Besides the statement just introduced, Python uses the usual flow control statements known from other languages, with some twists. Show
4.1. >>> 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 7 StatementsPerhaps the most well-known statement type is the statement. For example: >>> 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 parts, and the part is optional. The keyword ‘ >>> 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 lamb9’ is short for ‘else if’, and is useful to avoid excessive indentation. An >>> 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 lamb7 … >>> 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 lamb9 … >>> 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 lamb9 … sequence is a substitute for the >>> range(10) range(0, 10)5 or >>> range(10) range(0, 10)6 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 >>> range(10) range(0, 10)7 statement useful. For more details see . 4.2. >>> range(10) range(0, 10) 8 StatementsThe 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 >>> range(10) range(0, 10)8 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 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 FunctionIf you do need to iterate over a sequence of numbers, the built-in function comes in handy. It generates arithmetic progressions: >>> for i in range(5): ... print(i) ... 0 1 2 3 4 The given end point is never part of the generated sequence; >>> sum(range(4)) # 0 + 1 + 2 + 3 63 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 combine and as follows: >>> a = ['Mary', 'had', 'a', 'little', 'lamb'] >>> for i in range(len(a)): ... print(i, a[i]) ... 0 Mary 1 had 2 a 3 little 4 lamb In most such cases, however, it is convenient to use the function, see . A strange thing happens if you just print a range: >>> range(10) range(0, 10) In many ways the object returned by 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 , 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 statement is such a construct, while an example of a function that takes an iterable is : >>> sum(range(4)) # 0 + 1 + 2 + 3 6 Later we will see more functions that return iterables and take iterables as arguments. In chapter , we will discuss in more detail about . 4.4. >>> 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 1 and >>> 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 2 Statements, and >>> range(10) range(0, 10) 0 Clauses on LoopsThe statement, like in C, breaks out of the innermost enclosing or loop. Loop statements may have an >>> range(10) range(0, 10)0 clause; it is executed when the loop terminates through exhaustion of the iterable (with ) or when the condition becomes false (with ), but not when the loop is terminated by a statement. This is exemplified by the following loop, which searches for prime numbers: >>> for n in range(2, 10): ... for x in range(2, n): ... if n % x == 0: ... print(n, 'equals', x, '*', n//x) ... break ... else: ... # loop fell through without finding a factor ... print(n, 'is a prime number') ... 2 is a prime number 3 is a prime number 4 equals 2 * 2 5 is a prime number 6 equals 2 * 3 7 is a prime number 8 equals 2 * 4 9 equals 3 * 3 (Yes, this is the correct code. Look closely: the >>> range(10) range(0, 10)0 clause belongs to the loop, not the statement.) When used with a loop, the >>> range(10) range(0, 10)0 clause has more in common with the >>> range(10) range(0, 10)0 clause of a statement than it does with that of statements: a statement’s >>> range(10) range(0, 10)0 clause runs when no exception occurs, and a loop’s >>> range(10) range(0, 10)0 clause runs when no >>> 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 * 31 occurs. For more on 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 96 statement and exceptions, see . The statement, also borrowed from C, continues with the next iteration 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 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. >>> # Measure some strings: ... words = ['cat', 'window', 'defenestrate'] >>> for w in words: ... print(w, len(w)) ... cat 3 window 6 defenestrate 12 04 StatementsThe statement does nothing. It can be used when a statement is required syntactically but the program requires no action. For example: >>> # Measure some strings: ... words = ['cat', 'window', 'defenestrate'] >>> for w in words: ... print(w, len(w)) ... cat 3 window 6 defenestrate 120 This is commonly used for creating minimal classes: >>> # Measure some strings: ... words = ['cat', 'window', 'defenestrate'] >>> for w in words: ... print(w, len(w)) ... cat 3 window 6 defenestrate 121 Another place 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 >>> # Measure some strings: ... words = ['cat', 'window', 'defenestrate'] >>> for w in words: ... print(w, len(w)) ... cat 3 window 6 defenestrate 1204 is silently ignored: >>> # Measure some strings: ... words = ['cat', 'window', 'defenestrate'] >>> for w in words: ... print(w, len(w)) ... cat 3 window 6 defenestrate 122 4.6. >>> range(10) range(0, 10) 7 StatementsA 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. The