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Method nsmallest

pandas/core/groupby/generic.py:1659–1721  ·  view source on GitHub ↗

Return the smallest `n` elements. Parameters ---------- n : int, default 5 Return this many ascending sorted values. keep : {'first', 'last', 'all'}, default 'first' When there are duplicate values that cannot all fit in a

(
        self, n: int = 5, keep: Literal["first", "last", "all"] = "first"
    )

Source from the content-addressed store, hash-verified

1657 return result
1658
1659 def nsmallest(
1660 self, n: int = 5, keep: Literal["first", "last", "all"] = "first"
1661 ) -> Series:
1662 """
1663 Return the smallest `n` elements.
1664
1665 Parameters
1666 ----------
1667 n : int, default 5
1668 Return this many ascending sorted values.
1669 keep : {'first', 'last', 'all'}, default 'first'
1670 When there are duplicate values that cannot all fit in a
1671 Series of `n` elements:
1672
1673 - ``first`` : return the first `n` occurrences in order
1674 of appearance.
1675 - ``last`` : return the last `n` occurrences in reverse
1676 order of appearance.
1677 - ``all`` : keep all occurrences. This can result in a Series of
1678 size larger than `n`.
1679
1680 Returns
1681 -------
1682 Series
1683 The `n` smallest values in the Series, sorted in increasing order.
1684
1685 See Also
1686 --------
1687 Series.nlargest: Get the `n` largest elements.
1688 Series.sort_values: Sort Series by values.
1689 Series.head: Return the first `n` rows.
1690
1691 Notes
1692 -----
1693 Faster than ``.sort_values().head(n)`` for small `n` relative to
1694 the size of the ``Series`` object.
1695
1696 Examples
1697 --------
1698 >>> s = pd.Series([1, 2, 3, 4, 5, 6])
1699
1700 >>> s
1701 0 1
1702 1 2
1703 2 3
1704 3 4
1705 4 5
1706 5 6
1707 dtype: int64
1708
1709 >>> s.groupby([1, 1, 1, 2, 2, 2]).nsmallest(n=2)
1710 1 0 1
1711 1 2
1712 2 3 4
1713 4 5
1714 dtype: int64
1715 """
1716 f = partial(Series.nsmallest, n=n, keep=keep)

Callers

nothing calls this directly

Calls 1

_python_apply_generalMethod · 0.80

Tested by

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