Unit-4 Python Collections and Library

Python Collections and Library

Python Collections and Library.

Python Tuples: A Comprehensive Guide

A tuple is an ordered, immutable collection in Python that stores multiple items.
It is similar to a list but has fixed values, meaning we cannot modify, add, or remove items after creation.

Tuples are used when:

✅ We want data to remain unchanged.
✅ We need faster execution (tuples are quicker than lists).
✅ We need a hashable object (tuples can be used as dictionary keys).

📍 Key Characteristics of Tuples.

Ordered → Elements retain their position.
Immutable → Cannot modify elements after creation.
Heterogeneous → Can store multiple data types.
Faster than lists → Tuples have better performance.
Allow Duplicates → Unlike sets, tuples can have repeated values.
Can be used as dictionary keys → Because tuples are immutable.

📍 Properties of Tuples

✅ 1. Ordered

The order of elements remains fixed.

fruits = ("apple", "banana", "cherry")
print(fruits[0])  # apple
print(fruits[1])  # banana

✅ 2. Immutable
Cannot change elements after creation.

numbers = (1, 2, 3) numbers[0] = 10 # ❌ TypeError: 'tuple' object does not support item assignment

✅ 3. Can Store Mixed Data Types.

data = ("John", 25, 5.8, True) print(data)

✅ 4. Allows Duplicate Values.

numbers = (1, 2, 3, 1, 2, 3) print(numbers) # (1, 2, 3, 1, 2, 3)

✅ 5. Can be Nested.

nested_tuple = (("a", "b"), (1, 2, 3)) print(nested_tuple)

📍 Creating a Tuple.
Tuples can be created using parentheses () or the tuple() function.
🔹 Syntax
tuple_name = (element1, element2, element3, …)
🔹 Examples

# Tuple of integers numbers = (10, 20, 30, 40) # Tuple of strings fruits = ("apple", "banana", "cherry") # Mixed data types mixed = (1, "hello", 3.14, True) # Nested tuple (tuple inside a tuple) nested = ((1, 2, 3), ("a", "b", "c")) # Empty tuple empty_tuple = () print(numbers) print(fruits) print(mixed) print(nested) print(empty_tuple)

📍 Single-Element Tuples.

A tuple with one element must have a trailing comma ,.

single = (5,) # ✅ Tuple not_a_tuple = (5) # ❌ Just an integer print(type(single)) # Output: print(type(not_a_tuple)) # Output:

📍 Indexing in Tuples

Tuple elements are indexed starting from 0 (like lists).

Element "Apple" "Banana" "Cherry"

Index 0 1 2

Index -3 -2 -1

fruits = ("apple", "banana", "cherry") print(fruits[0]) # Apple print(fruits[-1]) # Cherry print(fruits[1]) # Banana

📍 Slicing in Tuples
Tuples support slicing, allowing us to extract specific portions.
🔹 Syntax
tuple[start:end:step]
start → Beginning index (default 0).
end → Exclusive stopping index.
step → Skips elements (default 1).

numbers = (10, 20, 30, 40, 50, 60, 70) print(numbers[1:4]) # (20, 30, 40) print(numbers[:3]) # (10, 20, 30) print(numbers[3:]) # (40, 50, 60, 70) print(numbers[::2]) # (10, 30, 50, 70) print(numbers[::-1]) # (70, 60, 50, 40, 30, 20, 10) (Reverse)

📍 Changing Tuples (Workarounds).

Tuples cannot be modified, but we can convert them to lists.

