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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 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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