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Python: creating IP ranges from an IP list

I have a CSV file with a list of IP addresses for several data centers. The list is currently similar to the table below:

Data_Center_Name IP DC_1 52.102.182.2 DC_1 52.102.182.4 DC_1 52.102.182.1 DC_1 52.102.182.5 DC_1 52.102.182.3 DC_1 27.101.178.17 DC_1 27.101.178.16 DC_1 27.101.178.15 DC_1 23.201.165.7 DC_2 55.200.162.10 DC_2 55.200.162.12 DC_2 55.200.162.13 DC_2 55.200.162.11 DC_3 30.101.102.4

I want to convert lists to separate lists, for example:

  DC_1 = [52.102.182.1-52.102.182.5, 27.101.178.15-27.101.178.17, 23.201.165.7] DC_2 = [55.200.162.10-55.200.162.13] DC_3 = [30.101.102.4]

Can someone help me with python?

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3 answers

Using python3 (I can use python2 if required)

Using the ipaddress and groupby built-in libraries and other built-in goodies:

Create a function that converts a list of ipaddress objects to ranges:

 def create_range(ip_addresses): groups=[] for _, g in itertools.groupby(enumerate(sorted(ip_addresses)), lambda (i,x):i-int(x)): group = map(operator.itemgetter(1), g) if len(group) > 1: groups.append("{}-{}".format(group[0], str(group[-1]).split('.')[-1])) else: groups.append(str(group[0])) return groups

import the necessary libraries, analyze values ​​from csv (using StringIO to simulate reading from a file):

 import csv ## for reading csv file import ipaddress ## for creating ip address objects import io ## for mimicking reading csv file import operator ## for grouping operation import itertools ## for grouping operation import collections ## for creating a defaultdict ips = defaultdict(list) csv_file = u"""Data_Center_Name, IP DC_1, 50.102.182.2 DC_1, 52.102.182.4 DC_1, 52.102.182.1 DC_1, 52.102.182.5 DC_1, 52.102.182.3 DC_1, 27.101.178.17 DC_1, 27.101.178.16 DC_1, 27.101.178.15 DC_1, 23.201.165.7 DC_2, 55.200.162.10 DC_2, 55.200.162.12 DC_2, 55.200.162.13 DC_2, 55.200.162.11 DC_3, 30.101.102.4 """ with io.StringIO(csv_file) as f: reader = list(csv.reader(f)) for (dc, ip) in reader[1:]: ip = ipaddress.IPv4Address(unicode(ip.strip())) ips[dc.strip()].append(ip) result = {dc: create_range(ip_range) for dc, ip_range in ips.items()}

Result:

 In [92]: result Out[92]: {'DC_1': ['23.201.165.7', '27.101.178.15-17', '50.102.182.2', '52.102.182.1', '52.102.182.3-5'], 'DC_2': ['55.200.162.10-13'], 'DC_3': ['30.101.102.4']}

python2

 import csv ## for reading csv file import ipaddress ## for creating ip address objects from StringIO import StringIO ## for mimicking reading csv file import operator ## for grouping operation import itertools ## for grouping operation import collections ## for creating a defaultdict def create_range(ip_addresses): groups=[] for _, g in itertools.groupby(enumerate(sorted(ip_addresses)), lambda (i,x):i-int(x)): group = map(operator.itemgetter(1), g) if len(group) > 1: groups.append("{}-{}".format(group[0], str(group[-1]).split('.')[-1])) else: groups.append(str(group[0])) return groups ips = collections.defaultdict(list) csv_file = """Data_Center_Name, IP DC_1, 50.102.182.2 DC_1, 52.102.182.4 DC_1, 52.102.182.1 DC_1, 52.102.182.5 DC_1, 52.102.182.3 DC_1, 27.101.178.17 DC_1, 27.101.178.16 DC_1, 27.101.178.15 DC_1, 23.201.165.7 DC_2, 55.200.162.10 DC_2, 55.200.162.12 DC_2, 55.200.162.13 DC_2, 55.200.162.11 DC_3, 30.101.102.4 """ reader = csv.reader(StringIO(csv_file)) next(reader) for (dc, ip) in reader: ip = ipaddress.IPv4Address(unicode(ip.strip())) ips[dc.strip()].append(ip) result = {dc: create_range(ip_range) for dc, ip_range in ips.items()}

result with py2 code

 print result {'DC_2': ['55.200.162.10-13'], 'DC_3': ['30.101.102.4'], 'DC_1': ['23.201.165.7', '27.101.178.15-17', '50.102.182.2', '52.102.182.1', '52.102.182.3-5']}

