REF: back IntervalArray by a single ndarray

pandas/core/arrays/interval.py

@@ -1,5 +1,6 @@
 from operator import le, lt
 import textwrap
+from typing import TYPE_CHECKING, Optional, Tuple, Union, cast
 
 import numpy as np
 
@@ -11,14 +12,17 @@
     IntervalMixin,
     intervals_to_interval_bounds,
 )
+from pandas._typing import ArrayLike, Dtype
 from pandas.compat.numpy import function as nv
 from pandas.util._decorators import Appender
 
 from pandas.core.dtypes.cast import maybe_convert_platform
 from pandas.core.dtypes.common import (
     is_categorical_dtype,
     is_datetime64_any_dtype,
+    is_dtype_equal,
     is_float_dtype,
+    is_integer,
     is_integer_dtype,
     is_interval_dtype,
     is_list_like,
@@ -45,6 +49,10 @@
 from pandas.core.indexers import check_array_indexer
 from pandas.core.indexes.base import ensure_index
 
+if TYPE_CHECKING:
+    from pandas import Index
+    from pandas.core.arrays import DatetimeArray, TimedeltaArray
+
 _interval_shared_docs = {}
 
 _shared_docs_kwargs = dict(
@@ -169,6 +177,17 @@ def __new__(
             left = data._left
             right = data._right
             closed = closed or data.closed
+
+            if dtype is None or data.dtype == dtype:
+                # This path will preserve id(result._combined)
+                # TODO: could also validate dtype before going to simple_new
+                combined = data._combined
+                if copy:
+                    combined = combined.copy()
+                result = cls._simple_new(combined, closed=closed)
+                if verify_integrity:
+                    result._validate()
+                return result
         else:
 
             # don't allow scalars
@@ -186,83 +205,22 @@ def __new__(
             )
             closed = closed or infer_closed
 
-        return cls._simple_new(
-            left,
-            right,
-            closed,
-            copy=copy,
-            dtype=dtype,
-            verify_integrity=verify_integrity,
-        )
+        closed = closed or "right"
+        left, right = _maybe_cast_inputs(left, right, copy, dtype)
+        combined = _get_combined_data(left, right)
+        result = cls._simple_new(combined, closed=closed)
+        if verify_integrity:
+            result._validate()
+        return result
 
     @classmethod
-    def _simple_new(
-        cls, left, right, closed=None, copy=False, dtype=None, verify_integrity=True
-    ):
+    def _simple_new(cls, data, closed="right"):
         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._combined = data
+        result._left = data[:, 0]
+        result._right = data[:, 1]
         result._closed = closed
-        if verify_integrity:
-            result._validate()
         return result
 
     @classmethod
@@ -397,10 +355,16 @@ def from_breaks(cls, breaks, closed="right", copy=False, dtype=None):
     def from_arrays(cls, left, right, closed="right", copy=False, dtype=None):
         left = maybe_convert_platform_interval(left)
         right = maybe_convert_platform_interval(right)
+        if len(left) != len(right):
+            raise ValueError("left and right must have the same length")
 
-        return cls._simple_new(
-            left, right, closed, copy=copy, dtype=dtype, verify_integrity=True
-        )
+        closed = closed or "right"
+        left, right = _maybe_cast_inputs(left, right, copy, dtype)
+        combined = _get_combined_data(left, right)
+
+        result = cls._simple_new(combined, closed)
+        result._validate()
+        return result
 
     _interval_shared_docs["from_tuples"] = textwrap.dedent(
         """
@@ -506,19 +470,6 @@ def _validate(self):
             msg = "left side of interval must be <= right side"
             raise ValueError(msg)
 
-    def _shallow_copy(self, left, right):
-        """
-        Return a new IntervalArray with the replacement attributes
-
-        Parameters
-        ----------
-        left : Index
-            Values to be used for the left-side of the intervals.
-        right : Index
-            Values to be used for the right-side of the intervals.
-        """
-        return self._simple_new(left, right, closed=self.closed, verify_integrity=False)
-
     # ---------------------------------------------------------------------
     # Descriptive
 
@@ -546,18 +497,20 @@ def __len__(self) -> int:
 
     def __getitem__(self, key):
         key = check_array_indexer(self, key)
-        left = self._left[key]
-        right = self._right[key]
 
