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614 lines
21 KiB
ReStructuredText
614 lines
21 KiB
ReStructuredText
.. _tut-morecontrol:
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***********************
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More Control Flow Tools
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***********************
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Besides the :keyword:`while` statement just introduced, Python knows the usual
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control flow statements known from other languages, with some twists.
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.. _tut-if:
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:keyword:`if` Statements
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========================
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Perhaps the most well-known statement type is the :keyword:`if` statement. For
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example::
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>>> x = int(raw_input("Please enter an integer: "))
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>>> if x < 0:
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... x = 0
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... print 'Negative changed to zero'
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... elif x == 0:
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... print 'Zero'
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... elif x == 1:
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... print 'Single'
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... else:
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... print 'More'
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...
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There can be zero or more :keyword:`elif` parts, and the :keyword:`else` part is
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optional. The keyword ':keyword:`elif`' is short for 'else if', and is useful
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to avoid excessive indentation. An :keyword:`if` ... :keyword:`elif` ...
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:keyword:`elif` ... sequence is a substitute for the ``switch`` or
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``case`` statements found in other languages.
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.. _tut-for:
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:keyword:`for` Statements
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=========================
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.. index::
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statement: for
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statement: for
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The :keyword:`for` statement in Python differs a bit from what you may be used
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to in C or Pascal. Rather than always iterating over an arithmetic progression
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of numbers (like in Pascal), or giving the user the ability to define both the
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iteration step and halting condition (as C), Python's :keyword:`for` statement
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iterates over the items of any sequence (a list or a string), in the order that
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they appear in the sequence. For example (no pun intended):
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.. One suggestion was to give a real C example here, but that may only serve to
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confuse non-C programmers.
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::
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>>> # Measure some strings:
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... a = ['cat', 'window', 'defenestrate']
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>>> for x in a:
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... print x, len(x)
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...
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cat 3
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window 6
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defenestrate 12
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It is not safe to modify the sequence being iterated over in the loop (this can
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only happen for mutable sequence types, such as lists). If you need to modify
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the list you are iterating over (for example, to duplicate selected items) you
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must iterate over a copy. The slice notation makes this particularly
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convenient::
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>>> for x in a[:]: # make a slice copy of the entire list
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... if len(x) > 6: a.insert(0, x)
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...
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>>> a
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['defenestrate', 'cat', 'window', 'defenestrate']
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.. _tut-range:
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The :func:`range` Function
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==========================
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If you do need to iterate over a sequence of numbers, the built-in function
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:func:`range` comes in handy. It generates lists containing arithmetic
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progressions::
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>>> range(10)
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[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
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The given end point is never part of the generated list; ``range(10)`` generates
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a list of 10 values, the legal indices for items of a sequence of length 10. It
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is possible to let the range start at another number, or to specify a different
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increment (even negative; sometimes this is called the 'step')::
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>>> range(5, 10)
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[5, 6, 7, 8, 9]
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>>> range(0, 10, 3)
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[0, 3, 6, 9]
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>>> range(-10, -100, -30)
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[-10, -40, -70]
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To iterate over the indices of a sequence, combine :func:`range` and :func:`len`
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as follows::
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>>> a = ['Mary', 'had', 'a', 'little', 'lamb']
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>>> for i in range(len(a)):
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... print i, a[i]
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...
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0 Mary
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1 had
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2 a
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3 little
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4 lamb
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.. _tut-break:
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:keyword:`break` and :keyword:`continue` Statements, and :keyword:`else` Clauses on Loops
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=========================================================================================
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The :keyword:`break` statement, like in C, breaks out of the smallest enclosing
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:keyword:`for` or :keyword:`while` loop.
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The :keyword:`continue` statement, also borrowed from C, continues with the next
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iteration of the loop.