✅ 1. Changing Values.

fruits = ("apple", "banana", "cherry") # Convert tuple to list, modify, and convert back temp_list = list(fruits) temp_list[1] = "orange" fruits = tuple(temp_list) print(fruits) # ('apple', 'orange', 'cherry')

✅ 2. Adding Elements.

numbers = (10, 20, 30) # Adding an element by creating a new tuple numbers = numbers + (40,) print(numbers) # (10, 20, 30, 40)

✅ 3. Removing Elements.

fruits = ("apple", "banana", "cherry") temp_list = list(fruits) temp_list.remove("banana") fruits = tuple(temp_list) print(fruits) # ('apple', 'cherry')

📍 Tuple Methods.
✅ 1. count() – Count Occurrences.

numbers = (1, 2, 2, 3, 2, 4) print(numbers.count(2)) # 3

✅ 2. index() – Find First Occurrence

fruits = ("apple", "banana", "cherry", "banana") print(fruits.index("banana")) # 1

Packing & Unpacking Tuples

✅ Tuple Packing

person = ("Alice", 30, "Engineer") print(person)

✅ Tuple Unpacking

name, age, job = person print(name) # Alice print(age) # 30 print(job) # Engineer

📍 Iterating Over Tuples.

✅ Using a for Loop

fruits = ("apple", "banana", "cherry") for fruit in fruits: print(fruit)

✅ Using while Loop

index = 0 while index < len(fruits): print(fruits[index]) index += 1

Checking for Elements.

fruits = ("apple", "banana", "cherry") print("banana" in fruits) # True print("orange" in fruits) # False

Using Tuples as Dictionary Keys

coordinates = { (10, 20): "Point A", (30, 40): "Point B" } print(coordinates[(10, 20)]) # "Point A"

Deleting a Tuple

fruits = ("apple", "banana", "cherry") del fruits # Deletes the tuple completely

Tuple vs List Comparison

Feature Tuple List

Mutable? ❌ No ✅ Yes

Speed ✅ Fast ❌ Slower

Syntax () []

Methods Few More

Uses Fixed data Dynamic data

Built-in Functions in Tuples

Function Description Example

Python Sets: A Comprehensive Guide

A set in Python is an unordered, mutable, and unique collection of elements.
Unlike lists and tuples, sets do not allow duplicate values and are defined using curly braces {}.
✅ Why Use Sets?
To remove duplicates from a collection.
To perform mathematical operations like union, intersection, and difference.
To store unique elements efficiently.

📍 1. Declaring a Set
Sets are declared using curly braces {} or the set() constructor.
✅ 1. Creating a Set

fruits = {"apple", "banana", "cherry"} print(fruits) # Output: {'banana', 'cherry', 'apple'} (Order is random)

✅ 2. Creating an Empty Set.

empty_set = set() # ✅ Correct way print(type(empty_set)) # Output:

Properties of Sets
✅ 1. Unordered – Elements do not maintain a fixed position.
✅ 2. Unique – Duplicate values are automatically removed.
✅ 3. Mutable – We can add or remove elements.
✅ 4. Cannot Contain Mutable Items – Lists and dictionaries cannot be set elements.

📍 Accessing Set Data
Since sets are unordered, we cannot access elements using an index like lists or tuples.
✅ 1. Using a Loop

fruits = {"apple", "banana", "cherry"} for fruit in fruits: print(fruit)

✅ 2. Checking for Membership (in Operator).

print("banana" in fruits) # Output: True print("orange" in fruits) # Output: False

📍 Set Methods.
Python provides several built-in set methods for adding, removing, and modifying sets.

🔹 1. add() – Adds an Element.

fruits = {"apple", "banana"} fruits.add("cherry") print(fruits) # {'apple', 'banana', 'cherry'}

Duplicate values are ignored:

fruits.add("banana") print(fruits) # {'apple', 'banana', 'cherry'} (No duplicate added)

🔹 2. clear() – Removes All Elements

fruits = {"apple", "banana", "cherry"} fruits.clear() print(fruits) # Output: set()

🔹 3. copy() – Creates a Copy

original_set = {1, 2, 3} new_set = original_set.copy() print(new_set) # Output: {1, 2, 3}

Changes to new_set won’t affect original_set.

new_set.add(4) print(original_set) # Output: {1, 2, 3} (unchanged)

🔹 4. discard() – Removes an Element (No Error if Not Found).

