December 2025 monthly summary for astropy/astropy focusing on weighted statistics improvements and maintainability. 1) Key features delivered: - Enhanced numpy's np.average to support weights of varied shapes, including proper unit handling, and returning the sum of weights. This enables accurate weighted statistics across diverse input shapes and improves correctness for downstream analyses. - Tests added to validate behavior with and without weights, enhancing reliability and mitigating regression risks when using weighted averages. - Maintenance work included a changelog entry and a MAINT note to ensure np.average also works with no weights, preserving backward compatibility. 2) Major bugs fixed: - Fixed edge-case handling for weighted averages with differing shapes and clarified no-weights behavior, reducing potential calculation errors in user workflows. 3) Overall impact and accomplishments: - Improves accuracy and usability of weighted statistics in data analysis workflows, reducing user errors and enabling robust analyses across varied datasets. - Strengthened code quality with focused tests, changelog updates, and maintenance practices. 4) Technologies/skills demonstrated: - Python, NumPy, and weighted statistics algorithms - Test-driven development with pytest, test coverage for weighted/unweighted cases - Documentation and changelog maintenance, collaborative code review and maintenance practices