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Loop statements may have an ``else`` clause; it is executed when the loop
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terminates through exhaustion of the list (with :keyword:`for`) or when the
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condition becomes false (with :keyword:`while`), but not when the loop is
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terminated by a :keyword:`break` statement. This is exemplified by the
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following loop, which searches for prime numbers::
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>>> for n in range(2, 10):
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... for x in range(2, n):
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... if n % x == 0:
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... print n, 'equals', x, '*', n/x
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... break
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... else:
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... # loop fell through without finding a factor
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... print n, 'is a prime number'
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...
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2 is a prime number
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3 is a prime number
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4 equals 2 * 2
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5 is a prime number
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6 equals 2 * 3
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7 is a prime number
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8 equals 2 * 4
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9 equals 3 * 3
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.. _tut-pass:
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:keyword:`pass` Statements
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==========================
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The :keyword:`pass` statement does nothing. It can be used when a statement is
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required syntactically but the program requires no action. For example::
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>>> while True:
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... pass # Busy-wait for keyboard interrupt
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...
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.. _tut-functions:
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Defining Functions
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==================
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We can create a function that writes the Fibonacci series to an arbitrary
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boundary::
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>>> def fib(n): # write Fibonacci series up to n
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... """Print a Fibonacci series up to n."""
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... a, b = 0, 1
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... while b < n:
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... print b,
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... a, b = b, a+b
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...
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>>> # Now call the function we just defined:
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... fib(2000)
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1 1 2 3 5 8 13 21 34 55 89 144 233 377 610 987 1597
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.. index::
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single: documentation strings
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single: docstrings
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single: strings, documentation
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The keyword :keyword:`def` introduces a function *definition*. It must be
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followed by the function name and the parenthesized list of formal parameters.
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The statements that form the body of the function start at the next line, and
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must be indented. The first statement of the function body can optionally be a
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string literal; this string literal is the function's documentation string, or
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:dfn:`docstring`.
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There are tools which use docstrings to automatically produce online or printed
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documentation, or to let the user interactively browse through code; it's good
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practice to include docstrings in code that you write, so try to make a habit of
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it.
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The *execution* of a function introduces a new symbol table used for the local
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variables of the function. More precisely, all variable assignments in a
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function store the value in the local symbol table; whereas variable references
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first look in the local symbol table, then in the local symbol tables of
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enclosing functions, then in the global symbol table, and finally in the table
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of built-in names. Thus, global variables cannot be directly assigned a value
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within a function (unless named in a :keyword:`global` statement), although they
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may be referenced.
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The actual parameters (arguments) to a function call are introduced in the local
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symbol table of the called function when it is called; thus, arguments are
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passed using *call by value* (where the *value* is always an object *reference*,
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not the value of the object). [#]_ When a function calls another function, a new
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local symbol table is created for that call.
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A function definition introduces the function name in the current symbol table.
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The value of the function name has a type that is recognized by the interpreter
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as a user-defined function. This value can be assigned to another name which
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can then also be used as a function. This serves as a general renaming
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mechanism::
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>>> fib
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<function fib at 10042ed0>
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>>> f = fib
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>>> f(100)
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1 1 2 3 5 8 13 21 34 55 89
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You might object that ``fib`` is not a function but a procedure. In Python,
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like in C, procedures are just functions that don't return a value. In fact,
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technically speaking, procedures do return a value, albeit a rather boring one.
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This value is called ``None`` (it's a built-in name). Writing the value
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``None`` is normally suppressed by the interpreter if it would be the only value
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written. You can see it if you really want to using :keyword:`print`::
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>>> fib(0)
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>>> print fib(0)
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None
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It is simple to write a function that returns a list of the numbers of the
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Fibonacci series, instead of printing it::
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>>> def fib2(n): # return Fibonacci series up to n
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... """Return a list containing the Fibonacci series up to n."""
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... result = []
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... a, b = 0, 1
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... while b < n:
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... result.append(b) # see below
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... a, b = b, a+b
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... return result
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...
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>>> f100 = fib2(100) # call it
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>>> f100 # write the result
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[1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89]
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This example, as usual, demonstrates some new Python features:
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* The :keyword:`return` statement returns with a value from a function.
