多线程python中的同步与数据合并问题

我有一个需要在paralle处理的zip文件列表。

我就是这么做的:

 for i in range(len(list_files)):
        p.apply_async(process_data,  args=(general_pd, list_files[i], TimeStamp[i], S1_Sync, S2_Sync, S3_Sync, S4_Sync, S5_Sync, S6_Sync, S7_Sync, T1_Sync, T2_Sync, T3_Sync, T4_Sync, T5_Sync, Time_Sync))
    p.close()
    p.join()

s1_sync等是列表

S1_Sync = manager.list()
S2_Sync = manager.list()

过程数据:

  def process_data(general_dic, path, time, s1, s2, s3, s4, s5, s6, s7, t1, t2, t3, t4, t5, t_sync):

  if os.path.isfile(path):
                 s1.append(df_conv['C_strain_COY'].T.max())
                 s2.append(df_conv['C_strain_CUY'].T.max())
                 s3.append(df_conv['C_strain_ROX'].T.max())
                 s4.append(df_conv['C_strain_CUX'].T.max())
                 s5.append(df_conv['C_strain_CMX'].T.max())
                 s6.append(df_conv['C_strain_COX'].T.max())
                 s7.append(df_conv['C_strain_LOX'].T.max())

                 t1.append(df_conv['C_temp_CUY'].T.max())
                 t2.append(df_conv['C_temp_COY'].T.max())
                 t3.append(df_conv['C_temp_CUX'].T.max())
                 t4.append(df_conv['C_temp_CMX'].T.max())
                 t5.append(df_conv['C_temp_COX'].T.max())
                 print("file does exist at this time")
            else:
                s1.append(np.nan)
                s2.append(np.nan)
                s3.append(np.nan)
                s4.append(np.nan)
                s5.append(np.nan)
                s6.append(np.nan)
                s7.append(np.nan)
                t1.append(np.nan)
                t2.append(np.nan)
                t3.append(np.nan)
                t4.append(np.nan)
                t5.append(np.nan)

如果有一个文件,我会得到该数据的中值,并将其存储在s1、s2等中。

在p.close()之后,我将数据写入excel

print("writing data")
    result['TimeStamp'] = pd.Series(convert_proxy(Time_Sync))
    result['C_strain_COY'] = pd.Series(convert_proxy(S1_Sync))
    result['C_strain_CUY'] = pd.Series(convert_proxy(S2_Sync))
    result['C_strain_ROX'] = pd.Series(convert_proxy(S3_Sync))
    result['C_strain_CUX'] = pd.Series(convert_proxy(S4_Sync))
    result['C_strain_CMX'] = pd.Series(convert_proxy(S5_Sync))
    result['C_strain_COX'] = pd.Series(convert_proxy(S6_Sync))
    result['C_strain_LOX'] = pd.Series(convert_proxy(S7_Sync))

    result['C_temp_CUY'] = pd.Series(convert_proxy(T1_Sync))
    result['C_temp_COY'] = pd.Series(convert_proxy(T2_Sync))
    result['C_temp_CUX'] = pd.Series(convert_proxy(T3_Sync))
    result['C_temp_CMX'] = pd.Series(convert_proxy(T4_Sync))
    result['C_temp_COX'] = pd.Series(convert_proxy(T5_Sync))
    #general_pd.sort_values(by='TimeStamp')
    writer = pd.ExcelWriter(r'c:\ahmed\median_data_meter_1_max.xlsx', engine='xlsxwriter')
    # Convert the dataframe to an XlsxWriter Excel object.
    general_pd.to_excel(writer, sheet_name='Sheet1')
    # Close the Pandas Excel writer and output the Excel file.
    writer.save()

我将每个代理列表转换为常规列表并保存在excel中。

问题是我得到的数据是在我生成的时间戳的随机位置处理的,例如一些数据放在excel表中的错误位置。

是同步问题吗如何正确合并每个线程中的数据?

我就是这样生成时间戳的:

ts = datetime.datetime.strptime('2018-08-21', '%Y-%m-%d')
    end_ts = datetime.datetime.strptime('2018-08-23', '%Y-%m-%d')
    while ts < end_ts:
           print(ts)
           ts += datetime.timedelta(minutes=15)
           path = os.path.join(
               r'\\file01\vnxmo\\TIT\NAS\\Baudynamik\\_meter\\SpeedFT-meter1\\peakdata\\' + ts.strftime("%Y-%m-%d") + r'\\peakdata_' + ts.strftime(
                   "%Y%m%d_%H%M") + r'.bz2')
           TimeStamp.append(ts)
           list_files.append(path)