chore: update type hints to 3.10+

pyproject.toml

@@ -105,25 +105,19 @@ required-version = ">=0.7.10"
 [tool.ruff]
 line-length = 120
 indent-width = 4
+target-version = "py310"
 
 [tool.ruff.lint]
 select = [
-    # "E",   # pycodestyle errors
-    "W",   # pycodestyle warnings
-    "F",   # pyflakes
-    "I",   # isort (import sorting)
-    # "N",   # pep8-naming
-    # "UP",  # pyupgrade (modern Python syntax)
-    # "ANN", # flake8-annotations (enforce type hints)
-    # "B",   # fla e8-bugbear (common bugs)
-    # "C4",  # flake8-comprehensions
-    # "DTZ", # flake8-datetimez (timezone awareness)
-    "ICN", # flake8-import-conventions
-    "PIE", # flake8-pie (misc lints)
-    # "RET", # flake8-return
-    # "SIM", # flake8-simplify
-    # "PTH", # flake8-use-pathlib
-    "TID", # flake8-tidy-imports (ban relative imports)
+    "W",     # pycodestyle warnings
+    "F",     # pyflakes
+    "I",     # isort (import sorting)
+    "ICN",   # flake8-import-conventions
+    "PIE",   # flake8-pie (misc lints)
+    "TID",   # flake8-tidy-imports (ban relative imports)
+    "UP006", # List[A] -> list[A]
+    "UP007", # Union[A, B] -> A | B
+    "UP045", # Optional[A] -> A | None
 ]
 ignore = [
     "ANN401", # Dynamically typed expressions (Any)

src/data_designer/config/analysis/column_profilers.py

@@ -3,7 +3,6 @@
 
 from abc import ABC
 from enum import Enum
-from typing import Optional, Union
 
 from pydantic import BaseModel, Field
 from rich.panel import Panel
@@ -61,7 +60,7 @@ class JudgeScoreProfilerConfig(ConfigBase):
     """
 
     model_alias: str
-    summary_score_sample_size: Optional[int] = Field(default=20, ge=1)
+    summary_score_sample_size: int | None = Field(default=20, ge=1)
 
 
 class JudgeScoreSample(BaseModel):
@@ -75,7 +74,7 @@ class JudgeScoreSample(BaseModel):
         reasoning: The reasoning or explanation provided by the judge for this score.
     """
 
-    score: Union[int, str]
+    score: int | str
     reasoning: str
 
 
@@ -94,11 +93,11 @@ class JudgeScoreDistributions(BaseModel):
         histograms: Mapping of each score dimension name to its histogram data.
     """
 
-    scores: dict[str, list[Union[int, str]]]
+    scores: dict[str, list[int | str]]
     reasoning: dict[str, list[str]]
     distribution_types: dict[str, ColumnDistributionType]
-    distributions: dict[str, Union[CategoricalDistribution, NumericalDistribution, MissingValue]]
-    histograms: dict[str, Union[CategoricalHistogramData, MissingValue]]
+    distributions: dict[str, CategoricalDistribution | NumericalDistribution | MissingValue]
+    histograms: dict[str, CategoricalHistogramData | MissingValue]
 
 
 class JudgeScoreSummary(BaseModel):
@@ -132,7 +131,7 @@ class JudgeScoreProfilerResults(ColumnProfilerResults):
 
     column_name: str
     summaries: dict[str, JudgeScoreSummary]
-    score_distributions: Union[JudgeScoreDistributions, MissingValue]
+    score_distributions: JudgeScoreDistributions | MissingValue
 
     def create_report_section(self) -> Panel:
         layout = Table.grid(Column(), expand=True, padding=(2, 0))

src/data_designer/config/analysis/column_statistics.py

@@ -5,7 +5,7 @@
 
 from abc import ABC, abstractmethod
 from enum import Enum
-from typing import Any, Literal, Optional, Union
+from typing import Any, Literal
 
 from pandas import Series
 from pydantic import BaseModel, ConfigDict, create_model, field_validator, model_validator
@@ -69,27 +69,27 @@ class GeneralColumnStatistics(BaseColumnStatistics):
     """
 
     column_name: str
-    num_records: Union[int, MissingValue]
-    num_null: Union[int, MissingValue]
-    num_unique: Union[int, MissingValue]
+    num_records: int | MissingValue
+    num_null: int | MissingValue
+    num_unique: int | MissingValue
     pyarrow_dtype: str
     simple_dtype: str
     column_type: Literal["general"] = "general"
 
