remove string type hints

Author: MarcoGorelli

.pre-commit-config.yaml

@@ -149,8 +149,7 @@ repos:
         entry: |
             (?x)
             \#\ type:\ (?!ignore)|
-            \#\ type:\s?ignore(?!\[)|
-            \)\ ->\ \"
+            \#\ type:\s?ignore(?!\[)
         language: pygrep
         types: [python]
     -   id: np-bool
@@ -185,3 +184,7 @@ repos:
     -   id: codespell
         types_or: [python, rst, markdown]
         files: ^pandas/core/
+-   repo: https://github.com/MarcoGorelli/no-string-hints
+    rev: v0.1.5
+    hooks:
+    -   id: no-string-hints

pandas/core/aggregation.py

@@ -3,6 +3,8 @@
 kwarg aggregations in groupby and DataFrame/Series aggregation
 """
 
+from __future__ import annotations
+
 from collections import defaultdict
 from functools import partial
 from typing import (
@@ -296,7 +298,7 @@ def relabel_result(
     func: Dict[str, List[Union[Callable, str]]],
     columns: Iterable[Hashable],
     order: Iterable[int],
-) -> Dict[Hashable, "Series"]:
+) -> Dict[Hashable, Series]:
     """
     Internal function to reorder result if relabelling is True for
     dataframe.agg, and return the reordered result in dict.
@@ -323,7 +325,7 @@ def relabel_result(
     reordered_indexes = [
         pair[0] for pair in sorted(zip(columns, order), key=lambda t: t[1])
     ]
-    reordered_result_in_dict: Dict[Hashable, "Series"] = {}
+    reordered_result_in_dict: Dict[Hashable, Series] = {}
     idx = 0
 
     reorder_mask = not isinstance(result, ABCSeries) and len(result.columns) > 1

pandas/core/algorithms.py

@@ -559,7 +559,7 @@ def factorize(
     sort: bool = False,
     na_sentinel: Optional[int] = -1,
     size_hint: Optional[int] = None,
-) -> Tuple[np.ndarray, Union[np.ndarray, "Index"]]:
+) -> Tuple[np.ndarray, Union[np.ndarray, Index]]:
     """
     Encode the object as an enumerated type or categorical variable.
 

pandas/core/arrays/boolean.py

@@ -72,7 +72,7 @@ def numpy_dtype(self) -> np.dtype:
         return np.dtype("bool")
 
     @classmethod
-    def construct_array_type(cls) -> Type["BooleanArray"]:
+    def construct_array_type(cls) -> Type[BooleanArray]:
         """
         Return the array type associated with this dtype.
 
@@ -94,7 +94,7 @@ def _is_numeric(self) -> bool:
         return True
 
     def __from_arrow__(
-        self, array: Union["pyarrow.Array", "pyarrow.ChunkedArray"]
+        self, array: Union[pyarrow.Array, pyarrow.ChunkedArray]
     ) -> BooleanArray:
         """
         Construct BooleanArray from pyarrow Array/ChunkedArray.

pandas/core/arrays/categorical.py

@@ -422,7 +422,7 @@ def dtype(self) -> CategoricalDtype:
         return self._dtype
 
     @property
-    def _constructor(self) -> Type["Categorical"]:
+    def _constructor(self) -> Type[Categorical]:
         return Categorical
 
     @classmethod
@@ -2162,7 +2162,7 @@ def _concat_same_type(
 
     # ------------------------------------------------------------------
 
-    def _encode_with_my_categories(self, other: "Categorical") -> Categorical:
+    def _encode_with_my_categories(self, other: Categorical) -> Categorical:
         """
         Re-encode another categorical using this Categorical's categories.
 
@@ -2179,7 +2179,7 @@ def _encode_with_my_categories(self, other: "Categorical") -> Categorical:
         )
         return self._from_backing_data(codes)
 
-    def _categories_match_up_to_permutation(self, other: "Categorical") -> bool:
+    def _categories_match_up_to_permutation(self, other: Categorical) -> bool:
         """
         Returns True if categoricals are the same dtype
           same categories, and same ordered
@@ -2527,7 +2527,7 @@ def _delegate_method(self, name, *args, **kwargs):
 # utility routines
 
 
-def _get_codes_for_values(values, categories: "Index") -> np.ndarray:
+def _get_codes_for_values(values, categories: Index) -> np.ndarray:
     """
     utility routine to turn values into codes given the specified categories
 

pandas/core/arrays/floating.py

@@ -47,7 +47,7 @@ def _is_numeric(self) -> bool:
         return True
 
     @classmethod
-    def construct_array_type(cls) -> Type["FloatingArray"]:
+    def construct_array_type(cls) -> Type[FloatingArray]:
         """
         Return the array type associated with this dtype.
 

pandas/core/arrays/integer.py

@@ -57,7 +57,7 @@ def _is_numeric(self) -> bool:
         return True
 
     @classmethod
-    def construct_array_type(cls) -> Type["IntegerArray"]:
+    def construct_array_type(cls) -> Type[IntegerArray]:
         """
         Return the array type associated with this dtype.
 

pandas/core/arrays/numeric.py

@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 import datetime
 import numbers
 from typing import TYPE_CHECKING, Any, List, Union
@@ -25,7 +27,7 @@
 
 class NumericDtype(BaseMaskedDtype):
     def __from_arrow__(
-        self, array: Union["pyarrow.Array", "pyarrow.ChunkedArray"]
+        self, array: Union[pyarrow.Array, pyarrow.ChunkedArray]
     ) -> BaseMaskedArray:
         """
         Construct IntegerArray/FloatingArray from pyarrow Array/ChunkedArray.

pandas/core/arrays/numpy_.py

@@ -89,7 +89,7 @@ def construct_from_string(cls, string: str) -> PandasDtype:
         return cls(dtype)
 
     @classmethod
-    def construct_array_type(cls) -> Type["PandasArray"]:
+    def construct_array_type(cls) -> Type[PandasArray]:
         """
         Return the array type associated with this dtype.
 
@@ -155,7 +155,7 @@ class PandasArray(
     # ------------------------------------------------------------------------
     # Constructors
 
-    def __init__(self, values: Union[np.ndarray, "PandasArray"], copy: bool = False):
+    def __init__(self, values: Union[np.ndarray, PandasArray], copy: bool = False):
         if isinstance(values, type(self)):
             values = values._ndarray
         if not isinstance(values, np.ndarray):

pandas/core/arrays/period.py

@@ -201,7 +201,7 @@ def _simple_new(
 
     @classmethod
     def _from_sequence(
-        cls: Type["PeriodArray"],
+        cls: Type[PeriodArray],
         scalars: Union[Sequence[Optional[Period]], AnyArrayLike],
         *,
         dtype: Optional[Dtype] = None,