4. More Control Flow Tools¶
Besides the while statement just introduced, Python uses the usual flow control statements known from other languages, with some twists.
4.1. if Statements¶
Perhaps the most well-known statement type is the if statement. For example:
There can be zero or more elif parts, and the else part is optional. The keyword ‘ elif ’ is short for ‘else if’, and is useful to avoid excessive indentation. An if … elif … elif … sequence is a substitute for the switch or case statements found in other languages.
If you’re comparing the same value to several constants, or checking for specific types or attributes, you may also find the match statement useful. For more details see match Statements .
4.2. for Statements¶
The for statement in Python differs a bit from what you may be used to in C or Pascal. Rather than always iterating over an arithmetic progression of numbers (like in Pascal), or giving the user the ability to define both the iteration step and halting condition (as C), Python’s for statement iterates over the items of any sequence (a list or a string), in the order that they appear in the sequence. For example (no pun intended):
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:
4.3. The range() Function¶
If you do need to iterate over a sequence of numbers, the built-in function range() comes in handy. It generates arithmetic progressions:
The given end point is never part of the generated sequence; range(10) generates 10 values, the legal indices for items of a sequence of length 10. It is possible to let the range start at another number, or to specify a different increment (even negative; sometimes this is called the ‘step’):
To iterate over the indices of a sequence, you can combine range() and len() as follows:
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:
In many ways the object returned by range() behaves as if it is a list, but in fact it isn’t. It is an object which returns the successive items of the desired sequence when you iterate over it, but it doesn’t really make the list, thus saving space.
We say such an object is iterable , that is, suitable as a target for functions and constructs that expect something from which they can obtain successive items until the supply is exhausted. We have seen that the for statement is such a construct, while an example of a function that takes an iterable is sum() :
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 list() .
4.4. break and continue Statements, and else Clauses on Loops¶
The break statement, like in C, breaks out of the innermost enclosing for or while loop.
Loop statements may have an else clause; it is executed when the loop terminates through exhaustion of the iterable (with for ) or when the condition becomes false (with while ), but not when the loop is terminated by a break statement. This is exemplified by the following loop, which searches for prime numbers:
(Yes, this is the correct code. Look closely: the else clause belongs to the for loop, not the if statement.)
When used with a loop, the else clause has more in common with the else clause of a try statement than it does with that of if statements: a try statement’s else clause runs when no exception occurs, and a loop’s else clause runs when no break occurs. For more on the try statement and exceptions, see Handling Exceptions .
The continue statement, also borrowed from C, continues with the next iteration of the loop:
4.5. pass Statements¶
The pass statement does nothing. It can be used when a statement is required syntactically but the program requires no action. For example:
This is commonly used for creating minimal classes:
Another place pass can be used is as a place-holder for a function or conditional body when you are working on new code, allowing you to keep thinking at a more abstract level. The pass is silently ignored:
4.6. match Statements¶
A match statement takes an expression and compares its value to successive patterns given as one or more case blocks. This is superficially similar to a switch statement in C, Java or JavaScript (and many other languages), but it’s more similar to pattern matching in languages like Rust or Haskell. Only the first pattern that matches gets executed and it can also extract components (sequence elements or object attributes) from the value into variables.
The simplest form compares a subject value against one or more literals:
Note the last block: the “variable name” _ 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 | (“or”):
Patterns can look like unpacking assignments, and can be used to bind variables:
Study that one carefully! The first pattern has two literals, and can be thought of as an extension of the literal pattern shown above. But the next two patterns combine a literal and a variable, and the variable binds a value from the subject ( point ). The fourth pattern captures two values, which makes it conceptually similar to the unpacking assignment (x, y) = point .
If you are using classes to structure your data you can use the class name followed by an argument list resembling a constructor, but with the ability to capture attributes into variables:
You can use positional parameters with some builtin classes that provide an ordering for their attributes (e.g. dataclasses). You can also define a specific position for attributes in patterns by setting the __match_args__ special attribute in your classes. If it’s set to (“x”, “y”), the following patterns are all equivalent (and all bind the y attribute to the var variable):
A recommended way to read patterns is to look at them as an extended form of what you would put on the left of an assignment, to understand which variables would be set to what. Only the standalone names (like var above) are assigned to by a match statement. Dotted names (like foo.bar ), attribute names (the x= and y= above) or class names (recognized by the “(…)” next to them like Point above) are never assigned to.
