This challenge is using csv and display a nice ranking based on calculated score. It’s as not fancy as wrangling the data with Pandas, still it’s very fun. I’ve not used namedtuple as much, but after this challenge I feel more comfortable with it. Actually it’s very handy compared to class.
Output:
$ python3 directors.py 1. Sergio Leone 8.5 ------------------------------------------------------------ 1984] Once Upon a Time in America 8.4 1968] Once Upon a Time in the West 8.6 1966] The Good, the Bad and the Ugly 8.9 1964] A Fistful of Dollars 8.0 2. Christopher Nolan 8.4 ------------------------------------------------------------ 2012] The Dark Knight Rises 8.5 2008] The Dark Knight 9.0 2014] Interstellar 8.6 2010] Inception 8.8 2005] Batman Begins 8.3 2002] Insomnia 7.2 2006] The Prestige 8.5 2000] Memento 8.5 3. Quentin Tarantino 8.2 ------------------------------------------------------------ 2012] Django Unchained 8.5 2009] Inglourious Basterds 8.3 2015] The Hateful Eight 7.9 2003] Kill Bill: Vol. 1 8.1 2004] Kill Bill: Vol. 2 8.0 1997] Jackie Brown 7.5 1994] Pulp Fiction 8.9 1992] Reservoir Dogs 8.4 4. Hayao Miyazaki 8.2 ------------------------------------------------------------ 2008] Ponyo 7.7 2004] Howl's Moving Castle 8.2 1997] Princess Mononoke 8.4 2001] Spirited Away 8.6 ...
Code:
import csv
from collections import defaultdict, namedtuple
MOVIE_DATA = 'movie_metadata.csv'
NUM_TOP_DIRECTORS = 20
MIN_MOVIES = 4
MIN_YEAR = 1960
def get_movies_by_director():
'''Extracts all movies from csv and stores them in a dictionary
where keys are directors, and values is a list of movies (named tuples)'''
directors = defaultdict(list)
Movie = namedtuple("movie", ('movie_title', 'title_year', 'imdb_score'))
with open(MOVIE_DATA) as f:
movies = csv.DictReader(f)
for movie in movies:
directors[movie['director_name']].append(Movie(movie['movie_title'], movie['title_year'], movie['imdb_score']))
return directors
def get_average_scores(directors):
'''Filter directors with < MIN_MOVIES and calculate averge score'''
nominated_directors = {}
for director, movies in directors.items():
if len(movies) >= MIN_MOVIES:
nominated_directors[(director, _calc_mean(movies))] = movies
return nominated_directors
def _calc_mean(movies):
'''Helper method to calculate mean of list of Movie namedtuples'''
scores = [float(movie.imdb_score) for movie in movies]
return round(sum(scores)/len(scores),1)
def print_results(directors):
'''Print directors ordered by highest average rating. For each director
print his/her movies also ordered by highest rated movie.
See http://pybit.es/codechallenge13.html for example output'''
fmt_director_entry = '{counter}. {director:<52} {avg}'
fmt_movie_entry = '{year}] {title:<50} {score}'
sep_line = '-' * 60
report = sorted(directors.items(), key=lambda x: float(x[0][1]), reverse=True)
for i in range(NUM_TOP_DIRECTORS):
print(fmt_director_entry.format(counter=i+1, director=report[i][0][0], avg=report[i][0][1]))
print(sep_line)
for movie in report[i][1]:
print(fmt_movie_entry.format(year=movie.title_year, title=movie.movie_title, score=movie.imdb_score))
print()
def main():
'''This is a template, feel free to structure your code differently.
We wrote some tests based on our solution: test_directors.py'''
directors = get_movies_by_director()
directors = get_average_scores(directors)
print_results(directors)
if __name__ == '__main__':
main()