📘 PAPER 2: OBJECT ORIENTED PROGRAMMING WITH PYTHON UNIT 4 – Dictionaries, Functions, File Handling & Regular Expressions (university of allahabad)

 

🔴 UNIT 4 – Dictionaries, Functions, File Handling & Regular Expressions

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1️⃣ Dictionaries in Python

✅ Definition

A dictionary is an unordered collection of data stored as key–value pairs.

Example:

student = {
    "name": "Amit",
    "age": 20,
    "course": "MCA"
}

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🔹 Characteristics

✔ Key-value based
✔ Mutable
✔ Keys must be unique
✔ Fast access

---

🔹 Accessing Dictionary Elements

print(student["name"])

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🔹 Adding & Updating Values

student["age"] = 21
student["city"] = "Delhi"

---

🔹 Deleting Elements

del student["age"]

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2️⃣ Counting Frequency Using Dictionary

Example:

text = "banana"
freq = {}

for ch in text:
    if ch in freq:
        freq[ch] += 1
    else:
        freq[ch] = 1

print(freq)

Output:

{'b':1, 'a':3, 'n':2}

---

3️⃣ Python Functions

✅ Definition

A function is a block of reusable code.

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🔹 Function Syntax

def function_name(parameters):
    statements
    return value

---

🔹 Types of Arguments

1. Positional

2. Keyword

3. Default

4. Variable-length

---

Example:

def add(a, b=5):
    return a + b

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4️⃣ Passing Function as Argument

def square(x):
    return x*x

def fun(f, value):
    return f(value)

print(fun(square, 5))

---

5️⃣ Lambda Function

Definition:

Anonymous function written in one line.

square = lambda x: x*x
print(square(5))

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6️⃣ Map Function

nums = [1,2,3]
result = list(map(lambda x: x*2, nums))

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7️⃣ List Comprehension

squares = [x*x for x in range(5)]

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8️⃣ File Handling in Python

---

🔹 Opening a File

f = open("data.txt", "r")

File Modes:

Mode	Meaning
r	Read
w	Write
a	Append
r+	Read + Write

---

🔹 Reading File

f.read()

---

🔹 Writing File

f.write("Hello Python")

---

🔹 Closing File

f.close()

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9️⃣ String Processing

Common String Functions:

s.upper()
s.lower()
s.replace()
s.split()
s.strip()

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🔟 Regular Expressions (RegEx)

Definition:

Regular expressions are used for pattern matching.

---

Common Symbols

Symbol	Meaning
.	Any character
^	Start of string
$	End of string
*	Zero or more
+	One or more
[a-z]	Range

---

Example:

import re
pattern = re.search("python", "I love python")

📘 PAPER 2: OBJECT ORIENTED PROGRAMMING WITH PYTHON UNIT 3 – Classes, Objects & OOP Concepts (university of allahabad)

 

🔴 UNIT 3 – Classes, Objects & OOP Concepts

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1️⃣ Class and Object

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✅ Class

A class is a blueprint or template used to create objects.

📌 It defines:

• Data members (variables)

• Member functions (methods)

Example:

class Student:
    def show(self):
        print("This is a student")

---

✅ Object

An object is an instance of a class.

s1 = Student()
s1.show()

---

🔹 Key Difference

Class	Object
Blueprint	Real-world entity
Logical	Physical
No memory	Uses memory

---

2️⃣ Abstract Data Type (ADT)

✅ Definition

An ADT defines:

• What operations are to be performed

• Not how they are implemented

It focuses on behavior, not implementation.

Example:

• Stack

• Queue

• List

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3️⃣ Classes and Objects in Python

Example Program:

class Person:
    def __init__(self, name):
        self.name = name

    def display(self):
        print("Name:", self.name)

p = Person("Rahul")
p.display()

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4️⃣ Constructor (__init__)

Definition:

A constructor is a special method used to initialize objects.