Patterns can be arbitrarily nested. For example, if we have a short list of points, we could match it like this:
We can add an if clause to a pattern, known as a “guard”. If the guard is false, match goes on to try the next case block. Note that value capture happens before the guard is evaluated:
Several other key features of this statement:
Like unpacking assignments, tuple and list patterns have exactly the same meaning and actually match arbitrary sequences. An important exception is that they don’t match iterators or strings.
Sequence patterns support extended unpacking: [x, y, *rest] and (x, y, *rest) work similar to unpacking assignments. The name after * may also be _ , so (x, y, *_) matches a sequence of at least two items without binding the remaining items.
Mapping patterns: <"bandwidth": b, "latency": l>captures the "bandwidth" and "latency" values from a dictionary. Unlike sequence patterns, extra keys are ignored. An unpacking like **rest is also supported. (But **_ would be redundant, so it is not allowed.)
Subpatterns may be captured using the as keyword:
will capture the second element of the input as p2 (as long as the input is a sequence of two points)
Most literals are compared by equality, however the singletons True , False and None are compared by identity.
Patterns may use named constants. These must be dotted names to prevent them from being interpreted as capture variable:
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:
The keyword def introduces a function definition. It must be followed by the function name and the parenthesized list of formal parameters. The statements that form the body of the function start at the next line, and must be indented.
The first statement of the function body can optionally be a string literal; this string literal is the function’s documentation string, or docstring. (More about docstrings can be found in the section Documentation Strings .) There are tools which use docstrings to automatically produce online or printed documentation, or to let the user interactively browse through code; it’s good practice to include docstrings in code that you write, so make a habit of it.
The execution of a function introduces a new symbol table used for the local variables of the function. More precisely, all variable assignments in a function store the value in the local symbol table; whereas variable references first look in the local symbol table, then in the local symbol tables of enclosing functions, then in the global symbol table, and finally in the table of built-in names. Thus, global variables and variables of enclosing functions cannot be directly assigned a value within a function (unless, for global variables, named in a global statement, or, for variables of enclosing functions, named in a nonlocal statement), although they may be referenced.
The actual parameters (arguments) to a function call are introduced in the local symbol table of the called function when it is called; thus, arguments are passed using call by value (where the value is always an object reference, not the value of the object). 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:
Coming from other languages, you might object that fib is not a function but a procedure since it doesn’t return a value. In fact, even functions without a return statement do return a value, albeit a rather boring one. This value is called None (it’s a built-in name). Writing the value None is normally suppressed by the interpreter if it would be the only value written. You can see it if you really want to using print() :
It is simple to write a function that returns a list of the numbers of the Fibonacci series, instead of printing it:
This example, as usual, demonstrates some new Python features:
The return statement returns with a value from a function. return without an expression argument returns None . Falling off the end of a function also returns None .
The statement result.append(a) calls a method of the list object result . A method is a function that ‘belongs’ to an object and is named obj.methodname , where obj is some object (this may be an expression), and methodname is the name of a method that is defined by the object’s type. Different types define different methods. Methods of different types may have the same name without causing ambiguity. (It is possible to define your own object types and methods, using classes, see Classes ) The method append() shown in the example is defined for list objects; it adds a new element at the end of the list. In this example it is equivalent to result = result + [a] , but more efficient.
4.8. 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:
This function can be called in several ways:
giving only the mandatory argument: ask_ok(‘Do you really want to quit?’)
giving one of the optional arguments: ask_ok(‘OK to overwrite the file?’, 2)
or even giving all arguments: ask_ok(‘OK to overwrite the file?’, 2, ‘Come on, only yes or no!’)
This example also introduces the in keyword. This tests whether or not a sequence contains a certain value.
