Add default values using Pandas assign in design_matrix

src/ert/config/design_matrix.py

@@ -112,12 +112,10 @@ def read_design_matrix(
             error_msg = "\n".join(error_list)
             raise ValueError(f"Design matrix is not valid, error:\n{error_msg}")
 
-        defaults = DesignMatrix._read_defaultssheet(
-            self.xls_filename, self.default_sheet
+        defaults_to_use = DesignMatrix._read_defaultssheet(
+            self.xls_filename, self.default_sheet, design_matrix_df.columns.to_list()
         )
-        for k, v in defaults.items():
-            if k not in design_matrix_df.columns:
-                design_matrix_df[k] = v
+        design_matrix_df = design_matrix_df.assign(**defaults_to_use)
 
         parameter_configuration: Dict[str, ParameterConfig] = {}
         transform_function_definitions: List[TransformFunctionDefinition] = []
@@ -200,11 +198,14 @@ def _validate_design_matrix(design_matrix: pd.DataFrame) -> List[str]:
 
     @staticmethod
     def _read_defaultssheet(
-        xls_filename: Union[Path, str], defaults_sheetname: str
+        xls_filename: Union[Path, str],
+        defaults_sheetname: str,
+        existing_parameters: List[str],
     ) -> Dict[str, Union[str, float]]:
         """
         Construct a dict of keys and values to be used as defaults from the
-        first two columns in a spreadsheet.
+        first two columns in a spreadsheet. Only returns the keys that are
+        different from the exisiting parameters.
 
         Returns a dict of default values
 
@@ -230,7 +231,11 @@ def _read_defaultssheet(
         if not default_df[0].is_unique:
             raise ValueError("Default sheet contains duplicate parameter names")
 
-        return {row[0]: convert_to_numeric(row[1]) for _, row in default_df.iterrows()}
+        return {
+            row[0]: convert_to_numeric(row[1])
+            for _, row in default_df.iterrows()
+            if row[0] not in existing_parameters
+        }
 
 
 def convert_to_numeric(x: str) -> Union[str, float]:

tests/ert/unit_tests/sensitivity_analysis/test_design_matrix.py

@@ -183,3 +183,30 @@ def test_reading_default_sheet_validation(tmp_path, data, error_msg):
     design_matrix = DesignMatrix(design_path, "DesignSheet01", "DefaultValues")
     with pytest.raises(ValueError, match=error_msg):
         design_matrix.read_design_matrix()
+
+
+def test_default_values_used(tmp_path):
+    design_path = tmp_path / "design_matrix.xlsx"
+    design_matrix_df = pd.DataFrame(
+        {
+            "REAL": [0, 1, 2, 3],
+            "a": [1, 2, 3, 4],
+            "b": [0, 2, 0, 1],
+            "c": ["low", "high", "medium", "low"],
+        }
+    )
+    default_sheet_df = pd.DataFrame([["one", 1], ["b", 4], ["d", "case_name"]])
+    with pd.ExcelWriter(design_path) as xl_write:
+        design_matrix_df.to_excel(xl_write, index=False, sheet_name="DesignSheet01")
+        default_sheet_df.to_excel(
+            xl_write, index=False, sheet_name="DefaultValues", header=False
+        )
+    design_matrix = DesignMatrix(design_path, "DesignSheet01", "DefaultValues")
+    design_matrix.read_design_matrix()
+    df = design_matrix.design_matrix_df
+    np.testing.assert_equal(df[DESIGN_MATRIX_GROUP, "one"], np.array([1, 1, 1, 1]))
+    np.testing.assert_equal(df[DESIGN_MATRIX_GROUP, "b"], np.array([0, 2, 0, 1]))
+    np.testing.assert_equal(
+        df[DESIGN_MATRIX_GROUP, "d"],
+        np.array(["case_name", "case_name", "case_name", "case_name"]),
+    )