# DataPrep.EDA: Task-Centric Exploratory Data Analysis for Statistical Modeling in Python

arXiv: https://arxiv.org/abs/2104.00841 · HTML: https://mlsanity.com/paper/2104.00841/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Apr 2021.

## Built on by (most cited first, 7 of 7)

- [AutoPrep: Natural Language Question-Aware Data Preparation with a Multi-Agent Framework](https://mlsanity.com/paper/2412.10422/) (2024)
- [SuperNOVA: Design Strategies and Opportunities for Interactive Visualization in Computational Notebooks](https://mlsanity.com/paper/2305.03039/) (2023)
- [Can Deep Neural Networks Predict Data Correlations from Column Names?](https://mlsanity.com/paper/2107.04553/) (2021)
- [KGLiDS: A Platform for Semantic Abstraction, Linking, and Automation of Data Science](https://mlsanity.com/paper/2303.02204/) (2023)
- [Towards Automated Cross-domain Exploratory Data Analysis through Large Language Models](https://mlsanity.com/paper/2412.07214/) (2024)
- [Auto-Test: Learning Semantic-Domain Constraints for Unsupervised Error Detection in Tables](https://mlsanity.com/paper/2504.10762/) (2025)
- [InData: Towards Secure Multi-Step, Tool-Based Data Analysis](https://mlsanity.com/paper/2511.11933/) (2025)

## Abstract (arXiv)

Exploratory Data Analysis (EDA) is a crucial step in any data science project. However, existing Python libraries fall short in supporting data scientists to complete common EDA tasks for statistical modeling. Their API design is either too low level, which is optimized for plotting rather than EDA, or too high level, which is hard to specify more fine-grained EDA tasks. In response, we propose DataPrep.EDA, a novel task-centric EDA system in Python. DataPrep.EDA allows data scientists to declaratively specify a wide range of EDA tasks in different granularity with a single function call. We identify a number of challenges to implement DataPrep.EDA, and propose effective solutions to improve the scalability, usability, customizability of the system. In particular, we discuss some lessons learned from using Dask to build the data processing pipelines for EDA tasks and describe our approaches to accelerate the pipelines. We conduct extensive experiments to compare DataPrep.EDA with Pandas-profiling, the state-of-the-art EDA system in Python. The experiments show that DataPrep.EDA significantly outperforms Pandas-profiling in terms of both speed and user experience. DataPrep.EDA is open-sourced as an EDA component of DataPrep: https://github.com/sfu-db/dataprep.