The Practical Guide to Python for Scientists and Engineers/

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Printing Top Rated Movies by Viewer Age

Printing Top Rated Movies by Viewer Age

Learn how to apply a filter on the data read.

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How to apply a filter on data?

Let’s compare how the tastes of different age brackets (13–19 vs. 60+) vary. If we want to see all the movies rated by people 60 and older, we’ll execute the code in line 21.

We’ll pass movie_data.age > 60 to itself, and pandas will return a DataFrame that meets this condition:

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import pandas as pd
import matplotlib.pyplot as plt
def movie(nogui=False, movielenspath=''):
    user_columns = ['user_id', 'age', 'gender']
    users = pd.read_csv(movielenspath + 'movie_lens/u.user', sep='|', names=user_columns, usecols=range(3))
    rating_columns = ['user_id', 'movie_id', 'rating']
    ratings = pd.read_csv(movielenspath + 'movie_lens/u.data', sep='\t', names=rating_columns,  usecols=range(3))
    movie_columns = ['movie_id', 'title']
    movies = pd.read_csv(movielenspath + 'movie_lens/u.item', sep='|', names=movie_columns, usecols=range(2), encoding="iso-8859-1")
    # create one merged DataFrame
    movie_ratings = pd.merge(movies, ratings)
    movie_data = pd.merge(movie_ratings, users)
    # Get the top rated ones by index
    elderly = movie_data[(movie_data.age > 60)]
    print("Top ten movies for elderly: \n", elderly[:10])
if __name__ == "__main__":
    movie()
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