Doc/tutorial/controlflow.rst

.. _tut-morecontrol:

***********************
More Control Flow Tools
***********************

Besides the :keyword:`while` statement just introduced, Python knows the usual
control flow statements known from other languages, with some twists.


.. _tut-if:

:keyword:`if` Statements
========================

Perhaps the most well-known statement type is the :keyword:`if` statement.  For
example::

   >>> x = int(input("Please enter an integer: "))
   >>> if x < 0:
   ...      x = 0
   ...      print('Negative changed to zero')
   ... elif x == 0:
   ...      print('Zero')
   ... elif x == 1:
   ...      print('Single')
   ... else:
   ...      print('More')
   ... 

There can be zero or more :keyword:`elif` parts, and the :keyword:`else` part is
optional.  The keyword ':keyword:`elif`' is short for 'else if', and is useful
to avoid excessive indentation.  An  :keyword:`if` ... :keyword:`elif` ...
:keyword:`elif` ... sequence is a substitute for the ``switch`` or
``case`` statements found in other languages.


.. _tut-for:

:keyword:`for` Statements
=========================

.. index::
   statement: for

The :keyword:`for` statement in Python differs a bit from what you may be used
to in C or Pascal.  Rather than always iterating over an arithmetic progression
of numbers (like in Pascal), or giving the user the ability to define both the
iteration step and halting condition (as C), Python's :keyword:`for` statement
iterates over the items of any sequence (a list or a string), in the order that
they appear in the sequence.  For example (no pun intended):

.. One suggestion was to give a real C example here, but that may only serve to
   confuse non-C programmers.

::

   >>> # Measure some strings:
   ... a = ['cat', 'window', 'defenestrate']
   >>> for x in a:
   ...     print(x, len(x))
   ... 
   cat 3
   window 6
   defenestrate 12

It is not safe to modify the sequence being iterated over in the loop (this can
only happen for mutable sequence types, such as lists).  If you need to modify
the list you are iterating over (for example, to duplicate selected items) you
must iterate over a copy.  The slice notation makes this particularly
convenient::

   >>> for x in a[:]: # make a slice copy of the entire list
   ...    if len(x) > 6: a.insert(0, x)
   ... 
   >>> a
   ['defenestrate', 'cat', 'window', 'defenestrate']


.. _tut-range:

The :func:`range` Function
==========================

If you do need to iterate over a sequence of numbers, the built-in function
:func:`range` comes in handy.  It generates arithmetic progressions::


    >>> for i in range(5):
    ...     print(i)
    ...
    0
    1
    2
    3
    4
    


The given end point is never part of the generated list; ``range(10)`` generates
10 values, the legal indices for items of a sequence of length 10.  It
is possible to let the range start at another number, or to specify a different
increment (even negative; sometimes this is called the 'step')::

    range(5, 10)          
       5 through 9

    range(0, 10, 3)       
       0, 3, 6, 9

    range(-10, -100, -30) 
      -10, -40, -70

To iterate over the indices of a sequence, combine :func:`range` and :func:`len`
as follows::

   >>> a = ['Mary', 'had', 'a', 'little', 'lamb']
   >>> for i in range(len(a)):
   ...     print(i, a[i])
   ... 
   0 Mary
   1 had
   2 a
   3 little
   4 lamb

A strange thing happens if you just print a range::

   >>> print(range(10))
   range(0, 10)

In many ways the object returned by :func:`range` behaves as if it is a list,
but in fact it isn't. It is an object which returns the successive items of 
the desired sequence when you iterate over it, but it doesn't really make 
the list, thus saving space. 

We say such an object is *iterable*, that is, suitable as a target for 
functions and constructs that expect something from which they can 
obtain successive items until the supply is exhausted. We have seen that
the :keyword:`for` statement is such an *iterator*. The function :func:`list`
is another; it creates lists from iterables::


   >>> list(range(5))
   [0, 1, 2, 3, 4]

Later we will see more functions that return iterables and take iterables as argument.

.. _tut-break:

:keyword:`break` and :keyword:`continue` Statements, and :keyword:`else` Clauses on Loops
=========================================================================================

The :keyword:`break` statement, like in C, breaks out of the smallest enclosing
:keyword:`for` or :keyword:`while` loop.

The :keyword:`continue` statement, also borrowed from C, continues with the next
iteration of the loop.

