nsmallest(self, n: 'int' = 5, keep: 'str' = 'first') -> 'Series'
Faster than .sort_values().head(n)
for small n relative to the size of the Series
object.
Return this many ascending sorted values.
When there are duplicate values that cannot all fit in a Series of n elements:
The n smallest values in the Series, sorted in increasing order.
Return the smallest n elements.
Series.head
Return the first :None:None:`n` rows.
Series.nlargest
Get the :None:None:`n` largest elements.
Series.sort_values
Sort Series by values.
>>> countries_population = {"Italy": 59000000, "France": 65000000,
... "Brunei": 434000, "Malta": 434000,
... "Maldives": 434000, "Iceland": 337000,
... "Nauru": 11300, "Tuvalu": 11300,
... "Anguilla": 11300, "Montserrat": 5200}
... s = pd.Series(countries_population)
... s Italy 59000000 France 65000000 Brunei 434000 Malta 434000 Maldives 434000 Iceland 337000 Nauru 11300 Tuvalu 11300 Anguilla 11300 Montserrat 5200 dtype: int64
The n smallest elements where n=5
by default.
>>> s.nsmallest() Montserrat 5200 Nauru 11300 Tuvalu 11300 Anguilla 11300 Iceland 337000 dtype: int64
The n smallest elements where n=3
. Default :None:None:`keep` value is 'first' so Nauru and Tuvalu will be kept.
>>> s.nsmallest(3) Montserrat 5200 Nauru 11300 Tuvalu 11300 dtype: int64
The n smallest elements where n=3
and keeping the last duplicates. Anguilla and Tuvalu will be kept since they are the last with value 11300 based on the index order.
>>> s.nsmallest(3, keep='last') Montserrat 5200 Anguilla 11300 Tuvalu 11300 dtype: int64
The n smallest elements where n=3
with all duplicates kept. Note that the returned Series has four elements due to the three duplicates.
>>> s.nsmallest(3, keep='all') Montserrat 5200 Nauru 11300 Tuvalu 11300 Anguilla 11300 dtype: int64See :
The following pages refer to to this document either explicitly or contain code examples using this.
pandas.core.groupby.generic.SeriesGroupBy.nlargest
pandas.core.series.Series.nlargest
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