meth:`~generator.__next__` "
"method, the function will resume executing."
msgstr ""
msgid "Here's a sample usage of the ``generate_ints()`` generator:"
msgstr ""
msgid ""
"You could equally write ``for i in generate_ints(5)``, or ``a, b, c = "
"generate_ints(3)``."
msgstr ""
msgid ""
"Inside a generator function, ``return value`` causes "
"``StopIteration(value)`` to be raised from the :meth:`~generator.__next__` "
"method. Once this happens, or the bottom of the function is reached, the "
"procession of values ends and the generator cannot yield any further values."
msgstr ""
msgid ""
"You could achieve the effect of generators manually by writing your own "
"class and storing all the local variables of the generator as instance "
"variables. For example, returning a list of integers could be done by "
"setting ``self.count`` to 0, and having the :meth:`~iterator.__next__` "
"method increment ``self.count`` and return it. However, for a moderately "
"complicated generator, writing a corresponding class can be much messier."
msgstr ""
msgid ""
"The test suite included with Python's library, :source:`Lib/test/"
"test_generators.py`, contains a number of more interesting examples. Here's "
"one generator that implements an in-order traversal of a tree using "
"generators recursively. ::"
msgstr ""
msgid ""
"# A recursive generator that generates Tree leaves in in-order.\n"
"def inorder(t):\n"
" if t:\n"
" for x in inorder(t.left):\n"
" yield x\n"
"\n"
" yield t.label\n"
"\n"
" for x in inorder(t.right):\n"
" yield x"
msgstr ""
msgid ""
"Two other examples in ``test_generators.py`` produce solutions for the N-"
"Queens problem (placing N queens on an NxN chess board so that no queen "
"threatens another) and the Knight's Tour (finding a route that takes a "
"knight to every square of an NxN chessboard without visiting any square "
"twice)."
msgstr ""
msgid "Passing values into a generator"
msgstr ""
msgid ""
"In Python 2.4 and earlier, generators only produced output. Once a "
"generator's code was invoked to create an iterator, there was no way to pass "
"any new information into the function when its execution is resumed. You "
"could hack together this ability by making the generator look at a global "
"variable or by passing in some mutable object that callers then modify, but "
"these approaches are messy."
msgstr ""
msgid ""
"In Python 2.5 there's a simple way to pass values into a generator. :keyword:"
"`yield` became an expression, returning a value that can be assigned to a "
"variable or otherwise operated on::"
msgstr ""
msgid "val = (yield i)"
msgstr ""
msgid ""
"I recommend that you **always** put parentheses around a ``yield`` "
"expression when you're doing something with the returned value, as in the "
"above example. The parentheses aren't always necessary, but it's easier to "
"always add them instead of having to remember when they're needed."
msgstr ""
msgid ""
"(:pep:`342` explains the exact rules, which are that a ``yield``-expression "
"must always be parenthesized except when it occurs at the top-level "
"expression on the right-hand side of an assignment. This means you can "
"write ``val = yield i`` but have to use parentheses when there's an "
"operation, as in ``val = (yield i) + 12``.)"
msgstr ""
msgid ""
"Values are sent into a generator by calling its :meth:`send(value) "
"<generator.send>` method. This method resumes the generator's code and the "
"``yield`` expression returns the specified value. If the regular :meth:"
"`~generator.__next__` method is called, the ``yield`` returns ``None``."
msgstr ""
msgid ""
"Here's a simple counter that increments by 1 and allows changing the value "
"of the internal counter."
msgstr ""
msgid ""
"def counter(maximum):\n"
" i = 0\n"
" while i < maximum:\n"
" val = (yield i)\n"
" # If value provided, change counter\n"
" if val is not None:\n"
" i = val\n"
" else:\n"
" i += 1"
msgstr ""
msgid "And here's an example of changing the counter:"
msgstr ""
msgid ""
"Because ``yield`` will often be returning ``None``, you should always check "
"for this case. Don't just use its value in expressions unless you're sure "
"that the :meth:`~generator.send` method will be the only method used to "
"resume your generator function."
msgstr ""
msgid ""
"In addition to :meth:`~generator.send`, there are two other methods on "
"generators:"
msgstr ""
msgid ""
":meth:`throw(value) <generator.throw>` is used to raise an exception inside "
"the generator; the exception is raised by the ``yield`` expression where the "
"generator's execution is paused."
msgstr ""
msgid ""
":meth:`~generator.close` sends a :exc:`GeneratorExit` exception to the "
"generator to terminate the iteration. On receiving this exception, the "
"generator's code must either raise :exc:`GeneratorExit` or :exc:"
"`StopIteration`; catching the exception and doing anything else is illegal "
"and will trigger a :exc:`RuntimeError`. :meth:`~generator.close` will also "
"be called by Python's garbage collector when the generator is garbage-"
"collected."
