dask

What problem does it solve?

Traditional Python libraries like pandas and NumPy struggle with datasets that exceed available RAM or require significant computation time. Dask solves this by enabling parallel and distributed computing, allowing you to process terabyte-scale data efficiently on single machines or clusters.

Core Features & Use Cases

• Larger-than-Memory Data Handling: Scale pandas DataFrames and NumPy Arrays to datasets that don't fit in memory, using familiar APIs.
• Parallel & Distributed Computing: Accelerate computations by distributing tasks across multiple CPU cores or machines, processing multiple files (CSV, Parquet, JSON) in parallel.
• Use Case: Analyze a 500GB dataset of sensor readings that's too large for pandas. Use Dask DataFrames to read, filter, and aggregate the data in parallel, completing the analysis in minutes instead of hours or crashing your system.

Quick Start

To read multiple CSV files into a Dask DataFrame: import dask.dataframe as dd ddf = dd.read_csv('data/*.csv') result = ddf.groupby('category').mean().compute()