Dask DataFrame.to_parquet fails on read - Stack Overflow
Mar 15, 2022 · Use dask.dataframe.read_parquet or other dask I/O implementations, not dask.delayed wrapping pandas I/O operations, whenever possible. Giving dask direct access …
Converting an DataFrame from pandas to dask - Stack Overflow
Oct 22, 2020 · I followed this documentation dask.dataframe.from_pandas and there are optional arguments called npartitions and chunksize. So I try write something like this: import …
How to transform Dask.DataFrame to pd.DataFrame?
Aug 18, 2016 · How can I transform my resulting dask.DataFrame into pandas.DataFrame (let's say I am done with heavy lifting, and just want to apply sklearn to my aggregate result)?
dask: difference between client.persist and client.compute
Jan 23, 2017 · More pragmatically, I recommend using persist when your result is large and needs to be spread among many computers and using compute when your result is small and …
Dask does not use all workers and behaves differently with …
Apr 21, 2023 · Workers: 15 Threads: 15 Memory: 22.02 GiB Dask Version: 2023.2.0 Dask.Distributed Version: 2023.2.0 10 nodes If I use 10 nodes the calculations interrupted …
python - Why does Dask perform so slower while multiprocessing …
Sep 6, 2019 · 36 dask delayed 10.288054704666138s my cpu has 6 physical cores Question Why does Dask perform so slower while multiprocessing perform so much faster? Am I using …
dask dataframe how to convert column to to_datetime
Sep 20, 2016 · When using black-box methods like map_partitions, dask.dataframe needs to know the type and names of the output. There are a few ways to do this listed in the docstring …
dask - Make Pandas DataFrame apply () use all cores? - Stack …
As of August 2017, Pandas DataFame.apply() is unfortunately still limited to working with a single core, meaning that a multi-core machine will waste the majority of its compute-time when you …
python - Difference between dask.distributed LocalCluster with …
Sep 2, 2019 · What is the difference between the following LocalCluster configurations for dask.distributed? Client(n_workers=4, processes=False, threads_per_worker=1) versus …
Strategy for partitioning dask dataframes efficiently
Jun 20, 2017 · The documentation for Dask talks about repartioning to reduce overhead here. They however seem to indicate you need some knowledge of what your dataframe will look …