def test_is_valid_strict_true(self):
"""Test the ``GreaterThan.is_valid`` method with strict True.
If strict is True, equal values should count as invalid.
Input:
- Table with a strictly valid row, a strictly invalid row and
a row that has the same value for both high and low.
Output:
- True should be returned for the strictly valid row and False
for the other two.
"""
# Setup
instance = GreaterThan(low='a', high='b', strict=False)
# Run
table_data = pd.DataFrame({
'a': [1, 2, 3],
'b': [4, 2, 2],
'c': [7, 8, 9]
})
out = instance.is_valid(table_data)
# Assert
expected_out = pd.Series([True, True, False])
pd.testing.assert_series_equal(expected_out, out)
def test_is_valid_false_not_strict(self):
"""Test the ``GreaterThan.is_valid`` method when the column values are not valid
and the comparison is not strict.
If the columns do not satisfy the costraint, result is a series of ``False`` values.
Input:
- Table data, where the values of the ``low`` column are higher
than the values of the ``high`` column (pandas.DataFrame)
Output:
- Series of ``True`` values (pandas.Series)
"""
# Setup
instance = GreaterThan(low='a', high='b')
# Run
table_data = pd.DataFrame({
'a': [1, 2, 3],
'b': [0, 1, 2],
'c': [7, 8, 9]
})
out = instance.is_valid(table_data)
# Assert
expected_out = pd.Series([False, False, False])
pd.testing.assert_series_equal(expected_out, out)