rating_data_raw = sc.textFile("%s/ml-100k/u.data" % PATH)
print rating_data_raw.first()
num_ratings = rating_data_raw.count()
print "Ratings: %d" % num_ratings
# In[35]:
rating_data = rating_data_raw.map(lambda line: line.split("\t"))
ratings = rating_data.map(lambda fields: int(fields[2]))
max_rating = ratings.reduce(lambda x, y: max(x, y))
min_rating = ratings.reduce(lambda x, y: min(x, y))
mean_rating = ratings.reduce(lambda x, y: x + y) / float(num_ratings)
median_rating = np.median(ratings.collect())
ratings_per_user = num_ratings / num_users
ratings_per_movie = num_ratings / num_movies
print "Min rating: %d" % min_rating
print "Max rating: %d" % max_rating
print "Average rating: %2.2f" % mean_rating
print "Median rating: %d" % median_rating
print "Average # of ratings per user: %2.2f" % ratings_per_user
print "Average # of ratings per movie: %2.2f" % ratings_per_movie
# In[36]:
# we can also use the stats function to get some similar information to the above
ratings.stats()上面是粗暴的做法
简单的做法：
>>> all_data = sc.parallelize([1,2,3,4,5,6,7,8,100])
>>> all_data.mean()
15.11111111111111
>>> all_data.max()
100
>>> all_data.min()
1
>>> all_data.median()
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
AttributeError: 'RDD' object has no attribute 'median'
>>> all_data.stats()
(count: 9, mean: 15.1111111111, stdev: 30.0903987804, max: 100.0, min: 1.0)