加快庞大数据集的Python文件处理速度

2019年8月3日 179次阅读

我有一个大型数据集存储为17GB的csv文件(fileData),其中包含每个customer_id的可变数量的记录(最多约30,000).我正在尝试搜索特定客户(在fileSelection中列出 – 总共90000中的大约1500个)并将每个客户的记录复制到单独的csv文件(fileOutput)中.

我是Python的新手,但使用它因为vba和matlab(我比较熟悉)无法处理文件大小. (我正在使用Aptana studio编写代码,但是直接从cmd行运行python以获得速度.运行64位Windows 7.)

我写的代码是提取一些客户,但有两个问题：
1)无法在大型数据集中找到大多数客户. (我相信它们都在数据集中,但不能完全确定.)
2)非常慢.任何加速代码的方法都将受到赞赏,包括可以更好地利用16核PC的代码.

这是代码：

 `def main():

    # Initialisation : 

    #  - identify columns in slection file
    #
    fS = open (fileSelection,"r")
    if fS.mode == "r":
        header = fS.readline()
        selheaderlist = header.split(",")
        custkey =   selheaderlist.index('CUSTOMER_KEY')

    #
    # Identify columns in dataset file
    fileData = path2+file_data
    fD = open (fileData,"r")
    if fD.mode == "r":
        header = fD.readline()
        dataheaderlist = header.split(",")
        custID =   dataheaderlist.index('CUSTOMER_ID')
    fD.close()

    # For each customer in the selection file
    customercount=1
    for sr in fS:
        # Find customer key and locate it in customer ID field in dataset  
        selrecord = sr.split(",")
        requiredcustomer = selrecord[custkey]

        #Look for required customer in dataset
        found = 0
        fD = open (fileData,"r")
        if fD.mode == "r":
            while found == 0:
                dr = fD.readline()
                if not dr: break
                datrecord = dr.split(",")
                if datrecord[custID] == requiredcustomer:
                    found = 1

                    # Open outputfile
                    fileOutput= path3+file_out_root + str(requiredcustomer)+ ".csv"
                    fO=open(fileOutput,"w+")
                    fO.write(str(header))

                    #copy all records for required customer number
                    while datrecord[custID] == requiredcustomer:
                        fO.write(str(dr))
                        dr = fD.readline()
                        datrecord = dr.split(",")
                    #Close Output file          
                    fO.close()           

            if found == 1:
                print ("Customer Count "+str(customercount)+ "  Customer ID"+str(requiredcustomer)+" copied.  ")
                customercount = customercount+1
            else:
                print("Customer ID"+str(requiredcustomer)+" not found in dataset")
                fL.write (str(requiredcustomer)+","+"NOT FOUND")
            fD.close()

    fS.close()

    `

提取几百个客户需要几天时间,但却找不到更多.

Sample Output

谢谢@Paul Cornelius.这样效率更高.我采用了你的方法,也使用@Bernardo建议的csv处理：

# Import Modules

import csv


def main():

    # Initialisation : 


    fileSelection = path1+file_selection
    fileData = path2+file_data


    # Step through selection file and create dictionary with required ID's as keys, and empty objects
    with open(fileSelection,'rb') as csvfile:
        selected_IDs = csv.reader(csvfile)
        ID_dict = {}
        for row in selected_IDs:
            ID_dict.update({row[1]:[]})

      # step through data file: for selected customer ID's, append records to dictionary objects
    with open(fileData,'rb') as csvfile:
        dataset = csv.reader(csvfile)
        for row in dataset:
            if row[0] in ID_dict:
                    ID_dict[row[0]].extend([row[1]+','+row[4]])

        # write all dictionary objects to csv files
    for row in ID_dict.keys():
        fileOutput = path3+file_out_root+row+'.csv'
        with open(fileOutput,'wb') as csvfile:
            output = csv.writer(csvfile, delimiter='\n')
            output.writerows([ID_dict[row]])

最佳答案 对于一个简单的答案,任务太过牵扯.但是你的方法非常低效,因为你有太多的嵌套循环.尝试通过客户列表进行一次传递,并为每个构建一个“客户”对象,其中包含您稍后需要使用的任何信息.你把它们放在字典里;键是不同的必需客户变量,值是客户对象.如果我是你,我会先让这部分工作,然后再对大文件进行愚弄.

现在,您将逐步浏览大量客户数据文件,每次遇到其datarecord [custID]字段位于字典中的记录时,都会在输出文件中附加一行.您可以使用相对高效的运算符来测试字典中的成员资格.

不需要嵌套循环.

您呈现它的代码无法运行,因为您在不打开它的情况下写入名为fL的某个对象.另外,正如Tim Pietzcker指出的那样,你没有关闭你的文件,因为你实际上没有调用close函数.

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