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08_13.py
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# Section 8.13 snippets
# Cleaning Your Data
# Data Validation
import pandas as pd
zips = pd.Series({'Boston': '02215', 'Miami': '3310'})
zips
zips.str.match(r'\d{5}')
cities = pd.Series(['Boston, MA 02215', 'Miami, FL 33101'])
cities
cities.str.contains(r' [A-Z]{2} ')
cities.str.match(r' [A-Z]{2} ')
# Reformatting Your Data
contacts = [['Mike Green', 'demo1@deitel.com', '5555555555'],
['Sue Brown', 'demo2@deitel.com', '5555551234']]
contactsdf = pd.DataFrame(contacts,
columns=['Name', 'Email', 'Phone'])
contactsdf
import re
def get_formatted_phone(value):
result = re.fullmatch(r'(\d{3})(\d{3})(\d{4})', value)
return '-'.join(result.groups()) if result else value
formatted_phone = contactsdf['Phone'].map(get_formatted_phone)
formatted_phone
contactsdf['Phone'] = formatted_phone
contactsdf
##########################################################################
# (C) Copyright 2019 by Deitel & Associates, Inc. and #
# Pearson Education, Inc. All Rights Reserved. #
# #
# DISCLAIMER: The authors and publisher of this book have used their #
# best efforts in preparing the book. These efforts include the #
# development, research, and testing of the theories and programs #
# to determine their effectiveness. The authors and publisher make #
# no warranty of any kind, expressed or implied, with regard to these #
# programs or to the documentation contained in these books. The authors #
# and publisher shall not be liable in any event for incidental or #
# consequential damages in connection with, or arising out of, the #
# furnishing, performance, or use of these programs. #
##########################################################################