dython VS Machine-Learning-for-Asset-Managers

Make a categorical heatmap instead (example see https://github.com/shakedzy/dython/issues/2)

to answer question one try just running a simple correlation matrix among your yearly and the average of your daily figures For years 2012+ when you have all four inputs. I frequently use the small convenience library Dython Dython in Github. If your features are very independent then you will not be able to fill in missing values and will need to find other surrogates such as “is my crop largely a fixed percentage of overall exports and are overall exports available for missing years?” If your features are highly dependent then essentially you don’t need them all - both XGBoost and LightGBM have simple fill-in-with-the-mean type imputation of missing values - run across all your data with imputation on and removing low impact features will remove all but one highly interdependent features.