| Python 3.6.5 Documentation > Control Flow
More Control Flow Tools
***********************
Besides the "while" statement just introduced, Python knows the usual
control flow statements known from other languages, with some twists.
"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.
"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
If you need to modify the sequence you are iterating over while inside
the loop (for example to duplicate selected items), it is recommended
that you first make a copy. Iterating over a sequence does not
implicitly make a copy. The slice notation makes this especially
convenient:
>>> for w in words[:]: # Loop over a slice copy of the entire list.
... if len(w) > 6:
... words.insert(0, w)
...
>>> words
['defenestrate', 'cat', 'window', 'defenestrate']
With "for w in words:", the example would attempt to create an
infinite list, inserting "defenestrate" over and over again.
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’):
range(5, 10)
5, 6, 7, 8, 9
range(0, 10, 3)
0, 3, 6, 9
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:
>>> print(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 an *iterator*. The function "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.
"break" and "continue" Statements, and "else" Clauses on Loops
==============================================================
The "break" statement, like in C, breaks out of the innermost
enclosing "for" or "while" loop.
Loop statements may have an "else" clause; it is executed when the
loop terminates through exhaustion of the list (with "for") or when
the condition becomes false (with "while"), but not when the loop is
terminated by a "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 "for" loop, **not** the "if" statement.)
When used with a loop, the "else" clause has more in common with the
"else" clause of a "try" statement than it does 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.
The "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 a number", num)
Found an even number 2
Found a number 3
Found an even number 4
Found a number 5
Found an even number 6
Found a number 7
Found an even number 8
Found a number 9
"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!
...
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
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 cannot be directly assigned a value within a function
(unless named in a "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). [1] 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)
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.
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.
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:
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 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
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 keyword 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.
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'
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 !
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')]
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.
Function Annotations
--------------------
Function annotations are completely optional metadata information
about the types used by user-defined functions (see **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
positional argument, a keyword 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'
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 "CamelCase" for classes and "lower_case_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).
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