函数在Python多处理池中不起作用

Question

这是我的代码，正在运行，但是有点慢。

def fsl(Df,p):
    NewDf = Df.copy()

    if p=='group1':
        try:
            del NewDf['sl']
            del NewDf['Y2030']
        except:
            pass

    selected_clusters = NewDf.loc[(NewDf['Group']==p) & (NewDf['Selected']=='Y'),'Clusters'].tolist()

    for i in selected_clusters:
        x = 0
        surplus = calc_surplus(x,i,p)
        while (surplus > 0) and (x < 100000):
            x += 100
            surplus = calc_surplus(x,i,p)
        NewDf.loc[(NewDf['Clusters']==i) & (NewDf['Group']==p),'sl']=x

    if p=='group1':
        NewDf['sl'] = NewDf['sl'].fillna(0)

    return NewDf

我希望每个surplus的{​​{1}}可以并行计算，以加快处理速度。

我将那些用于并行运行的代码移到了一个新函数上，并试图像这样在selected_cluster上运行它：

Multiprocessing.Pool

问题是，调用功能def parallel(i): x = 0 surplus = calc_surplus(x,i,p) while (surplus > 0) and (x < 100000): x += 100 surplus = calc_surplus(x,i,p) NewDf.loc[(NewDf['Clusters']==i) & (NewDf['Group']==p),'sl']=x if p=='group1': NewDf['sl'] = NewDf['sl'].fillna(0) def fsl(Df,p): NewDf = Df.copy() if p=='group1': try: del NewDf['sl'] del NewDf['Y2030'] except: pass selected_clusters = NewDf.loc[(NewDf['Group']==p) & (NewDf['Selected']=='Y'),'Clusters'].tolist() if __name__ == '__main__': with Pool(4) as pool: pool.map(parallel,[i for i in selected_clusters]) return NewDf 时，功能parallel永远不会运行。从未创建列fsl。我认为错误出在sl或pool.map中，但我似乎真的无法解决。

我已经在Multiprocessing上看到了其他线程，但是其中大多数并不完全适用于此。我的代码有什么问题？