The rise of wearable devices with integrated physiological (e.g., electrocardiography [ECG]) and inertial sensors (e.g., accelerometer) has generated extensive amounts of data, collected in various settings. However, extracting meaningful knowledge from data collected using such systems is typically achieved through costly and inflexible proprietary tools, or diverse and often redundant open-source code developed by researchers. This fragmentation and lack of standardized tools hinders progress. In this chapter we describe how BioSPPy addresses this gap as an open-source Python library for physiological data processing and analysis. It offers methods for ten common physiological signals in a user-friendly design, covering steps from data loading to knowledge extraction. By being open source, BioSPPy is freely available and promotes collaborative development, allowing a community of users to contribute to the library’s improvement, fix bugs, and add features, ensuring rapid evolution. Furthermore, it is flexible, enabling users to adapt the modules to their specific needs and goals, and transparent, since users can understand and verify the algorithms and methods employed, facilitating peer-reviewed validation. Overall, it accelerates innovation and ensures that the toolbox evolves in line with the latest research and user requirements.