def test1(self):
df = df_iris.copy()
out_df = sort(df,
input_cols=['species', 'petal_length'],
is_asc=['desc', 'asc'])['out_table']
print(df)
print(out_df)
def test_validation(self):
df = get_iris()
with self.assertRaises(BrighticsFunctionException) as bfe:
out_df = sort(df, input_cols=[], is_asc=['desc'])['out_table']
test_errors = bfe.exception.errors
self.assertTrue({'0033': ['input_cols']} in test_errors)
def test_default(self):
df = load_iris()
out_df = sort(df,
input_cols=['species', 'petal_length'],
is_asc=['desc', 'asc'])['out_table']
print(df)
print(out_df)
def test_example_sort(self):
in_table = self.example_table
expected_table = pd.DataFrame({'A': [3, 2, 1], 'B': ['c', 'b', 'a']})
sort_out = sort(table=in_table, input_cols=['B'],
is_asc=False)['out_table'].reset_index(drop=True)
print(sort_out)
pd.testing.assert_frame_equal(expected_table, sort_out)
def test_second(self):
st = sort(self.testdata,
input_cols=['petal_length', 'species'],
is_asc=False)
DF2 = st['out_table'].values
# print(DF2)
np.testing.assert_array_equal(DF2[0][0:4], [7.7, 2.6, 6.9, 2.3])
np.testing.assert_array_equal(DF2[1][0:4], [7.7, 3.8, 6.7, 2.2])
np.testing.assert_array_equal(DF2[2][0:4], [7.7, 2.8, 6.7, 2.0])
np.testing.assert_array_equal(DF2[3][0:4], [7.6, 3.0, 6.6, 2.1])
np.testing.assert_array_equal(DF2[4][0:4], [7.9, 3.8, 6.4, 2.0])
def test_first(self):
st = sort(self.testdata,
input_cols=['sepal_length', 'sepal_width'],
is_asc=True)
DF1 = st['out_table'].values
# print(DF1)
np.testing.assert_array_equal(DF1[0][0:4], [4.3, 3.0, 1.1, 0.1])
np.testing.assert_array_equal(DF1[1][0:4], [4.4, 2.9, 1.4, 0.2])
np.testing.assert_array_equal(DF1[2][0:4], [4.4, 3.0, 1.3, 0.2])
np.testing.assert_array_equal(DF1[3][0:4], [4.4, 3.2, 1.3, 0.2])
np.testing.assert_array_equal(DF1[4][0:4], [4.5, 2.3, 1.3, 0.3])
def test2(self):
df = get_iris()
out_df = sort(df, input_cols=[], is_asc=['desc'])['out_table']
print(df)
print(out_df)
