"file_mixed_ext": Unsupported,
"image_png": Unsupported,
"image_png_missing": Unsupported,
"str_int_leading_zeros": Numeric,
"str_float_non_leading_zeros": Numeric,
"str_int_zeros": Numeric,
"email_address_str": Categorical,
"str_complex_nan": Categorical,
"email_address": Unsupported,
"email_address_missing": Unsupported,
}
if int(pd.__version__[0]) >= 1:
inference_map["string_dtype_series"] = Categorical
@pytest.mark.parametrize(**get_inference_cases(series, inference_map, typeset))
def test_inference(series, type, typeset, difference):
"""Test the generated combinations for "inference(series) == type"
Args:
series: the series to test
type: the type to test against
"""
config["vars"]["num"]["low_categorical_threshold"].set(0)
result, message = infers(series, type, typeset, difference)
assert result, message
# Conversions in one single step
convert_map = [
# Model type, Relation type
"str_true_false_none": Boolean,
"str_true_false_nan": Boolean,
"somedate": DateTime,
"empty": Unsupported,
"list": Unsupported,
"mixed": Unsupported,
"dict": Unsupported,
"tuple": Unsupported,
"inf_only": Numeric,
"nullable_int": Numeric,
"catnum": Numeric,
}
@pytest.mark.parametrize(**patch_arg(
get_inference_cases(series, inference_map, my_typeset), "inference_type"))
def test_inference(name, series, inference_type, typeset, difference):
"""Test the generated combinations for "inference(series) == type_"
Args:
series: the series to test
type_: the type to test against
"""
result, message = infers(name, series, inference_type, typeset, difference)
assert result, message
# Conversions in one single step
convert_map = [
# Model type, Relation type
(
"image_png_missing": Object,
"str_int_leading_zeros": String,
"str_float_non_leading_zeros": Float,
"str_int_zeros": Integer,
"email_address": Object,
"email_address_missing": Object,
"email_address_str": String,
"all_null_none": Generic,
"all_null_nan": Generic,
"all_null_nat": Generic,
"all_null_empty_str": String,
"string_dtype_series": String,
}
@pytest.mark.parametrize(**get_inference_cases(array, inference_map, typeset))
def test_inference(name, series, inference_type, typeset, difference):
"""Test the generated combinations for "inference(array) == type"
Args:
series: the array to test
inference_type: the type to test against
"""
result, message = infers(name, series, inference_type, typeset, difference)
assert result, message
# Conversions in one single step
convert_map = [
# Model type, Relation type
(Integer, Float, {"int_nan_series", "float_series2"}),
"str_complex_nan": Categorical,
"email_address": Unsupported,
"email_address_missing": Unsupported,
"all_null_nat": Unsupported,
"all_null_empty_str": Categorical,
"py_datetime_str": Categorical,
"all_null_none": Unsupported,
"complex_series_py_float": Numeric,
"all_null_nan": Unsupported,
}
if int(pd.__version__[0]) >= 1:
inference_map["string_dtype_series"] = Categorical
@pytest.mark.parametrize(**patch_arg(
get_inference_cases(series, inference_map, my_typeset_default),
"inference_type"))
def test_inference(name, series, inference_type, typeset, difference):
"""Test the generated combinations for "inference(series) == type"
Args:
series: the series to test
inference_type: the type to test against
"""
result, message = infers(name, series, inference_type, typeset, difference)
assert result, message
# Conversions in one single step
convert_map = [
# Model type, Relation type