Marvel data analysis
Level: Advanced (score: 4)
Dive into the Marvel Universe! Your goal is to analyze a dataset of Marvel characters, focusing on popularity, historical introductions, and gender representation.
You'll implement three functions:
-most_popular_characters
- max_and_min_years_new_characters
- get_percentage_female_characters
You'll find more specific instructions in their docstrings.
The dataset is pre-loaded in the template code as a list of Character (typed) namedtuples, for example:
Character(pid='1678', name='Spider-Man', sid='Secret Identity', align='Good Characters', sex='Male Characters', appearances='4043', year='1962')
Note that each character is identified by a unique pid. Note that characters with the same name but different pids represent different characters (across universes or timelines). See Thor for example:
(Pdb) pp [ch for ch in characters if ch.name == "Thor"]
[Character(pid='2460', name='Thor', sid='No Dual Identity', align='Good Characters', sex='Male Characters', appearances='2258', year='1950'),
Character(pid='755266', name='Thor', sid='Secret Identity', align='Bad Characters', sex='Male Characters', appearances='1', year='1998'),
Character(pid='20704', name='Thor', sid='', align='Bad Characters', sex='Male Characters', appearances='', year='1954')]
These are distinct characters and should not be aggregated by name alone.
Tasks:
- Popularity: Find the most popular characters based on their total appearances.
- Yearly Introductions: Identify the years with the highest and lowest numbers of new character introductions.
- Gender Representation: Calculate the percentage of female characters, ignoring characters without a specified gender.
Enjoy the journey through Marvel data while honing your Python skills!