4.More Control Flow Tools

As well as thewhilestatement just introduced, Python uses a few more that we will encounter in this chapter.

4.1.ifStatements

Perhaps the most well-known statement type is theifstatement. For example:

>>>x=int(input("Please enter an integer:"))
Please enter an integer: 42
>>>ifx<0:
...x=0
...print('Negative changed to zero')
...elifx==0:
...print('Zero')
...elifx==1:
...print('Single')
...else:
...print('More')
...
More

There can be zero or moreelifparts, and theelsepart is optional. The keyword ‘elif’ is short for ‘else if’, and is useful to avoid excessive indentation. Anif…elif… elif…sequence is a substitute for theswitchor casestatements 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 thematchstatement useful. For more details seematch Statements.

4.2.forStatements

Theforstatement 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’sforstatement 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']
>>>forwinwords:
...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',' cảnh thái lang ':'active'}

# Strategy: Iterate over a copy
foruser,statusinusers.copy().items():
ifstatus=='inactive':
delusers[user]

# Strategy: Create a new collection
active_users={}
foruser,statusinusers.items():
ifstatus=='active':
active_users[user]=status

4.3.Therange()Function

If you do need to iterate over a sequence of numbers, the built-in function range()comes in handy. It generates arithmetic progressions:

>>>foriinrange(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 combinerange()and len()as follows:

>>>a=['Mary','had','a','little','lamb']
>>>foriinrange(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 theenumerate() function, seeLooping Techniques.

A strange thing happens if you just print a range:

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

In many ways the object returned byrange()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 isiterable,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 theforstatement is such a construct, while an example of a function that takes an iterable issum():

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

Later we will see more functions that return iterables and take iterables as arguments. In chapterData Structures,we will discuss in more detail about list().

4.4.breakandcontinueStatements

Thebreakstatement breaks out of the innermost enclosing fororwhileloop:

>>>forninrange(2,10):
...forxinrange(2,n):
...ifn%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

Thecontinuestatement continues with the next iteration of the loop:

>>>fornuminrange(2,10):
...ifnum%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.elseClauses on Loops

In afororwhileloop thebreakstatement may be paired with anelseclause. If the loop finishes without executing thebreak,theelseclause executes.

In aforloop, theelseclause is executed after the loop finishes its final iteration, that is, if no break occurred.

In awhileloop, it’s executed after the loop’s condition becomes false.

In either kind of loop, theelseclause isnotexecuted if the loop was terminated by abreak.Of course, other ways of ending the loop early, such as areturnor a raised exception, will also skip execution of theelseclause.

This is exemplified in the followingforloop, which searches for prime numbers:

>>>forninrange(2,10):
...forxinrange(2,n):
...ifn%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: theelseclause belongs to theforloop,nottheifstatement.)

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

When used with a loop, theelseclause has more in common with theelse clause of atrystatement than it does with that ofif statements: atrystatement’selseclause runs when no exception occurs, and a loop’selseclause runs when nobreakoccurs. For more on thetrystatement and exceptions, seeHandling Exceptions.

4.6.passStatements

Thepassstatement does nothing. It can be used when a statement is required syntactically but the program requires no action. For example:

>>>whileTrue:
...pass# Busy-wait for keyboard interrupt (Ctrl+C)
...

This is commonly used for creating minimal classes:

>>>classMyEmptyClass:
...pass
...

Another placepasscan 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. Thepassis silently ignored:

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

4.7.matchStatements

Amatchstatement 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:

defhttp_error(status):
matchstatus:
case400:
return"Bad request"
case404:
return"Not found"
case418:
return"I'm a teapot"
case_:
return"Something's wrong with the internet"

Note the last block: the “variable name”_acts as awildcardand 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” ):

case401|403|404:
return"Not allowed"

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

# point is an (x, y) tuple
matchpoint:
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_:
raiseValueError("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 variablebindsa 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:

classPoint:
def__init__(self,x,y):
self.x=x
self.y=y

defwhere_is(point):
matchpoint:
casePoint(x=0,y=0):
print("Origin")
casePoint(x=0,y=y):
print(f"Y={y}")
casePoint(x=x,y=0):
print(f"X={x}")
casePoint():
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 theyattribute to thevarvariable):

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 (likevarabove) are assigned to by a match statement. Dotted names (likefoo.bar), attribute names (thex=andy=above) or class names (recognized by the “(…)” next to them likePointabove) 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:

classPoint:
__match_args__=('x','y')
def__init__(self,x,y):
self.x=x
self.y=y

matchpoints:
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 anifclause to a pattern, known as a “guard”. If the guard is false,matchgoes on to try the next case block. Note that value capture happens before the guard is evaluated:

matchpoint:
casePoint(x,y)ifx==y:
print(f"Y=X at{x}")
casePoint(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**restis also supported. (But**_would be redundant, so it is not allowed.)

• Subpatterns may be captured using theaskeyword:

  case(Point(x1,y1),Point(x2,y2)asp2):...
  

  will capture the second element of the input asp2(as long as the input is a sequence of two points)

• Most literals are compared by equality, however the singletonsTrue, FalseandNoneare compared by identity.

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

  fromenumimportEnum
  classColor(Enum):
  RED='red'
  GREEN='green'
  BLUE='blue'
  
  color=Color(input("Enter your choice of 'red', 'blue' or 'green':"))
  
  matchcolor:
  caseColor.RED:
  print("I see red!")
  caseColor.GREEN:
  print("Grass is green")
  caseColor.BLUE:
  print("I'm feeling the blues:(")
  

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

4.8.Defining Functions

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

>>>deffib(n):# write Fibonacci series up to n
..."""Print a Fibonacci series up to n." ""
...a,b=0,1
...whilea<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 keyworddefintroduces a functiondefinition.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, ordocstring. (More about docstrings can be found in the sectionDocumentation 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.

