4. More Control Flow Tools (2024)

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

4.1. if Statements

Perhaps the most well-known statement type is the if statement. Forexample:

>>> 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 isoptional. The keyword ‘elif’ is short for ‘else if’, and is usefulto avoid excessive indentation. An ifelifelif … sequence is a substitute for the switch orcase statements found in other languages.

If you’re comparing the same value to several constants, or checking for specific types orattributes, you may also find the match statement useful. For moredetails see match Statements.

4.2. for Statements

The for statement in Python differs a bit from what you may be usedto in C or Pascal. Rather than always iterating over an arithmetic progressionof numbers (like in Pascal), or giving the user the ability to define both theiteration step and halting condition (as C), Python’s for statementiterates over the items of any sequence (a list or a string), in the order thatthey 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 3window 6defenestrate 12

Code that modifies a collection while iterating over that same collection canbe tricky to get right. Instead, it is usually more straight-forward to loopover a copy of the collection or to create a new collection:

# Create a sample collectionusers = {'Hans': 'active', 'Éléonore': 'inactive', '景太郎': 'active'}# Strategy: Iterate over a copyfor user, status in users.copy().items(): if status == 'inactive': del users[user]# Strategy: Create a new collectionactive_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 functionrange() comes in handy. It generates arithmetic progressions:

>>> for i in range(5):...  print(i)...01234

The given end point is never part of the generated sequence; range(10) generates10 values, the legal indices for items of a sequence of length 10. Itis possible to let the range start at another number, or to specify a differentincrement (even negative; sometimes this is called the ‘step’):

>>> list(range(5, 10))[5, 6, 7, 8, 9]>>> list(range(0, 10, 3))[0, 3, 6, 9]>>> list(range(-10, -100, -30))[-10, -40, -70]

To iterate over the indices of a sequence, you can combine range() andlen() as follows:

>>> a = ['Mary', 'had', 'a', 'little', 'lamb']>>> for i in range(len(a)):...  print(i, a[i])...0 Mary1 had2 a3 little4 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 ofthe desired sequence when you iterate over it, but it doesn’t really makethe list, thus saving space.

We say such an object is iterable, that is, suitable as a target forfunctions and constructs that expect something from which they canobtain successive items until the supply is exhausted. We have seen thatthe for statement is such a construct, while an example of a functionthat takes an iterable is sum():

>>> sum(range(4)) # 0 + 1 + 2 + 36

Later we will see more functions that return iterables and take iterables asarguments. In chapter Data Structures, we will discuss in more detail aboutlist().

4.4. break and continue Statements, and else Clauses on Loops

The break statement breaks out of the innermost enclosingfor or while loop.

A for or while loop can include an else clause.

In a for loop, the else clause is executedafter the loop reaches its final iteration.

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

In either kind of loop, the else clause is not executedif the loop was terminated by a break.

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

>>> for n in range(2, 10):...  for x in range(2, n):...  if n % x == 0:...  print(n, 'equals', x, '*', n//x)...  break...  else:...  # loop fell through without finding a factor...  print(n, 'is a prime number')...2 is a prime number3 is a prime number4 equals 2 * 25 is a prime number6 equals 2 * 37 is a prime number8 equals 2 * 49 equals 3 * 3

(Yes, this is the correct code. Look closely: the else clause belongs tothe for loop, not the if statement.)

When used with a loop, the else clause has more in common with theelse clause of a try statement than it does with that ofif statements: a try statement’s else clause runswhen no exception occurs, and a loop’s else clause runs when no breakoccurs. For more on the try statement and exceptions, seeHandling Exceptions.

The continue statement, also borrowed from C, continues with the nextiteration of the loop:

>>> for num in range(2, 10):...  if num % 2 == 0:...  print("Found an even number", num)...  continue...  print("Found an odd number", num)...Found an even number 2Found an odd number 3Found an even number 4Found an odd number 5Found an even number 6Found an odd number 7Found an even number 8Found an odd number 9

4.5. pass Statements

The pass statement does nothing. It can be used when a statement isrequired 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 orconditional body when you are working on new code, allowing you to keep thinkingat a more abstract level. The pass is silently ignored:

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

4.6. match Statements

A match statement takes an expression and compares its value to successivepatterns given as one or more case blocks. This is superficiallysimilar to a switch statement in C, Java or JavaScript (and manyother languages), but it’s more similar to pattern matching inlanguages like Rust or Haskell. Only the first pattern that matchesgets executed and it can also extract components (sequence elementsor object attributes) from the value into variables.