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:keyword:`return` without an expression argument returns ``None``. Falling off
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the end of a procedure also returns ``None``.
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* The statement ``result.append(b)`` calls a *method* of the list object
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``result``. A method is a function that 'belongs' to an object and is named
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``obj.methodname``, where ``obj`` is some object (this may be an expression),
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and ``methodname`` is the name of a method that is defined by the object's type.
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Different types define different methods. Methods of different types may have
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the same name without causing ambiguity. (It is possible to define your own
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object types and methods, using *classes*, as discussed later in this tutorial.)
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The method :meth:`append` shown in the example is defined for list objects; it
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adds a new element at the end of the list. In this example it is equivalent to
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``result = result + [b]``, but more efficient.
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.. _tut-defining:
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More on Defining Functions
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==========================
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It is also possible to define functions with a variable number of arguments.
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There are three forms, which can be combined.
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.. _tut-defaultargs:
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Default Argument Values
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-----------------------
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The most useful form is to specify a default value for one or more arguments.
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This creates a function that can be called with fewer arguments than it is
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defined to allow. For example::
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def ask_ok(prompt, retries=4, complaint='Yes or no, please!'):
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while True:
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ok = raw_input(prompt)
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if ok in ('y', 'ye', 'yes'): return True
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if ok in ('n', 'no', 'nop', 'nope'): return False
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retries = retries - 1
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if retries < 0: raise IOError, 'refusenik user'
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print complaint
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This function can be called either like this: ``ask_ok('Do you really want to
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quit?')`` or like this: ``ask_ok('OK to overwrite the file?', 2)``.
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This example also introduces the :keyword:`in` keyword. This tests whether or
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not a sequence contains a certain value.
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The default values are evaluated at the point of function definition in the
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*defining* scope, so that ::
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i = 5
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def f(arg=i):
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print arg
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i = 6
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f()
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will print ``5``.
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**Important warning:** The default value is evaluated only once. This makes a
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difference when the default is a mutable object such as a list, dictionary, or
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instances of most classes. For example, the following function accumulates the
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arguments passed to it on subsequent calls::
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def f(a, L=[]):
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L.append(a)
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return L
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print f(1)
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print f(2)
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print f(3)
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This will print ::
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[1]
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[1, 2]
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[1, 2, 3]
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If you don't want the default to be shared between subsequent calls, you can
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write the function like this instead::
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def f(a, L=None):
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if L is None:
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L = []
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L.append(a)
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return L
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.. _tut-keywordargs:
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Keyword Arguments
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-----------------
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Functions can also be called using keyword arguments of the form ``keyword =
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value``. For instance, the following function::
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def parrot(voltage, state='a stiff', action='voom', type='Norwegian Blue'):
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print "-- This parrot wouldn't", action,
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print "if you put", voltage, "volts through it."
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print "-- Lovely plumage, the", type
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print "-- It's", state, "!"
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could be called in any of the following ways::
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parrot(1000)
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parrot(action = 'VOOOOOM', voltage = 1000000)
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parrot('a thousand', state = 'pushing up the daisies')
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parrot('a million', 'bereft of life', 'jump')
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but the following calls would all be invalid::
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parrot() # required argument missing
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parrot(voltage=5.0, 'dead') # non-keyword argument following keyword
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parrot(110, voltage=220) # duplicate value for argument
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parrot(actor='John Cleese') # unknown keyword
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In general, an argument list must have any positional arguments followed by any
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keyword arguments, where the keywords must be chosen from the formal parameter
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names. It's not important whether a formal parameter has a default value or
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not. No argument may receive a value more than once --- formal parameter names
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corresponding to positional arguments cannot be used as keywords in the same
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calls. Here's an example that fails due to this restriction::
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>>> def function(a):
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... pass
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...
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>>> function(0, a=0)
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Traceback (most recent call last):
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File "<stdin>", line 1, in ?