The default values are evaluated at the point of function definition in the defining scope, so that
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:
This will print
If you don’t want the default to be shared between subsequent calls, you can write the function like this instead:
4.8.2. Keyword Arguments¶
Functions can also be called using keyword arguments of the form kwarg=value . For instance, the following function:
accepts one required argument ( voltage ) and three optional arguments ( state , action , and type ). This function can be called in any of the following ways:
but all the following calls would be invalid:
In a function call, keyword arguments must follow positional arguments. All the keyword arguments passed must match one of the arguments accepted by the function (e.g. actor is not a valid argument for the parrot function), and their order is not important. This also includes non-optional arguments (e.g. parrot(voltage=1000) is valid too). No argument may receive a value more than once. Here’s an example that fails due to this restriction:
When a final formal parameter of the form **name is present, it receives a dictionary (see Mapping Types — dict ) containing all keyword arguments except for those corresponding to a formal parameter. This may be combined with a formal parameter of the form *name (described in the next subsection) which receives a tuple containing the positional arguments beyond the formal parameter list. ( *name must occur before **name .) For example, if we define a function like this:
It could be called like this:
and of course it would print:
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:
where / and * are optional. If used, these symbols indicate the kind of parameter by how the arguments may be passed to the function: positional-only, positional-or-keyword, and keyword-only. Keyword parameters are also referred to as named parameters.
4.8.3.1. Positional-or-Keyword Arguments¶
If / and * are not present in the function definition, arguments may be passed to a function by position or by keyword.
4.8.3.2. 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 / (forward-slash). The / is used to logically separate the positional-only parameters from the rest of the parameters. If there is no / in the function definition, there are no positional-only parameters.
Parameters following the / may be positional-or-keyword or keyword-only.
4.8.3.3. Keyword-Only Arguments¶
To mark parameters as keyword-only, indicating the parameters must be passed by keyword argument, place an * in the arguments list just before the first keyword-only parameter.
4.8.3.4. Function Examples¶
Consider the following example function definitions paying close attention to the markers / and * :
The first function definition, standard_arg , the most familiar form, places no restrictions on the calling convention and arguments may be passed by position or keyword:
The second function pos_only_arg is restricted to only use positional parameters as there is a / in the function definition:
The third function kwd_only_args only allows keyword arguments as indicated by a * in the function definition:
And the last uses all three calling conventions in the same function definition:
Finally, consider this function definition which has a potential collision between the positional argument name and **kwds which has name as a key:
There is no possible call that will make it return True as the keyword ‘name’ will always bind to the first parameter. For example:
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:
In other words, the names of positional-only parameters can be used in **kwds without ambiguity.
4.8.3.5. Recap¶
The use case will determine which parameters to use in the function definition:
Use positional-only if you want the name of the parameters to not be available to the user. This is useful when parameter names have no real meaning, if you want to enforce the order of the arguments when the function is called or if you need to take some positional parameters and arbitrary keywords.
Use keyword-only when names have meaning and the function definition is more understandable by being explicit with names or you want to prevent users relying on the position of the argument being passed.
For an API, use positional-only to prevent breaking API changes if the parameter’s name is modified in the future.
4.8.4. 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.
Normally, these variadic arguments will be last in the list of formal parameters, because they scoop up all remaining input arguments that are passed to the function. Any formal parameters which occur after the *args parameter are ‘keyword-only’ arguments, meaning that they can only be used as keywords rather than positional arguments.
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 range() function expects separate start and stop arguments. If they are not available separately, write the function call with the * -operator to unpack the arguments out of a list or tuple:
In the same fashion, dictionaries can deliver keyword arguments with the ** -operator:
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 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:
The above example uses a lambda expression to return a function. Another use is to pass a small function as an argument:
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:
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 __annotations__ attribute of the function as a dictionary and have no effect on any other part of the function. Parameter annotations are defined by a colon after the parameter name, followed by an expression evaluating to the value of the annotation. Return annotations are defined by a literal -> , followed by an expression, between the parameter list and the colon denoting the end of the def statement. The following example has a required argument, an optional argument, and the return value annotated:
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:
Use 4-space indentation, and no tabs.
4 spaces are a good compromise between small indentation (allows greater nesting depth) and large indentation (easier to read). Tabs introduce confusion, and are best left out.
Wrap lines so that they don’t exceed 79 characters.
This helps users with small displays and makes it possible to have several code files side-by-side on larger displays.
Use blank lines to separate functions and classes, and larger blocks of code inside functions.
When possible, put comments on a line of their own.
Use spaces around operators and after commas, but not directly inside bracketing constructs: a = f(1, 2) + g(3, 4) .
Name your classes and functions consistently; the convention is to use UpperCamelCase for classes and lowercase_with_underscores for functions and methods. Always use self as the name for the first method argument (see A First Look at Classes for more on classes and methods).
