DCA3104 PYTHON PROGRAMMING APRIL 2025

Description

SESSION	APRIL 2025
PROGRAM	BACHELOR OF COMPUTER APPLICATIONS (BCA)
SEMESTER	5
COURSE CODE & NAME	DCA3104 PYTHON PROGRAMMING

 

 

SET-I

 

 

Q1. a) Explain mutable and immutable datatypes of python.

1. b) How do  membership and  identity  operators  work? Explain with example          5+5    

Ans 1.

Understanding Data Types in Python

In Python, every value has a data type, and based on the ability to change the value without altering the object’s identity, data types are classified into mutable and immutable. Understanding this distinction is essential for proper memory management and behavior prediction of variables during program execution.

Mutable Data Types

A mutable data type allows changes to its content after the object has been created. This means we can modify, add, or remove elements without changing the reference (i.e., memory location) of the

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Q2. a)  How  instance  variables  are  different  from  class variables?

1. b) Explain the use of following string functions: – upper(), lower(), isdigit(), isalpha(), split(), join() with example. 5+5

Ans 2.

1. a) How Instance Variables Are Different from Class Variables

Understanding Instance Variables

Instance variables are variables that are defined within a class but are specific to each object or instance of that class. They are created inside a constructor or any instance method using the self keyword and hold data that is unique for each instance.

For example:

class Student:

 

 

Q3. a) What is list? Explain insert() and append() methods with example.

1. b) How to create private and protected variables in class? Explain its importance. 5+5

Ans 3.

1. Definition of List in Python

A list in Python is an ordered, mutable, and heterogeneous collection that can hold a variety of object types, such as integers, strings, and even other lists. Lists are defined using square brackets.

Example:

my_list = [1, 2, 3, ‘Python’]

Python lists are dynamic and support operations such as addition, deletion, indexing, slicing, and iteration.

 

SET-II

 

Q4. a) How do variable length and keyword arguments works? Explain with program.

1. b) Explain differences between remove(), discard( ) and pop( ) method for deleting elements from set. 5+5

Ans 4.

1. Understanding Variable-Length Arguments

In Python, functions can be defined to accept a variable number of arguments using *args for non-keyworded and **kwargs for keyworded arguments. These are useful when the number of inputs is unknown during function definition.

The *args syntax is used to send a variable number of non-keyword arguments to a function. Internally, it converts the arguments into a tuple. For example:

def add_numbers(*args):

total = 0

 

Q5.  What  is  an  exception  handling?  How  do  you  handle multiple exceptions in python? Explain with example. 10

Ans 5.

Understanding Exception Handling in Python

Exception handling in Python is a mechanism to gracefully manage run-time errors. These errors, known as exceptions, occur during execution and can interrupt the flow of a program. Python provides the try-except block to handle such errors and continue program execution instead of crashing.

The try block contains code that might raise an exception. If an error occurs, it is caught by the

 

Q6. a) How to handle missing data using pandas? Explain dropna() and fillna() methods.

1. b) What are DDL and DML commands? Explain. 5+5

Ans 6.

1. Handling Missing Data in Pandas

In real-world data analysis, datasets often contain missing values. Pandas offers efficient tools to handle such data using methods like dropna() and fillna(). These methods allow either removing or imputing missing entries to maintain data integrity.

dropna() Method

The dropna() method is used to remove rows or columns containing missing (NaN) values. By default, it drops rows.

Example: