Finding the Best Distribution that Fits Your Data using Python???s Fitter Library

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 Fitter library developed by Thomas Cokelaer. This library is a lifesaver. It uses Scipy library in the backend for distribution fitting and supports 80 distributions, which is huge.

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