Selecting the smallest of the largest

This recipe can be used to create catchy news headlines such as Out of the Top 100 Universities, These 5 have the Lowest Tuition, or From the Top 50 Cities to Live, these 10 are the Most Affordable.

During analysis, it is possible that you will first need to find a grouping of data that contains the top n values in a single column and, from this subset, find the bottom m values based on a different column.

In this recipe, we find the five lowest budget movies from the top 100 scoring movies by taking advantage of the convenience methods: .nlargest and .nsmallest.

How to do it…

1. Read in the movie dataset, and select the columns: movie_title, imdb_score, and budget:

   >>> movie = pd.read_csv("data/movie.csv")
   >>> movie2 = movie[["movie_title", "imdb_score", "budget"]]
   >>> movie2.head()
      movie_title  imdb_score       budget
   0       Avatar    ...