Python | Pandas Series.idxmax()
Last Updated :
13 Feb, 2019
Pandas series is a One-dimensional ndarray with axis labels. The labels need not be unique but must be a hashable type. The object supports both integer- and label-based indexing and provides a host of methods for performing operations involving the index.
Pandas Series.idxmax()
function return the row label of the maximum value. If multiple values equal the maximum, the first row label with that value is returned.
Syntax: Series.idxmax(axis=0, skipna=True, *args, **kwargs)
Parameter :
skipna : Exclude NA/null values. If the entire Series is NA, the result will be NA.
axis : For compatibility with DataFrame.idxmax. Redundant for application on Series.
Returns : idxmax : Index of maximum of values.
Example #1: Use Series.idxmax()
function to find the index label corresponding to the maximum value in the given series object.
import pandas as pd
sr = pd.Series([ 10 , 25 , 3 , 25 , 24 , 6 ])
index_ = [ 'Coca Cola' , 'Sprite' , 'Coke' , 'Fanta' , 'Dew' , 'ThumbsUp' ]
sr.index = index_
print (sr)
|
Output :
Now we will use Series.idxmax()
function to find the index label corresponding to the maximum value in the series.
result = sr.idxmax()
print (result)
|
Output :
As we can see in the output, the Series.idxmax()
function has returned the index label of the maximum element in the given series object.
Example #2 : Use Series.idxmax()
function to find the index label corresponding to the maximum value in the given series object.
import pandas as pd
sr = pd.Series([ 11 , 21 , 8 , 18 , 65 , 84 , 32 , 10 , 5 , 24 , 32 ])
index_ = pd.date_range( '2010-10-09' , periods = 11 , freq = 'M' )
sr.index = index_
print (sr)
|
Output :
Now we will use Series.idxmax()
function to find the index label corresponding to the maximum value in the series.
result = sr.idxmax()
print (result)
|
Output :
As we can see in the output, the Series.idxmax()
function has returned the index label of the maximum element in the given series object.
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