This project is designed to load, process, and combine financial data from different sources. It allows for a unified and structured analysis of financial movements recorded on various platforms such as MoneyWiz, ING, and Revolut.
- Data Loading: Capability to read files in different formats (such as CSV and Excel) from various financial data sources.
- Processing and Normalization: Normalizes the data to maintain a consistent format, facilitating comparison and combined analysis.
- Data Analysis: Includes functions to categorize and label financial movements, allowing for more detailed analysis.
- Data Combination: Combines data from multiple sources into a single Pandas DataFrame for unified analysis.
- Security Backup: Functionality to create backup copies of processed files, including a timestamp for better version management.
To use this project, you will need to have Python installed along with the Pandas and OpenPyXL libraries. Set up the paths to your data files and run the corresponding processing functions. The script will combine all the data into a single DataFrame, which you can then analyze according to your needs.
Contributions are welcome. If you have ideas for improving the project or adding support for more data sources, feel free to create a pull request or open an issue.