This repository serves as a comprehensive resource for anyone eager to delve into the world of Python programming. Divided into two distinct parts, it provides a structured learning experience.

• The first part features a collection of Jupyter files within the 'practice_python' folder, offering a hands-on and interactive environment for Python practice. These files cover a myriad of topics, from foundational concepts to advanced exercises, facilitating a gradual mastery of the language.
• In the second part, a robust Flask project awaits exploration, showcasing the practical application of Python in a real-world scenario. With features spanning user management, location tracking, company operations, position handling, and user-company mappings, this Flask RESTful API project offers valuable insights into building scalable and functional applications.

Whether you're a novice honing your Python skills or an experienced developer seeking practical examples, this repository caters to a diverse audience eager to enhance their programming proficiency.

Explore the 'practice_python' folder to find Jupyter files for Python practice.

1. Python Basics
2. Data Structures in Python
3. Data Manipulation and Visualization

You can run these files using Jupyter Notebook.

This project is a Flask RESTful API that provides endpoints for managing users, locations, companies, positions, and user-company mappings. It follows best practices for structuring a Flask project and includes unit tests using pytest.

• User management (CRUD operations)
• Location management (CRUD operations)
• Company management (CRUD operations)
• Position management (CRUD operations)
• User-Company Mapping management (CRUD operations)

1. Install software: docker
2. Pull the image:

docker pull taranjitkaurme/datascience_practice_python:latest

3. Run the Docker container:

docker run -p 5000:5000 taranjitkaurme/datascience_practice_python:latest

1. Install all the softwares: python, pipenv, docker, intelliJ, git

   Note: You can also use - https://github.com/neurabytes/nb-automation-devtools

2. Clone the repository:

git clone https://github.com/taranjitkaurmee/datascience_practice_python.git
cd datascience_practice_python

3. Install dependencies:

pipenv install

4. Run the Flask application:

python app.py

The API will be accessible at http://localhost:5000.

• Users:

  • GET: /api/users
  • GET: /api/users/int:user_id
  • POST: /api/users
  • PUT: /api/users/int:user_id
  • DELETE: /api/users/int:user_id

• Locations:

  • GET: /api/location
  • GET: /api/location/int:location_id
  • POST: /api/location
  • PUT: /api/location/int:location_id
  • DELETE: /api/location/int:location_id

• Companies:

  • GET: /api/company
  • GET: /api/company/int:company_id
  • POST: /api/company
  • PUT: /api/company/int:company_id
  • DELETE: /api/company/int:company_id

• Positions:

  • GET: /api/position
  • GET: /api/position/int:position_id
  • POST: /api/position
  • PUT: /api/position/int:position_id
  • DELETE: /api/position/int:position_id

• User-Company Mappings:

  • GET: /api/usercompanymapping
  • GET: /api/usercompanymapping/int:mapping_id
  • POST: /api/usercompanymapping
  • PUT: /api/usercompanymapping/int:mapping_id
  • DELETE: /api/usercompanymapping/int:mapping_id

Run unit tests using pytest:

pytest

Feel free to contribute by reporting issues, suggesting enhancements, or submitting pull requests.

This project is licensed under the Apache License - see the LICENSE file for details.