Added test case for zero divsion for column_scaler in test_column_sca…

…ler.py

tests/test_column_scaler.py

@@ -9,6 +9,11 @@ def one_column_df():
     """Return DataFrame with one column of numeric values. Used for testing."""
     return pd.DataFrame({"price": [25, 50, 75]})
 
+@pytest.fixture
+def one_column_df_float():
+    """Return DataFrame with one column of floating values. Used for testing."""
+    return pd.DataFrame({"price": [25.0, 50.0, 75.0]})
+
 @pytest.fixture
 def single_val_df():
     """Return DataFrame with one column with single repeated value. Used for testing."""
@@ -26,11 +31,17 @@ def non_numeric_df():
 
 # Expected use case tests
 def test_minmax_scaling_default(one_column_df):
-    """Test min-max scaling with default new_min=0 and new_max=1."""
+    """Test min-max scaling with default new_min=0 and new_max=1. Use float values."""
     scaled_df = column_scaler(one_column_df, column="price", method="minmax")
     expected = [0.0, 0.5, 1.0]
     assert scaled_df["price"].tolist() == expected
 
+def test_minmax_scaling_default_float(one_column_df_float):
+    """Test min-max scaling with default new_min=0 and new_max=1."""
+    scaled_df = column_scaler(one_column_df_float, column="price", method="minmax")
+    expected = [0.0, 0.5, 1.0]
+    assert scaled_df["price"].tolist() == expected
+
 def test_minmax_scaling_custom(one_column_df):
     """Test min-max scaling with custom new_min=10 and new_max=20."""
     scaled_df = column_scaler(one_column_df, column="price", method="minmax", new_min=10, new_max=20)
@@ -58,6 +69,15 @@ def test_single_value_column_minmax(single_val_df):
     expected = [15.0, 15.0, 15.0]
     assert scaled_df["price"].tolist() == expected
 
+def test_single_value_column_standard(single_val_df):
+    """Test standard scaling with column with single repeated values to prevent division by zero."""
+    with pytest.warns(UserWarning, 
+                      match="Standard deviation is zero"):
+        scaled_df = column_scaler(single_val_df, column="price", method="standard")
+
+    expected = [0, 0, 0]
+    assert scaled_df["price"].tolist() == expected
+
 def test_empty_dataframe(empty_df):
     """Test scaling on empty DataFrame."""
     with pytest.warns(UserWarning, match="Empty DataFrame detected"):
@@ -73,6 +93,7 @@ def test_column_with_nan():
     expected = [0.0, np.nan, 1.0]
     assert np.allclose(scaled_df["price"], expected, equal_nan=True)
 
+
 # Erroneous case tests
 
 def test_non_numeric_column(non_numeric_df):