問題描述

這是我的問題.
帶有一堆 .csv 文件(或其他文件).Pandas 是一種讀取它們并保存為 Dataframe 格式的簡單方法.但是當文件量很大時，我想通過多處理讀取文件以節省一些時間.

Here is my question.
With bunch of .csv files(or other files). Pandas is an easy way to read them and save into Dataframe format. But when the amount of files was huge, I want to read the files with multiprocessing to save some time.

我手動將文件分成不同的路徑.單獨使用:

I manually divide the files into different path. Using severally:

os.chdir("./task_1")
files = os.listdir('.')
files.sort()
for file in files:
    filename,extname = os.path.splitext(file)
    if extname == '.csv':
        f = pd.read_csv(file)
        df = (f.VALUE.as_matrix()).reshape(75,90)   

然后將它們組合起來.

如何使用 pool 運行它們來解決我的問題?
任何建議將不勝感激！

How to run them with pool to achieve my problem?
Any advice would be appreciated!

推薦答案

使用Pool:

import os
import pandas as pd 
from multiprocessing import Pool

# wrap your csv importer in a function that can be mapped
def read_csv(filename):
    'converts a filename to a pandas dataframe'
    return pd.read_csv(filename)


def main():

    # get a list of file names
    files = os.listdir('.')
    file_list = [filename for filename in files if filename.split('.')[1]=='csv']

    # set up your pool
    with Pool(processes=8) as pool: # or whatever your hardware can support

        # have your pool map the file names to dataframes
        df_list = pool.map(read_csv, file_list)

        # reduce the list of dataframes to a single dataframe
        combined_df = pd.concat(df_list, ignore_index=True)

if __name__ == '__main__':
    main()

這篇關于在 Pandas 中使用多處理讀取 csv 文件的最簡單方法的文章就介紹到這了，希望我們推薦的答案對大家有所幫助，也希望大家多多支持html5模板網！