How to use pandas DataFrame in shared memory during multiprocessing?
In one answer to: Is shared readonly data copied to different processes for multiprocessing? a working solution for shared memory for a numpy array is given.
How would the same look like if a pandas DataFrame should be used?
Background: I would like to be able to write to the DataFrame during multiprocessing and would like to be able to process it further after the multiprocessing has finished.
python pandas multiprocessing python-multiprocessing python-multithreading
add a comment |
In one answer to: Is shared readonly data copied to different processes for multiprocessing? a working solution for shared memory for a numpy array is given.
How would the same look like if a pandas DataFrame should be used?
Background: I would like to be able to write to the DataFrame during multiprocessing and would like to be able to process it further after the multiprocessing has finished.
python pandas multiprocessing python-multiprocessing python-multithreading
Have you considered using dask?
– user32185
Nov 15 '18 at 17:52
Thanks for your input. I would like to add new rows to the Dataframe with ʼdf.loc[len(df)] = [x, x]ʼ. Would dask help me with this easily and take care that this happens in a synchronized way?
– user9098935
Nov 16 '18 at 4:13
Do you mean preserving order?
– user32185
Nov 16 '18 at 14:24
You might want to have a look as this toy example pastebin
– user32185
Nov 16 '18 at 14:36
add a comment |
In one answer to: Is shared readonly data copied to different processes for multiprocessing? a working solution for shared memory for a numpy array is given.
How would the same look like if a pandas DataFrame should be used?
Background: I would like to be able to write to the DataFrame during multiprocessing and would like to be able to process it further after the multiprocessing has finished.
python pandas multiprocessing python-multiprocessing python-multithreading
In one answer to: Is shared readonly data copied to different processes for multiprocessing? a working solution for shared memory for a numpy array is given.
How would the same look like if a pandas DataFrame should be used?
Background: I would like to be able to write to the DataFrame during multiprocessing and would like to be able to process it further after the multiprocessing has finished.
python pandas multiprocessing python-multiprocessing python-multithreading
python pandas multiprocessing python-multiprocessing python-multithreading
edited Nov 15 '18 at 17:36
asked Nov 15 '18 at 13:20
user9098935
Have you considered using dask?
– user32185
Nov 15 '18 at 17:52
Thanks for your input. I would like to add new rows to the Dataframe with ʼdf.loc[len(df)] = [x, x]ʼ. Would dask help me with this easily and take care that this happens in a synchronized way?
– user9098935
Nov 16 '18 at 4:13
Do you mean preserving order?
– user32185
Nov 16 '18 at 14:24
You might want to have a look as this toy example pastebin
– user32185
Nov 16 '18 at 14:36
add a comment |
Have you considered using dask?
– user32185
Nov 15 '18 at 17:52
Thanks for your input. I would like to add new rows to the Dataframe with ʼdf.loc[len(df)] = [x, x]ʼ. Would dask help me with this easily and take care that this happens in a synchronized way?
– user9098935
Nov 16 '18 at 4:13
Do you mean preserving order?
– user32185
Nov 16 '18 at 14:24
You might want to have a look as this toy example pastebin
– user32185
Nov 16 '18 at 14:36
Have you considered using dask?
– user32185
Nov 15 '18 at 17:52
Have you considered using dask?
– user32185
Nov 15 '18 at 17:52
Thanks for your input. I would like to add new rows to the Dataframe with ʼdf.loc[len(df)] = [x, x]ʼ. Would dask help me with this easily and take care that this happens in a synchronized way?
– user9098935
Nov 16 '18 at 4:13
Thanks for your input. I would like to add new rows to the Dataframe with ʼdf.loc[len(df)] = [x, x]ʼ. Would dask help me with this easily and take care that this happens in a synchronized way?
– user9098935
Nov 16 '18 at 4:13
Do you mean preserving order?
– user32185
Nov 16 '18 at 14:24
Do you mean preserving order?
– user32185
Nov 16 '18 at 14:24
You might want to have a look as this toy example pastebin
– user32185
Nov 16 '18 at 14:36
You might want to have a look as this toy example pastebin
– user32185
Nov 16 '18 at 14:36
add a comment |
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Have you considered using dask?
– user32185
Nov 15 '18 at 17:52
Thanks for your input. I would like to add new rows to the Dataframe with ʼdf.loc[len(df)] = [x, x]ʼ. Would dask help me with this easily and take care that this happens in a synchronized way?
– user9098935
Nov 16 '18 at 4:13
Do you mean preserving order?
– user32185
Nov 16 '18 at 14:24
You might want to have a look as this toy example pastebin
– user32185
Nov 16 '18 at 14:36