diff --git a/docs/examples/Asymmetric Calculation Example.ipynb b/docs/examples/Asymmetric Calculation Example.ipynb
index 54724adde..0b3175711 100644
--- a/docs/examples/Asymmetric Calculation Example.ipynb	
+++ b/docs/examples/Asymmetric Calculation Example.ipynb	
@@ -815,7 +815,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "venv",
    "language": "python",
    "name": "python3"
   },
@@ -829,12 +829,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.2"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "f2534ebf7a1a13ecc51c3a04ce741b49cf1feb97b5ca55170ed7b2036b4058c0"
-   }
+   "version": "3.13.0"
   }
  },
  "nbformat": 4,
diff --git a/docs/examples/Make Test Dataset.ipynb b/docs/examples/Make Test Dataset.ipynb
index 54e15205a..1258600be 100644
--- a/docs/examples/Make Test Dataset.ipynb	
+++ b/docs/examples/Make Test Dataset.ipynb	
@@ -431,7 +431,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "venv",
    "language": "python",
    "name": "python3"
   },
@@ -445,12 +445,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.2"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "39f035c5e58f53e973c1999fdd4393d6db2dd3f04fe05f524988f90f4bbdb576"
-   }
+   "version": "3.13.0"
   }
  },
  "nbformat": 4,
diff --git a/docs/examples/Power Flow Example.ipynb b/docs/examples/Power Flow Example.ipynb
index c084ac3a6..251332de1 100644
--- a/docs/examples/Power Flow Example.ipynb	
+++ b/docs/examples/Power Flow Example.ipynb	
@@ -51,9 +51,9 @@
     "    # suppress warning about pyarrow as future required dependency\n",
     "    import pandas as pd\n",
     "\n",
-    "from power_grid_model import LoadGenType, ComponentType, DatasetType\n",
+    "from power_grid_model import LoadGenType, ComponentType, DatasetType, ComponentAttributeFilterOptions\n",
     "from power_grid_model import PowerGridModel, CalculationMethod, CalculationType\n",
-    "from power_grid_model import initialize_array"
+    "from power_grid_model import initialize_array, power_grid_meta_data"
    ]
   },
   {
@@ -122,6 +122,42 @@
     "}"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "102d862d",
+   "metadata": {},
+   "source": [
+    "It is also possible to specify a component input data in a columnar data format.\n",
+    "\n",
+    "A columnar data format better integrates with most databases. In addition, it may bring memory and, in some cases, even computational performance improvements, because unused attribute columns can be omitted.\n",
+    "\n",
+    "Make sure to provide the correct `dtype` to the numpy arrays, exposed for each dataset type, component and attribute via the `power_grid_meta_data` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "1034f6f3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "source_attribute_dtypes = power_grid_meta_data[DatasetType.input][ComponentType.source].dtype\n",
+    "source_columns = {\n",
+    "    \"id\": np.array([10], dtype=source_attribute_dtypes[\"id\"]),\n",
+    "    \"node\": np.array([1], dtype=source_attribute_dtypes[\"node\"]),\n",
+    "    \"status\": np.array([1], dtype=source_attribute_dtypes[\"status\"]),\n",
+    "    \"u_ref\": np.array([1.0], dtype=source_attribute_dtypes[\"u_ref\"]),\n",
+    "    # We're not creating columns for u_ref_angle, sk, ... Instead, the default values are used. This saves us memory.\n",
+    "}\n",
+    "\n",
+    "input_data = {\n",
+    "    ComponentType.node: node,\n",
+    "    ComponentType.line: line,\n",
+    "    ComponentType.sym_load: sym_load,\n",
+    "    ComponentType.source: source_columns,\n",
+    "}"
+   ]
+  },
   {
    "attachments": {},
    "cell_type": "markdown",
@@ -133,7 +169,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "id": "f0c8c3e8",
    "metadata": {},
    "outputs": [
@@ -174,7 +210,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "id": "40509eaf",
    "metadata": {},
    "outputs": [],
@@ -207,7 +243,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "id": "7ef134e9",
    "metadata": {},
    "outputs": [],
@@ -244,7 +280,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "id": "44c2de63",
    "metadata": {},
