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# Python | Pandas Series.argmax()

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 pdimport pandas as pd  # Creating the Seriessr = pd.Series([34, 5, 13, 32, 4, 15])  # Create the Indexindex_ = ['Coca Cola', 'Sprite', 'Coke', 'Fanta', 'Dew', 'ThumbsUp']  # set the indexsr.index = index_  # Print the seriesprint(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 valueresult = sr.argmax()  # Print the resultprint(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 pdimport pandas as pd  # Creating the Seriessr = pd.Series([11, 21, 8, 18, 65, 18, 32, 10, 5, 32, None])  # Create the Index# apply yearly frequencyindex_ = pd.date_range('2010-10-09 08:45', periods = 11, freq ='Y')  # set the indexsr.index = index_  # Print the seriesprint(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 valueresult = sr.argmax()  # Print the resultprint(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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