CLN,TYP Remove string return annotations (#39174)

* remove string return annotations

* fixup conflict

* fixup merge conflict

Author: MarcoGorelli

.pre-commit-config.yaml

@@ -145,11 +145,12 @@ repos:
         language: pygrep
         types_or: [python, cython]
     -   id: unwanted-typing
-        name: Check for use of comment-based annotation syntax and missing error codes
+        name: Check for outdated annotation syntax and missing error codes
         entry: |
             (?x)
             \#\ type:\ (?!ignore)|
-            \#\ type:\s?ignore(?!\[)
+            \#\ type:\s?ignore(?!\[)|
+            \)\ ->\ \"
         language: pygrep
         types: [python]
     -   id: np-bool

pandas/compat/pickle_compat.py

@@ -1,6 +1,7 @@
 """
 Support pre-0.12 series pickle compatibility.
 """
+from __future__ import annotations
 
 import contextlib
 import copy
@@ -64,7 +65,7 @@ class _LoadSparseSeries:
     # https://github.com/python/mypy/issues/1020
     # error: Incompatible return type for "__new__" (returns "Series", but must return
     # a subtype of "_LoadSparseSeries")
-    def __new__(cls) -> "Series":  # type: ignore[misc]
+    def __new__(cls) -> Series:  # type: ignore[misc]
         from pandas import Series
 
         warnings.warn(
@@ -82,7 +83,7 @@ class _LoadSparseFrame:
     # https://github.com/python/mypy/issues/1020
     # error: Incompatible return type for "__new__" (returns "DataFrame", but must
     # return a subtype of "_LoadSparseFrame")
-    def __new__(cls) -> "DataFrame":  # type: ignore[misc]
+    def __new__(cls) -> DataFrame:  # type: ignore[misc]
         from pandas import DataFrame
 
         warnings.warn(

pandas/core/arrays/boolean.py

@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 import numbers
 from typing import TYPE_CHECKING, List, Optional, Tuple, Type, Union
 import warnings
@@ -93,7 +95,7 @@ def _is_numeric(self) -> bool:
 
     def __from_arrow__(
         self, array: Union["pyarrow.Array", "pyarrow.ChunkedArray"]
-    ) -> "BooleanArray":
+    ) -> BooleanArray:
         """
         Construct BooleanArray from pyarrow Array/ChunkedArray.
         """
@@ -276,7 +278,7 @@ def dtype(self) -> BooleanDtype:
     @classmethod
     def _from_sequence(
         cls, scalars, *, dtype: Optional[Dtype] = None, copy: bool = False
-    ) -> "BooleanArray":
+    ) -> BooleanArray:
         if dtype:
             assert dtype == "boolean"
         values, mask = coerce_to_array(scalars, copy=copy)
@@ -291,7 +293,7 @@ def _from_sequence_of_strings(
         copy: bool = False,
         true_values: Optional[List[str]] = None,
         false_values: Optional[List[str]] = None,
-    ) -> "BooleanArray":
+    ) -> BooleanArray:
         true_values_union = cls._TRUE_VALUES.union(true_values or [])
         false_values_union = cls._FALSE_VALUES.union(false_values or [])
 

pandas/core/arrays/categorical.py

@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 from csv import QUOTE_NONNUMERIC
 from functools import partial
 import operator
@@ -740,7 +742,7 @@ def _set_categories(self, categories, fastpath=False):
 
         self._dtype = new_dtype
 
-    def _set_dtype(self, dtype: CategoricalDtype) -> "Categorical":
+    def _set_dtype(self, dtype: CategoricalDtype) -> Categorical:
         """
         Internal method for directly updating the CategoricalDtype
 
@@ -1740,7 +1742,7 @@ def fillna(self, value=None, method=None, limit=None):
     def _ndarray(self) -> np.ndarray:
         return self._codes
 
-    def _from_backing_data(self, arr: np.ndarray) -> "Categorical":
+    def _from_backing_data(self, arr: np.ndarray) -> Categorical:
         return self._constructor(arr, dtype=self.dtype, fastpath=True)
 
     def _box_func(self, i: int):
@@ -2160,7 +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.
 

