NumPy | amax method
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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).
