Feature / Update Python validation to support optional id in update data

src/power_grid_model/_utils.py

@@ -539,9 +539,28 @@ def is_columnar(component_data: ComponentData) -> bool:
     return not isinstance(component_data, np.ndarray)
 
 
-def is_nan_or_equivalent(array):
+def is_nan_or_default(x: np.ndarray) -> np.ndarray:
+    """
+    Check if elements in the array are NaN or equal to the min of its dtype.
+
+    Args:
+        x: A NumPy array to check.
+
+    Returns:
+        A boolean NumPy array where each element is True if the corresponding element in x is NaN
+        or min of its dtype, and False otherwise.
+    """
+    if x.dtype == np.float64:
+        return np.isnan(x)
+    if x.dtype in (np.int32, np.int8):
+        return x == np.iinfo(x.dtype).min
+    raise TypeError(f"Unsupported data type: {x.dtype}")
+
+
+def is_nan_or_equivalent(array) -> bool:
     """
     Check if the array contains only nan values or equivalent nan values for specific data types.
+    This is the aggregrated version of `is_nan_or_default` for the whole array.
 
     Args:
         array: The array to check.
@@ -751,25 +770,6 @@ def get_dataset_type(data: Dataset) -> DatasetType:
     return next(iter(candidates))
 
 
-def is_nan_or_default(x: np.ndarray) -> np.ndarray:
-    """
-    Check if elements in the array are NaN or equal to the min of its dtype.
-
-    Args:
-        x: A NumPy array to check.
-
-    Returns:
-        A boolean NumPy array where each element is True if the corresponding element in x is NaN
-        or min of its dtype, and False otherwise.
-    """
-    if x.dtype == np.float64:
-        return np.isnan(x)
-    elif x.dtype in (np.int32, np.int8):
-        return x == np.iinfo(x.dtype).min
-    else:
-        raise TypeError(f"Unsupported data type: {x.dtype}")
-
-
 def get_comp_batch_size(comp_data: dict) -> int:
     """
     Get the batch size of the component update data.