msgstr ""
msgid ""
"If you need to run cleanup code when a :exc:`GeneratorExit` occurs, I "
"suggest using a ``try: ... finally:`` suite instead of catching :exc:"
"`GeneratorExit`."
msgstr ""
msgid ""
"The cumulative effect of these changes is to turn generators from one-way "
"producers of information into both producers and consumers."
msgstr ""
msgid ""
"Generators also become **coroutines**, a more generalized form of "
"subroutines. Subroutines are entered at one point and exited at another "
"point (the top of the function, and a ``return`` statement), but coroutines "
"can be entered, exited, and resumed at many different points (the ``yield`` "
"statements)."
msgstr ""
msgid "Built-in functions"
msgstr ""
msgid ""
"Let's look in more detail at built-in functions often used with iterators."
msgstr ""
msgid ""
"Two of Python's built-in functions, :func:`map` and :func:`filter` duplicate "
"the features of generator expressions:"
msgstr ""
msgid ""
":func:`map(f, iterA, iterB, ...) <map>` returns an iterator over the sequence"
msgstr ""
msgid ""
"``f(iterA[0], iterB[0]), f(iterA[1], iterB[1]), f(iterA[2], iterB[2]), ...``."
msgstr ""
msgid "You can of course achieve the same effect with a list comprehension."
msgstr ""
msgid ""
":func:`filter(predicate, iter) <filter>` returns an iterator over all the "
"sequence elements that meet a certain condition, and is similarly duplicated "
"by list comprehensions. A **predicate** is a function that returns the "
"truth value of some condition; for use with :func:`filter`, the predicate "
"must take a single value."
msgstr ""
msgid "This can also be written as a list comprehension:"
msgstr ""
msgid ""
":func:`enumerate(iter, start=0) <enumerate>` counts off the elements in the "
"iterable returning 2-tuples containing the count (from *start*) and each "
"element. ::"
msgstr ""
msgid ""
">>> for item in enumerate(['subject', 'verb', 'object']):\n"
"... print(item)\n"
"(0, 'subject')\n"
"(1, 'verb')\n"
"(2, 'object')"
msgstr ""
msgid ""
":func:`enumerate` is often used when looping through a list and recording "
"the indexes at which certain conditions are met::"
msgstr ""
msgid ""
"f = open('data.txt', 'r')\n"
"for i, line in enumerate(f):\n"
" if line.strip() == '':\n"
" print('Blank line at line #%i' % i)"
msgstr ""
msgid ""
":func:`sorted(iterable, key=None, reverse=False) <sorted>` collects all the "
"elements of the iterable into a list, sorts the list, and returns the sorted "
"result. The *key* and *reverse* arguments are passed through to the "
"constructed list's :meth:`~list.sort` method. ::"
msgstr ""
msgid ""
">>> import random\n"
">>> # Generate 8 random numbers between [0, 10000)\n"
">>> rand_list = random.sample(range(10000), 8)\n"
">>> rand_list\n"
"[769, 7953, 9828, 6431, 8442, 9878, 6213, 2207]\n"
">>> sorted(rand_list)\n"
"[769, 2207, 6213, 6431, 7953, 8442, 9828, 9878]\n"
">>> sorted(rand_list, reverse=True)\n"
"[9878, 9828, 8442, 7953, 6431, 6213, 2207, 769]"
msgstr ""
msgid ""
"(For a more detailed discussion of sorting, see the :ref:`sortinghowto`.)"
msgstr ""
msgid ""
"The :func:`any(iter) <any>` and :func:`all(iter) <all>` built-ins look at "
"the truth values of an iterable's contents. :func:`any` returns ``True`` if "
"any element in the iterable is a true value, and :func:`all` returns "
"``True`` if all of the elements are true values:"
msgstr ""
msgid ""
":func:`zip(iterA, iterB, ...) <zip>` takes one element from each iterable "
"and returns them in a tuple::"
msgstr ""
msgid ""
"zip(['a', 'b', 'c'], (1, 2, 3)) =>\n"
" ('a', 1), ('b', 2), ('c', 3)"
msgstr ""
msgid ""
"It doesn't construct an in-memory list and :term:`exhaust <exhausted>` all "
"the input iterators before returning; instead tuples are constructed and "
"returned only if they're requested. (The technical term for this behaviour "
"is `lazy evaluation <https://en.wikipedia.org/wiki/Lazy_evaluation>`__.)"
msgstr ""
msgid ""
"This iterator is intended to be used with iterables that are all of the same "
"length. If the iterables are of different lengths, the resulting stream "
"will be the same length as the shortest iterable. ::"
msgstr ""
msgid ""
"zip(['a', 'b'], (1, 2, 3)) =>\n"
" ('a', 1), ('b', 2)"
msgstr ""
msgid ""
"You should avoid doing this, though, because an element may be taken from "
"the longer iterators and discarded. This means you can't go on to use the "
"iterators further because you risk skipping a discarded element."
