Principle:Huggingface Datasets Dataset From Dict Construction
| Knowledge Sources | |
|---|---|
| Domains | Data_Engineering, NLP |
| Last Updated | 2026-02-14 18:00 GMT |
Overview
Creating datasets from Python dictionaries enables quick, in-memory dataset construction from columnar data already available in Python.
Description
Dictionary-based dataset construction is the most direct way to create a HuggingFace Dataset object when your data is already structured as column-name-to-values mappings. The input dictionary maps string column names to lists (or Arrow arrays) of values. Each key becomes a column and each list element becomes a row. The library converts these Python objects into an Apache Arrow in-memory table, optionally casting columns to a provided feature schema. Because the resulting dataset lives entirely in memory, it is best suited for small to medium datasets or as an intermediate step before persisting to disk with save_to_disk or uploading with push_to_hub.
Usage
Use dictionary-based construction when you have columnar data readily available in Python, such as outputs from an API, results of a computation, or data read from a custom format. It is the fastest path from raw Python data to a fully typed Dataset object with Arrow-backed storage.
Theoretical Basis
The core logic converts each column (list or Arrow array) into an optimized typed sequence, infers or validates feature types, and assembles the columns into a PyArrow Table wrapped by the Dataset class. If explicit features are provided, the data is cast to match the schema; otherwise, types are inferred from the data itself. This two-phase approach (build then optionally cast) allows both flexible usage without a schema and strict usage with one.