Principle:Huggingface Datasets Pandas Dataset Building
| Knowledge Sources | |
|---|---|
| Domains | Data_Engineering, NLP |
| Last Updated | 2026-02-14 18:00 GMT |
Overview
Pandas Dataset Building is the principle of constructing HuggingFace Datasets from Pandas pickle files, where an ArrowBasedBuilder reads pickled DataFrames and converts them to Arrow tables. This builder is deprecated and maintained for backward compatibility.
Description
Pandas pickle files store serialized DataFrame objects using Python's pickle protocol. The Pandas Dataset Building principle defines how the packaged Pandas builder, an ArrowBasedBuilder subclass, reads these pickled DataFrames and converts them into Arrow record batches for consumption by the HuggingFace Dataset ecosystem. The builder deserializes each pickle file into a Pandas DataFrame and then uses PyArrow's Table.from_pandas conversion to produce the corresponding Arrow table.
This builder exists as a legacy format builder that was introduced when pickle-based DataFrame serialization was a common data exchange pattern in the Python data science ecosystem. Because pickle files are not self-describing, not portable across Python versions, and carry inherent security risks from arbitrary code execution during deserialization, this format has been superseded by safer and more efficient alternatives such as Parquet and Arrow IPC. The builder is maintained solely for backward compatibility with existing datasets that were originally stored in this format.
Despite its deprecated status, the Pandas builder follows the same ArrowBasedBuilder contract as other format builders, producing Arrow tables through the _generate_tables method and integrating with the standard dataset preparation pipeline for caching and splitting.
Usage
Use Pandas Dataset Building only when you have legacy datasets stored as Pandas pickle files and need to load them into the HuggingFace Dataset format for backward compatibility. For all new datasets, prefer Parquet or other self-describing formats that provide better portability, safety, and performance. Be aware that loading pickle files from untrusted sources poses a security risk due to the potential for arbitrary code execution during deserialization.
Theoretical Basis
Pandas DataFrames store data in a row-oriented or mixed layout that must be converted to Arrow's columnar format during ingestion. The conversion relies on PyArrow's Table.from_pandas function, which maps Pandas dtypes to Arrow types and reorganizes the data from a column-of-arrays layout into Arrow's chunked columnar representation. Because pickle deserialization reconstructs arbitrary Python objects, the process is inherently slower and less safe than reading from self-describing columnar formats like Parquet, where the schema and data are encoded in a well-defined binary layout that can be parsed without executing arbitrary code.