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Python generators are a type of iterable, like lists or tuples, but instead of storing all the
values in memory, they generate values on the fly. This makes them more memory efficient,
especially when dealing with large datasets.
A generator is defined using a function that contains one or more yield statements.
When the function is called, it returns a generator object without executing the function. Each
time the generator's __next__() method is called (or when using it in a loop), the
function runs until it hits a yield statement, which returns the value to the caller and
pauses the function's execution, maintaining its state for the next call.
Here’s a simple example of a generator:
1 def count_up_to(n):
2 count = 1
3 while count <= n:
4 yield count
5 count += 1

In this example, calling count_up_to(5) returns a generator that will yield numbers
from 1 to 5. You can iterate over it using a loop:
1 for number in count_up_to(5):
2 print(number)

This will output:

1 1
2 2
3 3
4 4
5 5

Generators are particularly useful for reading large files, streaming data, or any situation
where you want to produce a series of values without holding them all in memory at once.
Python decorators are a powerful and flexible way to modify or enhance the behavior of
functions or methods. They allow you to wrap another function, modifying its behavior
without permanently modifying the function itself.
A decorator is typically defined as a function that takes another function as an argument and
returns a new function that usually extends or alters the behavior of the original function. This
is often done using the @decorator_name syntax above the function definition.
Here’s a simple example of a decorator:
1 def my_decorator(func):
2 def wrapper():
3 print("Something is happening before the function is called.")
4 func()
5 print("Something is happening after the function is called.")
6 return wrapper
7
8 @my_decorator
9 def say_hello():
10 print("Hello!")
11
12 say_hello()

In this example, when say_hello() is called, it does not just print "Hello!" but also
prints messages before and after the call, demonstrating how the decorator modifies its
behavior.
Decorators can also accept arguments, allowing for more customizable behavior. They are
commonly used in various frameworks and libraries, such as Flask for web applications, to
handle routes, authentication, and more.
Overall, decorators provide a clean and readable way to enhance functions and methods in
Python.
Python Database Connections
Python provides several libraries and modules to connect to various types of databases,
allowing developers to perform database operations such as querying, inserting, updating,
and deleting data. Here are some key points about Python database connections:
1. Database Drivers: Different databases require specific drivers to connect. Commonly
used libraries include:
SQLite: Built-in support with the sqlite3 module.
PostgreSQL: Use psycopg2 or asyncpg for asynchronous operations.
MySQL: Use mysql-connector-python or PyMySQL .
Oracle: Use cx_Oracle .
2. Connection Establishment: To establish a connection, you typically need to provide
parameters such as the database name, user credentials, and host information. For
example, using SQLite:
1 import sqlite3
2 connection = sqlite3.connect('example.db')

3. Creating a Cursor: After establishing a connection, you create a cursor object to execute
SQL commands:
1 cursor = connection.cursor()

4. Executing SQL Commands: You can execute SQL commands using the cursor. For
example:
1 cursor.execute('SELECT * FROM table_name')

5. Fetching Results: After executing a query, you can fetch results using methods like

fetchone() , fetchall() , or fetchmany(size) .

6. Transaction Management: Most database operations are transactional. You should

commit your changes using connection.commit() and handle exceptions

properly to roll back transactions if needed.
7. Closing Connections: Always close the cursor and connection when done to free up
resources:
1 cursor.close()
2 connection.close()

8. ORMs: Object-Relational Mappers like SQLAlchemy and Django ORM provide higher-level
abstractions for database interactions, making it easier to work with databases without
writing raw SQL.
9. Error Handling: Use try-except blocks to handle database errors gracefully.

10. Connection Pools: For applications with high database traffic, consider using connection
pooling to manage multiple connections efficiently.
Overall, Python's flexibility and the variety of libraries available make it easy to connect to
and interact with different databases.

