Dask
Dask is an open-source parallel computing library in Python that enables scalable analytics for data science and machine learning workflows. It extends the capabilities of NumPy, pandas, and scikit-learn to handle large datasets and computations across multiple cores or distributed clusters.
Key Features
Parallelized Computation: Runs on multicore systems and distributed environments with minimal code changes.
Native Python Integration: Works seamlessly with pandas, NumPy, scikit-learn, and XGBoost.
Dynamic Task Scheduling: Optimizes execution using a task graph engine.
Scalable DataFrames & Arrays: Supports out-of-core operations for massive datasets.
Cluster Support: Runs on local machines, cloud, or HPC clusters using Dask.distributed.
Example Use Cases
Scaling pandas workflows to large datasets
Distributed training of machine learning models
Real-time processing in data pipelines
Integrating with Jupyter for interactive parallel analysis