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TypeError: function() got multiple values for keyword argument 'a'
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When a final formal parameter of the form ``**name`` is present, it receives a
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dictionary (see :ref:`typesmapping`) containing all keyword arguments except for
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those corresponding to a formal parameter. This may be combined with a formal
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parameter of the form ``*name`` (described in the next subsection) which
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receives a tuple containing the positional arguments beyond the formal parameter
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list. (``*name`` must occur before ``**name``.) For example, if we define a
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function like this::
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def cheeseshop(kind, *arguments, **keywords):
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print "-- Do you have any", kind, '?'
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print "-- I'm sorry, we're all out of", kind
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for arg in arguments: print arg
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print '-'*40
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keys = keywords.keys()
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keys.sort()
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for kw in keys: print kw, ':', keywords[kw]
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It could be called like this::
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cheeseshop('Limburger', "It's very runny, sir.",
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"It's really very, VERY runny, sir.",
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client='John Cleese',
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shopkeeper='Michael Palin',
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sketch='Cheese Shop Sketch')
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and of course it would print::
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-- Do you have any Limburger ?
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-- I'm sorry, we're all out of Limburger
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It's very runny, sir.
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It's really very, VERY runny, sir.
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----------------------------------------
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client : John Cleese
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shopkeeper : Michael Palin
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sketch : Cheese Shop Sketch
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Note that the :meth:`sort` method of the list of keyword argument names is
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called before printing the contents of the ``keywords`` dictionary; if this is
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not done, the order in which the arguments are printed is undefined.
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.. _tut-arbitraryargs:
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Arbitrary Argument Lists
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------------------------
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.. index::
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statement: *
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Finally, the least frequently used option is to specify that a function can be
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called with an arbitrary number of arguments. These arguments will be wrapped
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up in a tuple. Before the variable number of arguments, zero or more normal
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arguments may occur. ::
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def write_multiple_items(file, separator, *args):
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file.write(separator.join(args))
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.. _tut-unpacking-arguments:
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Unpacking Argument Lists
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------------------------
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The reverse situation occurs when the arguments are already in a list or tuple
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but need to be unpacked for a function call requiring separate positional
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arguments. For instance, the built-in :func:`range` function expects separate
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*start* and *stop* arguments. If they are not available separately, write the
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function call with the ``*``\ -operator to unpack the arguments out of a list
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or tuple::
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>>> range(3, 6) # normal call with separate arguments
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[3, 4, 5]
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>>> args = [3, 6]
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>>> range(*args) # call with arguments unpacked from a list
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[3, 4, 5]
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.. index::
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statement: **
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In the same fashion, dictionaries can deliver keyword arguments with the ``**``\
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-operator::
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>>> def parrot(voltage, state='a stiff', action='voom'):
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... print "-- This parrot wouldn't", action,
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... print "if you put", voltage, "volts through it.",
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... print "E's", state, "!"
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...
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>>> d = {"voltage": "four million", "state": "bleedin' demised", "action": "VOOM"}
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>>> parrot(**d)
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-- This parrot wouldn't VOOM if you put four million volts through it. E's bleedin' demised !
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.. _tut-lambda:
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Lambda Forms
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------------
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By popular demand, a few features commonly found in functional programming
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languages like Lisp have been added to Python. With the :keyword:`lambda`
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keyword, small anonymous functions can be created. Here's a function that
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returns the sum of its two arguments: ``lambda a, b: a+b``. Lambda forms can be
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used wherever function objects are required. They are syntactically restricted
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to a single expression. Semantically, they are just syntactic sugar for a
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normal function definition. Like nested function definitions, lambda forms can
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reference variables from the containing scope::
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>>> def make_incrementor(n):
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... return lambda x: x + n
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...
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>>> f = make_incrementor(42)
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>>> f(0)
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42
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>>> f(1)
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43
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.. _tut-docstrings:
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Documentation Strings
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---------------------
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.. index::
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single: docstrings
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single: documentation strings
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single: strings, documentation
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There are emerging conventions about the content and formatting of documentation
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strings.
