def test_check_column_exists_and_order(self, d, m, r):
df = get_test_csv(cwd, d)
meta = read_json(cwd, m)
expected_result = read_json(cwd, r)
l = Linter(df, meta)
l.check_column_exists_and_order()
result = l.vlog.as_dict()
self.assertDictEqual(result, expected_result)
def test_data_types(self, d, m, r):
df = get_test_csv(cwd, d)
meta = read_json(cwd, m)
l = Linter(df, meta)
l.check_types()
result = l.success()
self.assertEqual(result, r)
def test_check_regex(self, d, m, r):
df = get_test_csv(cwd, d)
meta = read_json(cwd, m)
expected_result = read_json(cwd, r)
l = Linter(df, meta)
l.check_pattern()
result = l.vlog.as_dict()
self.assertDictEqual(result, expected_result)
def test_check_enums(self, d, m, r):
df = get_test_csv(cwd, d)
meta = read_json(cwd, m)
expected_result = read_json(cwd, r)
l = Linter(df, meta)
l.check_enums()
result = l.vlog["mychar"]["check_enums"].as_dict()
self.assertDictEqual(result, expected_result)
def test_overall_success(self, d, m, r):
df = get_test_csv(cwd, d)
meta = read_json(cwd, m)
l = Linter(df, meta)
# Should not return success before tests run
with self.assertRaises(Exception):
l.success()
l.check_all()
result = l.success()
self.assertEqual(result, r)
def test_detailed_markdown(self, d, m):
"""
This tests that a wide range of metadata/data combinations render correctly without erroring
"""
df = get_test_csv(cwd, d)
meta = read_json(cwd, m)
l = Linter(df, meta)
l.check_all()
l.markdown_report()
def test_metadata_correctly_imposed_on_valid_data(self):
df = get_test_csv(cwd, "test_csv_data_valid")
meta_data = read_json(cwd, "meta/test_meta_cols_valid.json")
meta_cols = meta_data["columns"]
self.assertFalse(
_pd_df_datatypes_match_metadata_data_types(df, meta_cols))
# We expect that, after impose_metadata_types_on_pd_df is run, the datatypes conform to the metadata
df = impose_metadata_types_on_pd_df(df, meta_data)
self.assertTrue(
_pd_df_datatypes_match_metadata_data_types(df, meta_cols))
def test_metadata_impose_does_not_work_on_invalid_data(self):
# What happens if we read in data that does NOT conform to the metadata
df = get_test_csv(cwd, "test_csv_data_invalid_data")
meta_data = read_json(cwd, "meta/test_meta_cols_valid.json")
meta_cols = meta_data["columns"]
self.assertFalse(
_pd_df_datatypes_match_metadata_data_types(df, meta_cols))
# We expect that, after impose_metadata_types_on_pd_df is run, the datatypes do NOT conform to the metadata
df = impose_metadata_types_on_pd_df(df, meta_data)
self.assertFalse(
_pd_df_datatypes_match_metadata_data_types(df, meta_cols))
def test_validate_meta_data(self):
meta = read_json(cwd, "meta/test_meta_cols_valid.json")
#Data is irrelevant but cannot instantiate linter without it
df = get_test_csv(cwd, "test_csv_data_valid")
# Test no error is raised
L = Linter(df, meta)
# Test invalid metadata raises an error
meta = read_json(cwd, "meta/test_invalid_meta_cols_missing_name.json")
with self.assertRaises(ValidationError):
L = Linter(df, meta)
# Test invalid metadata raises an error
meta = read_json(cwd, "meta/test_invalid_meta_columns_key_mispelt.json")
with self.assertRaises(ValidationError):
L = Linter(df, meta)
def test_data_types_ints(self):
df = get_test_csv(cwd, "test_csv_data_ints")
meta = read_json(cwd, "meta/test_meta_cols_ints.json")
l = Linter(df, meta)
l.check_types()
actual = {
k: v["check_data_type"]["success"]
for k, v in l.vlog.as_dict().items()
}
expected = {
'int_with_float': False,
'int_with_long': False,
'int_with_null': True,
'int_without_null': True
}
self.assertDictEqual(actual, expected)