NumPy | nanmin method
nanmin(~) method ignores all missing values and returns the smallest value in the Numpy array. The minimums can also be computed row-wise and column-wise.
The input array.
The allowed values are as follows:
Minimum computed column-wise
Minimum computed row-wise
Minimum computed from entire array
If the computed minimum is larger 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. The data-type is the same as that of your input array
Minimum of the entire array
Minimum of each column
np.nanmin([[2,np.NaN],[1,3]], axis=0)array([1., 3.])
Minimum of each row
np.nanmin([[2,np.NaN],[1,3]], axis=1)array([2., 1.])
Passing in initial parameter
Here, the computed minimum is 1, yet it is larger than the provided value of initial (i.e. -4), so -4 is returned instead.
Passing in a boolean mask
Instead of considering all the values, we can choose which values to compute the minimum of by providing a mask:
np.nanmin([2,5,3,4], where=[False,False,True,True], initial=8)3
Here, although 2 is technically the smallest 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 minimum cannot be computed (e.g. when the mask is all