     @field_validator("num_null", "num_unique", "num_records", mode="before")
-    def general_statistics_ensure_python_integers(cls, v: Union[int, MissingValue]) -> Union[int, MissingValue]:
+    def general_statistics_ensure_python_integers(cls, v: int | MissingValue) -> int | MissingValue:
         return v if isinstance(v, MissingValue) else prepare_number_for_reporting(v, int)
 
     @property
-    def percent_null(self) -> Union[float, MissingValue]:
+    def percent_null(self) -> float | MissingValue:
         return (
             self.num_null
             if self._is_missing_value(self.num_null)
             else prepare_number_for_reporting(100 * self.num_null / (self.num_records + EPSILON), float)
         )
 
     @property
-    def percent_unique(self) -> Union[float, MissingValue]:
+    def percent_unique(self) -> float | MissingValue:
         return (
             self.num_unique
             if self._is_missing_value(self.num_unique)
@@ -108,7 +108,7 @@ def _general_display_row(self) -> dict[str, str]:
     def create_report_row_data(self) -> dict[str, str]:
         return self._general_display_row
 
-    def _is_missing_value(self, v: Union[float, int, MissingValue]) -> bool:
+    def _is_missing_value(self, v: float | int | MissingValue) -> bool:
         return v in set(MissingValue)
 
 
@@ -128,12 +128,12 @@ class LLMTextColumnStatistics(GeneralColumnStatistics):
         column_type: Discriminator field, always "llm-text" for this statistics type.
     """
 
-    output_tokens_mean: Union[float, MissingValue]
-    output_tokens_median: Union[float, MissingValue]
-    output_tokens_stddev: Union[float, MissingValue]
-    input_tokens_mean: Union[float, MissingValue]
-    input_tokens_median: Union[float, MissingValue]
-    input_tokens_stddev: Union[float, MissingValue]
+    output_tokens_mean: float | MissingValue
+    output_tokens_median: float | MissingValue
+    output_tokens_stddev: float | MissingValue
+    input_tokens_mean: float | MissingValue
+    input_tokens_median: float | MissingValue
+    input_tokens_stddev: float | MissingValue
     column_type: Literal[DataDesignerColumnType.LLM_TEXT.value] = DataDesignerColumnType.LLM_TEXT.value
 
     @field_validator(
@@ -145,7 +145,7 @@ class LLMTextColumnStatistics(GeneralColumnStatistics):
         "input_tokens_stddev",
         mode="before",
     )
-    def llm_column_ensure_python_floats(cls, v: Union[float, int, MissingValue]) -> Union[float, int, MissingValue]:
+    def llm_column_ensure_python_floats(cls, v: float | int | MissingValue) -> float | int | MissingValue:
         return v if isinstance(v, MissingValue) else prepare_number_for_reporting(v, float)
 
     def create_report_row_data(self) -> dict[str, Any]:
@@ -225,7 +225,7 @@ class SamplerColumnStatistics(GeneralColumnStatistics):
 
     sampler_type: SamplerType
     distribution_type: ColumnDistributionType
-    distribution: Optional[Union[CategoricalDistribution, NumericalDistribution, MissingValue]]
+    distribution: CategoricalDistribution | NumericalDistribution | MissingValue | None
     column_type: Literal[DataDesignerColumnType.SAMPLER.value] = DataDesignerColumnType.SAMPLER.value
 
     def create_report_row_data(self) -> dict[str, str]:
@@ -273,15 +273,15 @@ class ValidationColumnStatistics(GeneralColumnStatistics):
         column_type: Discriminator field, always "validation" for this statistics type.
     """
 
-    num_valid_records: Union[int, MissingValue]
+    num_valid_records: int | MissingValue
     column_type: Literal[DataDesignerColumnType.VALIDATION.value] = DataDesignerColumnType.VALIDATION.value
 
     @field_validator("num_valid_records", mode="before")
-    def code_validation_column_ensure_python_integers(cls, v: Union[int, MissingValue]) -> Union[int, MissingValue]:
+    def code_validation_column_ensure_python_integers(cls, v: int | MissingValue) -> int | MissingValue:
         return v if isinstance(v, MissingValue) else prepare_number_for_reporting(v, int)
 
