PandasCorpus: A Resource of Real-World Pandas Workflows and Usage Patterns

LLM-based data-analysis tools are increasingly used to help users analyze messy spreadsheets and workbooks, from answering questions over uploaded files to generating code, summaries, and visualizations. These systems are often evaluated by the correctness of their final downstream answers. However, reliable data analysis also depends on an earlier step: understanding what the dataset contains before solving the requested task. For complex workbooks, this Data Exploration step includes identifying the logical tables behind physical sheets, interpreting column semantics, recovering keys and relationships, and detecting quality issues. In current tools and benchmarks, this step is usually left implicit, creating a gap between downstream task performance and the dataset understanding needed for reliable, human-checkable analysis. Our key contribution is to identify this overlooked gap, make Data Exploration a first-class evaluation target, and show through downstream experiments that stronger Data Exploration support improves task performance. To evaluate dataset understanding directly, we introduce two benchmark settings: a real multi-sheet workbook benchmark based on a Vitamin D study dataset, and an extension of DSBench with schema-fixed Data Exploration artifacts. In both settings, systems are evaluated by the quality of a structured artifact capturing tables, columns, semantic roles, relationships, and profiling signals. Our results show that strong LLMs and data-analysis agents still miss important logical structure even when they read spreadsheet content. Furthermore, explicit Data Exploration support often improves downstream correctness, suggesting it should be treated as a first-class, inspectable stage in LLM data-analysis workflows and a natural human-in-the-loop checkpoint where domain experts can review and correct the artifact before downstream analysis proceeds.

Data preparation is a central and time-consuming stage in data analysis workflows. Traditionally, commercial tools have relied on graphical user interfaces (GUIs) to simplify data preparation, allowing users to define transformations through visual operators and workflows. Recent advances in large language models (LLMs) raise the possibility of a paradigm shift toward natural language (NL)-driven data preparation, in which users can specify preparation intents in NL directly. However, it remains unclear how far current LLM-based agents are from this paradigm shift in practice. Existing code generation benchmarks do not capture key characteristics of data preparation, including ambiguous user intents, imperfect real-world data, and the need to translate code into interpretable workflows for validation. To bridge this gap, we present PrepBench, a benchmark designed to evaluate NL-driven data preparation along three core capabilities: interactive disambiguation, prep-code generation, and code-to-workflow translation. We crawl data from the Preppin' Data Challenges, and then extend it into a systematically designed benchmark. The benchmark covers diverse domains, and each task involves 3 to 18 data preparation steps. Nearly half of the tasks require over 100 lines of Python code, and the longest solutions approach 300 lines. Our evaluation shows that, despite recent progress, realizing this paradigm shift remains challenging for state-of-the-art LLMs. PrepBench provides a principled benchmark for measuring this gap and helps identify key challenges toward realizing NL-driven data preparation.

Real-world data analysis is a multi-step process over heterogeneous inputs rather than merely producing a final answer. A practical system should autonomously organize multi-step workflows, execute generated code in a sandboxed and controllable environment, and remain inspectable through visible action traces and intermediate artifacts. Existing LLM-based analysis tools, however, often emphasize isolated subtasks, leaving limited support for complete execution-grounded workflows. We present DA-Studio (Data Analysis Studio), an interactive web-based demo system for end-to-end data analysis that is autonomous, sandboxed, and inspectable. DA-Studio integrates an action-structured analysis backend, a sandboxed execution workspace, and a browser interface for task setup, streamed action traces, artifact preview, code editing and rerunning, and report export. Through iterative action generation, code execution, and feedback incorporation, it incrementally constructs executable analysis steps from raw files and natural-language requests while exposing intermediate results and artifacts throughout the process.