Python csv切割柱的部分_Python_Csv

Python csv切割柱的部分

python csv

Python csv切割柱的部分,python,csv,Python,Csv,我遇到了这个奇怪的问题我还应该提到这在过去是有效的，所以我也在想可能是.csv或特定行本身出了问题快速故障。我有一个脚本，可以从CVE（漏洞）数据的.csv文件中提取数据。然后，它使用cvss模块重新审视发现，我们使用输出作为衡量修补优先级和紧迫性的方法（在我们实现新工具之前，此脚本是临时修复）这就是问题的症结所在。下面是我的摄取文件输出现在的样子 Vulnerability Title,Plugin ID,Original CVSS Score,Default Vector,Origi

我遇到了这个奇怪的问题

我还应该提到这在过去是有效的，所以我也在想可能是.csv或特定行本身出了问题

快速故障。我有一个脚本，可以从CVE（漏洞）数据的.csv文件中提取数据。然后，它使用cvss模块重新审视发现，我们使用输出作为衡量修补优先级和紧迫性的方法

（在我们实现新工具之前，此脚本是临时修复）

这就是问题的症结所在。下面是我的摄取文件输出现在的样子

Vulnerability Title,Plugin ID,Original CVSS Score,Default Vector,Original Severity,AWS Score,AWS Vector,AWS Severity,Hosts,Host Type,Percentage Impacted
Cisco IOS IKEv1 Packet Handling Remote Information Disclosure (cisco-sa-20160916-ikev1) (BENIGNCERTAIN),NES-93736,4.6,CVSS2#AV:N/AC:L/Au:N/C:P/I:N/A:N,,,AV:N/AC:L/Au:N/C:P/I:N/A:N,,26,26,
Cisco IOS Software TCP Memory Leak DoS (cisco-sa-20150325-tcpleak),NES-82568,4.9,CVSS2#AV:N/AC:L/Au:N/C:N/I:N/A:C,,,AV:N/AC:L/Au:N/C:N/I:N/A:C,,30,26,
RHEL 5 / 6 / 7 : nss and nss-util (RHSA-2016:2779),NES-94912,4.2,CVSS2#AV:N/AC:M/Au:N/C:C/I:C/A:C/E:F/RL:OF/RC:ND,,,AV:N/AC:M/Au:N/C:C/I:C/A:C/E:F/RL:OF/RC:ND,,5112,23,

这是我的脚本之后的输出（附在下面）

为了进一步解释，第1行以“ium”开头，这是单词Medium的一个截断点，它来自我脚本第128行的底部部分（表示#原始分数的部分）。应该说是中等。所以基本上，如果你看我输入的2个字，和输出比较，它删掉了整行，只添加了脚本试图添加的单词的一半。我想可能是因为所有的支架什么的，但我不确定

Cisco IOS IKEv1 Packet Handling Remote Information Disclosure (cisco-sa-20160916-ikev1) (BENIGNCERTAIN),NES-93736,4.6,CVSS2#AV:N/AC:L/Au:N/C:P/I:N/A:N,

下面是执行此功能的脚本。这有点难看，我知道，改进建议是受欢迎的，但找出它为什么会弄乱我的文件是我现在的首要任务。我曾考虑过换成熊猫，但这需要一点时间，因为我从来没有使用过它，所以我还不知道如何做到这一点

def rescore_function():
#headers
    print 'Starting Rescore'
    csv_in = open('/tmp/rescore_test.csv', 'rb')
    csv_out = open('/tmp/rescored_vulnerabilities.csv', 'wb')
    writer = csv.writer(csv_out)
    reader = csv.reader(csv_in)
    headers = next(reader, None)
    if headers:
        writer.writerow(headers)

    print 'Creating Target Distrobution'
    for row in csv.reader(csv_in):
    #This is a terrible way of setting up the percentage of hosts impacted for target distrobution. Its ugly and horrible. Host count defines the host impacted, host_type identifies what kind of host it is. Such as Alinux, Rhel5, or Cisco IOS
        host_count = float(row[8])
        host_type = float(row[9])
        alinux_impact = host_count / ALINUX_HOST
        cisco_impact = host_count / CISCO_COUNT
        juniper_impact = host_count / JUNIPER_COUNT
        citrix_impact = host_count / CITRIX_COUNT        
        all_linux= host_count / LINUX_TOTAL
        print 'math set'

