nlargest(self, n: 'int' = 5, keep: 'str' = 'first')
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 :
Hover to see nodes names; edges to Self not shown, Caped at 50 nodes.
Using a canvas is more power efficient and can get hundred of nodes ; but does not allow hyperlinks; , arrows or text (beyond on hover)
SVG is more flexible but power hungry; and does not scale well to 50 + nodes.
All aboves nodes referred to, (or are referred from) current nodes; Edges from Self to other have been omitted (or all nodes would be connected to the central node "self" which is not useful). Nodes are colored by the library they belong to, and scaled with the number of references pointing them