如何使用并发将数据帧追加到空数据帧 [英] How to append dataframe to an empty dataframe using concurrent

问题描述

我想在Python中使用 concurrent 运行一个函数。这就是我所拥有的功能：

I want to run a function using concurrent in Python. This is the function that I have :

import concurrent.futures
import pandas as pd
import time

def putIndf(file):
    listSel = getline(file)
    datFram = savetoDataFrame(listSel)
    return datFram #datatype : dataframe

def main():
    newData = pd.DataFrame()
    with concurrent.futures.ProcessPoolExecutor(max_workers=30) as executor:
        for i,file in zip(fileList, executor.map(dp.putIndf, fileList)):
            df = newData.append(file, ignore_index=True)
    return df

if __name__ == '__main__':
    main()

我想将数据框加入为一个数据框 newData ，但结果只是该函数的最后一个数据帧

I want to join dataframe to be one dataframe newData, but the result is only the last dataframe from that function

推荐答案

基本上，每次迭代都分配 df ，并且永远不要增长它。您可能的意思（建议）是初始化一个空的 df 并迭代附加：

Essentially you are re-assigning df with each iteration and never growing it. What you probably meant (ill-advised) is to initialize an empty df and append iteratively:

df = pd.DataFrame()
...
df = df.append(file, ignore_index=True)

不过，首选方法是构建要在循环外一次附加到一起的数据帧的集合，并避免在循环内增加任何复杂的对象，例如数据帧。

Nonetheless, the preferred method is to build a collection of data frames to be appended all together once outside a loop and avoid growing any complex objects like data frames inside loop.

def main():
    with concurrent.futures.ProcessPoolExecutor(max_workers=30) as executor:
        # LIST COMPREHENSION
        df_list = [file for i,file in zip(fileList, executor.map(dp.putIndf, fileList))]

        # DICTIONARY COMPREHENSION
        # df_dict = {i:file for i,file in zip(fileList, executor.map(dp.putIndf, fileList))}

    df = pd.concat(df_list, ignore_index=True)
    return df

或者由于您的合并过程，附加数据fr ames到一个列表，仍然在循环外串联一次：

Alternatively due to your pool process, append data frames to a list, still concatenating once outside the loop:

def main():
    df_list = []      # df_dict = {}
    with concurrent.futures.ProcessPoolExecutor(max_workers=30) as executor:
        for i,file in zip(fileList, executor.map(dp.putIndf, fileList)):
            df_list.append(file)
            # df_dict[i] = file

    df = pd.concat(df_list, ignore_index=True)
    return df

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