Functions In Python Explained with Examples

Sharing is Caring

In Python, a function is a group of linked statements that perform a certain task. Our coder can be divided into smaller, more manageable pieces with the use of functions. As it expands, our developer becomes more structured and in charge. Additionally, it makes the program modular and avoids duplication.

Functions In Python Explained with Examples

Function Syntax

def function_name(number of parameters):

        code definition


The components listed below are included in the function definition above.

  •  The function header’s keyword def marks the start of it.
  • A function name that acts as a special identification number. The naming guidelines for Python functions are the same as those for constructing identifiers.
  • the arguments (variables) we utilise to feed data into a function. They’re not necessary.
  • A colon marks where the function header terminates (:).
  • An optional description string (docstring) that outlines the function’s goals.
  • The function’s body is made up of one or more valid Python statements. Each statement must have the exact same level of indentation (usually 4 spaces).
  • An optional return statement that enables the function to return a value.

Explanation with an Example

There are two parts to functions such as creating a function and calling it. Let us explain it with an example.

def print_function():                                        #function creation   print(“My first Program Using Function”)   print_function()  #function calling

Function Parameters

Functions will take parameters with data in them.

  • The list of arguments is included in parenthesis after the function name. You can add as many arguments as you like by simply separating each one with a comma.


The function in the example that follows only accepts one argument (name). When the function is invoked, 1st name is supplied to it, and it is used  to display the complete name:

def my_name(name):   print(name)   my_name(“Aiman”) my_name(“Mubashir”) my_name(“khan”)

Scope of a Variables

It’s possible that not all of a program’s variables are accessible at all times. This relies on the location at which a variable was declared. The area of the program where a specific identifier can be accessed depends on a variable’s scope. Python has two different types of variable scopes.

  • Global variable
  • Local variable

 The variables inside a function are thought of as having a local scope, whilst the variables outside the function are thought of as having a global scope. This indicates that local variables can only be retrieved within the function in which they are defined, whereas global variables can be accessed by all methods throughout the program body. When a function is invoked, the local variables inside of it are brought into scope.

Also Read: Procurement Analysis in Python and C++

The Below example is given to demonstrate how a function’s internal variable scope can be understood.

def var_scope():             a = 100             print(“varible inside function:”,a)   b = 20 var_scope() print(“varible outside function:”,b)

The output will be:

varible inside function: 100 varible outside function: 20

Function Types

Basically, there are two categories into which we can divide functions:

  • Built-in functions are those that Python comes with.
  • User-defined functions are those that have been set forth by users themselves.

User-defined Functions in Python

  • The functions that we create on our own to accomplish a certain task are known as user-defined functions. We’ve already discussed using and calling functions in Python.
  • The pre-installed functions in Python are referred to as built-in functions. The term “library function” can be used to describe functions that are utilized but were authored by others.
  • All extra functions we develop on our own are considered user-defined functions. As a result, our user-defined function may appear to someone else to be a library function.

Merits  of User-defined Functions

  • User-defined functions are those we develop on our own to carry out certain tasks. Python’s using and calling of functions have already been covered.
  • Built-in functions are the ones that come with Python by default. Library functions are functions that are used but were created by someone else.
  • User-defined functions are all additional functions that we create on our own. As a consequence, our user-defined function might be mistaken for a library function by someone else.

Let we explain it with an example.

# user-defined functions   def sum_numbers(a,b):    sum = a + b    return sum   number1 = 100 number2 = 200   print(“The sum is”, sum_numbers(number1, number2))

Built-in-Functions in Python

“Function NameDescription
abs()Returns the absolute value of a number
all()Returns True if all items in an iterable object are true
any()Returns True if any item in an iterable object is true
ascii()Returns a readable version of an object. Replaces none-ascii characters with escape character
bin()Returns the binary version of a number
bool()Returns the boolean value of the specified object
bytearray()Returns an array of bytes
bytes()Returns a bytes object
callable()Returns True if the specified object is callable, otherwise False
chr()Returns a character from the specified Unicode code.
classmethod()Converts a method into a class method
compile()Returns the specified source as an object, ready to be executed
complex()Returns a complex number
delattr()Deletes the specified attribute (property or method) from the specified object
dict()Returns a dictionary (Array)
dir()Returns a list of the specified object’s properties and methods
divmod()Returns the quotient and the remainder when argument1 is divided by argument2
enumerate()Takes a collection (e.g. a tuple) and returns it as an enumerate object
eval()Evaluates and executes an expression
exec()Executes the specified code (or object)
filter()Use a filter function to exclude items in an iterable object
float()Returns a floating point number
format()Formats a specified value
frozenset()Returns a frozenset object
getattr()Returns the value of the specified attribute (property or method)
globals()Returns the current global symbol table as a dictionary
hasattr()Returns True if the specified object has the specified attribute (property/method)
hash()Returns the hash value of a specified object
help()Executes the built-in help system
hex()Converts a number into a hexadecimal value
id()Returns the id of an object
input()Allowing user input
int()Returns an integer number
isinstance()Returns True if a specified object is an instance of a specified object
issubclass()Returns True if a specified class is a subclass of a specified object
iter()Returns an iterator object
len()Returns the length of an object
list()Returns a list
locals()Returns an updated dictionary of the current local symbol table
map()Returns the specified iterator with the specified function applied to each item
max()Returns the largest item in an iterable
memoryview()Returns a memory view object
min()Returns the smallest item in an iterable
next()Returns the next item in an iterable
object()Returns a new object
oct()Converts a number into an octal
open()Opens a file and returns a file object
ord()Convert an integer representing the Unicode of the specified character
pow()Returns the value of x to the power of y
print()Prints to the standard output device
property()Gets, sets, deletes a property
range()Returns a sequence of numbers, starting from 0 and increments by 1 (by default)
repr()Returns a readable version of an object
reversed()Returns a reversed iterator
round()Rounds a numbers
set()Returns a new set object
setattr()Sets an attribute (property/method) of an object
slice()Returns a slice object
sorted()Returns a sorted list
staticmethod()Converts a method into a static method
str()Returns a string object
sum()Sums the items of an iterator
super()Returns an object that represents the parent class
tuple()Returns a tuple
type()Returns the type of an object
vars()Returns the __dict__ property of an object
zip()Returns an iterator, from two or more iterators”


Is using a Python function faster?

These built-in functions, like min, max, all, map, and others in Python, are quick because they are written in the C programming language. To speed up the execution of your code, you should utilize these built-in functions rather than creating your own.

Why are functions required?

With the use of functions, programmers can break a problem down into smaller, easier-to-manage sections, each of which can do a single task. Once a function is established, the nuances of how it functions can almost completely be forgotten.

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