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
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