     @property
-    def percent_valid(self) -> Union[float, MissingValue]:
+    def percent_valid(self) -> float | MissingValue:
         return (
             self.num_valid_records
             if self._is_missing_value(self.num_valid_records)
@@ -303,7 +303,7 @@ class CategoricalHistogramData(BaseModel):
         counts: List of occurrence counts for each category.
     """
 
-    categories: list[Union[float, int, str]]
+    categories: list[float | int | str]
     counts: list[int]
 
     @model_validator(mode="after")
@@ -328,12 +328,12 @@ class CategoricalDistribution(BaseModel):
         histogram: Complete frequency distribution showing all categories and their counts.
     """
 
-    most_common_value: Union[str, int]
-    least_common_value: Union[str, int]
+    most_common_value: str | int
+    least_common_value: str | int
     histogram: CategoricalHistogramData
 
     @field_validator("most_common_value", "least_common_value", mode="before")
-    def ensure_python_types(cls, v: Union[str, int]) -> Union[str, int]:
+    def ensure_python_types(cls, v: str | int) -> str | int:
         return str(v) if not is_int(v) else prepare_number_for_reporting(v, int)
 
     @classmethod
@@ -357,14 +357,14 @@ class NumericalDistribution(BaseModel):
         median: Median value of the distribution.
     """
 
-    min: Union[float, int]
-    max: Union[float, int]
+    min: float | int
+    max: float | int
     mean: float
     stddev: float
     median: float
 
     @field_validator("min", "max", "mean", "stddev", "median", mode="before")
-    def ensure_python_types(cls, v: Union[float, int]) -> Union[float, int]:
+    def ensure_python_types(cls, v: float | int) -> float | int:
         return prepare_number_for_reporting(v, int if is_int(v) else float)
 
     @classmethod
@@ -378,17 +378,17 @@ def from_series(cls, series: Series) -> Self:
         )
 
 
-ColumnStatisticsT: TypeAlias = Union[
-    GeneralColumnStatistics,
-    LLMTextColumnStatistics,
-    LLMCodeColumnStatistics,
-    LLMStructuredColumnStatistics,
-    LLMJudgedColumnStatistics,
-    SamplerColumnStatistics,
-    SeedDatasetColumnStatistics,
-    ValidationColumnStatistics,
-    ExpressionColumnStatistics,
-]
+ColumnStatisticsT: TypeAlias = (
+    GeneralColumnStatistics
+    | LLMTextColumnStatistics
+    | LLMCodeColumnStatistics
+    | LLMStructuredColumnStatistics
+    | LLMJudgedColumnStatistics
+    | SamplerColumnStatistics
+    | SeedDatasetColumnStatistics
+    | ValidationColumnStatistics
+    | ExpressionColumnStatistics
+)
 
 
 DEFAULT_COLUMN_STATISTICS_MAP = {

src/data_designer/config/analysis/dataset_profiler.py

@@ -3,7 +3,7 @@
 
 from functools import cached_property
 from pathlib import Path
-from typing import Annotated, Optional, Union
+from typing import Annotated
 
 from pydantic import BaseModel, Field, field_validator
 
@@ -34,8 +34,8 @@ class DatasetProfilerResults(BaseModel):
     num_records: int
     target_num_records: int
     column_statistics: list[Annotated[ColumnStatisticsT, Field(discriminator="column_type")]] = Field(..., min_length=1)
-    side_effect_column_names: Optional[list[str]] = None
-    column_profiles: Optional[list[ColumnProfilerResultsT]] = None
+    side_effect_column_names: list[str] | None = None
+    column_profiles: list[ColumnProfilerResultsT] | None = None
 
     @field_validator("num_records", "target_num_records", mode="before")
     def ensure_python_integers(cls, v: int) -> int:
@@ -61,8 +61,8 @@ def get_column_statistics_by_type(self, column_type: DataDesignerColumnType) ->
 
     def to_report(
         self,
-        save_path: Optional[Union[str, Path]] = None,
-        include_sections: Optional[list[Union[ReportSection, DataDesignerColumnType]]] = None,
+        save_path: str | Path | None = None,
+        include_sections: list[ReportSection | DataDesignerColumnType] | None = None,
     ) -> None:
         """Generate and print an analysis report based on the dataset profiling results.
 

