OOPS Object oriented Programming PPT Tutorial

Name of the School: School of computer science and engineering
Course Code: OOP Course Name: E2UC201
Faculty Name: Rahul Anjana Programe Name:
Topic:
 FUNCTIONAL PROGRAMMING:
 First class,
 Higher order
 Proxy function
 Lambda function

Functional Programming
Imperative programming is a paradigm in computer science that uses statements to
change a program's state. It's based on a statement-at-a-time paradigm, and the
order in which operations occur is crucial. Imperative programming languages
require an understanding of the functions necessary to solve a problem, rather than
a reliance on models that are able to solve it.
Example: C, Java, and Python.
Declarative programming is a coding style that tells a program what to do, not how
to do it. It uses a domain-specific language (DSL) that is usually closer to natural
language than pseudocode, making it more readable and easier to learn. The DSL
abstracts much of the boilerplate, leaving fewer lines of code to do the same work.
Example: HTML, SQL, CSS and XML. (eXtensible Markup Language)-
XML stands for Extensible Markup Language. It is a type of markup language and
file format used to store, transport, and recreate arbitrary data.CSS-Cascading Style
Sheets

Pure and Impure Function:
 Functions that don’t modify their arguments or produce any other side-effects are called pure.
 Functions that modify their arguments or cause other actions to occur are called impure.

#Example Impure:
def
remove_last(input_list):
input_list.pop()
names = [‘A', ‘B', ‘C']
remove_last(names)
print(names)
remove_last(names)
print(names)
#Example Pure:
def remove_last(input_list):
new_list = input_list.copy()
new_list.pop()
return new_list
names = ['A', 'B', 'C']
new_names = remove_last(names)
print(names)
print(new_names)
[‘A', ‘B']
[‘A']
[‘A', ‘B‘,’C’]
[‘A‘,’B’]

Functional programming is a declarative programming paradigm style where one applies
pure functions in sequence to solve complex problems.
Functions take an input value and produce an output value without being affected by
the program.

Functional Programming Concepts
Functional Programming Concepts:
 First-class functions
 Recursion
 Immutability
 Pure functions
 High order functions

1. Functions are objects: Python functions are first class objects. In the example below, we
are assigning function to a variable. This assignment doesn’t call the function. It takes the
function object referenced by shout and creates a second name pointing to it, yell.

Functional Programming Concepts:
 Recursion: Functional programming languages rely on recursion rather than iteration.
Instead of iterating over a loop, a function in a functional programming language will call
itself.
# Solving for a factorial using recursion
def recursiveFactorial(n):
if (n == 0):
return 1;
# recursion call
return n *recursiveFactorial(n - 1);
print(recursiveFactorial(5))

First-class functions First-class functions
In Python, the term “first-class function” refers to a function’s ability to be treated as an :
 object that can be assigned to a variable,
 used as an argument for other functions, and
 returned as a value.
As a result, functions in Python are identical to other objects like strings, integers, and lists.

 Function can be assigned to a variable:This allows for easy manipulation and reuse of
functions.
Example:
def square(x):
return x ** 2
my_func = square
print(my_func(3)) # Output: 9

 Functions can be passed as arguments to other functions: This is helpful for writing more
modular, reusable code as well as higher-order functions.
Exmple:
def apply_operation(func, x):
return func(x)
def square(x):
return x ** 2
print(apply_operation(square, 3)) # Output: 9

 Functions can also return values from other functions: This is useful for returning functions
based on specific criteria or for creating functions on the fly.
Example:
def get_operation(op):
if op == '+':
def add(x, y):
return x + y
return add
elif op == '-':
def subtract(x, y):
return x - y
return subtract
add_func = get_operation('+')
subtract_func = get_operation('-')
print(add_func(3, 4)) # Output: 7
print(subtract_func(10, 5)) # Output: 5

Higher order functions
 A function that accepts other functions as parameters or returns functions as outputs is
called a high order function.
 This process applies a function to its parameters at each iteration while returning a new
function that accepts the next parameter.
 Common examples of higher-order functions include filter, map, and reduce.
 The idea of first-class functions in Python makes higher-order functions possible.
 Higher-order functions operate by accepting a function as an argument, altering it, and then
returning the altered function.
 More modular and reusable code can be produced as a result.
Built-in Higher-Order Functions in Python
Map, Filter, and Reduce

Higher order functions
 Example
def apply_func(func, lst):
return [func(x) for x in lst]
def square(x):
return x ** 2
numbers = [1, 2, 3, 4, 5]
squared_numbers = apply_func(square,
numbers) print(squared_numbers)
# Output: [1, 4, 9, 16, 25]
Example 2
def make_adder(n):
def adder(x):
return x + n
return adder
add_five = make_adder(5)
print(add_five(10))
# Output: 15

Proxy function
 The Proxy method is Structural design pattern that allows you to provide the replacement
for an another object.
 Here, we use different classes to represent the functionalities of another class.
 The most important part is that here we create an object having original object functionality
to provide to the outer world.