Python Object-Oriented Programming (OOP) concepts are fundamental to structuring code

in a way that is modular, reusable, and easier to maintain. Here are the key concepts:
1. Classes: A class is a blueprint for creating objects. It defines a set of attributes and
methods that the created objects (instances) will have.
Example:
1 class Dog:
2 def __init__(self, name):
3 self.name = name

2. Objects: An object is an instance of a class. It contains data (attributes) and methods that
operate on that data.
Example:
1 my_dog = Dog("Buddy")

3. Encapsulation: This principle involves bundling the data (attributes) and methods that
operate on the data within a single unit (class). It restricts direct access to some of the
object's components, which is a means of preventing unintended interference and misuse.
Example:
1 class BankAccount:
2 def __init__(self, balance):
3 self.__balance = balance # Private attribute
4
5 def deposit(self, amount):
6 self.__balance += amount

4. Inheritance: Inheritance allows a class (child class) to inherit attributes and methods from
another class (parent class). This promotes code reusability.
Example:
1 class Animal:
2 def speak(self):
3 return "Animal speaks"
4
5 class Dog(Animal):
6 def bark(self):
7 return "Woof!"

5. Polymorphism: This concept allows methods to do different things based on the object it
is acting upon. It enables the same method name to be used for different types.
Example:
1 class Cat(Animal):
2 def speak(self):
3 return "Meow"
4
5 def animal_sound(animal):
 6 print(animal.speak())
7
8 animal_sound(Dog()) # Outputs: Animal speaks
9 animal_sound(Cat()) # Outputs: Meow

6. Abstraction: Abstraction is the concept of hiding complex implementation details and

showing only the essential features of the object. This can be achieved using abstract
classes and interfaces.
Example:
1 from abc import ABC, abstractmethod
2
3 class Shape(ABC):
4 @abstractmethod
5 def area(self):
6 pass
7
8 class Circle(Shape):
9 def __init__(self, radius):
10 self.radius = radius
11
12 def area(self):
13 return 3.14 * self.radius * self.radius

These concepts form the backbone of OOP in Python, allowing developers to create more
organized and manageable code.
Python iterators are objects that allow you to traverse through a collection, such as lists,
tuples, or dictionaries, without needing to know the underlying structure of the collection.
They implement two main methods defined in the iterator protocol: __iter__() and
__next__() .
1. Iterator Protocol:

An object is considered an iterator if it implements the __iter__() method, which

returns the iterator object itself, and the __next__() method, which returns the
next value from the iterator. When there are no more items to return, __next__()
raises a StopIteration exception.
2. Creating an Iterator:

You can create an iterator from an iterable (like a list) using the iter() function. For
example:
1 my_list = [1, 2, 3]
2 my_iterator = iter(my_list)
3. Using an Iterator:

You can retrieve items from the iterator using the next() function:
1 print(next(my_iterator)) # Outputs: 1
2 print(next(my_iterator)) # Outputs: 2

4. Iteration in Loops:

Python's for loop automatically handles the iterator protocol, making it easy to iterate
through items:
1 for item in my_list:
2 print(item)

5. Custom Iterators:

You can create your own iterator by defining a class with the __iter__() and
__next__() methods. Here’s a simple example:
1 class Countdown:
2 def __init__(self, start):
3 self.current = start
4
5 def __iter__(self):
6 return self
7
8 def __next__(self):
9 if self.current <= 0:
10 raise StopIteration
11 else:
12 self.current -= 1
13 return self.current + 1

6. Benefits:
Iterators provide a way to work with large datasets efficiently, as they generate items on-
the-fly and do not require loading the entire dataset into memory.
Overall, iterators are a powerful feature in Python that enhance the ability to work with
sequences and collections in a memory-efficient manner.
LISP Lists
1 (defun control-structure-example (n)
2 (if (<= n 0)
3 (format t "The number is non-positive: ~A" n)
4 (progn
5 (format t "The number is positive: ~A" n)
6 (if (evenp n)
7 (format t "It's an even number.")
8 (format t "It's an odd number.")))))
9
10 ;; Example usage:
11 (control-structure-example 5) ; Output: The number is positive: 5 It's an odd number.
12 (control-structure-example -3) ; Output: The number is non-positive: -3
13 (control-structure-example 4) ; Output: The number is positive: 4 It's an even number.

