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.. _tut-morecontrol:

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

Besides the :keyword:`while` statement just introduced, Python uses the usual
flow control 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: "))
   Please enter an integer: 42
   >>> if x < 0:
   ...     x = 0
   ...     print('Negative changed to zero')
   ... elif x == 0:
   ...     print('Zero')
   ... elif x == 1:
   ...     print('Single')
   ... else:
   ...     print('More')
   ...
   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.

If you're comparing the same value to several constants, or checking for specific types or
attributes, you may also find the :keyword:`!match` statement useful. For more
details see :ref:`tut-match`.

.. _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:
   ... words = ['cat', 'window', 'defenestrate']
   >>> for w in words:
   ...     print(w, len(w))
   ...
   cat 3
   window 6
   defenestrate 12

Code that modifies a collection while iterating over that same collection can
be tricky to get right.  Instead, it is usually more straight-forward to loop
over a copy of the collection or to create a new collection::

    # Create a sample collection
    users = {'Hans': 'active', 'Éléonore': 'inactive', '景太郎': 'active'}

    # Strategy:  Iterate over a copy
    for user, status in users.copy().items():
        if status == 'inactive':
            del users[user]

    # Strategy:  Create a new collection
    active_users = {}
    for user, status in users.items():
        if status == 'active':
            active_users[user] = status


.. _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 sequence; ``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')::

    >>> list(range(5, 10))
    [5, 6, 7, 8, 9]

    >>> list(range(0, 10, 3))
    [0, 3, 6, 9]

    >>> list(range(-10, -100, -30))
    [-10, -40, -70]

To iterate over the indices of a sequence, you can 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

In most such cases, however, it is convenient to use the :func:`enumerate`
function, see :ref:`tut-loopidioms`.

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

   >>> 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 :term:`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 a construct, while an example of a function
that takes an iterable is :func:`sum`::

    >>> sum(range(4))  # 0 + 1 + 2 + 3
    6

Later we will see more functions that return iterables and take iterables as
arguments.  In chapter :ref:`tut-structures`, we will discuss in more detail about
:func:`list`.

.. _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 innermost enclosing
:keyword:`for` or :keyword:`while` loop.

Loop statements may have an :keyword:`!else` clause; it is executed when the loop
terminates through exhaustion of the iterable (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

(Yes, this is the correct code.  Look closely: the ``else`` clause belongs to
the :keyword:`for` loop, **not** the :keyword:`if` statement.)

When used with a loop, the ``else`` clause has more in common with the
``else`` clause of a :keyword:`try` statement than it does with that of
:keyword:`if` statements: a :keyword:`try` statement's ``else`` clause runs
when no exception occurs, and a loop's ``else`` clause runs when no ``break``
occurs. For more on the :keyword:`!try` statement and exceptions, see
:ref:`tut-handling`.

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

    >>> for num in range(2, 10):
    ...     if num % 2 == 0:
    ...         print("Found an even number", num)
    ...         continue
    ...     print("Found an odd number", num)
    ...
    Found an even number 2
    Found an odd number 3
    Found an even number 4
    Found an odd number 5
    Found an even number 6
    Found an odd number 7
    Found an even number 8
    Found an odd number 9

.. _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 (Ctrl+C)
   ...

This is commonly used for creating minimal classes::

   >>> class MyEmptyClass:
   ...     pass
   ...

Another place :keyword:`pass` can be used is as a place-holder for a function or
conditional body when you are working on new code, allowing you to keep thinking
at a more abstract level.  The :keyword:`!pass` is silently ignored::

   >>> def initlog(*args):
   ...     pass   # Remember to implement this!
   ...


.. _tut-match:

:keyword:`!match` Statements
============================

A match statement takes an expression and compares its value to successive
patterns given as one or more case blocks.  This is superficially
similar to a switch statement in C, Java or JavaScript (and many
other languages), but it can also extract components (sequence elements or
object attributes) from the value into variables.

The simplest form compares a subject value against one or more literals::

    def http_error(status):
        match status:
            case 400:
                return "Bad request"
            case 404:
                return "Not found"
            case 418:
                return "I'm a teapot"
            case _:
                return "Something's wrong with the internet"

Note the last block: the "variable name" ``_`` acts as a *wildcard* and
never fails to match. If no case matches, none of the branches is executed.

You can combine several literals in a single pattern using ``|`` ("or")::

            case 401 | 403 | 404:
                return "Not allowed"

Patterns can look like unpacking assignments, and can be used to bind
variables::

    # point is an (x, y) tuple
    match point:
        case (0, 0):
            print("Origin")
        case (0, y):
            print(f"Y={y}")
        case (x, 0):
            print(f"X={x}")
        case (x, y):
            print(f"X={x}, Y={y}")
        case _:
            raise ValueError("Not a point")

Study that one carefully!  The first pattern has two literals, and can
be thought of as an extension of the literal pattern shown above.  But
the next two patterns combine a literal and a variable, and the
variable *binds* a value from the subject (``point``).  The fourth
pattern captures two values, which makes it conceptually similar to
the unpacking assignment ``(x, y) = point``.

