STYLE add future annotations where possible

.pre-commit-config.yaml

@@ -94,8 +94,6 @@ repos:
         stages: [manual]
         additional_dependencies: &pyright_dependencies
         - [email protected]
--   repo: local
-    hooks:
     -   id: pyright_reportGeneralTypeIssues
         name: pyright reportGeneralTypeIssues
         entry: pyright --skipunannotated -p pyright_reportGeneralTypeIssues.json
@@ -105,8 +103,6 @@ repos:
         types: [python]
         stages: [manual]
         additional_dependencies: *pyright_dependencies
--   repo: local
-    hooks:
     -   id: mypy
         name: mypy
         entry: mypy
@@ -115,8 +111,6 @@ repos:
         pass_filenames: false
         types: [python]
         stages: [manual]
--   repo: local
-    hooks:
     -   id: flake8-rst
         name: flake8-rst
         description: Run flake8 on code snippets in docstrings or RST files
@@ -237,3 +231,15 @@ repos:
         additional_dependencies:
         - flake8==4.0.1
         - flake8-pyi==22.5.1
+    -   id: future-annotations
+        name: import annotations from __future__
+        entry: 'from __future__ import annotations'
+        language: pygrep
+        args: [--negate]
+        files: ^pandas/
+        types: [python]
+        exclude: |
+            (?x)
+            /(__init__\.py)|(api\.py)|(_version\.py)|(testing\.py)|(conftest\.py)$
+            |/tests/
+            |/_testing/

pandas/_config/dates.py

@@ -1,6 +1,8 @@
 """
 config for datetime formatting
 """
+from __future__ import annotations
+
 from pandas._config import config as cf
 
 pc_date_dayfirst_doc = """

pandas/compat/chainmap.py

@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 from typing import (
     ChainMap,
     TypeVar,

pandas/compat/pyarrow.py

@@ -1,5 +1,7 @@
 """ support pyarrow compatibility across versions """
 
+from __future__ import annotations
+
 from pandas.util.version import Version
 
 try:

pandas/core/_numba/kernels/shared.py

@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 import numba
 import numpy as np
 

pandas/core/array_algos/transforms.py

@@ -2,6 +2,8 @@
 transforms.py is for shape-preserving functions.
 """
 
+from __future__ import annotations
+
 import numpy as np
 
 

pandas/core/arraylike.py

@@ -4,6 +4,8 @@
     Index
     ExtensionArray
 """
+from __future__ import annotations
+
 import operator
 from typing import Any
 import warnings

pandas/core/computation/check.py

@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 from pandas.compat._optional import import_optional_dependency
 
 ne = import_optional_dependency("numexpr", errors="warn")

pandas/core/computation/common.py

@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 from functools import reduce
 
 import numpy as np

pandas/core/config_init.py

@@ -9,6 +9,8 @@
 module is imported, register them here rather than in the module.
 
 """
+from __future__ import annotations
+
 import os
 from typing import Callable
 import warnings

pandas/core/dtypes/inference.py

@@ -1,5 +1,7 @@
 """ basic inference routines """
 
+from __future__ import annotations
+
 from collections import abc
 from numbers import Number
 import re

pandas/core/exchange/buffer.py

@@ -1,7 +1,4 @@
-from typing import (
-    Optional,
-    Tuple,
-)
+from __future__ import annotations
 
 import numpy as np
 from packaging import version
@@ -60,7 +57,7 @@ def __dlpack__(self):
             return self._x.__dlpack__()
         raise NotImplementedError("__dlpack__")
 
-    def __dlpack_device__(self) -> Tuple[DlpackDeviceType, Optional[int]]:
+    def __dlpack_device__(self) -> tuple[DlpackDeviceType, int | None]:
         """
         Device type and device ID for where the data in the buffer resides.
         """

pandas/core/exchange/dataframe_protocol.py

@@ -2,18 +2,17 @@
 A verbatim copy (vendored) of the spec from https://github.com/data-apis/dataframe-api
 """
 
+from __future__ import annotations
+
 from abc import (
     ABC,
     abstractmethod,
 )
 import enum
 from typing import (
     Any,
-    Dict,
     Iterable,
-    Optional,
     Sequence,
-    Tuple,
     TypedDict,
 )
 
@@ -90,18 +89,18 @@ class ColumnNullType(enum.IntEnum):
 class ColumnBuffers(TypedDict):
     # first element is a buffer containing the column data;
     # second element is the data buffer's associated dtype
-    data: Tuple["Buffer", Any]
+    data: tuple[Buffer, Any]
 
