# remember in 4.py above, we put the data is in a dataframe, countries.

# 1. use the default numeric index
print(countries[1:3]) # same as print(countries[1:3:1])
# 2. above returns:
#           Country   Capital  PopulationMillions
#     Code                                      
#     CA    Canada    Ottawa                  37
#     BR    Brazil  Brasilia                 211

# 3.
print(countries[2:]) # same as print(countries[2::1])
# 4. above returns:
#          Country   Capital  PopulationMillions
#     Code                                      
#     BR    Brazil  Brasilia                 211
#     CH     China   Beijing                1000
#     FR    France     Paris                  11

# 5. 
print(countries[:3]) # same as print(countries[:3:1]) or print(countries[0:3:1]) or print(countries[0:3])
# 6. above returns:
#           Country   Capital  PopulationMillions
#     Code                                       
#     NG    Nigeria     Abuja                 206
#     CA     Canada    Ottawa                  37
#     BR     Brazil  Brasilia                 211

# 7.
print(countries[:]) # sames as print(countries[::1]) or print(countries[0::1])
# 8. above returns:
#          Country   Capital  PopulationMillions
#     Code                                       
#     NG    Nigeria     Abuja                 206
#     CA     Canada    Ottawa                  37
#     BR     Brazil  Brasilia                 211
#     CH      China   Beijing                1000
#     FR     France     Paris                  11

# 9.
print(countries[::2]) # sames as print(countries[0::2])
# 10. above returns:
#          Country   Capital  PopulationMillions
#     Code                                       
#     NG    Nigeria     Abuja                 206
#     BR     Brazil  Brasilia                 211
#     FR     France     Paris                  11

# 10.
print(countries[-1:]) # same as print(countries[-1::1])
# 11. above returns:
#         Country Capital  PopulationMillions
#     Code                                    
#     FR    France   Paris                  11

# 12.
print(countries[:-1]) # same as print(countries[0:-1]) or print(countries[0:-1:1]) or print(countries[:-1:1])
# 13. above returns:
#          Country   Capital  PopulationMillions
#     Code                                       
#     NG    Nigeria     Abuja                 206
#     CA     Canada    Ottawa                  37
#     BR     Brazil  Brasilia                 211
#     CH      China   Beijing                1000

# 14.
print(countries[-2:])
# 15. above returns:
#         Country  Capital  PopulationMillions
#     Code                                     
#     CH     China  Beijing                1000
#     FR    France    Paris                  11

# 16.
print(countries[:-2]) # same as print(countries[0:-2])
# 17. above returns:
#          Country   Capital  PopulationMillions
#     Code                                       
#     NG    Nigeria     Abuja                 206
#     CA     Canada    Ottawa                  37
#     BR     Brazil  Brasilia                 211

# 18.
print(countries[-1:-1])
# 19. above returns:
# Empty DataFrame
# Columns: [Country, Capital, PopulationMillions]
# Index: []