msgstr ""
msgid "The itertools module"
msgstr ""
msgid ""
"The :mod:`itertools` module contains a number of commonly used iterators as "
"well as functions for combining several iterators. This section will "
"introduce the module's contents by showing small examples."
msgstr ""
msgid "The module's functions fall into a few broad classes:"
msgstr ""
msgid "Functions that create a new iterator based on an existing iterator."
msgstr ""
msgid "Functions for treating an iterator's elements as function arguments."
msgstr ""
msgid "Functions for selecting portions of an iterator's output."
msgstr ""
msgid "A function for grouping an iterator's output."
msgstr ""
msgid "Creating new iterators"
msgstr ""
msgid ""
":func:`itertools.count(start, step) <itertools.count>` returns an infinite "
"stream of evenly spaced values. You can optionally supply the starting "
"number, which defaults to 0, and the interval between numbers, which "
"defaults to 1::"
msgstr ""
msgid ""
"itertools.count() =>\n"
" 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, ...\n"
"itertools.count(10) =>\n"
" 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, ...\n"
"itertools.count(10, 5) =>\n"
" 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, ..."
msgstr ""
msgid ""
":func:`itertools.cycle(iter) <itertools.cycle>` saves a copy of the contents "
"of a provided iterable and returns a new iterator that returns its elements "
"from first to last. The new iterator will repeat these elements "
"infinitely. ::"
msgstr ""
msgid ""
"itertools.cycle([1, 2, 3, 4, 5]) =>\n"
" 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, ..."
msgstr ""
msgid ""
":func:`itertools.repeat(elem, [n]) <itertools.repeat>` returns the provided "
"element *n* times, or returns the element endlessly if *n* is not "
"provided. ::"
msgstr ""
msgid ""
"itertools.repeat('abc') =>\n"
" abc, abc, abc, abc, abc, abc, abc, abc, abc, abc, ...\n"
"itertools.repeat('abc', 5) =>\n"
" abc, abc, abc, abc, abc"
msgstr ""
msgid ""
":func:`itertools.chain(iterA, iterB, ...) <itertools.chain>` takes an "
"arbitrary number of iterables as input, and returns all the elements of the "
"first iterator, then all the elements of the second, and so on, until all of "
"the iterables have been :term:`exhausted`. ::"
msgstr ""
msgid ""
"itertools.chain(['a', 'b', 'c'], (1, 2, 3)) =>\n"
" a, b, c, 1, 2, 3"
msgstr ""
msgid ""
":func:`itertools.islice(iter, [start], stop, [step]) <itertools.islice>` "
"returns a stream that's a slice of the iterator. With a single *stop* "
"argument, it will return the first *stop* elements. If you supply a "
"starting index, you'll get *stop-start* elements, and if you supply a value "
"for *step*, elements will be skipped accordingly. Unlike Python's string "
"and list slicing, you can't use negative values for *start*, *stop*, or "
"*step*. ::"
msgstr ""
msgid ""
"itertools.islice(range(10), 8) =>\n"
" 0, 1, 2, 3, 4, 5, 6, 7\n"
"itertools.islice(range(10), 2, 8) =>\n"
" 2, 3, 4, 5, 6, 7\n"
"itertools.islice(range(10), 2, 8, 2) =>\n"
" 2, 4, 6"
msgstr ""
msgid ""
":func:`itertools.tee(iter, [n]) <itertools.tee>` replicates an iterator; it "
"returns *n* independent iterators that will all return the contents of the "
"source iterator. If you don't supply a value for *n*, the default is 2. "
"Replicating iterators requires saving some of the contents of the source "
"iterator, so this can consume significant memory if the iterator is large "
"and one of the new iterators is consumed more than the others. ::"
msgstr ""
msgid ""
"itertools.tee( itertools.count() ) =>\n"
" iterA, iterB\n"
"\n"
"where iterA ->\n"
" 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, ...\n"
"\n"
"and iterB ->\n"
" 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, ..."
msgstr ""
msgid "Calling functions on elements"
msgstr ""
msgid ""
"The :mod:`operator` module contains a set of functions corresponding to "
"Python's operators. Some examples are :func:`operator.add(a, b) <operator."
"add>` (adds two values), :func:`operator.ne(a, b) <operator.ne>` (same as "
"``a != b``), and :func:`operator.attrgetter('id') <operator.attrgetter>` "
"(returns a callable that fetches the ``.id`` attribute)."
msgstr ""
msgid ""
":func:`itertools.starmap(func, iter) <itertools.starmap>` assumes that the "
"iterable will return a stream of tuples, and calls *func* using these tuples "
"as the arguments::"
msgstr ""
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