dataforgoodfr/13_brigade_coupes_rases
Nov 2025 – Nov 2025
1 Month active
Languages Used
Python
Technical Skills
Jupyter Notebooksdata analysisdata visualizationgeospatial analysis
LiliOnillon
PROFILE
Lilionillon
Lili Onillon developed two Jupyter notebooks for the dataforgoodfr/13_brigade_coupes_rases repository, enabling automated generation of statistical tables and visualizations by region and department to support environmental monitoring. Using Python and leveraging geospatial analysis and data visualization skills, Lili structured the notebooks to save outputs in a dedicated directory, ensuring reproducibility and consistency in reporting. The implementation included descriptive statistics and integration of validated geodata references, accelerating regional insight generation. Lili emphasized production readiness by incorporating pre-commit checks, unit tests, and thorough documentation, with all changes peer-reviewed. The work focused on maintainable, transparent workflows for environmental data analysis.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
10,752
Activity Months1
Your Network
17 peopleShared Repositories
17
Work History
November 2025
1 Commits • 1 Features
Nov 1, 2025November 2025 — Key feature delivery: Two Jupyter notebooks (Regional and Departmental Statistical Reports) were delivered to generate tables and plots by region and department, with outputs saved to an output/ directory to support reproducible environmental monitoring reporting. Impact: accelerates decision-making with standardized regional insights and improves reporting consistency for environmental data. Tech/Process: Python-based notebooks with descriptive statistics and visualizations, leveraging region/department data and associated geodata references; PRs include quality checks and documentation so that changes are production-ready.
1 Commits • 1 Features
Nov 1, 2025November 2025 — Key feature delivery: Two Jupyter notebooks (Regional and Departmental Statistical Reports) were delivered to generate tables and plots by region and department, with outputs saved to an output/ directory to support reproducible environmental monitoring reporting. Impact: accelerates decision-making with standardized regional insights and improves reporting consistency for environmental data. Tech/Process: Python-based notebooks with descriptive statistics and visualizations, leveraging region/department data and associated geodata references; PRs include quality checks and documentation so that changes are production-ready.
November 2025
Activity
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Quality Metrics
Correctness80.0%
Maintainability80.0%
Architecture80.0%
Performance80.0%
AI Usage60.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
Jupyter Notebooksdata analysisdata visualizationgeospatial analysis
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