update dtype name

Author: topper-123

pandas/core/arrays/categorical.py

@@ -453,7 +453,7 @@ def __init__(
                     ) from err
 
             # we're inferring from values
-            dtype = CategoricalDtype(categories, dtype.ordered, dtype.categories_dtype)
+            dtype = CategoricalDtype(categories, dtype.ordered)
 
         elif isinstance(values.dtype, CategoricalDtype):
             old_codes = extract_array(values)._codes
@@ -2524,7 +2524,7 @@ class CategoricalAccessor(PandasDelegate, PandasObject, NoNewAttributesMixin):
     3    c
     4    c
     5    c
-    dtype: category
+    dtype: category[object]
     Categories (3, object): ['a', 'b', 'c']
 
     >>> s.cat.categories
@@ -2537,7 +2537,7 @@ class CategoricalAccessor(PandasDelegate, PandasObject, NoNewAttributesMixin):
     3    a
     4    a
     5    a
-    dtype: category
+    dtype: category[object]
     Categories (3, object): ['c', 'b', 'a']
 
     >>> s.cat.reorder_categories(list("cba"))
@@ -2547,7 +2547,7 @@ class CategoricalAccessor(PandasDelegate, PandasObject, NoNewAttributesMixin):
     3    c
     4    c
     5    c
-    dtype: category
+    dtype: category[object]
     Categories (3, object): ['c', 'b', 'a']
 
     >>> s.cat.add_categories(["d", "e"])
@@ -2557,7 +2557,7 @@ class CategoricalAccessor(PandasDelegate, PandasObject, NoNewAttributesMixin):
     3    c
     4    c
     5    c
-    dtype: category
+    dtype: category[object]
     Categories (5, object): ['a', 'b', 'c', 'd', 'e']
 
     >>> s.cat.remove_categories(["a", "c"])
@@ -2567,7 +2567,7 @@ class CategoricalAccessor(PandasDelegate, PandasObject, NoNewAttributesMixin):
     3    NaN
     4    NaN
     5    NaN
-    dtype: category
+    dtype: category[object]
     Categories (1, object): ['b']
 
     >>> s1 = s.cat.add_categories(["d", "e"])
@@ -2578,7 +2578,7 @@ class CategoricalAccessor(PandasDelegate, PandasObject, NoNewAttributesMixin):
     3    c
     4    c
     5    c
-    dtype: category
+    dtype: category[object]
     Categories (3, object): ['a', 'b', 'c']
 
     >>> s.cat.set_categories(list("abcde"))
@@ -2588,7 +2588,7 @@ class CategoricalAccessor(PandasDelegate, PandasObject, NoNewAttributesMixin):
     3    c
     4    c
     5    c
-    dtype: category
+    dtype: category[object]
     Categories (5, object): ['a', 'b', 'c', 'd', 'e']
 
     >>> s.cat.as_ordered()
@@ -2598,7 +2598,7 @@ class CategoricalAccessor(PandasDelegate, PandasObject, NoNewAttributesMixin):
     3    c
     4    c
     5    c
-    dtype: category
+    dtype: category[object]
     Categories (3, object): ['a' < 'b' < 'c']
 
     >>> s.cat.as_unordered()
@@ -2608,7 +2608,7 @@ class CategoricalAccessor(PandasDelegate, PandasObject, NoNewAttributesMixin):
     3    c
     4    c
     5    c
-    dtype: category
+    dtype: category[object]
     Categories (3, object): ['a', 'b', 'c']
     """
 

pandas/core/dtypes/dtypes.py

@@ -185,13 +185,13 @@ class CategoricalDtype(PandasExtensionDtype, ExtensionDtype):
 
     Examples
     --------
-    >>> t = pd.CategoricalDtype(['b', 'a'], ordered=True)
+    >>> t = pd.CategoricalDtype(categories=['b', 'a'], ordered=True)
     >>> pd.Series(['a', 'b', 'a', 'c'], dtype=t)
     0      a
     1      b
     2      a
     3    NaN
-    dtype: category
+    dtype: category[object]
     Categories (2, object): ['b' < 'a']
 
     An empty CategoricalDtype with a specific dtype can be created
@@ -208,7 +208,7 @@ class CategoricalDtype(PandasExtensionDtype, ExtensionDtype):
     base = np.dtype("O")
     _metadata = ("categories", "ordered")
     _cache_dtypes: dict[str_type, PandasExtensionDtype] = {}
-    _categories_dtype: Dtype | None = None
+    _categories_dtype: Dtype
     _match = re.compile(r"category\[(?P<categories_dtype>.+)\]")
 
     def __init__(
@@ -392,13 +392,6 @@ def _get_categories_dtype_from_string(cls, string: str_type) -> str_type | None:
         d = match.groupdict()
         return d.get("categories_dtype")
 
