pandas.Series.nlargest — pandas 3.0.0rc0+33.g1fd184de2a documentation (original) (raw)

Series.nlargest(n=5, keep='first')[source]#

Return the largest n elements.

Parameters:

nint, default 5

Return this many descending sorted values.

keep{‘first’, ‘last’, ‘all’}, default ‘first’

When there are duplicate values that cannot all fit in a Series of n elements:

• first : return the first n occurrences in order of appearance.
• last : return the last n occurrences in reverse order of appearance.
• all : keep all occurrences. This can result in a Series of size larger than n.

Returns:

Series

The n largest values in the Series, sorted in decreasing order.

Notes

Faster than .sort_values(ascending=False).head(n) for small nrelative to the size of the Series object.

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.

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.

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