pandas’s library, combined with Regex Library, allows for the filtering of emails to check their validity.
For details about Regex’s syntax, please visit here.
In this method, we iterate over the input_data
series and match each entry with the valid email pattern using regex match()
. The function returns True
if the exact match is found; otherwise, it returns False
.
Then, the resultant values are mapped to the input_data
using the map()
function.
#importing pandas and regex librariesimport pandas as pdimport re as regex#initialiing pandas seriesinput_data = pd.Series(['educative.io', 'jobs@educative.io', 'edpresso@educative.io'])#initializing valid email pattern (may vary)pattern ='[0-9a-zA-Z._%+-]+@[0-9a-zA-Z.-]+\\.[A-Za-z]{2,4}'#mapping valid emailsmapped_result = input_data.map(lambda i: bool(regex.match(pattern, i)))print("Valid Emails are: ")print(input_data[mapped_result])
In this method, we use str findall()
to find all matching occurrences of the valid email pattern in input_data
.
#importing pandas libraryimport pandas as pd#initializing pandas seriesinput_data = pd.Series(['educative.io', 'jobs@educative.io', 'edpresso@educative.io'])#initializing valid email pattern (may vary)pattern ='[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\\.[A-Za-z]{2,4}'#finding all matching occurrencesresult = input_data.str.findall(pattern)print("Valid Emails are: ")print([i for i in result if len(i) > 0])
In this method, we use regex findall()
to find all matching occurrences of the valid email pattern in input_data
.
#importing pandas and regex librariesimport pandas as pdimport re as regex#initializing pandas seriesinput_data = pd.Series(['educative.io', 'jobs@educative.io', 'edpresso@educative.io'])#initializing valid email pattern (may vary)pattern ='[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\\.[A-Za-z]{2,4}'#finding all matching occurrencesresult = [regex.findall(pattern, email) for email in input_data]print("Valid Emails are: ")print([i for i in result if len(i) > 0])