Parse vertical data

rawText.txt

Asia
     Instance REGION-1:
       ABCD_RV
         Interface: fastethernet01/01
         Last state change: 0h54m44s ago
         Sysid: 01441
         Speaks: IPv4
         Topologies:
           ipv4-unicast
         SAPA: point-to-point
         Area Address(es):
           441
         IPv4 Address(es):
           1.1.1.1
 
       EFGH_RV
         Interface: fastethernet01/01
         Last state change: 0h54m44s ago
         Sysid: 01442
         Speaks: IPv4
         Topologies:
           ipv4-unicast
         SAPA: point-to-point
         Area Address(es):
           442
         IPv4 Address(es):
           1.1.1.2
 
 EU
     Instance REGION-2:
       IJKL_RV
         Interface: fastethernet01/01
         Last state change: 0h54m44s ago
         Sysid: 01443
         Speaks: IPv4
         Topologies:
           ipv4-unicast
         SAPA: point-to-point
         Area Address(es):
           443
         IPv4 Address(es):
           1.1.1.3

ReadVertical.py

import re
import pandas as pd
from collections import defaultdict
 
 
def parse_text(data_blocks):
   """
   Generate a dict which could be converted easily to a pandas dataframe
   :param data_blocks: splittable data
   :return: dict with row values for every column
   """
   final_data = defaultdict(list)
   for key1 in data_blocks.keys():
       for key2 in data_blocks.get(key1):
           for key3 in data_blocks.get(key1).get(key2):
               final_data['area'].append(key1)
               final_data['instance'].append(key2)
               final_data['sub_instance'].append(key3)
               for items in data_blocks[key1][key2][key3].split(";"):
                   print(items)
                   if items.isspace() or len(items) == 0:
                       continue
                   a,b = re.split(r':\s*', items)
                   final_data[a].append(b)
   return final_data

def nested_dict(depth=3):
   return (defaultdict(str) if depth<2 else defaultdict(lambda: nested_dict(depth-1)))

def main():
   _level_1 = re.compile(r'instance region.*', re.IGNORECASE)
   with open('stack_formatting.txt') as f:
       data = f.readlines()

   """
   Format data so that it could be split easily
   """
   data_blocks = nested_dict()
   header = None
   instance = None
   top_header = None
   for line in data:
       line = line.strip()
       if line == "Asia" or line == "EU":
           top_header = line
       elif _level_1.match(line):
           header = line
       else:
           if "_RV" in line:
               instance = line
           elif not line.endswith(":"):
               data_blocks[top_header][header][instance] += line + ";"
           else:
               data_blocks[top_header][header][instance] += line


   print(pd.DataFrame(parse_text(data_blocks)))

if __name__ == "__main__":
   main()