pandas-dev/pandas (original) (raw)

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')