REF: de-duplicate ExtensionIndex methods

pandas/core/arrays/_mixins.py

@@ -346,6 +346,36 @@ def where(
         res_values = np.where(mask, self._ndarray, value)
         return self._from_backing_data(res_values)
 
+    # ------------------------------------------------------------------------
+    # Index compat methods
+
+    def insert(
+        self: NDArrayBackedExtensionArrayT, loc: int, item
+    ) -> NDArrayBackedExtensionArrayT:
+        """
+        Make new ExtensionArray inserting new item at location. Follows
+        Python list.append semantics for negative values.
+
+        Parameters
+        ----------
+        loc : int
+        item : object
+
+        Returns
+        -------
+        type(self)
+        """
+        code = self._validate_scalar(item)
+
+        new_vals = np.concatenate(
+            (
+                self._ndarray[:loc],
+                np.asarray([code], dtype=self._ndarray.dtype),
+                self._ndarray[loc:],
+            )
+        )
+        return self._from_backing_data(new_vals)
+
     # ------------------------------------------------------------------------
     # Additional array methods
     #  These are not part of the EA API, but we implement them because

pandas/core/dtypes/missing.py

@@ -37,7 +37,11 @@
     is_string_dtype,
     needs_i8_conversion,
 )
-from pandas.core.dtypes.dtypes import ExtensionDtype
+from pandas.core.dtypes.dtypes import (
+    ExtensionDtype,
+    IntervalDtype,
+    PeriodDtype,
+)
 from pandas.core.dtypes.generic import (
     ABCDataFrame,
     ABCExtensionArray,
@@ -630,7 +634,13 @@ def is_valid_na_for_dtype(obj, dtype: DtypeObj) -> bool:
         # This is needed for Categorical, but is kind of weird
         return True
 
-    # must be PeriodDType
+    elif isinstance(dtype, PeriodDtype):
+        return not isinstance(obj, (np.datetime64, np.timedelta64, Decimal))
+
+    elif isinstance(dtype, IntervalDtype):
+        return lib.is_float(obj) or obj is None or obj is libmissing.NA
+
+    # fallback, default to allowing NaN, None, NA, NaT
     return not isinstance(obj, (np.datetime64, np.timedelta64, Decimal))
 
 

pandas/core/indexes/extension.py

@@ -13,17 +13,12 @@
 
 from pandas._typing import ArrayLike
 from pandas.compat.numpy import function as nv
-from pandas.errors import AbstractMethodError
 from pandas.util._decorators import (
     cache_readonly,
     doc,
 )
 from pandas.util._exceptions import rewrite_exception
 
-from pandas.core.dtypes.cast import (
-    find_common_type,
-    infer_dtype_from,
-)
 from pandas.core.dtypes.common import (
     is_dtype_equal,
     is_object_dtype,
@@ -34,6 +29,7 @@
     ABCSeries,
 )
 
+from pandas.core.array_algos.putmask import validate_putmask
 from pandas.core.arrays import (
     Categorical,
     DatetimeArray,
@@ -297,6 +293,21 @@ def searchsorted(self, value, side="left", sorter=None) -> np.ndarray:
         # overriding IndexOpsMixin improves performance GH#38083
         return self._data.searchsorted(value, side=side, sorter=sorter)
 
+    def putmask(self, mask, value) -> Index:
+        mask, noop = validate_putmask(self._data, mask)
+        if noop:
+            return self.copy()
+
+        try:
+            self._validate_fill_value(value)
+        except (ValueError, TypeError):
+            dtype = self._find_common_type_compat(value)
+            return self.astype(dtype).putmask(mask, value)
+
+        arr = self._data.copy()
+        arr.putmask(mask, value)
+        return type(self)._simple_new(arr, name=self.name)
+
     # ---------------------------------------------------------------------
 
     def _get_engine_target(self) -> np.ndarray:
@@ -323,9 +334,30 @@ def repeat(self, repeats, axis=None):
         result = self._data.repeat(repeats, axis=axis)
         return type(self)._simple_new(result, name=self.name)
 
-    def insert(self, loc: int, item):
-        # ExtensionIndex subclasses must override Index.insert
-        raise AbstractMethodError(self)
+    def insert(self, loc: int, item) -> Index:
+        """
+        Make new Index inserting new item at location. Follows
+        Python list.append semantics for negative values.
+
+        Parameters
+        ----------
+        loc : int
+        item : object
+
+        Returns
+        -------
+        new_index : Index
+        """
+        try:
+            result = self._data.insert(loc, item)
+        except (ValueError, TypeError):
+            # e.g. trying to insert an integer into a DatetimeIndex
+            #  We cannot keep the same dtype, so cast to the (often object)
+            #  minimal shared dtype before doing the insert.
+            dtype = self._find_common_type_compat(item)
+            return self.astype(dtype).insert(loc, item)
+        else:
+            return type(self)._simple_new(result, name=self.name)
 
