dataforgoodfr/13_pollution_eau
Feb 2025 – Feb 2025
1 Month active
Languages Used
PythonSQL
Technical Skills
Data AnalysisData CleaningData ValidationDuckDBJupyter NotebooksPandas
Murielepp
PROFILE
Murielepp
During February 2025, Muriel Eppinger developed a cross-dataset data quality validation feature for the dataforgoodfr/13_pollution_eau repository, focusing on water quality datasets. She designed a notebook-based workflow using Python, Pandas, and DuckDB to load and process data from multiple sources, including EDR CAP, EDR TTP, EDC prelevements, and EDC resultats. This approach enabled systematic identification of discrepancies and missing sampling points between EDR and EDC datasets, improving data coverage and consistency checks. Muriel updated data schemas and loading logic to support the validation process, laying the groundwork for future automated quality metrics and enhanced data governance within the project.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
1
Lines of code
21,146
Activity Months1
Your Network
12 peopleShared Repositories
12
Work History
February 2025
2 Commits • 1 Features
Feb 1, 2025February 2025 monthly summary: Delivered cross-dataset data quality validation feature for water quality data (EDR vs EDC) in dataforgoodfr/13_pollution_eau, enabling notebook-based loading/processing across EDR CAP, EDR TTP, EDC prelevements, and EDC resultats to identify discrepancies and missing sampling points. This work enhances data coverage, consistency checks, and paves the way for automated quality metrics and governance. Two commits updated data schemas and loading logic to support the validation workflow.
2 Commits • 1 Features
Feb 1, 2025February 2025 monthly summary: Delivered cross-dataset data quality validation feature for water quality data (EDR vs EDC) in dataforgoodfr/13_pollution_eau, enabling notebook-based loading/processing across EDR CAP, EDR TTP, EDC prelevements, and EDC resultats to identify discrepancies and missing sampling points. This work enhances data coverage, consistency checks, and paves the way for automated quality metrics and governance. Two commits updated data schemas and loading logic to support the validation workflow.
February 2025
Activity
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Quality Metrics
Correctness60.0%
Maintainability60.0%
Architecture60.0%
Performance60.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
PythonSQL
Technical Skills
Data AnalysisData CleaningData ValidationDuckDBJupyter NotebooksPandas
Repositories Contributed To
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