SeriesGroupBy.
nlargest
Return the largest n elements.
Return this many descending sorted values.
When there are duplicate values that cannot all fit in a Series of n elements:
first
of appearance.
last
order of appearance.
all
size larger than n.
The n largest values in the Series, sorted in decreasing order.
See also
Series.nsmallest
Get the n smallest elements.
Series.sort_values
Sort Series by values.
Series.head
Return the first n rows.
Notes
Faster than .sort_values(ascending=False).head(n) for small n relative to the size of the Series object.
.sort_values(ascending=False).head(n)
Series
Examples
>>> 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.
n=5
>>> s.nlargest() France 65000000 Italy 59000000 Malta 434000 Maldives 434000 Brunei 434000 dtype: int64
The n largest elements where n=3. Default keep value is ‘first’ so Malta will be kept.
n=3
>>> 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: int64