The simplest form compares a subject value against one or more literals:

def http_error(status): match status: case 400: return "Bad request" case 404: return "Not found" case 418: return "I'm a teapot" case _: return "Something's wrong with the internet"

Note the last block: the “variable name” _ acts as a wildcard andnever fails to match. If no case matches, none of the branches is executed.

You can combine several literals in a single pattern using | (“or”):

case 401 | 403 | 404: return "Not allowed"

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

# point is an (x, y) tuplematch point: case (0, 0): print("Origin") case (0, y): print(f"Y={y}") case (x, 0): print(f"X={x}") case (x, y): print(f"X={x}, Y={y}") case _: raise ValueError("Not a point")

Study that one carefully! The first pattern has two literals, and canbe thought of as an extension of the literal pattern shown above. Butthe next two patterns combine a literal and a variable, and thevariable binds a value from the subject (point). The fourthpattern captures two values, which makes it conceptually similar tothe unpacking assignment (x, y) = point.

If you are using classes to structure your datayou can use the class name followed by an argument list resembling aconstructor, but with the ability to capture attributes into variables:

class Point: def __init__(self, x, y): self.x = x self.y = ydef where_is(point): match point: case Point(x=0, y=0): print("Origin") case Point(x=0, y=y): print(f"Y={y}") case Point(x=x, y=0): print(f"X={x}") case Point(): print("Somewhere else") case _: print("Not a point")

You can use positional parameters with some builtin classes that provide anordering for their attributes (e.g. dataclasses). You can also define a specificposition for attributes in patterns by setting the __match_args__ specialattribute in your classes. If it’s set to (“x”, “y”), the following patterns are allequivalent (and all bind the y attribute to the var variable):

Point(1, var)Point(1, y=var)Point(x=1, y=var)Point(y=var, x=1)

A recommended way to read patterns is to look at them as an extended form of what youwould put on the left of an assignment, to understand which variables would be set towhat.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 shortlist of Points, with __match_args__ added, we could match it like this:

class Point: __match_args__ = ('x', 'y') def __init__(self, x, y): self.x = x self.y = ymatch points: case []: print("No points") case [Point(0, 0)]: print("The origin") case [Point(x, y)]: print(f"Single point {x}, {y}") case [Point(0, y1), Point(0, y2)]: print(f"Two on the Y axis at {y1}, {y2}") case _: print("Something else")

We can add an if clause to a pattern, known as a “guard”. If theguard is false, match goes on to try the next case block. Notethat value capture happens before the guard is evaluated:

match point: case Point(x, y) if x == y: print(f"Y=X at {x}") case Point(x, y): print(f"Not on the diagonal")

Several other key features of this statement:

  • Like unpacking assignments, tuple and list patterns have exactly thesame meaning and actually match arbitrary sequences. An importantexception 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. Thename after * may also be _, so (x, y, *_) matches a sequenceof 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 sequencepatterns, extra keys are ignored. An unpacking like **rest is alsosupported. (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 isa 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 namesto prevent them from being interpreted as capture variable:

    from enum import Enumclass Color(Enum): RED = 'red' GREEN = 'green' BLUE = 'blue'color = Color(input("Enter your choice of 'red', 'blue' or 'green': "))match color: case Color.RED: print("I see red!") case Color.GREEN: print("Grass is green") case Color.BLUE: print("I'm feeling the blues :(")

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

4.7. Defining Functions

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

>>> 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 befollowed 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, andmust 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 printeddocumentation, or to let the user interactively browse through code; it’s goodpractice 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 localvariables of the function. More precisely, all variable assignments in afunction store the value in the local symbol table; whereas variable referencesfirst look in the local symbol table, then in the local symbol tables ofenclosing functions, then in the global symbol table, and finally in the tableof built-in names. Thus, global variables and variables of enclosing functionscannot be directly assigned a value within a function (unless, for globalvariables, named in a global statement, or, for variables of enclosingfunctions, named in a nonlocal statement), although they may bereferenced.

The actual parameters (arguments) to a function call are introduced in the localsymbol table of the called function when it is called; thus, arguments arepassed using call by value (where the value is always an object reference,not the value of the object). [1] When a function calls another function,or calls itself recursively, a newlocal symbol table is created for that call.

A function definition associates the function name with the function object inthe current symbol table. The interpreter recognizes the object pointed to bythat name as a user-defined function. Other names can also point to that samefunction object and can also be used to access the function:

>>> fib<function fib at 10042ed0>>>> f = fib>>> f(100)0 1 1 2 3 5 8 13 21 34 55 89

Coming from other languages, you might object that fib is not a function buta procedure since it doesn’t return a value. In fact, even functions without areturn statement do return a value, albeit a rather boring one. Thisvalue is called None (it’s a built-in name). Writing the value None isnormally 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 theFibonacci 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 offthe end of a function also returns None.

