Welcome to the repository for the **Data Analysis with Pandas and Python** course on Udemy by Boris Paskhaver. 🎓

This repository serves as a comprehensive resource for learning and applying the fundamentals of data analysis using the powerful Pandas library in Python. It contains well-documented code examples, Jupyter Notebooks, and real-world datasets that are covered in the course. 💡📊

Feel free to explore the code, review the notebooks, and experiment with the provided materials. Engage with the course community, start the repository, and fork it as you see fit. 🚀🔍

Let's embark on this data-driven adventure together and unleash the power of Python and Pandas for insightful data analysis! 🚀✨

Good luck on your journey to learn Data Analysis with Pandas and Python! May you gain valuable insights and excel in your data-driven endeavors. 🎉🔥

• Installation
• Usage
• Contributing
• License

To use the projects in this repository, you need to have Python and the Pandas library installed on your machine. You can install Python from the official Python website and Pandas using pip, the Python package installer.

pip install pandas

It is recommended to use a virtual environment to keep the project dependencies isolated. You can create a virtual environment using venv or conda, depending on your preference.

Each project in this repository is located in its own Jupyter Notebook file (.ipynb). To use a specific project, open the notebook in Jupyter Notebook or JupyterLab.

To run the code in the notebook, make sure you have the necessary dependencies installed. You can install the required dependencies by running the following command:

After installing the dependencies, launch Jupyter Notebook or JupyterLab from the terminal:

jupyter notebook

or

jupyter lab

In your web browser, navigate to the URL provided by Jupyter Notebook/Lab and open the desired notebook (.ipynb) file. Execute the cells in the notebook to see the code and its outputs.

Feel free to modify the code cells and adapt them to your specific needs. Each project contains comments and documentation to help you understand the code and its purpose.

🎉 Contributions to this repository are welcome! If you have a project or improvement to suggest, please follow these steps:

1. 🍴 Fork the repository.
2. 🔧 Create a new branch for your feature or bug fix.
3. 🚀 Implement your changes.
4. ✔️ Test your changes to ensure they work correctly.
5. 💾 Commit your changes and push them to your forked repository.
6. 📩 Submit a pull request detailing your changes.

Please make sure to follow the repository's code style and include appropriate documentation for your changes.

📄 The code in this repository is available under the MIT License. You are free to use, modify, and distribute it for personal or commercial purposes. Please refer to the LICENSE file for more information.