February 2026 (2026-02) monthly summary for malariagen/malariagen-data-python focused on reliability and developer UX improvements in NJT plotting. Key features delivered: - NJT plotting error handling improvements: introduced descriptive errors when insufficient SNPs or samples are present; added context to ValueErrors and improved user guidance to resolve issues; new tests ensure descriptive error messages are raised for this failure mode. Major bugs fixed: - Fixed uninformative error path in NJT plotting by wrapping distance computation in _njt and validating minimum sample count; updated messaging to reflect actual data requirements. Enhanced Not Enough SNPs messages to include counts and actionable remedies; corresponding tests added. Overall impact and accomplishments: - Increased reliability and user experience for NJT plotting workflows by providing clear, actionable error messages and precondition checks. - Strengthened test coverage (distance.py, snp_data.py, test_distance.py) ensuring future changes preserve descriptive errors. - Reduced time-to-diagnose for data sufficiency issues, improving data QC throughput and onboarding for new users. Technologies/skills demonstrated: - Python coding practices, exception handling, and precondition validation. - Test-driven development with targeted unit tests for error messaging. - Cross-module changes in distance.py and snp_data.py to improve UX. Business value: - Clear guidance during failures reduces support time and accelerates data debugging, enabling faster decision-making and more reliable visualization pipelines.