Works! Thank you You can get the output: {'DC_2': ['55 .200.162.10-55.200.162.13 '],' DC_3 ': ['30 .101.102.4'], 'DC_1': ['23 .201.165.7 ', '27 .101.178.15-27.101 .178.17 ', '50 .102.182.2', '52 .102.182.1 ', '52 .102.182.3-52.102.182.5']} -

Yes, change this line:

 groups.append("{}-{}".format(group[0], str(group[-1]).split('.')[-1]))

For this:

 groups.append("{}-{}".format(group[0], group[-1]))
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* This answer has been modified, causing a careless reading of the question *

For one range list

 df[['P1','P2']]=df.IP.str.rsplit('.',1).apply(pd.Series) d=df.sort_values(['Data_Center_Name','P1','P2']).\ groupby(['Data_Center_Name','P1']).\ IP.apply(lambda x : x.iloc[0]+'-'+x.iloc[-1] if len(x)>1 else x.iloc[0] ) d Out[388]: Data_Center_Name P1 DC_1 23.201.165 23.201.165.7 27.101.178 27.101.178.15-27.101.178.17 50.102.182 50.102.182.2 52.102.182 52.102.182.1-52.102.182.5 DC_2 55.200.162 55.200.162.10-55.200.162.13 DC_3 30.101.102 30.101.102.4 Name: IP, dtype: object

To get the result

 d.groupby(level=0).apply(list) Out[392]: Data_Center_Name DC_1 [23.201.165.7, 27.101.178.15-27.101.178.17, 50... DC_2 [55.200.162.10-55.200.162.13] DC_3 [30.101.102.4] Name: IP, dtype: object
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My decision:

  • Convert each IP to decimal

  • Sort and enter ranges (interval) from list numbers

  • Convert them to IP format.

Input:

 ips = [ "52.102.182.2", "52.102.182.4", "52.102.182.1", "52.102.182.5", "52.102.182.3", "27.101.178.17", "27.101.178.16", "27.101.178.15", "23.201.165.7", ]

Step 1:

IP => Binary => Decimal

 # Convert ips to binary strings bins = [''.join([bin(int(i))[2:].zfill(8) for i in ip.split('.')]) for ip in ips] # Convert binary strings to decimal numbers numbers = [int(b, 2) for b in bins]

or IP => Decimal

 # Convert ips to decimal numbers numbers = [sum((256 ** (3 - k)) * int(n) for k, n in enumerate(ip.split('.'))) for ip in ips]

Step 2:

 # Sort decimal numbers numbers.sort() # Get ranges from decimal numbers ranges = [] tmp = [] for i in range(len(numbers)): tmp.append(numbers[i]) if (i == len(numbers) - 1) or (numbers[i + 1] > numbers[i] + 1): if len(tmp) == 1: ranges.append(tmp[0]) else: ranges.append((tmp[0], tmp[-1])) tmp = []

Step 3:

 # Convert dec ranges to ip ranges def dec_to_ip(n): return '.'.join([str(int(n % 256 ** (4 - k) / 256 ** (3 - k))) for k in range(4)]) # Final result ip_ranges = [(dec_to_ip(r[0]), dec_to_ip(r[1])) if type(r) == tuple else dec_to_ip(r) for r in ranges]

Output:

 ['23.201.165.7', ('27.101.178.15', '27.101.178.17'), ('52.102.182.1', '52.102.182.5')]
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Source: https://habr.com/ru/post/1273027/

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