def test_leading_trailing_whitespace_column_name(self):
col1 = CategoricalMetadataColumn(
pd.Series(['foo', ' bar ', 'baz'],
name=' col ',
index=pd.Index(['a', 'b', 'c'], name='id')))
col2 = CategoricalMetadataColumn(
pd.Series(['foo', ' bar ', 'baz'],
name='col',
index=pd.Index(['a', 'b', 'c'], name='id')))
self.assertEqual(col1, col2)
def test_leading_trailing_whitespace_value(self):
with self.assertRaisesRegex(
ValueError, "CategoricalMetadataColumn.*leading or trailing "
"whitespace characters.*Column 'col1'.*' bar '"):
CategoricalMetadataColumn(
pd.Series(['foo', ' bar ', 'baz'],
name='col1',
index=pd.Index(['a', 'b', 'c'], name='id')))
def test_empty_str_value(self):
with self.assertRaisesRegex(
ValueError, "CategoricalMetadataColumn.*empty strings.*"
"column 'col1'"):
CategoricalMetadataColumn(
pd.Series(['foo', '', 'bar'],
name='col1',
index=pd.Index(['a', 'b', 'c'], name='id')))
def test_unsupported_type_value(self):
with self.assertRaisesRegex(
TypeError, "CategoricalMetadataColumn.*strings or missing "
r"values.*42\.5.*float.*'col1'"):
CategoricalMetadataColumn(
pd.Series(['foo', 'bar', 42.5],
name='col1',
index=pd.Index(['a', 'b', 'c'], name='id')))
def test_unsupported_dtype(self):
with self.assertRaisesRegex(
TypeError, "CategoricalMetadataColumn 'col1' does not support"
".*Series.*dtype.*float64"):
CategoricalMetadataColumn(
pd.Series([42.5, 42.6, 42.7],
name='col1',
index=pd.Index(['a', 'b', 'c'], name='id')))
def test_wrong_obj(self):
with self.assertRaisesRegex(
TypeError, 'NumericMetadataColumn constructor.*pandas.Series'):
NumericMetadataColumn(pd.DataFrame([[1, 2, 3]]))
with self.assertRaisesRegex(
TypeError,
'CategoricalMetadataColumn constructor.*pandas.Series'):
CategoricalMetadataColumn({})
def test_all_missing_data(self):
mdc = CategoricalMetadataColumn(
pd.Series(np.array([np.nan, np.nan, np.nan], dtype=object),
name='col1',
index=pd.Index(['a', 'b', 'c'], name='id')))
obs = mdc.to_series()
exp = pd.Series(np.array([np.nan, np.nan, np.nan], dtype=object),
name='col1',
index=pd.Index(['a', 'b', 'c'], name='id'))
pdt.assert_series_equal(obs, exp)
self.assertEqual(obs.dtype, object)
def test_numeric_strings_preserved_as_strings(self):
series = pd.Series(['1', np.nan, '2.5', '3.0'],
name='my column',
index=pd.Index(['a', 'b', 'c', 'd'], name='id'))
mdc = CategoricalMetadataColumn(series)
self.assertEqual(mdc.id_count, 4)
self.assertEqual(mdc.id_header, 'id')
self.assertEqual(mdc.ids, ('a', 'b', 'c', 'd'))
self.assertEqual(mdc.name, 'my column')
obs_series = mdc.to_series()
pdt.assert_series_equal(obs_series, series)
self.assertEqual(obs_series.dtype, object)
def test_supported_dtype(self):
series = pd.Series(['foo', np.nan, 'bar', 'foo'],
name='my column',
index=pd.Index(['a', 'b', 'c', 'd'], name='id'))
mdc = CategoricalMetadataColumn(series)
self.assertEqual(mdc.id_count, 4)
self.assertEqual(mdc.id_header, 'id')
self.assertEqual(mdc.ids, ('a', 'b', 'c', 'd'))
self.assertEqual(mdc.name, 'my column')
obs_series = mdc.to_series()
pdt.assert_series_equal(obs_series, series)
self.assertEqual(obs_series.dtype, object)
def test_type_mismatch(self):
dummy = DummyMetadataColumn(
pd.Series([1.0, 2.0, 3.0],
name='col1',
index=pd.Index(['id1', 'id2', 'id3'], name='id')))
numeric = NumericMetadataColumn(
pd.Series([1.0, 2.0, 3.0],
name='col1',
index=pd.Index(['id1', 'id2', 'id3'], name='id')))
categorical = CategoricalMetadataColumn(
pd.Series(['a', 'b', 'c'],
name='col1',
index=pd.Index(['id1', 'id2', 'id3'], name='id')))
self.assertReallyNotEqual(dummy, numeric)
self.assertReallyNotEqual(dummy, categorical)
def test_missing_data_normalized(self):
# Different missing data representations should be normalized to np.nan
mdc = CategoricalMetadataColumn(
pd.Series([np.nan, 'foo', float('nan'), None],
name='col1',
index=pd.Index(['a', 'b', 'c', 'd'], name='id')))
obs = mdc.to_series()
exp = pd.Series([np.nan, 'foo', np.nan, np.nan],
name='col1',
index=pd.Index(['a', 'b', 'c', 'd'], name='id'))
pdt.assert_series_equal(obs, exp)
self.assertEqual(obs.dtype, object)
self.assertTrue(np.isnan(obs['a']))
self.assertTrue(np.isnan(obs['c']))
self.assertTrue(np.isnan(obs['d']))
def test_categorical_metadata_column(self):
mdc = CategoricalMetadataColumn(
pd.Series(['foo', 'bar', '42.50'],
name='categorical-column',
index=pd.Index(['id1', 'id2', 'id3'], name='id')))
mdc.save(self.filepath)
with open(self.filepath, 'r') as fh:
obs = fh.read()
exp = ("id\tcategorical-column\n"
"#q2:types\tcategorical\n"
"id1\tfoo\n"
"id2\tbar\n"
"id3\t42.50\n")
self.assertEqual(obs, exp)