# ptype: Probabilistic Type Inference

arXiv: https://arxiv.org/abs/1911.10081 · HTML: https://mlsanity.com/paper/1911.10081/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Nov 2019.

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

- [ptype-cat: Inferring the Type and Values of Categorical Variables](https://mlsanity.com/paper/2111.11956/) (2021)
- [Identifying the Units of Measurement in Tabular Data](https://mlsanity.com/paper/2111.11959/) (2021)
- [From Strings to Data Science: a Practical Framework for Automated String Handling](https://mlsanity.com/paper/2111.01868/) (2021)
- [Robustness of AutoML on Dirty Categorical Data](https://mlsanity.com/paper/2602.00412/) (2026)

## Abstract (arXiv)

Type inference refers to the task of inferring the data type of a given column of data. Current approaches often fail when data contains missing data and anomalies, which are found commonly in real-world data sets. In this paper, we propose ptype, a probabilistic robust type inference method that allows us to detect such entries, and infer data types. We further show that the proposed method outperforms the existing methods.