How to use the pandarallel._pandarallel._DataFrameGroupBy.apply function in pandarallel

To help you get started, we’ve selected a few pandarallel examples, based on popular ways it is used in public projects.

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nalepae / pandarallel / pandarallel / _pandarallel.py View on Github

cls.__store_ctx = _plasma.start_plasma_store(int(shm_size_mb * 1e6))
        plasma_store_name, _ = cls.__store_ctx.__enter__()

        plasma_client = _plasma.connect(plasma_store_name)

        args = plasma_store_name, nb_workers, plasma_client

        _pd.DataFrame.parallel_apply = _DataFrame.apply(*args, progress_bar)
        _pd.DataFrame.parallel_applymap = _DataFrame.applymap(*args, progress_bar)

        _pd.Series.parallel_map = _Series.map(*args, progress_bar)
        _pd.Series.parallel_apply = _Series.apply(*args, progress_bar)

        _pd.core.window.Rolling.parallel_apply = _SeriesRolling.apply(*args, progress_bar)

        _pd.core.groupby.DataFrameGroupBy.parallel_apply = _DataFrameGroupBy.apply(*args)

        _pd.core.window.RollingGroupby.parallel_apply = _RollingGroupby.apply(*args)

How to use the pandarallel._pandarallel._DataFrameGroupBy.apply function in pandarallel

To help you get started, we’ve selected a few pandarallel examples, based on popular ways it is used in public projects.

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