fruits = {"apple", "banana", "cherry"} fruits.discard("banana") print(fruits) # {'apple', 'cherry'} fruits.discard("orange") # No error

🔹 5. pop() – Removes and Returns a Random Element.

numbers = {10, 20, 30, 40} removed_item = numbers.pop() print(removed_item) # Randomly removes an element print(numbers)

🔹 6. remove() – Removes an Element (Throws Error if Not Found)

fruits = {"apple", "banana", "cherry"} fruits.remove("banana") print(fruits) # {'apple', 'cherry'} fruits.remove("orange") # ❌ KeyError: 'orange'

🔹 7. union() – Combines Two Sets (Returns a New Set)

A = {1, 2, 3} B = {3, 4, 5} result = A.union(B) print(result) # Output: {1, 2, 3, 4, 5}

🔹 8. update() – Adds Elements from Another Set.

A = {1, 2, 3} B = {3, 4, 5} A.update(B) # Modifies A print(A) # {1, 2, 3, 4, 5}

📍 Set Operations.
Python sets support mathematical operations like union, intersection, and difference.

✅ 1. | (Union) – Combines Sets.

A = {1, 2, 3} B = {3, 4, 5} print(A | B) # {1, 2, 3, 4, 5}

✅ 2. & (Intersection) – Common Elements.

print(A & B) # {3}

✅ 3. - (Difference) – Elements in A but not in B

print(A - B) # {1, 2}

✅ 4. ^ (Symmetric Difference) – Elements Not in Both Sets

print(A ^ B) # {1, 2, 4, 5}

📍 Iterating Over a Set.

✅ Using a for Loop.

fruits = {"apple", "banana", "cherry"} for fruit in fruits: print(fruit)

📍 Checking if a Set is a Subset, Superset, or Disjoint

✅ 1. issubset() – Checks if A is a Subset of B

A = {1, 2} B = {1, 2, 3, 4} print(A.issubset(B)) # True

✅ 2. issuperset() – Checks if A is a Superset of B.

print(B.issuperset(A)) # True

✅ 3. isdisjoint() – Checks if Two Sets Have No Common Elements

C = {5, 6, 7} print(A.isdisjoint(C)) # True

Frozen Sets (Immutable Sets).
A frozen set is an immutable version of a set.

fs = frozenset({1, 2, 3}) print(fs) # fs.add(4) # ❌ AttributeError: 'frozenset' object has no attribute 'add'

Set vs List vs Tuple.

Feature Set List Tuple

Mutable? ✅ Yes ✅ Yes ❌ No

Duplicates? ❌ No ✅ Yes ✅ Yes

Ordered? ❌ No ✅ Yes ✅ Yes

Indexing? ❌ No ✅ Yes ✅ Yes

Performance 🔥 Fastest 🔸 Medium 🔸 Medium

Python Dictionary: A Comprehensive Guide.

A dictionary in Python is a mutable, unordered collection of key-value pairs.
Unlike lists and tuples, which are indexed by numbers, dictionaries use keys to access values.
✅ Why Use Dictionaries?
Fast lookups (searching for a value using a key is efficient).
Flexible keys (can be strings, numbers, or even tuples).
Stores structured data (like JSON format).

📍 1. Creating a Dictionary
Dictionaries are defined using curly braces {} with key-value pairs separated by colons :.
✅ 1. Creating a Dictionary with Values

student = { "name": "John", "age": 22, "course": "Computer Science" } print(student)

🔹 Keys – "name", "age", "course"
🔹 Values – "John", 22, "Computer Science"

✅ 2. Creating an Empty Dictionary.

empty_dict = {} # ✅ Correct way print(type(empty_dict)) # Output:

✅ 3. Using dict() Constructor.

student = dict(name="John", age=22, course="CS") print(student) # {'name': 'John', 'age': 22, 'course': 'CS'}