simplest form compares a subject value against one or more literals: >>> # Measure some strings: ... words = ['cat', 'window', 'defenestrate'] >>> for w in words: ... print(w, len(w)) ... cat 3 window 6 defenestrate 123 Note the last block: the “variable name” >>> # Measure some strings: ... words = ['cat', 'window', 'defenestrate'] >>> for w in words: ... print(w, len(w)) ... cat 3 window 6 defenestrate 1210 acts as a wildcard and never fails to match. If no case matches, none of the branches is executed. You can combine several literals in a single pattern using >>> # Measure some strings: ... words = ['cat', 'window', 'defenestrate'] >>> for w in words: ... print(w, len(w)) ... cat 3 window 6 defenestrate 1211 (“or”): >>> # Measure some strings: ... words = ['cat', 'window', 'defenestrate'] >>> for w in words: ... print(w, len(w)) ... cat 3 window 6 defenestrate 124 Patterns can look like unpacking assignments, and can be used to bind variables: >>> # Measure some strings: ... words = ['cat', 'window', 'defenestrate'] >>> for w in words: ... print(w, len(w)) ... cat 3 window 6 defenestrate 125 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 ( >>> # Measure some strings: ... words = ['cat', 'window', 'defenestrate'] >>> for w in words: ... print(w, len(w)) ... cat 3 window 6 defenestrate 1212). The fourth pattern captures two values, which makes it conceptually similar to the unpacking assignment >>> # Measure some strings: ... words = ['cat', 'window', 'defenestrate'] >>> for w in words: ... print(w, len(w)) ... cat 3 window 6 defenestrate 1213. 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: >>> # Measure some strings: ... words = ['cat', 'window', 'defenestrate'] >>> for w in words: ... print(w, len(w)) ... cat 3 window 6 defenestrate 126 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 >>> # Measure some strings: ... words = ['cat', 'window', 'defenestrate'] >>> for w in words: ... print(w, len(w)) ... cat 3 window 6 defenestrate 1214 special attribute in your classes. If it’s set to (“x”, “y”), the following patterns are all equivalent (and all bind the >>> # Measure some strings: ... words = ['cat', 'window', 'defenestrate'] >>> for w in words: ... print(w, len(w)) ... cat 3 window 6 defenestrate 1215 attribute to the >>> # Measure some strings: ... words = ['cat', 'window', 'defenestrate'] >>> for w in words: ... print(w, len(w)) ... cat 3 window 6 defenestrate 1216 variable): >>> # Measure some strings: ... words = ['cat', 'window', 'defenestrate'] >>> for w in words: ... print(w, len(w)) ... cat 3 window 6 defenestrate 127 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 >>> # Measure some strings: ... words = ['cat', 'window', 'defenestrate'] >>> for w in words: ... print(w, len(w)) ... cat 3 window 6 defenestrate 1216 above) are assigned to by a match statement. Dotted names (like >>> # Measure some strings: ... words = ['cat', 'window', 'defenestrate'] >>> for w in words: ... print(w, len(w)) ... cat 3 window 6 defenestrate 1218), attribute names (the >>> # Measure some strings: ... words = ['cat', 'window', 'defenestrate'] >>> for w in words: ... print(w, len(w)) ... cat 3 window 6 defenestrate 1219 and >>> # Measure some strings: ... words = ['cat', 'window', 'defenestrate'] >>> for w in words: ... print(w, len(w)) ... cat 3 window 6 defenestrate 1220 above) or class names (recognized by the “(…)” next to them like >>> # Measure some strings: ... words = ['cat', 'window', 'defenestrate'] >>> for w in words: ... print(w, len(w)) ... cat 3 window 6 defenestrate 1221 above) are never assigned to. Patterns can be arbitrarily nested. For example, if we have a short list of points, we could match it like this: >>> # Measure some strings: ... words = ['cat', 'window', 'defenestrate'] >>> for w in words: ... print(w, len(w)) ... cat 3 window 6 defenestrate 128 We can add an >>> 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 lamb7 clause to a pattern, known as a “guard”. If the guard is false, >>> range(10) range(0, 10)7 goes on to try the next case block. Note that value capture happens before the guard is evaluated: >>> # Measure some strings: ... words = ['cat', 'window', 'defenestrate'] >>> for w in words: ... print(w, len(w)) ... cat 3 window 6 defenestrate 129 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 FunctionsWe can create a function that writes the Fibonacci series to an arbitrary boundary: # 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] = status2 The keyword 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 .) 