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# NumPy | amax method

schedule Aug 12, 2023
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Numpy's `amax(~)` method returns the largest value in the Numpy array. The maximums can also be computed row-wise and column-wise.

# Parameters

1. `a` | `array_like`

The input array.

2. `axis`link | `None` or `int` | `optional`

The allowed values are as follows:

Parameter value

Meaning

axis=0

Maximum computed column-wise

axis=1

Maximum computed row-wise

None

Maximum computed from entire array

By default, `axis=None`.

3. `initial`link | `int` | `optional`

If the computed maximum is less than `initial`, then `initial` will be returned instead.

4. `where`link | `array-like` of `booleans` | `optional`

Instead of considering all the values, we can choose which values to consider by providing this parameter. Only values corresponding to `True` in the mask will be considered.

# Return value

A scalar is returned if the axis parameter is not supplied. Otherwise, a Numpy array is returned.

# Examples

## Maximum of the entire array

``` np.amax([[2,5],[1,3]]) 5 ```

## Maximum of each column

``` np.amax([[2,5],[1,3]], axis=0) array([2, 5]) ```

## Maximum of each row

``` np.amax([[2,5],[1,3]], axis=1) array([5, 3]) ```

## Handling maximum values

When your array contains missing values (e.g. NaNs), then `NaN` is returned:

``` np.amax([2,np.NaN,1,3]) nan ```

If you want to ignore missing values, then use `np.nanmax(~)` method instead.

## Passing in initial parameter

``` np.amax([[2,5],[1,3]], initial=8) 8 ```

Here, the computed maximum is 5, yet it is smaller than the provided value of initial (i.e. 8), so 8 is returned instead.

## Passing in a boolean mask

Instead of considering all the values, we can choose which values to compute the maximum of by providing a mask:

``` np.amax([2,5,3,4], where=[True,False,False,True], initial=-1) 4 ```

Here, although 5 is technically the largest value, it is ignored since its corresponding value in the mask is `False`. Note that we need to supply the parameter `initial` here, which will be the returned value if the maximum cannot be computed (e.g. when the mask is all `False`).

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