2. Accessing Dictionary Elements.
We use keys to access dictionary values.

✅ 1. Accessing Using [] (Bracket Notation)

student = {"name": "Alice", "age": 21} print(student["name"]) # Output: Alice

🔹 KeyError: If the key does not exist:

print(student["address"]) # ❌ KeyError: 'address'

✅ 2. Accessing Using get() Method (Safer)

print(student.get("name")) # Output: Alice print(student.get("address", "Not Found")) # Output: Not Found

📍 Adding Elements to a Dictionary

student["address"] = "New York" print(student) # {'name': 'Alice', 'age': 21, 'address': 'New York'}

📍 Updating Dictionary Values

student["age"] = 22 print(student) # {'name': 'Alice', 'age': 22, 'address': 'New York'}

📍 Removing Elements from a Dictionary.
Python provides several methods to remove dictionary elements.

✅ 1. pop() – Removes a Specific Key

student = {"name": "Alice", "age": 21, "course": "CS"} age = student.pop("age") print(student) # {'name': 'Alice', 'course': 'CS'} print(age) # 21

🔹 If key does not exist, pop() throws a KeyError:

student.pop("address") # ❌ KeyError: 'address'

✅ 2. popitem() – Removes the Last Inserted Key-Value Pair.

student = {"name": "Alice", "age": 21, "course": "CS"} item = student.popitem() print(student) # {'name': 'Alice', 'age': 21} print(item) # ('course', 'CS') (last added pair)

🔹 If dictionary is empty, popitem() raises an error.

✅ 3. del – Deletes a Specific Key

del student["age"] print(student) # {'name': 'Alice'}

🔹 Deleting the Entire Dictionary

del student print(student) # ❌ NameError: name 'student' is not defined

✅ 4. clear() – Removes All Elements.

student = {"name": "Alice", "age": 21} student.clear() print(student) # Output: {}

📍 Dictionary Methods
Python provides useful dictionary methods for accessing and modifying data.

🔹 1. get() – Retrieves a Value (Safer than [])

student = {"name": "Alice", "age": 21} print(student.get("name")) # Alice print(student.get("address", "Not Found")) # Not Found

🔹 2. pop() – Removes a Key and Returns its Value.

student = {"name": "Alice", "age": 21} age = student.pop("age") print(student) # {'name': 'Alice'} print(age) # 21

🔹 3. popitem() – Removes and Returns the Last Inserted Pair.

student = {"name": "Alice", "age": 21} item = student.popitem() print(item) # ('age', 21) print(student) # {'name': 'Alice'}

🔹 4. clear() – Removes All Elements

student.clear() print(student) # {}

🔹 5. copy() – Creates a Copy.

student = {"name": "Alice", "age": 21} copy_student = student.copy() print(copy_student) # {'name': 'Alice', 'age': 21}

🔹 Modifying copy_student does not affect the original dictionary:

copy_student["age"] = 22 print(student) # {'name': 'Alice', 'age': 21} (unchanged) print(copy_student) # {'name': 'Alice', 'age': 22}

📍 Looping Through a Dictionary
✅ 1. Looping Over Keys

student = {"name": "Alice", "age": 21} for key in student: print(key) # name, age

✅ 2. Looping Over Values

for value in student.values(): print(value) # Alice, 21

✅ 3. Looping Over Key-Value Pairs

for key, value in student.items(): print(f"{key}: {value}")

📍 Dictionary vs List vs Tuple

Feature Dictionary List Tuple

Mutable? ✅ Yes ✅ Yes ❌ No

Key-Based Access? ✅ Yes ❌ No ❌ No

Indexed Access? ❌ No ✅ Yes ✅ Yes

Order Maintained? ✅ Yes (Python 3.7+) ✅ Yes ✅ Yes

Duplicates? ❌ No (Unique Keys) ✅ Yes ✅ Yes

Introduction to NumPy and Pandas in Python.

