Numerical Computing with Python

How to do it...

1. Read the movie dataset, set the movie title as the index, and create the criteria:

>>> movie = pd.read_csv('data/movie.csv', index_col='movie_title')>>> c1 = movie['title_year'] >= 2010>>> c2 = movie['title_year'].isnull()>>> criteria = c1 | c2

2. Use the mask method on a DataFrame to make all the values in rows with movies that were made from 2010 onward missing. Any movie that originally had a missing value for title_year is also masked:

>>> movie.mask(criteria).head()

3. Notice how all the values in the third, fourth, and fifth rows from the preceding DataFrame are missing. Chain the dropna method to remove rows that have ...