REF: be stricter about what we pass to _simple_new

pandas/core/arrays/period.py

@@ -169,8 +169,9 @@ def __init__(self, values, freq=None, dtype=None, copy=False):
         self._dtype = PeriodDtype(freq)
 
     @classmethod
-    def _simple_new(cls, values, freq=None, **kwargs):
+    def _simple_new(cls, values: np.ndarray, freq=None, **kwargs):
         # alias for PeriodArray.__init__
+        assert isinstance(values, np.ndarray) and values.dtype == "i8"
         return cls(values, freq=freq, **kwargs)
 
     @classmethod

pandas/core/indexes/base.py

@@ -50,7 +50,6 @@
 from pandas.core.dtypes.generic import (
     ABCCategorical,
     ABCDataFrame,
-    ABCDatetimeArray,
     ABCDatetimeIndex,
     ABCIndexClass,
     ABCIntervalIndex,
@@ -459,11 +458,7 @@ def _simple_new(cls, values, name=None, dtype=None):
 
         Must be careful not to recurse.
         """
-        if isinstance(values, (ABCSeries, ABCIndexClass)):
-            # Index._data must always be an ndarray.
-            # This is no-copy for when _values is an ndarray,
-            # which should be always at this point.
-            values = np.asarray(values._values)
+        assert isinstance(values, np.ndarray), type(values)
 
         result = object.__new__(cls)
         result._data = values
@@ -509,17 +504,14 @@ def _get_attributes_dict(self):
     def _shallow_copy(self, values=None, **kwargs):
         if values is None:
             values = self.values
+
         attributes = self._get_attributes_dict()
         attributes.update(kwargs)
         if not len(values) and "dtype" not in kwargs:
             attributes["dtype"] = self.dtype
 
         # _simple_new expects an the type of self._data
         values = getattr(values, "_values", values)
-        if isinstance(values, ABCDatetimeArray):
-            # `self.values` returns `self` for tz-aware, so we need to unwrap
-            #  more specifically
-            values = values.asi8
 
         return self._simple_new(values, **attributes)
 

pandas/core/indexes/datetimes.py

@@ -274,10 +274,6 @@ def _simple_new(cls, values, name=None, freq=None, tz=None, dtype=None):
             freq = values.freq
             values = values._data
 
-        # DatetimeArray._simple_new will accept either i8 or M8[ns] dtypes
-        if isinstance(values, DatetimeIndex):
-            values = values._data
-
         dtype = tz_to_dtype(tz)
         dtarr = DatetimeArray._simple_new(values, freq=freq, dtype=dtype)
         assert isinstance(dtarr, DatetimeArray)

pandas/core/indexes/interval.py

@@ -259,6 +259,8 @@ def _simple_new(cls, array, name, closed=None):
         closed : Any
             Ignored.
         """
+        assert isinstance(array, IntervalArray), type(array)
+
         result = IntervalMixin.__new__(cls)
         result._data = array
         result.name = name

pandas/core/indexes/numeric.py

@@ -52,6 +52,7 @@ class NumericIndex(Index):
 
     def __new__(cls, data=None, dtype=None, copy=False, name=None):
         cls._validate_dtype(dtype)
+        name = maybe_extract_name(name, data, cls)
 
         # Coerce to ndarray if not already ndarray or Index
         if not isinstance(data, (np.ndarray, Index)):
@@ -77,7 +78,7 @@ def __new__(cls, data=None, dtype=None, copy=False, name=None):
             # GH#13601, GH#20285, GH#27125
             raise ValueError("Index data must be 1-dimensional")
 
-        name = maybe_extract_name(name, data, cls)
+        subarr = np.asarray(subarr)
         return cls._simple_new(subarr, name=name)
 
     @classmethod

pandas/core/indexes/period.py

@@ -249,8 +249,6 @@ def _simple_new(cls, values, name=None, freq=None, **kwargs):
             freq = Period._maybe_convert_freq(freq)
         values = PeriodArray(values, freq=freq)
 
-        if not isinstance(values, PeriodArray):
-            raise TypeError("PeriodIndex._simple_new only accepts PeriodArray")
         result = object.__new__(cls)
         result._data = values
         # For groupby perf. See note in indexes/base about _index_data

pandas/core/indexes/range.py

@@ -112,7 +112,7 @@ def __new__(
         return cls._simple_new(rng, dtype=dtype, name=name)
 
     @classmethod
-    def from_range(cls, data, name=None, dtype=None):
+    def from_range(cls, data: range, name=None, dtype=None) -> "RangeIndex":
         """
         Create RangeIndex from a range object.
 

pandas/core/indexes/timedeltas.py

@@ -177,12 +177,13 @@ def __new__(
         tdarr = TimedeltaArray._from_sequence(
             data, freq=freq, unit=unit, dtype=dtype, copy=copy
         )
-        return cls._simple_new(tdarr._data, freq=tdarr.freq, name=name)
+        return cls._simple_new(tdarr, name=name)
 
     @classmethod
     def _simple_new(cls, values, name=None, freq=None, dtype=_TD_DTYPE):
         # `dtype` is passed by _shallow_copy in corner cases, should always
         #  be timedelta64[ns] if present
+
         if not isinstance(values, TimedeltaArray):
             values = TimedeltaArray._simple_new(values, dtype=dtype, freq=freq)
         else:
@@ -420,7 +421,8 @@ def insert(self, loc, item):
             new_i8s = np.concatenate(
                 (self[:loc].asi8, [item.view(np.int64)], self[loc:].asi8)
             )
-            return self._shallow_copy(new_i8s, freq=freq)
+            tda = type(self._data)._simple_new(new_i8s, freq=freq)
+            return self._shallow_copy(tda)
         except (AttributeError, TypeError):
 
             # fall back to object index
@@ -507,4 +509,4 @@ def timedelta_range(
 
     freq, freq_infer = dtl.maybe_infer_freq(freq)
     tdarr = TimedeltaArray._generate_range(start, end, periods, freq, closed=closed)
-    return TimedeltaIndex._simple_new(tdarr._data, freq=tdarr.freq, name=name)
+    return TimedeltaIndex._simple_new(tdarr, name=name)