NumPy | amax method
amax(~) method returns the largest value in the Numpy array. The maximums can also be computed row-wise and column-wise.
The input array.
The allowed values are as follows:
Maximum computed column-wise
Maximum computed row-wise
Maximum computed from entire array
If the computed maximum is less than
initial will be returned instead.
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.
A scalar is returned if the axis parameter is not supplied. Otherwise, a Numpy array is returned.
Maximum of the entire array
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:
If you want to ignore missing values, then use
np.nanmax(~) method instead.
Passing in initial parameter
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