在 Pandas groupby 之后并行化应用 [英] Parallelize apply after pandas groupby

问题描述

我在groupby之后使用了rosetta.parallel.pandas_easy来并行化apply，例如:

I have used rosetta.parallel.pandas_easy to parallelize apply after groupby, for example:

from rosetta.parallel.pandas_easy import groupby_to_series_to_frame
df = pd.DataFrame({'a': [6, 2, 2], 'b': [4, 5, 6]},index= ['g1', 'g1', 'g2'])
groupby_to_series_to_frame(df, np.mean, n_jobs=8, use_apply=True, by=df.index)

但是，有没有人想出如何并行化返回 DataFrame 的函数?正如预期的那样，此代码对于 rosetta 失败.

However, has anyone figured out how to parallelize a function that returns a DataFrame? This code fails for rosetta, as expected.

def tmpFunc(df):
    df['c'] = df.a + df.b
    return df

df.groupby(df.index).apply(tmpFunc)
groupby_to_series_to_frame(df, tmpFunc, n_jobs=1, use_apply=True, by=df.index)

推荐答案

这似乎有效，尽管它确实应该内置到 Pandas 中

This seems to work, although it really should be built in to pandas

import pandas as pd
from joblib import Parallel, delayed
import multiprocessing

def tmpFunc(df):
    df['c'] = df.a + df.b
    return df

def applyParallel(dfGrouped, func):
    retLst = Parallel(n_jobs=multiprocessing.cpu_count())(delayed(func)(group) for name, group in dfGrouped)
    return pd.concat(retLst)

if __name__ == '__main__':
    df = pd.DataFrame({'a': [6, 2, 2], 'b': [4, 5, 6]},index= ['g1', 'g1', 'g2'])
    print 'parallel version: '
    print applyParallel(df.groupby(df.index), tmpFunc)

    print 'regular version: '
    print df.groupby(df.index).apply(tmpFunc)

    print 'ideal version (does not work): '
    print df.groupby(df.index).applyParallel(tmpFunc)

这篇关于在 Pandas groupby 之后并行化应用的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！