I don't understand why sometimes a direct assignment of a new dtype is possible (but messes up the values), and why at other times a seemingly harmless upcast (in my potentially ignorant point of view) is not possible. So, maybe a direct assignment of a new dtype is actually never a good idea? (I'm asking), and one should always go the route of newarray= array(oldarray, dtype=newdtype), but why then sometimes the upcast provides an error and forbids it and sometimes not? Examples: In [140]: slope.read_center_window() In [141]: slope.data.dtype Out[141]: dtype('float32') In [142]: slope.data[1,1] Out[142]: 10.044398 In [143]: val = slope.data[1,1] In [144]: slope.data.dtype='float64' In [145]: slope.data[1,1] Out[145]: 586.98938070189865 #----- #Here, the value of data[1,1] has completely changed (and so has the rest of the array), and no error was given. # But then... #---- In [146]: val.dtype Out[146]: dtype('float32') In [147]: val Out[147]: 10.044398 In [148]: val.dtype='float64' --------------------------------------------------------------------------- AttributeError Traceback (most recent call last) <ipython-input-148-52a373a41cac> in <module>() ----> 1 val.dtype='float64' AttributeError: attribute 'dtype' of 'numpy.generic' objects is not writable === end of code So why is there an error in the 2nd case, but no error in the first case? Is there a logic to it? Thanks, Michael