In this thesis, I developed an application called Biostat for processing raw data based on the Drosophila melanogaster (D. melanogaster) activities. A web-based software solution is presented to extend an existing tool’s functionality including filtering, accumulating, and visualising data. It was applied to a particular use case wherein its data contained recorded sensor data for the activity of D. melanogaster over time, which varied both during the day and between days. However, the software’s utility is widely generalizable, easy to scale, and applicable to many other use cases. The end-result web application offers an input form and file to upload, after which it converts the data to the pandas' dataframe format and displays a graph. Some limitations of the application are discussed, including the scarce time period in which a full-fledged solution, one which would cover all necessary functionalities, could not have been made, substantially due to a lack of testing and time it would take to fully implement. This thesis contributes to the understanding and usage of existing tools for researchers working in the field of biotechnology with Python.