Advanced Data Analytics Using Python_ With Machine Learning, Deep Learning and NLP Examples ( 2023)

Chapter 2
ETL with Python (Structured Data)
start = istart,
).execute()
import json
with open('data1.json', 'w') as fp:
json.dump(res, fp)
# pprint.pprint(type(res))
# pprint.pprint(res)
def get_email_ph(link_text, pdf=None):
if pdf==True:
from textract import process
text = process(link_text)
else:
text = link_text
# print text
import re
email = []
ph = []
valid_ph = re.compile("[789][0-9]{9}$")
valid = re.compile("[A-Za-z]+[@]{1}[A-Za-z]+\.[a-z]+")
for token in re.split(r'[,\s]',text):
# for token in nltk.tokenize(text):
# print token
a = valid.match(token)
b = valid_ph.match(token)
if a != None:
print a.group()
email.append(a.group())
if b != None:
print b.group()
ph.append(b.group())
return email, ph
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