     def _validate_fill_value(self, value):
         """
@@ -426,60 +458,3 @@ def _get_engine_target(self) -> np.ndarray:
     def _from_join_target(self, result: np.ndarray) -> ArrayLike:
         assert result.dtype == self._data._ndarray.dtype
         return self._data._from_backing_data(result)
-
-    def insert(self: _T, loc: int, item) -> Index:
-        """
-        Make new Index inserting new item at location. Follows
-        Python list.append semantics for negative values.
-
-        Parameters
-        ----------
-        loc : int
-        item : object
-
-        Returns
-        -------
-        new_index : Index
-
-        Raises
-        ------
-        ValueError if the item is not valid for this dtype.
-        """
-        arr = self._data
-        try:
-            code = arr._validate_scalar(item)
-        except (ValueError, TypeError):
-            # e.g. trying to insert an integer into a DatetimeIndex
-            #  We cannot keep the same dtype, so cast to the (often object)
-            #  minimal shared dtype before doing the insert.
-            dtype, _ = infer_dtype_from(item, pandas_dtype=True)
-            dtype = find_common_type([self.dtype, dtype])
-            return self.astype(dtype).insert(loc, item)
-        else:
-            new_vals = np.concatenate(
-                (
-                    arr._ndarray[:loc],
-                    np.asarray([code], dtype=arr._ndarray.dtype),
-                    arr._ndarray[loc:],
-                )
-            )
-            new_arr = arr._from_backing_data(new_vals)
-            return type(self)._simple_new(new_arr, name=self.name)
-
-    def putmask(self, mask, value) -> Index:
-        res_values = self._data.copy()
-        try:
-            res_values.putmask(mask, value)
-        except (TypeError, ValueError):
-            return self.astype(object).putmask(mask, value)
-
-        return type(self)._simple_new(res_values, name=self.name)
-
-    # error: Argument 1 of "_wrap_joined_index" is incompatible with supertype
-    # "Index"; supertype defines the argument type as "Union[ExtensionArray, ndarray]"
-    def _wrap_joined_index(  # type: ignore[override]
-        self: _T, joined: NDArrayBackedExtensionArray, other: _T
-    ) -> _T:
-        name = get_op_result_name(self, other)
-
-        return type(self)._simple_new(joined, name=name)

pandas/core/indexes/interval.py

@@ -67,7 +67,6 @@
     take_nd,
     unique,
 )
-from pandas.core.array_algos.putmask import validate_putmask
 from pandas.core.arrays.interval import (
     IntervalArray,
     _interval_shared_docs,
@@ -854,46 +853,6 @@ def mid(self) -> Index:
     def length(self) -> Index:
         return Index(self._data.length, copy=False)
 
-    def putmask(self, mask, value) -> Index:
-        mask, noop = validate_putmask(self._data, mask)
-        if noop:
-            return self.copy()
-
-        try:
-            self._validate_fill_value(value)
-        except (ValueError, TypeError):
-            dtype = self._find_common_type_compat(value)
-            return self.astype(dtype).putmask(mask, value)
-
-        arr = self._data.copy()
-        arr.putmask(mask, value)
-        return type(self)._simple_new(arr, name=self.name)
-
-    def insert(self, loc: int, item):
-        """
-        Return a new IntervalIndex inserting new item at location. Follows
-        Python list.append semantics for negative values.  Only Interval
-        objects and NA can be inserted into an IntervalIndex
-
-        Parameters
-        ----------
-        loc : int
-        item : object
-
-        Returns
-        -------
-        IntervalIndex
-        """
-        try:
-            result = self._data.insert(loc, item)
-        except (ValueError, TypeError):
-            # e.g trying to insert a string
-            dtype, _ = infer_dtype_from_scalar(item, pandas_dtype=True)
-            dtype = find_common_type([self.dtype, dtype])
-            return self.astype(dtype).insert(loc, item)
-
-        return type(self)._simple_new(result, name=self.name)
-
     # --------------------------------------------------------------------
     # Rendering Methods
     # __repr__ associated methods are based on MultiIndex

pandas/tests/dtypes/test_missing.py

@@ -24,6 +24,7 @@
 )
 from pandas.core.dtypes.missing import (
     array_equivalent,
+    is_valid_na_for_dtype,
     isna,
     isnull,
     na_value_for_dtype,
@@ -729,3 +730,12 @@ def test_is_matching_na_nan_matches_none(self):
 
         assert libmissing.is_matching_na(None, np.nan, nan_matches_none=True)
         assert libmissing.is_matching_na(np.nan, None, nan_matches_none=True)
+
+
+class TestIsValidNAForDtype:
+    def test_is_valid_na_for_dtype_interval(self):
+        dtype = IntervalDtype("int64", "left")
+        assert not is_valid_na_for_dtype(NaT, dtype)
+
+        dtype = IntervalDtype("datetime64[ns]", "both")
+        assert not is_valid_na_for_dtype(NaT, dtype)