def _homogenize(data, index: Index, dtype: DtypeObj | None) -> list[ArrayLike]:
oindex = None
homogenized = []
for val in data:
if isinstance(val, ABCSeries):
if dtype is not None:
val = val.astype(dtype, copy=False)
if val.index is not index:
# Forces alignment. No need to copy data since we
# are putting it into an ndarray later
val = val.reindex(index, copy=False)
val = val._values
else:
if isinstance(val, dict):
if oindex is None:
oindex = index.astype("O")
if isinstance(index, (DatetimeIndex, TimedeltaIndex)):
# see test_constructor_dict_datetime64_index
val = dict_compat(val)
else:
# see test_constructor_subclass_dict
val = dict(val)
val = lib.fast_multiget(val, oindex._values, default=np.nan)
val = sanitize_array(val,
index,
dtype=dtype,
copy=False,
raise_cast_failure=False)
homogenized.append(val)
return homogenized
def _homogenize(data, index: Index, dtype: Optional[DtypeObj]):
oindex = None
homogenized = []
for val in data:
if isinstance(val, ABCSeries):
if dtype is not None:
val = val.astype(dtype)
if val.index is not index:
# Forces alignment. No need to copy data since we
# are putting it into an ndarray later
val = val.reindex(index, copy=False)
# TODO extract_array should be preferred, but that gives failures for
# `extension/test_numpy.py` (extract_array will convert numpy arrays
# to PandasArray), see https://github.com/pandas-dev/pandas/issues/40021
# val = extract_array(val, extract_numpy=True)
val = val._values
else:
if isinstance(val, dict):
if oindex is None:
oindex = index.astype("O")
if isinstance(index, (ABCDatetimeIndex, ABCTimedeltaIndex)):
val = dict_compat(val)
else:
val = dict(val)
val = lib.fast_multiget(val, oindex._values, default=np.nan)
val = sanitize_array(
val, index, dtype=dtype, copy=False, raise_cast_failure=False
)
homogenized.append(val)
return homogenized
def _homogenize(data, index: Index, dtype: Optional[DtypeObj]):
oindex = None
homogenized = []
for val in data:
if isinstance(val, ABCSeries):
if dtype is not None:
val = val.astype(dtype)
if val.index is not index:
# Forces alignment. No need to copy data since we
# are putting it into an ndarray later
val = val.reindex(index, copy=False)
else:
if isinstance(val, dict):
if oindex is None:
oindex = index.astype("O")
if isinstance(index, (ABCDatetimeIndex, ABCTimedeltaIndex)):
val = dict_compat(val)
else:
val = dict(val)
val = lib.fast_multiget(val, oindex._values, default=np.nan)
val = sanitize_array(val,
index,
dtype=dtype,
copy=False,
raise_cast_failure=False)
homogenized.append(val)
return homogenized
def test_intersection(self, sort):
# intersect with Int64Index
index = RangeIndex(start=0, stop=20, step=2)
other = Index(np.arange(1, 6))
result = index.intersection(other, sort=sort)
expected = Index(np.sort(np.intersect1d(index.values, other.values)))
tm.assert_index_equal(result, expected)
result = other.intersection(index, sort=sort)
expected = Index(
np.sort(np.asarray(np.intersect1d(index.values, other.values))))
tm.assert_index_equal(result, expected)
# intersect with increasing RangeIndex
other = RangeIndex(1, 6)
result = index.intersection(other, sort=sort)
expected = Index(np.sort(np.intersect1d(index.values, other.values)))
tm.assert_index_equal(result, expected, exact="equiv")
# intersect with decreasing RangeIndex
other = RangeIndex(5, 0, -1)
result = index.intersection(other, sort=sort)
expected = Index(np.sort(np.intersect1d(index.values, other.values)))
tm.assert_index_equal(result, expected, exact="equiv")
# reversed (GH 17296)
result = other.intersection(index, sort=sort)
tm.assert_index_equal(result, expected, exact="equiv")
# GH 17296: intersect two decreasing RangeIndexes
first = RangeIndex(10, -2, -2)
other = RangeIndex(5, -4, -1)
expected = first.astype(int).intersection(other.astype(int), sort=sort)
result = first.intersection(other, sort=sort).astype(int)
tm.assert_index_equal(result, expected)
# reversed
result = other.intersection(first, sort=sort).astype(int)
tm.assert_index_equal(result, expected)
index = RangeIndex(5, name="foo")
# intersect of non-overlapping indices
other = RangeIndex(5, 10, 1, name="foo")
result = index.intersection(other, sort=sort)
expected = RangeIndex(0, 0, 1, name="foo")
tm.assert_index_equal(result, expected)
other = RangeIndex(-1, -5, -1)
result = index.intersection(other, sort=sort)
expected = RangeIndex(0, 0, 1)
tm.assert_index_equal(result, expected)
# intersection of empty indices
other = RangeIndex(0, 0, 1)
result = index.intersection(other, sort=sort)
expected = RangeIndex(0, 0, 1)
tm.assert_index_equal(result, expected)
result = other.intersection(index, sort=sort)
tm.assert_index_equal(result, expected)
