PERF: column_arrays

pandas/core/internals/blocks.py

@@ -228,6 +228,11 @@ def get_values(self, dtype: DtypeObj | None = None) -> np.ndarray:
         # expected "ndarray")
         return self.values  # type: ignore[return-value]
 
+    def values_for_json(self) -> np.ndarray:
+        # Incompatible return value type (got "Union[ndarray[Any, Any],
+        # ExtensionArray]", expected "ndarray[Any, Any]")
+        return self.values  # type: ignore[return-value]
+
     @final
     @cache_readonly
     def fill_value(self):
@@ -1375,6 +1380,9 @@ def get_values(self, dtype: DtypeObj | None = None) -> np.ndarray:
         # TODO(EA2D): reshape not needed with 2D EAs
         return np.asarray(values).reshape(self.shape)
 
+    def values_for_json(self) -> np.ndarray:
+        return np.asarray(self.values)
+
     def interpolate(
         self, method="pad", axis=0, inplace=False, limit=None, fill_value=None, **kwargs
     ):
@@ -1805,6 +1813,11 @@ class DatetimeLikeBlock(NDArrayBackedExtensionBlock):
     is_numeric = False
     values: DatetimeArray | TimedeltaArray
 
+    def values_for_json(self) -> np.ndarray:
+        # special casing datetimetz to avoid conversion through
+        #  object dtype
+        return self.values._ndarray
+
 
 class DatetimeTZBlock(DatetimeLikeBlock):
     """implement a datetime64 block with a tz attribute"""

pandas/core/internals/managers.py

@@ -973,24 +973,25 @@ def column_arrays(self) -> list[np.ndarray]:
         """
         Used in the JSON C code to access column arrays.
         This optimizes compared to using `iget_values` by converting each
-        block.values to a np.ndarray only once up front
         """
-        # special casing datetimetz to avoid conversion through object dtype
-        arrays = [
-            blk.values._ndarray
-            if isinstance(blk, DatetimeTZBlock)
-            else np.asarray(blk.values)
-            for blk in self.blocks
-        ]
-        result = []
-        for i in range(len(self.items)):
-            arr = arrays[self.blknos[i]]
-            if arr.ndim == 2:
-                values = arr[self.blklocs[i]]
+        # This is an optimized equivalent to
+        #  result = [self.iget_values(i) for i in range(len(self.items))]
+        result: list[np.ndarray | None] = [None] * len(self.items)
+
+        for blk in self.blocks:
+            mgr_locs = blk._mgr_locs
+            values = blk.values_for_json()
+            if values.ndim == 1:
+                # TODO(EA2D): special casing not needed with 2D EAs
+                result[mgr_locs[0]] = values
+
             else:
-                values = arr
-            result.append(values)
-        return result
+                for i, loc in enumerate(mgr_locs):
+                    result[loc] = values[i]
+
+        # error: Incompatible return value type (got "List[None]",
+        # expected "List[ndarray[Any, Any]]")
+        return result  # type: ignore[return-value]
 
     def iset(self, loc: int | slice | np.ndarray, value: ArrayLike):
         """