def test_getitem_arraylike_mask(self):
arr = SparseArray([0, 1, 2])
result = arr[[True, False, True]]
expected = SparseArray([0, 2])
tm.assert_sp_array_equal(result, expected)
def _check_roundtrip(obj):
unpickled = tm.round_trip_pickle(obj)
tm.assert_sp_array_equal(unpickled, obj)
def test_constructor_sparse_dtype(self):
result = SparseArray([1, 0, 0, 1], dtype=SparseDtype("int64", -1))
expected = SparseArray([1, 0, 0, 1], fill_value=-1, dtype=np.int64)
tm.assert_sp_array_equal(result, expected)
assert result.sp_values.dtype == np.dtype("int64")
def test_constructor_sparse_dtype_str(self):
result = SparseArray([1, 0, 0, 1], dtype="Sparse[int32]")
expected = SparseArray([1, 0, 0, 1], dtype=np.int32)
tm.assert_sp_array_equal(result, expected)
assert result.sp_values.dtype == np.dtype("int32")
def test_invert(fill_value):
arr = np.array([True, False, False, True])
sparray = SparseArray(arr, fill_value=fill_value)
result = ~sparray
expected = SparseArray(~arr, fill_value=not fill_value)
tm.assert_sp_array_equal(result, expected)
def test_astype_more(self, array, dtype, expected):
result = array.astype(dtype)
tm.assert_sp_array_equal(result, expected)
def test_take_all_empty(self):
a = pd.array([0, 0], dtype=SparseDtype("int64"))
result = a.take([0, 1], allow_fill=True, fill_value=np.nan)
tm.assert_sp_array_equal(a, result)
def test_unique_all_sparse():
# https://github.com/pandas-dev/pandas/issues/23168
arr = SparseArray([0, 0])
result = arr.unique()
expected = SparseArray([0])
tm.assert_sp_array_equal(result, expected)
def test_sparray_inplace():
sparray = SparseArray([0, 2, 0, 0])
ndarray = np.array([0, 1, 2, 3])
sparray += ndarray
expected = SparseArray([0, 3, 2, 3], fill_value=0)
tm.assert_sp_array_equal(sparray, expected)
def test_take(self):
exp = SparseArray(np.take(self.arr_data, [2, 3]))
tm.assert_sp_array_equal(self.arr.take([2, 3]), exp)
exp = SparseArray(np.take(self.arr_data, [0, 1, 2]))
tm.assert_sp_array_equal(self.arr.take([0, 1, 2]), exp)
def test_ufuncs(ufunc, arr):
result = ufunc(arr)
fill_value = ufunc(arr.fill_value)
expected = SparseArray(ufunc(np.asarray(arr)), fill_value=fill_value)
tm.assert_sp_array_equal(result, expected)
def test_with_list(op):
arr = SparseArray([0, 1], fill_value=0)
result = op(arr, [0, 1])
expected = op(arr, SparseArray([0, 1]))
tm.assert_sp_array_equal(result, expected)
def test_unary_op(op, fill_value):
arr = np.array([0, 1, np.nan, 2])
sparray = SparseArray(arr, fill_value=fill_value)
result = op(sparray)
expected = SparseArray(op(arr), fill_value=op(fill_value))
tm.assert_sp_array_equal(result, expected)
def test_fillna(self):
s = SparseArray([1, np.nan, np.nan, 3, np.nan])
res = s.fillna(-1)
exp = SparseArray([1, -1, -1, 3, -1], fill_value=-1, dtype=np.float64)
tm.assert_sp_array_equal(res, exp)
s = SparseArray([1, np.nan, np.nan, 3, np.nan], fill_value=0)
res = s.fillna(-1)
exp = SparseArray([1, -1, -1, 3, -1], fill_value=0, dtype=np.float64)
tm.assert_sp_array_equal(res, exp)
s = SparseArray([1, np.nan, 0, 3, 0])
res = s.fillna(-1)
exp = SparseArray([1, -1, 0, 3, 0], fill_value=-1, dtype=np.float64)
tm.assert_sp_array_equal(res, exp)
s = SparseArray([1, np.nan, 0, 3, 0], fill_value=0)
res = s.fillna(-1)
exp = SparseArray([1, -1, 0, 3, 0], fill_value=0, dtype=np.float64)
tm.assert_sp_array_equal(res, exp)
s = SparseArray([np.nan, np.nan, np.nan, np.nan])
res = s.fillna(-1)
exp = SparseArray([-1, -1, -1, -1], fill_value=-1, dtype=np.float64)
tm.assert_sp_array_equal(res, exp)
s = SparseArray([np.nan, np.nan, np.nan, np.nan], fill_value=0)
res = s.fillna(-1)
exp = SparseArray([-1, -1, -1, -1], fill_value=0, dtype=np.float64)
tm.assert_sp_array_equal(res, exp)
# float dtype's fill_value is np.nan, replaced by -1
s = SparseArray([0.0, 0.0, 0.0, 0.0])
res = s.fillna(-1)
exp = SparseArray([0.0, 0.0, 0.0, 0.0], fill_value=-1)
tm.assert_sp_array_equal(res, exp)
