Jul 1, 2016 · I am using dask as in how to parallelize many (fuzzy) string comparisons using apply in Pandas?. Basically I do some computations (without writing anything to disk) that invoke Pandas and Fuzzywuzzy (that may not be releasing the …

Sep 20, 2016 · Use astype. You can use the astype method to convert the dtype of a series to a NumPy dtype. df.time.astype('M8[us]')

Jan 23, 2017 · So if you persist a dask dataframe with 100 partitions you get back a dask dataframe with 100 partitions, with each partition pointing to a future currently running on the cluster. Client.compute returns a single Future for each collection. This future refers to a single Python object result collected on one worker.

Mar 2, 2017 · This is my first venture into parallel processing and I have been looking into Dask but I am having trouble actually coding it. I have had a look at their examples and documentation and I think d...

Dec 19, 2018 · Dask does not impose a timeout on tasks by default. The cancelled future that you're seeing isn't a Dask future, it's a Tornado future (Tornado is the library that Dask uses for network communication). So unfortunately all this is saying is that something failed.

Mar 15, 2018 · Simple question: I have a dataframe in dask containing about 300 mln records. I need to know the exact number of rows that the dataframe contains. Is there an easy way to do this? When I try to run dataframe.x.count().compute() it looks like it tries to load the entire data into RAM, for which there is no space and it crashes.

Sep 6, 2019 · In your example, dask is slower than python multiprocessing, because you don't specify the scheduler, so dask uses the multithreading backend, which is the default. As mdurant has pointed out, your code does not release the GIL, therefore multithreading cannot execute the task graph in parallel.

Additionally Dask won't support row-wise element insertion. This kind of workload is difficult to scale. Instead I recommend using dd.Series.where (See this answer ) or else doing your iteration in a function (after making a copy so as not to operate in place) and then using map_partitions to call that function across all of the Pandas ...

Jun 8, 2017 · (dask_df.groupby('Column B') .apply(lambda d: len(d), meta=pd.DataFrame({'Column B': 'int'}))).compute() same if I try having the dtype be int instead of "int" or for that matter 'f8' or np.float64 so it doesn't seem like …

Oct 21, 2021 · The input seen by the outer chunk lambda function here (the lambda function of x) is a grouped Pandas dataframe (from one Dask partition) and the input seen by the inner lambda function (the lambda function of y) is a Series representing one …