REF/TYP: Rename ABCIndexClass->ABCIndex and use cast (pandas-dev#38329)

pandas/core/algorithms.py

@@ -50,7 +50,7 @@
 from pandas.core.dtypes.generic import (
     ABCDatetimeArray,
     ABCExtensionArray,
-    ABCIndexClass,
+    ABCIndex,
     ABCMultiIndex,
     ABCRangeIndex,
     ABCSeries,
@@ -63,6 +63,7 @@
 
 if TYPE_CHECKING:
     from pandas import Categorical, DataFrame, Index, Series
+    from pandas.core.arrays import DatetimeArray, TimedeltaArray
 
 _shared_docs: Dict[str, str] = {}
 
@@ -215,7 +216,7 @@ def _reconstruct_data(
         values = values.astype(dtype, copy=False)
 
         # we only support object dtypes bool Index
-        if isinstance(original, ABCIndexClass):
+        if isinstance(original, ABCIndex):
             values = values.astype(object, copy=False)
     elif dtype is not None:
         if is_datetime64_dtype(dtype):
@@ -437,9 +438,7 @@ def isin(comps: AnyArrayLike, values: AnyArrayLike) -> np.ndarray:
             f"to isin(), you passed a [{type(values).__name__}]"
         )
 
-    if not isinstance(
-        values, (ABCIndexClass, ABCSeries, ABCExtensionArray, np.ndarray)
-    ):
+    if not isinstance(values, (ABCIndex, ABCSeries, ABCExtensionArray, np.ndarray)):
         values = _ensure_arraylike(list(values))
     elif isinstance(values, ABCMultiIndex):
         # Avoid raising in extract_array
@@ -700,7 +699,7 @@ def factorize(
         and values.freq is not None
     ):
         codes, uniques = values.factorize(sort=sort)
-        if isinstance(original, ABCIndexClass):
+        if isinstance(original, ABCIndex):
             uniques = original._shallow_copy(uniques, name=None)
         elif isinstance(original, ABCSeries):
             from pandas import Index
@@ -739,8 +738,11 @@ def factorize(
     uniques = _reconstruct_data(uniques, dtype, original)
 
     # return original tenor
-    if isinstance(original, ABCIndexClass):
+    if isinstance(original, ABCIndex):
         if original.dtype.kind in ["m", "M"] and isinstance(uniques, np.ndarray):
+            original._data = cast(
+                "Union[DatetimeArray, TimedeltaArray]", original._data
+            )
             uniques = type(original._data)._simple_new(uniques, dtype=original.dtype)
         uniques = original._shallow_copy(uniques, name=None)
     elif isinstance(original, ABCSeries):

pandas/core/arrays/base.py

@@ -41,7 +41,7 @@
     pandas_dtype,
 )
 from pandas.core.dtypes.dtypes import ExtensionDtype
-from pandas.core.dtypes.generic import ABCDataFrame, ABCIndexClass, ABCSeries
+from pandas.core.dtypes.generic import ABCDataFrame, ABCIndex, ABCSeries
 from pandas.core.dtypes.missing import isna
 
 from pandas.core import ops
@@ -1361,7 +1361,7 @@ def convert_values(param):
                     ovalues = [param] * len(self)
                 return ovalues
 
-            if isinstance(other, (ABCSeries, ABCIndexClass, ABCDataFrame)):
+            if isinstance(other, (ABCSeries, ABCIndex, ABCDataFrame)):
                 # rely on pandas to unbox and dispatch to us
                 return NotImplemented
 

pandas/core/arrays/categorical.py

@@ -38,7 +38,7 @@
     needs_i8_conversion,
 )
 from pandas.core.dtypes.dtypes import CategoricalDtype
-from pandas.core.dtypes.generic import ABCIndexClass, ABCSeries
+from pandas.core.dtypes.generic import ABCIndex, ABCSeries
 from pandas.core.dtypes.missing import is_valid_nat_for_dtype, isna, notna
 
 from pandas.core import ops
@@ -321,7 +321,7 @@ def __init__(
         if is_categorical_dtype(values):
             if dtype.categories is None:
                 dtype = CategoricalDtype(values.categories, dtype.ordered)
-        elif not isinstance(values, (ABCIndexClass, ABCSeries)):
+        elif not isinstance(values, (ABCIndex, ABCSeries)):
             # sanitize_array coerces np.nan to a string under certain versions
             # of numpy
             values = maybe_infer_to_datetimelike(values, convert_dates=True)
@@ -2514,7 +2514,7 @@ def _get_codes_for_values(values, categories) -> np.ndarray:
         values = ensure_object(values)
         categories = ensure_object(categories)
 