Theexecutionof 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 aglobalstatement, or, for variables of enclosing functions, named in anonlocalstatement), 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 usingcall by value(where thevalueis always an objectreference, 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 thatfibis not a function but a procedure since it doesn’t return a value. In fact, even functions without a returnstatement do return a value, albeit a rather boring one. This value is calledNone(it’s a built-in name). Writing the valueNoneis normally suppressed by the interpreter if it would be the only value written. You can see it if you really want to usingprint():

>>>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:

>>>deffib2(n):# return Fibonacci series up to n
..."""Return a list containing the Fibonacci series up to n." ""
...result=[]
...a,b=0,1
...whilea<n:
...result.append(a)# see below
...a,b=b,a+b
...returnresult
...
>>>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:

• Thereturnstatement returns with a value from a function. returnwithout an expression argument returnsNone.Falling off the end of a function also returnsNone.

• The statementresult.append(a)calls amethodof the list object result.A method is a function that ‘belongs’ to an object and is named obj.methodname,whereobjis some object (this may be an expression), andmethodnameis 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, usingclasses,seeClasses) The methodappend()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:

defask_ok(prompt,retries=4,reminder='Please try again!'):
whileTrue:
reply=input(prompt)
ifreplyin{'y','ye','yes'}:
returnTrue
ifreplyin{'n','no','nop','nope'}:
returnFalse
retries=retries-1
ifretries<0:
raiseValueError('invalid user response')
print(reminder)

This function can be called in several ways:

• giving only the mandatory argument: ask_ok('Doyoureallywanttoquit?')

• giving one of the optional arguments: ask_ok('OKtooverwritethefile?',2)

• or even giving all arguments: ask_ok('OKtooverwritethefile?',2,'Comeon,onlyyesorno!')

This example also introduces theinkeyword. This tests whether or not a sequence contains a certain value.

The default values are evaluated at the point of function definition in the definingscope, so that

i=5

deff(arg=i):
print(arg)

i=6
f()

will print5.

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:

deff(a,L=[]):
L.append(a)
returnL

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:

deff(a,L=None):
ifLisNone:
L=[]
L.append(a)
returnL

4.9.2.Keyword Arguments

Functions can also be called usingkeyword arguments of the formkwarg=value.For instance, the following function:

defparrot(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,andtype). 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.actoris not a valid argument for the parrotfunction), 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:

>>>deffunction(a):
...pass
...
>>>function(0,a=0)
Traceback (most recent call last):
File"<stdin>",line1,in<module>
TypeError:function() got multiple values for argument 'a'

When a final formal parameter of the form**nameis present, it receives a dictionary (seeMapping 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 atuplecontaining the positional arguments beyond the formal parameter list. (*namemust occur before**name.) For example, if we define a function like this:

defcheeseshop(kind,*arguments,**keywords):
print("-- Do you have any",kind,"?")
print("-- I'm sorry, we're all out of",kind)
forarginarguments:
print(arg)
print("-"*40)
forkwinkeywords:
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 aspositional-only.Ifpositional-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 bepositional-or-keywordorkeyword-only.

4.9.3.3.Keyword-Only Arguments

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

4.9.3.4.Function Examples

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

>>>defstandard_arg(arg):
...print(arg)
...
>>>defpos_only_arg(arg,/):
...print(arg)
...
>>>defkwd_only_arg(*,arg):
...print(arg)
...
>>>defcombined_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 functionpos_only_argis 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>",line1,in<module>
TypeError:pos_only_arg() got some positional-only arguments passed as keyword arguments: 'arg'

The third functionkwd_only_argsonly allows keyword arguments as indicated by a*in the function definition:

>>>kwd_only_arg(3)
Traceback (most recent call last):
File"<stdin>",line1,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>",line1,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>",line1,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 argumentnameand**kwdswhich hasnameas a key:

deffoo(name,**kwds):
return'name'inkwds

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

>>>foo(1,**{'name':2})
Traceback (most recent call last):
File"<stdin>",line1,in<module>
TypeError:foo() got multiple values for argument 'name'
>>>

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

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

In other words, the names of positional-only parameters can be used in **kwdswithout ambiguity.

4.9.3.5.Recap

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

deff(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 (seeTuples and Sequences). Before the variable number of arguments, zero or more normal arguments may occur.

defwrite_multiple_items(file,separator,*args):
file.write(separator.join(args))

Normally, thesevariadicarguments 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.

>>>defconcat(*args,sep="/"):
...returnsep.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-inrange()function expects separate startandstoparguments. 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:

>>>defparrot(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 thelambdakeyword. This function returns the sum of its two arguments:lambdaa,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:

>>>defmake_incrementor(n):
...returnlambdax: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=lambdapair: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 afterthe 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:

>>>defmy_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 annotationsare completely optional metadata information about the types used by user-defined functions (seePEP 3107and PEP 484for more information).

Annotationsare 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 thedefstatement. The following example has a required argument, an optional argument, and the return value annotated:

>>>deff(ham:str,eggs:str='eggs')->str:
...print("Annotations:",f.__annotations__)
...print("Arguments:",ham,eggs)
...returnham+' 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 aboutcoding 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 8has 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 UpperCamelCasefor classes andlowercase_with_underscoresfor functions and methods. Always useselfas the name for the first method argument (seeA First Look at Classesfor 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 referencewould 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).