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# print(len(l))
Simple Regex
Regex on strings¶
import re
egstring = '''
Jessica is 15 years old, and Daniel is 27 years old.
Edward is 97 years old, and his grandfather, Oscar, is 108 years old
'''
ages = re.findall(r'\d{1,3}', egstring)
names = re.findall(r'[A-Z][a-z]*', egstring)
print(ages)
print(names)
result = re.split(r'\d{1,3}', egstring)
print(result)
string = "Python is fun"
match = re.search('\APython', string)
if match:
print("pattern found inside the string")
else:
print("pattern not found")
Email
# example
#pattern = r'\w{4}_\d{2}\w{5}.\w{4}@w{5}.\w{3}
#pattern1 = r'[a-z]+_[0-9a-z]+.[a-z]+@[a-z.]+'
#email_string = "bill_05gates.mics@gmail.com"
generic_pattern = r'[a-zA-Z0-9._]+@[a-z]+.[a-z]+'
email_string1 = "lalitisdashing_6969@gmail.com"
if(re.match(generic_pattern, email_string1) != None):
print(True)
else:
print(False)
# Entering an Email
email = input ("Enter an email")
email_list = ["bill_05gates.mics@gmail.com", "sahithi12_kanithi12@hotmail.com", " xyz@gmail.com",
"sachin.tripathi007@hotmail.com", "tripathi.sachin13@gmail.com", "qtdash@yahoo.com"]
email_list.append(email)
print(email_list)
#Function definition
def email_match(email_ls):
count = len(email_ls)
gmail_pattern = r'[a-zA-Z0-9._]+@gmail.[a-z]+'
hotmail_pattern = r'[a-zA-Z0-9._]+@hotmail.[a-z]+'
yahoo_pattern = r'[a-zA-Z0-9._]+@yahoo.[a-z]+'
print("---")
print("GMAIL MAILS")
for i in range(0,count):
if(re.match(gmail_pattern, email_ls[i]) != None):
print(email_ls[i])
print("---")
print("HOTMAIL MAILS")
for i in range(0,count):
if(re.match(hotmail_pattern, email_ls[i]) != None):
print(email_ls[i])
print("---")
print("YAHOO MAILS")
for i in range(0,count):
if(re.match(yahoo_pattern, email_ls[i]) !=None):
print(email_ls[i])
#Calling the function
email_match(email_list)
POS
import nltk
from nltk import pos_tag
from nltk import word_tokenize
sample_text = word_tokenize("The classes are reopening on 15th March in St. Joseph's College of Commerce")
sample_text
pos_tag(sample_text)
nltk.help.upenn_tagset("DT")
nltk.help.upenn_tagset("VBP")
# do for what is asked or how many ever are asked
#nltk.help.upenn_tagset("NNS")
text = nltk.Text(word.lower() for word in nltk.corpus.brown.words())
text