Completed bite 30

Author: cisco7507

days/01-03-datetimes/byte67/byte67.py renamed to days/01-03-datetimes/bite67/byte67.py

@@ -0,0 +1,81 @@
+'''
+from: https://codechalleng.es/bites/30/#console
+In this Bite we are going to parse a csv movie dataset to identify the directors with the highest rated movies.
+
+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.
+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),  ....
+    ], ...
+}
+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.
+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!
+'''
+
+import csv
+from collections import defaultdict, namedtuple
+import os
+from urllib.request import urlretrieve
+
+BASE_URL = 'https://bites-data.s3.us-east-2.amazonaws.com/'
+TMP = '/tmp'
+
+fname = 'movie_metadata.csv'
+remote = os.path.join(BASE_URL, fname)
+local = os.path.join(TMP, fname)
+urlretrieve(remote, local)
+
+MOVIE_DATA = local
+MIN_MOVIES = 4
+MIN_YEAR = 1960
+
+Movie = namedtuple('Movie', 'title year score')
+
+
+def get_movies_by_director():
+    """Extracts all movies from csv and stores them in a dict,
+    where keys are directors, and values are a list of movies,
+    use the defined Movie namedtuple"""
+    directors = defaultdict(list)
+
+    with open(local, 'r') as movie_data:
+        for element in csv.DictReader(movie_data):
+            directors[element['director_name']].append(
+                Movie(title=element['movie_title'], year=element['title_year'], score=element['imdb_score']))
+
+    return directors
+
+
+def calc_mean_score(movies):
+    """Helper method to calculate mean of list of Movie namedtuples,
+       round the mean to 1 decimal place"""
+    score = 0
+    for movie in movies:
+        score += float(movie.score)
+    mean_value = score / len(movies)
+    return round(mean_value, ndigits=1)
+
+
+def get_average_scores(directors):
+    """Iterate through the directors dict (returned by get_movies_by_director),
+       return a list of tuples (director, average_score) ordered by highest
+       score in descending order. Only take directors into account
+       with >= MIN_MOVIES"""
+
+    to_sort = [(director, calc_mean_score(movie_stats)) for director, movie_stats in directors.items() if
+               len(movie_stats) >= MIN_MOVIES]
+    k = sorted(to_sort, key=lambda x: x[1], reverse=True)
+    return k
+
+
+if __name__ == '__main__':
+    directors_hash = get_movies_by_director()
+    [print(avg) for avg in get_average_scores(directors_hash)]