Example:

class Student:
    def __init__(self, roll):
        self.roll = roll

---

5️⃣ Features of OOP in Python

---

🔹 1. Encapsulation

Binding data and methods together.

class Bank:
    def __init__(self):
        self.__balance = 5000

✔ Data hiding
✔ Security

---

🔹 2. Abstraction

Showing essential features and hiding internal details.

Achieved using:

• Abstract classes

• Interfaces

---

🔹 3. Inheritance

Child class inherits properties of parent class.

class Parent:
    def show(self):
        print("Parent")

class Child(Parent):
    pass

---

Types of Inheritance:

1. Single

2. Multiple

3. Multilevel

4. Hierarchical

5. Hybrid

---

🔹 4. Polymorphism

Same function behaves differently.

Example:

print(len("Python"))
print(len([1,2,3]))

---

6️⃣ Abstract Class

Definition:

An abstract class contains abstract methods (methods without body).

Using abc module:

from abc import ABC, abstractmethod

class Shape(ABC):
    @abstractmethod
    def area(self):
        pass

---

7️⃣ Scope in Python

Types of Scope:

1. Local

2. Global

3. Non-local

4. Built-in

---

Example:

x = 10  # global

def fun():
    x = 5  # local
    print(x)

---

8️⃣ Multithreading in Python

---

✅ Definition

Multithreading means executing multiple threads simultaneously.

---

Is Multithreading Supported in Python?

✔ Yes
❌ But limited due to GIL (Global Interpreter Lock)

---

Example:

import threading

def display():
    print("Hello")

t = threading.Thread(target=display)
t.start()

---

Advantages:

• Faster execution

• Better CPU utilization

• Useful in I/O operations

---

Disadvantages:

• Complex debugging

• GIL limits performance

📘 PAPER 2: OBJECT ORIENTED PROGRAMMING WITH PYTHON UNIT 2 – Python Data Structures (university of allahabad)

 

🔴 UNIT 2 – Python Data Structures

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1️⃣ Introduction to Python Data Structures

✅ What is a Data Structure?

A data structure is a way to store, organize, and manage data efficiently so that operations can be performed easily.

Python provides built-in data structures, such as:

• List

• Tuple

• Set

• Dictionary

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2️⃣ Python Data Types (Revisited)

🔹 Mutable Data Types

Data that can be changed after creation.

• List

• Dictionary

• Set

🔹 Immutable Data Types

Data that cannot be changed.

• Integer

• Float

• String

• Tuple

---

3️⃣ Python Lists

✅ Definition

A list is an ordered, mutable collection of elements.

Example:

numbers = [10, 20, 30, 40]

---

🔹 List Characteristics

✔ Ordered
✔ Mutable
✔ Allows duplicate values
✔ Indexed

---

🔹 List Operations

Accessing elements

print(numbers[0])

Slicing

numbers[1:3]

Adding elements

numbers.append(50)

Removing elements

numbers.remove(20)

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4️⃣ List Slicing

Syntax:

list[start : end : step]

Example:

a = [1,2,3,4,5]
print(a[1:4])

Output:

[2, 3, 4]

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5️⃣ Indexing

• Positive indexing → Left to right

• Negative indexing → Right to left

a = [10, 20, 30]
print(a[-1])   # 30

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6️⃣ List Concatenation

a = [1,2]
b = [3,4]
c = a + b

Output:

[1,2,3,4]

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7️⃣ Searching in Python

🔹 Linear Search

Searches element one by one.

def linear_search(lst, key):
    for i in lst:
        if i == key:
            return True
    return False

✔ Simple
❌ Slow for large data

---

🔹 Binary Search

Works only on sorted lists

def binary_search(arr, key):
    low = 0
    high = len(arr)-1
    while low <= high:
        mid = (low+high)//2
        if arr[mid] == key:
            return True
        elif arr[mid] < key:
            low = mid+1
        else:
            high = mid-1
    return False

✔ Fast
✔ Efficient

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8️⃣ Inductive Function Definition

Meaning:

A function defined using:

1. Base case

2. Recursive case

Example:

Factorial

def fact(n):
    if n == 0:
        return 1
    return n * fact(n-1)

