def test_yield_correct_results(self, df_with_numeric_values):
df = df_with_numeric_values
base_check = Check(CheckLevel.EXCEPTION, description="a description")
analyzers = [
Minimum("att1"),
Maximum("att1"),
Mean("att1"),
StandardDeviation("att1"),
Sum("att1"),
Quantile("att1", 0.5),
]
engine = PandasEngine(df)
repo = InMemoryMetadataRepository()
context_numeric = do_analysis_run(engine, repo, analyzers)
assert is_success(base_check.has_min("att1", lambda v: v == 1.0),
context_numeric)
assert is_success(base_check.has_max("att1", lambda v: v == 6.0),
context_numeric)
assert is_success(base_check.has_mean("att1", lambda v: v == 3.5),
context_numeric)
assert is_success(
base_check.has_standard_deviation("att1", lambda v: v == 1.870829),
context_numeric,
)
assert is_success(base_check.has_sum("att1", lambda v: v == 21.0),
context_numeric)
assert is_success(
base_check.has_approx_quantile("att1", 0.5, lambda v: v == 4.0),
context_numeric)
def test_multiple_quantiles_are_computed(self, df_with_numeric_values):
df = df_with_numeric_values
analyzers = [
Quantile("att1", 0.1),
Quantile("att1", 0.5),
Quantile("att1", 0.9),
]
engine = PandasEngine(df)
repo = InMemoryMetadataRepository()
context_numeric = do_analysis_run(engine, repo, analyzers)
assert len(context_numeric.metric_map) == 3
print(context_numeric)
base_check = Check(CheckLevel.EXCEPTION, description="a description")
assert is_success(
base_check.has_approx_quantile("att1", 0.5, lambda v: v == 4.0),
context_numeric)
assert is_success(
base_check.has_approx_quantile("att1", 0.9, lambda v: v == 6.0),
context_numeric)
assert is_success(
base_check.has_approx_quantile("att1", 0.1, lambda v: v == 1.0),
context_numeric)
