dataforgoodfr/13_odis
Mar 2025 – Apr 2025
2 Months active
Languages Used
BruJSONMarkdownPythonYAMLyaml
Technical Skills
API IntegrationAPI integrationConfiguration ManagementData AnalysisData CollectionData Configuration
antoningar
PROFILE
Antoningar
Antonin Garanto enhanced the dataforgoodfr/13_odis repository by building and refining data extraction pipelines across housing, population, and education domains. Using Python and Jupyter Notebooks, Antonin implemented robust API integrations, including INSEE Melodi and Data Education Gouv, to expand data coverage and reliability. He introduced configuration-driven extraction, added new endpoints for granular analytics, and improved service configurations for better data availability. Addressing maintainability, Antonin updated documentation and fixed a case-sensitivity bug in JSON parsing. His work demonstrated depth in data engineering and configuration management, enabling richer, more accurate analytics and reporting for stakeholders in a short project timeframe.
Overall Statistics
Feature vs Bugs
83%Features
Repository Contributions
15Total
Bugs
1
Commits
15
Features
5
Lines of code
19,984
Activity Months2
Your Network
18 peopleShared Repositories
18
Arthur HavlicekMember
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apibracMember
gustelleMember
HAMIDI AminMember
Henry D.Member
salma-hnanaMember
Jean-Baptiste-NMember
Kimberly BlackMember
Work History
April 2025
1 Commits • 1 Features
Apr 1, 2025April 2025 monthly summary for dataforgoodfr/13_odis: Delivered an Education Data Extraction Configuration Enhancement to support more granular analytics for education-domain metrics. The change expands the fields selected and grouped by for 'moyenne d'eleve par classe par commune' and 'moyenne d'eleve par classe par departement', and reduces the data extraction limit from -1 to 10 to enable richer data pulls. Implemented via targeted commit a5ce8a35ed8e37acdc722fe5b47e27f0f2a6b6be with message 'fix(education): extract more datas'. This work improves reporting readiness, data quality, and analytics depth for stakeholders. Key achievements delivered this month: - Expanded Education Data Extraction Configuration for education domain to include more granular data points for moyennes per classe par commune and moyennes per classe par departement. - Fixed data extraction limit by lowering from -1 to 10, enabling larger data pulls for analytics. - Implemented changes through commit a5ce8a35ed8e37acdc722fe5b47e27f0f2a6b6be (fix(education): extract more datas). - Enhanced analytics readiness and reporting accuracy for education-domain insights, demonstrating domain-specific data modeling and configuration-driven ETL improvements.
1 Commits • 1 Features
Apr 1, 2025April 2025 monthly summary for dataforgoodfr/13_odis: Delivered an Education Data Extraction Configuration Enhancement to support more granular analytics for education-domain metrics. The change expands the fields selected and grouped by for 'moyenne d'eleve par classe par commune' and 'moyenne d'eleve par classe par departement', and reduces the data extraction limit from -1 to 10 to enable richer data pulls. Implemented via targeted commit a5ce8a35ed8e37acdc722fe5b47e27f0f2a6b6be with message 'fix(education): extract more datas'. This work improves reporting readiness, data quality, and analytics depth for stakeholders. Key achievements delivered this month: - Expanded Education Data Extraction Configuration for education domain to include more granular data points for moyennes per classe par commune and moyennes per classe par departement. - Fixed data extraction limit by lowering from -1 to 10, enabling larger data pulls for analytics. - Implemented changes through commit a5ce8a35ed8e37acdc722fe5b47e27f0f2a6b6be (fix(education): extract more datas). - Enhanced analytics readiness and reporting accuracy for education-domain insights, demonstrating domain-specific data modeling and configuration-driven ETL improvements.
April 2025
March 2025
14 Commits • 4 Features
Mar 1, 2025March 2025: Delivered end-to-end data extraction enhancements and new data endpoints across housing, population, and education domains in dataforgoodfr/13_odis. Implemented Melodi API throttling, introduced MelodiExtractor, expanded data sources, and integrated Bruno API requests for housing, alongside updated service configurations to improve data availability and reliability for housing statistics. Added INSEE population statistics endpoints to capture key indicators (socio-professional categories, density, households, sex and age breakdowns) for 2023. Introduced Education data extraction from Data Education Gouv API, with endpoints for average students per class by commune, region, and department for the 2023 school year. Added Notebook data extraction via NotebookExtractor and updated documentation to reflect notebook-based workflows. Fixed a case-sensitivity bug in MelodiExtractor format handling to ensure JSON data is recognized and extraction errors are prevented. These efforts increased data availability, reliability, and coverage, enabling richer, timely insights for housing, demographics, and education while improving maintainability through configuration and documentation updates.
March 2025
14 Commits • 4 Features
Mar 1, 2025March 2025: Delivered end-to-end data extraction enhancements and new data endpoints across housing, population, and education domains in dataforgoodfr/13_odis. Implemented Melodi API throttling, introduced MelodiExtractor, expanded data sources, and integrated Bruno API requests for housing, alongside updated service configurations to improve data availability and reliability for housing statistics. Added INSEE population statistics endpoints to capture key indicators (socio-professional categories, density, households, sex and age breakdowns) for 2023. Introduced Education data extraction from Data Education Gouv API, with endpoints for average students per class by commune, region, and department for the 2023 school year. Added Notebook data extraction via NotebookExtractor and updated documentation to reflect notebook-based workflows. Fixed a case-sensitivity bug in MelodiExtractor format handling to ensure JSON data is recognized and extraction errors are prevented. These efforts increased data availability, reliability, and coverage, enabling richer, timely insights for housing, demographics, and education while improving maintainability through configuration and documentation updates.
Activity
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Quality Metrics
Correctness89.4%
Maintainability90.6%
Architecture90.6%
Performance84.0%
AI Usage21.4%
Skills & Technologies
Programming Languages
BruJSONMarkdownPythonYAMLyaml
Technical Skills
API IntegrationAPI integrationConfiguration ManagementData AnalysisData CollectionData ConfigurationData EngineeringData ExtractionDocumentationJupyter NotebooksPythondata extraction
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