def test_astype_bool(self):
a = SparseArray([1, 0, 0, 1], dtype=SparseDtype(int, 0))
result = a.astype(bool)
expected = SparseArray([True, 0, 0, True], dtype=SparseDtype(bool, 0))
tm.assert_sp_array_equal(result, expected)
# update fill value
result = a.astype(SparseDtype(bool, False))
expected = SparseArray([True, False, False, True],
dtype=SparseDtype(bool, False))
tm.assert_sp_array_equal(result, expected)
def test_astype_bool(self):
a = SparseArray([1, 0, 0, 1], dtype=SparseDtype(int, 0))
with tm.assert_produces_warning(FutureWarning,
match="astype from Sparse"):
result = a.astype(bool)
expected = SparseArray([True, False, False, True],
dtype=SparseDtype(bool, False))
tm.assert_sp_array_equal(result, expected)
# update fill value
result = a.astype(SparseDtype(bool, False))
expected = SparseArray([True, False, False, True],
dtype=SparseDtype(bool, False))
tm.assert_sp_array_equal(result, expected)
def test_astype_copy_false(self):
# GH#34456 bug caused by using .view instead of .astype in astype_nansafe
arr = SparseArray([1, 2, 3])
result = arr.astype(float, copy=False)
expected = SparseArray([1.0, 2.0, 3.0], fill_value=0.0)
tm.assert_sp_array_equal(result, expected)
def test_astype_all(self, any_real_dtype):
vals = np.array([1, 2, 3])
arr = SparseArray(vals, fill_value=1)
typ = np.dtype(any_real_dtype)
res = arr.astype(typ)
assert res.dtype == SparseDtype(typ, 1)
assert res.sp_values.dtype == typ
tm.assert_numpy_array_equal(np.asarray(res.to_dense()), vals.astype(typ))
def test_astype_all(self, any_real_numpy_dtype):
vals = np.array([1, 2, 3])
arr = SparseArray(vals, fill_value=1)
typ = np.dtype(any_real_numpy_dtype)
with tm.assert_produces_warning(FutureWarning,
match="astype from Sparse"):
res = arr.astype(typ)
assert res.dtype == SparseDtype(typ, 1)
assert res.sp_values.dtype == typ
tm.assert_numpy_array_equal(np.asarray(res.to_dense()),
vals.astype(typ))
def test_astype(self):
# float -> float
arr = SparseArray([None, None, 0, 2])
result = arr.astype("Sparse[float32]")
expected = SparseArray([None, None, 0, 2], dtype=np.dtype("float32"))
tm.assert_sp_array_equal(result, expected)
dtype = SparseDtype("float64", fill_value=0)
result = arr.astype(dtype)
expected = SparseArray._simple_new(
np.array([0.0, 2.0], dtype=dtype.subtype), IntIndex(4, [2, 3]),
dtype)
tm.assert_sp_array_equal(result, expected)
dtype = SparseDtype("int64", 0)
result = arr.astype(dtype)
expected = SparseArray._simple_new(np.array([0, 2], dtype=np.int64),
IntIndex(4, [2, 3]), dtype)
tm.assert_sp_array_equal(result, expected)
arr = SparseArray([0, np.nan, 0, 1], fill_value=0)
with pytest.raises(ValueError, match="NA"):
arr.astype("Sparse[i8]")
def test_astype_nan_raises(self):
arr = SparseArray([1.0, np.nan])
with pytest.raises(ValueError, match="Cannot convert non-finite"):
arr.astype(int)
def test_astype_nan_raises(self):
arr = SparseArray([1.0, np.nan])
with pytest.raises(ValueError, match="Cannot convert non-finite"):
msg = "astype from SparseDtype"
with tm.assert_produces_warning(FutureWarning, match=msg):
arr.astype(int)
