def test_warning_tf(self):
test_root_path = os.path.dirname(
os.path.dirname(os.path.realpath(__file__)))
test_dir = os.path.join(test_root_path, 'data')
path = os.path.join(test_dir, 'csv/diamonds.csv')
data = dp.Data(path)
profile_options = dp.ProfilerOptions()
profile_options.structured_options.set({
"text.is_enabled": False,
"int.is_enabled": False,
"float.is_enabled": False,
"order.is_enabled": False,
"category.is_enabled": False,
"chi2_homogeneity.is_enabled": False,
"datetime.is_enabled": False
})
profile = dp.StructuredProfiler(data, options=profile_options)
results = profile.report()
columns = []
predictions = []
for i in range(len(results['data_stats'])):
columns.append(i)
predictions.append(results['data_stats'][i]['data_label'])
def test_null_list(self, *mocks):
data = [None, None, None]
profiler = dp.StructuredProfiler(data, options=self.options)
fig = graphs.plot_missing_values_matrix(profiler)
self.assertIsInstance(fig, plt.Figure)
self.assertEqual(1, len(fig.axes))
ax = fig.axes[0]
patches, labels = ax.get_legend_handles_labels()
self.assertEqual(['"None"'], labels)
expected_patch_values = [
{
"xy": (0.1, -0.5),
"width": 0.8,
"height": 3
},
]
for patch, expected in zip(patches, expected_patch_values):
np.testing.assert_almost_equal(expected["xy"], patch.xy)
self.assertEqual(expected["width"], patch.get_width())
self.assertEqual(expected["height"], patch.get_height())
xtick_labels = [xtick.get_text() for xtick in ax.get_xticklabels()]
self.assertListEqual(['"0"'], xtick_labels)
self.assertEqual("column name", ax.get_xlabel())
self.assertEqual("row index", ax.get_ylabel())
def test_warning_tf_run_dp_multiple_times(self):
test_root_path = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))
test_dir = os.path.join(test_root_path, "data")
path = os.path.join(test_dir, "csv/diamonds.csv")
for i in range(3):
print("running dp =============================", i)
data = dp.Data(path)
profile_options = dp.ProfilerOptions()
profile_options.structured_options.set(
{
"text.is_enabled": False,
"int.is_enabled": False,
"float.is_enabled": False,
"order.is_enabled": False,
"category.is_enabled": False,
"chi2_homogeneity.is_enabled": False,
"datetime.is_enabled": False,
}
)
profile = dp.StructuredProfiler(data, options=profile_options)
results = profile.report()
columns = []
predictions = []
for j in range(len(results["data_stats"])):
columns.append(j)
predictions.append(results["data_stats"][j]["data_label"])
def test_2_null_types_multicol(self, *mocks):
data = pd.DataFrame(
[
[None, "", 1.0, "1/2/2021"],
[3, None, 3.5, ""],
[1, None, 1.0, "2/5/2020"],
[None, 1, 10.0, "3/5/2020"],
],
columns=["integer", "str", "float", "datetime"],
dtype=object,
)
profiler = dp.StructuredProfiler(data, options=self.options)
fig = graphs.plot_missing_values_matrix(profiler)
self.assertIsInstance(fig, plt.Figure)
self.assertEqual(1, len(fig.axes))
ax = fig.axes[0]
patches, labels = ax.get_legend_handles_labels()
self.assertEqual(['"None"', '"None"', '""', '"None"', '""'], labels)
expected_patch_values = [
{
"xy": (0.1, -0.5),
"width": 0.8,
"height": 1
},
{
"xy": (0.1, 2.5),
"width": 0.8,
"height": 1
},
{
"xy": (1.1, -0.5),
"width": 0.8,
"height": 1
},
{
"xy": (1.1, 0.5),
"width": 0.8,
"height": 2
},
{
"xy": (3.1, 0.5),
"width": 0.8,
"height": 1
},
]
for patch, expected in zip(patches, expected_patch_values):
np.testing.assert_almost_equal(expected["xy"], patch.xy)
self.assertEqual(expected["width"], patch.get_width())
self.assertEqual(expected["height"], patch.get_height())
xtick_labels = [xtick.get_text() for xtick in ax.get_xticklabels()]
self.assertListEqual(['"integer"', '"str"', '"float"', '"datetime"'],
xtick_labels)
self.assertEqual("column name", ax.get_xlabel())
self.assertEqual("row index", ax.get_ylabel())
def test_no_data(self, *mocks):
profiler = dp.StructuredProfiler([], options=self.options)
with self.assertWarnsRegex(
UserWarning,
"There was no data in the profiles to plot "
"missing column values.",
):
graphs.plot_missing_values_matrix(profiler)
def test_empty_profiler(self, plot_col_mock, plt_mock):
with self.assertWarnsRegex(
Warning, "No plots were constructed"
" because no int or float columns "
"were found in columns"):
fig = graphs.plot_histograms(
dp.StructuredProfiler(data=None, options=self.options))
self.assertIsNone(fig)
def setUpClass(cls):
cls.data = pd.DataFrame(
[[1, 'a', 1.0, '1/2/2021'], [None, 'b', None, '1/2/2020'],