If you are using classes to structure your data
you can use the class name followed by an argument list resembling a
constructor, but with the ability to capture attributes into variables::

    class Point:
        x: int
        y: int

    def where_is(point):
        match point:
            case Point(x=0, y=0):
                print("Origin")
            case Point(x=0, y=y):
                print(f"Y={y}")
            case Point(x=x, y=0):
                print(f"X={x}")
            case Point():
                print("Somewhere else")
            case _:
                print("Not a point")

You can use positional parameters with some builtin classes that provide an
ordering for their attributes (e.g. dataclasses). You can also define a specific
position for attributes in patterns by setting the ``__match_args__`` special
attribute in your classes. If it's set to ("x", "y"), the following patterns are all
equivalent (and all bind the ``y`` attribute to the ``var`` variable)::

    Point(1, var)
    Point(1, y=var)
    Point(x=1, y=var)
    Point(y=var, x=1)

A recommended way to read patterns is to look at them as an extended form of what you
would put on the left of an assignment, to understand which variables would be set to
what.
Only the standalone names (like ``var`` above) are assigned to by a match statement.
Dotted names (like ``foo.bar``), attribute names (the ``x=`` and ``y=`` above) or class names
(recognized by the "(...)" next to them like ``Point`` above) are never assigned to.

Patterns can be arbitrarily nested.  For example, if we have a short
list of points, we could match it like this::

    match points:
        case []:
            print("No points")
        case [Point(0, 0)]:
            print("The origin")
        case [Point(x, y)]:
            print(f"Single point {x}, {y}")
        case [Point(0, y1), Point(0, y2)]:
            print(f"Two on the Y axis at {y1}, {y2}")
        case _:
            print("Something else")

We can add an ``if`` clause to a pattern, known as a "guard".  If the
guard is false, ``match`` goes on to try the next case block.  Note
that value capture happens before the guard is evaluated::

    match point:
        case Point(x, y) if x == y:
            print(f"Y=X at {x}")
        case Point(x, y):
            print(f"Not on the diagonal")

Several other key features of this statement:

- Like unpacking assignments, tuple and list patterns have exactly the
  same meaning and actually match arbitrary sequences.  An important
  exception is that they don't match iterators or strings.

- Sequence patterns support extended unpacking: ``[x, y, *rest]`` and ``(x, y,
  *rest)`` work similar to unpacking assignments.  The
  name after ``*`` may also be ``_``, so ``(x, y, *_)`` matches a sequence
  of at least two items without binding the remaining items.

- Mapping patterns: ``{"bandwidth": b, "latency": l}`` captures the
  ``"bandwidth"`` and ``"latency"`` values from a dictionary.  Unlike sequence
  patterns, extra keys are ignored.  An unpacking like ``**rest`` is also
  supported.  (But ``**_`` would be redundant, so it is not allowed.)

- Subpatterns may be captured using the ``as`` keyword::

      case (Point(x1, y1), Point(x2, y2) as p2): ...

  will capture the second element of the input as ``p2`` (as long as the input is
  a sequence of two points)

- Most literals are compared by equality, however the singletons ``True``,
  ``False`` and ``None`` are compared by identity.

- Patterns may use named constants.  These must be dotted names
  to prevent them from being interpreted as capture variable::

      from enum import Enum
      class Color(Enum):
          RED = 'red'
          GREEN = 'green'
          BLUE = 'blue'

      color = Color(input("Enter your choice of 'red', 'blue' or 'green': "))

      match color:
          case Color.RED:
              print("I see red!")
          case Color.GREEN:
              print("Grass is green")
          case Color.BLUE:
              print("I'm feeling the blues :(")

For a more detailed explanation and additional examples, you can look into
:pep:`636` which is written in a tutorial format.

.. _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 a < n:
   ...         print(a, end=' ')
   ...         a, b = b, a+b
   ...     print()
   ...
   >>> # Now call the function we just defined:
   ... fib(2000)
   0 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`.
(More about docstrings can be found in the section :ref:`tut-docstrings`.)
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 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 local symbol tables of
enclosing functions, then in the global symbol table, and finally in the table
of built-in names. Thus, global variables and variables of enclosing functions
cannot be directly assigned a value within a function (unless, for global
variables, named in a :keyword:`global` statement, or, for variables of enclosing
functions, named in a :keyword:`nonlocal` 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,
or calls itself recursively, a new
local symbol table is created for that call.

A function definition associates the function name with the function object in
the current symbol table.  The interpreter recognizes the object pointed to by
that name as a user-defined function.  Other names can also point to that same
function object and can also be used to access the function::

   >>> fib
   <function fib at 10042ed0>
   >>> f = fib
   >>> f(100)
   0 1 1 2 3 5 8 13 21 34 55 89

Coming from other languages, you might object that ``fib`` is not a function but
a procedure since it doesn't return a value.  In fact, even functions without a
:keyword:`return` statement 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 :func:`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 a < n:
   ...         result.append(a)    # see below
   ...         a, b = b, a+b
   ...     return result
   ...
   >>> f100 = fib2(100)    # call it
   >>> f100                # write the result
   [0, 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 function also returns ``None``.

* The statement ``result.append(a)`` 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*, see :ref:`tut-classes`)
  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 + [a]``, but more efficient.


.. _tut-defining:

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

It is also possible to define functions with a variable number of arguments.


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