Bite 30. Movie data analysis

In this Bite we are going to parse a csv movie dataset to identify the directors with the highest rated movies.

1. Write get_movies_by_director: use csv.DictReader to convert movie_metadata.csv into a (default)dict of lists of Movie namedtuples. Convert/filter the data:
   
   • Only extract director_name, movie_title, title_year and imdb_score, ignoring movies without all of these fields.
   • Type conversions: title_year -> int / imdb_score -> float
   • Discard any movies older than 1960.

   Here is an extract:

   ....
   { 'Woody Allen': [
       Movie(title='Midnight in Paris', year=2011, score=7.7),
       Movie(title='The Curse of the Jade Scorpion', year=2001, score=6.8),
       Movie(title='To Rome with Love', year=2012, score=6.3),  ....
       ], ...
   }
   

2. Write the calc_mean_score helper that takes a list of Movie namedtuples and calculates the mean IMDb score, returning the score rounded to 1 decimal place.
3. Complete get_average_scores which takes the directors data structure returned by get_movies_by_director (see 1.) and returns a list of tuples (director, average_score) ordered by highest score in descending order. Only take directors into account with >= MIN_MOVIES

See the tests for more info. This could be tough one, but we really hope you learn a thing or two. Good luck and keep calm and code in Python!