4. More Control Flow Tools — Python 3.14.4 documentation

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4.
More Control Flow Tools
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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:
>>>
int
input
"Please enter an integer: "
))
Please enter an integer: 42
>>>
if
...
...
'Negative changed to zero'
...
elif
==
...
'Zero'
...
elif
==
...
'Single'
...
else
...
'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
in
words
...
len
))
...
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
in
range
):
...
...
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
10
))
[5, 6, 7, 8, 9]
>>>
list
range
10
))
[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:
>>>
'Mary'
'had'
'a'
'little'
'lamb'
>>>
for
in
range
len
)):
...
])
...
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
))
# 0 + 1 + 2 + 3
Later we will see more functions that return iterables and take iterables as
arguments. In chapter
Data Structures
, we will discuss
list()
in more
detail.
4.4.
break
and
continue
Statements
The
break
statement breaks out of the innermost enclosing
for
or
while
loop:
>>>
for
in
range
10
):
...
for
in
range
):
...
if
==
...
equals
//
...
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
10
):
...
if
num
==
...
"Found an even number
num
...
continue
...
"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
in
range
10
):
...
for
in
range
):
...
if
==
...
'equals'
'*'
//
...
break
...
else
...
# loop fell through without finding a factor
...
'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!
...
For this last case, many people use the ellipsis literal
...
instead of
pass
. This use has no special meaning to Python, and is not part of
the language definition (you could use any constant expression here), but
...
is used conventionally as a placeholder body as well.
See
The Ellipsis Object
4.7.
match
Statements
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. If no case matches,
none of the branches is executed.
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.
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
):
"Origin"
case
):
"Y=
case
):
"X=
case
):
"X=
, 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
):
self
self
def
where_is
point
):
match
point
case
Point
):
"Origin"
case
Point
):
"Y=
case
Point
):
"X=
case
Point
():
"Somewhere else"
case
"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
attribute to the
var
variable):
Point
var
Point
var
Point
var
Point
var
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
):
self
self
match
points
case
[]:
"No points"
case
Point
)]:
"The origin"
case
Point
)]:
"Single point
case
Point
y1
),
Point
y2
)]:
"Two on the Y axis at
y1
y2
case
"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
if
==
"Y=X at
case
Point
):
"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 variables:
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
"I see red!"
case
Color
GREEN
"Grass is green"
case
Color
BLUE
"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
):
# write Fibonacci series less than n
...
"""Print a Fibonacci series less than n."""
...
...
while
...
end
' '
...
...
()
...
>>>
# 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).
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

>>>
fib
>>>
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
>>>
fib
))
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
):
# return Fibonacci series up to n
...
"""Return a list containing the Fibonacci series up to n."""
...
result
[]
...
...
while
...
result
append
# see below
...
...
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
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
if
retries
raise
ValueError
'invalid user response'
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
def
arg
):
arg
()
will print
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
[]):
append
return
))
))
))
This will print
If you don’t want the default to be shared between subsequent calls, you can
write the function like this instead:
def
None
):
if
is
None
[]
append
return
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'
):
"-- This parrot wouldn't"
action
end
' '
"if you put"
voltage
"volts through it."
"-- Lovely plumage, the"
type
"-- 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
):
...
pass
...
>>>
function
Traceback (most recent call last):
File
""
, line
, in

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
):
"-- Do you have any"
kind
"?"
"-- I'm sorry, we're all out of"
kind
for
arg
in
arguments
arg
"-"
40
for
kw
in
keywords
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
):
...
arg
...
>>>
def
pos_only_arg
arg
):
...
arg
...
>>>
def
kwd_only_arg
arg
):
...
arg
...
>>>
def
combined_example
pos_only
standard
kwd_only
):
...
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
>>>
standard_arg
arg
The second function
pos_only_arg
is restricted to only use positional
parameters as there is a
in the function definition:
>>>
pos_only_arg
>>>
pos_only_arg
arg
Traceback (most recent call last):
File
""
, line
, in

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
Traceback (most recent call last):
File
""
, line
, in

TypeError
kwd_only_arg() takes 0 positional arguments but 1 was given
>>>
kwd_only_arg
arg
And the last uses all three calling conventions in the same function
definition:
>>>
combined_example
Traceback (most recent call last):
File
""
, line
, in

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

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
**
'name'
})
Traceback (most recent call last):
File
""
, line
, in

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
**
'name'
})
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
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
))
# normal call with separate arguments
[3, 4, 5]
>>>
args
>>>
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'
):
...
"-- This parrot wouldn't"
action
end
' '
...
"if you put"
voltage
"volts through it."
end
' '
...
"E's"
state
"!"
...
>>>
"voltage"
"four million"
"state"
"bleedin' demised"
"action"
"VOOM"
>>>
parrot
**
-- 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
):
...
return
lambda
...
>>>
make_incrementor
42
>>>
42
>>>
43
The above example uses a lambda expression to return a function. Another use
is to pass a small function as an argument. For instance,
list.sort()
takes a sorting key function
key
which can be a lambda function:
>>>
pairs
[(
'one'
),
'two'
),
'three'
),
'four'
)]
>>>
pairs
sort
key
lambda
pair
pair
])
>>>
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 strips indentation from multi-line string literals when they
serve as module, class, or function docstrings.
Here is an example of a multi-line docstring:
>>>
def
my_function
():
...
"""Do nothing, but document it.
...
...
No, really, it doesn't do anything:
...
...
>>> my_function()
...
>>>
...
"""
...
pass
...
>>>
my_function
__doc__
Do nothing, but document it.
No, really, it doesn't do anything:
>>> my_function()
>>>
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
ham
str
eggs
str
'eggs'
->
str
...
"Annotations:"
__annotations__
...
"Arguments:"
ham
eggs
...
return
ham
' and '
eggs
...
>>>
'spam'
Annotations: {'ham': , 'return': , 'eggs': }
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
concisely,
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:
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
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).
Table of Contents
4. More Control Flow Tools
4.1.
if
Statements
4.2.
for
Statements
4.3. The
range()
Function
4.4.
break
and
continue
Statements
4.5.
else
Clauses on Loops
4.6.
pass
Statements
4.7.
match
Statements
4.8. Defining Functions
4.9. More on Defining Functions
4.9.1. Default Argument Values
4.9.2. Keyword Arguments
4.9.3. Special parameters
4.9.3.1. Positional-or-Keyword Arguments
4.9.3.2. Positional-Only Parameters
4.9.3.3. Keyword-Only Arguments
4.9.3.4. Function Examples
4.9.3.5. Recap
4.9.4. Arbitrary Argument Lists
4.9.5. Unpacking Argument Lists
4.9.6. Lambda Expressions
4.9.7. Documentation Strings
4.9.8. Function Annotations
4.10. Intermezzo: Coding Style
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3.
An Informal Introduction to Python
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5.
Data Structures
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