Learning Data Mining with Python

movie_name_data.columns = ["MovieID", "Title", "Release Date",
"Video Release", "IMDB", "", "Action", "Adventure",
"Animation", "Children's", "Comedy", "Crime", "Documentary",
"Drama", "Fantasy", "Film-Noir",
"Horror", "Musical", "Mystery", "Romance", "Sci-Fi", "Thriller",
"War", "Western"]
Chapter 4
Getting the movie title is important, so we will create a function that will return a
movie's title from its MovieID, saving us the trouble of looking it up each time. Let's
look at the code:
def get_movie_name(movie_id):
We look up the movie_name_data DataFrame for the given MovieID and return only
the title column:
title_object = movie_name_data[movie_name_data["MovieID"] ==
movie_id]["Title"]
We use the values parameter to get the actual value (and not the pandas Series
object that is currently stored in title_object). We are only interested in the first
value—there should only be one title for a given MovieID anyway!
title = title_object.values[0]
We end the function by returning the title as needed. Let's look at the code:
return title
In a new IPython Notebook cell, we adjust our previous code for printing out the top
rules to also include the titles:
for index in range(5):
print("Rule #{0}".format(index + 1))
(premise, conclusion) = sorted_confidence[index][0]
premise_names = ", ".join(get_movie_name(idx) for idx
in premise)
conclusion_name = get_movie_name(conclusion)
print("Rule: If a person recommends {0} they will
also recommend {1}".format(premise_names, conclusion_name))
print(" - Confidence: {0:.3f}".format(confidence[(premise,
conclusion)]))
print("")
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