This program demonstrates the use of control structures in Lisp, including if , progn ,
and evenp . The function control-structure-example checks if a given
number n is positive, negative, or zero, and determines if it is even or odd.
1 (defun greet (name)
2 "This function takes a name as an argument and prints a greeting."
3 (format t "Hello, ~A! Welcome to the Lisp programming world!~%" name))
4
5 (defun add (a b)
6 "This function takes two numbers and returns their sum."
7 (+ a b))
8
9 (defun factorial (n)
10 "This function computes the factorial of a number n."
11 (if (<= n 1)
12 1
13 (* n (factorial (1- n)))))
14
15 ;; Example usage:
16 (greet "Alice") ; Output: Hello, Alice! Welcome to the Lisp programming world!
17 (format t "The sum of 5 and 3 is: ~A~%" (add 5 3)) ; Output: The sum of 5 and 3 is: 8
18 (format t "The factorial of 5 is: ~A~%" (factorial 5)) ; Output: The factorial of 5 is: 120

This program defines three functions in Lisp: greet , add , and factorial . Each
function demonstrates different aspects of function usage in Lisp, such as taking arguments,
performing calculations, and returning values. The example usage at the end shows how to
call these functions and what output they produce.
Lisp lists are one of the fundamental data structures in the Lisp programming language. A list
in Lisp is a sequence of elements, which can be of any type, including numbers, symbols, or
even other lists. Here are some key points about Lisp lists:
1. Structure: A list is typically represented with parentheses. For example, (1 2 3) is a
list containing three elements: 1, 2, and 3.
2. Cons Cells: Internally, lists are constructed using cons cells. A cons cell is a pair of values,
where the first value is called the "car" (the first element of the list), and the second value
is called the "cdr" (the rest of the list). For example, the list (1 2 3) can be thought of
as a cons cell where the car is 1 and the cdr is (2 3) .
3. Nesting: Lists can contain other lists, allowing for the creation of complex data structures.

For example, ((1 2) (3 4)) is a list containing two sublists.

4. Functions: Lisp provides a variety of built-in functions to manipulate lists, such as:

car : Returns the first element of a list.

cdr : Returns the rest of the list after the first element.
cons : Constructs a new list by adding an element to the front of an existing list.
list : Creates a new list from its arguments.
5. Immutability: In Lisp, lists are immutable, meaning that once a list is created, it cannot be

changed. However, functions like cons can create new lists based on existing ones.
6. Evaluation: Lists are also used to represent code in Lisp, as Lisp is a homoiconic language.
This means that the structure of the code is the same as the structure of the data, allowing
for powerful metaprogramming capabilities.
7. Examples:

Creating a list: (list 1 2 3) produces the list (1 2 3) .

Accessing elements: (car '(1 2 3)) returns 1 , and (cdr '(1 2 3))
returns (2 3) .
Overall, lists are a versatile and essential part of programming in Lisp, serving both as a data
structure for storing collections of items and as a means of representing code.
Lisp sets are a type of data structure used to store unique elements, similar to sets in
mathematics. Here are some key points about sets in Lisp:
1. Uniqueness: Sets in Lisp automatically handle uniqueness, meaning that no duplicate
elements can be stored within a set.
2. Implementation: In Common Lisp, sets can be implemented using hash tables or lists. The

set data type is not built-in, but you can use lists or hash tables to create set-like
behavior.
3. Common Functions:

union : Combines two sets to create a new set that contains all unique elements from
both.
intersection : Creates a new set containing only the elements that are present in
both sets.
difference : Produces a new set with elements from the first set that are not in the
second set.
 subsetp : Checks if one set is a subset of another.
4. Example Using Lists:
To create a simple set using lists, you can use functions to ensure uniqueness:
1 (defun add-to-set (item set)
2 (if (member item set)
3 set
4 (cons item set)))
5
6 (defparameter *my-set* nil)
7 (setf *my-set* (add-to-set 1 *my-set*))
8 (setf *my-set* (add-to-set 2 *my-set*))
9 (setf *my-set* (add-to-set 1 *my-set*)) ; 1 will not be added again

