googleapis/python-bigquery-pandas
Oct 2025 – Dec 2025
2 Months active
Languages Used
Python
Technical Skills
BigQueryPythondata engineeringunit testingGoogle BigQuerydata analysis
antoineeripret
PROFILE
Antoineeripret
Antoine Eripret contributed to the googleapis/python-bigquery-pandas repository by developing two features focused on data organization and cost estimation in BigQuery workflows. He enhanced the to_gbq function to support time-based and range-based partitioning as well as clustering columns, using Python and data engineering best practices to improve query performance and cost efficiency for large datasets. Additionally, he implemented a dry_run mode for read_gbq, enabling users to estimate data processed without executing queries, which aids in cost planning and analytics. His work demonstrated depth in BigQuery integration, pandas interoperability, and collaborative open-source development, with thorough testing and documentation updates.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
2
Lines of code
486
Activity Months2
Your Network
10 peopleWork History
December 2025
1 Commits • 1 Features
Dec 1, 2025December 2025 monthly summary for googleapis/python-bigquery-pandas: Delivered dry_run estimation for read_gbq to estimate data processed without executing the query, enabling cost planning and data-volume awareness for large datasets. Feature returns GB data size instead of a DataFrame when dry_run is True. The change is implemented in commit 516f986f6935c9a6426e6a9b1702cb2002916362 with co-authorship from Shenyang Cai and Shuowei Li. No major bugs fixed this month. Overall impact: improved customer cost visibility, safer query planning, and expanded analytics capabilities. Technologies demonstrated: Python, BigQuery integration, pandas interoperability, collaboration on open-source contributions, and commit-driven development.
1 Commits • 1 Features
Dec 1, 2025December 2025 monthly summary for googleapis/python-bigquery-pandas: Delivered dry_run estimation for read_gbq to estimate data processed without executing the query, enabling cost planning and data-volume awareness for large datasets. Feature returns GB data size instead of a DataFrame when dry_run is True. The change is implemented in commit 516f986f6935c9a6426e6a9b1702cb2002916362 with co-authorship from Shenyang Cai and Shuowei Li. No major bugs fixed this month. Overall impact: improved customer cost visibility, safer query planning, and expanded analytics capabilities. Technologies demonstrated: Python, BigQuery integration, pandas interoperability, collaboration on open-source contributions, and commit-driven development.
December 2025
October 2025
1 Commits • 1 Features
Oct 1, 2025October 2025 (2025-10) performance review summary for googleapis/python-bigquery-pandas: Implemented partitioning and clustering enhancements in to_gbq, enabling time-based and range-based partitioning and clustering columns. Included end-to-end tests and corrected a documentation error. This work lays groundwork for more efficient data organization in BigQuery, improving query performance and potential cost reductions for users with large datasets. Commit e7213c7760582a58b03d75886e5754fd3083e622 (feat: add partitioning and clustering to the to_gbq function; co-authored by Tim Sweña) adds the feature and tests.
October 2025
1 Commits • 1 Features
Oct 1, 2025October 2025 (2025-10) performance review summary for googleapis/python-bigquery-pandas: Implemented partitioning and clustering enhancements in to_gbq, enabling time-based and range-based partitioning and clustering columns. Included end-to-end tests and corrected a documentation error. This work lays groundwork for more efficient data organization in BigQuery, improving query performance and potential cost reductions for users with large datasets. Commit e7213c7760582a58b03d75886e5754fd3083e622 (feat: add partitioning and clustering to the to_gbq function; co-authored by Tim Sweña) adds the feature and tests.
Activity
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Quality Metrics
Correctness100.0%
Maintainability80.0%
Architecture100.0%
Performance80.0%
AI Usage40.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
BigQueryGoogle BigQueryPythondata analysisdata engineeringpandasunit testing
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