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 statement, or, for variables of enclosing functions, named in a 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: # 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] = status3 Coming from other languages, you might object that >>> # Measure some strings: ... words = ['cat', 'window', 'defenestrate'] >>> for w in words: ... print(w, len(w)) ... cat 3 window 6 defenestrate 1242 is not a function but a procedure since it doesn’t return a value. In fact, even functions without a statement do return a value, albeit a rather boring one. This value is called >>> # Measure some strings: ... words = ['cat', 'window', 'defenestrate'] >>> for w in words: ... print(w, len(w)) ... cat 3 window 6 defenestrate 1238 (it’s a built-in name). Writing the value >>> # Measure some strings: ... words = ['cat', 'window', 'defenestrate'] >>> for w in words: ... print(w, len(w)) ... cat 3 window 6 defenestrate 1238 is normally suppressed by the interpreter if it would be the only value written. You can see it if you really want to using : # 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] = status4 It is simple to write a function that returns a list of the numbers of the Fibonacci series, instead of printing it: # 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] = status5 This example, as usual, demonstrates some new Python features:
4.8. More on Defining FunctionsIt 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 ValuesThe 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: # 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] = status6 This function can be called in several ways:
This example also introduces the 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 # 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] = status7 will print >>> # Measure some strings: ... words = ['cat', 'window', 'defenestrate'] >>> for w in words: ... print(w, len(w)) ... cat 3 window 6 defenestrate 1262. 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: # 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] = status8 This will print # 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] = status9 If you don’t want the default to be shared between subsequent calls, you can write the function like this instead: >>> for i in range(5): ... print(i) ... 0 1 2 3 40 4.8.2. Keyword ArgumentsFunctions can also be called using of the form >>> # Measure some strings: ... words = ['cat', 'window', 'defenestrate'] >>> for w in words: ... print(w, len(w)) ... cat 3 window 6 defenestrate 1263. For instance, the following function: >>> for i in range(5): ... print(i) ... 0 1 2 3 41 accepts one required argument ( >>> # Measure some strings: ... words = ['cat', 'window', 'defenestrate'] >>> for w in words: ... print(w, len(w)) ... cat 3 window 6 defenestrate 1264) and three optional arguments ( >>> # Measure some strings: ... words = ['cat', 'window', 'defenestrate'] >>> for w in words: ... print(w, len(w)) ... cat 3 window 6 defenestrate 1265, >>> # Measure some strings: ... words = ['cat', 'window', 'defenestrate'] >>> for w in words: ... print(w, len(w)) ... cat 3 window 6 defenestrate 1266, and >>> # Measure some strings: ... words = ['cat', 'window', 'defenestrate'] >>> for w in words: ... print(w, len(w)) ... cat 3 window 6 defenestrate 1267). This function can be called in any of the following ways: >>> for i in range(5): ... print(i) ... 0 1 2 3 42 but all the following calls would be invalid: >>> for i in range(5): ... print(i) ... 0 1 2 3 43 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. >>> # Measure some strings: ... words = ['cat', 'window', 'defenestrate'] >>> for w in words: ... print(w, len(w)) ... cat 3 window 6 defenestrate 1268 is not a valid argument for the >>> # Measure some strings: ... words = ['cat', 'window', 'defenestrate'] >>> for w in words: ... print(w, len(w)) ... cat 3 window 6 defenestrate 1269 function), and their order is not important. This also includes non-optional arguments (e.g. >>> # Measure some strings: ... words = ['cat', 'window', 'defenestrate'] >>> for w in words: ... print(w, len(w)) ... cat 3 window 6 defenestrate 1270 is valid too). No argument may receive a value more than once. Here’s an example that fails due to this restriction: >>> for i in range(5): ... print(i) ... 0 1 2 3 44 When a final formal parameter of the form >>> # Measure some strings: ... words = ['cat', 'window', 'defenestrate'] >>> for w in words: ... print(w, len(w)) ... cat 3 window 6 defenestrate 1271 is present, it receives a dictionary (see ) containing all keyword arguments except for those corresponding to a formal parameter. This may be combined with a formal parameter of the form >>> # Measure some strings: ... words = ['cat', 'window', 'defenestrate'] >>> for w in words: ... print(w, len(w)) ... cat 3 window 6 defenestrate 1272 (described in the next subsection) which receives a containing the positional arguments beyond the formal parameter list. ( >>> # Measure some strings: ... words = ['cat', 'window', 'defenestrate'] >>> for w in words: ... print(w, len(w)) ... cat 3 window 6 defenestrate 1272 must occur before >>> # Measure some strings: ... words = ['cat', 'window', 'defenestrate'] >>> for w in words: ... print(w, len(w)) ... cat 3 window 6 defenestrate 1271.) For example, if we define a function like this: >>> for i in range(5): ... print(i) ... 0 1 2 3 45 It could be called like this: >>> for i in range(5): ... print(i) ... 0 1 2 3 46 and of course it would print: >>> for i in range(5): ... print(i) ... 