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9️⃣ Sorting Techniques

🔹 Selection Sort

Steps:

1. Find minimum

2. Swap with first element

3. Repeat

def selection_sort(arr):
    for i in range(len(arr)):
        min = i
        for j in range(i+1, len(arr)):
            if arr[j] < arr[min]:
                min = j
        arr[i], arr[min] = arr[min], arr[i]

---

🔹 Insertion Sort

def insertion_sort(arr):
    for i in range(1, len(arr)):
        key = arr[i]
        j = i - 1
        while j >= 0 and key < arr[j]:
            arr[j+1] = arr[j]
            j -= 1
        arr[j+1] = key

---

🔟 In-Place Sorting

Definition:

Sorting without using extra memory.

✔ Selection Sort
✔ Insertion Sort

---

1️⃣1️⃣ Tuples

Definition:

Tuple is an ordered but immutable collection.

t = (10, 20, 30)

✔ Faster than list
✔ Used for fixed data

---

1️⃣2️⃣ Mutability Concept

Data Type	Mutable
List	Yes
Tuple	No
String	No
Dictionary	Yes

---

1️⃣3️⃣ Programs Using Lists

Find Maximum Element

lst = [4, 8, 2, 9]
print(max(lst))

Find Minimum Element

print(min(lst))

Find Mean

mean = sum(lst) / len(lst)

📘 PAPER 2: OBJECT ORIENTED PROGRAMMING WITH PYTHON (MCA547) UNIT -1 Object Oriented Programming & Python Basics (university of allahabad)

 

🔴 UNIT 1 Object Oriented Programming & Python Basics

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1️⃣ Introduction to Object-Oriented Programming (OOP)

✅ What is OOP?

Object-Oriented Programming is a programming approach that organizes software using objects and classes instead of functions and logic.

✅ Objective of OOP

• Code reusability

• Data security

• Easy maintenance

• Real-world modeling

---

2️⃣ Basic Concepts of OOP

🔹 1. Object

An object is a real-world entity that has:

• Properties (data)

• Behavior (methods)

📌 Example:

car = Car()

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🔹 2. Class

A class is a blueprint for creating objects.

📌 Example:

class Car:
    def start(self):
        print("Car started")

---

🔹 3. Encapsulation

Wrapping data and methods together.

✔ Data hiding
✔ Security

Example:

class Student:
    def __init__(self):
        self.__marks = 90

---

🔹 4. Abstraction

Showing only essential details and hiding internal implementation.

✔ Achieved using abstract classes

---

🔹 5. Inheritance

One class acquires properties of another.

class Child(Parent):
    pass

---

🔹 6. Polymorphism

Same function behaves differently.

Example:

print(len("Hello"))
print(len([1,2,3]))

---

3️⃣ Benefits of OOP

✔ Code reusability
✔ Modularity
✔ Easy debugging
✔ Security
✔ Real-world modeling

---

4️⃣ Applications of OOP

• Software development

• Web applications

• Game development

• AI & ML

• Mobile apps

• GUI applications

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5️⃣ Algorithms and Programming

✅ Algorithm

A finite set of steps to solve a problem.

Characteristics:

• Finite

• Unambiguous

• Effective

• Input & Output defined

---

6️⃣ Introduction to Python

✅ Features of Python

• Easy syntax

• Interpreted language

• Platform independent

• Object-oriented

• Large standard library

---

7️⃣ Structure of Python Program

# Comment
print("Hello World")

Components:

1. Comments

2. Statements

3. Indentation

4. Functions

---

8️⃣ Variables in Python

Definition:

A variable is a container to store data.

x = 10
name = "Python"

---

9️⃣ Data Types in Python

Built-in Data Types

Type	Example
int	10
float	10.5
str	"Hello"
bool	True
list	[1,2,3]
tuple	(1,2)
set	{1,2}
dict	{"a":1}

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🔟 Operators in Python

Types of Operators:

1. Arithmetic (+, -, *, /)

2. Relational (>, <, ==)

3. Logical (and, or, not)

4. Assignment (=, +=)

5. Bitwise (&, |)

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1️⃣1️⃣ Control Flow in Python