5. Example Using Hash Tables:

You can also use hash tables to represent sets:
1 (defparameter *my-set* (make-hash-table))
2
3 (defun add-to-hash-set (item)
4 (setf (gethash item *my-set*) t))
5
6 (add-to-hash-set 'a)
7 (add-to-hash-set 'b)
8 (add-to-hash-set 'a) ; 'a' will not be added again

6. Performance: Using hash tables for sets generally provides better performance for
membership tests and set operations compared to using lists, especially for larger
datasets.
Sets in Lisp are versatile and can be used for various applications where uniqueness and set
operations are required.
Lisp symbols are fundamental data types in the Lisp programming language, representing
identifiers or names. Here are some key points about Lisp symbols:
1. Definition: A symbol is an atomic data type that consists of a name, which can be used to
represent variables, functions, or any other identifiers in the code.
2. Creation: Symbols are created by simply using a name that starts with a letter and can

include letters, digits, and special characters (like - or * ). For example, my-
variable , foo , and + are all valid symbols.
3. Uniqueness: Each symbol is unique within its namespace. This means that two symbols
with the same name are considered the same, regardless of where they are defined.
4. Interning: When a symbol is created, it is interned in a package, which means it is stored
in a symbol table. This allows for efficient lookup and comparison.
5. Usage: Symbols are often used as variable names, function names, and keys in data
structures. They can also be manipulated as data, making them powerful for
metaprogramming.
6. Equality: In Lisp, symbols can be compared using the eq function, which checks if two
symbols are the same.
7. Global and Local Scope: Symbols can exist in a global scope or be locally scoped within a
function or block, allowing for flexible variable management.
8. Example: Defining a symbol and using it in a function:

1 (defparameter *my-symbol* 'example)

2 (print *my-symbol*) ; Outputs: EXAMPLE

Overall, symbols are a core aspect of Lisp, enabling the creation of dynamic and flexible code
structures.
Structural recursion is a method of defining functions that operate on data structures, such
as lists, by breaking them down into simpler components. This approach leverages the
inherent structure of the data, allowing for elegant and efficient solutions. Here are key points
about structural recursion with lists:
1. Base Case: Every recursive function must have a base case that defines when the
recursion should stop. For lists, a common base case is an empty list.
2. Recursive Case: This part of the function processes the list by examining its first element
(the head) and then recursively calling itself on the remainder of the list (the tail). This
allows the function to build up a solution incrementally.
3. Examples:
Sum of Elements: A function that calculates the sum of all elements in a list can be
defined recursively by adding the first element to the sum of the rest of the list.
Length of a List: To find the length of a list, the function can return 1 (for the head) plus
the length of the tail.
4. Advantages: Structural recursion simplifies the implementation of functions that need to
traverse or manipulate lists, making the code more readable and maintainable.
5. Performance: While structural recursion is often clear and concise, it may lead to stack
overflow errors for very large lists due to deep recursion. Tail recursion optimization can
mitigate this in some languages.
Overall, structural recursion is a powerful technique for working with lists, emphasizing the
natural recursive nature of list structures.
Polymorphism in Haskell refers to the ability of functions and data types to operate on
different types without being explicitly defined for each type. Haskell supports two main
forms of polymorphism: parametric polymorphism and ad-hoc polymorphism.
1. Parametric Polymorphism: This allows functions to be written generically so that they can
handle values uniformly without depending on their type. For example, a function that
takes a list of any type and returns the first element can be defined as follows:
1 head :: [a] -> a
2 head (x:_) = x
3 head [] = error "Empty list"

