DEV Community: Seenivasan A

Python-Sets

Seenivasan A — Thu, 04 Jun 2026 17:47:14 +0000

Set is used to store a collection of items with the following properties.

No duplicate elements. If you try to insert the same item again, it is ignored because sets store only unique values.
An unordered collection. When we access all items, they are accessed without any specific order and we cannot access items using indexes as we do in lists.
Internally use hashing that makes set efficient for search, insert and delete operations. It gives a major advantage over a list for problems with these operations.
Mutable, meaning we can add or remove elements after their creation, the individual elements within the set cannot be changed directly.

s = {10, 50, 20}
print(s)
print(type(s))

Output
{10, 50, 20}

Method |Shortcut |Description

add() Adds an element to the set
clear() Removes all the elements from the set
copy() Returns a copy of the set
difference() - Returns a set containing the difference between two or more sets
difference_update() -= Removes the items in this set that are also included in another, specified set
discard() Remove the specified item
intersection() & Returns a set, that is the intersection of two other sets
intersection_update() &= Removes the items in this set that are not present in other, specified set(s)
isdisjoint() Returns True if NO items of this set is present in another set
issubset() <= Returns True if all items of this set is present in another set
< Returns True if all items of this set is present in another, larger set
issuperset() >= Returns True if all items of another set is present in this set
> Returns True if all items of another, smaller set is present in this set
pop() Removes an element from the set
remove() Removes the specified element
symmetric_difference() ^ Returns a set with the symmetric differences of two sets
symmetric_difference_update() ^= Inserts the symmetric differences from this set and another
union() | Return a set containing the union of sets
update() |= Update the set with the union of this set and others

Check unique and Immutable
Sets cannot have duplicate values. While you cannot modify the individual elements directly, you can still add or remove elements from the set.

# a set cannot have duplicate values
s = {"Geeks", "for", "Geeks"}
print(s)

# values of a set cannot be changed
s[1] = "Hello"
print(s)

Output

{'Geeks', 'for'}
TypeError: 'set' object does not support item assignment

Python frozenset

frozenset is an immutable version of a set.
Like sets, it contains unique, unordered, unchangeable elements.
Unlike sets, elements cannot be added or removed from a frozenset.

a = frozenset([1, 2, 3, 4])
b = frozenset([3, 4, 5, 6])

c = a.copy()
print(c)

print(a.union(b))
print(a.intersection(b))
print(a.difference(b))
print(a.symmetric_difference(b))

Output
frozenset({1, 2, 3, 4})
frozenset({1, 2, 3, 4, 5, 6})
frozenset({3, 4})
frozenset({1, 2})
frozenset({1, 2, 5, 6})

Method |Shortcut |Description

copy() Returns a shallow copy
difference() - Returns a new frozenset with the difference
intersection() & Returns a new frozenset with the intersection
isdisjoint() Returns True if there is NO intersection between two frozensets
issubset() <= / < Returns True if this frozenset is a (proper) subset of another
issuperset() >= / > Returns True if this frozenset is a (proper) superset of another
symmetric_difference() ^ Returns a new frozenset with the symmetric differences
union() | Returns a new frozenset containing the union

Internal working of Set

Python sets are implemented using a hash table, similar to dictionaries, where the set elements are stored as keys with dummy values. If multiple elements map to the same index, Python handles the collision by storing them in the same bucket.

The diagram below illustrates how sets internally manage collisions to support efficient insertion, deletion, and lookup operations.

Each index in the hash table represents a possible hash value where elements are stored based on their computed hash key.
When two elements map to the same index, they are linked together using a linked list, forming a chain at that position.
This chaining mechanism helps efficiently handle collisions, allowing multiple elements to exist at the same hash index without overwriting each other.

References
https://www.geeksforgeeks.org/python/sets-in-python/
https://www.w3schools.com/python/python_sets.asp

Python-List,Tuples

Seenivasan A — Wed, 03 Jun 2026 17:25:48 +0000

List

A List is an ordered collection of items that can store different types of data such as numbers, strings, or even other lists. Lists are mutable, which means their contents can be modified after creation.

