diff options
Diffstat (limited to 'numpy/lib/nanfunctions.py')
| -rw-r--r-- | numpy/lib/nanfunctions.py | 24 |
1 files changed, 20 insertions, 4 deletions
diff --git a/numpy/lib/nanfunctions.py b/numpy/lib/nanfunctions.py index 2c2c3435b..766bf3c82 100644 --- a/numpy/lib/nanfunctions.py +++ b/numpy/lib/nanfunctions.py @@ -160,8 +160,12 @@ def _remove_nan_1d(arr1d, overwrite_input=False): True if `res` can be modified in place, given the constraint on the input """ + if arr1d.dtype == object: + # object arrays do not support `isnan` (gh-9009), so make a guess + c = np.not_equal(arr1d, arr1d, dtype=bool) + else: + c = np.isnan(arr1d) - c = np.isnan(arr1d) s = np.nonzero(c)[0] if s.size == arr1d.size: warnings.warn("All-NaN slice encountered", RuntimeWarning, @@ -214,7 +218,11 @@ def _divide_by_count(a, b, out=None): return np.divide(a, b, out=out, casting='unsafe') else: if out is None: - return a.dtype.type(a / b) + # Precaution against reduced object arrays + try: + return a.dtype.type(a / b) + except AttributeError: + return a / b else: # This is questionable, but currently a numpy scalar can # be output to a zero dimensional array. @@ -1551,7 +1559,13 @@ def nanvar(a, axis=None, dtype=None, out=None, ddof=0, keepdims=np._NoValue): # Compute variance. var = np.sum(sqr, axis=axis, dtype=dtype, out=out, keepdims=keepdims) - if var.ndim < cnt.ndim: + + # Precaution against reduced object arrays + try: + var_ndim = var.ndim + except AttributeError: + var_ndim = np.ndim(var) + if var_ndim < cnt.ndim: # Subclasses of ndarray may ignore keepdims, so check here. cnt = cnt.squeeze(axis) dof = cnt - ddof @@ -1671,6 +1685,8 @@ def nanstd(a, axis=None, dtype=None, out=None, ddof=0, keepdims=np._NoValue): keepdims=keepdims) if isinstance(var, np.ndarray): std = np.sqrt(var, out=var) - else: + elif hasattr(var, 'dtype'): std = var.dtype.type(np.sqrt(var)) + else: + std = np.sqrt(var) return std |