Taking advantage of the emergence of Open Data platforms dedicated to urban services, one can try to understand the functioning of cities. The biggest challenge is no more to get the data; but to structure it, analyze it, extract new information from it, and design clever representations in order to visualize it. This presentation will focus on a recent open source study made by Oslandia about bike-sharing systems in France. Our dev stack is largely based on Python tools, from back-end (a data pipeline designed with Luigi) to front-end (with Flask API and web application). The most important data processing steps will be detailed, and a particular attention will be paid to inherent machine learning problems, like bike stand classification, or bike availability prediction. To that matter, we target a better comprehension of urban areas, and value creation for bike-sharing system users. A live demo of the web application will end the presentation.