def test_wholetable_empty(self):
df = pd.DataFrame()
df["c1"] = []
df["c2"] = []
r = completeness(df=df)[0]
self.assertEqual(r, 100.)
def test_wholetable_allnull(self):
df = pd.DataFrame()
df["c1"] = [None for _ in range(100)]
df["c2"] = [np.NaN for _ in range(100)]
df["c3"] = [np.NaN for _ in range(100)]
r = completeness(df=df)[0]
self.assertEqual(r, 0.0)
def test_wholetable_full(self):
df = pd.DataFrame()
df["c1"] = [chr(i) for i in range(100)]
df["c2"] = [i for i in range(100)]
df["c3"] = [i / 0.7 for i in range(100)]
r = completeness(df=df)[0]
self.assertEqual(r, 100.0)
def test_singlecolumns_empty(self):
df = pd.DataFrame()
df["c1"] = []
df["c2"] = []
r1, r2 = completeness(["c1", "c2"], df)
self.assertEqual(r1, 100.)
self.assertEqual(r2, 100.)
def test_singlecolumns_allnull(self):
df = pd.DataFrame()
df["c1"] = [None for _ in range(100)]
df["c2"] = [np.NaN for _ in range(100)]
df["c3"] = [np.NaN for _ in range(100)]
r1, r2, r3 = completeness(["c1", "c2", "c3"], df)
self.assertEqual(r1, 0.0)
self.assertEqual(r2, 0.0)
self.assertEqual(r3, 0.0)
def test_singlecolumns_full(self):
df = pd.DataFrame()
df["c1"] = [chr(i) for i in range(100)]
df["c2"] = [i for i in range(100)]
df["c3"] = [i / 0.7 for i in range(100)]
r1, r2, r3 = completeness(["c1", "c2", "c3"], df)
self.assertEqual(r1, 100.0)
self.assertEqual(r2, 100.0)
self.assertEqual(r3, 100.0)
def test_wholetable_partial(self):
df = pd.DataFrame()
# create and assign columns to df
l1 = [chr(i) for i in range(100)]
l2 = [i for i in range(100)]
l3 = [i / 0.7 for i in range(100)]
for i in range(20):
l1[i] = None
l2[i] = np.NaN
l3[i] = np.NaN
random.shuffle(l1)
random.shuffle(l2)
random.shuffle(l3)
df["c1"] = l1
df["c2"] = l2
df["c3"] = l3
r = completeness(df=df)[0]
self.assertEqual(r, 80.0)
def test_singlecolumns_partial(self):
df = pd.DataFrame()
# create and assign columns to df
l1 = [chr(i) for i in range(100)]
l2 = [i for i in range(100)]
l3 = [i / 0.7 for i in range(100)]
for i in range(20):
l1[i] = None
l2[i] = np.NaN
l3[i] = np.NaN
random.shuffle(l1)
random.shuffle(l2)
random.shuffle(l3)
df["c1"] = l1
df["c2"] = l2
df["c3"] = l3
r1, r2, r3 = completeness(["c1", "c2", "c3"], df)
self.assertEqual(r1, 80.0)
self.assertEqual(r2, 80.0)
self.assertEqual(r3, 80.0)
#!/usr/bin/python3
import pandas as pd
from haychecker.chc.metrics import completeness
df = pd.read_csv("examples/resources/employees.csv")
pd.set_option('max_columns', 10)
print(df)
r1, r2 = completeness(["region", "reportsTo"], df)
print("Completeness region: {}, completeness reportsTo: {}".format(r1, r2))
task1 = completeness(["region", "reportsTo"])
task2 = completeness(["city"])
task3 = task1.add(task2)
result = task3.run(df)
r1, r2 = result[0]["scores"]
r3 = result[1]["scores"][0]
print(
"Completeness region: {}, completeness reportsTo: {}, completeness city: {}"
.format(r1, r2, r3))
def test_groping_multile_columns(self):
df = pd.DataFrame()
c1 = [0, 0, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 5]
c2 = ["a", "a", "b", "b", "c", "c", "d", "d", "d", "d", "a", "a", "a"]
c3 = [0.0, 0.0, 0.1, 0.1, 2.2, 2.2, 2.2, 3.1, 3.2, 3.3, 40, 40, 50]
c4 = [10.0, 20.0, 10.0, 20.0, 10.0, 20.0, 10.0, 20.0, 10.0, 20.0, 10.0, 20.0, 10.0]
c5 = ["09:10:10" for _ in range(10)]
c5.extend(["00:11:10" for _ in range(3)])
df["c1"] = c1
df["c2"] = c2
df["c3"] = c3
df["c4"] = c4
df["c5"] = c5
task = Task()
task.add(completeness())
task.add(completeness([0, 1, 2]))
task.add(deduplication([0, 1]))
task.add(deduplication())
task.add(timeliness(["c5"], value="10:10:10", timeFormat="%S:%M:%H"))
task.add(completeness())
condition1 = {"column": "c3", "operator": "lt", "value": 50}
condition2 = {"column": "c3", "operator": "gt", "value": 1.0}
conditions = [condition1, condition2]
task.add(rule(conditions))
condition1 = {"column": "c5", "operator": "eq", "value": "00:11:10"}
conditions = [condition1]
task.add(rule(conditions))
condition1 = {"column": "c3", "operator": "lt", "value": 50}
condition2 = {"column": "c3", "operator": "gt", "value": 1.0}
conditions = [condition1, condition2]
having1 = {"column": "*", "operator": "gt", "value": 1, "aggregator": "count"}
having2 = {"column": "c4", "operator": "eq", "value": 50 / 3, "aggregator": "avg"}
havings = [having1, having2]
task.add(grouprule([0, "c2"], havings, conditions))
result = task.run(df)
# c1
r = result[0]["scores"][0]
self.assertEqual(r, 100.)
# c2
r1, r2, r3 = result[1]["scores"]
self.assertEqual(r1, 100.)
self.assertEqual(r2, 100.)
self.assertEqual(r3, 100.)
# d1
r1, r2 = result[2]["scores"]
self.assertEqual(r1, (6 / 13) * 100)
self.assertEqual(r2, (4 / 13) * 100)
# d2
r = result[3]["scores"][0]
self.assertEqual(r, 100.)
# t
r = result[4]["scores"][0]
self.assertEqual(r, (10 / 13) * 100)
# c3
r = result[5]["scores"][0]
self.assertEqual(r, 100.)
# r1
r = result[6]["scores"][0]
self.assertEqual(r, (8 / 13) * 100)
# r2
r = result[7]["scores"][0]
self.assertEqual(r, (3 / 13) * 100)
# gr1
r = result[8]["scores"][0]
self.assertEqual(r, 25.0)