pandas多进程加速apply

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from pandarallel import pandarallel import pandas as pd   pandarallel.initialize(nb_workers=4)   res = pd.read_csv('./8.csv', low_memory=False) res['SFRZM'] = res['SFRZM'].parallel_apply(lambda x: str(x) + 'x') res.to_csv('./9.csv', index=False)

　github地址: https://github.com/nalepae/pandarallel/

Without parallelization	With parallelization
df.apply(func)	df.parallel_apply(func)
df.applymap(func)	df.parallel_applymap(func)
df.groupby(args).apply(func)	df.groupby(args).parallel_apply(func)
df.groupby(args1).col_name.rolling(args2).apply(func)	df.groupby(args1).col_name.rolling(args2).parallel_apply(func)
df.groupby(args1).col_name.expanding(args2).apply(func)	df.groupby(args1).col_name.expanding(args2).parallel_apply(func)
series.map(func)	series.parallel_map(func)
series.apply(func)	series.parallel_apply(func)
series.rolling(args).apply(func)	series.rolling(args).parallel_apply(func)