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4.1. if Statements¶Perhaps the most well-known statement type is the >>> 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 If you’re comparing the same value to several
constants, or checking for specific types or attributes, you may also find the 4.2. for Statements¶The >>> # Measure some strings: ... words = ['cat', 'window', 'defenestrate'] >>> for w in words: ... 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', '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 >>> for i in range(5): ... print(i) ... 0 1 2 3 4 The given end point is never part of the generated sequence; >>> 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 >>> a = ['Mary', 'had', 'a', 'little', 'lamb'] >>> for i in range(len(a)): ... print(i, a[i]) ... 0 Mary 1 had 2 a 3 little 4 lamb In most such cases, however, it is convenient to use the A strange thing happens if you just print a range: >>> range(10) range(0, 10) In many ways the object returned by 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 >>> sum(range(4)) # 0 + 1 + 2 + 3 6 Later we will see more functions that return iterables and take iterables as arguments. In chapter Data
Structures, we will discuss in more detail about 4.4. break and continue Statements, and else Clauses on Loops¶The Loop statements may have an >>> for n in range(2, 10): ... for x in range(2, n): ... if n % x == 0: ... print(n, 'equals', x, '*', n//x) ... break ... else: ... # loop fell through without finding a factor ... print(n, 'is a prime number') ... 2 is a prime number 3 is a prime number 4 equals 2 * 2 5 is a prime number 6 equals 2 * 3 7 is a prime number 8 equals 2 * 4 9 equals 3 * 3 (Yes, this is the correct code. Look closely: the When used with a loop, the The >>> 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 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. pass Statements¶The >>> while True: ... pass # Busy-wait for keyboard interrupt (Ctrl+C) ... This is commonly used for creating minimal classes: >>> class MyEmptyClass: ... pass ... Another place >>> def initlog(*args): ... pass # Remember to implement this! ... 4.6. match Statements¶A 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” You can combine several literals in a single pattern using 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 (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 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 ( 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: x: int y: int def 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 an
ordering for their attributes (e.g. dataclasses). You can also define a specific position for attributes in patterns by setting the 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 (like Patterns can be arbitrarily nested. For example, if we have a short list of points, we could match it like this: match 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 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:
For a more detailed explanation and additional examples, you can look into PEP 636 which is written in a tutorial format. 4.7. Defining Functions¶We can create a function that writes the Fibonacci series to an arbitrary boundary: >>> def fib(n): # write Fibonacci series up to n ... """Print a Fibonacci series up to n.""" ... a, b = 0, 1 ... while a < n: ... print(a, end=' ') ... a, b = b, a+b ... print() ... >>> # Now call the function we just defined: ... fib(2000) 0 1 1 2 3 5 8 13 21 34 55 89 144 233 377 610 987 1597 The keyword 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 The actual parameters (arguments) to a function call are introduced in the local symbol table of the called function when it is called; thus, arguments are passed using call by value (where the value is always an object reference, not the value of the object). 1 When a function calls another function, 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 Coming from other languages, you might object that >>> fib(0) >>> print(fib(0)) None It is simple to write a function that returns a list of the numbers of the Fibonacci series, instead of printing it: >>> def fib2(n): # return Fibonacci series up to n ... """Return a list containing the Fibonacci series up to n.""" ... result = [] ... a, b = 0, 1 ... while a < n: ... result.append(a) # see below ... a, b = b, a+b ... return result ... >>> f100 = fib2(100) # call it >>> f100 # write the result [0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89] This example, as usual, demonstrates some new Python features:
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 is defined to allow. For example: def ask_ok(prompt, retries=4, reminder='Please try again!'): while True: ok = input(prompt) if ok in ('y', 'ye', 'yes'): return True if ok in ('n', 'no', 'nop', 'nope'): return False retries = retries - 1 if retries < 0: raise ValueError('invalid user response') print(reminder) This function can be called in several ways:
This example also introduces the The default values are evaluated at the point of function definition in the defining scope, so that i = 5 def f(arg=i): print(arg) i = 6 f() will print Important warning: The default value is evaluated only once. This makes a difference when the default is a mutable object such as a list, dictionary, or instances of most classes. For example, the following function accumulates the arguments passed to it on subsequent calls: def f(a, L=[]): L.append(a) return L print(f(1)) print(f(2)) print(f(3)) This will print If you don’t want the default to be shared between subsequent calls, you can write the function like this instead: def f(a, L=None): if L is None: L = [] L.append(a) return L 4.8.2. Keyword Arguments¶Functions
