# PyTond: Efficient Python Data Science on the Shoulders of Databases

arXiv: https://arxiv.org/abs/2407.11616 · HTML: https://mlsanity.com/paper/2407.11616/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jul 2024.

## Built on by (most cited first, 4 of 4)

- [TableRAG: A Retrieval Augmented Generation Framework for Heterogeneous Document Reasoning](https://mlsanity.com/paper/2506.10380/) (2025)
- [InferF: Declarative Factorization of AI/ML Inferences over Joins](https://mlsanity.com/paper/2511.20489/) (2025)
- [Automated Tensor-Relational Decomposition for Large-Scale Sparse Tensor Computation](https://mlsanity.com/paper/2603.08957/) (2026)
- [ORCA: Evaluating LLMs on Data Science Code Translation](https://mlsanity.com/paper/2609.30749/) (2026)

## Abstract (arXiv)

Python data science libraries such as Pandas and NumPy have recently gained immense popularity. Although these libraries are feature-rich and easy to use, their scalability limitations require more robust computational resources. In this paper, we present PyTond, an efficient approach to push the processing of data science workloads down into the database engines that are already known for their big data handling capabilities. Compared to the previous work, by introducing TondIR, our approach can capture a more comprehensive set of workloads and data layouts. Moreover, by doing IR-level optimizations, we generate better SQL code that improves the query processing by the underlying database engine. Our evaluation results show promising performance improvement compared to Python and other alternatives for diverse data science workloads.