avatar Bite 124. Marvel data analysis

marvel spiderman coffee mugThis is a simplified version of our Marvel Data Analysis we held at the Alicante PyChallengeDay.

Complete most_popular_characters, max_and_min_years_new_characters and percentage_female functions below, following the instructions in the docstrings.

We already loaded the Marvel csv data into a list of Character namedtuples:

[Character(pid='1678', name='Spider-Man', 
           sid='Secret Identity', align='Good Characters', 
           sex='Male Characters', appearances='4043', year='1962'),
 Character(pid='7139', name='Captain America', 
           sid='Public Identity', align='Good Characters', 
           sex='Male Characters', appearances='3360', year='1941'),
 Character(pid='64786', name='Wolverine', 
           sid='Public Identity', align='Neutral Characters', 
           sex='Male Characters', appearances='3061', year='1974'),

Note that if a character appears in multiple eras / universes they should be treated as separate unique characters. For example:

Susan Storm (Earth-616)
Susan Storm (Heroes Reborn) (Earth-616)
Susan Storm (Onslaught Reborn) (Earth-616)
Susan Storm (Retro, Skrull) (Earth-616)

are 4 characters, not 1!

Ready to get some interesting facts from this Marvel data set? Enjoy and learn more Python!

Login and get coding
go back Advanced level
Bitecoin 4X

188 out of 190 users completed this Bite.
Will you be Pythonista #189 to crack this Bite?
Resolution time: ~65 min. (avg. submissions of 5-240 min.)
Pythonistas rate this Bite 4.25 on a 1-10 difficulty scale.
» Up for a challenge? 💪

Focus on this Bite hiding sidebars, turn on Focus Mode.

Ask for Help