You can use the following methods to get the index of the max value in a NumPy array:

**Method 1: Get Index of Max Value in One-Dimensional Array**

x.argmax()

**Method 2: Get Index of Max Value in Each Row of Multi-Dimensional Array**

x.argmax(axis=1)

**Method 3: Get Index of Max Value in Each Column of Multi-Dimensional Array**

x.argmax(axis=0)

The following examples show how to use each method in practice.

**Example 1: Get Index of Max Value in One-Dimensional Array**

The following code shows how to get the index of the max value in a one-dimensional NumPy array:

**import numpy as np
#create NumPy array of values
x = np.array([2, 7, 9, 4, 4, 6, 3])
#find index that contains max value
x.argmax()
2**

The **argmax()** function returns a value of **2**.

This tells us that the value in index position **2** of the array contains the maximum value.

If we look at the original array, we can see that the value in index position **2** is **9**, which is indeed the maximum value in the array.

**Example 2: Get Index of Max Value in Each Row of Multi-Dimensional Array**

The following code shows how to get the index of the max value in each row of a multi-dimensional NumPy array:

**import numpy as np
#create multi-dimentsional NumPy array
x = np.array([[4, 2, 1, 5], [7, 9, 2, 0]])
#view NumPy array
print(x)
[[4 2 1 5]
[7 9 2 0]]
#find index that contains max value in each row
x.argmax(axis=1)
array([3, 1], dtype=int32)
**

From the results we can see:

- The max value in the first row is located in index position
**3**. - The max value in the second row is located in index position
**1**.

**Example 3: Get Index of Max Value in Each Column of Multi-Dimensional Array**

The following code shows how to get the index of the max value in each column of a multi-dimensional NumPy array:

**import numpy as np
#create multi-dimentsional NumPy array
x = np.array([[4, 2, 1, 5], [7, 9, 2, 0]])
#view NumPy array
print(x)
[[4 2 1 5]
[7 9 2 0]]
#find index that contains max value in each column
x.argmax(axis=0)
array([1, 1, 1, 0], dtype=int32)
**

From the results we can see:

- The max value in the first column is located in index position
**1**. - The max value in the second column is located in index position
**1**. - The max value in the third column is located in index position
**1**. - The max value in the fourth column is located in index position
**0**.

**Related:** A Simple Explanation of NumPy Axes

**Additional Resources**

The following tutorials explain how to perform other common operations in Python:

How to Fill NumPy Array with Values

How to Replace Elements in NumPy Array

How to Get Specific Row from NumPy Array