4. More Control Flow Tools
**************************

As well as the "while" statement just introduced, Python uses a few
more that we will encounter in this chapter.


4.1. "if" Statements
====================

Perhaps the most well-known statement type is the "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 "elif" parts, and the "else" part is
optional.  The keyword ‘"elif"’ is short for ‘else if’, and is useful
to avoid excessive indentation.  An  "if" … "elif" … "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 "match"
statement useful. For more details see match Statements.


4.2. "for" Statements
=====================

The "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 "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):

   >>> # 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


4.3. The "range()" Function
===========================

If you do need to iterate over a sequence of numbers, the built-in
function "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 "range()"
and "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 "enumerate()"
function, see Looping Techniques.

A strange thing happens if you just print a range:

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

In many ways the object returned by "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 "for" statement is such a construct, while an example of a
function that takes an iterable is "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 Data Structures, we will discuss
in more detail about "list()".


4.4. "break" and "continue" Statements
======================================

The "break" statement breaks out of the innermost enclosing "for" or
"while" loop:

   >>> for n in range(2, 10):
   ...     for x in range(2, n):
   ...         if n % x == 0:
   ...             print(f"{n} equals {x} * {n//x}")
   ...             break
   ...
   4 equals 2 * 2
   6 equals 2 * 3
   8 equals 2 * 4
   9 equals 3 * 3

The "continue" statement continues with the next iteration of the
loop:

   >>> for num in range(2, 10):
   ...     if num % 2 == 0:
   ...         print(f"Found an even number {num}")
   ...         continue
   ...     print(f"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


4.5. "else" Clauses on Loops
============================

In a "for" or "while" loop the "break" statement may be paired with an
"else" clause.  If the loop finishes without executing the "break",
the "else" clause executes.

In a "for" loop, the "else" clause is executed after the loop finishes
its final iteration, that is, if no break occurred.

In a "while" loop, it’s executed after the loop’s condition becomes
false.

In either kind of loop, the "else" clause is **not** executed if the
loop was terminated by a "break".  Of course, other ways of ending the
loop early, such as a "return" or a raised exception, will also skip
execution of the "else" clause.

This is exemplified in the following "for" 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 "for" loop, **not** the "if" statement.)

One way to think of the else clause is to imagine it paired with the
"if" inside the loop.  As the loop executes, it will run a sequence
like if/if/if/else. The "if" is inside the loop, encountered a number
of times. If the condition is ever true, a "break" will happen. If the
condition is never true, the "else" clause outside the loop will
execute.

When used with a loop, the "else" clause has more in common with the
"else" clause of a "try" statement than it does with that of "if"
statements: a "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 "try" statement and exceptions, see Handling Exceptions.


4.6. "pass" Statements
======================

The "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 "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 "pass" is silently
ignored:

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


4.7. "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’s more similar to pattern matching
in languages like Rust or Haskell. Only the first pattern that matches
gets executed and 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:
       def __init__(self, x, y):
           self.x = x
           self.y = y

   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, with "__match_args__" added, we could match it like
this:

   class Point:
       __match_args__ = ('x', 'y')
       def __init__(self, x, y):
           self.x = x
           self.y = y

   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.


4.8. Defining Functions
=======================

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

   >>> def fib(n):    # write Fibonacci series less than n
   ...     """Print a Fibonacci series less than 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

The 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 *docstring*. (More about docstrings can be found in the section
Documentation Strings.) 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 "global" statement, or, for variables of enclosing
functions, named in a "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). [1] 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 "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 "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 "return" statement returns with a value from a function.
  "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 Classes) The method "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.


4.9. 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.


4.9.1. 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, reminder='Please try again!'):
       while True:
           reply = input(prompt)
           if reply in {'y', 'ye', 'yes'}:
               return True
           if reply in {'n', 'no', 'nop', 'nope'}:
               return False
           retries = retries - 1
           if retries < 0:
               raise ValueError('invalid user response')
           print(reminder)

This function can be called in several ways:

* giving only the mandatory argument: "ask_ok('Do you really want to
  quit?')"

* giving one of the optional arguments: "ask_ok('OK to overwrite the
  file?', 2)"

* or even giving all arguments: "ask_ok('OK to overwrite the file?',
  2, 'Come on, only yes or no!')"