Lists are one of 4 built-in data types in Python used to store collections of data, the other 3 are Tuple, Set, and Dictionary, all with different qualities and usage.
List is a built-in data structure used to store an ordered collection of items. They are dynamic, resizable and capable of storing multiple data types.
Mutable: list elements can be changed, updated, added, or removed after the list is created.
Ordered: elements maintain the order in which they are inserted.
Index-based: elements are accessed using their position, starting from index 0.

a = [1, 2, 3]
print(a)

b = ["apple", "banana"]
print(b)

a = list((1, 2, 3, 'apple', 4.5))  
print(a)

b = list("GFG")
print(b)

Output
[1, 2, 3]
['apple', 'banana']
[1, 2, 3, 'apple', 4.5]
['G', 'F', 'G']

Method Description

append() Adds an element at the end of the list
clear() Removes all the elements from the list
copy() Returns a copy of the list
count() Returns the number of elements with the specified value
extend() Add the elements of a list (or any iterable), to the end of the current list
index() Returns the index of the first element with the specified value
insert() Adds an element at the specified position
pop() Removes the element at the specified position
remove() Removes the item with the specified value
reverse() Reverses the order of the list
sort() Sorts the list

Use Lists When:

Data needs frequent updates.
Elements need to be added or removed.
Working with dynamic datasets.

Tuples

A Tuple is also an ordered collection of items, but unlike lists, tuples are immutable. Once a tuple is created, its values cannot be changed.

Tuples are similar to lists, but unlike lists, they cannot be changed after their creation.
Can hold elements of different data types.
These are ordered, heterogeneous and immutable

Tuples can store elements of different data types, such as integers, strings, lists and dictionaries, within a single structure.

Tuples can be concatenated using the + operator. This operation combines two or more tuples to create a new tuple

Method Description

count() Returns the number of times a specified value occurs in a tuple
index() Searches the tuple for a specified value and returns the position of where it was found

tup = ()
print(tup)

# Using String
tup = ('Geeks', 'For')
print(tup)

# Using List
li = [1, 2, 4, 5, 6]
print(tuple(li))

# Using Built-in Function
tup = tuple('Geeks')
print(tup)

Output
()
('Geeks', 'For')
(1, 2, 4, 5, 6)
('G', 'e', 'e', 'k', 's')

Slicing of Tuple
Slicing a tuple means creating a new tuple from a subset of elements of the original tuple. The slicing syntax is tuple[start:stop:step].

tup = tuple('GEEKSFORGEEKS')
print(tup[1:])
print(tup[::-1])
print(tup[4:9])

Output
('E', 'E', 'K', 'S', 'F', 'O', 'R', 'G', 'E', 'E', 'K', 'S')
('S', 'K', 'E', 'E', 'G', 'R', 'O', 'F', 'S', 'K', 'E', 'E', 'G')
('S', 'F', 'O', 'R', 'G')

Use Tuples When:
Data should remain unchanged.
Storing fixed information.
Better performance is required.

Lists and Tuples are fundamental data structures in Python. Lists provide flexibility because they can be modified, while Tuples provide safety and better performance because they cannot be changed. Choosing the right data structure depends on the application's requirements. Understanding these concepts helps programmers write more efficient and organized Python code.

References
https://www.w3schools.com/python/python_lists.asp
https://www.geeksforgeeks.org/python/python-lists/
https://www.geeksforgeeks.org/python/python-tuples/
https://www.w3schools.com/python/python_tuples.asp

AI in Data Analytics

Seenivasan A — Mon, 25 May 2026 19:02:59 +0000

Why AI is Used in Data Analytics

AI is used because it saves time and reduces manual work. Data Analysts often spend many hours cleaning data and writing code. AI tools help complete these tasks faster and more accurately.

Benefits of AI include:

Faster analysis
Better accuracy
Easy automation
Quick visualization
Faster code generation

AI also helps beginners understand coding concepts more easily.

Recently, I attended a webinar conducted by Brand Monk Academy about how Artificial Intelligence (AI) is used in Data Analytics. The session explained how Data Analysts use AI tools to make their work easier, faster, and more efficient.

The webinar mainly focused on four important topics:

Data Cleaning
Data Visualization
Automation
Data Generation

These concepts are very important in modern Data Analytics because companies work with huge amounts of data every day.

Data Cleaning

Data cleaning is the process of correcting or removing unwanted data from a dataset. Before analyzing data, it is important to make sure the data is accurate and organized properly.

The speakers explained that datasets often contain:

Missing values
Duplicate records
Incorrect information
Empty rows

Cleaning the data helps improve accuracy and makes analysis easier. AI tools can now help automate this process and reduce manual work.

Data Visualization

Data visualization means presenting data using charts, graphs, and dashboards. It helps people understand information more clearly.

The webinar showed different visualization methods such as:

Bar charts
Pie charts
Line graphs

Visualization helps businesses identify trends and make better decisions. AI tools can also suggest suitable charts automatically based on the data.

Automation

Automation is one of the biggest advantages of AI in Data Analytics. Instead of doing repetitive tasks manually, analysts can use automation tools and code to complete tasks quickly.

The speakers also explained how Excel Developer Options and VBA macros are used for automation.

Some automated tasks include:

Data formatting
Removing duplicates
Creating reports
Performing calculations automatically

Automation saves time and improves productivity.

