The apyxl package (Another PYthon package for eXplainable Learning) is a simple wrapper around XGBoost, Hyperopt, and SHAP. It allows users to build high-performing regression or classification models and leverage SHAP analysis to gain insights into the relationships between inputs and outputs. With apyxl, processing categorical features, fitting models using Bayesian hyperparameter search, and creating SHAP explainers can all be done with a single line of code, streamlining the entire process from data preparation to model interpretation. Additionally, the package offers methods for time series normalization, with upcoming support for econometric tasks such as Difference-in-Differences and Regression Discontinuity.