[pre-commit.ci] auto fixes from pre-commit.com hooks

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tests/data_connector/test_dataframe_connector.py

@@ -4,6 +4,7 @@
 
 from sdgx.data_connectors.dataframe_connector import DataFrameConnector
 
+
 @pytest.fixture
 def data_for_test():
     return pd.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})

tests/models/test_nlabelencoder.py

@@ -3,31 +3,37 @@
 import pandas as pd
 import pytest
 
-from sdgx.models.components.sdv_rdt.transformers.categorical import NormalizedLabelEncoder
+from sdgx.models.components.sdv_rdt.transformers.categorical import (
+    NormalizedLabelEncoder,
+)
 
-@pytest.fixture(scope='module')
+
+@pytest.fixture(scope="module")
 def data_test():
-    return pd.DataFrame({
-        'x': [str(i) for i in range(100)],
-        'y': [str(-i) for i in range(50)]*2,
-        'z': [str(i) for i in range(25)]*4
-    }, columns=['x', 'y', 'z'])
+    return pd.DataFrame(
+        {
+            "x": [str(i) for i in range(100)],
+            "y": [str(-i) for i in range(50)] * 2,
+            "z": [str(i) for i in range(25)] * 4,
+        },
+        columns=["x", "y", "z"],
+    )
+
 
-def test_encoder(
-    data_test: pd.DataFrame
-):
+def test_encoder(data_test: pd.DataFrame):
 
-    for col in ['x', 'y', 'z']:
+    for col in ["x", "y", "z"]:
         nlabel_encoder = NormalizedLabelEncoder()
         nlabel_encoder.fit(data_test, col)
         td = nlabel_encoder.transform(data_test.copy())
         rd = nlabel_encoder.reverse_transform(td.copy())
-        td.rename(columns={f'{col}.value': f'{col}'}, inplace=True)
+        td.rename(columns={f"{col}.value": f"{col}"}, inplace=True)
         assert (rd[col].sort_values().values == data_test[col].sort_values().values).any()
         assert (td[col] >= 0).any()
         assert (td[col] <= 1).any()
         assert td[col].shape == data_test[col].shape
         assert len(td[col].unique()) == len(data_test[col].unique())
 
+
 if __name__ == "__main__":
     pytest.main(["-vv", "-s", __file__])