Lambda function: What is a Lambda Function?
 Lambda functions are similar to user-defined functions but without a name.
 They're commonly referred to as anonymous functions.
 Lambda functions are efficient whenever you want to create a function that will only
contain simple expressions – that is, expressions that are usually a single line of a
statement.
 They're also useful when you want to use the function once.
Note: The anonymous function does not have a return keyword. This is because the
anonymous function will automatically return the result of the expression in the function once
it is executed.

Lambda function: When Should You Use a Lambda Function?
You should use the lambda function to create simple expressions. For example,
expressions that do not include complex structures such as if-else, for-loops, and so
on.
So, for example, if you want to create a function with a for-loop, you should use a user-
defined function.

Lambda function: How to Define a Lambda Function?
lambda argument(s) : expression
1.lambda is a keyword in Python for defining the anonymous function.
2.argument(s) is a placeholder, that is a variable that will be used to hold the value you
want to pass into the function expression. A lambda function can have multiple variables
depending on what you want to achieve.
3.expression is the code you want to execute in the lambda function.

lambda argument(s) : expression
Example:
(lambda x : x * 2)(3)
>> 6
def f(x):
return x * 2
print(f(3))
x=lambda x:x*3
print(x(2))

Immediately Invoked Function Expression
(lambda x, y: x + y)(2, 3)
(lambda x: x + 1)(2)
>>>high_ord_func = lambda x, func: x + func(x)
>>> high_ord_func(2, lambda x: x * x)
6
>>> high_ord_func(2, lambda x: x + 3)
7

Examples:
(lambda x, y, z: x + y + z)(3, 8, 1)
12
print((lambda x: x if(x > 10) else 10)(5))
10
print((lambda x: x if(x > 10) else 10)(12))
12

Examples:
(lambda x: x * 10 if x > 10 else (x * 5 if x < 5 else x))(11)
>>> high_ord_func = lambda x, func: x + func(x)
>>> high_ord_func(2, lambda x: x * x)
(lambda x, y, z=3: x + y + z)(1, 2)
(lambda x, y, z=3: x + y + z)(1, y=2)
(lambda *args: sum(args))(1,2,3)
y = 6
z = lambda x: x * y
print (z(8))

Examples:
def myfunc(n):
return lambda a : a * n

Examples:
min = (lambda x, y: x if x < y else y)
print(min(2, 3))
def myfunc(n):
return lambda a : a * n
mydoubler = myfunc(2)
print(mydoubler(11))

The functions map(), filter(), and reduce() all do the same thing: They
each take a function and a list of elements, and then return the result of
applying the function to each element in the list. As previously stated,
Python has built-in functions like map(), filter(), and reduce().

we have three main functions:
•map()
•filter()
•reduce()
The map() function-The map() function or map and filter in Python (also
called as map filter in Python) is a higher-order function.
SYNTAX: map(function, iterables)
EXAMPLE-
def function(a):
return a*a
x = map(function, (1,2,3,4)) #x is the map object
print(set(x))

x is a map object, as you can see. The map function is displayed next, which takes
“function()” as a parameter and then applies “a * a” to all ‘iterables’. As a result, all iterables’
values are multiplied by themselves before being returned.
The filter() function-
The filter() function is used to generate an output list of values that return true when the
function is called. It has the following syntax:
SYNTAX: filter (function, iterables)
This function like python map function map(), can take user-defined functions and lambda
functions as parameters.
EXAMPLE-
def func(x):
if x>=3:
return x
y = filter(func, (1,2,3,4))
print(y)

OUTPUT-[3, 4]
As you can see, y is the filter object, and the list is a collection of true values for the condition
(x>=3).
The reduce() function-
The reduce() function applies a provided function to ‘iterables’ and returns a single value, as the name
implies.
SYNTAX: reduce(function, iterables)
The function specifies which expression should be applied to the ‘iterables’ in this case. The function tools
module must be used to import this function.
EXAMPLE-
from functools import reduce
reduce(lambda a,b: a+b,[23,21,45,98])
OUTPUT-187

The reduce function in the preceding example adds each iterable in the list one by one and returns a single
result.

OOPS Object oriented Programming PPT Tutorial

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