When possible, Index objects are implemented using hash tables that allow for very fast selection and data alignment. They are similar to Python sets in that they support operations such as intersection and union, but are dissimilar because they are ordered with duplicates allowed.
Python dictionaries and sets are also implemented with hash tables that allow for membership checking to happen very fast in constant time, regardless of the size of the object.
Notice how the values DataFrame attribute returned a NumPy n-dimensional array, or ndarray. Most of pandas relies heavily on the ndarray. Beneath the index, columns, and data are NumPy ndarrays. They could be considered the base object for pandas that many other objects are built upon. To see this, we can look at the values of the index and columns:
>>> index.values
array([ 0, 1, 2, ..., 4913, 4914, 4915])
>>> columns.values
array(['color', 'director_name', 'num_critic_for_reviews',
...
'imdb_score', 'aspect_ratio', 'movie_facebook_likes'],
dtype=object)