Loop statements may have an ``else`` clause; it is executed when the loop
terminates through exhaustion of the list (with :keyword:`for`) or when the
condition becomes false (with :keyword:`while`), but not when the loop is
terminated by a :keyword:`break` statement.  This is exemplified by the
following loop, which searches for prime numbers::

   >>> for n in range(2, 10):
   ...     for x in range(2, n):
   ...         if n % x == 0:
   ...             print(n, 'equals', x, '*', n/x)
   ...             break
   ...     else:
   ...         # loop fell through without finding a factor
   ...         print(n, 'is a prime number')
   ... 
   2 is a prime number
   3 is a prime number
   4 equals 2 * 2
   5 is a prime number
   6 equals 2 * 3
   7 is a prime number
   8 equals 2 * 4
   9 equals 3 * 3


.. _tut-pass:

:keyword:`pass` Statements
==========================

The :keyword:`pass` statement does nothing. It can be used when a statement is
required syntactically but the program requires no action. For example::

   >>> while True:
   ...       pass # Busy-wait for keyboard interrupt
   ... 


.. _tut-functions:

Defining Functions
==================

We can create a function that writes the Fibonacci series to an arbitrary
boundary::

   >>> def fib(n):    # write Fibonacci series up to n
   ...     """Print a Fibonacci series up to n."""
   ...     a, b = 0, 1
   ...     while b < n:
   ...         print(b, end=' ')
   ...         a, b = b, a+b
   ...     print()
   ... 
   >>> # Now call the function we just defined:
   ... fib(2000)
   1 1 2 3 5 8 13 21 34 55 89 144 233 377 610 987 1597

.. index::
   single: documentation strings
   single: docstrings
   single: strings, documentation

The keyword :keyword:`def` introduces a function *definition*.  It must be
followed by the function name and the parenthesized list of formal parameters.
The statements that form the body of the function start at the next line, and
must be indented.  The first statement of the function body can optionally be a
string literal; this string literal is the function's documentation string, or
:dfn:`docstring`.

There are tools which use docstrings to automatically produce online or printed
documentation, or to let the user interactively browse through code; it's good
practice to include docstrings in code that you write, so try to make a habit of
it.

The *execution* of a function introduces a new symbol table used for the local
variables of the function.  More precisely, all variable assignments in a
function store the value in the local symbol table; whereas variable references
first look in the local symbol table, then in the global symbol table, and then
in the table of built-in names. Thus,  global variables cannot be directly
assigned a value within a function (unless named in a :keyword:`global`
statement), although they may be referenced.

The actual parameters (arguments) to a function call are introduced in the local
symbol table of the called function when it is called; thus, arguments are
passed using *call by value* (where the *value* is always an object *reference*,
not the value of the object). [#]_ When a function calls another function, a new
local symbol table is created for that call.

A function definition introduces the function name in the current symbol table.
The value of the function name has a type that is recognized by the interpreter
as a user-defined function.  This value can be assigned to another name which
can then also be used as a function.  This serves as a general renaming
mechanism::

   >>> fib
   <function fib at 10042ed0>
   >>> f = fib
   >>> f(100)
   1 1 2 3 5 8 13 21 34 55 89

You might object that ``fib`` is not a function but a procedure.  In Python,
like in C, procedures are just functions that don't return a value.  In fact,
technically speaking, procedures do return a value, albeit a rather boring one.
This value is called ``None`` (it's a built-in name).  Writing the value
``None`` is normally suppressed by the interpreter if it would be the only value
written.  You can see it if you really want to using :keyword:`print`::

   >>> fib(0)
   >>> print(fib(0))
   None

It is simple to write a function that returns a list of the numbers of the
Fibonacci series, instead of printing it::

   >>> def fib2(n): # return Fibonacci series up to n
   ...     """Return a list containing the Fibonacci series up to n."""
   ...     result = []
   ...     a, b = 0, 1
   ...     while b < n:
   ...         result.append(b)    # see below
   ...         a, b = b, a+b
   ...     return result
   ... 
   >>> f100 = fib2(100)    # call it
   >>> f100                # write the result
   [1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89]

This example, as usual, demonstrates some new Python features:

* The :keyword:`return` statement returns with a value from a function.
  :keyword:`return` without an expression argument returns ``None``. Falling off
  the end of a procedure also returns ``None``.

