dtype, val = infer_dtype_from_scalar(data)
assert dtype == np.object_
+ @pytest.mark.intel
@pytest.mark.parametrize(
"arr, expected",
[('foo', np.object_),
expected = np.rec.array([('x', 'y')], dtype=[('a', 'O'), ('b', 'O')])
tm.assert_almost_equal(result, expected)
+ @pytest.mark.intel
def test_to_records_with_unicode_column_names(self):
# xref issue: https://github.com/numpy/numpy/issues/2407
# Issue #11879. to_records used to raise an exception when used
import numpy as np
+import pytest
import pandas.util.testing as tm
import pandas as pd
-
+@pytest.mark.intel
def test_to_native_types():
index = DatetimeIndex(freq='1D', periods=3, start='2017-01-01')
import numpy as np
+import pytest
import pandas.util.testing as tm
import pandas as pd
-
+@pytest.mark.intel
def test_to_native_types():
index = PeriodIndex(['2017-01-01', '2017-01-02',
'2017-01-03'], freq='D')
return create_data()
+@pytest.mark.intel
def check_arbitrary(a, b):
if isinstance(a, (list, tuple)) and isinstance(b, (list, tuple)):
else:
tm.assert_frame_equal(result, expected)
+ @pytest.mark.intel
@pytest.mark.parametrize('version', legacy_packers_versions())
def test_msgpacks_legacy(self, current_packers_data, all_packers_data,
version):