This example also introduces the "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


4.9.2. Keyword Arguments
------------------------

Functions can also be called using *keyword arguments* of the form
"kwarg=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, "!")

accepts one required argument ("voltage") and three optional arguments
("state", "action", and "type").  This function can be called in any
of the following ways:

   parrot(1000)                                          # 1 positional argument
   parrot(voltage=1000)                                  # 1 keyword argument
   parrot(voltage=1000000, action='VOOOOOM')             # 2 keyword arguments
   parrot(action='VOOOOOM', voltage=1000000)             # 2 keyword arguments
   parrot('a million', 'bereft of life', 'jump')         # 3 positional arguments
   parrot('a thousand', state='pushing up the daisies')  # 1 positional, 1 keyword

but all the following calls would be invalid:

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

In a function call, keyword arguments must follow positional
arguments. All the keyword arguments passed must match one of the
arguments accepted by the function (e.g. "actor" is not a valid
argument for the "parrot" function), and their order is not important.
This also includes non-optional arguments (e.g. "parrot(voltage=1000)"
is valid too). No argument may receive a value more than once. 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 <module>
   TypeError: function() got multiple values for argument 'a'

When a final formal parameter of the form "**name" is present, it
receives a dictionary (see Mapping Types — dict) 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)
       for kw in keywords:
           print(kw, ":", keywords[kw])

It could be called like this:

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

Note that the order in which the keyword arguments are printed is
guaranteed to match the order in which they were provided in the
function call.


4.9.3. Special parameters
-------------------------

By default, arguments may be passed to a Python function either by
position or explicitly by keyword. For readability and performance, it
makes sense to restrict the way arguments can be passed so that a
developer need only look at the function definition to determine if
items are passed by position, by position or keyword, or by keyword.

A function definition may look like:

   def f(pos1, pos2, /, pos_or_kwd, *, kwd1, kwd2):
         -----------    ----------     ----------
           |             |                  |
           |        Positional or keyword   |
           |                                - Keyword only
            -- Positional only

where "/" and "*" are optional. If used, these symbols indicate the
kind of parameter by how the arguments may be passed to the function:
positional-only, positional-or-keyword, and keyword-only. Keyword
parameters are also referred to as named parameters.


4.9.3.1. Positional-or-Keyword Arguments
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

If "/" and "*" are not present in the function definition, arguments
may be passed to a function by position or by keyword.


4.9.3.2. Positional-Only Parameters
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Looking at this in a bit more detail, it is possible to mark certain
parameters as *positional-only*. If *positional-only*, the parameters’
order matters, and the parameters cannot be passed by keyword.
Positional-only parameters are placed before a "/" (forward-slash).
The "/" is used to logically separate the positional-only parameters
from the rest of the parameters. If there is no "/" in the function
definition, there are no positional-only parameters.

Parameters following the "/" may be *positional-or-keyword* or
*keyword-only*.


4.9.3.3. Keyword-Only Arguments
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

To mark parameters as *keyword-only*, indicating the parameters must
be passed by keyword argument, place an "*" in the arguments list just
before the first *keyword-only* parameter.


4.9.3.4. Function Examples
~~~~~~~~~~~~~~~~~~~~~~~~~~

Consider the following example function definitions paying close
attention to the markers "/" and "*":

   >>> def standard_arg(arg):
   ...     print(arg)
   ...
   >>> def pos_only_arg(arg, /):
   ...     print(arg)
   ...
   >>> def kwd_only_arg(*, arg):
   ...     print(arg)
   ...
   >>> def combined_example(pos_only, /, standard, *, kwd_only):
   ...     print(pos_only, standard, kwd_only)

The first function definition, "standard_arg", the most familiar form,
places no restrictions on the calling convention and arguments may be
passed by position or keyword:

   >>> standard_arg(2)
   2

   >>> standard_arg(arg=2)
   2

The second function "pos_only_arg" is restricted to only use
positional parameters as there is a "/" in the function definition:

   >>> pos_only_arg(1)
   1

   >>> pos_only_arg(arg=1)
   Traceback (most recent call last):
     File "<stdin>", line 1, in <module>
   TypeError: pos_only_arg() got some positional-only arguments passed as keyword arguments: 'arg'

The third function "kwd_only_arg" only allows keyword arguments as
indicated by a "*" in the function definition:

   >>> kwd_only_arg(3)
   Traceback (most recent call last):
     File "<stdin>", line 1, in <module>
   TypeError: kwd_only_arg() takes 0 positional arguments but 1 was given

   >>> kwd_only_arg(arg=3)
   3

And the last uses all three calling conventions in the same function
definition:

   >>> combined_example(1, 2, 3)
   Traceback (most recent call last):
     File "<stdin>", line 1, in <module>
   TypeError: combined_example() takes 2 positional arguments but 3 were given

   >>> combined_example(1, 2, kwd_only=3)
   1 2 3

   >>> combined_example(1, standard=2, kwd_only=3)
   1 2 3

   >>> combined_example(pos_only=1, standard=2, kwd_only=3)
   Traceback (most recent call last):
     File "<stdin>", line 1, in <module>
   TypeError: combined_example() got some positional-only arguments passed as keyword arguments: 'pos_only'

Finally, consider this function definition which has a potential
collision between the positional argument "name"  and "**kwds" which
has "name" as a key:

   def foo(name, **kwds):
       return 'name' in kwds

There is no possible call that will make it return "True" as the
keyword "'name'" will always bind to the first parameter. For example:

   >>> foo(1, **{'name': 2})
   Traceback (most recent call last):
     File "<stdin>", line 1, in <module>
   TypeError: foo() got multiple values for argument 'name'
   >>>

But using "/" (positional only arguments), it is possible since it
allows "name" as a positional argument and "'name'" as a key in the
keyword arguments:

   >>> def foo(name, /, **kwds):
   ...     return 'name' in kwds
   ...
   >>> foo(1, **{'name': 2})
   True

In other words, the names of positional-only parameters can be used in
"**kwds" without ambiguity.


4.9.3.5. Recap
~~~~~~~~~~~~~~

