narwhals-dev/narwhals
Apr 2026 – May 2026
2 Months active
Languages Used
Python
Technical Skills
PySparkPythondata analysisdata processingstatistical modelingtesting
Pedro
PROFILE
Pedro
Worked on the narwhals-dev/narwhals repository to expand analytics and mathematical capabilities across PySpark-like and pandas-like backends. Delivered cross-backend quantile aggregation with linear interpolation, improving statistical modeling and data processing accuracy. Addressed a critical timezone naming issue to prevent deprecation risks in production. Enhanced the Ibis expression library by implementing a log function with a configurable base and a negation unary operator, simplifying arithmetic operations for end users. Emphasized robust testing and unit testing throughout, ensuring correctness and reliability. Leveraged Python, PySpark, and data analysis skills to strengthen backend consistency, analytic workflows, and overall platform reliability at scale.
Overall Statistics
Feature vs Bugs
75%Features
Repository Contributions
5Total
Bugs
1
Commits
5
Features
3
Lines of code
141
Activity Months2
Your Network
39 peopleSame Organization
@booking.com5
Work History
May 2026
2 Commits • 2 Features
May 1, 2026May 2026 focused on strengthening math capabilities and usability in narwhals. Delivered two key features in the Ibis expression library: 1) a log function with configurable base and a default base of e, backed by tests to ensure correctness across bases; 2) a negation unary operator for expressions and series to simplify arithmetic expressions. These changes reduce the risk of incorrect log results, improve analytic workflows, and enhance library flexibility for end users. The work is underpinned by targeted tests and clear commit traceability.
2 Commits • 2 Features
May 1, 2026May 2026 focused on strengthening math capabilities and usability in narwhals. Delivered two key features in the Ibis expression library: 1) a log function with configurable base and a default base of e, backed by tests to ensure correctness across bases; 2) a negation unary operator for expressions and series to simplify arithmetic expressions. These changes reduce the risk of incorrect log results, improve analytic workflows, and enhance library flexibility for end users. The work is underpinned by targeted tests and clear commit traceability.
May 2026
April 2026
3 Commits • 1 Features
Apr 1, 2026Month 2026-04 had a focused set of analytics and reliability improvements across narwhals. Key features delivered include cross-backend quantile support and enhanced tests, while a critical timezone naming fix eliminates a deprecation risk. The work improves data accuracy, expands statistical capabilities across PySpark-like and pandas-like backends, and strengthens overall platform reliability. 1) Key features delivered: Implemented quantile aggregation across multiple backends (PySpark-like quantile with linear interpolation; group-by quantile for pandas-like backends). Added tests and noted SQLFrame behavior. 2) Major bugs fixed: Corrected Asia/Kathmandu timezone reference from Asia/Katmandu to Asia/Kathmandu to align with naming conventions and avoid deprecation issues. 3) Overall impact and accomplishments: Expanded analytics capabilities, improved data correctness, and reduced risk of deprecated time zone naming affecting production configurations. Strengthened cross-backend consistency and test coverage, enabling more robust analytics at scale. 4) Technologies/skills demonstrated: PySpark-like and pandas-like backend integration, quantile calculations, testing strategy, and documentation notes around known issues (SQLFrame). Business value delivered through accurate statistics and reliability.
April 2026
3 Commits • 1 Features
Apr 1, 2026Month 2026-04 had a focused set of analytics and reliability improvements across narwhals. Key features delivered include cross-backend quantile support and enhanced tests, while a critical timezone naming fix eliminates a deprecation risk. The work improves data accuracy, expands statistical capabilities across PySpark-like and pandas-like backends, and strengthens overall platform reliability. 1) Key features delivered: Implemented quantile aggregation across multiple backends (PySpark-like quantile with linear interpolation; group-by quantile for pandas-like backends). Added tests and noted SQLFrame behavior. 2) Major bugs fixed: Corrected Asia/Kathmandu timezone reference from Asia/Katmandu to Asia/Kathmandu to align with naming conventions and avoid deprecation issues. 3) Overall impact and accomplishments: Expanded analytics capabilities, improved data correctness, and reduced risk of deprecated time zone naming affecting production configurations. Strengthened cross-backend consistency and test coverage, enabling more robust analytics at scale. 4) Technologies/skills demonstrated: PySpark-like and pandas-like backend integration, quantile calculations, testing strategy, and documentation notes around known issues (SQLFrame). Business value delivered through accurate statistics and reliability.
Activity
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Quality Metrics
Correctness96.0%
Maintainability88.0%
Architecture88.0%
Performance88.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
PySparkPythonPython programmingdata analysisdata manipulationdata processingstatistical modelingtestingunit testing
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