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