def test_reshape_failure_cases_exceptions():
"""Tests that the reshape_failure_cases method correctly produces a
TypeError."""
# pylint: disable=W0212, E1121
# disabling pylint because this function should be private to the class and
# it's ok to access it because the function needs to be tested.
check = Check(lambda x: x.isna().sum() == 0)
for data in [1, "foobar", 1.0, {"key": "value"}, list(range(10))]:
with pytest.raises(TypeError):
error_formatters.reshape_failure_cases(data, check.n_failure_cases)
def numpy_pandas_coerce_failure_cases(data_container: Union[PandasObject,
np.ndarray],
type_: Any) -> PandasObject:
"""
Get the failure cases resulting from trying to coerce a pandas/numpy object
into particular data type.
"""
# pylint: disable=import-outside-toplevel,cyclic-import
from pandera import error_formatters
from pandera.engines import pandas_engine
data_type = pandas_engine.Engine.dtype(type_)
if isinstance(data_container, np.ndarray):
if len(data_container.shape) == 1:
data_container = pd.Series(data_container)
elif len(data_container.shape) == 2:
data_container = pd.DataFrame(data_container)
else:
raise ValueError(
"only numpy arrays of 1 or 2 dimensions are supported")
if check_utils.is_index(data_container):
data_container = data_container.to_series() # type: ignore[union-attr]
if check_utils.is_table(data_container):
check_output = data_container.apply( # type: ignore[union-attr]
numpy_pandas_coercible,
args=(data_type, ),
)
_, failure_cases = check_utils.prepare_dataframe_check_output(
data_container,
check_output,
ignore_na=False,
)
elif check_utils.is_field(data_container):
check_output = numpy_pandas_coercible(data_container, data_type)
_, failure_cases = check_utils.prepare_series_check_output(
data_container,
check_output,
ignore_na=False,
)
else:
raise TypeError(
f"type of data_container {type(data_container)} not understood. "
"Must be a pandas Series, Index, or DataFrame.")
return error_formatters.reshape_failure_cases(failure_cases,
ignore_na=False)