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Python | Pandas Series.argmax()

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  • Last Updated : 27 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.argmax() function returns the row label of the maximum value in the given series object.

Syntax: Series.argmax(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.
*args, **kwargs : Additional keywords have no effect but might be accepted for compatibility with NumPy.

Returns : idxmax : Index of maximum of values.

Example #1: Use Series.argmax() function to return the row label of the maximum value in the given series object




# importing pandas as pd
import pandas as pd
  
# Creating the Series
sr = pd.Series([34, 5, 13, 32, 4, 15])
  
# Create the Index
index_ = ['Coca Cola', 'Sprite', 'Coke', 'Fanta', 'Dew', 'ThumbsUp']
  
# set the index
sr.index = index_
  
# Print the series
print(sr)


Output :

Coca Cola    34
Sprite        5
Coke         13
Fanta        32
Dew           4
ThumbsUp     15
dtype: int64

Now we will use Series.argmax() function to return the row label of the maximum value in the given series object.




# return the row label for
# the maximum value
result = sr.argmax()
  
# Print the result
print(result)


Output :

Coca Cola

As we can see in the output, the Series.argmax() function has successfully returned the row label of the maximum value in the given series object.
 
Example #2 : Use Series.argmax() function to return the row label of the maximum value in the given series object.




# importing pandas as pd
import pandas as pd
  
# Creating the Series
sr = pd.Series([11, 21, 8, 18, 65, 18, 32, 10, 5, 32, None])
  
# Create the Index
# apply yearly frequency
index_ = pd.date_range('2010-10-09 08:45', periods = 11, freq ='Y')
  
# set the index
sr.index = index_
  
# Print the series
print(sr)


Output :

2010-12-31 08:45:00    11.0
2011-12-31 08:45:00    21.0
2012-12-31 08:45:00     8.0
2013-12-31 08:45:00    18.0
2014-12-31 08:45:00    65.0
2015-12-31 08:45:00    18.0
2016-12-31 08:45:00    32.0
2017-12-31 08:45:00    10.0
2018-12-31 08:45:00     5.0
2019-12-31 08:45:00    32.0
2020-12-31 08:45:00     NaN
Freq: A-DEC, dtype: float64

Now we will use Series.argmax() function to return the row label of the maximum value in the given series object.




# return the row label for
# the maximum value
result = sr.argmax()
  
# Print the result
print(result)


Output :

2014-12-31 08:45:00

As we can see in the output, the Series.argmax() function has successfully returned the row label of the maximum value in the given series object.


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