-    if isinstance(categories, ABCIndexClass):
+    if isinstance(categories, ABCIndex):
         return coerce_indexer_dtype(categories.get_indexer_for(values), categories)
 
     # Only hit here when we've already coerced to object dtypee.

pandas/core/arrays/datetimes.py

@@ -44,7 +44,7 @@
     pandas_dtype,
 )
 from pandas.core.dtypes.dtypes import DatetimeTZDtype
-from pandas.core.dtypes.generic import ABCIndexClass, ABCPandasArray, ABCSeries
+from pandas.core.dtypes.generic import ABCIndex, ABCPandasArray, ABCSeries
 from pandas.core.dtypes.missing import isna
 
 from pandas.core.algorithms import checked_add_with_arr
@@ -216,7 +216,7 @@ class DatetimeArray(dtl.TimelikeOps, dtl.DatelikeOps):
     _freq = None
 
     def __init__(self, values, dtype=DT64NS_DTYPE, freq=None, copy=False):
-        if isinstance(values, (ABCSeries, ABCIndexClass)):
+        if isinstance(values, (ABCSeries, ABCIndex)):
             values = values._values
 
         inferred_freq = getattr(values, "_freq", None)
@@ -1947,7 +1947,7 @@ def sequence_to_dt64ns(
     # if dtype has an embedded tz, capture it
     tz = validate_tz_from_dtype(dtype, tz)
 
-    if isinstance(data, ABCIndexClass):
+    if isinstance(data, ABCIndex):
         if data.nlevels > 1:
             # Without this check, data._data below is None
             raise TypeError("Cannot create a DatetimeArray from a MultiIndex.")

pandas/core/arrays/period.py

@@ -40,7 +40,7 @@
 )
 from pandas.core.dtypes.dtypes import PeriodDtype
 from pandas.core.dtypes.generic import (
-    ABCIndexClass,
+    ABCIndex,
     ABCPeriodIndex,
     ABCSeries,
     ABCTimedeltaArray,
@@ -964,14 +964,14 @@ def dt64arr_to_periodarr(data, freq, tz=None):
         raise ValueError(f"Wrong dtype: {data.dtype}")
 
     if freq is None:
-        if isinstance(data, ABCIndexClass):
+        if isinstance(data, ABCIndex):
             data, freq = data._values, data.freq
         elif isinstance(data, ABCSeries):
             data, freq = data._values, data.dt.freq
 
     freq = Period._maybe_convert_freq(freq)
 
-    if isinstance(data, (ABCIndexClass, ABCSeries)):
+    if isinstance(data, (ABCIndex, ABCSeries)):
         data = data._values
 
     base = freq._period_dtype_code

pandas/core/arrays/sparse/array.py

@@ -35,7 +35,7 @@
     is_string_dtype,
     pandas_dtype,
 )
-from pandas.core.dtypes.generic import ABCIndexClass, ABCSeries
+from pandas.core.dtypes.generic import ABCIndex, ABCSeries
 from pandas.core.dtypes.missing import isna, na_value_for_dtype, notna
 
 import pandas.core.algorithms as algos
@@ -746,7 +746,7 @@ def value_counts(self, dropna=True):
                 keys = np.insert(keys, 0, self.fill_value)
                 counts = np.insert(counts, 0, fcounts)
 
-        if not isinstance(keys, ABCIndexClass):
+        if not isinstance(keys, ABCIndex):
             keys = Index(keys)
         return Series(counts, index=keys)
 

pandas/core/base.py

@@ -32,7 +32,7 @@
     is_object_dtype,
     is_scalar,
 )
-from pandas.core.dtypes.generic import ABCDataFrame, ABCIndexClass, ABCSeries
+from pandas.core.dtypes.generic import ABCDataFrame, ABCIndex, ABCSeries
 from pandas.core.dtypes.missing import isna, remove_na_arraylike
 
 from pandas.core import algorithms
@@ -199,7 +199,7 @@ def _selection_name(self):
     @property
     def _selection_list(self):
         if not isinstance(
-            self._selection, (list, tuple, ABCSeries, ABCIndexClass, np.ndarray)
+            self._selection, (list, tuple, ABCSeries, ABCIndex, np.ndarray)
         ):
             return [self._selection]
         return self._selection
@@ -254,7 +254,7 @@ def __getitem__(self, key):
         if self._selection is not None:
             raise IndexError(f"Column(s) {self._selection} already selected")
 