In this example, a is a type variable that can represent any type, making the head
function applicable to lists of any type.
2. Ad-hoc Polymorphism: This is achieved through type classes, which allow functions to

operate on different types that are instances of the same type class. For example, the Eq
type class allows for equality checks on different types:
1 class Eq a where
2 (==) :: a -> a -> Bool

You can define instances of Eq for different types, such as integers and custom data types,
allowing you to use the (==) operator on any type that is an instance of Eq .
Here’s an example of using ad-hoc polymorphism with a custom data type:
1 data Point = Point Int Int
2
3 instance Eq Point where
4 (Point x1 y1) == (Point x2 y2) = x1 == x2 && y1 == y2

In this case, the Point type can now be compared using the (==) operator because it
has an instance of the Eq type class.
Polymorphism in Haskell enhances code reusability and abstraction, allowing developers to
write more general and flexible code.
In Haskell, strings and characters are fundamental data types used to represent text.
1. Strings:
Strings in Haskell are represented as lists of characters. The type of a string is
[Char] .
 For example, the string "Hello" can be represented as ['H', 'e', 'l', 'l',
'o'] .
Haskell provides various functions to manipulate strings, such as length ,
concat , and map .
Strings are immutable, meaning once a string is created, it cannot be changed.
2. Characters:

Characters in Haskell are represented by the Char type.

A character is defined using single quotes, for example, 'a' , '1' , or '!' .
Haskell provides functions to work with characters, such as isUpper , isLower ,
and toUpper .
3. Common Operations:

You can concatenate strings using the ++ operator.

Accessing individual characters can be done using the !! operator, where indexing
starts at 0.
Haskell also supports string literals and allows for multi-line strings using triple quotes.
4. Performance:
Since strings are lists of characters, operations that involve modifying strings can be less
efficient. For performance-sensitive applications, Haskell provides the Text type from
the Data.Text module, which is more efficient for string manipulation.
Overall, Haskell's treatment of strings and characters emphasizes immutability and functional
programming principles, making it easy to work with text while maintaining safety and clarity
in code.
Data types using patterns refer to the structured ways in which data can be organized and
manipulated in programming languages, especially in functional programming. Here are some
key points about data types using patterns:
1. Definition: Data types define the nature of data that can be stored and manipulated within
a program. Patterns refer to the specific structures or arrangements of data types that
facilitate operations and functions.
2. Primitive Data Types: These are the basic building blocks of data types, including
integers, floats, characters, and booleans. They are often used as the foundation for more
complex data types.
3. Composite Data Types: These types are formed by combining primitive data types.
Common examples include:
Lists: Ordered collections of elements that can be of various types.
Tuples: Fixed-size collections of elements, which can be of different types.
Arrays: Collections of elements of the same type, often used for indexed access.
4. User-Defined Data Types: Programmers can create custom data types to suit specific
needs. Examples include:
Structures: Group different data types together, like a Person structure containing a
name and age.
Classes: Define objects with attributes and methods, encapsulating data and behavior.
5. Pattern Matching: This is a powerful feature in many programming languages that allows
for the deconstruction of data types based on their structure. It simplifies the process of
accessing and manipulating data within complex types.
6. Examples:
In Haskell, pattern matching can be used to define functions that operate on lists or
tuples by specifying how to handle different shapes of data.
In Lisp, functions can be designed to take advantage of the list structure, allowing for
elegant recursive processing.
7. Benefits: Using data types with patterns enhances code readability, maintainability, and
reusability. It allows developers to express complex operations succinctly and clearly,
making it easier to reason about code behavior.
Overall, data types using patterns are integral to effective programming, providing a
framework for organizing and manipulating data efficiently.

Data types in programming define the kind of data that can be stored and manipulated within
a program. Here’s a brief overview of common data types using patterns:
1. Primitive Data Types:

Integer: Represents whole numbers, e.g., 5 , -10 .

