Union to |

Signed-off-by: Santiago Figueroa Manrique <[email protected]>

Author: figueroa1395

src/power_grid_model_io/converters/pandapower_converter.py

@@ -8,7 +8,7 @@
 
 import logging
 from functools import lru_cache
-from typing import Dict, List, MutableMapping, Optional, Tuple, Type, Union
+from typing import Dict, List, MutableMapping, Optional, Tuple, Type
 
 import numpy as np
 import pandas as pd
@@ -2237,7 +2237,7 @@ def _generate_ids(self, pp_table: str, pp_idx: pd.Index, name: Optional[str] = N
     def _get_pgm_ids(
         self,
         pp_table: str,
-        pp_idx: Optional[Union[pd.Series, np.ndarray]] = None,
+        pp_idx: Optional[pd.Series | np.ndarray] = None,
         name: Optional[str] = None,
     ) -> pd.Series:
         """
@@ -2515,7 +2515,7 @@ def _get_pp_attr(
         table: str,
         attribute: str,
         expected_type: Optional[str] = None,
-        default: Optional[Union[float, bool, str]] = None,
+        default: Optional[float | bool | str] = None,
     ) -> np.ndarray:
         """
         Returns the selected PandaPower attribute from the selected PandaPower table.
@@ -2529,7 +2529,7 @@ def _get_pp_attr(
         """
         pp_component_data = self.pp_input_data[table]
 
-        exp_dtype: Union[str, Type] = "O"
+        exp_dtype: str | Type = "O"
         if expected_type is not None:
             exp_dtype = expected_type
         elif default is not None:
@@ -2569,7 +2569,7 @@ def get_id(self, pp_table: str, pp_idx: int, name: Optional[str] = None) -> int:
         """
         return self.idx[(pp_table, name)][pp_idx]
 
-    def lookup_id(self, pgm_id: int) -> Dict[str, Union[str, int]]:
+    def lookup_id(self, pgm_id: int) -> Dict[str, str | int]:
         """
         Retrieve the original name / key combination of a pgm object
 

src/power_grid_model_io/converters/pgm_json_converter.py

@@ -10,7 +10,7 @@
 import warnings
 from enum import Enum
 from pathlib import Path
-from typing import Any, Dict, List, Optional, Union
+from typing import Any, Dict, List, Optional
 
 import numpy as np
 from power_grid_model import ComponentType, DatasetType, initialize_array
@@ -45,8 +45,8 @@ class PgmJsonConverter(BaseConverter[StructuredData]):
 
     def __init__(
         self,
-        source_file: Optional[Union[Path, str]] = None,
-        destination_file: Optional[Union[Path, str]] = None,
+        source_file: Optional[Path | str] = None,
+        destination_file: Optional[Path | str] = None,
         log_level: int = logging.INFO,
     ):
         source = JsonFileStore(file_path=Path(source_file)) if source_file else None

src/power_grid_model_io/converters/tabular_converter.py

@@ -9,7 +9,7 @@
 import logging
 from enum import Enum
 from pathlib import Path
-from typing import Any, Collection, Dict, List, Mapping, Optional, Union, cast
+from typing import Any, Collection, Dict, List, Mapping, Optional, cast
 
 import numpy as np
 import pandas as pd
@@ -146,9 +146,9 @@ def _parse_data(self, data: TabularData, data_type: DatasetType, extra_info: Opt
     def _convert_table_to_component(  # pylint: disable = too-many-arguments,too-many-positional-arguments
         self,
         data: TabularData,
-        data_type: Union[str, Enum],
+        data_type: str | Enum,
         table: str,
-        component: Union[str, Enum],
+        component: str | Enum,
         attributes: InstanceAttributes,
         extra_info: Optional[ExtraInfo],
     ) -> Optional[np.ndarray]:
@@ -166,9 +166,9 @@ def _convert_table_to_component(  # pylint: disable = too-many-arguments,too-man
           extra_info: an optional dictionary where extra component info (that can't be specified in
         power-grid-model data) can be specified
           data: TabularData:
-          data_type: Union[str, Enum]:
+          data_type: str | Enum:
           table: str:
-          component: Union[str, Enum]:
+          component: str | Enum:
           attributes: InstanceAttributes:
           extra_info: Optional[ExtraInfo]:
 
@@ -236,7 +236,7 @@ def _convert_col_def_to_attribute(  # pylint: disable = too-many-arguments,too-m
         data: TabularData,
         pgm_data: np.ndarray,
         table: str,
-        component: Union[str, Enum],
+        component: str | Enum,
         attr: str,
         col_def: Any,
         table_mask: Optional[np.ndarray],
@@ -258,7 +258,7 @@ def _convert_col_def_to_attribute(  # pylint: disable = too-many-arguments,too-m
           data: TabularData:
           pgm_data: np.ndarray:
           table: str:
-          component: Union[str, Enum]:
+          component: str | Enum:
           attr: str:
           col_def: Any:
           extra_info: Optional[ExtraInfo]:
@@ -428,16 +428,15 @@ def _parse_col_def(  # pylint: disable = too-many-arguments,too-many-positional-
     def _parse_col_def_const(
         data: TabularData,
         table: str,
-        col_def: Union[int, float],
+        col_def: int | float,
         table_mask: Optional[np.ndarray] = None,
     ) -> pd.DataFrame:
         """Create a single column pandas DataFrame containing the const value.
 
