day 5 movie data analysis script

Author: vladlind

days/04-06-collections/.movie_data_analysis.py.un~

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+import csv
+from collections import defaultdict, namedtuple
+import os
+from urllib.request import urlretrieve
+
+BASE_URL = 'http://projects.bobbelderbos.com/pcc/movies/'
+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():
+    with open(local) as f:
+        reader = csv.DictReader(f)
+        dict_movies = {}
+        for row in reader:
+            if int(row['title_year'] or 0) >= MIN_YEAR:
+                movie_tuple = Movie(row['movie_title'].strip(),int(row['title_year'] or 0),float(row['imdb_score']))
+                if row['director_name'] not in dict_movies:
+                    dict_movies[row['director_name']] = [movie_tuple]
+                else:
+                    dict_movies[row['director_name']].append(movie_tuple)
+    return dict_movies
+
+
+def calc_mean_score(movies):
+    """Helper method to calculate mean of list of Movie namedtuples,
+       round the mean to 1 decimal place"""
+    list_score = [movie.score for movie in movies]
+    return round(sum(list_score)/len(list_score), 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"""
+    return sorted([(director,calc_mean_score(movies)) for director, movies in directors.items() if len(movies) >= 4], key=lambda x: x[1], reverse=True)