-        if isinstance(key, (list, tuple, ABCSeries, ABCIndexClass, np.ndarray)):
+        if isinstance(key, (list, tuple, ABCSeries, ABCIndex, np.ndarray)):
             # pandas\core\base.py:217: error: "SelectionMixin" has no attribute
             # "obj"  [attr-defined]
             if len(

pandas/core/common.py

@@ -24,7 +24,7 @@
     is_extension_array_dtype,
     is_integer,
 )
-from pandas.core.dtypes.generic import ABCExtensionArray, ABCIndexClass, ABCSeries
+from pandas.core.dtypes.generic import ABCExtensionArray, ABCIndex, ABCSeries
 from pandas.core.dtypes.inference import iterable_not_string
 from pandas.core.dtypes.missing import isna, isnull, notnull  # noqa
 
@@ -100,7 +100,7 @@ def is_bool_indexer(key: Any) -> bool:
     check_array_indexer : Check that `key` is a valid array to index,
         and convert to an ndarray.
     """
-    if isinstance(key, (ABCSeries, np.ndarray, ABCIndexClass)) or (
+    if isinstance(key, (ABCSeries, np.ndarray, ABCIndex)) or (
         is_array_like(key) and is_extension_array_dtype(key.dtype)
     ):
         if key.dtype == np.object_:
@@ -199,7 +199,7 @@ def asarray_tuplesafe(values, dtype=None):
 
     if not (isinstance(values, (list, tuple)) or hasattr(values, "__array__")):
         values = list(values)
-    elif isinstance(values, ABCIndexClass):
+    elif isinstance(values, ABCIndex):
         return values._values
 
     if isinstance(values, list) and dtype in [np.object_, object]:
@@ -466,9 +466,7 @@ def convert_to_list_like(
     Convert list-like or scalar input to list-like. List, numpy and pandas array-like
     inputs are returned unmodified whereas others are converted to list.
     """
-    if isinstance(
-        values, (list, np.ndarray, ABCIndexClass, ABCSeries, ABCExtensionArray)
-    ):
+    if isinstance(values, (list, np.ndarray, ABCIndex, ABCSeries, ABCExtensionArray)):
         # np.ndarray resolving as Any gives a false positive
         return values  # type: ignore[return-value]
     elif isinstance(values, abc.Iterable) and not isinstance(values, str):

pandas/core/construction.py

@@ -41,7 +41,7 @@
 )
 from pandas.core.dtypes.generic import (
     ABCExtensionArray,
-    ABCIndexClass,
+    ABCIndex,
     ABCPandasArray,
     ABCSeries,
 )
@@ -281,9 +281,7 @@ def array(
         msg = f"Cannot pass scalar '{data}' to 'pandas.array'."
         raise ValueError(msg)
 
-    if dtype is None and isinstance(
-        data, (ABCSeries, ABCIndexClass, ABCExtensionArray)
-    ):
+    if dtype is None and isinstance(data, (ABCSeries, ABCIndex, ABCExtensionArray)):
         dtype = data.dtype
 
     data = extract_array(data, extract_numpy=True)
@@ -392,7 +390,7 @@ def extract_array(obj: object, extract_numpy: bool = False) -> Union[Any, ArrayL
     >>> extract_array(pd.Series([1, 2, 3]), extract_numpy=True)
     array([1, 2, 3])
     """
-    if isinstance(obj, (ABCIndexClass, ABCSeries)):
+    if isinstance(obj, (ABCIndex, ABCSeries)):
         obj = obj.array
 
     if extract_numpy and isinstance(obj, ABCPandasArray):

pandas/core/dtypes/base.py

@@ -9,7 +9,7 @@
 from pandas._typing import DtypeObj
 from pandas.errors import AbstractMethodError
 
-from pandas.core.dtypes.generic import ABCDataFrame, ABCIndexClass, ABCSeries
+from pandas.core.dtypes.generic import ABCDataFrame, ABCIndex, ABCSeries
 
 if TYPE_CHECKING:
     from pandas.core.arrays import ExtensionArray
@@ -277,7 +277,7 @@ def is_dtype(cls, dtype: object) -> bool:
         """
         dtype = getattr(dtype, "dtype", dtype)
 
