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Disney Lorcana Data Hub

A centralized repository for collecting, analyzing, and exploring Disney Lorcana card data from various sources. Perfect for data enthusiasts, data scientists, and machine learning practitioners looking to explore structured datasets and gain insights into the Disney Lorcana universe.

Directory Structure

├── README.md               <- Overview of the project and setup instructions.
├── data/                   <- Raw and processed data files (CSV, JSON, etc.).
│   ├── raw/                <- Raw data from scraping or other sources.
│   └── processed/          <- Cleaned and preprocessed data ready for analysis.
│
├── images/                 <- Directory for storing card images or visual assets.
│
├── notebooks/              <- Jupyter Notebooks for data exploration and ML.
│   ├── exploration/        <- EDA (Exploratory Data Analysis) notebooks.
│   ├── visualization/      <- Visualization notebooks (e.g., trends or stats).
│   └── machine_learning/   <- ML models and experiments.
│
├── scripts/                <- Python scripts for data collection and processing.
│   ├── scrape_data.py      <- Scripts for scraping from sources (like APIs).
│   ├── clean_data.py       <- Preprocess raw data into clean datasets.
│   └── utils/              <- Helper utilities.
│
├── requirements.txt        <- List of Python dependencies.
└── LICENSE                 <- MIT license.

Data Sources

The data used in this project is sourced from various Disney Lorcana card datasets. The raw data is stored in the data/raw directory, and the processed data is stored in the data/processed.

List of Data Sources

  1. Disney Lorcana: The official resources for all Disney Lorcana cards by Ravensburger. This data source provide only the name and number of the cards, which is not sufficient for our analysis. Therefore, we are using alternative sources that offer more comprehensive datasets. However, we have included this source for completeness.

  2. Lorcast API: Lorcast is a third-party API that provides comprehensive data on Disney Lorcana cards. We are using this API to collect detailed information about the cards, including their attributes, images, and more.

Feel free to explore and analyze the data using the provided Jupyter Notebooks in the notebooks/ directory

Usage

Note on Development Container

This project includes a .devcontainer configuration for Visual Studio Code. If you are using VS Code, you can open the project in a development container to ensure a consistent development environment. The container will automatically install the required dependencies specified in the requirements.txt file.

Prerequisites

Ensure you have the following installed:

  • Docker
  • Visual Studio Code with the Dev Containers extension

Instructions

  1. Clone the repository:

    git clone https://github.com/bert-cafecito/disney-lorcana-data-hub.git
cd disney-lorcana-data-hub

  2. Reopen in Container:

    • Press F1 and select >Dev Containers: Rebuild Container.

  3. Start Jupyter Notebook:

    jupyter notebook --ip=0.0.0.0 --port=8888 --no-browser --allow-root

  4. Access Jupyter Notebook: Open your browser and navigate to http://localhost:8888.

  5. Explore Notebooks:

    • Navigate to the notebooks/ directory to explore the available Jupyter Notebooks.

Running Python Scripts

You can run the Python scripts in the scripts/ directory to scrape data, clean data, or perform other tasks. To run a script, use the following command:

python scripts/<script_name>.py

Replace <script_name> with the name of the script you want to run (e.g., scrape_data.py, clean_data.py).

Contributing

Contributions are welcome and encouraged! To get started, please read the contributing guidelines to learn how you can contribute to this project.

Support

If you would like to support this project or me, you can do so in the following ways:

Reporting Issues

If you find a bug or have a feature request, please open an issue on the GitHub Issues page. Provide as much detail as possible to help us understand and resolve the issue quickly.

Contributing

If you would like to contribute to the project, please read the contributing guidelines to learn how you can help. We appreciate all contributions, whether they are big or small.

Follow Me on Social Media

Star the Repository

If you find this project useful, please consider starring the repository on GitHub. Starring a repository helps increase its visibility and lets others know that the project is valuable. It also provides motivation and support to the maintainers to continue improving the project.

Acknowledgements

I would like to mention the following codebases and projects that inspired this repository:

  • Data Hub Template: A template for creating centralized data repositories for various datasets.

License

This project is licensed under the MIT License. See the LICENSE file for details.