Don’t use fancy encodings if your code is meant to be used in international environments. Python’s default, UTF-8, or even plain ASCII work best in any case.
Likewise, don’t use non-ASCII characters in identifiers if there is only the slightest chance people speaking a different language will read or maintain the code.
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).
Функции в Python
Функция — это именованный блок кода, к которому можно обратиться из любого места программы. У функции есть имя и список входных параметров, а также возвращаемое значение.
Функция позволяет использовать в программе один и тот же фрагмент кода несколько раз.
Oбъявление функции в Python выглядит так:
def function_name ( argument1 , argument2 , . ):
# код функции
# def — DEclare Function — «объявить функцию»
# function_name — имя функции
# (argument1, argument2, . ) — список аргументов, поступающих на вход функции при ее вызове
# тело функции — это весь код, который идет после двоеточия
# Объявление функции
def hello (name):
print ( ‘Hello, ‘ + name)
# Вызовы функции
hello ( ‘Max’ )
hello ( ‘Ivan’ )
hello ( ‘Alex’ )
hello ( ‘Kate’ )
# Вывод
>> Hello, Max
>> Hello, Ivan
>> Hello, Alex
>> Hello, Kate
Оператор return возвращает значение из функции.
Представьте себе обычный калькулятор. Вы вводите первое число, операцию и второе число. Калькулятор возвращает нам результат операции над этими числами. Всё просто, не так ли? Функции точно так же умеют возвращать значение с помощью специального оператора return.
# Объявление функции
def sum2 ( a , b ):
return a + b
# Вызовы функции
s1 = sum2 ( 10 , 2 )
s2 = sum2 ( 108 , 100 )
s3 = sum2 ( 3 , 1 )
print ( f ‘s1 =
print ( f ‘s2 =
print ( f ‘s3 =
>> s1 = 12
>> s2 = 208
>> s3 = 4
# Функция умножения двух чисел
def mult2 ( a , b ):
return a * b
# Вызовем нашу функцию
m1 = mult2 ( 10 , 2 )
m2 = mult2 ( 108 , 100 )
m3 = mult2 ( 3 , 1 )
print ( f ‘m1 =
print ( f ‘m2 =
print ( f ‘m3 =
>> m1 = 20
>> m2 = 10800
>> m3 = 3
Для параметров функции можно указывать значения по умолчанию. Это дает возможность вызывать функцию с меньшим числом параметров.
# Аргумент name по умолчанию равен ‘world’
def hello ( name = ‘world’ ):
print ( ‘Hello, ‘ + name)
hello ()
hello ( ‘Ivan’ )
>> Hello, world
>> Hello, Ivan
Примеры
# Написать функцию square(), вычисляющую квадрат числа.
def square ( number ):
return number * number # Возвращаем результат работы функции обратно в программу
a = square ( 2 )
print (a)
# Напишите функцию perimetr, вычисляющую периметр прямоугольника со сторонами a и b.
def perimetr ( a , b ):
return 2 * (a + b)
p = perimetr ( 4 , 3 )
print (p)
# Напишите функцию isEven, возвращающую True, если число четное, и False, если — нечетное.
def isEven ( x ):
return x % 2 == 0
print ( isEven ( 10 ))
print ( isEven ( 11 ))
# Напишите функцию amountList, которая возвращает сумму всех элементов списка.
def amountList ( lst ):
amount = 0
f or x in lst:
amount += x
return amount
print ( amountList ([ 1 , 2 , 3 ]))
mylist = [ 1 , 2 , 4 , 8 , 16 ]
s = amountList (mylist)
print ( f ‘Сумма списка ‘ )
>> 6
>> Сумма списка [ 1 , 2 , 4 , 8 , 16 ] равна 31
# Напишите функцию fib, которая возвращает n-ное число Фибоначчи.
# Последовательность Фибоначчи выглядит так: 1 1 2 3 5 8 13 21 34
def fib ( n ):
a, b = 0 , 1
if n == 0 : return 0
f or i in range ( 1 , n):
a, b = b, a + b
return b
print ( fib ( 2 ))
print ( fib ( 3 ))
print ( fib ( 4 ))
print ( fib ( 5 ))
print ( fib ( 10 ))
# Напишите функцию fact, вычисляющую значение факториала числа N.
# Факториал числа — это произведение всех чисел от 1 до N.
# Например, факториал числа 5 равен 120 (5! = 120).
def fact ( n ):
result = 1
while n > 1 :
result *= n
n -= 1
return result
print ( fact ( 2 ))
print ( fact ( 3 ))
print ( fact ( 4 ))
print ( fact ( 5 ))
Решение задач
Напишите функцию, которая получает в качестве аргумента радиус круга и находит его площадь.