  • The statement result.append(a) calls a method of the list objectresult. A method is a function that ‘belongs’ to an object and is namedobj.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 havethe same name without causing ambiguity. (It is possible to define your ownobject types and methods, using classes, see Classes)The method append() shown in the example is defined for list objects; itadds a new element at the end of the list. In this example it is equivalent toresult = result + [a], but more efficient.

4.8. 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.

4.8.1. 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 isdefined to allow. For example:

def ask_ok(prompt, retries=4, reminder='Please try again!'): while True: reply = input(prompt) if reply in {'y', 'ye', 'yes'}: return True if reply in {'n', 'no', 'nop', 'nope'}: return False retries = retries - 1 if retries < 0: raise ValueError('invalid user response') print(reminder)

This function can be called in several ways:

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

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

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

This example also introduces the in keyword. This tests whether ornot a sequence contains a certain value.

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

i = 5def f(arg=i): print(arg)i = 6f()

will print 5.

Important warning: The default value is evaluated only once. This makes adifference when the default is a mutable object such as a list, dictionary, orinstances of most classes. For example, the following function accumulates thearguments passed to it on subsequent calls:

def f(a, L=[]): L.append(a) return Lprint(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 canwrite the function like this instead:

def f(a, L=None): if L is None: L = [] L.append(a) return L

4.8.2. Keyword Arguments

Functions can also be called using keyword argumentsof 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 anyof the following ways:

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

but all the following calls would be invalid:

parrot() # required argument missingparrot(voltage=5.0, 'dead') # non-keyword argument after a keyword argumentparrot(110, voltage=220) # duplicate value for the same argumentparrot(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 argumentsaccepted by the function (e.g. actor is not a valid argument for theparrot function), and their order is not important. This also includesnon-optional arguments (e.g. parrot(voltage=1000) is valid too).No argument may receive a value more than once.Here’s an example that fails due to this restriction:

>>> def function(a):...  pass...>>> function(0, a=0)Traceback (most recent call last): File "<stdin>", line 1, in <module>TypeError: function() got multiple values for argument 'a'

When a final formal parameter of the form **name is present, it receives adictionary (see Mapping Types — dict) containing all keyword arguments except forthose corresponding to a formal parameter. This may be combined with a formalparameter of the form *name (described in the next subsection) whichreceives a tuple containing the positionalarguments beyond the formal parameter list. (*name must occurbefore **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 LimburgerIt's very runny, sir.It's really very, VERY runny, sir.----------------------------------------shopkeeper : Michael Palinclient : John Cleesesketch : Cheese Shop Sketch

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

4.8.3. Special parameters

By default, arguments may be passed to a Python function either by positionor explicitly by keyword. For readability and performance, it makes sense torestrict the way arguments can be passed so that a developer need only lookat the function definition to determine if items are passed by position, byposition 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 ofparameter by how the arguments may be passed to the function:positional-only, positional-or-keyword, and keyword-only. Keyword parametersare also referred to as named parameters. Positional-or-Keyword Arguments

If / and * are not present in the function definition, arguments maybe passed to a function by position or by keyword. Positional-Only Parameters

Looking at this in a bit more detail, it is possible to mark certain parametersas positional-only. If positional-only, the parameters’ order matters, andthe parameters cannot be passed by keyword. Positional-only parameters areplaced before a / (forward-slash). The / is used to logicallyseparate the positional-only parameters from the rest of the parameters.If there is no / in the function definition, there are no positional-onlyparameters.

Parameters following the / may be positional-or-keyword or keyword-only. Keyword-Only Arguments

To mark parameters as keyword-only, indicating the parameters must be passedby keyword argument, place an * in the arguments list just before the firstkeyword-only parameter. Function Examples

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

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

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

>>> standard_arg(2)2>>> standard_arg(arg=2)2

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

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

The third function kwd_only_args only allows keyword arguments as indicatedby a * in the function definition:

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

And the last uses all three calling conventions in the same functiondefinition:

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

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

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

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

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

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

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

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

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

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

As guidance:

  • Use positional-only if you want the name of the parameters to not beavailable to the user. This is useful when parameter names have no realmeaning, if you want to enforce the order of the arguments when the functionis called or if you need to take some positional parameters and arbitrarykeywords.

