Speeding up scalar selection

Both the .iloc and .loc indexers are capable of selecting a single element, a scalar value, from a Series or DataFrame. However, there exist the indexers, .iat and .at, which respectively achieve the same thing at faster speeds. Like .iloc, the .iat indexer uses integer location to make its selection and must be passed two integers separated by a comma. Similar to .loc, the .at index uses labels to make its selection and must be passed an index and column label separated by a comma.

 

Getting ready

This recipe is valuable if computational time is of utmost importance. It shows the performance improvement of .iat and .at over .iloc and .loc when using scalar selection.

How to do it...

1. Read in the college scoreboard dataset with the institution name as the index. Pass a college name and column name to.loc in order to select a scalar value:

>>> college = pd.read_csv('data/college.csv', index_col='INSTNM')
>>> cn = 'Texas A & M University-College...