The speakers also explained how Excel Developer Options and VBA macros are used for automation.

Sub RemoveDuplicates()

    Range("A1:A10").RemoveDuplicates Columns:=1, Header:=xlNo

    MsgBox "Duplicate values removed successfully!"

End Sub

After the code, continue with this explanation:

This VBA macro removes duplicate values from Excel data automatically and displays a completion message. VBA helps Data Analysts automate repetitive tasks and improve productivity while working with large datasets.

Google Colab and Gemini AI

One of the most interesting topics in the webinar was using Google Colab and Gemini AI for Data Analytics.

Google Colab allows users to write and run Python code online without installing software. Gemini AI helps users generate code, create datasets, analyze data, and visualize information using simple prompts.

The speakers demonstrated how AI can:

Generate Python code
Create sample datasets
Analyze CSV files
Generate charts and graphs
Explain coding errors
Automate repetitive tasks

For example, users can simply ask Gemini AI to:

“Create a bar chart for sales data”

and AI can generate the required code automatically.

This makes learning Data Analytics easier for beginners and improves productivity for professionals.

Will AI Replace Jobs?
One important discussion in the webinar was whether AI will replace Data Analyst jobs.

The speakers explained that AI may automate some repetitive tasks, but it will mainly improve jobs rather than completely replace them. Human thinking, decision-making, creativity, and business understanding are still very important.

AI works as a supporting tool for analysts by helping them:

Work faster
Reduce errors
Improve productivity
Focus on important decisions

Therefore, learning AI tools can help Data Analysts improve their skills and grow in their careers.

Conclusion

The webinar provided a simple understanding of how AI is transforming Data Analytics. Concepts like data cleaning, visualization, automation, and AI-assisted coding are becoming essential in modern workplaces.

Learning tools like Google Colab and Gemini AI can help students and professionals improve their technical knowledge and work more efficiently in the future.

Certificate

CRACKING CODING INTERVIEW

Seenivasan A — Sun, 24 May 2026 17:59:51 +0000

Technical interviews at top companies like Google, Amazon, and Meta are often centered around coding and algorithm-based problem-solving. Many students feel nervous about these interviews because they are very different from regular academic exams. However, understanding the interview process can make preparation easier and more effective.
In most software engineering interviews, candidates are given one or two coding problems and are expected to explain their thought process while solving them. Interviewers are not only checking whether the final answer is correct, but also evaluating how candidates think, communicate, and approach difficult problems.

What Interviewers Evaluate

Interviewers generally focus on five important areas:

1. Analytical Skills

- This is one of the most important aspects of coding interviews. Interviewers observe how candidates analyze a problem, identify patterns, and choose efficient solutions.
- For example, if a candidate is asked to find duplicate elements in an array, a brute-force solution may work, but an optimized approach using a hash set demonstrates stronger problem-solving ability.

Example

nums = [1, 2, 3, 2, 4, 5, 1]
seen = set()

for num in nums:
    if num in seen:
        print("Duplicate:", num)
    else:
        seen.add(num)

Output

Duplicate: 2
Duplicate: 1

2. Coding Skills

Writing clean and readable code is very important. Interviewers expect proper variable names, structured logic, and fewer syntax mistakes.

Good coding style includes:

Meaningful variable names
Proper indentation
Error handling
Optimized logic

Even small mistakes are acceptable if the candidate explains their logic clearly.

3. Computer Science Fundamentals

Strong knowledge of data structures and algorithms is extremely useful in interviews. Concepts like arrays, linked lists, stacks, queues, trees, graphs, sorting, and searching are commonly asked.

For example, Binary Search is a popular interview topic because it demonstrates understanding of efficient searching techniques.

f(n)=log2n

Binary Search reduces the search space repeatedly, making it much faster than linear search.

Important Data Structures and Algorithms Topics for Coding Interviews

4. Experience and Projects

Interviewers also evaluate the projects candidates have worked on. Real-world projects demonstrate practical knowledge and problem-solving ability.

For example, developing a Doctor Appointment Booking System with features like:

Appointment scheduling
Patient management
Database handling
Authentication systems

shows strong practical exposure to development concepts.

5. Communication Skills and Culture Fit

Candidates who explain their ideas clearly usually perform better. Interviewers prefer people who can collaborate well with teams and communicate effectively.

During interviews:

Think aloud
Explain your approach
Discuss alternative solutions
Ask clarifying questions when necessary

Good communication often creates a positive impression.

Why Do Companies Use Coding Interviews?

Many candidates question why companies focus heavily on algorithms and whiteboard coding. There are several reasons behind this approach.

Problem-Solving Ability

Companies believe that candidates who solve difficult algorithmic problems are usually strong logical thinkers. Smart problem solvers often adapt quickly to new technologies and challenges.