#The reason for vul_id is 3 lists combined is simple. alinux_impact NEEDS to be 24, cisco NEEDs to be 26, juniper NEEDS to match 27, because vul_id is the softwares 'vulnerability ID type
#range falls into all_linux. So fillvalue=vul_os[-1]  means if its not 24,26,27, it is "all_linux" which means it compares it to the All linux number.       
        vul_id = [24, 26, 27, 25] + range(24) + range(28,101)
        vul_os = [alinux_impact, cisco_impact, juniper_impact, all_linux]

        append_file = open('/tmp/rescored_vulnerabilities.csv', 'ab')
        append_write = csv.writer(append_file)

#Does the for loop with the fillvalue as mentioned above. Basically Y is the host type (linux, Cisco IOS, etc) and X is the vulnerability type. So it runs through and figures out the TD and rescore methods.
#X equals the percetange of impacted, so the Metric will be based on amount/percentage of X impacted and does a regex search and replace based on that using the CVSS calculations.
        print vul_id
        print vul_os
        for x,y in izip_longest(vul_os, vul_id, fillvalue=vul_os[-1]):
            print x,y
            print host_type
     #VECTOR REGEXP, host_type is which OS/Device type. 23 = RHEL5, 24 = Alinux, 26 = Cisco, 27 = Juniper   
            if host_type == y:
                row[10] = x
                if  x <= 0.25:
                    AC_Metric = 'A:C/CDP:L/TD:L/CR:H/IR:H/AR:H'
                    AP_Metric = 'A:P/CDP:L/TD:L/CR:H/IR:H/AR:H'
                    AN_Metric = 'A:N/CDP:L/TD:L/CR:H/IR:H/AR:H'
                    RCUC_Metric = 'RC:UC/CDP:L/TD:L/CR:H/IR:H/AR:H'
                    RCUR_Metric = 'RC:UR/CDP:L/TD:L/CR:H/IR:H/AR:H'
                    RCC_Metric = 'RC:C/CDP:L/TD:L/CR:H/IR:H/AR:H'
                    RCND_Metric = 'RC:ND/CDP:L/TD:L/CR:H/IR:H/AR:H'
                elif 0.26 <= x <= 0.75:
                    AC_Metric = 'A:C/CDP:L/TD:M/CR:H/IR:H/AR:H'
                    AP_Metric = 'A:P/CDP:L/TD:M/CR:H/IR:H/AR:H'
                    AN_Metric = 'A:N/CDP:L/TD:M/CR:H/IR:H/AR:H'
                    RCUC_Metric = 'RC:UC/CDP:L/TD:M/CR:H/IR:H/AR:H'
                    RCUR_Metric = 'RC:UR/CDP:L/TD:M/CR:H/IR:H/AR:H'
                    RCC_Metric = 'RC:C/CDP:L/TD:M/CR:H/IR:H/AR:H'
                    RCND_Metric = 'RC:ND/CDP:L/TD:M/CR:H/IR:H/AR:H'
                else:
                    AC_Metric = 'A:C/CDP:L/TD:H/CR:H/IR:H/AR:H'
                    AP_Metric = 'A:P/CDP:L/TD:H/CR:H/IR:H/AR:H'
                    AN_Metric = 'A:N/CDP:L/TD:H/CR:H/IR:H/AR:H'
                    RCUC_Metric = 'RC:UC/CDP:L/TD:H/CR:H/IR:H/AR:H'
                    RCUR_Metric = 'RC:UR/CDP:L/TD:H/CR:H/IR:H/AR:H'
                    RCC_Metric = 'RC:C/CDP:L/TD:H/CR:H/IR:H/AR:H'
                    RCND_Metric = 'RC:ND/CDP:L/TD:H/CR:H/IR:H/AR:H'


