Dias: Dynamic Rewriting of Pandas Code
Summary: Dynamic, cross-library source-to-source rewriting of Pandas code for ad-hoc EDA; fine-grained dynamic preconditions ensure safe rewrites. Dias rewrites notebook cells to speed up EDA, up to 57x vs pandas, 1909x vs modin, and 3.6x notebook-wide gains. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Stefanos Baziotis (University of Illinois Urbana-Champaign)
- 2. Daniel Kang (University of Illinois Urbana-Champaign)
- 3. Charith Mendis (University of Illinois Urbana-Champaign)
BibTeX Citation
@inproceedings{baziotis_sigmod24,
title = {{Dias: Dynamic Rewriting of Pandas Code}},
author = {Baziotis, Stefanos and Kang, Daniel and Mendis, Charith},
series = {{SIGMOD} '24},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3639313},
url = {https://dl.acm.org/doi/10.1145/3639313},
year = {2024}
}
Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 6,742 | Mitigating the Impedance Mismatch between Prediction Query Execution and Database Engine | 2025 | SIGMOD | 5.6910432e-05 |
Previous
Page 1 / 1
Next
Outgoing Citations (Sorted by Pagerank)
Showing 3 of 3 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 2,207 | Magpie: Python at Speed and Scale using Cloud Backends | 2021 | CIDR | 8.8487039e-05 |
| 3,930 | Flexible Rule-Based Decomposition and Metadata Independence in Modin: A Parallel Dataframe System | 2022 | VLDB | 6.9185978e-05 |
| 4,815 | PolyFrame: A Retargetable Query-based Approach to Scaling Dataframes | 2021 | VLDB | 6.3992929e-05 |
Previous
Page 1 / 1
Next
Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 1,193 | Weld: A Common Runtime for High Performance Data Analytics | 2017 | CIDR |
| 2 | 9,246 | DQDF: Data-Quality-Aware Dataframes | 2022 | VLDB |
| 3 | 13,807 | Towards Understanding Data Analysis Workflows using a Large Notebook Corpus | 2019 | SIGMOD |
| 4 | 3,098 | Lux: Always-on Visualization Recommendations for Exploratory Dataframe Workflows | 2022 | VLDB |
| 5 | 3,930 | Flexible Rule-Based Decomposition and Metadata Independence in Modin: A Parallel Dataframe System | 2022 | VLDB |
| 6 | 3,384 | Putting Pandas in a Box | 2021 | CIDR |
| 7 | 1,369 | Towards Scalable Dataframe Systems | 2020 | VLDB |
| 8 | 2,249 | Evaluating End-to-End Optimization for Data Analytics Applications in Weld | 2018 | VLDB |
| 9 | 10,995 | Painless and Efficient Scaling of Pandas Programs: Demo | 2026 | VLDB |
| 10 | 5,640 | DataPrep.EDA: Task-Centric Exploratory Data Analysis for Statistical Modeling in Python | 2021 | SIGMOD |