filename = "data/users.txt"
with open(filename, 'r') as file :
# read the first line
line = file.readline()
print("ID".ljust(10), "First".ljust(20), "Middle".ljust(20), "Last".ljust(20), "Dept")
print('-' * 100)
# keep reading until end of file
while line != "" :
# extract columns from each line
items = line.strip().split(':')
print(items[0].ljust(10), items[1].ljust(20), items[2].ljust(20), items[3].ljust(20), items[4])
line = file.readline()
# close file
file.close()
ID First Middle Last Dept ---------------------------------------------------------------------------------------------------- 1080 Priscillia Forbes Shepard Cleaning Services 4382 Devan Fielder Public Relations 6285 Grey Collyer Public Relations 6201 Kierah Battaile Catering 6671 Madilyn Helling Public Relations 4898 Deri-J Watherston Research 8954 Alec Leng Production 6263 Rebbekkah Clifford Cleaning Services 1704 Elorm Lynes Sales 2064 Kinza Roxanna Farrer Research 4663 Zackery Poyntz Research 1601 Albert Lukas Montgomery Legal 3702 Albert Lukas Montgomery Sales 4730 Nadelle Landale Warehousing 4191 Cory Diljeet Stockill Sales 6119 Rhae Forrester Sales 4454 Kieara Milner Cleaning Services 5502 Taylore Amellia Granse Cleaning Services 7505 Tyra Deborah Elting Research 7506 Tyra Deborah Elting Catering 1183 Bridget Beverley Pace Catering 7337 Nathen Lemerrie Customer Service 2462 Ashlee Hooten Production 0641 Kelci-Louise Blakey Sales 8866 Asir Cowthwaite Human Resources 0196 Niven Cary Sproule Warehousing 0199 Niven Cary Sproule Sales 0243 Niven Cary Sproule Legal 4541 Keirien Blenkinsop Customer Service
filename = "data/users.txt"
with open(filename, 'r') as file :
# read the first line
line = file.readline()
print("ID".ljust(10), "First".ljust(20), "Middle".ljust(20), "Last".ljust(20), "Dept")
print('-' * 100)
# keep reading until end of file
while line != "" :
# extract columns from each line
(id, first, middle, last, dept) = line.strip().split(':')
print(id.ljust(10), first.ljust(20), middle.ljust(20), last.ljust(20), dept)
line = file.readline()
# close file
file.close()
ID First Middle Last Dept ---------------------------------------------------------------------------------------------------- 1080 Priscillia Forbes Shepard Cleaning Services 4382 Devan Fielder Public Relations 6285 Grey Collyer Public Relations 6201 Kierah Battaile Catering 6671 Madilyn Helling Public Relations 4898 Deri-J Watherston Research 8954 Alec Leng Production 6263 Rebbekkah Clifford Cleaning Services 1704 Elorm Lynes Sales 2064 Kinza Roxanna Farrer Research 4663 Zackery Poyntz Research 1601 Albert Lukas Montgomery Legal 3702 Albert Lukas Montgomery Sales 4730 Nadelle Landale Warehousing 4191 Cory Diljeet Stockill Sales 6119 Rhae Forrester Sales 4454 Kieara Milner Cleaning Services 5502 Taylore Amellia Granse Cleaning Services 7505 Tyra Deborah Elting Research 7506 Tyra Deborah Elting Catering 1183 Bridget Beverley Pace Catering 7337 Nathen Lemerrie Customer Service 2462 Ashlee Hooten Production 0641 Kelci-Louise Blakey Sales 8866 Asir Cowthwaite Human Resources 0196 Niven Cary Sproule Warehousing 0199 Niven Cary Sproule Sales 0243 Niven Cary Sproule Legal 4541 Keirien Blenkinsop Customer Service