                text = row[6]
                text = re.sub(r'AV:N','AV:A',text)
                text = re.sub(r'AC:L','AC:H',text)
                text = re.sub(r'AC:M','AC:H',text)
                text = re.sub(r'Au:N','Au:M',text)
                text = re.sub(r'Au:S','Au:M',text)
                text = re.sub(r'A:C$',AC_Metric,text)
                text = re.sub(r'A:P$',AP_Metric,text)
                text = re.sub(r'A:N$',AP_Metric,text)
                text = re.sub(r'RC:UC',RCUC_Metric,text)
                text = re.sub(r'RC:UR',RCUR_Metric,text)
                text = re.sub(r'RC:C',RCC_Metric,text)
                text = re.sub(r'RC:ND',RCND_Metric,text)
                row[6] = text
    #NEW SCORE, uses CVSS module to take the previous vector and find out the the numbered score. It then uses that number to define the severity word.
                try:
                    vector = row[6]
                    c = CVSS2(vector)
                    row[5] = c.scores()[2]
                    vul_score = row[5]
                    if 0 <= vul_score <= 3.9:
                        vuln_word = 'Low'
                    elif 4.0 <= vul_score <=6.9:
                        vuln_word = 'Medium'
                    elif 7.0 <= vul_score <= 9.9:
                        vuln_word = 'High'
                    else:
                        vuln_word = 'Critical'
                    row[7] = vuln_word
                except CVSS2MalformedError:
                    rescored_success = False
                    pass
    #ORIGINAL SCORE, does the same as above for the original vector since NESSUS does not provide the Severity "word". This only finds the word, not the number value.
                default_score = float(row[2])
                if 0 <= default_score <= 3.9:
                    default_severity = 'Low'
                elif 4.0 <= default_score <=6.9:
                    default_severity = 'Medium'
                elif 7.0 <= default_score <= 9.9:
                    default_severity = 'High'
                else:
                    default_severity = 'Critical'
                row[4] = default_severity
                append_write.writerow(row)

def rescore_函数（）：
#标题
打印“开始重新存储”
csv_in=open（'/tmp/rescore_test.csv'，'rb'）
csv_out=open（'/tmp/rescored_漏洞.csv'，wb'）
writer=csv.writer（csv\u out）
读卡器=csv。读卡器（csv_in）
headers=next（读卡器，无）
如果标题为：
writer.writerow（标题）
打印“正在创建目标分发”
对于csv.reader中的行（csv\u in）：
#这是设置受目标分发影响的主机百分比的糟糕方法。它丑陋可怕。主机计数定义受影响的主机，主机类型标识主机的类型。例如Alinux、Rhel5或Cisco IOS
主机计数=浮动（第[8]行）
主机类型=浮动（第[9]行）
alinux\u影响=主机计数/alinux\u主机
cisco\u影响=主机\u计数/cisco\u计数
juniper\u impact=主机计数/juniper\u计数
citrix_impact=主机_计数/citrix_计数
所有linux=主机计数/linux总数
打印“数学集”
#vul_id为3个列表的原因很简单。alinux_影响需要是24，cisco需要是26，juniper需要匹配27，因为vul_id是软件的漏洞id类型
#范围包括所有linux。所以fillvalue=vul_os[-1]意味着如果不是24,26,27，那么它就是“all_linux”，这意味着它将它与all-linux编号进行比较。
vul_id=[24,26,27,25]+范围（24）+范围（28101）
vul_os=[alinux_影响、cisco_影响、juniper_影响、all_linux]
append_file=open（'/tmp/rescorred_漏洞.csv'，'ab'）
append\u write=csv.writer（append\u文件）
#使用上面提到的fillvalue执行for循环。基本上Y是主机类型（linux、Cisco IOS等），X是漏洞类型。因此，它贯穿并计算出TD和rescore方法。
#X等于受影响的百分比，因此该度量将基于受影响X的数量/百分比，并使用CVSS计算基于该数量/百分比进行正则表达式搜索和替换。
打印vul_id
打印vul_os
对于izip_最长的x，y（vul_os，vul_id，fillvalue=vul_os[-1]）：
打印x，y
打印主机类型
#VECTOR REGEXP，主机类型是哪个操作系统/设备类型。23=RHEL5，24=Alinux，26=Cisco，27=Juniper
如果主机类型==y：
行[10]=x
如果x您的代码非常大，很难复制，但我怀疑写文件句柄和所有在写模式下进行的缓冲/并发缓冲文件访问有问题。一团糟
首先，您使用csv\u out=open（'/tmp/rescored\u-volabilities.csv'，wb'）打开/截断文件。
你写标题
对于每个迭代，尽管前面提到的句柄没有关闭，但可以在追加模式下打开文件：
append_file=open（'/tmp/rescored_-volabilities.csv'，'ab'）
您也不能关闭append\u文件