Float: Represents decimal numbers, e.g., 3.14 , -0.001 .
Character: Represents single characters, e.g., 'a' , 'Z' .
Boolean: Represents truth values, true or false .
2. Composite Data Types:
 String: A sequence of characters, e.g., "Hello, World!" .
Array: A collection of elements of the same type, e.g., [1, 2, 3] .
List: An ordered collection that can contain elements of different types, e.g., [1,
"two", 3.0] .
3. User-Defined Data Types:

Struct: A composite data type that groups different data types together, e.g., struct
Person { string name; int age; } .
Class: A blueprint for creating objects that encapsulate data and methods, e.g., class
Dog { string breed; void bark(); } .
4. Specialized Data Types:

Null: Represents the absence of a value, e.g., null or None .

Function: Represents a callable block of code, e.g., function add(a, b) {
return a + b; } .
Tuple: An ordered collection of elements that can be of different types, e.g., (1,
"two", 3.0) .
Understanding these data types and their patterns is essential for effective programming and
data manipulation.
It looks like you're interested in understanding polymorphic functions in Haskell.
Polymorphism in Haskell allows functions to operate on different types without being
explicitly defined for each type. There are two main forms of polymorphism in Haskell:
parametric polymorphism and ad-hoc polymorphism.
1. Parametric Polymorphism: This allows you to write functions that can handle values
uniformly without depending on their type. For example, a function that takes a list of any
type and returns the first element can be defined using a type variable:
1 head :: [a] -> a
2 head (x:_) = x
3 head [] = error "Empty list"

Here, a is a type variable that can represent any type, making the head function
applicable to lists of any type.
2. Ad-hoc Polymorphism: This is achieved through type classes, which allow functions to

operate on different types that are instances of the same type class. For example, the Eq
 type class allows for equality checks on different types:
1 class Eq a where
2 (==) :: a -> a -> Bool

You can define instances of Eq for different types, such as integers and custom data
types, allowing you to use the (==) operator on any type that is an instance of Eq .
Here's an example of using ad-hoc polymorphism with a custom data type:
1 data Point = Point Int Int
2
3 instance Eq Point where
4 (Point x1 y1) == (Point x2 y2) = x1 == x2 && y1 == y2

In this case, the Point type can now be compared using the (==) operator because
it has an instance of the Eq type class.
Polymorphism in Haskell enhances code reusability and abstraction, allowing developers to
write more general and flexible code. If you have more questions or need further assistance,
feel free to ask!

