amples
        --------
        >>> df = pd.DataFrame({'population': [59000000, 65000000, 434000,
        ...                                   434000, 434000, 337000, 337000,
        ...                                   11300, 11300],
        ...                    'GDP': [1937894, 2583560 , 12011, 4520, 12128,
        ...                            17036, 182, 38, 311],
        ...                    'alpha-2': ["IT", "FR", "MT", "MV", "BN",
        ...                                "IS", "NR", "TV", "AI"]},
        ...                   index=["Italy", "France", "Malta",
        ...                          "Maldives", "Brunei", "Iceland",
        ...                          "Nauru", "Tuvalu", "Anguilla"])
        >>> df
                  population      GDP alpha-2
        Italy       59000000  1937894      IT
        France      65000000  2583560      FR
        Malta         434000    12011      MT
        Maldives      434000     4520      MV
        Brunei        434000    12128      BN
        Iceland       337000    17036      IS
        Nauru         337000      182      NR
        Tuvalu         11300       38      TV
        Anguilla       11300      311      AI

        In the following example, we will use ``nsmallest`` to select the
        three rows having the smallest values in column "population".

        >>> df.nsmallest(3, 'population')
                  population    GDP alpha-2
        Tuvalu         11300     38      TV
        Anguilla       11300    311      AI
        Iceland       337000  17036      IS

        When using ``keep='last'``, ties are resolved in reverse order:

        >>> df.nsmallest(3, 'population', keep='last')
                  population  GDP alpha-2
        Anguilla       11300  311      AI
        Tuvalu         11300   38      TV
        Nauru         337000  182      NR

        When using ``keep='all'``, all duplicate items are maintained:

        >>> df.nsmallest(3, 'population', keep='all')
                  population    GDP alpha-2
        Tuvalu         11300     38      TV
        Anguilla       11300    311      AI
        Iceland       337000  17036      IS
        Nauru         337000    182      NR

        To order by the smallest values in column "population" and then "GDP", we can
        specify multiple columns like in the next example.

        >>> df.nsmallest(3, ['population', 'GDP'])
                  population  GDP alpha-2
        Tuvalu         11300   38      TV
        Anguilla       11300  311      AI
        Nauru         337000  182      NR
        r™