can also be called using keyword arguments of the form 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 ( 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. >>> def function(a): ... pass ... >>> function(0, a=0) Traceback (most recent call last): File " When a final formal parameter of the form def cheeseshop(kind, *arguments, **keywords): print("-- Do you have any", kind, "?") print("-- I'm sorry, we're all out of", kind) for arg in arguments: print(arg) print("-" * 40) for kw in keywords: print(kw, ":", keywords[kw]) It could be called like this: cheeseshop("Limburger", "It's very runny, sir.", "It's really very, VERY runny, sir.", shopkeeper="Michael Palin", client="John Cleese", sketch="Cheese Shop Sketch") and of course it would print: -- Do you have any Limburger ? -- I'm sorry, we're all out of Limburger It's very runny, sir. It's really very, VERY runny, sir. ---------------------------------------- shopkeeper : Michael Palin client : John Cleese sketch : Cheese Shop Sketch Note that the order in which the keyword arguments are printed is guaranteed to match the order in which they were provided in the function call. 4.8.3. 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 4.8.3.1. Positional-or-Keyword Arguments¶If 4.8.3.2. 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 Parameters following the 4.8.3.3. Keyword-Only Arguments¶To mark parameters as keyword-only, indicating the parameters must be passed by keyword argument, place an 4.8.3.4. Function Examples¶Consider the following example function definitions paying close attention to the markers >>> 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(2) 2 >>> standard_arg(arg=2) 2 The second function >>> pos_only_arg(1) 1 >>> pos_only_arg(arg=1) Traceback (most recent call last): File " The third function
>>> kwd_only_arg(3) Traceback (most recent call last): File " And the last uses all three calling conventions in the same function definition: >>> combined_example(1, 2, 3) Traceback (most recent call last): File " Finally, consider this function definition which has a potential collision between the positional argument def foo(name, **kwds): return 'name' in kwds There is no possible call that will make it return >>> foo(1, **{'name': 2}) Traceback (most recent call last): File " But using 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 4.8.3.5. 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:
4.8.4. 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 >>> 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 tuple but need to be unpacked for a function call requiring separate positional arguments. For instance, the built-in >>> 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 >>> 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 >>> def make_incrementor(n): ... return lambda x: x + n ... >>> f = make_incrementor(42) >>> f(0) 42 >>> f(1) 43 The above example uses a lambda expression to return a function. Another use is to pass a small function as an argument: >>> pairs = [(1, 'one'), (2, 'two'), (3, 'three'), (4, 'four')] >>> pairs.sort(key=lambda pair: pair[1]) >>> pairs [(4, 'four'), (1, 'one'), (3, 'three'), (2, 'two')] 4.8.7. 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 after the first line of the string determines the amount of indentation for the entire documentation string. (We can’t use the first line since it is generally adjacent to the string’s opening quotes so its indentation is not apparent in the string literal.) Whitespace “equivalent” to this indentation is then stripped from the start of all lines of the string. Lines that are indented less should not occur, but if they occur all their leading whitespace should be stripped. Equivalence of whitespace should be tested after expansion of tabs (to 8 spaces, normally). Here is an example of a multi-line docstring: >>> def my_function(): ... """Do nothing, but document it. ... ... No, really, it doesn't do anything. ... """ ... pass ... >>> print(my_function.__doc__) Do nothing, but document it. No, really, it doesn't do anything. 4.8.8. 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 >>> def f(ham: str, eggs: str = 'eggs') -> str: ... print("Annotations:", f.__annotations__) ... print("Arguments:", ham, eggs) ... return ham + ' and ' + eggs ... >>> f('spam') Annotations: {'ham': 4.9. Intermezzo: Coding Style¶Now that you are about to write longer, more complex pieces of Python, it is a good time to talk about coding style. Most languages can be written (or more concise, formatted) in different styles; some are more readable than others. Making it easy for others to read your code is always a good idea, and adopting a nice coding style helps tremendously for that. For Python, PEP 8 has emerged as the style guide that most projects adhere to; it promotes a very readable and eye-pleasing coding style. Every Python developer should read it at some point; here are the most important points extracted for you:
Footnotes 1Actually, 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). |