diff options
Diffstat (limited to 'numpy/lib/nanfunctions.py')
| -rw-r--r-- | numpy/lib/nanfunctions.py | 20 |
1 files changed, 13 insertions, 7 deletions
diff --git a/numpy/lib/nanfunctions.py b/numpy/lib/nanfunctions.py index a02ad779f..2c2c3435b 100644 --- a/numpy/lib/nanfunctions.py +++ b/numpy/lib/nanfunctions.py @@ -962,12 +962,16 @@ def _nanmedian1d(arr1d, overwrite_input=False): Private function for rank 1 arrays. Compute the median ignoring NaNs. See nanmedian for parameter usage """ - arr1d, overwrite_input = _remove_nan_1d(arr1d, - overwrite_input=overwrite_input) - if arr1d.size == 0: - return np.nan + arr1d_parsed, overwrite_input = _remove_nan_1d( + arr1d, overwrite_input=overwrite_input, + ) - return np.median(arr1d, overwrite_input=overwrite_input) + if arr1d_parsed.size == 0: + # Ensure that a nan-esque scalar of the appropiate type (and unit) + # is returned for `timedelta64` and `complexfloating` + return arr1d[-1] + + return np.median(arr1d_parsed, overwrite_input=overwrite_input) def _nanmedian(a, axis=None, out=None, overwrite_input=False): @@ -1008,10 +1012,12 @@ def _nanmedian_small(a, axis=None, out=None, overwrite_input=False): for i in range(np.count_nonzero(m.mask.ravel())): warnings.warn("All-NaN slice encountered", RuntimeWarning, stacklevel=4) + + fill_value = np.timedelta64("NaT") if m.dtype.kind == "m" else np.nan if out is not None: - out[...] = m.filled(np.nan) + out[...] = m.filled(fill_value) return out - return m.filled(np.nan) + return m.filled(fill_value) def _nanmedian_dispatcher( |