     # first element is a buffer containing mask values indicating missing data;
     # second element is the mask value buffer's associated dtype.
     # None if the null representation is not a bit or byte mask
-    validity: Optional[Tuple["Buffer", Any]]
+    validity: tuple[Buffer, Any] | None
 
     # first element is a buffer containing the offset values for
     # variable-size binary data (e.g., variable-length strings);
     # second element is the offsets buffer's associated dtype.
     # None if the data buffer does not have an associated offsets buffer
-    offsets: Optional[Tuple["Buffer", Any]]
+    offsets: tuple[Buffer, Any] | None
 
 
 class CategoricalDescription(TypedDict):
@@ -111,7 +110,7 @@ class CategoricalDescription(TypedDict):
     is_dictionary: bool
     # Python-level only (e.g. ``{int: str}``).
     # None if not a dictionary-style categorical.
-    mapping: Optional[dict]
+    mapping: dict | None
 
 
 class Buffer(ABC):
@@ -161,7 +160,7 @@ def __dlpack__(self):
         raise NotImplementedError("__dlpack__")
 
     @abstractmethod
-    def __dlpack_device__(self) -> Tuple[DlpackDeviceType, Optional[int]]:
+    def __dlpack_device__(self) -> tuple[DlpackDeviceType, int | None]:
         """
         Device type and device ID for where the data in the buffer resides.
         Uses device type codes matching DLPack.
@@ -239,7 +238,7 @@ def offset(self) -> int:
 
     @property
     @abstractmethod
-    def dtype(self) -> Tuple[DtypeKind, int, str, str]:
+    def dtype(self) -> tuple[DtypeKind, int, str, str]:
         """
         Dtype description as a tuple ``(kind, bit-width, format string, endianness)``.
 
@@ -293,7 +292,7 @@ def describe_categorical(self) -> CategoricalDescription:
 
     @property
     @abstractmethod
-    def describe_null(self) -> Tuple[ColumnNullType, Any]:
+    def describe_null(self) -> tuple[ColumnNullType, Any]:
         """
         Return the missing value (or "null") representation the column dtype
         uses, as a tuple ``(kind, value)``.
@@ -306,7 +305,7 @@ def describe_null(self) -> Tuple[ColumnNullType, Any]:
 
     @property
     @abstractmethod
-    def null_count(self) -> Optional[int]:
+    def null_count(self) -> int | None:
         """
         Number of null elements, if known.
 
@@ -316,7 +315,7 @@ def null_count(self) -> Optional[int]:
 
     @property
     @abstractmethod
-    def metadata(self) -> Dict[str, Any]:
+    def metadata(self) -> dict[str, Any]:
         """
         The metadata for the column. See `DataFrame.metadata` for more details.
         """
@@ -330,7 +329,7 @@ def num_chunks(self) -> int:
         pass
 
     @abstractmethod
-    def get_chunks(self, n_chunks: Optional[int] = None) -> Iterable["Column"]:
+    def get_chunks(self, n_chunks: int | None = None) -> Iterable[Column]:
         """
         Return an iterator yielding the chunks.
 
@@ -395,7 +394,7 @@ def __dataframe__(self, nan_as_null: bool = False, allow_copy: bool = True):
 
     @property
     @abstractmethod
-    def metadata(self) -> Dict[str, Any]:
+    def metadata(self) -> dict[str, Any]:
         """
         The metadata for the data frame, as a dictionary with string keys. The
         contents of `metadata` may be anything, they are meant for a library
@@ -415,7 +414,7 @@ def num_columns(self) -> int:
         pass
 
     @abstractmethod
-    def num_rows(self) -> Optional[int]:
+    def num_rows(self) -> int | None:
         # TODO: not happy with Optional, but need to flag it may be expensive
         #       why include it if it may be None - what do we expect consumers
         #       to do here?
@@ -460,21 +459,21 @@ def get_columns(self) -> Iterable[Column]:
         pass
 
     @abstractmethod
-    def select_columns(self, indices: Sequence[int]) -> "DataFrame":
+    def select_columns(self, indices: Sequence[int]) -> DataFrame:
         """
         Create a new DataFrame by selecting a subset of columns by index.
         """
         pass
 
     @abstractmethod
-    def select_columns_by_name(self, names: Sequence[str]) -> "DataFrame":
+    def select_columns_by_name(self, names: Sequence[str]) -> DataFrame:
         """
         Create a new DataFrame by selecting a subset of columns by name.
         """
         pass
 
     @abstractmethod
-    def get_chunks(self, n_chunks: Optional[int] = None) -> Iterable["DataFrame"]:
+    def get_chunks(self, n_chunks: int | None = None) -> Iterable[DataFrame]:
         """
         Return an iterator yielding the chunks.
 

