pandas.Series.nlargest#

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

Return the largestn 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 aSeries ofn elements:

• first : return the firstn occurrences in orderof appearance.

• last : return the lastn occurrences in reverseorder of appearance.

• all : keep all occurrences. This can result in a Series ofsize larger thann.

Returns:

Series

Then largest values in the Series, sorted in decreasing order.

See also

Series.nsmallest

Get then smallest elements.

Series.sort_values

Sort Series by values.

Series.head

Return the firstn rows.

Notes

Faster than.sort_values(ascending=False).head(n) for smallnrelative to the size of theSeries 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)>>>sItaly       59000000France      65000000Malta         434000Maldives      434000Brunei        434000Iceland       337000Nauru          11300Tuvalu         11300Anguilla       11300Montserrat      5200dtype: int64

Then largest elements wheren=5 by default.

>>>s.nlargest()France      65000000Italy       59000000Malta         434000Maldives      434000Brunei        434000dtype: int64

Then largest elements wheren=3. Defaultkeep value is ‘first’so Malta will be kept.

>>>s.nlargest(3)France    65000000Italy     59000000Malta       434000dtype: int64

Then largest elements wheren=3 and keeping the last duplicates.Brunei will be kept since it is the last with value 434000 based onthe index order.

>>>s.nlargest(3,keep='last')France      65000000Italy       59000000Brunei        434000dtype: int64

Then largest elements wheren=3 with all duplicates kept. Notethat the returned Series has five elements due to the three duplicates.

>>>s.nlargest(3,keep='all')France      65000000Italy       59000000Malta         434000Maldives      434000Brunei        434000dtype: int64