ptype: Probabilistic Type Inference
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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.
Key facts. Cited by 4 papers in the map (data through 13 Oct 2026). First posted on arXiv 22 Nov 2019.
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Citing papers per year: 2021: 3; 2022: 0; 2023: 0; 2024: 0; 2025: 0; 2026: 1.
4 papers.
- 2021ptype-cat: Inferring the Type and Values of Categorical Variablescited by 1
- 2021Identifying the Units of Measurement in Tabular Datacited by 1
- 2021From Strings to Data Science: a Practical Framework for Automated String Handlingcited by 0
- 2026Robustness of AutoML on Dirty Categorical Datacited by 0
- 2026Robustness of AutoML on Dirty Categorical Data31 Jan 2026
- 2021Identifying the Units of Measurement in Tabular Data23 Nov 2021
- 2021ptype-cat: Inferring the Type and Values of Categorical Variables23 Nov 2021
- 2021From Strings to Data Science: a Practical Framework for Automated String Handling2 Nov 2021