# int dtype shouldn't have missing. No changes.
s = SparseArray([0, 0, 0, 0])
assert s.dtype == SparseDtype(np.int64)
assert s.fill_value == 0
res = s.fillna(-1)
tm.assert_sp_array_equal(res, s)
s = SparseArray([0, 0, 0, 0], fill_value=0)
assert s.dtype == SparseDtype(np.int64)
assert s.fill_value == 0
res = s.fillna(-1)
exp = SparseArray([0, 0, 0, 0], fill_value=0)
tm.assert_sp_array_equal(res, exp)
# fill_value can be nan if there is no missing hole.
# only fill_value will be changed
s = SparseArray([0, 0, 0, 0], fill_value=np.nan)
assert s.dtype == SparseDtype(np.int64, fill_value=np.nan)
assert np.isnan(s.fill_value)
res = s.fillna(-1)
exp = SparseArray([0, 0, 0, 0], fill_value=-1)
tm.assert_sp_array_equal(res, exp)
def test_take_negative(self):
exp = SparseArray(np.take(self.arr_data, [-1]))
tm.assert_sp_array_equal(self.arr.take([-1]), exp)
exp = SparseArray(np.take(self.arr_data, [-4, -3, -2]))
tm.assert_sp_array_equal(self.arr.take([-4, -3, -2]), exp)
def test_ufunc(self):
# GH 13853 make sure ufunc is applied to fill_value
sparse = SparseArray([1, np.nan, 2, np.nan, -2])
result = SparseArray([1, np.nan, 2, np.nan, 2])
tm.assert_sp_array_equal(abs(sparse), result)
tm.assert_sp_array_equal(np.abs(sparse), result)
sparse = SparseArray([1, -1, 2, -2], fill_value=1)
result = SparseArray([1, 2, 2],
sparse_index=sparse.sp_index,
fill_value=1)
tm.assert_sp_array_equal(abs(sparse), result)
tm.assert_sp_array_equal(np.abs(sparse), result)
sparse = SparseArray([1, -1, 2, -2], fill_value=-1)
result = SparseArray([1, 2, 2],
sparse_index=sparse.sp_index,
fill_value=1)
tm.assert_sp_array_equal(abs(sparse), result)
tm.assert_sp_array_equal(np.abs(sparse), result)
sparse = SparseArray([1, np.nan, 2, np.nan, -2])
result = SparseArray(np.sin([1, np.nan, 2, np.nan, -2]))
tm.assert_sp_array_equal(np.sin(sparse), result)
sparse = SparseArray([1, -1, 2, -2], fill_value=1)
result = SparseArray(np.sin([1, -1, 2, -2]), fill_value=np.sin(1))
tm.assert_sp_array_equal(np.sin(sparse), result)
sparse = SparseArray([1, -1, 0, -2], fill_value=0)
result = SparseArray(np.sin([1, -1, 0, -2]), fill_value=np.sin(0))
tm.assert_sp_array_equal(np.sin(sparse), result)
def test_constructor_dtype_str(self):
result = SparseArray([1, 2, 3], dtype="int")
expected = SparseArray([1, 2, 3], dtype=int)
tm.assert_sp_array_equal(result, expected)
def test_map_missing():
arr = SparseArray([0, 1, 2])
expected = SparseArray([10, 11, None], fill_value=10)
result = arr.map({0: 10, 1: 11})
tm.assert_sp_array_equal(result, expected)
def test_pickle(self, fix, request):
obj = request.getfixturevalue(fix)
unpickled = tm.round_trip_pickle(obj)
tm.assert_sp_array_equal(unpickled, obj)
