def test_index_copy_float(name, dtype, deep=True):
"""Test for NumericIndex Copy Casts
"""
cidx = cudf.Float64Index([1.0, 2.0, 3.0])
pidx = cidx.to_pandas()
pidx_copy = pidx.copy(name=name, deep=deep, dtype=dtype)
cidx_copy = cidx.copy(name=name, deep=deep, dtype=dtype)
assert_eq(pidx_copy, cidx_copy)
def test_float_index_apis(data, name, dtype):
pindex = pd.Float64Index(data, dtype=dtype, name=name)
# Float32Index
gindex = cudf.Float32Index(data, dtype=dtype, name=name)
assert_eq(pindex, gindex)
assert gindex.dtype == np.dtype("float32")
# Float64Index
gindex = cudf.Float64Index(data, dtype=dtype, name=name)
assert_eq(pindex, gindex)
assert gindex.dtype == np.dtype("float64")
pidx = cidx.to_pandas()
pidx_copy = pidx.copy(name=name, deep=deep, dtype=dtype)
cidx_copy = cidx.copy(name=name, deep=deep, dtype=dtype)
assert_eq(pidx_copy, cidx_copy)
@pytest.mark.parametrize("deep", [True, False])
@pytest.mark.parametrize(
"idx",
[
cudf.DatetimeIndex(["2001", "2002", "2003"]),
cudf.core.index.StringIndex(["a", "b", "c"]),
cudf.Int64Index([1, 2, 3]),
cudf.Float64Index([1.0, 2.0, 3.0]),
cudf.CategoricalIndex([1, 2, 3]),
cudf.CategoricalIndex(["a", "b", "c"]),
],
)
def test_index_copy_deep(idx, deep):
"""Test if deep copy creates a new instance for device data.
The general criterion is to compare `Buffer.ptr` between two data objects.
Specifically for:
- CategoricalIndex, this applies to both `.codes` and `.categories`
- StringIndex, to every element in `._base_children`
- Others, to `.base_data`
No test is defined for RangeIndex.
"""
idx_copy = idx.copy(deep=deep)
same_ref = not deep