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 …

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 …

May 24, 2022 · However, passing a meta attribute to read_sql_query and setting head_rows=0 is completely ok as long as there's an efficient way to retrieve/create while dask-sql might work …

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 …

Find the dask package suitable for your python version from the release history page of the pypi dask page. Go back to colab and remove dask completely !pip uninstall dask

May 15, 2018 · How should I get the shape of a dask dataframe? Asked 7 years, 6 months ago Modified 1 year, 7 months ago Viewed 26k times

Nov 20, 2018 · 19 I am trying to use dask-distributed on my laptop using a LocalCluster, but I have still not found a way to let my application close without raising some warnings or …

Jul 2, 2021 · I am importing a very large csv file ~680GB using Dask, however, the output is not what I expect. My aim is to select only some columns (6/50), and perhaps filter them (this I am …

Jun 8, 2017 · This is required because apply() is flexible enough that it can produce just about anything from a dataframe. As you can see, if you don't provide a meta, then dask actually …

Running len () or compute () on a dask dataframe with several million entries takes longer than the equivalent in pandas. I know I can find the number of partitions with df_dask.npartitions …