* The statement ``result.append(b)`` calls a *method* of the list object
  ``result``.  A method is a function that 'belongs' to an object and is named
  ``obj.methodname``, where ``obj`` is some object (this may be an expression),
  and ``methodname`` is the name of a method that is defined by the object's type.
  Different types define different methods.  Methods of different types may have
  the same name without causing ambiguity.  (It is possible to define your own
  object types and methods, using *classes*, as discussed later in this tutorial.)
  The method :meth:`append` shown in the example is defined for list objects; it
  adds a new element at the end of the list.  In this example it is equivalent to
  ``result = result + [b]``, but more efficient.


.. _tut-defining:

More on Defining Functions
==========================

It is also possible to define functions with a variable number of arguments.
There are three forms, which can be combined.


.. _tut-defaultargs:

Default Argument Values
-----------------------

The most useful form is to specify a default value for one or more arguments.
This creates a function that can be called with fewer arguments than it is
defined to allow.  For example::

   def ask_ok(prompt, retries=4, complaint='Yes or no, please!'):
       while True:
           ok = input(prompt)
           if ok in ('y', 'ye', 'yes'): return True
           if ok in ('n', 'no', 'nop', 'nope'): return False
           retries = retries - 1
           if retries < 0:
               raise IOError('refusenik user')
           print(complaint)

This function can be called either like this: ``ask_ok('Do you really want to
quit?')`` or like this: ``ask_ok('OK to overwrite the file?', 2)``.

This example also introduces the :keyword:`in` keyword. This tests whether or
not a sequence contains a certain value.

The default values are evaluated at the point of function definition in the
*defining* scope, so that ::

   i = 5

   def f(arg=i):
       print(arg)

   i = 6
   f()

will print ``5``.

**Important warning:**  The default value is evaluated only once. This makes a
difference when the default is a mutable object such as a list, dictionary, or
instances of most classes.  For example, the following function accumulates the
arguments passed to it on subsequent calls::

   def f(a, L=[]):
       L.append(a)
       return L

   print(f(1))
   print(f(2))
   print(f(3))

This will print ::

   [1]
   [1, 2]
   [1, 2, 3]

If you don't want the default to be shared between subsequent calls, you can
write the function like this instead::

   def f(a, L=None):
       if L is None:
           L = []
       L.append(a)
       return L


.. _tut-keywordargs:

Keyword Arguments
-----------------

Functions can also be called using keyword arguments of the form ``keyword =
value``.  For instance, the following function::

   def parrot(voltage, state='a stiff', action='voom', type='Norwegian Blue'):
       print("-- This parrot wouldn't", action, end=' ')
       print("if you put", voltage, "volts through it.")
       print("-- Lovely plumage, the", type)
       print("-- It's", state, "!")

could be called in any of the following ways::

   parrot(1000)
   parrot(action = 'VOOOOOM', voltage = 1000000)
   parrot('a thousand', state = 'pushing up the daisies')
   parrot('a million', 'bereft of life', 'jump')

but the following calls would all be invalid::

   parrot()                     # required argument missing
   parrot(voltage=5.0, 'dead')  # non-keyword argument following keyword
   parrot(110, voltage=220)     # duplicate value for argument
   parrot(actor='John Cleese')  # unknown keyword

In general, an argument list must have any positional arguments followed by any
keyword arguments, where the keywords must be chosen from the formal parameter
names.  It's not important whether a formal parameter has a default value or
not.  No argument may receive a value more than once --- formal parameter names
corresponding to positional arguments cannot be used as keywords in the same
calls. Here's an example that fails due to this restriction::

   >>> def function(a):
   ...     pass
   ... 
   >>> function(0, a=0)
   Traceback (most recent call last):
     File "<stdin>", line 1, in ?
   TypeError: function() got multiple values for keyword argument 'a'

When a final formal parameter of the form ``**name`` is present, it receives a
dictionary (see :ref:`typesmapping`) containing all keyword arguments except for
those corresponding to a formal parameter.  This may be combined with a formal
parameter of the form ``*name`` (described in the next subsection) which
receives a tuple containing the positional arguments beyond the formal parameter
list.  (``*name`` must occur before ``**name``.) For example, if we define a
function like this::

   def cheeseshop(kind, *arguments, **keywords):
       print("-- Do you have any", kind, '?')
       print("-- I'm sorry, we're all out of", kind)
       for arg in arguments: print arg
       print('-'*40)
       keys = sorted(keywords.keys())
       for kw in keys: print(kw, ':', keywords[kw])

