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pythonpython-3.xpandasdataframeconcurrent.futures

How to append dataframe to an empty dataframe using concurrent


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()

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


Solution

  • 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)
    

    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
    

    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