PD-Explain: A Unified Python-native Framework for Query Explanations Over DataFrames
Summary: PD-Explain brings multiple query-explanation techniques into a unified, Python-native Pandas library. It supports automatic discovery of interesting result parts, visual explanations, and natural-language descriptions for exploratory data analysis. (summarized by gpt-5.6-luna on Jul 24 2026)
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Authors
- 1. Itay Elyashiv (Bar-Ilan University)
- 2. Amir Gilad (Hebrew University)
- 3. Edna Isakov (Bar-Ilan University)
- 4. Tal Tikochinsky (Bar-Ilan University)
- 5. Amit Somech (Bar-Ilan University)
BibTeX Citation
@article{elyashiv_vldb24,
title = {{PD-Explain: A Unified Python-native Framework for Query Explanations Over DataFrames}},
author = {Elyashiv, Itay and Gilad, Amir and Isakov, Edna and Tikochinsky, Tal and Somech, Amit},
journal = {PVLDB},
series = {{VLDB} '24},
volume = {17},
number = {12},
pages = {4473--4476},
doi = {10.14778/3685800.3685903},
url = {https://doi.org/10.14778/3685800.3685903},
year = {2024}
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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 |
|---|---|---|---|---|
| 191 | Scorpion: Explaining Away Outliers in Aggregate Queries | 2013 | VLDB | 0.00026096009 |
| 605 | The Complexity of Causality and Responsibility for Query Answers and non-Answers | 2011 | VLDB | 0.00015839628 |
| 663 | A Formal Approach to Finding Explanations for Database Queries | 2014 | SIGMOD | 0.00015174751 |
| 2,424 | Computing the Shapley Value of Facts in Query Answering | 2022 | SIGMOD | 8.6009068e-05 |
| 2,432 | Tracing Data Errors with View-Conditioned Causality | 2011 | SIGMOD | 8.5870067e-05 |
| 3,072 | Lux: Always-on Visualization Recommendations for Exploratory Dataframe Workflows | 2022 | VLDB | 7.7873864e-05 |
| 7,383 | ExplainED: Explanations for EDA Notebooks | 2020 | VLDB | 5.6284447e-05 |
| 8,184 | FEDEX: An Explainability Framework for Data Exploration Steps | 2022 | VLDB | 5.4709725e-05 |
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