Recently we learned of the package seaborne. With this wonderful package came the method sns.displot() which returns two visualizations we have already learned in matplotlib.pyplot. This method combines together a histogram along with a KDE (Fit and plot a univariate or bivariate kernel density estimate). Along with a rugplot if you really want to dig deep into the data’s distrubutions. I found this to be a very interesting visual technique because it brough together so many methods wev’ve learned thus far, all in a single line of code.
I think this truly shows the beauty and true power of Python. The Seaborn Distplot can easily show a user whether his or her univariate distribution is uniformed or not. This is a great tool to take a quick snapshot of one’s data. This will help a data scientist choose whether or not to use a variable in his or her regression tests. Also, this could serve as a starting point of data cleaning. The Displot method would show someone whether or not data cleaning was needed to try and affect the fit of the data. This is crucial for proper data analysis.