TYP: improve type annotations and remove unnecessary type ignores

- Add and refine type hints in core modules (algorithms, base, indexes, _version).
- Remove redundant `# type: ignore[no-untyped-call]` comments in groupby/categorical.py.
- Refactor variable annotations for clarity and consistency.
- No functional changes; improves code quality  and type safety.

Author: simonjayhawkins

pandas/_version.py

@@ -17,6 +17,7 @@
 import re
 import subprocess
 import sys
+from typing import Any
 
 
 def get_keywords():
@@ -640,7 +641,7 @@ def render(pieces, style):
     }
 
 
-def get_versions():
+def get_versions() -> dict[str, Any]:
     """Get version information or return default if unable to do so."""
     # I am in _version.py, which lives at ROOT/VERSIONFILE_SOURCE. If we have
     # __file__, we can work backwards from there to the root. Some

pandas/core/algorithms.py

@@ -12,6 +12,7 @@
     TYPE_CHECKING,
     Literal,
     cast,
+    overload,
 )
 import warnings
 
@@ -314,6 +315,18 @@ def _check_object_for_strings(values: np.ndarray) -> str:
 # --------------- #
 
 
+@overload
+def unique(values: np.ndarray) -> np.ndarray: ...
+@overload
+def unique(values: Index) -> Index: ...
+@overload
+def unique(values: Series) -> np.ndarray: ...
+@overload
+def unique(values: Categorical) -> Categorical: ...
+@overload
+def unique(values: ExtensionArray) -> ExtensionArray: ...
+
+
 def unique(values):
     """
     Return unique values based on a hash table.

pandas/core/base.py

@@ -1100,7 +1100,7 @@ def unique(self):
         values = self._values
         if not isinstance(values, np.ndarray):
             # i.e. ExtensionArray
-            result = values.unique()
+            result: np.ndarray | ExtensionArray = values.unique()
         else:
             result = algorithms.unique1d(values)
         return result

pandas/core/groupby/categorical.py

@@ -46,7 +46,7 @@ def recode_for_groupby(c: Categorical, sort: bool, observed: bool) -> Categorica
         # In cases with c.ordered, this is equivalent to
         #  return c.remove_unused_categories(), c
 
-        take_codes = unique1d(c.codes[c.codes != -1])  # type: ignore[no-untyped-call]
+        take_codes = unique1d(c.codes[c.codes != -1])
 
         if sort:
             take_codes = np.sort(take_codes)
@@ -68,7 +68,7 @@ def recode_for_groupby(c: Categorical, sort: bool, observed: bool) -> Categorica
 
     # GH:46909: Re-ordering codes faster than using (set|add|reorder)_categories
     # GH 38140: exclude nan from indexer for categories
-    unique_notnan_codes = unique1d(c.codes[c.codes != -1])  # type: ignore[no-untyped-call]
+    unique_notnan_codes = unique1d(c.codes[c.codes != -1])
     if sort:
         unique_notnan_codes = np.sort(unique_notnan_codes)
     if (num_cat := len(c.categories)) > len(unique_notnan_codes):

pandas/core/indexes/base.py

@@ -3301,7 +3301,9 @@ def _intersection(self, other: Index, sort: bool = False):
                 if is_numeric_dtype(self.dtype):
                     # This is faster, because Index.unique() checks for uniqueness
                     # before calculating the unique values.
-                    res = algos.unique1d(res_indexer)
+                    res: Index | ExtensionArray | np.ndarray = algos.unique1d(
+                        res_indexer
+                    )
                 else:
                     result = self.take(indexer)
                     res = result.drop_duplicates()

pandas/util/_print_versions.py

@@ -32,7 +32,7 @@ def _get_commit_hash() -> str | None:
     except ImportError:
         from pandas._version import get_versions
 
-        versions = get_versions()  # type: ignore[no-untyped-call]
+        versions = get_versions()
         return versions["full-revisionid"]