A number of nanops functions rely on the private internal function nanops._maybe_null_out. These include nansum, nanmin, nanmax, and nanprod. All of these, in principle, should work with arrays with a complex dtype. However, due to a bug in _maybe_null_out, they convert the array to a float under certain situations, resulting in erroneous results. The easiest way to trigger this is to have a complex array where all the imaginary values are nan, and make axis argument something other than None.
>>> from pandas.core import nanops
>>> import bottleneck
>>> import numpy as np
>>> nanops._USE_BOTTLENECK = False
>>>
>>> val = np.tile(1, (11, 7)) + np.tile(np.nan*1j, (11, 7))
>>>
>>> bottleneck.nansum(val, axis=0)
array([ nan+0.j, nan+0.j, nan+0.j, nan+0.j, nan+0.j, nan+0.j, nan+0.j])
>>> nanops.nansum(val, axis=0)
array([ nan, nan, nan, nan, nan, nan, nan])
>>> bottleneck.nansum(val, axis=0).dtype
dtype('complex128')
>>> nanops.nansum(val, axis=0).dtype
dtype('float64')
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