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The first line should always be a short, concise summary of the object's
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purpose. For brevity, it should not explicitly state the object's name or type,
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since these are available by other means (except if the name happens to be a
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verb describing a function's operation). This line should begin with a capital
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letter and end with a period.
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If there are more lines in the documentation string, the second line should be
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blank, visually separating the summary from the rest of the description. The
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following lines should be one or more paragraphs describing the object's calling
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conventions, its side effects, etc.
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The Python parser does not strip indentation from multi-line string literals in
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Python, so tools that process documentation have to strip indentation if
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desired. This is done using the following convention. The first non-blank line
|
|
*after* the first line of the string determines the amount of indentation for
|
|
the entire documentation string. (We can't use the first line since it is
|
|
generally adjacent to the string's opening quotes so its indentation is not
|
|
apparent in the string literal.) Whitespace "equivalent" to this indentation is
|
|
then stripped from the start of all lines of the string. Lines that are
|
|
indented less should not occur, but if they occur all their leading whitespace
|
|
should be stripped. Equivalence of whitespace should be tested after expansion
|
|
of tabs (to 8 spaces, normally).
|
|
|
|
Here is an example of a multi-line docstring::
|
|
|
|
>>> def my_function():
|
|
... """Do nothing, but document it.
|
|
...
|
|
... No, really, it doesn't do anything.
|
|
... """
|
|
... pass
|
|
...
|
|
>>> print my_function.__doc__
|
|
Do nothing, but document it.
|
|
|
|
No, really, it doesn't do anything.
|
|
|
|
|
|
.. _tut-codingstyle:
|
|
|
|
Intermezzo: Coding Style
|
|
========================
|
|
|
|
.. sectionauthor:: Georg Brandl <georg@python.org>
|
|
.. index:: pair: coding; style
|
|
|
|
Now that you are about to write longer, more complex pieces of Python, it is a
|
|
good time to talk about *coding style*. Most languages can be written (or more
|
|
concise, *formatted*) in different styles; some are more readable than others.
|
|
Making it easy for others to read your code is always a good idea, and adopting
|
|
a nice coding style helps tremendously for that.
|
|
|
|
For Python, :pep:`8` has emerged as the style guide that most projects adhere to;
|
|
it promotes a very readable and eye-pleasing coding style. Every Python
|
|
developer should read it at some point; here are the most important points
|
|
extracted for you:
|
|
|
|
* Use 4-space indentation, and no tabs.
|
|
|
|
4 spaces are a good compromise between small indentation (allows greater
|
|
nesting depth) and large indentation (easier to read). Tabs introduce
|
|
confusion, and are best left out.
|
|
|
|
* Wrap lines so that they don't exceed 79 characters.
|
|
|
|
This helps users with small displays and makes it possible to have several
|
|
code files side-by-side on larger displays.
|
|
|
|
* Use blank lines to separate functions and classes, and larger blocks of
|
|
code inside functions.
|
|
|
|
* When possible, put comments on a line of their own.
|
|
|
|
* Use docstrings.
|
|
|
|
* Use spaces around operators and after commas, but not directly inside
|
|
bracketing constructs: ``a = f(1, 2) + g(3, 4)``.
|
|
|
|
* Name your classes and functions consistently; the convention is to use
|
|
``CamelCase`` for classes and ``lower_case_with_underscores`` for functions
|
|
and methods. Always use ``self`` as the name for the first method argument.
|
|
|
|
* Don't use fancy encodings if your code is meant to be used in international
|
|
environments. Plain ASCII works best in any case.
|
|
|
|
|
|
.. rubric:: Footnotes
|
|
|
|
.. [#] Actually, *call by object reference* would be a better description,
|
|
since if a mutable object is passed, the caller will see any changes the
|
|
callee makes to it (items inserted into a list).
|
|
|