0 1 2 3 47 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 parametersBy 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: >>> for i in range(5): ... print(i) ... 0 1 2 3 48 where >>> # Measure some strings: ... words = ['cat', 'window', 'defenestrate'] >>> for w in words: ... print(w, len(w)) ... cat 3 window 6 defenestrate 1275 and >>> # Measure some strings: ... words = ['cat', 'window', 'defenestrate'] >>> for w in words: ... print(w, len(w)) ... cat 3 window 6 defenestrate 1226 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. 4.8.3.1. Positional-or-Keyword ArgumentsIf >>> # Measure some strings: ... words = ['cat', 'window', 'defenestrate'] >>> for w in words: ... print(w, len(w)) ... cat 3 window 6 defenestrate 1275 and >>> # Measure some strings: ... words = ['cat', 'window', 'defenestrate'] >>> for w in words: ... print(w, len(w)) ... cat 3 window 6 defenestrate 1226 are not present in the function definition, arguments may be passed to a function by position or by keyword. 4.8.3.2. Positional-Only ParametersLooking 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 >>> # Measure some strings: ... words = ['cat', 'window', 'defenestrate'] >>> for w in words: ... print(w, len(w)) ... cat 3 window 6 defenestrate 1275 (forward-slash). The >>> # Measure some strings: ... words = ['cat', 'window', 'defenestrate'] >>> for w in words: ... print(w, len(w)) ... cat 3 window 6 defenestrate 1275 is used to logically separate the positional-only parameters from the rest of the parameters. If there is no >>> # Measure some strings: ... words = ['cat', 'window', 'defenestrate'] >>> for w in words: ... print(w, len(w)) ... cat 3 window 6 defenestrate 1275 in the function definition, there are no positional-only parameters. Parameters following the >>> # Measure some strings: ... words = ['cat', 'window', 'defenestrate'] >>> for w in words: ... print(w, len(w)) ... cat 3 window 6 defenestrate 1275 may be positional-or-keyword or keyword-only. 4.8.3.3. Keyword-Only ArgumentsTo mark parameters as keyword-only, indicating the parameters must be passed by keyword argument, place an >>> # Measure some strings: ... words = ['cat', 'window', 'defenestrate'] >>> for w in words: ... print(w, len(w)) ... cat 3 window 6 defenestrate 1226 in the arguments list just before the first keyword-only parameter. 4.8.3.4. Function ExamplesConsider the following example function definitions paying close attention to the markers >>> # Measure some strings: ... words = ['cat', 'window', 'defenestrate'] >>> for w in words: ... print(w, len(w)) ... cat 3 window 6 defenestrate 1275 and >>> # Measure some strings: ... words = ['cat', 'window', 'defenestrate'] >>> for w in words: ... print(w, len(w)) ... cat 3 window 6 defenestrate 1226: >>> for i in range(5): ... print(i) ... 0 1 2 3 49 The first function definition, >>> # Measure some strings: ... words = ['cat', 'window', 'defenestrate'] >>> for w in words: ... print(w, len(w)) ... cat 3 window 6 defenestrate 1286, the most familiar form, places no restrictions on the calling convention and arguments may be passed by position or keyword: >>> 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]0 The second function >>> # Measure some strings: ... words = ['cat', 'window', 'defenestrate'] >>> for w in words: ... print(w, len(w)) ... cat 3 window 6 defenestrate 1287 is restricted to only use positional parameters as there is a >>> # Measure some strings: ... words = ['cat', 'window', 'defenestrate'] >>> for w in words: ... print(w, len(w)) ... cat 3 window 6 defenestrate 1275 in the function definition: >>> 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]1 The third function >>> # Measure some strings: ... words = ['cat', 'window', 'defenestrate'] >>> for w in words: ... print(w, len(w)) ... cat 3 window 6 defenestrate 1289 only allows keyword arguments as indicated by a >>> # Measure some strings: ... words = ['cat', 'window', 'defenestrate'] >>> for w in words: ... print(w, len(w)) ... cat 3 window 6 defenestrate 1226 in the function definition: >>> 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]2 And the last uses all three calling conventions in the same function definition: >>> 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]3 Finally, consider this function definition which has a potential collision between the positional argument >>> # Measure some strings: ... words = ['cat', 'window', 'defenestrate'] >>> for w in words: ... print(w, len(w)) ... cat 3 window 6 defenestrate 1291 and >>> # Measure some strings: ... words = ['cat', 'window', 'defenestrate'] >>> for w in words: ... print(w, len(w)) ... cat 3 window 6 defenestrate 1292 which has >>> # Measure some strings: ... words = ['cat', 'window', 'defenestrate'] >>> for w in words: ... print(w, len(w)) ... cat 3 window 6 defenestrate 1291 as a key: >>> 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]4 There is no possible call that will make it return >>> # Measure some strings: ... words = ['cat', 'window', 'defenestrate'] >>> for w in words: ... print(w, len(w)) ... cat 3 window 6 defenestrate 1236 as the keyword >>> # Measure some strings: ... words = ['cat', 'window', 'defenestrate'] >>> for