# Не забудьте написать функцию circle.
print ( circle ( 4 ))
print ( circle ( 1 ))
Напишите функцию, которая возвращает True, если число делится на 3, и False, если — нет.
# Не забудьте написать функцию three.
print ( three ( 4 ))
print ( three ( 3 ))
3. Максимум в списке
Напишите функцию, которая возвращает максимальный элемент из переданного в нее списка.
# Напишите функцию maxList.
mylist = [ 1 , 3 , 2 ]
print ( maxList (mylist))
4. Сколько четных
Напишите функцию, которая возвращает количество четных элементов в списке.
# Напишите функцию evenCounter.
mylist = [ 1 , 10 , 2 , 4 , 6 ]
evens = evenCounter (mylist)
print (even)
Напишите функцию, которая возвращает список с уникальными (неповторяющихся) элементам.
# Напишите функцию unique.
mylist = [ 1 , 1 , 2 , 1 , 3 , 2 , 3 ]
print ( unique (mylist))
Just Python Part 6— Functions
A function is a block of code which only runs when it is called. Python function in any programming language is a sequence of statements in a certain order, given a name. When called, those statements are executed. So we don’t have to write the code again and again for each [type of] data that we want to apply it to. This is called code re-usability.
We will study the different types of functions in Python: Python built-in functions, Python recursion function, Python lambda function, and Python user-defined functions with their syntax and examples. As you already know, Python gives you many built-in functions like print, int, float, bin, hex, string, list, set, dictionary, and so.
You can also create your own functions. These functions are called user-defined functions.
Syntax of Function
Defining a Function
You can define functions to provide the required functionality. Here are simple rules to define a function in Python.
- Function blocks begin with the keyword def followed by the function name and parentheses “ ( ) “.
- Any input parameters or arguments should be placed within these parentheses. You can also define parameters inside these parentheses.
- The first statement of a function can be an optional statement — the documentation string of the function or docstring.
- The code block within every function starts with a colon (:) and is indented.
- The statement return [expression] exits a function, optionally passing back an expression to the caller. A return statement with no arguments is the same as return None.
Rules for naming python function (identifier)
We follow the same rules when naming a function as we do when naming a variable.
- It can begin with either of the following: A-Z, a-z, and underscore(_).
- The rest of it can contain either of the following: A-Z, a-z, digits(0–9), and underscore(_).
- A reserved keyword may not be chosen as an identifier.
It is good practice to name a Python function according to what it does.
You may use a docstring right under the first line of a function declaration. This is a documentation string, and it explains what the function does.
You can access this docstring using the __doc__ attribute of the function.
Calling a Function
To call a function, use the function name followed by parenthesis:
If you don’t yet know what to put in the function, then you should put the pass statement in its body. If you leave its body empty, you get an error “Expected an indented block”. You can even reassign a function by defining it again.
Python Function Argument
Arguments are specified after the function name, inside the parentheses. You can add as many arguments as you want, just separate them with a comma.
Arguments are often shortened to args in Python documentations.
Parameters or Arguments?
The terms parameter and argument can be used for the same thing: information that are passed into a function.
From a function’s perspective:
A parameter is the variable listed inside the parentheses in the function definition.
An argument is the value that are sent to the function when it is called.
There are different types of arguments:
- Default argument
- Keyword argument
- Arbitrary argument
Default argument
A default argument is an argument that assumes a default value if a value is not provided in the function call for that argument. The following example gives an idea on default arguments, it prints default age if it is not passed −
When the above code is executed, it produces the following result −
Any number of arguments can have a default value. But you must make sure to not have a non-default argument after a default argument. In other words, if you provide a default argument, all others succeeding it must have default values as well. The reason is simple. Imagine you have a function with two parameters. The first argument has a default value, but the second doesn’t. Now when you call it(if it was allowed), you provide only one argument. The interpreter takes it to be the first argument. What happens to the second argument, then? It has no clue.
