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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. axislink | 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. initiallink | int | optional

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

4. wherelink | 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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