This repository is a personal revision workspace rather than a single production project. It combines Python practice notebooks, data-analysis labs, a Django + React full-stack practice app, Git course exercises, and supporting notes/PDFs used for interview prep and concept review.
The two most substantial areas are:
Fast Labs/: a sequence of lab notebooks covering core Python, file handling, text processing, data analysis, plotting, and introductory ML-style dataset work.Django-Practice/: a full-stack practice project with a Django REST backend and a React + TypeScript + Vite frontend.
This folder contains multiple Jupyter notebooks and small supporting files used for hands-on lab work.
Topics covered across the labs include:
- basic Python problem solving and syntax exercises
- strings, loops, list comprehensions, random values, and sequence manipulation
- inventory/order-history style notebook work
- file I/O with supporting text files such as
orders.txt,spam.txt,reg_no.txt, andtext.txt - regular-expression and text-processing practice
- data analysis with
pandas - plotting and exploratory analysis
- CSV-based exercises using datasets like:
Titanic-Dataset.csvstock_prices.csvQS_2025.csv
Representative files:
Fast Labs/lab2.ipynbFast Labs/lab3.ipynbFast Labs/lab4.ipynbFast Labs/lab-5/lab5_inventory.ipynbFast Labs/lab-5/lab5_ordersHistory.ipynbFast Labs/lab-6/lab6.ipynbFast Labs/lab-7/lab7.ipynbFast Labs/lab-8/lab8.ipynbFast Labs/lab-9/lab9.ipynbFast Labs/lab-10/lab10.ipynbFast Labs/lab-11/lab11.ipynbFast Labs/Solutions.ipynb
In practice, this folder reads like a progression from beginner-to-intermediate Python labs into more data-oriented notebook work.
This is the main application-style section of the repository.
The backend is a Django project using Django REST Framework, django-cors-headers, and PostgreSQL-oriented configuration via environment variables.
Current backend structure and behavior:
- Django project:
client_portal - apps:
clientsschoolManagement
- dependencies defined in
pyproject.toml - environment-driven settings using
python-decouple - CORS enabled for the frontend dev server at
http://localhost:5173
Implemented backend models:
Client- name, email, phone, company, company size, industry, website
- address fields
- account manager foreign key
- annual revenue, notes, priority level, active flag
SchoolStudentTeacherSubject
Implemented API patterns:
clientsapp:GET /api/clients/POST /api/clients/
schoolManagementapp via DRF router:/api/management/schools//api/management/students//api/management/teachers//api/management/subjects/
- custom actions currently present:
/api/management/schools/ids_only//api/management/subjects/subject_names/
There is also a notes.ipynb file in this folder documenting the author’s learning process around:
- Django project/app structure
- models and migrations
- serializers and DRF views
- React frontend integration
- CORS setup
- Tailwind setup notes
The frontend is a React app built with:
- React 19
- TypeScript
- Vite
- Tailwind CSS
- React Router
react-hook-form,zod, and Radix/shadcn-style UI components
Current frontend pages/components include:
- a simple landing page with routes into the practice areas
ClientPage.tsx- fetches client data from Django
- posts new client records to the backend
management.tsx- dynamic route-based management page
- builds forms for school, teacher, student, and subject flows
- calls backend management endpoints
ChatBot.tsx- UI-focused mock chat layout with sidebar/navbar components
This part of the repo is clearly a practice integration project for learning how a React frontend communicates with a Django REST backend.
This folder contains revision notebooks and documents focused on fundamentals.
Covered material includes:
- NumPy basics
- Pandas basics
- Matplotlib basics
- markdown usage in Jupyter
- Python strings, lists, sets, dictionaries, and tuples
- database concepts such as schemas, joins, keys, and normalization
- data structures and OOP review
- linked list/BST examples
- extra course/reference notebooks on linear systems and arrays
It also includes several PDFs for interview-style revision and concept review.
This appears to be practice work from a Git/GitHub course.
Contents include:
- simple text exercises in
gitone/ - basic HTML fragments/pages in
gittwo/andgitthree/ - notes in
Notes.txt
This section is separate from the Python/Django work and functions more like course artifact storage.
At the repository root there are additional study/reference assets, including:
genai_interview_crash_course.md- PDF notes on React/React Router
- machine learning Q&A/reference PDFs
If you are trying to understand this repository quickly, this order makes the most sense:
README.mdFast Labs/Django-Practice/Basics - Revision/GItHub Course/
If your focus is the application project specifically, start here instead:
Django-Practice/Backend/client_portal/client_portal/settings.pyDjango-Practice/Backend/client_portal/client_portal/urls.pyDjango-Practice/Backend/client_portal/clients/Django-Practice/Backend/client_portal/schoolManagement/Django-Practice/Frontend/react/src/App.tsxDjango-Practice/Frontend/react/src/pages/ClientPage.tsxDjango-Practice/Frontend/react/src/pages/management.tsxDjango-Practice/notes.ipynb
This repo is not normalized into a single setup flow, but the Django practice app can be understood as follows.
From Django-Practice/Backend/client_portal/:
- install dependencies from
pyproject.toml - provide environment variables for:
SECRET_KEYPOSTGRESQL_NAMEPOSTGRESQL_USERPOSTGRESQL_PASSWORD- optionally
POSTGRESQL_HOST - optionally
POSTGRESQL_PORT
- run migrations
- start Django on port
8000
From Django-Practice/Frontend/react/:
- install dependencies with
npm install - run the Vite dev server
- frontend expects backend access at
http://localhost:8000
The repository is best understood as a combined:
- Python revision notebook collection
- data-analysis lab archive
- Django/React integration practice project
- Git/GitHub course workspace
- interview-prep/reference notes directory
It is broad by design. The README should not frame it as one narrowly scoped app, because the contents are clearly multi-track and study-oriented.