SyntaxError: non-default argument follows default argument
This was all about the default arguments in Python
Keyword Arguments
With keyword arguments in python, we can change the order of passing the arguments without any consequences. Let’s take a function to divide two numbers, and return the quotient.
The phrase Keyword Arguments are often shortened to kwargs in Python documentations.
Arbitrary Arguments, *args
If you do not know how many arguments that will be passed into your function, add a * before the parameter name in the function definition.
This way the function will receive a tuple of arguments, and can access the items accordingly:
Arbitrary Keyword Arguments, **kwargs
If you do not know how many keyword arguments that will be passed into your function, add two asterisk: ** before the parameter name in the function definition.
This way the function will receive a dictionary of arguments, and can access the items accordingly:
Arbitrary Kword Arguments are often shortened to **kwargs in Python documentations.
Passing a List as an Argument
You can send any data types of argument to a function (string, number, list, dictionary etc.), and it will be treated as the same data type inside the function.
E.g. if you send a List as an argument, it will still be a List when it reaches the function:
Python return statement
A Python function may optionally return a value. This value can be a result that it produced on its execution. Or it can be something you specify- an expression or a value. A return statement with no arguments is the same as return None.
When the above code is executed, it produces the following result −
Scope and Lifetime of Variables in Python
All variables in a program may not be accessible at all locations in that program. This depends on where you have declared a variable.
The scope of a variable determines the portion of the program where you can access a particular identifier. There are two basic scopes of variables in Python
- Global variables
- Local variables
Local Scope— A variable that’s declared inside a function has a local scope. In other words, it is local to that function.
If you then try to access the variable x outside the function, you cannot.
Traceback (most recent call last):
File “<pyshell#96>”, line 1, in <module>
NameError: name ‘x’ is not defined
Global Scope— When you declare a variable outside python function, or anything else, it has global scope. It means that it is visible everywhere within the program.
However, you can’t change its value from inside a local scope(here, inside a function). To do so, you must declare it global inside the function, using the ‘global’ keyword.
Lifetime
A variable’s lifetime is the period of time for which it resides in the memory.
A variable that’s declared inside python function is destroyed after the function stops executing. So the next time the function is called, it does not remember the previous value of that variable.
As you can see here, the function func1() doesn’t print 2 the second time.
Deleting Python function
Till now, we have seen how to delete a variable. Similarly, you can delete a function with the ‘del’ keyword.
Python Lambda Expressions
As we said earlier, a function doesn’t need to have a name. A lambda expression in Python allows us to create anonymous python function, and we use the ‘lambda’ keyword for it. The following is the syntax for a lambda expression.
lambda arguments : expression
It’s worth noting that it can have any number of arguments, but only one expression. It evaluates the value of that expression, and returns the result. Let’s take an example.
This code takes the numbers 5 as arguments a and put it in the expression (a+10). This makes it 5 + 10 which is 15. Finally, it returns 15.
Actually, the function object is assigned to the identifier myvar.
Lambda functions can take any number of arguments:
Why Use Lambda Functions?
The power of lambda is better shown when you use them as an anonymous function inside another function.
Say you have a function definition that takes one argument, and that argument will be multiplied with an unknown number:
These functions are called anonymous because they are not declared in the standard manner by using the def keyword. You can use the lambda keyword to create small anonymous functions.
- Lambda forms can take any number of arguments but return just one value in the form of an expression. They cannot contain commands or multiple expressions.
- An anonymous function cannot be a direct call to print because lambda requires an expression
- Lambda functions have their own local namespace and cannot access variables other than those in their parameter list and those in the global namespace.
Python Recursion Function
In Python function, recursion is when a function calls itself. To see how this could be useful, let’s try calculating the factorial of a number. Mathematically, a number’s factorial is:
To code this, we type the following.
Try recursive functions using the print statements in between the functions to get the clear understanding how recursive functions work.
Conclusion: Python Functions
It is important to revise in order to retain information. In this lesson, we learned about the Python function. First, we saw the different types of functions. Now we can create, update, and delete a function. And we know that a function may take arguments and may return a value. We also looked at the scope and lifetime of a variable. Hope you like the Python Function Tutorial.
Don’t forget to revise the various built-in functions supported by Python. Refer to our tutorials for the same.