src/data_designer/config/analysis/utils/reporting.py

@@ -5,7 +5,7 @@
 
 from enum import Enum
 from pathlib import Path
-from typing import TYPE_CHECKING, Optional, Union
+from typing import TYPE_CHECKING
 
 from rich.align import Align
 from rich.console import Console, Group
@@ -48,8 +48,8 @@ class ReportSection(str, Enum):
 
 def generate_analysis_report(
     analysis: DatasetProfilerResults,
-    save_path: Optional[Union[str, Path]] = None,
-    include_sections: Optional[list[Union[ReportSection, DataDesignerColumnType]]] = None,
+    save_path: str | Path | None = None,
+    include_sections: list[ReportSection | DataDesignerColumnType] | None = None,
 ) -> None:
     """Generate an analysis report for dataset profiling results.
 

src/data_designer/config/base.py

@@ -4,7 +4,7 @@
 from __future__ import annotations
 
 from pathlib import Path
-from typing import Any, Optional, Union
+from typing import Any
 
 import yaml
 from pydantic import BaseModel, ConfigDict
@@ -31,7 +31,7 @@ def to_dict(self) -> dict[str, Any]:
         """
         return self.model_dump(mode="json")
 
-    def to_yaml(self, path: Optional[Union[str, Path]] = None, *, indent: Optional[int] = 2, **kwargs) -> Optional[str]:
+    def to_yaml(self, path: str | Path | None = None, *, indent: int | None = 2, **kwargs) -> str | None:
         """Convert the configuration to a YAML string or file.
 
         Args:
@@ -49,7 +49,7 @@ def to_yaml(self, path: Optional[Union[str, Path]] = None, *, indent: Optional[i
         with open(path, "w") as f:
             f.write(yaml_str)
 
-    def to_json(self, path: Optional[Union[str, Path]] = None, *, indent: Optional[int] = 2, **kwargs) -> Optional[str]:
+    def to_json(self, path: str | Path | None = None, *, indent: int | None = 2, **kwargs) -> str | None:
         """Convert the configuration to a JSON string or file.
 
         Args:

src/data_designer/config/column_configs.py

@@ -2,7 +2,7 @@
 # SPDX-License-Identifier: Apache-2.0
 
 from abc import ABC
-from typing import Annotated, Literal, Optional, Type, Union
+from typing import Annotated, Literal
 
 from pydantic import BaseModel, Discriminator, Field, model_validator
 from typing_extensions import Self
@@ -91,7 +91,7 @@ class SamplerColumnConfig(SingleColumnConfig):
     sampler_type: SamplerType
     params: Annotated[SamplerParamsT, Discriminator("sampler_type")]
     conditional_params: dict[str, Annotated[SamplerParamsT, Discriminator("sampler_type")]] = {}
-    convert_to: Optional[str] = None
+    convert_to: str | None = None
     column_type: Literal["sampler"] = "sampler"
 
     @model_validator(mode="before")
@@ -146,8 +146,8 @@ class LLMTextColumnConfig(SingleColumnConfig):
 
     prompt: str
     model_alias: str
-    system_prompt: Optional[str] = None
-    multi_modal_context: Optional[list[ImageContext]] = None
+    system_prompt: str | None = None
+    multi_modal_context: list[ImageContext] | None = None
     column_type: Literal["llm-text"] = "llm-text"
 
     @property
@@ -222,7 +222,7 @@ class LLMStructuredColumnConfig(LLMTextColumnConfig):
         column_type: Discriminator field, always "llm-structured" for this configuration type.
     """
 
-    output_format: Union[dict, Type[BaseModel]]
+    output_format: dict | type[BaseModel]
     column_type: Literal["llm-structured"] = "llm-structured"
 
     @model_validator(mode="after")
@@ -255,7 +255,7 @@ class Score(ConfigBase):
 
     name: str = Field(..., description="A clear name for this score.")
     description: str = Field(..., description="An informative and detailed assessment guide for using this score.")
-    options: dict[Union[int, str], str] = Field(..., description="Score options in the format of {score: description}.")
+    options: dict[int | str, str] = Field(..., description="Score options in the format of {score: description}.")
 
 
 class LLMJudgeColumnConfig(LLMTextColumnConfig):