在Pandas中使用多处理读取csv文件的最简单方法

Question

这是我的问题.
随着一堆.csv文件(或其他文件).熊猫是一种简单的阅读方式并保存为Dataframe格式.但是当文件量很大时,我想用多处理来读取文件以节省一些时间.

我早期的尝试

我手动将文件分成不同的路径.分别使用:

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)   

然后结合它们.

如何运行它们pool来实现我的问题？
任何建议将不胜感激!

Answer 1

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()

Answer 2

dask 图书馆旨在不仅解决您的问题，而且肯定会解决您的问题。