The use case will determine which parameters to use in the function
definition:

   def f(pos1, pos2, /, pos_or_kwd, *, kwd1, kwd2):

As guidance:

* Use positional-only if you want the name of the parameters to not be
  available to the user. This is useful when parameter names have no
  real meaning, if you want to enforce the order of the arguments when
  the function is called or if you need to take some positional
  parameters and arbitrary keywords.

* Use keyword-only when names have meaning and the function definition
  is more understandable by being explicit with names or you want to
  prevent users relying on the position of the argument being passed.

* For an API, use positional-only to prevent breaking API changes if
  the parameter’s name is modified in the future.


4.9.4. 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 (see Tuples and Sequences).
Before the variable number of arguments, zero or more normal arguments
may occur.

   def write_multiple_items(file, separator, *args):
       file.write(separator.join(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'


4.9.5. 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 "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 !


4.9.6. Lambda Expressions
-------------------------

Small anonymous functions can be created with the "lambda" keyword.
This function returns the sum of its two arguments: "lambda a, b:
a+b". Lambda functions 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 functions 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

The above example uses a lambda expression to return a function.
Another use is to pass a small function as an argument:

   >>> pairs = [(1, 'one'), (2, 'two'), (3, 'three'), (4, 'four')]
   >>> pairs.sort(key=lambda pair: pair[1])
   >>> pairs
   [(4, 'four'), (1, 'one'), (3, 'three'), (2, 'two')]


4.9.7. Documentation Strings
----------------------------

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.


4.9.8. Function Annotations
---------------------------

Function annotations are completely optional metadata information
about the types used by user-defined functions (see **PEP 3107** and
**PEP 484** for more information).

*Annotations* are stored in the "__annotations__" attribute of the
function as a dictionary and have no effect on any other part of the
function.  Parameter annotations are defined by a colon after the
parameter name, followed by an expression evaluating to the value of
the annotation.  Return annotations are defined by a literal "->",
followed by an expression, between the parameter list and the colon
denoting the end of the "def" statement.  The following example has a
required argument, an optional argument, and the return value
annotated:

   >>> def f(ham: str, eggs: str = 'eggs') -> str:
   ...     print("Annotations:", f.__annotations__)
   ...     print("Arguments:", ham, eggs)
   ...     return ham + ' and ' + eggs
   ...
   >>> f('spam')
   Annotations: {'ham': <class 'str'>, 'return': <class 'str'>, 'eggs': <class 'str'>}
   Arguments: spam eggs
   'spam and eggs'


4.10. Intermezzo: 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 "UpperCamelCase" for classes and "lowercase_with_underscores"
  for functions and methods.  Always use "self" as the name for the
  first method argument (see A First Look at Classes for more on
  classes and methods).

* Don’t use fancy encodings if your code is meant to be used in
  international environments.  Python’s default, UTF-8, or even plain
  ASCII work best in any case.

* Likewise, don’t use non-ASCII characters in identifiers if there is
  only the slightest chance people speaking a different language will
  read or maintain the code.

-[ Footnotes ]-

[1] 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).
