Comments (1)
Infinitay
commented on June 10, 2024
I ended up resolving this by moving all my function definitions and their function dependents into a separate module. From there I changed the signatures to take in my main_df and pass it around as needed. As a result I changed my apply statement to `combinations_df.parallel_apply(process_combination, args=(main_df,), axis=1, result_type="expand").
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Related Issues (20)
- Notice on maintenance HOT 2
- Is there a way to make the progress bar more compact? HOT 1
- Error displaying widget: model not found HOT 1
- Does pandarallel not support parallel_apply with multiple columns groupby? HOT 5
- parallel_apply not working with Pandas >= 2.1 HOT 3
- AttributeError: 'DataFrameGroupBy' object has no attribute 'parallel_apply' HOT 2
- Pandarallel very slow after loading huge dataframe HOT 2
- consider taking in global environment variable for nb_workers and possibly other parameters too HOT 3
- DataFrame.applymap has been deprecated. HOT 2
- use pool as spawn HOT 2
- Python 3.12.1 with pandarallel==1.6.5 usage of parallel_apply time increase X3 HOT 1
- Pandarallel could stuck without raising any errors when using all the physical cores HOT 2
- Parallel_apply gets stuck HOT 4
- Memory usage increases across multiple `parallel_apply` HOT 2
- Dataframe with 64000 rows is being processed twice HOT 3
- Is there an implementation that do not require picke dump to any file system?
- Pandarallel is failing with SSLContext error HOT 4
- Suggest to be a no-code-change accelerator for pandas.
- Handle virtual cores more explicitly HOT 1
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