# NumPy amax()

The `amax()` function computes maximum value along a specified axis in an array.

### Example

``````import numpy as np

array1 = np.array([5, 2, 8, 1, 9])

# find maximum value  in array1
maxValue = np.amax(array1)

print(maxValue)

# Output: 9``````

## amax() Syntax

The syntax of `amax()` is:

``numpy.amax(a, axis = None, keepdims = False)``

## amax() Arguments

The `amax()` function takes following arguments:

• `a` - the input array
• `axis` (optional) - the axis along which the maximum value is computed
• `keepdims` (optional) - whether to preserve the input array's dimension (`bool`)

## amax() Return Value

The `amax()` function returns the maximum element from an array or along a specified axis.

## Example 1: amax() With 2-D Array

The `axis` argument defines how we can find the maximum element in a 2-D array.

• If `axis` = `None`, the array is flattened and the maximum value of the flattened array is returned.
• If `axis` = 0, the maximum value is calculated column-wise.
• If `axis` = 1, the maximum value is calculated row-wise.
``````import numpy as np

array1 = np.array([[10, 17, 25],
[15, 11, 22]])

# calculate the maximum value of the flattened array
result1 = np.amax(array1)

print('The maximum value of the flattened array:', result1)

# calculate the column-wise maximum values
result2 = np.amax(array1, axis=0)

print('Column-wise maximum values (axis 0):', result2)

# calculate the row-wise maximum values
result3 = np.amax(array1, axis=1)

print('Row-wise maximum values (axis 1):', result3)``````

Output

```The maximum value of the flattened array: 25
Column-wise maximum values (axis 0): [15 17 25]
Row-wise maximum values (axis 1): [25 22]```

Here,

1. `np.amax(array1)` calculates the maximum value of the flattened array. It returns the largest element in the entire array.
2. `np.amax(array1, axis=0)` calculates the column-wise maximum values. It returns an array containing the maximum value for each column.
3. `np.amax(array1, axis=1)` calculates the row-wise maximum values. It returns an array containing the maximum value for each row

## Example 2: amax() With keepdims

When `keepdims = True`, the dimensions of the resulting array matches the dimension of the input array.

``````import numpy as np

array1 = np.array([[10, 17, 25],
[15, 11, 22]])

print('Dimensions of original array:', array1.ndim)

result = np.amax(array1, axis=1)

print('\nWithout keepdims:')
print(result)
print('Dimensions of array:', result.ndim)

# set keepdims to True to retain the dimension of the input array
result = np.amax(array1, axis=1, keepdims=True)

print('\nWith keepdims:')
print(result)
print('Dimensions of array:', result.ndim)``````

Output

```Dimensions of original array: 2

Without keepdims:
[25 22]
Dimensions of array: 1

With keepdims:
[[25]
[22]]
Dimensions of array: 2```

Without `keepdims`, the result is simply a one-dimensional array containing the maximum values along the specified axis.

With `keepdims`, the resulting array has the same number of dimensions as the input array.