PART-A
Python constructors, inheritance, destructors, and Lisp control structures each offer distinct
advantages in programming. Here’s a summary of their benefits:
Python Constructors:
Initialization: Constructors allow for the initialization of object attributes when an instance
of a class is created, ensuring that objects are in a valid state from the start.
Encapsulation: They help encapsulate the setup logic within the class, promoting cleaner
and more organized code.
Flexibility: Constructors can take parameters, allowing for the creation of objects with
varying initial states based on input values.
Inheritance in Python:
Code Reusability: Inheritance allows new classes to inherit attributes and methods from
existing classes, reducing code duplication and promoting reuse.
Hierarchical Classification: It enables the creation of a class hierarchy, making it easier to
model real-world relationships and behaviors.
 Polymorphism: Inheritance supports polymorphism, allowing methods to be overridden in
derived classes, which can lead to more flexible and maintainable code.
Destructors in Python:
Resource Management: Destructors are used to define cleanup behavior when an object
is about to be destroyed, allowing for the release of resources such as file handles or
network connections.
Automatic Cleanup: They ensure that necessary cleanup actions are performed
automatically, reducing the risk of resource leaks and improving program stability.
Custom Behavior: Developers can define custom behavior during object destruction,
tailoring the cleanup process to specific needs.
Lisp Control Structures:
Flexibility: Lisp control structures (like if , cond , and loop ) provide flexible ways to
manage program flow, allowing for complex decision-making and iteration.
Readability: The syntax of Lisp control structures can lead to more readable and
expressive code, especially for recursive algorithms and functional programming
paradigms.
Homoiconicity: Since code is represented as lists, Lisp allows for powerful
metaprogramming capabilities, enabling developers to manipulate code as data and create
dynamic control structures.
These features enhance the capabilities of Python and Lisp, making them powerful tools for
developers.
Polymorphism: Polymorphism is a programming concept that allows objects of different
classes to be treated as objects of a common superclass. It enables a single interface to
represent different underlying forms (data types). The most common use of polymorphism is
in method overriding, where a subclass can provide a specific implementation of a method
that is already defined in its superclass.
Data Abstraction: Data abstraction is the principle of hiding the complex implementation
details of a system and exposing only the essential features to the user. It allows
programmers to interact with data without needing to understand the underlying
complexities. This is often achieved through abstract classes and interfaces.
Encapsulation: Encapsulation is the bundling of data (attributes) and methods (functions) that
operate on the data into a single unit, typically a class. It restricts direct access to some of
the object's components, which is a means of preventing unintended interference and
misuse. Encapsulation promotes data hiding and protects the integrity of the object's state.
Inheritance: Inheritance is a mechanism in object-oriented programming that allows a new
class (subclass) to inherit properties and behaviors (methods) from an existing class
(superclass). This promotes code reusability and establishes a hierarchical relationship
between classes.
Object: An object is an instance of a class that contains data (attributes) and methods
(functions) that operate on that data. Objects are the fundamental building blocks of object-
oriented programming, representing real-world entities or concepts.
Super Class: A superclass (or parent class) is a class that is extended or inherited by one or
more subclasses. It contains common attributes and methods that can be shared among its
subclasses, providing a base for further specialization.
Sub Class: A subclass (or child class) is a class that inherits from a superclass. It can extend
or modify the behavior of the superclass by adding new attributes and methods or overriding
existing ones. Subclasses promote code reuse and facilitate the creation of more specific
types based on a general class.
Modularity: Modularity is the design principle of breaking a program into separate,
independent modules or components, each responsible for a specific functionality. This
enhances code organization, maintainability, and reusability, allowing developers to work on
individual modules without affecting the entire system.
Here are examples of user-defined polymorphic functions, patterns, Lisp data types, and Lisp
symbols:
User-Defined Polymorphic Functions
1. Polymorphic Function Using Type Classes:

1 (defclass shape ()
2 ())
3
4 (defmethod area ((s shape))
5 0)
6
7 (defclass rectangle (shape)
8 ((width :initarg :width :accessor width)
9 (height :initarg :height :accessor height)))
10
11 (defmethod area ((r rectangle))
12 (* (width r) (height r)))
13
14 (defclass circle (shape)
 15 ((radius :initarg :radius :accessor radius)))
16
17 (defmethod area ((c circle))
18 (* pi (expt (radius c) 2)))

Patterns
1. Pattern Matching:

1 (defun describe-thing (x)

2 (cond
3 ((numberp x) (format t "It's a number: ~A" x))
4 ((stringp x) (format t "It's a string: ~A" x))
5 ((listp x) (format t "It's a list: ~A" x))
6 (t (format t "It's something else: ~A" x))))

Lisp Data Types

1. List:

1 (defparameter *my-list* '(1 2 3 4 5))

2. Vector:

1 (defparameter *my-vector* #(1 2 3 4 5))

3. Hash Table:

1 (defparameter *my-hash* (make-hash-table))

2 (setf (gethash 'key1 *my-hash*) 'value1)

4. Structure:

1 (defstruct person
2 name
3 age)

Lisp Symbols
1. Creating Symbols:

1 (defparameter *my-symbol* 'example)

2. Using Symbols:

1 (defparameter *symbol-list* '(apple banana cherry))

3. Comparing Symbols:

1 (defun compare-symbols (sym1 sym2)

2 (if (eq sym1 sym2)
3 (format t "~A and ~A are the same symbol." sym1 sym2)
4 (format t "~A and ~A are different symbols." sym1 sym2)))
These examples illustrate how to define polymorphic functions, use various data types, and
work with symbols in Lisp.