2 个仓库
Conversion of query results between different tabular memory formats and data science frameworks.
Distinct from Framework Array Conversions: Existing candidates are too narrow, focusing on specific tensor conversions rather than general dataframe format interoperability.
Explore 2 awesome GitHub repositories matching data & databases · Dataframe Interoperability. Refine with filters or upvote what's useful.
Ibis is a portable Python dataframe library and multi-backend query engine that provides a unified interface for executing data transformations across diverse compute engines. It functions as a Python SQL expression compiler and dialect transpiler, allowing users to define data logic once and execute it across cloud warehouses, embedded databases, and distributed clusters without rewriting code. The project distinguishes itself through a database backend abstraction that decouples transformation logic from the underlying execution engine. It enables polyglot data workflows by mixing raw SQL s
Transforms query results into common data structures such as Pandas, Polars, and PyArrow.
cuml is a GPU-accelerated machine learning library and framework that uses CUDA to accelerate tabular data preprocessing and model execution. It provides a suite of tools for training and deploying classification, regression, and clustering models on NVIDIA GPUs and GPU clusters. The library is designed for scalability, offering a distributed GPU machine learning environment that can spread computation and data across multiple hardware accelerators and nodes to handle datasets exceeding single-device memory. It mirrors standard estimator interfaces to allow the replacement of CPU-based models
Enables seamless data movement between processing libraries by accepting various input formats like arrays and dataframes.