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 the wrong way? If yes, what is the right way? Note: Please discuss with this particular case or other specific and concrete cases. Please do NOT talk generally.
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 like beforehand (ie that there w...
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 for this case, using it is not an option, unfortunately How can I go about correctly reading an SQL query into dask dataframe?
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 run df.
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
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 triggering some strange iterations with matplotlib (I am using the tkAgg backend).
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 unsure of because ...
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 computes part of the data, to see what the types should be - which is fine, but you should know it is happening.
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 (which is very fast) but is there no attribute that stores the total length/ length of each partition?
