SFrames

SFrames are a family of data-structure constructs used for scalable tabular data analysis, originally developed as part of GraphLab Create and later continued in Turicreate. An SFrame stores data in a columnar fashion, where the frame consists of multiple columns, and each column is an SArray that holds values of a single data type. A key feature is its support for out-of-core processing: data can be stored on disk and loaded as needed, enabling operations on datasets that exceed available memory.

The SFrame API provides typical data-manipulation capabilities required in data science workflows. Users can create SFrames from dictionaries, CSV files, or databases, select or rename columns, add or remove columns, and index rows. Operations often support filtering, sorting, grouping and aggregation, and joining with other frames. Custom transformations can be applied to columns, and the structure is designed to integrate with machine learning pipelines for feature engineering and model training. Data types supported include numeric, boolean, string, and more complex types such as vectors or lists.

SFrames are designed to be efficient and flexible for large-scale data work. They enable lazy loading, parallel processing on multi-core machines, and efficient memory usage through columnar storage. While they can be converted to in-memory formats such as pandas DataFrames for smaller datasets, SFrames excel in handling datasets that do not fit entirely in memory.

Historically associated with GraphLab Create, SFrames later became central in Turicreate workflows for building and deploying machine learning models. They are often used for preparing data, feature extraction, and pre-processing steps before model training.