<jupyter_start><jupyter_text>We're working with a CSV file, which contains employee information. Each record has a name field, followed by a phone number field, and a role field. The phone number field contains U.S. phone numbers, and needs to be modified to the international format, with "+1-" in front of the phone number.<jupyter_code>import re
def transform_record(record):
  new_record = re.sub(r"([\d-]+)",r"+1-\1",record)
  return new_record

print(transform_record("<NAME>,802-867-5309,System Administrator")) 
# <NAME>,+1-802-867-5309,System Administrator

print(transform_record("<NAME>,684-3481127,IT specialist")) 
# <NAME>,+1-684-3481127,IT specialist

print(transform_record("<NAME>,846-687-7436,Programmer")) 
# <NAME>,+1-846-687-7436,Programmer

print(transform_record("<NAME>,698-746-3357,Web Developer")) 
# <NAME>,+1-698-746-3357,Web Developer<jupyter_output><NAME>,+1-802-867-5309,System Administrator
<NAME>,+1-684-3481127,IT specialist
<NAME>,+1-846-687-7436,Programmer
<NAME>,+1-698-746-3357,Web Developer
{'fraction_non_alphanumeric': 0.15451895043731778, 'fraction_numerical': 0.12730806608357628, 'mean_word_length': 6.62962962962963, 'pattern_counts': {'":': 0, '<': 16, '<?xml version=': 0, '>': 16, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 3, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '624824', 'license': 'MIT', 'n_tokens_mistral': 430, 'n_tokens_neox': 354, 'n_words': 78, 'path': 'Exercises/14.ipynb', 'repo_name': 'pdesai878/Regexes', 'size': '1107'}