         Args:
           data: TabularData:
           table: str:
-          col_def: Union[int:
-          float]:
+          col_def: int | float:
 
         Returns:
 
@@ -609,7 +608,7 @@ def _parse_auto_id(  # pylint: disable = too-many-arguments,too-many-positional-
         table: str,
         ref_table: Optional[str],
         ref_name: Optional[str],
-        key_col_def: Union[str, List[str], Dict[str, str]],
+        key_col_def: str | List[str] | Dict[str, str],
         table_mask: Optional[np.ndarray],
         extra_info: Optional[ExtraInfo],
     ) -> pd.DataFrame:
@@ -852,7 +851,7 @@ def get_id(row: pd.Series) -> int:
 
         return keys.apply(get_id, axis=1).to_list()
 
-    def lookup_id(self, pgm_id: int) -> Dict[str, Union[str, Dict[str, int]]]:
+    def lookup_id(self, pgm_id: int) -> Dict[str, str | Dict[str, int]]:
         """
         Retrieve the original name / key combination of a pgm object
         Args:

src/power_grid_model_io/converters/vision_excel_converter.py

@@ -8,7 +8,7 @@
 import logging
 from dataclasses import dataclass
 from pathlib import Path
-from typing import Any, Mapping, Optional, Union
+from typing import Any, Mapping, Optional
 
 from power_grid_model_io.converters.tabular_converter import TabularConverter
 from power_grid_model_io.data_stores.base_data_store import LANGUAGE_EN
@@ -37,10 +37,10 @@ class VisionExcelConverter(TabularConverter):
 
     def __init__(  # pylint: disable=too-many-arguments,too-many-positional-arguments
         self,
-        source_file: Optional[Union[Path, str]] = None,
+        source_file: Optional[Path | str] = None,
         language: str = LANGUAGE_EN,
         terms_changed: Optional[dict] = None,
-        mapping_file: Optional[Union[Path, str]] = None,
+        mapping_file: Optional[Path | str] = None,
         log_level: int = logging.INFO,
     ):
         _mapping_file = Path(

src/power_grid_model_io/data_stores/excel_file_store.py

@@ -7,7 +7,7 @@
 
 import re
 from pathlib import Path
-from typing import Dict, List, Optional, Set, Tuple, Union
+from typing import Dict, List, Optional, Set, Tuple
 
 import pandas as pd
 
@@ -185,10 +185,10 @@ def _handle_duplicate_columns(self, data: pd.DataFrame, sheet_name: str) -> pd.D
 
         return data
 
-    def _check_duplicate_values(self, sheet_name: str, data: pd.DataFrame) -> Dict[int, Union[str, Tuple[str, ...]]]:
+    def _check_duplicate_values(self, sheet_name: str, data: pd.DataFrame) -> Dict[int, str | Tuple[str, ...]]:
         grouped = self._group_columns_by_index(data=data)
 
-        to_rename: Dict[int, Union[str, Tuple[str, ...]]] = {}
+        to_rename: Dict[int, str | Tuple[str, ...]] = {}
 
         for col_name, col_idxs in grouped.items():
             # No duplicate column names
@@ -251,8 +251,8 @@ def _update_column_names(self, data: pd.DataFrame) -> pd.DataFrame:
         return data
 
     @staticmethod
-    def _group_columns_by_index(data: pd.DataFrame) -> Dict[Union[str, Tuple[str, ...]], Set[int]]:
-        grouped: Dict[Union[str, Tuple[str, ...]], Set[int]] = {}
+    def _group_columns_by_index(data: pd.DataFrame) -> Dict[str | Tuple[str, ...], Set[int]]:
+        grouped: Dict[str | Tuple[str, ...], Set[int]] = {}
         columns = data.columns.values
         for col_idx, col_name in enumerate(columns):
             if col_name[0] not in grouped:

src/power_grid_model_io/data_stores/json_file_store.py

@@ -105,5 +105,5 @@ def _validate(self, data: StructuredData) -> None:
             if len(type_names) == 1:
                 type_str = type_names.pop()
             else:
-                type_str = "Union[" + ", ".join(type_names) + "]"
+                type_str = type_str = " | ".join(type_names)
             raise TypeError(f"Invalid data type for {type(self).__name__}: List[{type_str}]")

src/power_grid_model_io/data_types/_data_types.py

@@ -5,7 +5,7 @@
 Common data types used in the Power Grid Model project
 """
 
