def _get_combined_data(
left: Union["Index", ArrayLike], right: Union["Index", ArrayLike]
) -> Union[np.ndarray, "DatetimeArray", "TimedeltaArray"]:
# For dt64/td64 we want DatetimeArray/TimedeltaArray instead of ndarray
from pandas.core.ops.array_ops import maybe_upcast_datetimelike_array
left = maybe_upcast_datetimelike_array(left)
left = extract_array(left, extract_numpy=True)
right = maybe_upcast_datetimelike_array(right)
right = extract_array(right, extract_numpy=True)
lbase = getattr(left, "_ndarray", left).base
rbase = getattr(right, "_ndarray", right).base
if lbase is not None and lbase is rbase:
# If these share data, then setitem could corrupt our IA
right = right.copy()
if isinstance(left, np.ndarray):
assert isinstance(right, np.ndarray) # for mypy
combined = np.concatenate(
[left.reshape(-1, 1), right.reshape(-1, 1)],
axis=1,
)
else:
left = cast(Union["DatetimeArray", "TimedeltaArray"], left)
right = cast(Union["DatetimeArray", "TimedeltaArray"], right)
combined = type(left)._concat_same_type(
[left.reshape(-1, 1), right.reshape(-1, 1)],
axis=1,
)
return combined
def _get_combined_data(
left: Union["Index", ArrayLike], right: Union["Index", ArrayLike]
) -> Union[np.ndarray, "DatetimeArray", "TimedeltaArray"]:
# For dt64/td64 we want DatetimeArray/TimedeltaArray instead of ndarray
from pandas.core.ops.array_ops import maybe_upcast_datetimelike_array
left = maybe_upcast_datetimelike_array(left)
left = extract_array(left, extract_numpy=True)
right = maybe_upcast_datetimelike_array(right)
right = extract_array(right, extract_numpy=True)
lbase = getattr(left, "_ndarray", left).base
rbase = getattr(right, "_ndarray", right).base
if lbase is not None and lbase is rbase:
# If these share data, then setitem could corrupt our IA
right = right.copy()
if isinstance(left, np.ndarray):
assert isinstance(right, np.ndarray) # for mypy
combined = np.concatenate(
[left.reshape(-1, 1), right.reshape(-1, 1)],
axis=1,
)
else:
# error: Item "type" of "Union[Type[Index], Type[ExtensionArray]]" has
# no attribute "_concat_same_type" [union-attr]
# error: Unexpected keyword argument "axis" for "_concat_same_type" of
# "ExtensionArray" [call-arg]
# error: Item "Index" of "Union[Index, ExtensionArray]" has no
# attribute "reshape" [union-attr]
# error: Item "ExtensionArray" of "Union[Index, ExtensionArray]" has no
# attribute "reshape" [union-attr]
combined = type(
left)._concat_same_type( # type: ignore[union-attr,call-arg]
[left.reshape(-1, 1),
right.reshape(-1, 1)], # type: ignore[union-attr]
axis=1,
)
return combined
def _simple_new(
cls, left, right, closed=None, copy=False, dtype=None, verify_integrity=True
):
result = IntervalMixin.__new__(cls)
closed = closed or "right"
left = ensure_index(left, copy=copy)
right = ensure_index(right, copy=copy)
if dtype is not None:
# GH 19262: dtype must be an IntervalDtype to override inferred
dtype = pandas_dtype(dtype)
if not is_interval_dtype(dtype):
msg = f"dtype must be an IntervalDtype, got {dtype}"
raise TypeError(msg)
elif dtype.subtype is not None:
left = left.astype(dtype.subtype)
right = right.astype(dtype.subtype)
# coerce dtypes to match if needed
if is_float_dtype(left) and is_integer_dtype(right):
right = right.astype(left.dtype)
elif is_float_dtype(right) and is_integer_dtype(left):
left = left.astype(right.dtype)
if type(left) != type(right):
msg = (
f"must not have differing left [{type(left).__name__}] and "
f"right [{type(right).__name__}] types"
)
raise ValueError(msg)
elif is_categorical_dtype(left.dtype) or is_string_dtype(left.dtype):
# GH 19016
msg = (
"category, object, and string subtypes are not supported "
"for IntervalArray"
)
raise TypeError(msg)
elif isinstance(left, ABCPeriodIndex):
msg = "Period dtypes are not supported, use a PeriodIndex instead"
raise ValueError(msg)
elif isinstance(left, ABCDatetimeIndex) and str(left.tz) != str(right.tz):
msg = (
"left and right must have the same time zone, got "
f"'{left.tz}' and '{right.tz}'"
)
raise ValueError(msg)
# For dt64/td64 we want DatetimeArray/TimedeltaArray instead of ndarray
from pandas.core.ops.array_ops import maybe_upcast_datetimelike_array
left = maybe_upcast_datetimelike_array(left)
left = extract_array(left, extract_numpy=True)
right = maybe_upcast_datetimelike_array(right)
right = extract_array(right, extract_numpy=True)
lbase = getattr(left, "_ndarray", left).base
rbase = getattr(right, "_ndarray", right).base
if lbase is not None and lbase is rbase:
# If these share data, then setitem could corrupt our IA
right = right.copy()
result._left = left
result._right = right
result._closed = closed
if verify_integrity:
result._validate()
return result