我建议：

第一个是可以的
删除append_file=open（'/tmp/rescorred_-volabilities.csv'，'ab'）
用write
替换append\u write
（它会工作，write
指向同一个文件，并且仍然打开）
不要忘记在最后关闭csv\u out
（或者将所有代码放在中，打开（…）作为csv\u out:
块


请注意，此问题仅适用于Un*x。在Windows文件系统上，它会立即抛出异常，因为文件不能在写入模式下打开两次（有时也是如此）。
实际上，在python 2中r或rb是可以的。大多数python 2版本（但最新版本）在写入时需要“wb”或插入空行（在windows上）.这是一个错误，检查这里：啊，是的，是那个附加文件。我删除了它，并将其切换为只使用原始编写器，从而修复了所有问题。非常感谢您的帮助！我确实会在脚本完成后关闭文件写入文件，但我也会修复所有问题。太好了！我没有看到还有什么其他问题。
def rescore_function():
#headers
    print 'Starting Rescore'
    csv_in = open('/tmp/rescore_test.csv', 'rb')
    csv_out = open('/tmp/rescored_vulnerabilities.csv', 'wb')
    writer = csv.writer(csv_out)
    reader = csv.reader(csv_in)
    headers = next(reader, None)
    if headers:
        writer.writerow(headers)

    print 'Creating Target Distrobution'
    for row in csv.reader(csv_in):
    #This is a terrible way of setting up the percentage of hosts impacted for target distrobution. Its ugly and horrible. Host count defines the host impacted, host_type identifies what kind of host it is. Such as Alinux, Rhel5, or Cisco IOS
        host_count = float(row[8])
        host_type = float(row[9])
        alinux_impact = host_count / ALINUX_HOST
        cisco_impact = host_count / CISCO_COUNT
        juniper_impact = host_count / JUNIPER_COUNT
        citrix_impact = host_count / CITRIX_COUNT        
        all_linux= host_count / LINUX_TOTAL
        print 'math set'

#The reason for vul_id is 3 lists combined is simple. alinux_impact NEEDS to be 24, cisco NEEDs to be 26, juniper NEEDS to match 27, because vul_id is the softwares 'vulnerability ID type
#range falls into all_linux. So fillvalue=vul_os[-1]  means if its not 24,26,27, it is "all_linux" which means it compares it to the All linux number.       
        vul_id = [24, 26, 27, 25] + range(24) + range(28,101)
        vul_os = [alinux_impact, cisco_impact, juniper_impact, all_linux]

        append_file = open('/tmp/rescored_vulnerabilities.csv', 'ab')
        append_write = csv.writer(append_file)