pandas/core/exchange/from_dataframe.py

@@ -1,13 +1,8 @@
+from __future__ import annotations
+
 import ctypes
 import re
-from typing import (
-    Any,
-    Dict,
-    List,
-    Optional,
-    Tuple,
-    Union,
-)
+from typing import Any
 
 import numpy as np
 
@@ -24,7 +19,7 @@
     Endianness,
 )
 
-_NP_DTYPES: Dict[DtypeKind, Dict[int, Any]] = {
+_NP_DTYPES: dict[DtypeKind, dict[int, Any]] = {
     DtypeKind.INT: {8: np.int8, 16: np.int16, 32: np.int32, 64: np.int64},
     DtypeKind.UINT: {8: np.uint8, 16: np.uint16, 32: np.uint32, 64: np.uint64},
     DtypeKind.FLOAT: {32: np.float32, 64: np.float64},
@@ -108,7 +103,7 @@ def protocol_df_chunk_to_pandas(df: DataFrameXchg) -> pd.DataFrame:
     """
     # We need a dict of columns here, with each column being a NumPy array (at
     # least for now, deal with non-NumPy dtypes later).
-    columns: Dict[str, Any] = {}
+    columns: dict[str, Any] = {}
     buffers = []  # hold on to buffers, keeps memory alive
     for name in df.column_names():
         if not isinstance(name, str):
@@ -140,7 +135,7 @@ def protocol_df_chunk_to_pandas(df: DataFrameXchg) -> pd.DataFrame:
     return pandas_df
 
 
-def primitive_column_to_ndarray(col: Column) -> Tuple[np.ndarray, Any]:
+def primitive_column_to_ndarray(col: Column) -> tuple[np.ndarray, Any]:
     """
     Convert a column holding one of the primitive dtypes to a NumPy array.
 
@@ -165,7 +160,7 @@ def primitive_column_to_ndarray(col: Column) -> Tuple[np.ndarray, Any]:
     return data, buffers
 
 
-def categorical_column_to_series(col: Column) -> Tuple[pd.Series, Any]:
+def categorical_column_to_series(col: Column) -> tuple[pd.Series, Any]:
     """
     Convert a column holding categorical data to a pandas Series.
 
@@ -205,7 +200,7 @@ def categorical_column_to_series(col: Column) -> Tuple[pd.Series, Any]:
     return data, buffers
 
 
-def string_column_to_ndarray(col: Column) -> Tuple[np.ndarray, Any]:
+def string_column_to_ndarray(col: Column) -> tuple[np.ndarray, Any]:
     """
     Convert a column holding string data to a NumPy array.
 
@@ -268,7 +263,7 @@ def string_column_to_ndarray(col: Column) -> Tuple[np.ndarray, Any]:
             null_pos = ~null_pos
 
     # Assemble the strings from the code units
-    str_list: List[Union[None, float, str]] = [None] * col.size
+    str_list: list[None | float | str] = [None] * col.size
     for i in range(col.size):
         # Check for missing values
         if null_pos is not None and null_pos[i]:
@@ -324,7 +319,7 @@ def parse_datetime_format_str(format_str, data):
     raise NotImplementedError(f"DateTime kind is not supported: {format_str}")
 
 
-def datetime_column_to_ndarray(col: Column) -> Tuple[np.ndarray, Any]:
+def datetime_column_to_ndarray(col: Column) -> tuple[np.ndarray, Any]:
     """
     Convert a column holding DateTime data to a NumPy array.
 
@@ -362,9 +357,9 @@ def datetime_column_to_ndarray(col: Column) -> Tuple[np.ndarray, Any]:
 
 def buffer_to_ndarray(
     buffer: Buffer,
-    dtype: Tuple[DtypeKind, int, str, str],
+    dtype: tuple[DtypeKind, int, str, str],
     offset: int = 0,
-    length: Optional[int] = None,
+    length: int | None = None,
 ) -> np.ndarray:
     """
     Build a NumPy array from the passed buffer.
@@ -470,9 +465,9 @@ def bitmask_to_bool_ndarray(
 
 
 def set_nulls(
-    data: Union[np.ndarray, pd.Series],
+    data: np.ndarray | pd.Series,
     col: Column,
-    validity: Optional[Tuple[Buffer, Tuple[DtypeKind, int, str, str]]],
+    validity: tuple[Buffer, tuple[DtypeKind, int, str, str]] | None,
     allow_modify_inplace: bool = True,
 ):
     """

pandas/core/exchange/utils.py

@@ -2,6 +2,8 @@
 Utility functions and objects for implementing the exchange API.
 """
 
+from __future__ import annotations
+
 import re
 import typing