I can impute the mean and most frequent value using dask-ml like so, this works fine: But what if I have 100 million rows of data it seems that dask would do two loops when it could have done only one, is it possible to run both imputers simultaneously and/or in parallel instead of sequentially? What would be a sample
Tag: dask
Using set_index() on a Dask Dataframe and writing to parquet causes memory explosion
I have a large set of Parquet files that I am trying to sort on a column. Uncompressed, the data is around ~14Gb, so Dask seemed like the right tool for the job. All I’m doing with Dask is: Reading the parquet files Sorting on one of the columns (called “friend”) Writing as parquet files in a separate directory I
ERROR: Could not find a version that satisfies the requirement dask-cudf (from versions: none)
Describe the bug When I am trying to import dask_cudf I get the following ERROR: I have dask and RAPIDS installed with pip when I search for: pip install dask_cudf original site is not exists anymore: https://pypi.org/project/dask-cudf/ google stored site history: https://webcache.googleusercontent.com/search?q=cache:8in7y2jQFQIJ:https://pypi.org/project/dask-cudf/+&cd=1&hl=en&ct=clnk&gl=uk I am trying to install it with the following code in the Google Colab Window %pip install dask-cudf
Dask concatenate 2 dataframes into 1 single dataframe
Objective To merge df_labelled file with a portion of labelled points to df where contains all the point. What I have tried Referring to Simple way to Dask concatenate (horizontal, axis=1, columns), I tried the code below But I get the error ValueError: Not all divisions are known, can’t align partitions. Please use set_index to set the index. Another thing
Dask distributed.scheduler – ERROR – Couldn’t gather keys
I created a dask cluster using two local machines using I am trying to find best parameters using dask gridsearchcv. I am facing the following error. I hope someone helps in solving this issue. Thanks in advance. Answer I also meet the same issue, and I find it’s likely to be caused by firewall. Suppose we have two machines, 191.168.1.1
Writing dask bag to DB using custom function
I’m running a function on dask bag to dump data into NoSQL DB like: Now when I look at the dask task graph, after each partition completes the write_to_db function, it is being shown as memory instead ofreleased. My Questions: How to tell dask that there is no return value and hence mark memory as released? For example in the
Load oracle Dataframe in dask dataframe
I used to work with pandas and cx_Oracle until now. But I haver to switch to dask now due to RAM limitations. I tried to do it similar to how I used cx_oracle with pandas. But I receive an AttributeError named: Any ideas if its just a problem with the package? Answer Please read the dask doc on SQL: you
Losing index information when using dask.dataframe.to_parquet() with partitioning
When I was using dask=1.2.2 with pyarrow 0.11.1 I did not observe this behavior. After updating (dask=2.10.1 and pyarrow=0.15.1), I cannot save the index when I use to_parquet method with given partition_on and write_index arguments. Here I have created a minimal example which shows the issue: Which gives: I did not see that described anywhere in the dask documentation. Does