从txt文件中提取文本并将其转换为df

将此txt文件包含值

google.com('172.217.163.46', 443)
        commonName: \*google.com
        issuer: GTS CA 1O1
        notBefore: 2020-02-12 11:47:11
        notAfter:  2020-05-06 11:47:11

facebook.com('31.13.79.35', 443)
        commonName: \*facebook.com
        issuer: DigiCert SHA2 High Assurance Server CA
        notBefore: 2020-01-16 00:00:00
        notAfter:  2020-04-15 12:00:00

如何将其转换为df

尝试了一下，并获得了部分成功：

f = open("out.txt", "r")
a=(f.read())


a=(pd.read_csv(StringIO(data),
              header=None,
     #use a delimiter not present in the text file
     #forces pandas to read data into one column
              sep="/",
              names=['string'])
     #limit number of splits to 1
  .string.str.split(':',n=1,expand=True)
  .rename({0:'Name',1:'temp'},axis=1)
  .assign(temp = lambda x: np.where(x.Name.str.strip()
                             #look for string that ends 
                             #with a bracket
                              .str.match(r'(.\*)]$)'),
                              x.Name,
                              x.temp),
          Name = lambda x: x.Name.str.replace(r'(.\*)]$)','Name')
          )
   #remove whitespace
 .assign(Name = lambda x: x.Name.str.strip())
 .pivot(columns='Name',values='temp')
 .ffill()
 .dropna(how='any')
 .reset_index(drop=True)
 .rename_axis(None,axis=1)
 .filter(['Name','commonName','issuer','notBefore','notAfter'])      
  )

但这是循环的，并给我多个数据，例如单行有多个重复项

问题来源：stackoverflow