A Grammar of Data Analysis

2025-11-04

AI Summary

This paper addresses the lack of a unified, semantically clear syntactic framework for data analysis. It proposes a novel, operation-agnostic analytical syntax grounded in two primitive constructs—“measures” and “dimensions”—thereby abstracting analytical logic and decoupling semantic intent from underlying data sources. This design significantly enhances readability, reusability, and maintainability of analytical expressions. To our knowledge, this is the first systematic formulation of such a syntax model. Based on it, we design and open-source Meterstick, a Python library supporting unified analysis across heterogeneous backends—including pandas DataFrames and SQL databases. Empirical evaluation demonstrates that the framework delivers efficient execution while enabling cross-platform, declarative, and composable analytical modeling. By providing a foundational syntax layer, it advances the Analytics-as-Code paradigm.

Technology Category

• Machine Learning: Evaluation and Analysis
• Natural Language Processing: Syntax — Tagging, Chunking & Parsing
• Data Mining & Knowledge Management: Intelligent Query Processing

Application Category

• Web Mining and Content Analysis: Web measurements
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding

Abstract

This paper outlines a grammar of data analysis, as distinct from grammars of data manipulation, in which the primitives are metrics and dimensions. We describe a Python implementation of this grammar called Meterstick, which is agnostic to the underlying data source, which may be a DataFrame or a SQL database.

Problem

• Defining grammar for data analysis using metrics and dimensions
• Implementing Meterstick in Python for flexible data sources
• Distinguishing analysis grammar from data manipulation approaches

Innovation

• Grammar of data analysis with metrics and dimensions
• Python implementation called Meterstick
• Agnostic to DataFrame or SQL data sources