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@mindey
Last active Oct 20, 2017
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import pandas
from dask import dataframe
from dask.diagnostics import ProgressBar
def parallel_apply(df, func, progress=True, chunkrows=100, scheduler_address=None, *args, **kwargs):
if scheduler_address:
from dask.distributed import Client
client = Client(scheduler_address)
sd = dataframe.from_pandas(df, npartitions=int(len(df)/chunkrows))
if progress:
with ProgressBar():
return sd.apply(func, *args, **kwargs).compute()
else:
return sd.apply(func, *args, **kwargs).compute()
# For testing progress bar :)
test_df = pandas.DataFrame({'x': range(1000000), 'y': range(1000000)[::-1]})
test_df['z'] = parallel_apply(test_df, lambda row: row['x'] * row['y'], axis=1)
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