Pierre (or anyone else who cares to chime in), I'm using stack_arrays to combine data from two different files into a single array. In one of these files, the data from one entire record comes back missing, which, thanks to your recent change, ends up having a boolean dtype. There is actual data for this same field in the 2nd file, so it ends up having the dtype of float64. When I try to combine the two arrays, I end up with the following traceback: data = stack_arrays((old_data, data)) File "/home/rmay/.local/lib64/python2.5/site-packages/metpy/cbook.py", line 260, in stack_arrays output = ma.masked_all((np.sum(nrecords),), newdescr) File "/home/rmay/.local/lib64/python2.5/site-packages/numpy/ma/extras.py", line 79, in masked_all a = masked_array(np.empty(shape, dtype), ValueError: two fields with the same name Which is unsurprising. Do you think there is any reasonable way to get stack_arrays() to find a common dtype for fields with the same name? Or another suggestion on how to approach this? If you think coercing one/both of the fields to a common dtype is the way to go, just point me to a function that could figure out the dtype and I'll try to put together a patch. Thanks, Ryan P.S. Thanks so much for your work on putting those utility functions in recfunctions.py It makes it so much easier to have these functions available in the library itself rather than needing to reinvent the wheel over and over. -- Ryan May Graduate Research Assistant School of Meteorology University of Oklahoma