-from typing import Any, Dict, List, Union
+from typing import Any, Dict, List
 
 ExtraInfo = Dict[int, Any]
 """
@@ -36,7 +36,7 @@
 Legacy type name; use ExtraInfo instead!
 """
 
-StructuredData = Union[Dict[str, List[Dict[str, Any]]], List[Dict[str, List[Dict[str, Any]]]]]
+StructuredData = Dict[str, List[Dict[str, Any]]] | List[Dict[str, List[Dict[str, Any]]]]
 """
 Structured data is a multi dimensional structure (component_type -> objects -> attribute -> value) or a list of those
 dictionaries:

src/power_grid_model_io/data_types/tabular_data.py

@@ -2,12 +2,12 @@
 #
 # SPDX-License-Identifier: MPL-2.0
 """
-The TabularData class is a wrapper around Dict[str, Union[pd.DataFrame, np.ndarray]],
+The TabularData class is a wrapper around Dict[str, pd.DataFrame | np.ndarray],
 which supports unit conversions and value substitutions
 """
 
 import logging
-from typing import Callable, Dict, Generator, Iterable, Optional, Tuple, Union
+from typing import Callable, Dict, Generator, Iterable, Optional, Tuple
 
 import numpy as np
 import pandas as pd
@@ -21,11 +21,11 @@
 
 class TabularData:
     """
-    The TabularData class is a wrapper around Dict[str, Union[pd.DataFrame, np.ndarray]],
+    The TabularData class is a wrapper around Dict[str, pd.DataFrame | np.ndarray],
     which supports unit conversions and value substitutions
     """
 
-    def __init__(self, logger=None, **tables: Union[pd.DataFrame, np.ndarray, LazyDataFrame]):
+    def __init__(self, logger=None, **tables: pd.DataFrame | np.ndarray | LazyDataFrame):
         """
         Tabular data can either be a collection of pandas DataFrames and/or numpy structured arrays.
         The key word arguments will define the keys of the data.
@@ -48,7 +48,7 @@ def __init__(self, logger=None, **tables: Union[pd.DataFrame, np.ndarray, LazyDa
                     f"Invalid data type for table '{table_name}'; "
                     f"expected a pandas DataFrame or NumPy array, got {type(table_data).__name__}."
                 )
-        self._data: Dict[str, Union[pd.DataFrame, np.ndarray, LazyDataFrame]] = tables
+        self._data: Dict[str, pd.DataFrame | np.ndarray | LazyDataFrame] = tables
         self._units: Optional[UnitMapping] = None
         self._substitution: Optional[ValueMapping] = None
 
@@ -181,7 +181,7 @@ def __contains__(self, table_name: str) -> bool:
         """
         return table_name in self._data
 
-    def __getitem__(self, table_name: str) -> Union[pd.DataFrame, np.ndarray]:
+    def __getitem__(self, table_name: str) -> pd.DataFrame | np.ndarray:
         """
         Mimic the dictionary [] operator. It returns the 'raw' table data as stored in memory. This can be either a
         pandas DataFrame or a numpy structured array. It is possible that some unit conversions have been applied by
@@ -206,7 +206,7 @@ def keys(self) -> Iterable[str]:
 
         return self._data.keys()
 
-    def items(self) -> Generator[Tuple[str, Union[pd.DataFrame, np.ndarray]], None, None]:
+    def items(self) -> Generator[Tuple[str, pd.DataFrame | np.ndarray], None, None]:
         """
         Mimic the dictionary .items() function
 

src/power_grid_model_io/functions/filters.py

@@ -5,7 +5,7 @@
 These functions can be used in the mapping files to apply filter functions to vision data
 """
 
-from typing import List, Union
+from typing import List
 
 import pandas as pd
 
@@ -24,7 +24,7 @@ def exclude_empty(row: pd.Series, col: str) -> bool:
     return result
 
 
-def exclude_value(row: pd.Series, col: str, value: Union[float, str]) -> bool:
+def exclude_value(row: pd.Series, col: str, value: float | str) -> bool:
     """
     filter out by match value
     """

src/power_grid_model_io/mappings/tabular_mapping.py

@@ -5,13 +5,13 @@
 Tabular data mapping helper class
 """
 
-from typing import Dict, Generator, List, Tuple, Union
+from typing import Dict, Generator, List, Tuple
 
 import structlog
 
-AttributeValue = Union[int, float, str, Dict, List]
+AttributeValue = int | float | str | Dict | List
 InstanceAttributes = Dict[str, AttributeValue]
-Components = Dict[str, Union[InstanceAttributes, List[InstanceAttributes]]]
+Components = Dict[str, InstanceAttributes | List[InstanceAttributes]]
 Tables = Dict[str, Components]