diff options
Diffstat (limited to 'numpy')
| -rw-r--r-- | numpy/lib/nanfunctions.py | 29 | ||||
| -rw-r--r-- | numpy/lib/tests/test_nanfunctions.py | 8 |
2 files changed, 18 insertions, 19 deletions
diff --git a/numpy/lib/nanfunctions.py b/numpy/lib/nanfunctions.py index 12f4a8376..81f5aee2e 100644 --- a/numpy/lib/nanfunctions.py +++ b/numpy/lib/nanfunctions.py @@ -301,9 +301,10 @@ def nanmax(a, axis=None, out=None, keepdims=False): def nanargmin(a, axis=None): """ - Return indices of the minimum values over an axis, ignoring NaNs. For - all NaN slices the minimum value of the np.intp type is returned and a - `NanWarning` is raised. + Return the indices of the minimum values in the specified axis ignoring + NaNs. For all-NaN slices, the negative number ``np.iinfo('intp').min`` + is returned. It is platform dependent. Warning: the results cannot be + trusted if a slice contains only NaNs and Infs. Parameters ---------- @@ -348,9 +349,11 @@ def nanargmin(a, axis=None): def nanargmax(a, axis=None): """ - Return indices of the maximum values over an axis, ignoring NaNs. For - all NaN slices the minimum value of the np.intp type is returned and - a `NanWarning` is raised. + Return the indices of the maximum values in the specified axis ignoring + NaNs. For all-NaN slices, the negative number ``np.iinfo('intp').min`` + is returned. It is platform dependent. Warning: the results cannot be + trusted if a slice contains only NaNs and -Infs. + Parameters ---------- @@ -399,8 +402,7 @@ def nansum(a, axis=None, dtype=None, out=None, keepdims=0): Not a Numbers (NaNs) as zero. FutureWarning: In Numpy versions <= 1.8 Nan is returned for slices that - are all NaN or empty. In later versions zero will be returned. - + are all-NaN or empty. In later versions zero will be returned. Parameters ---------- @@ -467,14 +469,11 @@ def nansum(a, axis=None, dtype=None, out=None, keepdims=0): 3.0 >>> np.nansum(a, axis=0) array([ 2., 1.]) - - When positive infinity and negative infinity are present - >>> np.nansum([1, np.nan, np.inf]) inf >>> np.nansum([1, np.nan, np.NINF]) -inf - >>> np.nansum([1, np.nan, np.inf, np.NINF]) + >>> np.nansum([1, np.nan, np.inf, -np.inf]) # both +/- infinity present nan """ @@ -501,7 +500,7 @@ def nanmean(a, axis=None, dtype=None, out=None, keepdims=False): the flattened array by default, otherwise over the specified axis. `float64` intermediate and return values are used for integer inputs. - For all NaN slices NaN is returned and a `NanWarning` is raised. + For all-NaN slices, NaN is returned and a `NanWarning` is raised. .. versionadded:: 1.8.0 @@ -597,7 +596,7 @@ def nanvar(a, axis=None, dtype=None, out=None, ddof=0, distribution. The variance is computed for the flattened array by default, otherwise over the specified axis. - For all NaN slices NaN is returned and a `NanWarning` is raised. + For all-NaN slices, NaN is returned and a `NanWarning` is raised. .. versionadded:: 1.8.0 @@ -728,7 +727,7 @@ def nanstd(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False): of the non-NaN array elements. The standard deviation is computed for the flattened array by default, otherwise over the specified axis. - For all NaN slices NaN is returned and a `NanWarning` is raised. + For all-NaN slices, NaN is returned and a `NanWarning` is raised. .. versionadded:: 1.8.0 diff --git a/numpy/lib/tests/test_nanfunctions.py b/numpy/lib/tests/test_nanfunctions.py index 41d6e7cf5..93a5ef855 100644 --- a/numpy/lib/tests/test_nanfunctions.py +++ b/numpy/lib/tests/test_nanfunctions.py @@ -35,7 +35,7 @@ class TestNanFunctions_MinMax(TestCase): stdfuncs = [np.min, np.max] def test_mutation(self): - # Check that passes array is no modified. + # Check that passed array is not modified. ndat = _ndat.copy() for f in self.nanfuncs: f(ndat) @@ -99,7 +99,7 @@ class TestNanFunctions_ArgminArgmax(TestCase): nanfuncs = [nanargmin, nanargmax] def test_mutation(self): - # Check that passes array is no modified. + # Check that passed array is not modified. ndat = _ndat.copy() for f in self.nanfuncs: f(ndat) @@ -177,7 +177,7 @@ class TestNanFunctions_IntTypes(TestCase): class TestNanFunctions_Sum(TestCase): def test_mutation(self): - # Check that passes array is no modified. + # Check that passed array is not modified. ndat = _ndat.copy() nansum(ndat) assert_equal(ndat, _ndat) @@ -282,7 +282,7 @@ class TestNanFunctions_MeanVarStd(TestCase): stdfuncs = [np.mean, np.var, np.std] def test_mutation(self): - # Check that passes array is no modified. + # Check that passed array is not modified. ndat = _ndat.copy() for f in self.nanfuncs: f(ndat) |