I reckon you're more likely to get a good response on their Github page than here. Unless a dev happens to see this post. Source: almost 3 years ago

Since you are using python, pandas, scikit-learn, scipy, and statsmodels are what you are looking for. Source: about 3 years ago

In case you're really worried about cold start latency and your application load shows high variance in the number of concurrent requests, you might want to get a bit fancier. You could use time-series forecasting to anticipate how many containers should be warmed at each point in time. StatsModels is an open-source project that offers the most common algorithms for working with time-series. Here's a good... - Source: dev.to / about 4 years ago

Can't you get a student discount for Stata? R would definitely be able to handle everything. For Python, have a look through the statsmodel package https://github.com/statsmodels/statsmodels. Source: over 4 years ago

So to get this out of the way first, xlrd has less features than openpyxl and in addition only works with the old '.xls' format, not the newer '.xlsx' format. Even on the xlrd's Github repo it says: 'Please use openpyxl where you can... '. Source: over 3 years ago

There are few alternative libraries for reading and writing excel files: Pandas, Xlrd , openpyxl among others. In the end I settled for openpyxl as I had the most experience Using it and it had support for .xlsx files. - Source: dev.to / over 4 years ago