The XYT project is a Python library that contributes to the growing practice of open research data in the field of Human Urban Mobility and City Science. With the increasing amount of urban dynamics generating extensive and heterogeneous digital data, there is a need to effectively handle large, multi-sourced datasets in various formats and standards. While geolocation data is private and sensitive, public transit schedules are open data, presenting a mix of restriction levels. Traditionally, travel surveys such as *transport micro-census* or paper-based surveys have been used for travel analysis. However, the pervasiveness of digital technologies in the past decade has given data collection a whole new dimension, providing researchers with the opportunity to collect accurate travel data using GPS receivers. There is an increasing need from practitioners and researchers in the field of transport and mobility analysis and planning to leverage GPS data. Yet, GPS data in its raw form only provides information about a geolocalized time-stamp, which is limited in its insight. The XYT Python library proposes an integrated pipeline to transform raw GPS data into mobility data. Several instances were developed to generate fake GPS data for privacy-based manipulation of the library, pre-process, label and enrich the GPS data, obfuscate and aggregate data for privacy purposes, parse the GPS data into a format readable by mobility experts, including a pipeline for studying the action space of users, and abstract mobility diaries to graph to perform fast and scalable privacy-based operations. By packaging these contributions into an open-source Python library, XYT aims to enhance accessibility and foster collaboration within the transport and urban researcher community. Lastly, a user-friendly Streamlit app is also available to showcase the library's capabilities and facilitate integration into diverse projects in the field of urban data analytics.