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(raw_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 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:
... 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']
The range() Function
If you do need to iterate over a sequence of numbers, the built-in function
range() comes in handy. It generates lists containing
arithmetic progressions:
>>> range(10)
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
The given end point is never part of the generated list; range(10)
generates a list of 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'):
To iterate over the indices of a sequence, 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
break and continue Statements,
and else Clauses on Loops
The break statement, like in C, breaks out of the
smallest enclosing for or while
loop.
The 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 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
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
...
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,
... a, b = b, a+b
...
>>> # 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
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.
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
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:
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:
>>> 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 return statement returns with a value from
a function. 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 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.
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, complaint='Yes or no, please!'):
while True:
ok = raw_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 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:
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 "keyword
= value". For instance, the following function:
def parrot(voltage, state='a stiff', action='voom', type='Norwegian Blue'):
print "-- This parrot wouldn't", action,
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
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 = keywords.keys()
keys.sort()
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 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.
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)
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:
>>> range(3, 6) # normal call with separate arguments
[3, 4, 5]
>>> args = [3, 6]
>>> 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,
... print "if you put", voltage, "volts through it.",
... 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 Forms
By popular demand, a few features commonly found in functional programming
languages like Lisp have been added to Python. With the
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
Documentation Strings
There are emerging 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.
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