🔹 Conditional Statements

if a > b:
    print("A is greater")
else:
    print("B is greater")

---

🔹 Looping Statements

For Loop

for i in range(5):
    print(i)

While Loop

i = 0
while i < 5:
    print(i)
    i += 1

📘 PAPER 1: THEORY OF COMPUTATION (MCA546) UNIT 5: TURING MACHINES & UNDECIDABILITY (university of allahabad )

 

🔴 UNIT 5: TURING MACHINES & UNDECIDABILITY

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1️⃣ Turing Machine (TM)

✅ Definition

A Turing Machine is the most powerful abstract machine used to model computation.

It can solve any problem that is algorithmically computable.

---

✅ Components of Turing Machine

A Turing Machine is a 7-tuple:

M = (Q, Σ, Γ, δ, q₀, B, F)

Where:

• Q → Finite set of states

• Σ → Input alphabet

• Γ → Tape alphabet

• δ → Transition function

• q₀ → Initial state

• B → Blank symbol

• F → Final (accepting) states

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✅ Structure of TM

A Turing Machine has:

• Infinite tape

• Read/Write head

• Finite control

• Move Left / Right / Stay

---

2️⃣ Instantaneous Description (ID)

✅ Definition

An Instantaneous Description (ID) represents the current state of the Turing Machine.

Format:

$$α q β$$

Where:

• α → Tape content to left of head

• q → Current state

• β → Tape content under and right of head

---

✅ Example

101q011

Means:

• Head is reading 0

• Current state = q

• Tape contains 101011

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3️⃣ Language Accepted by Turing Machine

A language L is said to be accepted by a TM if:

• TM halts in accepting state for every string in L

• TM may loop for strings not in L

---

4️⃣ Turing Computability of Functions

A function is Turing computable if:

• There exists a TM that computes it

• TM halts for all valid inputs

---

Important Fact

All recursive functions are Turing computable

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5️⃣ Equivalence of Turing Machine and Recursive Functions

Theorem:

A function is Turing computable if and only if it is partial recursive.

This proves:

• Turing Machine

• Recursive Functions

• Lambda Calculus

➡ All are equivalent models of computation

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6️⃣ Recursively Enumerable (RE) Languages

✅ Definition

A language L is recursively enumerable if:

• There exists a Turing Machine that accepts L

• TM may not halt for strings not in L

---

✅ Types of Languages

Language Type	Description
Recursive	TM halts for all inputs
Recursively Enumerable	TM may loop
Non-RE	Not accepted by any TM

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7️⃣ Recursively Decidable Languages

✅ Definition

A language is recursive (decidable) if:

• TM halts for all inputs

• Gives YES or NO answer

---

Relationship

Recursive ⊂ Recursively Enumerable

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8️⃣ Undecidability

✅ Definition

A problem is undecidable if:

• No algorithm exists to solve it for all inputs

• TM cannot decide it

---

9️⃣ Undecidability of Type-0 Grammar

Type-0 grammars generate:

• Recursively enumerable languages

Problem:

Whether a given string belongs to a Type-0 grammar?

Result:

❌ Undecidable

Because:

• Equivalent to Halting Problem

• No general algorithm exists

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🔟 Church–Turing Thesis

✅ Statement

Any function that can be computed by an algorithm can be computed by a Turing Machine.

---

⚠ Important Points

• Not a theorem (cannot be proved)

• Based on strong evidence

• Foundation of computer science

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1️⃣1️⃣ Halting Problem

✅ Problem Statement

Given a program P and input I, determine:

Will P halt or run forever?

---

✅ Result

🚫 Halting Problem is Undecidable

---

✅ Proof Idea (Simple)

• Assume halting problem is decidable

• Construct a contradictory machine

• Leads to logical contradiction

• Hence undecidable

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1️⃣2️⃣ Importance of Halting Problem

• Shows limits of computation

• Basis of undecidability theory

• Used in compiler design & verification