-        if not isinstance(left, (np.ndarray, ExtensionArray)):
-            # scalar
-            if is_scalar(left) and isna(left):
+        result = self._combined[key]
+
+        if is_integer(key):
+            left, right = result[0], result[1]
+            if isna(left):
                 return self._fill_value
             return Interval(left, right, self.closed)
-        if np.ndim(left) > 1:
+
+        # TODO: need to watch out for incorrectly-reducing getitem
+        if np.ndim(result) > 2:
             # GH#30588 multi-dimensional indexer disallowed
             raise ValueError("multi-dimensional indexing not allowed")
-        return self._shallow_copy(left, right)
+        return type(self)._simple_new(result, closed=self.closed)
 
     def __setitem__(self, key, value):
         value_left, value_right = self._validate_setitem_value(value)
@@ -651,7 +604,8 @@ def fillna(self, value=None, method=None, limit=None):
 
         left = self.left.fillna(value=value_left)
         right = self.right.fillna(value=value_right)
-        return self._shallow_copy(left, right)
+        combined = _get_combined_data(left, right)
+        return type(self)._simple_new(combined, closed=self.closed)
 
     def astype(self, dtype, copy=True):
         """
@@ -693,7 +647,9 @@ def astype(self, dtype, copy=True):
                     f"Cannot convert {self.dtype} to {dtype}; subtypes are incompatible"
                 )
                 raise TypeError(msg) from err
-            return self._shallow_copy(new_left, new_right)
+            # TODO: do astype directly on self._combined
+            combined = _get_combined_data(new_left, new_right)
+            return type(self)._simple_new(combined, closed=self.closed)
         elif is_categorical_dtype(dtype):
             return Categorical(np.asarray(self))
         elif isinstance(dtype, StringDtype):
@@ -734,9 +690,11 @@ def _concat_same_type(cls, to_concat):
             raise ValueError("Intervals must all be closed on the same side.")
         closed = closed.pop()
 
+        # TODO: will this mess up on dt64tz?
         left = np.concatenate([interval.left for interval in to_concat])
         right = np.concatenate([interval.right for interval in to_concat])
-        return cls._simple_new(left, right, closed=closed, copy=False)
+        combined = _get_combined_data(left, right)  # TODO: 1-stage concat
+        return cls._simple_new(combined, closed=closed)
 
     def copy(self):
         """
@@ -746,11 +704,8 @@ def copy(self):
         -------
         IntervalArray
         """
-        left = self._left.copy()
-        right = self._right.copy()
-        closed = self.closed
-        # TODO: Could skip verify_integrity here.
-        return type(self).from_arrays(left, right, closed=closed)
+        combined = self._combined.copy()
+        return type(self)._simple_new(combined, closed=self.closed)
 
     def isna(self) -> np.ndarray:
         return isna(self._left)
@@ -843,7 +798,8 @@ def take(self, indices, allow_fill=False, fill_value=None, axis=None, **kwargs):
             self._right, indices, allow_fill=allow_fill, fill_value=fill_right
         )
 
-        return self._shallow_copy(left_take, right_take)
+        combined = _get_combined_data(left_take, right_take)
+        return type(self)._simple_new(combined, closed=self.closed)
 
     def _validate_listlike(self, value):
         # list-like of intervals
@@ -1170,10 +1126,7 @@ def set_closed(self, closed):
         if closed not in VALID_CLOSED:
             msg = f"invalid option for 'closed': {closed}"
             raise ValueError(msg)
-
-        return type(self)._simple_new(
-            left=self._left, right=self._right, closed=closed, verify_integrity=False
-        )
+        return type(self)._simple_new(self._combined, closed=closed)
 
     _interval_shared_docs[
         "is_non_overlapping_monotonic"
@@ -1314,9 +1267,8 @@ def to_tuples(self, na_tuple=True):
     @Appender(_extension_array_shared_docs["repeat"] % _shared_docs_kwargs)
     def repeat(self, repeats, axis=None):
         nv.validate_repeat(tuple(), dict(axis=axis))
-        left_repeat = self.left.repeat(repeats)
-        right_repeat = self.right.repeat(repeats)
-        return self._shallow_copy(left=left_repeat, right=right_repeat)
+        combined = self._combined.repeat(repeats, 0)
+        return type(self)._simple_new(combined, closed=self.closed)
 
     _interval_shared_docs["contains"] = textwrap.dedent(
         """
@@ -1399,3 +1351,92 @@ def maybe_convert_platform_interval(values):
         values = np.asarray(values)
 
     return maybe_convert_platform(values)
+
+
+def _maybe_cast_inputs(
+    left_orig: Union["Index", ArrayLike],
+    right_orig: Union["Index", ArrayLike],
+    copy: bool,
+    dtype: Optional[Dtype],
+) -> Tuple["Index", "Index"]:
+    left = ensure_index(left_orig, copy=copy)
+    right = ensure_index(right_orig, 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)
+        dtype = cast(IntervalDtype, dtype)
+        if 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 not is_dtype_equal(
+        left.dtype, right.dtype
+    ):
+        left_arr = cast("DatetimeArray", left._data)
+        right_arr = cast("DatetimeArray", right._data)
+        msg = (
+            "left and right must have the same time zone, got "
+            f"'{left_arr.tz}' and '{right_arr.tz}'"
+        )
+        raise ValueError(msg)
+
+    return left, right
+
+
+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