def assert_series_equal(cls, left, right, *args, **kwargs):
# base class tests hard-code expected values with numpy dtypes,
# whereas we generally want the corresponding PandasDtype
if (isinstance(right, pd.Series)
and not isinstance(right.dtype, ExtensionDtype)
and isinstance(left.dtype, PandasDtype)):
right = right.astype(PandasDtype(right.dtype))
return tm.assert_series_equal(left, right, *args, **kwargs)
def test_setitem_nullable_mask(self, data):
# GH 31446
# TODO: there is some issue with PandasArray, therefore,
# TODO: skip the setitem test for now, and fix it later
if data.dtype != PandasDtype("object"):
arr = data[:5]
expected = data.take([0, 0, 0, 3, 4])
mask = pd.array([True, True, True, False, False])
arr[mask] = data[0]
self.assert_extension_array_equal(expected, arr)
def test_fillna_fill_other(self, data_missing):
# Same as the parent class test, but with PandasDtype for expected["B"]
# instead of equivalent numpy dtype
data = data_missing
result = pd.DataFrame({
"A": data,
"B": [np.nan] * len(data)
}).fillna({"B": 0.0})
expected = pd.DataFrame({"A": data, "B": [0.0] * len(result)})
expected["B"] = expected["B"].astype(PandasDtype(expected["B"].dtype))
self.assert_frame_equal(result, expected)
def test_constructor_from_string():
result = PandasDtype.construct_from_string("int64")
expected = PandasDtype(np.dtype("int64"))
assert result == expected
def dtype(request):
return PandasDtype(np.dtype(request.param))
def test_constructor_from_string():
result = PandasDtype.construct_from_string("int64")
expected = PandasDtype(np.dtype("int64"))
assert result == expected
def test_repr():
dtype = PandasDtype(np.dtype("int64"))
assert repr(dtype) == "PandasDtype('int64')"
def test_is_boolean(dtype, expected):
dtype = PandasDtype(dtype)
assert dtype._is_boolean is expected
def test_is_numeric(dtype, expected):
dtype = PandasDtype(dtype)
assert dtype._is_numeric is expected
def dtype():
return PandasDtype(np.dtype('float'))
def dtype():
return PandasDtype(np.dtype('object'))