malariagen/malariagen-data-python
Feb 2026 – Feb 2026
1 Month active
Languages Used
Python
Technical Skills
Continuous IntegrationDevOpsPythonPython programmingalgorithm optimizationbioinformatics
31puneet
PROFILE
31puneet
Puneet worked on the malariagen/malariagen-data-python repository, developing robust data tooling for malaria genomics analytics. He implemented dynamic gene labeling by replacing hardcoded attributes with a flexible, attribute-driven approach, improving gene-name attribution and test reliability. Using Python and pandas, Puneet enhanced time-series data integrity by correcting DataFrame handling and cohort indexing, and introduced error handling for window size in statistical analyses. He refactored the AnophelesHetAnalysis module for maintainability, moving logic into dedicated files and adding unit tests. His work strengthened continuous integration reliability and improved the overall quality, maintainability, and speed of iteration for downstream bioinformatics analyses.
Overall Statistics
Feature vs Bugs
50%Features
Repository Contributions
9Total
Bugs
2
Commits
9
Features
2
Lines of code
1,644
Activity Months1
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Work History
February 2026
9 Commits • 2 Features
Feb 1, 2026February 2026 (2026-02) focused on delivering robust data tooling for malaria genomics analytics. Key work spanned: dynamic gene labeling, time-series data integrity, AnophelesHetAnalysis refactor with unit tests, and CI maintenance. Outcomes include more reliable gene-name attribution, corrected DataFrame handling, safer graph construction, and stronger test coverage. These changes improve pipeline reliability, data quality, and speed of iteration for downstream analyses and decision-making.
9 Commits • 2 Features
Feb 1, 2026February 2026 (2026-02) focused on delivering robust data tooling for malaria genomics analytics. Key work spanned: dynamic gene labeling, time-series data integrity, AnophelesHetAnalysis refactor with unit tests, and CI maintenance. Outcomes include more reliable gene-name attribution, corrected DataFrame handling, safer graph construction, and stronger test coverage. These changes improve pipeline reliability, data quality, and speed of iteration for downstream analyses and decision-making.
February 2026
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Quality Metrics
Correctness95.6%
Maintainability88.8%
Architecture88.8%
Performance88.8%
AI Usage22.2%
Skills & Technologies
Programming Languages
Python
Technical Skills
Continuous IntegrationDevOpsPythonPython programmingalgorithm optimizationbioinformaticsdata analysisdata manipulationdata visualizationerror handlinggraph theorypandassoftware engineeringstatistical modelingtesting
Repositories Contributed To
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