pandas/core/arrays/datetimes.py

@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 from datetime import datetime, time, timedelta, tzinfo
 from typing import Optional, Union, cast
 import warnings
@@ -291,7 +293,7 @@ def __init__(self, values, dtype=DT64NS_DTYPE, freq=None, copy=False):
     @classmethod
     def _simple_new(
         cls, values, freq: Optional[BaseOffset] = None, dtype=DT64NS_DTYPE
-    ) -> "DatetimeArray":
+    ) -> DatetimeArray:
         assert isinstance(values, np.ndarray)
         if values.dtype != DT64NS_DTYPE:
             assert values.dtype == "i8"

pandas/core/arrays/floating.py

@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 from typing import List, Optional, Tuple, Type
 import warnings
 
@@ -242,14 +244,14 @@ def __init__(self, values: np.ndarray, mask: np.ndarray, copy: bool = False):
     @classmethod
     def _from_sequence(
         cls, scalars, *, dtype=None, copy: bool = False
-    ) -> "FloatingArray":
+    ) -> FloatingArray:
         values, mask = coerce_to_array(scalars, dtype=dtype, copy=copy)
         return FloatingArray(values, mask)
 
     @classmethod
     def _from_sequence_of_strings(
         cls, strings, *, dtype=None, copy: bool = False
-    ) -> "FloatingArray":
+    ) -> FloatingArray:
         scalars = to_numeric(strings, errors="raise")
         return cls._from_sequence(scalars, dtype=dtype, copy=copy)
 

pandas/core/arrays/integer.py

@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 from typing import Dict, List, Optional, Tuple, Type
 import warnings
 
@@ -303,14 +305,14 @@ def __abs__(self):
     @classmethod
     def _from_sequence(
         cls, scalars, *, dtype: Optional[Dtype] = None, copy: bool = False
-    ) -> "IntegerArray":
+    ) -> IntegerArray:
         values, mask = coerce_to_array(scalars, dtype=dtype, copy=copy)
         return IntegerArray(values, mask)
 
     @classmethod
     def _from_sequence_of_strings(
         cls, strings, *, dtype: Optional[Dtype] = None, copy: bool = False
-    ) -> "IntegerArray":
+    ) -> IntegerArray:
         scalars = to_numeric(strings, errors="raise")
         return cls._from_sequence(scalars, dtype=dtype, copy=copy)
 

pandas/core/arrays/interval.py

@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 import operator
 from operator import le, lt
 import textwrap
@@ -861,7 +863,7 @@ def copy(self: IntervalArrayT) -> IntervalArrayT:
     def isna(self) -> np.ndarray:
         return isna(self._left)
 
-    def shift(self, periods: int = 1, fill_value: object = None) -> "IntervalArray":
+    def shift(self, periods: int = 1, fill_value: object = None) -> IntervalArray:
         if not len(self) or periods == 0:
             return self.copy()
 

pandas/core/arrays/masked.py

@@ -346,7 +346,7 @@ def factorize(self, na_sentinel: int = -1) -> Tuple[np.ndarray, ExtensionArray]:
         uniques = type(self)(uniques, np.zeros(len(uniques), dtype=bool))
         return codes, uniques
 
-    def value_counts(self, dropna: bool = True) -> "Series":
+    def value_counts(self, dropna: bool = True) -> Series:
         """
         Returns a Series containing counts of each unique value.
 

pandas/core/arrays/numpy_.py

@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 import numbers
 from typing import Optional, Tuple, Type, Union
 
@@ -75,7 +77,7 @@ def _is_boolean(self) -> bool:
         return self.kind == "b"
 
     @classmethod
-    def construct_from_string(cls, string: str) -> "PandasDtype":
+    def construct_from_string(cls, string: str) -> PandasDtype:
         try:
             dtype = np.dtype(string)
         except TypeError as err:
@@ -174,7 +176,7 @@ def __init__(self, values: Union[np.ndarray, "PandasArray"], copy: bool = False)
     @classmethod
     def _from_sequence(
         cls, scalars, *, dtype: Optional[Dtype] = None, copy: bool = False
-    ) -> "PandasArray":
+    ) -> PandasArray:
         if isinstance(dtype, PandasDtype):
             dtype = dtype._dtype
 
@@ -184,10 +186,10 @@ def _from_sequence(
         return cls(result)
 
     @classmethod
-    def _from_factorized(cls, values, original) -> "PandasArray":
+    def _from_factorized(cls, values, original) -> PandasArray:
         return cls(values)
 
-    def _from_backing_data(self, arr: np.ndarray) -> "PandasArray":
+    def _from_backing_data(self, arr: np.ndarray) -> PandasArray:
         return type(self)(arr)
 
     # ------------------------------------------------------------------------