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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 larger-than-memory or distributed environments. These parallel collections run on top of dynamic task schedulers. This package contains the dask array class. Dask arrays implement a subset of the NumPy interface on large arrays using blocked algorithms and task scheduling.
| Package | Summary | Distribution | Download |
| python310-dask-array-2024.11.1-1.1.noarch.html | Numpy-like array data structure for dask | OpenSuSE Tumbleweed for noarch | python310-dask-array-2024.11.1-1.1.noarch.rpm |
| Numpy-like array data structure for dask | python310-dask-array-2024.11.1-1.1.noarch.rpm | ||
| python310-dask-array-2024.8.2-1.1.noarch.html | Numpy-like array data structure for dask | OpenSuSE Ports Tumbleweed for noarch | python310-dask-array-2024.8.2-1.1.noarch.rpm |
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