def test_container_attrs(self) -> None:
for attr in UFUNC_AXIS_SKIPNA.keys() | UFUNC_SHAPE_SKIPNA.keys():
c = ContainerOperand
self.assertTrue(hasattr(c, attr))
with self.assertRaises(NotImplementedError):
c().display()
def get_jinja_contexts() -> tp.Dict[str, tp.List[tp.Tuple[str, str]]]:
post = {}
performance_cls = []
for name in dir(core):
obj = getattr(core, name)
if inspect.isclass(obj) and issubclass(obj, PerfTest):
performance_cls.append(obj.__name__)
post['performance_cls'] = performance_cls
def get_func_doc(
cls: type,
func_iter: tp.Iterable[str]) -> tp.List[tp.Tuple[str, str]]:
return [(f, getattr(cls, f).__doc__) for f in sorted(func_iter)]
for cls in (sf.Index, sf.Series, sf.Frame):
label = cls.__name__
post[label + '_operator_unary'] = get_func_doc(cls,
_UFUNC_UNARY_OPERATORS)
post[label + '_operator_binary'] = get_func_doc(
cls, _UFUNC_BINARY_OPERATORS)
for cls in (sf.Index, sf.Series, sf.Frame):
label = cls.__name__
post[label + '_ufunc_axis'] = sorted(UFUNC_AXIS_SKIPNA.keys())
post['interface'] = []
for target in (
sf.Series,
sf.Frame,
sf.FrameGO,
sf.Index,
sf.IndexGO,
sf.IndexHierarchy,
sf.IndexHierarchyGO,
sf.IndexYear,
sf.IndexYearGO,
sf.IndexYearMonth,
sf.IndexYearMonthGO,
sf.IndexDate,
sf.IndexDateGO,
sf.IndexMinute,
sf.IndexMinuteGO,
sf.IndexSecond,
sf.IndexSecondGO,
sf.IndexMillisecond,
sf.IndexMillisecondGO,
sf.IndexNanosecond,
sf.IndexNanosecondGO,
):
# import ipdb; ipdb.set_trace()
post['interface'].append((target.__name__, target.interface))
return post
def test_ufunc_skipna_1d(self, array: np.ndarray) -> None:
has_na = util.isna_array(array).any()
for ufunc, ufunc_skipna, dtype in UFUNC_AXIS_SKIPNA.values():
with np.errstate(over='ignore', under='ignore'):
v1 = ufunc_skipna(array)
# this should return a single value
self.assertFalse(isinstance(v1, np.ndarray))
if has_na:
v2 = ufunc(array)
self.assertFalse(isinstance(v2, np.ndarray))
def test_ufunc_axis(self, f1: Frame) -> None:
for attr, attrs in UFUNC_AXIS_SKIPNA.items():
if attr in ('std', 'var'):
continue
for axis in (0, 1):
values = f1.values
# must coerce all blocks to same type to compare to what NP does
# f2 = f1.astype(values.dtype)
a = getattr(f1, attr)(axis=axis).values # call the method
b = attrs.ufunc_skipna(values, axis=axis)
def test_ufunc_axis_skipna(self, array: np.ndarray) -> None:
has_na = util.isna_array(array).any()
for nt in UFUNC_AXIS_SKIPNA.values():
ufunc = nt.ufunc
ufunc_skipna = nt.ufunc_skipna
# dtypes = nt.dtypes
# composable = nt.composable
# doc = nt.doc_header
# size_one_unity = nt.size_one_unity
with np.errstate(over='ignore', under='ignore', divide='ignore'):
post = util.ufunc_axis_skipna(array=array,
skipna=True,
axis=0,
ufunc=ufunc,
ufunc_skipna=ufunc_skipna)
if array.ndim == 2:
self.assertTrue(post.ndim == 1)
def test_ufunc_axis(self, s1: Series) -> None:
for attr, attrs in UFUNC_AXIS_SKIPNA.items():
a = getattr(s1, attr)()
func = attrs.ufunc_skipna
b = func(s1.values)
self.assertEqualWithNaN(a, b)