update

.github/workflows/build.yml

@@ -29,7 +29,7 @@ jobs:
  mypy .
  - name: Test with pytest
  run: |
- coverage run -m pytest --typeguard-packages=strictly_typed_pandas,tests
+ coverage run -m pytest
  coverage report -m
  - name: Run notebooks
  run: |

strictly_typed_pandas/typeguard.py

@@ -7,21 +7,15 @@
 def check_dataset(value: Any, origin_type: Any, args: Tuple[Any, ...], memo: typeguard.TypeCheckMemo) -> None:
  schema_expected = args[0]
  if not isinstance(value, DataSet):
- msg = "Type must be a DataSet[{schema_expected}]; got {class_observed} instead".format(
- schema_expected=typeguard.qualified_name(schema_expected),
- class_observed=typeguard.qualified_name(value)
- )
+ msg = f"Type must be a DataSet[{schema_expected}]; got {value} instead"
  if memo.config.typecheck_fail_callback:
  memo.config.typecheck_fail_callback(typeguard.TypeCheckError(msg), memo)
  else:
  raise TypeError(msg)
 
  schema_observed = value.__orig_class__.__args__[0]
  if schema_observed != schema_expected:
- msg = "Type must be a DataSet[{schema_expected}]; got DataSet[{schema_observed}] instead".format(
- schema_expected=typeguard.qualified_name(schema_expected),
- schema_observed=typeguard.qualified_name(schema_observed)
- )
+ msg = f"Type must be a DataSet[{schema_expected}]; got DataSet[{schema_observed}] instead"
  if memo.config.typecheck_fail_callback:
  memo.config.typecheck_fail_callback(typeguard.TypeCheckError(msg), memo)
  else:
@@ -33,13 +27,9 @@ def check_indexed_dataset(value: Any, origin_type: Any, args: Tuple[Any, ...],
  schema_data_expected = args[1]
  if not isinstance(value, IndexedDataSet):
  msg = (
- "Type must be a IndexedDataSet[{schema_index_expected},{schema_data_expected}];" +
- "got {class_observed} instead"
- ).format(
- schema_index_expected=typeguard.qualified_name(schema_index_expected),
- schema_data_expected=typeguard.qualified_name(schema_data_expected),
- class_observed=typeguard.qualified_name(value)
- )
+ f"Type must be a IndexedDataSet[{schema_index_expected},{schema_data_expected}]; " +
+ f"got {value} instead"
+ )
 
  if memo.config.typecheck_fail_callback:
  memo.config.typecheck_fail_callback(typeguard.TypeCheckError(msg), memo)
@@ -50,13 +40,8 @@ def check_indexed_dataset(value: Any, origin_type: Any, args: Tuple[Any, ...],
  schema_data_observed = value.__orig_class__.__args__[1]
  if schema_index_observed != schema_index_expected or schema_data_observed != schema_data_expected:
  msg = (
- "Type must be a IndexedDataSet[{schema_index_expected},{schema_data_expected}];" +
- "got IndexedDataSet[{schema_index_observed},{schema_data_observed}] instead"
- ).format(
- schema_index_expected=typeguard.qualified_name(schema_index_expected),
- schema_data_expected=typeguard.qualified_name(schema_data_expected),
- schema_index_observed=typeguard.qualified_name(schema_index_observed),
- schema_data_observed=typeguard.qualified_name(schema_data_observed)
+ f"Type must be a IndexedDataSet[{schema_index_expected},{schema_data_expected}];" +
+ f"got IndexedDataSet[{schema_index_observed},{schema_data_observed}] instead"
  )
  if memo.config.typecheck_fail_callback:
  memo.config.typecheck_fail_callback(typeguard.TypeCheckError(msg), memo)

tests/test_dataset.py

@@ -2,6 +2,8 @@
 import pandas as pd
 import numpy as np # type: ignore
 
+from typeguard import typechecked
+
 from strictly_typed_pandas import DataSet
 from strictly_typed_pandas.pandas_types import StringDtype
 
@@ -93,6 +95,7 @@ def test_dataset_to_dataframe() -> None:
  assert isinstance(df.to_frame(), pd.DataFrame)
 
 
+@typechecked
 def foo(df: DataSet[Schema]) -> DataSet[Schema]:
  return df
 

tests/test_indexed_dataset.py

@@ -2,6 +2,8 @@
 import pandas as pd
 import numpy as np # type: ignore
 
+from typeguard import typechecked
+
 from strictly_typed_pandas import IndexedDataSet
 from strictly_typed_pandas.pandas_types import StringDtype
 
@@ -58,6 +60,7 @@ def test_overlapping_columns():
  IndexedDataSet[IndexSchema, IndexSchema]()
 
 
+@typechecked
 def foo(df: IndexedDataSet[IndexSchema, DataSchema]) -> IndexedDataSet[IndexSchema, DataSchema]:
  return df