# EDAssistant: Supporting Exploratory Data Analysis in Computational Notebooks with In-Situ Code Search and Recommendation

arXiv: https://arxiv.org/abs/2112.07858 · HTML: https://mlsanity.com/paper/2112.07858/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 66 references that are in the map.
- First posted on arXiv 15 Dec 2021.

## Builds on (most cited first, 9 of 9)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [CodeBERT: A Pre-Trained Model for Programming and Natural Languages](https://mlsanity.com/paper/2002.08155/) (2020)
- [Distributed Representations of Sentences and Documents](https://mlsanity.com/paper/1405.4053/) (2014)
- [GraphCodeBERT: Pre-training Code Representations with Data Flow](https://mlsanity.com/paper/2009.08366/) (2020)
- [Data2Vis: Automatic Generation of Data Visualizations Using Sequence to Sequence Recurrent Neural Networks](https://mlsanity.com/paper/1804.03126/) (2018)
- [VizML: A Machine Learning Approach to Visualization Recommendation](https://mlsanity.com/paper/1808.04819/) (2018)
- [Falx: Synthesis-Powered Visualization Authoring](https://mlsanity.com/paper/2102.01024/) (2021)
- [NBSearch: Semantic Search and Visual Exploration of Computational Notebooks](https://mlsanity.com/paper/2102.01275/) (2021)

## Built on by (most cited first, 8 of 8)

- [LightVA: Lightweight Visual Analytics with LLM Agent-Based Task Planning and Execution](https://mlsanity.com/paper/2411.05651/) (2024)
- [Auto-Formula: Recommend Formulas in Spreadsheets using Contrastive Learning for Table Representations](https://mlsanity.com/paper/2404.12608/) (2024)
- [Text-to-Pipeline: Bridging Natural Language and Data Preparation Pipelines](https://mlsanity.com/paper/2505.15874/) (2025)
- [SuperNOVA: Design Strategies and Opportunities for Interactive Visualization in Computational Notebooks](https://mlsanity.com/paper/2305.03039/) (2023)
- [Contextualized Data-Wrangling Code Generation in Computational Notebooks](https://mlsanity.com/paper/2409.13551/) (2024)
- [ReSpark: Leveraging Previous Data Reports as References to Generate New Reports with LLMs](https://mlsanity.com/paper/2502.02329/) (2025)
- [TA3: Testing Against Adversarial Attacks on Machine Learning Models](https://mlsanity.com/paper/2410.05334/) (2024)
- [Survey for Categorising Explainable AI Studies Using Data Analysis Task Frameworks](https://mlsanity.com/paper/2507.10208/) (2025)

## Abstract (arXiv)

Using computational notebooks (e.g., Jupyter Notebook), data scientists rationalize their exploratory data analysis (EDA) based on their prior experience and external knowledge such as online examples. For novices or data scientists who lack specific knowledge about the dataset or problem to investigate, effectively obtaining and understanding the external information is critical to carry out EDA. This paper presents EDAssistant, a JupyterLab extension that supports EDA with in-situ search of example notebooks and recommendation of useful APIs, powered by novel interactive visualization of search results. The code search and recommendation are enabled by state-of-the-art machine learning models, trained on a large corpus of EDA notebooks collected online. A user study is conducted to investigate both EDAssistant and data scientists' current practice (i.e., using external search engines). The results demonstrate the effectiveness and usefulness of EDAssistant, and participants appreciated its smooth and in-context support of EDA. We also report several design implications regarding code recommendation tools.