Finding the Best Distribution that Fits Your Data using Python’s Fitter Library
<p>If you are dealing with data then it is very likely that you have heard of probability distributions. I’m a transportation researcher and my speciality is pedestrian safety. For that reason, I’m very fortunate that I get to work with lots of data every day. As a pedestrian safety researcher, I often work with pedestrian crossing speed (average speed maintained by pedestrians while crossing a road) or waiting time at intersections. For this type of continuous data, I often need to identify the best-suited distribution. One of the common way of doing this using a paid software. Last week I started searching open-source libraries for fitting distributions. Even though there are several libraries available for R and Python they are fragmented. Fragmented in the sense that they only support very common distributions. After going through so many libraries and their documentation, I came across the <strong><em>Fitter </em></strong>library developed by <strong><em>Thomas Cokelaer</em></strong>. This library is a lifesaver. It uses <strong><em>Scipy library </em></strong>in the backend for distribution fitting<strong><em> </em></strong>and supports 80 distributions, which is huge.</p>
<p><a href="https://medium.com/the-researchers-guide/finding-the-best-distribution-that-fits-your-data-using-pythons-fitter-library-319a5a0972e9"><strong>Website</strong></a></p>