NumPy and Pandas are two essential libraries in Python for data analysis, numerical computing, and scientific computing.
✅ Why Use NumPy and Pandas?
NumPy provides high-performance array operations.
Pandas is used for data manipulation and analysis (works well with tabular data like Excel or CSV).
Faster than Python lists because they use vectorized operations.
Supports large datasets efficiently.

📍 Introduction to NumPy
NumPy (Numerical Python) is a high-performance library for numerical computations in Python.
✅ Features of NumPy:
Provides multi-dimensional arrays (ndarray).
Supports mathematical operations (addition, multiplication, statistics).
Optimized for performance and memory efficiency.
Used in Machine Learning, Data Science, and AI.
📍 Installing and Importing NumPy
✅ Installation (if not installed)

pip install numpy

✅ Importing NumPy

import numpy as np

📍 Creating a NumPy Array.
A NumPy array is similar to a Python list but faster and more efficient.
✅ Creating a 1D NumPy Array from a List

import numpy as np arr = np.array([1, 2, 3, 4, 5]) print(arr) # Output: [1 2 3 4 5] print(type(arr)) # Output:

✅ Creating a 2D NumPy Array

arr_2d = np.array([[1, 2, 3], [4, 5, 6]]) print(arr_2d)

📍 4. NumPy Statistical Methods
NumPy provides powerful statistical functions for numerical analysis.

🔹 1. mean() – Calculates the Mean (Average)
The mean is the sum of all values divided by the total number of values.

import numpy as np data = np.array([10, 20, 30, 40, 50]) mean_value = np.mean(data) print(mean_value) # Output: 30.0

🔹 2. median() – Finds the Middle Value
The median is the middle value of a sorted dataset.

data = np.array([1, 3, 5, 7, 9]) median_value = np.median(data) print(median_value) # Output: 5

🔹 If the dataset has an even number of values, the median is the average of the two middle numbers.

🔹 3. mode() – Finds the Most Frequent Value
NumPy does not have a built-in mode() function, but we can use SciPy for mode calculation.

from scipy import stats data = np.array([1, 2, 2, 3, 3, 3, 4, 5]) mode_value = stats.mode(data) print(mode_value.mode) # Output: [3]

🔹 The mode is the value that appears most frequently in the dataset.

🔹 4. std() – Standard Deviation
Standard Deviation measures how spread out the values are in the dataset.

data = np.array([10, 20, 30, 40, 50]) std_dev = np.std(data) print(std_dev) # Output: 14.142135623730951

🔹 Higher Standard Deviation → More spread out data.
🔹 Lower Standard Deviation → Data is closer to the mean.

🔹 5. var() – Variance
Variance is the square of Standard Deviation and measures how far values are from the mean.

data = np.array([10, 20, 30, 40, 50]) variance = np.var(data) print(variance) # Output: 200.0

📍 Applying NumPy Methods on a List
We can use NumPy functions on a numerical list.
✅ Example:

import numpy as np data_list = [10, 20, 30, 40, 50] # Convert list to NumPy array data = np.array(data_list) # Compute statistical values print("Mean:", np.mean(data)) print("Median:", np.median(data)) print("Standard Deviation:", np.std(data)) print("Variance:", np.var(data))

Element	"Apple"	"Banana"	"Cherry"
Index	0	1	2
Index	-3	-2	-1

Feature	Tuple	List
Mutable?	❌ No	✅ Yes
Speed	✅ Fast	❌ Slower
Syntax	()	[]
Methods	Few	More
Uses	Fixed data	Dynamic data

Feature	Set	List	Tuple
Mutable?	✅ Yes	✅ Yes	❌ No
Duplicates?	❌ No	✅ Yes	✅ Yes
Ordered?	❌ No	✅ Yes	✅ Yes
Indexing?	❌ No	✅ Yes	✅ Yes
Performance	🔥 Fastest	🔸 Medium	🔸 Medium

Feature	Dictionary	List	Tuple
Mutable?	✅ Yes	✅ Yes	❌ No
Key-Based Access?	✅ Yes	❌ No	❌ No
Indexed Access?	❌ No	✅ Yes	✅ Yes
Order Maintained?	✅ Yes (Python 3.7+)	✅ Yes	✅ Yes
Duplicates?	❌ No (Unique Keys)	✅ Yes	✅ Yes

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Unit-4 Python Collections and Library

Python Collections and Library

Python Collections and Library.