It could be called like this::

   cheeseshop('Limburger', "It's very runny, sir.",
              "It's really very, VERY runny, sir.",
              client='John Cleese',
              shopkeeper='Michael Palin',
              sketch='Cheese Shop Sketch')

and of course it would print::

   -- Do you have any Limburger ?
   -- I'm sorry, we're all out of Limburger
   It's very runny, sir.
   It's really very, VERY runny, sir.
   ----------------------------------------
   client : John Cleese
   shopkeeper : Michael Palin
   sketch : Cheese Shop Sketch

Note that the :meth:`sort` method of the list of keyword argument names is
called before printing the contents of the ``keywords`` dictionary; if this is
not done, the order in which the arguments are printed is undefined.


.. _tut-arbitraryargs:

Arbitrary Argument Lists
------------------------

Finally, the least frequently used option is to specify that a function can be
called with an arbitrary number of arguments.  These arguments will be wrapped
up in a tuple.  Before the variable number of arguments, zero or more normal
arguments may occur. ::

   def fprintf(file, format, *args):
       file.write(format % args)

 
Normally, these ``variadic`` arguments will be last in the list of formal
parameters, because they scoop up all remaining input arguments that are 
passed to the function. Any formal parameters which occur after the ``*args``
parameter are 'keyword-only' arguments, meaning that they can only be used as 
keywords rather than positional arguments. ::
 
   >>> def concat(*args, sep="/"):
   ...    return sep.join(args)
   ...
   >>> concat("earth", "mars", "venus")
   'earth/mars/venus'
   >>> concat("earth", "mars", "venus", sep=".")
   'earth.mars.venus'

.. _tut-unpacking-arguments:

Unpacking Argument Lists
------------------------

The reverse situation occurs when the arguments are already in a list or tuple
but need to be unpacked for a function call requiring separate positional
arguments.  For instance, the built-in :func:`range` function expects separate
*start* and *stop* arguments.  If they are not available separately, write the
function call with the  ``*``\ -operator to unpack the arguments out of a list
or tuple::

   >>> list(range(3, 6))            # normal call with separate arguments
   [3, 4, 5]
   >>> args = [3, 6]
   >>> list(range(*args))            # call with arguments unpacked from a list
   [3, 4, 5]

In the same fashion, dictionaries can deliver keyword arguments with the ``**``\
-operator::

   >>> def parrot(voltage, state='a stiff', action='voom'):
   ...     print("-- This parrot wouldn't", action, end=' ')
   ...     print("if you put", voltage, "volts through it.", end=' ')
   ...     print("E's", state, "!")
   ...
   >>> d = {"voltage": "four million", "state": "bleedin' demised", "action": "VOOM"}
   >>> parrot(**d)
   -- This parrot wouldn't VOOM if you put four million volts through it. E's bleedin' demised !


.. _tut-lambda:

Lambda Forms
------------

By popular demand, a few features commonly found in functional programming
languages like Lisp have been added to Python.  With the :keyword:`lambda`
keyword, small anonymous functions can be created. Here's a function that
returns the sum of its two arguments: ``lambda a, b: a+b``.  Lambda forms can be
used wherever function objects are required.  They are syntactically restricted
to a single expression.  Semantically, they are just syntactic sugar for a
normal function definition.  Like nested function definitions, lambda forms can
reference variables from the containing scope::

   >>> def make_incrementor(n):
   ...     return lambda x: x + n
   ...
   >>> f = make_incrementor(42)
   >>> f(0)
   42
   >>> f(1)
   43


.. _tut-docstrings:

Documentation Strings
---------------------

.. index::
   single: docstrings
   single: documentation strings
   single: strings, documentation

Here are some conventions about the content and formatting of documentation
strings. 

The first line should always be a short, concise summary of the object's
purpose.  For brevity, it should not explicitly state the object's name or type,
since these are available by other means (except if the name happens to be a
verb describing a function's operation).  This line should begin with a capital
letter and end with a period.

If there are more lines in the documentation string, the second line should be
blank, visually separating the summary from the rest of the description.  The
following lines should be one or more paragraphs describing the object's calling
conventions, its side effects, etc.

The Python parser does not strip indentation from multi-line string literals in
Python, so tools that process documentation have to strip indentation if
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.



.. 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).