Python多处理代码不支持

Question

由于我使用许多.csv文件，并且经常需要将它们转换为不同的格式，因此我非常希望用Python为其制作一个应用程序，这样我就不必每次都用notepad++手动完成。你知道吗

下面的代码在单核模式下工作。但是，我希望合并多处理，一次处理几个线程。你知道吗

我的问题是，当我使用多重处理时，代码完成时没有任何错误，也没有打印出来。你知道吗

你能帮我排除故障吗？你知道吗

import pandas as pd
from multiprocessing import Process
import os
import time
start_time = time.time()
thread_count = 2

def timer():
    print('Script ran for ' + time.strftime("%H:%M:%S", time.gmtime(time.time() - start_time)))

class Imported:

    def __init__(self, filename, convert_type):
        self.filename, self.convert_type = filename, convert_type

        if self.convert_type == 'to_hana':
            self.delim = ';'
            self.decim = ','

        if self.convert_type == 'from_hana':
            self.delim = ','
            self.decim = '.'

        if self.convert_type not in ['to_hana' , 'from_hana']:
            raise ValueError('Convert type is wrong!')

    def convert(self):
        if self.convert_type == 'to_hana':

            self.file = pd.read_csv(self.filename, encoding = 'utf-8', sep=self.delim, decimal=self.decim)
            self.error_check()

            self.delim = ','
            self.decim = '.'

            process_id = os.getpid()
            print(f'Process ID: {process_id}')

            self.file.to_csv(self.filename, index = None, header=True, decimal=self.decim, sep=self.delim)

            print('test1')
            timer()

        if self.convert_type == 'from_hana':

            self.file = pd.read_csv(self.filename, encoding = 'utf-8', sep=self.delim, decimal=self.decim)
            self.error_check()

            self.delim = ';'
            self.decim = ','

            process_id = os.getpid()
            print(f'Process ID: {process_id}')

            self.file.to_csv(self.filename, index = None, header=True, decimal=self.decim, sep=self.delim)

            print('test2')
            timer()

    def error_check(self):
        if len(list(self.file.columns.values)[0]) > 20:
            raise ValueError('Pravědpodobně poškozený soubor. Název prvního sloupce je příliš dlouhý.')

if __name__ == '__main__':

    filenames = [['data.csv','from_hana'],['klasifikace.csv','to_hana'],['klasifikace_statistiky.csv','to_hana']]

    processes = []

    #for i in enumerate(filenames):
    #    Imported.convert(Imported(filenames[i[0]][0], filenames[i[0]][1]))

    for i in enumerate(filenames):
        process = Process(target=Imported.convert, args=(filenames[i[0]][0], filenames[i[0]][1]))
        processes.append(process)
        process.start()

    for process in processes:
        process.join()

    print('DONE')

Answer 1

您可以通过创建类的对象，然后通过将target作为对象转换你知道吗

import pandas as pd
from multiprocessing import Process
import os
import time

start_time = time.time()
thread_count = 2


def timer():
    print('Script ran for ' + time.strftime("%H:%M:%S", time.gmtime(time.time() - start_time)))


class Imported:

    def __init__(self, filename, convert_type):
        self.filename, self.convert_type = filename, convert_type

        if self.convert_type == 'to_hana':
            self.delim = ';'
            self.decim = ','

        if self.convert_type == 'from_hana':
            self.delim = ','
            self.decim = '.'

        if self.convert_type not in ['to_hana', 'from_hana']:
            raise ValueError('Convert type is wrong!')

    def convert(self):
        if self.convert_type == 'to_hana':
            self.file = pd.read_csv(self.filename, encoding='utf-8', sep=self.delim, decimal=self.decim)
            self.error_check()

            self.delim = ','
            self.decim = '.'

            process_id = os.getpid()
            print('Process ID:', process_id)

            self.file.to_csv(self.filename, index=None, header=True, decimal=self.decim, sep=self.delim)

            print('test1')
            timer()

        if self.convert_type == 'from_hana':
            self.file = pd.read_csv(self.filename, encoding='utf-8', sep=self.delim, decimal=self.decim)
            self.error_check()

            self.delim = ';'
            self.decim = ','

            process_id = os.getpid()
            print('Process ID', process_id)

            self.file.to_csv(self.filename, index=None, header=True, decimal=self.decim, sep=self.delim)

            print('test2')
            timer()

    def error_check(self):
        if len(list(self.file.columns.values)[0]) > 20:
            raise ValueError('Pravědpodobně poškozený soubor. Název prvního sloupce je příliš dlouhý.')


if __name__ == '__main__':

    filenames = [['data.csv', 'from_hana'], ['klasifikace.csv', 'to_hana'], ['klasifikace_statistiky.csv', 'to_hana']]

    processes = []

    # for i in enumerate(filenames):
    #    Imported.convert(Imported(filenames[i[0]][0], filenames[i[0]][1]))

    for i in enumerate(filenames):
        obj = Imported(filenames[i[0]][0], filenames[i[0]][1])
        process = Process(target=obj.convert)
        processes.append(process)
        process.start()

    for process in processes:
        process.join()

    print('DONE')

Answer 2

import subprocess
from multiprocessing import Pool, cpu_count
import pandas as pd

def multi_processor(function_name, file_path, output_format):

    file_list = []
    #file_list = str(subprocess.check_output(f"find {file_path} -type f -iname \"*.csv*\" ", shell=True)).split('\\n')
    #file_list = sorted(file_list)

    # Test, put 6 strings in the list so your_function should run six times with 6 processors in parallel
    file_list.append("file_path1")
    file_list.append("file_path2")
    file_list.append("file_path3")
    file_list.append("file_path4")
    file_list.append("file_path5")
    file_list.append("file_path6")

    # Use max number of system processors - 1
    pool = Pool(processes=cpu_count()-1)
    pool.daemon = True

    results = {}
    # for every file in the file list, start a new process
    for each_file in file_list:
        results[each_file] = pool.apply_async(your_function, args=(output_format, each_file))

    # Wait for all processes to finish before proceeding
    pool.close()
    pool.join()

    # Results and any errors are returned
    return {your_function: result.get() for your_function, result in results.items()}


def your_function(output_format, file_name):
    try:
        df = pd.read_csv(file_name)
        writer = pd.ExcelWriter(f"{file_name}{output_format}")
        df.to_excel(writer)
        writer.save()
        return "Success!"
    except Exception as e:
        return str(e)

if __name__ == "__main__":
    some_results = multi_processor("your_function", "some_path_to_csv_files", ".xlsx")
    print(some_results)