w in words: ... print(w, len(w)) ... cat 3 window 6 defenestrate 1295 will always bind to the first parameter. For example: >>> 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]5 But using >>> # Measure some strings: ... words = ['cat', 'window', 'defenestrate'] >>> for w in words: ... print(w, len(w)) ... cat 3 window 6 defenestrate 1275 (positional only arguments), it is possible since it allows >>> # Measure some strings: ... words = ['cat', 'window', 'defenestrate'] >>> for w in words: ... print(w, len(w)) ... cat 3 window 6 defenestrate 1291 as a positional argument and >>> # Measure some strings: ... words = ['cat', 'window', 'defenestrate'] >>> for w in words: ... print(w, len(w)) ... cat 3 window 6 defenestrate 1295 as a key in the keyword arguments: >>> 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]6 In other words, the names of positional-only parameters can be used in >>> # Measure some strings: ... words = ['cat', 'window', 'defenestrate'] >>> for w in words: ... print(w, len(w)) ... cat 3 window 6 defenestrate 1292 without ambiguity. 4.8.3.5. RecapThe use case will determine which parameters to use in the function definition: >>> 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]7 As guidance:
4.8.4. Arbitrary Argument ListsFinally, 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 ). Before the variable number of arguments, zero or more normal arguments may occur. >>> 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]8 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 # 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] = status00 parameter are ‘keyword-only’ arguments, meaning that they can only be used as keywords rather than positional arguments. >>> 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]9 4.8.5. Unpacking Argument ListsThe 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 function expects separate start and stop arguments. If they are not available separately, write the function call with the >>> # Measure some strings: ... words = ['cat', 'window', 'defenestrate'] >>> for w in words: ... print(w, len(w)) ... cat 3 window 6 defenestrate 1226-operator to unpack the arguments out of a list or tuple: >>> 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 lamb0 In the same fashion, dictionaries can deliver keyword arguments with the # 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] = status03-operator: >>> 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 lamb1 4.8.6. Lambda ExpressionsSmall anonymous functions can be created with the keyword. This function returns the sum of its two arguments: # 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] = status05. 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: >>> 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 lamb2 The above example uses a lambda expression to return a function. Another use is to pass a small function as an argument: >>> 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 lamb3 4.8.7. Documentation StringsHere 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: >>> 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 lamb4 4.8.8. Function Annotationsare completely optional metadata information about the types used by user-defined functions (see PEP 3107 and PEP 484 for more information). are stored in the # 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] = status06 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 # 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] = status07, followed by an expression, between the parameter list and the colon denoting the end of the statement. The following example has a required argument, an optional argument, and the return value annotated: >>> 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 lamb5 4.9. Intermezzo: Coding StyleNow 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 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). What is the Elif in Python?The elif keyword is used in conditional statements (if statements), and is short for else if.
What is Elif in Python with example?The elif clause in Python
In case else is not specified, and all the statements are false , none of the blocks would be executed. Here's an example: if 51<5: print("False, statement skipped") elif 0<5: print("true, block executed") elif 0<3: print("true, but block will not execute") else: print("If all fails.")
What is the difference between if and Elif in Python?Difference between if and if elif else
Python will evaluate all three if statements to determine if they are true. Once a condition in the if elif else statement is true, Python stop evaluating the other conditions. Because of this, if elif else is faster than three if statements.
When should you use an Elif statement?Use the elif condition is used to include multiple conditional expressions after the if condition or between the if and else conditions. The elif block is executed if the specified condition evaluates to True . In the above example, the elif conditions are applied after the if condition.
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