DataPrep.EDA: Task-Centric Exploratory Data Analysis for Statistical Modeling in Python
Summary: DataPrep.EDA is a task-centric, declarative EDA system in Python that lets researchers specify diverse EDA tasks with a single function call. Its Dask-backed pipelines scale the workflow, delivering faster, more usable EDA than Pandas-profiling; open-sourced as part of DataPrep. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Jinglin Peng (Simon Fraser University)
- 2. Weiyuan Wu (Simon Fraser University)
- 3. Brandon Lockhart (Simon Fraser University)
- 4. Song Bian (Chinese University of Hong Kong)
- 5. Jing Nathan Yan (Cornell University)
- 6. Linghao Xu (Simon Fraser University)
- 7. Zhixuan Chi (Simon Fraser University)
- 8. Jeffrey M. Rzeszotarski (Cornell University)
- 9. Jiannan Wang (Simon Fraser University)
BibTeX Citation
@inproceedings{peng_sigmod21,
title = {{DataPrep.EDA: Task-Centric Exploratory Data Analysis for Statistical Modeling in Python}},
author = {Peng, Jinglin and Wu, Weiyuan and Lockhart, Brandon and Bian, Song and Yan, Jing Nathan and Xu, Linghao and Chi, Zhixuan and Rzeszotarski, Jeffrey M. and Wang, Jiannan},
series = {{SIGMOD} '21},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3448016.3457330},
url = {https://dl.acm.org/doi/10.1145/3448016.3457330},
year = {2021}
}Incoming Citations (Sorted by Pagerank)
Showing 6 of 6 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 5,357 | Can Large Language Models Predict Data Correlations from Column Names? | 2023 | VLDB | 6.1619918e-05 |
| 7,068 | How do Categorical Duplicates Affect ML? A New Benchmark and Empirical Analyses | 2024 | VLDB | 5.6083188e-05 |
| 7,270 | AutoPrep: Natural Language Question-Aware Data Preparation with a Multi-Agent Framework | 2025 | VLDB | 5.5694935e-05 |
| 9,229 | Auto-Test: Learning Semantic-Domain Constraints for Unsupervised Error Detection in Tables | 2025 | SIGMOD | 5.2056825e-05 |
| 11,270 | Weak-to-Strong Prompts with Lightweight-to-Powerful LLMs for High-Accuracy, Low-Cost, and Explainable Data Transformation | 2025 | VLDB | 4.9793485e-05 |
| 11,379 | Towards Automated Cross-domain Exploratory Data Analysis through Large Language Models | 2025 | VLDB | 4.9793485e-05 |
Previous
Page 1 / 1
Next
Outgoing Citations (Sorted by Pagerank)
Showing 8 of 8 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 95 | Potter's Wheel: An Interactive Data Cleaning System | 2001 | VLDB | 0.00034382643 |
| 395 | SeeDB: Efficient Data-Driven Visualization Recommendations to Support Visual Analytics | 2015 | VLDB | 0.00019165452 |
| 1,369 | Towards Scalable Dataframe Systems | 2020 | VLDB | 0.00010899832 |
| 1,395 | Data Profiling with Metanome | 2015 | VLDB | 0.00010794808 |
| 3,188 | Extracting Top-K Insights from Multi-dimensional Data | 2017 | SIGMOD | 7.5535445e-05 |
| 3,493 | Foresight: Recommending Visual Insights | 2017 | VLDB | 7.2593029e-05 |
| 4,725 | QuickInsights: Quick and Automatic Discovery of Insights from Multi-Dimensional Data | 2019 | SIGMOD | 6.4504342e-05 |
| 7,508 | ExplainED: Explanations for EDA Notebooks | 2020 | VLDB | 5.5066763e-05 |
Previous
Page 1 / 1
Next
Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 9,246 | DQDF: Data-Quality-Aware Dataframes | 2022 | VLDB |
| 2 | 11,647 | PD-Explain: A Unified Python-native Framework for Query Explanations Over DataFrames | 2024 | VLDB |
| 3 | 10,171 | Towards Autonomous, Hands-Free Data Exploration | 2020 | CIDR |
| 4 | 11,797 | To UDFs and Beyond: Demonstration of a Fully Decomposed Data Processor for General Data Wrangling Tasks | 2023 | VLDB |
| 5 | 6,878 | DAPHNE: An Open and Extensible System Infrastructure for Integrated Data Analysis Pipelines | 2022 | CIDR |
| 6 | 4,497 | Automating Exploratory Data Analysis via Machine Learning: An Overview | 2020 | SIGMOD |
| 7 | 8,695 | From Papers to Practice: The openclean Open-Source Data Cleaning Library | 2021 | VLDB |
| 8 | 7,508 | ExplainED: Explanations for EDA Notebooks | 2020 | VLDB |
| 9 | 3,984 | Data Canopy: Accelerating Exploratory Statistical Analysis | 2017 | SIGMOD |
| 10 | 10,278 | Dias: Dynamic Rewriting of Pandas Code | 2024 | SIGMOD |