Python Tuples: A Comprehensive Guide

A tuple is an ordered, immutable collection in Python that stores multiple items.

It is similar to a list but has fixed values, meaning we cannot modify, add, or remove items after creation.

Tuples are used when:

✅ We want data to remain unchanged.

✅ We need faster execution (tuples are quicker than lists).

✅ We need a hashable object (tuples can be used as dictionary keys).

📍 Key Characteristics of Tuples.

Ordered → Elements retain their position.

Immutable → Cannot modify elements after creation.

Heterogeneous → Can store multiple data types.

Faster than lists → Tuples have better performance.

Allow Duplicates → Unlike sets, tuples can have repeated values.

Can be used as dictionary keys → Because tuples are immutable.

📍 Properties of Tuples

✅ 1. Ordered

The order of elements remains fixed.

fruits = ("apple", "banana", "cherry") print(fruits[0]) # apple print(fruits[1]) # banana

✅ 2. Immutable

Cannot change elements after creation.

numbers = (1, 2, 3) numbers[0] = 10 # ❌ TypeError: 'tuple' object does not support item assignment

✅ 3. Can Store Mixed Data Types.

data = ("John", 25, 5.8, True) print(data)

✅ 4. Allows Duplicate Values.

numbers = (1, 2, 3, 1, 2, 3) print(numbers) # (1, 2, 3, 1, 2, 3)

✅ 5. Can be Nested.

nested_tuple = (("a", "b"), (1, 2, 3)) print(nested_tuple)

📍 Creating a Tuple.

Tuples can be created using parentheses () or the tuple() function.

🔹 Syntax

tuple_name = (element1, element2, element3, …)

🔹 Examples

📍 Single-Element Tuples.

A tuple with one element must have a trailing comma ,.

single = (5,) # ✅ Tuple not_a_tuple = (5) # ❌ Just an integer print(type(single)) # Output: print(type(not_a_tuple)) # Output:

📍 Indexing in Tuples

Tuple elements are indexed starting from 0 (like lists).

fruits = ("apple", "banana", "cherry") print(fruits[0]) # Apple print(fruits[-1]) # Cherry print(fruits[1]) # Banana

📍 Slicing in Tuples

Tuples support slicing, allowing us to extract specific portions.

🔹 Syntax

tuple[start:end:step]

start → Beginning index (default 0).

end → Exclusive stopping index.

step → Skips elements (default 1).

numbers = (10, 20, 30, 40, 50, 60, 70) print(numbers[1:4]) # (20, 30, 40) print(numbers[:3]) # (10, 20, 30) print(numbers[3:]) # (40, 50, 60, 70) print(numbers[::2]) # (10, 30, 50, 70) print(numbers[::-1]) # (70, 60, 50, 40, 30, 20, 10) (Reverse)

📍 Changing Tuples (Workarounds).

Tuples cannot be modified, but we can convert them to lists.

✅ 1. Changing Values.

fruits = ("apple", "banana", "cherry") # Convert tuple to list, modify, and convert back temp_list = list(fruits) temp_list[1] = "orange" fruits = tuple(temp_list) print(fruits) # ('apple', 'orange', 'cherry')

✅ 2. Adding Elements.

numbers = (10, 20, 30) # Adding an element by creating a new tuple numbers = numbers + (40,) print(numbers) # (10, 20, 30, 40)

✅ 3. Removing Elements.

fruits = ("apple", "banana", "cherry") temp_list = list(fruits) temp_list.remove("banana") fruits = tuple(temp_list) print(fruits) # ('apple', 'cherry')

📍 Tuple Methods.