  • Use keyword-only when names have meaning and the function definition ismore understandable by being explicit with names or you want to preventusers relying on the position of the argument being passed.

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

4.8.4. Arbitrary Argument Lists

Finally, the least frequently used option is to specify that a function can becalled with an arbitrary number of arguments. These arguments will be wrappedup 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 formalparameters, because they scoop up all remaining input arguments that arepassed to the function. Any formal parameters which occur after the *argsparameter are ‘keyword-only’ arguments, meaning that they can only be used askeywords 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'

4.8.5. Unpacking Argument Lists

The reverse situation occurs when the arguments are already in a list or tuplebut need to be unpacked for a function call requiring separate positionalarguments. For instance, the built-in range() function expects separatestart and stop arguments. If they are not available separately, write thefunction call with the *-operator to unpack the arguments out of a listor tuple:

>>> list(range(3, 6)) # normal call with separate arguments[3, 4, 5]>>> args = [3, 6]>>> list(range(*args)) # call with arguments unpacked from a list[3, 4, 5]

In the same fashion, dictionaries can deliver keyword arguments with the**-operator:

>>> def parrot(voltage, state='a stiff', action='voom'):...  print("-- This parrot wouldn't", action, end=' ')...  print("if you put", voltage, "volts through it.", end=' ')...  print("E's", state, "!")...>>> d = {"voltage": "four million", "state": "bleedin' demised", "action": "VOOM"}>>> parrot(**d)-- This parrot wouldn't VOOM if you put four million volts through it. E's bleedin' demised !

4.8.6. 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 aresyntactically restricted to a single expression. Semantically, they are justsyntactic sugar for a normal function definition. Like nested functiondefinitions, lambda functions can reference variables from the containingscope:

>>> 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 useis to pass a small function as an argument:

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

4.8.7. Documentation Strings

Here are some conventions about the content and formatting of documentationstrings.

The first line should always be a short, concise summary of the object’spurpose. 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 averb describing a function’s operation). This line should begin with a capitalletter and end with a period.

If there are more lines in the documentation string, the second line should beblank, visually separating the summary from the rest of the description. Thefollowing lines should be one or more paragraphs describing the object’s callingconventions, its side effects, etc.

The Python parser does not strip indentation from multi-line string literals inPython, so tools that process documentation have to strip indentation ifdesired. This is done using the following convention. The first non-blank lineafter the first line of the string determines the amount of indentation forthe entire documentation string. (We can’t use the first line since it isgenerally adjacent to the string’s opening quotes so its indentation is notapparent in the string literal.) Whitespace “equivalent” to this indentation isthen stripped from the start of all lines of the string. Lines that areindented less should not occur, but if they occur all their leading whitespaceshould be stripped. Equivalence of whitespace should be tested after expansionof tabs (to 8 spaces, normally).

Here is an example of a multi-line docstring:

>>> def my_function():...  """Do nothing, but document it.......  No, really, it doesn't do anything....  """...  pass...>>> print(my_function.__doc__)Do nothing, but document it. No, really, it doesn't do anything.

4.8.8. Function Annotations

Function annotations are completely optional metadatainformation about the types used by user-defined functions (see PEP 3107 andPEP 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 thefunction. Parameter annotations are defined by a colon after the parameter name, followedby an expression evaluating to the value of the annotation. Return annotations aredefined by a literal ->, followed by an expression, between the parameterlist and the colon denoting the end of the def statement. Thefollowing example has a required argument, an optional argument, and the returnvalue annotated:

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

4.9. Intermezzo: Coding Style

Now that you are about to write longer, more complex pieces of Python, it is agood time to talk about coding style. Most languages can be written (or moreconcise, 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 adoptinga 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 Pythondeveloper should read it at some point; here are the most important pointsextracted for you:

  • Use 4-space indentation, and no tabs.

    4 spaces are a good compromise between small indentation (allows greaternesting depth) and large indentation (easier to read). Tabs introduceconfusion, 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 severalcode files side-by-side on larger displays.

  • Use blank lines to separate functions and classes, and larger blocks ofcode inside functions.

  • When possible, put comments on a line of their own.

  • Use docstrings.

  • Use spaces around operators and after commas, but not directly insidebracketing constructs: a = f(1, 2) + g(3, 4).

  • Name your classes and functions consistently; the convention is to useUpperCamelCase for classes and lowercase_with_underscores for functionsand 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 internationalenvironments. Python’s default, UTF-8, or even plain ASCII work best in anycase.

  • Likewise, don’t use non-ASCII characters in identifiers if there is only theslightest chance people speaking a different language will read or maintainthe code.


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