-        if isinstance(dtype, (ABCSeries, ABCIndexClass, ABCDataFrame, np.dtype)):
+        if isinstance(dtype, (ABCSeries, ABCIndex, ABCDataFrame, np.dtype)):
             # https://github.com/pandas-dev/pandas/issues/22960
             # avoid passing data to `construct_from_string`. This could
             # cause a FutureWarning from numpy about failing elementwise

pandas/core/dtypes/common.py

@@ -19,7 +19,7 @@
     IntervalDtype,
     PeriodDtype,
 )
-from pandas.core.dtypes.generic import ABCCategorical, ABCIndexClass
+from pandas.core.dtypes.generic import ABCCategorical, ABCIndex
 from pandas.core.dtypes.inference import (  # noqa:F401
     is_array_like,
     is_bool,
@@ -1389,7 +1389,7 @@ def is_bool_dtype(arr_or_dtype) -> bool:
         arr_or_dtype = arr_or_dtype.categories
         # now we use the special definition for Index
 
-    if isinstance(arr_or_dtype, ABCIndexClass):
+    if isinstance(arr_or_dtype, ABCIndex):
 
         # TODO(jreback)
         # we don't have a boolean Index class

pandas/core/dtypes/dtypes.py

@@ -25,7 +25,7 @@
 from pandas._typing import DtypeObj, Ordered
 
 from pandas.core.dtypes.base import ExtensionDtype, register_extension_dtype
-from pandas.core.dtypes.generic import ABCCategoricalIndex, ABCIndexClass
+from pandas.core.dtypes.generic import ABCCategoricalIndex, ABCIndex
 from pandas.core.dtypes.inference import is_bool, is_list_like
 
 if TYPE_CHECKING:
@@ -499,7 +499,7 @@ def validate_categories(categories, fastpath: bool = False):
             raise TypeError(
                 f"Parameter 'categories' must be list-like, was {repr(categories)}"
             )
-        elif not isinstance(categories, ABCIndexClass):
+        elif not isinstance(categories, ABCIndex):
             categories = Index(categories, tupleize_cols=False)
 
         if not fastpath:

pandas/core/dtypes/generic.py

@@ -9,6 +9,7 @@
         DataFrame,
         DatetimeIndex,
         Float64Index,
+        Index,
         Int64Index,
         IntervalIndex,
         MultiIndex,
@@ -76,24 +77,28 @@ def _check(cls, inst) -> bool:
     "Type[IntervalIndex]",
     create_pandas_abc_type("ABCIntervalIndex", "_typ", ("intervalindex",)),
 )
-ABCIndexClass = create_pandas_abc_type(
-    "ABCIndexClass",
-    "_typ",
-    {
-        "index",
-        "int64index",
-        "rangeindex",
-        "float64index",
-        "uint64index",
-        "multiindex",
-        "datetimeindex",
-        "timedeltaindex",
-        "periodindex",
-        "categoricalindex",
-        "intervalindex",
-    },
+ABCIndex = cast(
+    "Type[Index]",
+    create_pandas_abc_type(
+        "ABCIndex",
+        "_typ",
+        {
+            "index",
+            "int64index",
+            "rangeindex",
+            "float64index",
+            "uint64index",
+            "multiindex",
+            "datetimeindex",
+            "timedeltaindex",
+            "periodindex",
+            "categoricalindex",
+            "intervalindex",
+        },
+    ),
 )
 
+
 ABCNDFrame = cast(
     "Type[NDFrame]",
     create_pandas_abc_type("ABCNDFrame", "_typ", ("series", "dataframe")),

pandas/core/dtypes/missing.py

@@ -34,7 +34,7 @@
 from pandas.core.dtypes.generic import (
     ABCDataFrame,
     ABCExtensionArray,
-    ABCIndexClass,
+    ABCIndex,
     ABCMultiIndex,
     ABCSeries,
 )
@@ -156,7 +156,7 @@ def _isna(obj, inf_as_na: bool = False):
         raise NotImplementedError("isna is not defined for MultiIndex")
     elif isinstance(obj, type):
         return False
-    elif isinstance(obj, (ABCSeries, np.ndarray, ABCIndexClass, ABCExtensionArray)):
+    elif isinstance(obj, (ABCSeries, np.ndarray, ABCIndex, ABCExtensionArray)):
         return _isna_ndarraylike(obj, inf_as_na=inf_as_na)
     elif isinstance(obj, ABCDataFrame):
         return obj.isna()