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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 DataFrame class. 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.
| Package | Summary | Distribution | Download |
| python311-dask-dataframe-2024.12.0-1.2.noarch.html | Pandas-like DataFrame data structure for dask | OpenSuSE Tumbleweed for noarch | python311-dask-dataframe-2024.12.0-1.2.noarch.rpm |
| Pandas-like DataFrame data structure for dask | python311-dask-dataframe-2024.12.0-1.2.noarch.rpm | ||
| python311-dask-dataframe-2024.12.0-1.2.noarch.html | Pandas-like DataFrame data structure for dask | OpenSuSE Ports Tumbleweed for noarch | python311-dask-dataframe-2024.12.0-1.2.noarch.rpm |
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