-    @property
-    def categories_dtype(self) -> Dtype:
-        if self.categories is None:
-            return self._categories_dtype
-
-        return self.categories.dtype
-
     def _finalize(self, categories, ordered: Ordered, fastpath: bool = False) -> None:
         if ordered is not None:
             self.validate_ordered(ordered)
@@ -604,7 +597,7 @@ def validate_categories(self, categories, fastpath: bool = False) -> Index:
             raise TypeError(
                 f"Parameter 'categories' must be list-like, was {repr(categories)}"
             )
-        dtype = self._categories_dtype
+        dtype = self.categories_dtype
         if not isinstance(categories, ABCIndex):
             categories = Index._with_infer(categories, dtype=dtype, tupleize_cols=False)
         elif dtype is not None:
@@ -662,6 +655,13 @@ def categories(self) -> Index:
         """
         return self._categories
 
+    @property
+    def categories_dtype(self) -> Dtype | None:
+        try:
+            return self.categories.dtype
+        except AttributeError:
+            return getattr(self, "_categories_dtype", None)
+
     @property
     def ordered(self) -> Ordered:
         """

pandas/core/generic.py

@@ -6343,7 +6343,7 @@ def astype(
         >>> ser.astype('category')
         0    1
         1    2
-        dtype: category
+        dtype: category[int32]
         Categories (2, int32): [1, 2]
 
         Convert to ordered categorical type with custom ordering:
@@ -6354,7 +6354,7 @@ def astype(
         >>> ser.astype(cat_dtype)
         0    1
         1    2
-        dtype: category
+        dtype: category[int64]
         Categories (2, int64): [2 < 1]
 
         Create a series of dates:

pandas/core/indexes/category.py

@@ -147,16 +147,16 @@ class CategoricalIndex(NDArrayBackedExtensionIndex):
 
     Examples
     --------
-    >>> pd.CategoricalIndex(["a", "b", "c", "a", "b", "c"])
+    >>> pd.CategoricalIndex(["a", "b", "c", "a", "b", "c"], orderer=True)
     CategoricalIndex(['a', 'b', 'c', 'a', 'b', 'c'],
-                     categories=['a', 'b', 'c'], ordered=False, dtype='category')
+                     categories=['a', 'b', 'c'], ordered=True, dtype='category[object]')
 
     ``CategoricalIndex`` can also be instantiated from a ``Categorical``:
 
-    >>> c = pd.Categorical(["a", "b", "c", "a", "b", "c"])
+    >>> c = pd.Categorical(["a", "b", "c", "a", "b", "c"], ordered=True)
     >>> pd.CategoricalIndex(c)
     CategoricalIndex(['a', 'b', 'c', 'a', 'b', 'c'],
-                     categories=['a', 'b', 'c'], ordered=False, dtype='category')
+                     categories=['a', 'b', 'c'], ordered=True, dtype='category[object]')
 
     Ordered ``CategoricalIndex`` can have a min and max value.
 
@@ -165,7 +165,7 @@ class CategoricalIndex(NDArrayBackedExtensionIndex):
     ... )
     >>> ci
     CategoricalIndex(['a', 'b', 'c', 'a', 'b', 'c'],
-                     categories=['c', 'b', 'a'], ordered=True, dtype='category')
+                     categories=['c', 'b', 'a'], ordered=True, dtype='category[object]')
     >>> ci.min()
     'c'
     """
@@ -438,24 +438,24 @@ def map(self, mapper, na_action: Literal["ignore"] | None = None):
         >>> idx = pd.CategoricalIndex(['a', 'b', 'c'])
         >>> idx
         CategoricalIndex(['a', 'b', 'c'], categories=['a', 'b', 'c'],
-                          ordered=False, dtype='category')
+                          ordered=False, dtype='category[object]')
         >>> idx.map(lambda x: x.upper())
         CategoricalIndex(['A', 'B', 'C'], categories=['A', 'B', 'C'],
-                         ordered=False, dtype='category')
+                         ordered=False, dtype='category[object]')
         >>> idx.map({'a': 'first', 'b': 'second', 'c': 'third'})
         CategoricalIndex(['first', 'second', 'third'], categories=['first',
-                         'second', 'third'], ordered=False, dtype='category')
+                         'second', 'third'], ordered=False, dtype='category[object]')
 
         If the mapping is one-to-one the ordering of the categories is
         preserved:
 
         >>> idx = pd.CategoricalIndex(['a', 'b', 'c'], ordered=True)
         >>> idx
         CategoricalIndex(['a', 'b', 'c'], categories=['a', 'b', 'c'],
-                         ordered=True, dtype='category')
+                         ordered=True, dtype='category[object]')
         >>> idx.map({'a': 3, 'b': 2, 'c': 1})
         CategoricalIndex([3, 2, 1], categories=[3, 2, 1], ordered=True,
-                         dtype='category')
+                         dtype='category[int64]')
 
         If the mapping is not one-to-one an :class:`~pandas.Index` is returned:
 

pandas/core/reshape/tile.py

@@ -199,7 +199,7 @@ def cut(
     c    (4.667, 7.333]
     d     (7.333, 10.0]
     e     (7.333, 10.0]
-    dtype: category
+    dtype: category[interval[float64, right]]
     Categories (3, interval[float64, right]): [(1.992, 4.667] < (4.667, ...
 
     Passing a Series as an input returns a Series with mapping value.