How to validate IP address in Pandas   

To validate IP addresses in a Pandas DataFrame, we can use

• the `pd.Series.apply() method
• custom function or regex

Here are the 2 ways to validate IP addresses in Pandas:

(1) validate with regex

df['ip'].str.contains(r"^\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}$")

(2) custom validation function

def validate_ip(ip):
	try:
    	parts = ip.split('.')
    	return len(parts) == 4 and all(0 <= int(part) < 256 for part in parts)
	except ValueError:
    	return False
	except (AttributeError, TypeError):
    	return False

df['valid_ip'] = df['ip'].apply(validate_ip)

Suppose we work with custom DataFrame like:

import pandas as pd

data = {'ip': ['192.168.0.1', '192.256.0.1', '192.168.0.2']}

df = pd.DataFrame(data)

validate with regex

To validate IP addresses with regex we have freedom of how strict the validation will be:

• strict regex for IP validation - "^(([0-9]|[1-9][0-9]|1[0-9]{2}|2[0-4][0-9]|25[0-5])\.){3}([0-9]|[1-9][0-9]|1[0-9]{2}|2[0-4][0-9]|25[0-5])$"
• basic IP validation - r"^\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}$"

So the Pandas validation will be applied by method str.contains and passing the regex:

df['ip'].str.contains(r"^\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}$")

So it the basic generation we get:

0	True
1	True
2	True
Name: ip, dtype: bool

Using the strict validation we get the correct result:

regex = "^(([0-9]|[1-9][0-9]|1[0-9]{2}|2[0-4][0-9]|25[0-5])\.){3}([0-9]|[1-9][0-9]|1[0-9]{2}|2[0-4][0-9]|25[0-5])$"
df['ip'].str.contains(regex)

result:

0 	True
1	False
2 	True
Name: ip, dtype: bool

validate with custom function

Alternatively we can use a custom function to validate IP addresses in Pandas.

This creates a new column 'valid_ip' in the DataFrame with a Boolean value. The column indicates whether each IP address is valid or not:

def validate_ip(ip):
	try:
    	parts = ip.split('.')
    	return len(parts) == 4 and all(0 <= int(part) < 256 for part in parts)
	except ValueError:
    	return False
	except (AttributeError, TypeError):
    	return False

df['valid_ip'] = df['ip'].apply(validate_ip)

	ip	valid_ip
0	192.168.0.1	True
1	192.256.0.1	False
2	192.168.0.2	True