Understanding Fundamentals

Basic knowledge of computer science concepts helps developers choose better solutions in real-world applications. For example, knowing when to use a hash map or binary search tree can improve software performance significantly.

Whiteboard Coding Encourages Discussion

Although whiteboard coding feels artificial, it helps interviewers understand how candidates think. It also encourages communication and explanation rather than relying completely on syntax or IDE suggestions.

Important Reality About Interviews

One important thing to understand is that interview evaluation is relative. Interviewers compare your performance with other candidates who solved the same question.

Getting a difficult question does not mean failure. If a problem is hard for you, it is likely difficult for everyone else too.

Interviewers mainly observe:

Your thinking process
Problem-solving approach
Optimization ability
Communication skills
Confidence under pressure

Coding interviews are not perfect, but they remain one of the most common methods companies use to evaluate software engineers. Success in these interviews depends not only on technical knowledge but also on communication, confidence, and structured thinking.
Consistent practice, strong fundamentals, and real project experience can greatly improve interview performance. Instead of fearing coding interviews, candidates should treat them as opportunities to demonstrate their problem-solving abilities and technical skills.

*Reference *

Strings and Loops in Python

Seenivasan A — Fri, 22 May 2026 12:58:30 +0000

String

Strings are sequence of characters written inside quotes. It can include letters, numbers, symbols and spaces. Python does not have a separate character type.
Strings are immutable, meaning their values cannot be changed after creation. Any modification to a string creates a new string instead of altering the original one.

Common String Methods

Python string methods is a collection of in-built Python functions that operates on strings.

upper()
lower()
find()
split()
strip()
format()
join()

Discussed problems

Reverse a String
Password Authentication
Parse csv "java","1","2","3".."6"
Convert uppercase to lowercase

Examples

Parse csv "java","1","2","3".."6"

data = input("Enter CSV data: ")
values = data.split(",")
total = 0
for i in values:
    i = i.strip('"')
    if i.isdigit():
        total += int(i)

print("Total =", total)

Input-"java","1","2","3","4","5","6"

Output-Total = 21

Loop

Loops are used to execute a block of code repeatedly until a condition is met or all items in a sequence are processed. The main types are For loops (iterating over sequences) and While loops (executing code based on a condition).
break is used to exit a for loop or a while loop, whereas continue is used to skip the current block, and return to the "for" or "while" statement.

While Loop Syntax

while condition:
statement

For Loop Syntax

for variable in range(start, stop, step):
statement

Discussed problems

Sum of (N) numbers
Multiplication table
Print even numbers

Examples
Sum of (N) numbers

def sum_of_numbers(n):
    i=1
    sum=0
    while i<=n:
        sum+=i
        i+=1
    print("The sum of first",n,"numbers is:",sum)

n=int(input("Enter the number of terms: "))
sum_of_numbers(n)

Input-> N=10

Output-> Sum=55

Algorithms Discussed

Merge Sort

Binary Search And Linear Search

References
https://www.geeksforgeeks.org/dsa/merge-sort/

https://tutorialhorizon.com/algorithms/linear-search-vs-binary-search/

GitLab

Seenivasan A — Fri, 15 May 2026 17:38:57 +0000

GitLab was found by Dmitriy Zaporozhets and Valery Sizov in October 2011. It was distributed under MIT license and the stable version of GitLab is 10.4 released in January 22, 2018.

Gitlab is a service that provides remote access to Git repositories.

These additional features include managing the sharing of code between different people, bug tracking, wiki space and other tools for 'social coding'.

Git: A local tool installed on your computer to track code changes.

GitHub / Bitbucket: Remote websites used to host and share your Git code.

GitLab: A complete, all-in-one platform that hosts your code and automates your entire development process.

Why GitLab?
GitLab is a github like service that organizations can use to provide internal management of git repositories. It is a self hosted Git-repository management system that keeps the user code private and can easily deploy the changes of the code.

GitLab is a user friendly web interface layer on top of Git, which increases the speed of working with Git.

GitLab - Git Commands

$ git --version
$ git status
$ git add *
$ git push origin branch-name
$ git pull origin NAME-OF-BRANCH -u
$git checkout branch-name
$ git merge master

Authentication
Id-(@username) in the profile.
Create personal access tokens. When you use 2FA, you can use these tokens to access the GitLab API.

To Be Discuss?

GitLab provides its own Continuous Integration (CI) system for managing the projects and provides user interface along with other features of GitLab.
GitLab - SSH Key Setup

References
https://www.tutorialspoint.com/gitlab/gitlab_introduction.htm
https://suresoft.dev/knowledge-hub/continuous-integration/gitlab-ci/
https://docs.gitlab.com/administration/get_started/