我在一个文件夹中有多个.csv文件。每个.csv文件都有来自股票列表的交易数据。我想从每个.csv文件中提取特定的数据部分（在本例中是从“BABA”股票代码中提取的），然后将多天的数据部分组合起来。由于全局解释器锁定，使用标准的For循环对150个.csv文件执行此操作需要大约15分钟。
目标：使用多处理加速For循环
问题：使用多处理时，我收到错误：属性错误：无法pickle局部对象“main.locals.compile”底部错误的完整追溯。
下面的代码速度较慢，但使用for循环可以正常工作：

def main():

    import pandas as pd
    import glob
    import numpy as np
    import multiprocessing
    from multiprocessing import Pool

    path = '/Users/DataFiles' #multiple .csv files located here
    file_list = glob.glob(path + '/*.csv')
    stock_list = []

    def compile(file):
        df = pd.read_csv(file)
        df = df.loc[df['UnderlyingSymbol'] == 'BABA'] #select a certain section of data for ticker from .csv file

#=== make some changes to dataframe ===

        def market_delta():
            return np.sum(df['Delta'] * df['OpenInterest']) / np.sum(df['OpenInterest'])
        df['DMarket'] = market_delta().round(4)

#=== append dataframes to list ===

        stock_list.append(df)

#=== using FOR LOOP ===

    for file in file_list:
        compile(file)

#=== combine dataframes from list into one dataframe and export

stock_list_merged = pd.concat(stock_list)
stock_list_merged.to_csv('scholes expanded.csv', index = False)

if __name__ == '__main__':
    main()

当我将for循环更改为多处理代码时，收到错误。

def main():

    import pandas as pd
    import glob
    import numpy as np
    import multiprocessing
    from multiprocessing import Pool

    path = '/Users/DataFiles' #multiple .csv files located here
    file_list = glob.glob(path + '/*.csv')
    stock_list = []

    def compile(file):
        df = pd.read_csv(file)
        df = df.loc[df['UnderlyingSymbol'] == 'BABA'] #select a certain section of data for ticker from .csv file

#=== make some changes to dataframe ===

        def market_delta():
            return np.sum(df['Delta'] * df['OpenInterest']) / np.sum(df['OpenInterest'])
        df['DMarket'] = market_delta().round(4)

#=== append dataframes to list ===

        stock_list.append(df)

#=== using MULTIPROCESSING ===

    pool = Pool(processes = (multiprocessing.cpu_count()-1))
    results = pool.map(compile, file_list)
    pool.close()
    pool.join()
    results_df = pd.concat(results)

#=== combine dataframes from list into one dataframe and export

stock_list_merged = pd.concat(stock_list)
stock_list_merged.to_csv('scholes expanded.csv', index = False)

if __name__ == '__main__':
    main()

错误追溯： 追溯（最近调用最后调用）：文件“/用户/andrewbochat/桌面/选项数据/practicemultiprocessing.py“，第54行，在main（）文件“/用户/andrewbochat/桌面/选项数据/practice multiprocessing.py”，第41行，在主结果=pool.map（编译，文件_列表）文件“/用户/andrewbochat/opt/anaconda 3/lib/python3.9/multiprocessing/pool.py“，第364行，在Map中返回self._map_async（函数、可迭代、Map星、块大小）.get（）文件“/用户/安德鲁波查/opt/anaconda 3/lib/python3.9/多处理/pool.py”，第771行，在获取提升自身中。_value文件“/用户/安德鲁波查/opt/anaconda 3/lib/python3.9/多处理/pool.py”，第537行，在_句柄_任务放置（任务）文件“/用户/安德鲁波查/opt/anaconda 3/lib/python3.9/多处理/ www.example.com ”，第211行，在发送自己。_send_bytes（_ForkingPickler. dump（obj））文件“/用户/andrewbochat/opt/anaconda 3/lib/python3.9/多处理/reduction.py“，第51行，在转储文件cls（buf，protocol）.dump（obj）中属性错误：无法pickle局部对象“main.locals.compile”