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This 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
andpercentage_female
functions below, following the instructions in the docstrings.We already loaded the Marvel
csv
data into alist
of Characternamedtuple
s:[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!
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