extract a text from txt file and convert it into df

vebey74412 :

have this txt files with values

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

How to convert this into df

tried this and got partially successfull:

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'])      
  )

But this is looping and giving me multiple data like single rows has a multiple duplicates

Serge Ballesta :

The file is not is csv format, so you should not read it with read_csv but parse it by hand. Here you could do:

with open("out.txt") as fd:
    cols = {'commonName','issuer','notBefore','notAfter'}  # columns to keep
    rows = []                                              # list of records
    for line in fd:
        line = line.strip()
        if ':' in line:
            elt = line.split(':', 1)                       # data line: parse it
            if elt[0] in cols:
                rec[elt[0]] = elt[1]
        elif len(line) > 0:
            rec = {'Name': line}                           # initial line of a block
            rows.append(rec)

a = pd.DataFrame(rows)         # and build the dataframe from the list of records

It gives:

                                Name       commonName                                   issuer               notAfter             notBefore
0  google.com('172.217.163.46', 443)     *.google.com                               GTS CA 1O1    2020-05-06 11:47:11   2020-02-12 11:47:11
1   facebook.com('31.13.79.35', 443)   *.facebook.com   DigiCert SHA2 High Assurance Server CA    2020-04-15 12:00:00   2020-01-16 00:00:00