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docs.dask.org
A Dask DataFrame is a large parallel DataFrame composed of many smaller pandas DataFrames, split along the index. These pandas DataFrames may live on disk for larger-than-memory computing on a single machine, or on many different machines in a cluster. One Dask DataFrame operation triggers many operations on the constituent pandas DataFrames. Design¶ Dask DataFrames coordinate many pandas DataFram
Dask¶ Dask is a flexible library for parallel computing in Python. Dask is composed of two parts: Dynamic task scheduling optimized for computation. This is similar to Airflow, Luigi, Celery, or Make, but optimized for interactive computational workloads. “Big Data” collections like parallel arrays, dataframes, and lists that extend common interfaces like NumPy, Pandas, or Python iterators to larg
Dask is a Python library for parallel and distributed computing. Dask is: Easy to use and set up (it’s just a Python library) Powerful at providing scale, and unlocking complex algorithms and Fun 🎉 How to Use Dask¶ Dask provides several APIs. Choose one that works best for you: Dask Futures parallelize arbitrary for-loop style Python code, providing: Flexible tooling allowing you to construct cus
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