✅ 1. count() – Count Occurrences.

numbers = (1, 2, 2, 3, 2, 4) print(numbers.count(2)) # 3

✅ 2. index() – Find First Occurrence

fruits = ("apple", "banana", "cherry", "banana") print(fruits.index("banana")) # 1

Packing & Unpacking Tuples

✅ Tuple Packing

person = ("Alice", 30, "Engineer") print(person)

✅ Tuple Unpacking

name, age, job = person print(name) # Alice print(age) # 30 print(job) # Engineer

📍 Iterating Over Tuples.

✅ Using a for Loop

fruits = ("apple", "banana", "cherry") for fruit in fruits: print(fruit)

✅ Using while Loop

index = 0 while index < len(fruits): print(fruits[index]) index += 1

Checking for Elements.

fruits = ("apple", "banana", "cherry") print("banana" in fruits) # True print("orange" in fruits) # False

Using Tuples as Dictionary Keys

coordinates = { (10, 20): "Point A", (30, 40): "Point B" } print(coordinates[(10, 20)]) # "Point A"

Deleting a Tuple

fruits = ("apple", "banana", "cherry") del fruits # Deletes the tuple completely

Tuple vs List Comparison

Feature Tuple List Mutable? ❌ No ✅ Yes Speed ✅ Fast ❌ Slower Syntax () [] Methods Few More Uses Fixed data Dynamic data

Tuples can be created using parentheses `()` or the `tuple()` function.

A tuple with one element must have a trailing comma `,`.

Tuple elements are indexed starting from `0` (like lists).

`start` → Beginning index (default `0`).

`end` → Exclusive stopping index.

`step` → Skips elements (default `1`).

✅ 1. `count()` – Count Occurrences.

✅ 2. `index()` – Find First Occurrence

✅ Using a `for` Loop

✅ Using `while` Loop

Feature Tuple List

Mutable? ❌ No ✅ Yes

Speed ✅ Fast ❌ Slower

Syntax () []

Methods Few More

Uses Fixed data Dynamic data

Unlike lists and tuples, sets do not allow duplicate values and are defined using curly braces `{}`.

Sets are declared using curly braces `{}` or the `set()` constructor.

✅ 1. Unordered – Elements do not maintain a fixed position.
✅ 2. Unique – Duplicate values are automatically removed.
✅ 3. Mutable – We can add or remove elements.
✅ 4. Cannot Contain Mutable Items – Lists and dictionaries cannot be set elements.

✅ 2. Checking for Membership (`in` Operator).

🔹 1. `add()` – Adds an Element.

🔹 2. `clear()` – Removes All Elements

🔹 3. `copy()` – Creates a Copy

Changes to `new_set` won’t affect `original_set`.

🔹 4. `discard()` – Removes an Element (No Error if Not Found).

🔹 5. `pop()` – Removes and Returns a Random Element.

🔹 6. `remove()` – Removes an Element (Throws Error if Not Found)

🔹 7. `union()` – Combines Two Sets (Returns a New Set)

🔹 8. `update()` – Adds Elements from Another Set.

✅ 1. `|` (Union) – Combines Sets.

✅ 2. `&` (Intersection) – Common Elements.

✅ 3. `-` (Difference) – Elements in A but not in B

✅ 4. `^` (Symmetric Difference) – Elements Not in Both Sets

✅ Using a `for` Loop.

✅ 1. `issubset()` – Checks if A is a Subset of B

✅ 2. `issuperset()` – Checks if A is a Superset of B.

✅ 3. `isdisjoint()` – Checks if Two Sets Have No Common Elements

Feature Set List Tuple

Mutable? ✅ Yes ✅ Yes ❌ No

Duplicates? ❌ No ✅ Yes ✅ Yes

Ordered? ❌ No ✅ Yes ✅ Yes

Indexing? ❌ No ✅ Yes ✅ Yes

Performance 🔥 Fastest 🔸 Medium 🔸 Medium