#Does the for loop with the fillvalue as mentioned above. Basically Y is the host type (linux, Cisco IOS, etc) and X is the vulnerability type. So it runs through and figures out the TD and rescore methods.
#X equals the percetange of impacted, so the Metric will be based on amount/percentage of X impacted and does a regex search and replace based on that using the CVSS calculations.
        print vul_id
        print vul_os
        for x,y in izip_longest(vul_os, vul_id, fillvalue=vul_os[-1]):
            print x,y
            print host_type
     #VECTOR REGEXP, host_type is which OS/Device type. 23 = RHEL5, 24 = Alinux, 26 = Cisco, 27 = Juniper   
            if host_type == y:
                row[10] = x
                if  x <= 0.25:
                    AC_Metric = 'A:C/CDP:L/TD:L/CR:H/IR:H/AR:H'
                    AP_Metric = 'A:P/CDP:L/TD:L/CR:H/IR:H/AR:H'
                    AN_Metric = 'A:N/CDP:L/TD:L/CR:H/IR:H/AR:H'
                    RCUC_Metric = 'RC:UC/CDP:L/TD:L/CR:H/IR:H/AR:H'
                    RCUR_Metric = 'RC:UR/CDP:L/TD:L/CR:H/IR:H/AR:H'
                    RCC_Metric = 'RC:C/CDP:L/TD:L/CR:H/IR:H/AR:H'
                    RCND_Metric = 'RC:ND/CDP:L/TD:L/CR:H/IR:H/AR:H'
                elif 0.26 <= x <= 0.75:
                    AC_Metric = 'A:C/CDP:L/TD:M/CR:H/IR:H/AR:H'
                    AP_Metric = 'A:P/CDP:L/TD:M/CR:H/IR:H/AR:H'
                    AN_Metric = 'A:N/CDP:L/TD:M/CR:H/IR:H/AR:H'
                    RCUC_Metric = 'RC:UC/CDP:L/TD:M/CR:H/IR:H/AR:H'
                    RCUR_Metric = 'RC:UR/CDP:L/TD:M/CR:H/IR:H/AR:H'
                    RCC_Metric = 'RC:C/CDP:L/TD:M/CR:H/IR:H/AR:H'
                    RCND_Metric = 'RC:ND/CDP:L/TD:M/CR:H/IR:H/AR:H'
                else:
                    AC_Metric = 'A:C/CDP:L/TD:H/CR:H/IR:H/AR:H'
                    AP_Metric = 'A:P/CDP:L/TD:H/CR:H/IR:H/AR:H'
                    AN_Metric = 'A:N/CDP:L/TD:H/CR:H/IR:H/AR:H'
                    RCUC_Metric = 'RC:UC/CDP:L/TD:H/CR:H/IR:H/AR:H'
                    RCUR_Metric = 'RC:UR/CDP:L/TD:H/CR:H/IR:H/AR:H'
                    RCC_Metric = 'RC:C/CDP:L/TD:H/CR:H/IR:H/AR:H'
                    RCND_Metric = 'RC:ND/CDP:L/TD:H/CR:H/IR:H/AR:H'


                text = row[6]
                text = re.sub(r'AV:N','AV:A',text)
                text = re.sub(r'AC:L','AC:H',text)
                text = re.sub(r'AC:M','AC:H',text)
                text = re.sub(r'Au:N','Au:M',text)
                text = re.sub(r'Au:S','Au:M',text)
                text = re.sub(r'A:C$',AC_Metric,text)
                text = re.sub(r'A:P$',AP_Metric,text)
                text = re.sub(r'A:N$',AP_Metric,text)
                text = re.sub(r'RC:UC',RCUC_Metric,text)
                text = re.sub(r'RC:UR',RCUR_Metric,text)
                text = re.sub(r'RC:C',RCC_Metric,text)
                text = re.sub(r'RC:ND',RCND_Metric,text)
                row[6] = text
    #NEW SCORE, uses CVSS module to take the previous vector and find out the the numbered score. It then uses that number to define the severity word.
                try:
                    vector = row[6]
                    c = CVSS2(vector)
                    row[5] = c.scores()[2]
                    vul_score = row[5]
                    if 0 <= vul_score <= 3.9:
                        vuln_word = 'Low'
                    elif 4.0 <= vul_score <=6.9:
                        vuln_word = 'Medium'
                    elif 7.0 <= vul_score <= 9.9:
                        vuln_word = 'High'
                    else:
                        vuln_word = 'Critical'
                    row[7] = vuln_word
                except CVSS2MalformedError:
                    rescored_success = False
                    pass
    #ORIGINAL SCORE, does the same as above for the original vector since NESSUS does not provide the Severity "word". This only finds the word, not the number value.
                default_score = float(row[2])
                if 0 <= default_score <= 3.9:
                    default_severity = 'Low'
                elif 4.0 <= default_score <=6.9:
                    default_severity = 'Medium'
                elif 7.0 <= default_score <= 9.9:
                    default_severity = 'High'
                else:
                    default_severity = 'Critical'
                row[4] = default_severity
                append_write.writerow(row)