**Syntax:**

Index –

Series –

DataFrame –

**Parameters:**

- Where the
**skipna**parameter excludes the NA/null values, the outcome is NA if the whole series is NA. **axis:**To be compatible with the DataFrame.idxmax, redundant use with the Series.- Additional keywords
***args**and****kwargs**have no impact. However, they may be accepted for NumPy compatibility. **idxmax:**The index of the highest value is returned.

## Example 1: Index()

Create an Index that stores 7 values which includes the None/NaN values.

- Return the maximum value index position by ignoring the NaN values.
- Return the maximum value index position by considering the NaN values.

import numpy

# Create the Index

shops=pandas.Index([10,345,67,89,90,None,numpy.nan])

print(shops,"\n")

# Return the Maximum element index position

print(shops.argmax(),"\n")

# Return the Maximum element index position by considering NaN values

print(shops.argmax(skipna=False))

**Output:**

**Explanation:**

First, we display the entire Index.

- In the second output, 345 is the largest value among 7 values and its index position is 1.
- In the last output, we consider the NaN values. Since there is a NaN value, -1 is returned.

## Example 2: Series()

Create a Pandas Series named “shops” that stores 5 values which include the NaN value.

- Return the maximum value index position by ignoring the NaN values.
- Return the maximum value index position by considering the NaN values.

import numpy

# Consider the Series data

shops=pandas.Series([100,45,67,78,numpy.nan])

print(shops,"\n")

# Return the Maximum element index position

print(shops.argmax(),"\n")

# Return the Maximum element index position by considering NaN values

print(shops.argmax(skipna=False))

**Output:**

**Explanation:**

First, we display the entire Series.

- In the second output, 100 is the largest value among 5 values and its index position is 0.
- In the last output, we consider the NaN values. Since there is a NaN value at the last position, -1 is returned.

## Example 3: DataFrame()

So far, we have seen how to find the maximum value’s index position, Now, we will see how to find it in the DataFrame column. Quickly create a Pandas DataFrame named “results” which stores 4 columns and 5 rows having None/NaN values.

- Return the maximum value index position by ignoring the NaN values.
- Return the maximum value index position by considering the NaN values.

import numpy

results = pandas.DataFrame([["Internal", 98,"pass",numpy.nan],

["Internal", 45,"fail",None],

["External", None,"pass",None],

["External", numpy.nan,"pass",None],

[None, 18,"fail",90]],

columns=["Exam","Score","Res","Other"],

index = ['Ram','Sravan','Govind','Anup', 'bob']

)

print(results,"\n")

# Return the Maximum element index position in the "Exam" column

print(results['Other'].argmax())

# Return the Maximum element index position in the "Score" column

print(results['Score'].argmax())

**Output:**

**Explanation:**

First, we display the entire DataFrame.

- In the second output, 90.0 is the largest value among 5 values in the “Other” column. Its index position is 4.
- In the last output, 98.0 is the largest value among 5 values in the “Marks” column. Its index position is 0.

## Conclusion

This article showed how to locate the index location of the maximum value (or values) in a DataFrame or Series using the Index.argmax() function, Series.argmax, and DataFrame[‘column’].argmax functions in this tutorial. Initially, we showed how to comprehend the function’s parameters before discovering how to use the argmax() function on various Python built-in functions.