aa = np.array([2.0, np.NaN])
aa[aa>1.0] = np.NaN
On running the code above, I get the foll. warning, I understand the reason for this warning, but how to avoid it?
RuntimeWarning: invalid value encountered in greater
1 Answer
Store the indices of the valid ones (non - NaNs). First off, we will use these indices to index into the array and perform the comparison to get a mask and then again index into those indices with that mask to retrieve back the indices corresponding to original order. Using the original-ordered indices, we could then assign elements in the input array to NaNs.
Thus, an implementation/solution would be -
idx = np.flatnonzero(~np.isnan(aa))
aa[idx[aa[idx] > 1.0]] = np.nan
Sample run -
In [106]: aa # Input array with NaNs
Out[106]: array([ 0., 3., nan, 0., 9., 6., 6., nan, 18., 6.])
In [107]: idx = np.flatnonzero(~np.isnan(aa)) # Store valid indices
In [108]: idx
Out[108]: array([0, 1, 3, 4, 5, 6, 8, 9])
In [109]: aa[idx[aa[idx] > 1.0]] = np.nan # Do the assignment
In [110]: aa # Verify
Out[110]: array([ 0., nan, nan, 0., nan, nan, nan, nan, nan, nan])
3 Comments
hpaulj
In Py3, comparisons with
np.nan produce False without warnings.
Darcy
Does this work for multidimensional arrays? I am getting errors because
idx uses linear indexing.
Divakar
@Darcy For multidim arrays, you would need to use
np.where that would give us indices along all dims and then use those to index and assign.
RuntimeWarning? If the latter,np.seterr(invalid='ignore')is enough. Refer this question