Lecture 4 : Sets and Dictionary in Python | DSA in Python

Many programmers face situations where their code becomes slow because it repeatedly searches through large lists to find values. When working with thousands or even millions of records, checking items one by one can take a lot of time and reduce performance. This article helps solve that problem by explaining Sets and Dictionary in Python collections and showing how these data structures improve data retrieval speed.

What Are Sets and Dictionary in Python?

Python provides several built-in data structures for storing and managing data. While lists and tuples store data in a sequence, sets and dictionaries use a different system that allows much faster searching and access to data.

A Python set is an unordered collection of unique elements in Python. Sets are changeable, meaning that after you create a set, you can add or remove objects. However, values inside a set have to be immutable, like numbers, strings or tuples.

A Python Dictionary is a mutable data structure that holds data in key-value pair form. The key must be unique and unchangeable . The value can be any data type : integers , strings , lists , other dictionaries , etc .

Sets and Dictionary in Python Example

# Quick declaration of sets and dictionaries

sample_set = {1, 2, 3, 4, 5}

sample_dict = {

    "name": "Joe",

    "age": 10,

    "city": "Paris"

}

In this example, the set stores a collection of unique numbers, while the dictionary stores information using keys and values.

In DSA in Python, sets and dictionaries are very important because they provide fast ways to store, search, and manage data. Instead of checking items one by one like a list, these structures use hashing to find values quickly. Because of their speed and flexibility, sets and dictionaries are widely used in algorithms, data processing tasks, and real-world software applications. Learning how they work helps you build faster and more efficient programs..

How do Sets and Dictionary in Python Optimise Lookups?

The primary advantage of using a set or a dictionary over a list lies in how elements are found inside system memory. A list requires sequential scanning from the very first element to the last, leading to time consumption that increases linearly with data size.

Sets and dictionaries in Python eliminate sequential searching entirely by implementing a technique called hashing. In simple terms, they act as hash maps in Python, where a mathematical hash function maps data elements directly to fixed memory slots.

Sets and Dictionary in Python Operations

Building a functional command over Sets and dictionaries in Python requires a deep understanding of their basic built-in methods, constructors, and item manipulation protocols.

Operations for Python Sets

You can initialize an empty set by invoking the explicit set() constructor. Using raw curly brackets without items ({}) creates a dictionary by default, so the constructor is mandatory for empty sets.

• Adding items: Use the .add() method to pass an element into the set. Duplicates are filtered out automatically.

• Removing items: Use the .remove() method to eliminate a specific element. If the value does not exist, it triggers an error.

• Membership testing: Use the in operator to verify if an item exists within the set instantaneously.

Python

# Initializing and altering a set
my_set = {1, 2, 3, 4, 5, 6, 7}
my_set.add(8)        # Result: {1, 2, 3, 4, 5, 6, 7, 8}
my_set.remove(7)     # Result: {1, 2, 3, 4, 5, 6, 8}

# Testing membership
print(6 in my_set)   # Returns: True

Operations for Python Dictionaries

Dictionaries store elements as explicit key-value pairs separated by a colon, wrapped entirely in curly brackets.

• Accessing values: Access entries by specifying the target key name inside standard square brackets [] or by calling the .get() method.

• Modifying/Adding values: Assign a value to an existing key to update it, or declare a completely new index key to append a pair.

• Removing values: Use the del keyword or call .pop() with the key name to clear specific entries. The .clear() method empties the dictionary entirely.

Python

# Operating a Python dictionary
my_dict = {"name": "Joe", "age": 10, "city": "Paris"}

# Accessing an item
print(my_dict["name"])  # Returns: 'Joe'

# Adding a new key-value pair
my_dict["new_key"] = "new_value"

# Deleting an entry
del my_dict["new_key"]

Mathematical Sets and Dictionary in Python

One of the main purposes of sets in Python DSA is comparing or combining different groups of data records. Python provides built-in methods to perform standard mathematical set operations effortlessly.

1. Union

The union operation combines all elements from both sets, discarding any duplicate entries that appear in both collections.

Python

set1 = {1, 3, 5, 6}
set2 = {1, 2, 3, 4}
print(set1.union(set2))  # Returns: {1, 2, 3, 4, 5, 6}

2. Intersection

The intersection operation targets and returns only the common elements present simultaneously in both datasets. This is highly useful for identifying mutual attributes, friends, or values.

Python

print(set1.intersection(set2))  # Returns: {1, 3}

3. Difference

The difference operation isolates elements that reside in the primary set but are completely absent from the comparison set.

Python

print(set1.difference(set2))  # Returns: {5, 6}

4. Subset Validation

You can check if an entire dataset is completely enclosed within another collection by running a boolean validation test.

False

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