[3, 'c', 3.5, '1/2/2022'], [4, 'd', 4.5, '1/2/2023'],
[5, 'e', 6.0, '5/2/2020'], [None, 'f', None, '1/5/2020'],
[1, 'g', 1.0, '2/5/2020'], [None, 1, 10.0, '3/5/2020']],
columns=['int', 'str', 'float', 'datetime'])
cls.options = dp.ProfilerOptions()
cls.options.set({"data_labeler.is_enabled": False})
cls.options.set({"multiprocess.is_enabled": False})
cls.profiler = dp.StructuredProfiler(cls.data, options=cls.options)
def test_2_null_types_multicol(self, *mocks):
data = pd.DataFrame(
[[None, '', 1.0, '1/2/2021'], [3, None, 3.5, ''],
[1, None, 1.0, '2/5/2020'], [None, 1, 10.0, '3/5/2020']],
columns=['integer', 'str', 'float', 'datetime'],
dtype=object)
profiler = dp.StructuredProfiler(data, options=self.options)
fig = graphs.plot_missing_values_matrix(profiler)
self.assertIsInstance(fig, plt.Figure)
self.assertEqual(1, len(fig.axes))
ax = fig.axes[0]
patches, labels = ax.get_legend_handles_labels()
self.assertEqual(['"None"', '"None"', '""', '"None"', '""'], labels)
expected_patch_values = [
{
'xy': (0.1, -0.5),
'width': 0.8,
'height': 1
},
{
'xy': (0.1, 2.5),
'width': 0.8,
'height': 1
},
{
'xy': (1.1, -0.5),
'width': 0.8,
'height': 1
},
{
'xy': (1.1, 0.5),
'width': 0.8,
'height': 2
},
{
'xy': (3.1, 0.5),
'width': 0.8,
'height': 1
},
]
for patch, expected in zip(patches, expected_patch_values):
np.testing.assert_almost_equal(expected['xy'], patch.xy)
self.assertEqual(expected['width'], patch.get_width())
self.assertEqual(expected['height'], patch.get_height())
xtick_labels = [xtick.get_text() for xtick in ax.get_xticklabels()]
self.assertListEqual(['"integer"', '"str"', '"float"', '"datetime"'],
xtick_labels)
self.assertEqual('column name', ax.get_xlabel())
self.assertEqual('row index', ax.get_ylabel())
def test_warning_tf_multiple_dp_with_update(self):
test_root_path = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))
test_dir = os.path.join(test_root_path, "data")
path = os.path.join(test_dir, "csv/diamonds.csv")
data = dp.Data(path)
profile_options = dp.ProfilerOptions()
profile_options.structured_options.set(
{
"text.is_enabled": False,
"int.is_enabled": False,
"float.is_enabled": False,
"order.is_enabled": False,
"category.is_enabled": False,
"datetime.is_enabled": False,
"chi2_homogeneity.is_enabled": False,
"correlation.is_enabled": False,
}
)
print("running dp1")
profile1 = dp.StructuredProfiler(data, options=profile_options)
data = dp.Data(path)
profile_options = dp.ProfilerOptions()
profile_options.structured_options.set(
{
"text.is_enabled": False,
"int.is_enabled": False,
"float.is_enabled": False,
"order.is_enabled": False,
"category.is_enabled": False,
"datetime.is_enabled": False,
"chi2_homogeneity.is_enabled": False,
"correlation.is_enabled": False,
}
)
print("running dp2")
profile2 = dp.StructuredProfiler(data, options=profile_options)
profile1.update_profile(data)
def test_warning_tf_run_dp_merge(self):
test_root_path = os.path.dirname(
os.path.dirname(os.path.realpath(__file__)))
test_dir = os.path.join(test_root_path, 'data')
path = os.path.join(test_dir, 'csv/diamonds.csv')
data = dp.Data(path)
profile_options = dp.ProfilerOptions()
profile_options.structured_options.set({
"text.is_enabled": False,
"int.is_enabled": False,
"float.is_enabled": False,
"order.is_enabled": False,
"category.is_enabled": False,
"datetime.is_enabled": False,
"chi2_homogeneity.is_enabled": False,
"correlation.is_enabled": False
})
print('running dp1')
profile1 = dp.StructuredProfiler(data, options=profile_options)
data = dp.Data(path)
profile_options = dp.ProfilerOptions()
profile_options.structured_options.set({
"text.is_enabled": False,
"int.is_enabled": False,
"float.is_enabled": False,
"order.is_enabled": False,
"category.is_enabled": False,
"datetime.is_enabled": False,
"chi2_homogeneity.is_enabled": False,
"correlation.is_enabled": False
})
print('running dp2')
profile2 = dp.StructuredProfiler(data, options=profile_options)
profile = profile1 + profile2
def setUpClass(cls):
cls.data = pd.DataFrame(
[
[1, "a", 1.0, "1/2/2021"],
[None, "b", None, "1/2/2020"],
[3, "c", 3.5, "1/2/2022"],
[4, "d", 4.5, "1/2/2023"],
[5, "e", 6.0, "5/2/2020"],
[None, "f", None, "1/5/2020"],
[1, "g", 1.0, "2/5/2020"],
[None, 1, 10.0, "3/5/2020"],
],
columns=["int", "str", "float", "datetime"],
)
cls.options = dp.ProfilerOptions()
cls.options.set({"data_labeler.is_enabled": False})
cls.options.set({"multiprocess.is_enabled": False})
cls.profiler = dp.StructuredProfiler(cls.data, options=cls.options)