    "outputs": [],
@@ -267,7 +303,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "id": "a581a36e",
    "metadata": {},
    "outputs": [
@@ -306,7 +342,7 @@
    "id": "24893218",
    "metadata": {},
    "source": [
-    "### Select component types in the result dataset\n",
+    "### Select components / attributes and format of result dataset\n",
     "\n",
     "By default `power-grid-model` calculates the result attributes for all components. If you do not need all the component types in your results, for example if you are only interested in `node`, you can specify the list of the component types you want in the result dataset in the argument `output_component_types`.\n",
     "\n",
@@ -315,7 +351,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "id": "30fdf18a",
    "metadata": {},
    "outputs": [
@@ -324,7 +360,7 @@
      "output_type": "stream",
      "text": [
       "List of component types in result dataset\n",
-      "['node']\n",
+      "[<ComponentType.node: 'node'>]\n",
       "------node result------\n",
       "   id  energized      u_pu             u   u_angle             p             q\n",
       "0   1          1  0.998988  10489.375043 -0.003039  3.121451e+07  6.991358e+06\n",
@@ -348,6 +384,155 @@
     "print(pd.DataFrame(output_data[ComponentType.node]))"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "d129b8de",
+   "metadata": {},
+   "source": [
+    "You can also request the output as a dictionary of attribute and their values columns for each component type."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "95ee7a35",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "List of component types in result dataset\n",
+      "[<ComponentType.line: 'line'>]\n",
+      "List of attribute types in line result\n",
+      "['id', 'p_from']\n",
+      "------line result------\n",
+      "{'id': array([3, 5, 8], dtype=int32), 'p_from': array([17360100.20222374, -3365613.74450156, 13854413.52498137])}\n"
+     ]
+    }
+   ],
+   "source": [
+    "output_data = model.calculate_power_flow(\n",
+    "    symmetric=True,\n",
+    "    error_tolerance=1e-8,\n",
+    "    max_iterations=20,\n",
+    "    calculation_method=CalculationMethod.newton_raphson,\n",
+    "    output_component_types={\n",
+    "        ComponentType.line: [\"id\", \"p_from\"],  # line output columns id and p_from\n",
+    "    },\n",
+    ")\n",
+    "print(\"List of component types in result dataset\")\n",
+    "print(list(output_data))\n",
+    "print(\"List of attribute types in line result\")\n",
+    "print(list(output_data[ComponentType.line]))\n",
+    "print(\"------line result------\")\n",
+    "print(output_data[ComponentType.line])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e564346b",
+   "metadata": {},
+   "source": [
+    "You can also mix output types between components. In this example:\n",
+    "\n",
+    "* `None` requests row-based data\n",
+    "* a list of attribute names requests columns for those attributes (as before)\n",
+    "* `ComponentAttributeFilterOptions.everything` requests columns for all supported attributes of a component."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "f52dbdd7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "List of component types in result dataset and associated data types\n",
+      "{'ComponentType.node': <class 'numpy.ndarray'>, 'ComponentType.line': <class 'dict'>, 'ComponentType.sym_load': <class 'dict'>}\n",
+      "------node result------\n",
+      "('id', 'energized', 'u_pu', 'u', 'u_angle', 'p', 'q')\n",
+      "------line result attributes------\n",
+      "['id', 'p_from']\n",
+      "------sym_load result attributes------\n",
+      "['id', 'energized', 'p', 'q', 'i', 's', 'pf']\n"
+     ]
+    }
+   ],
+   "source": [
+    "output_data = model.calculate_power_flow(\n",
+    "    symmetric=True,\n",
+    "    error_tolerance=1e-8,\n",
+    "    max_iterations=20,\n",
+    "    calculation_method=CalculationMethod.newton_raphson,\n",
+    "    output_component_types={\n",
+    "        ComponentType.node: None,  # node output as row-based\n",
+    "        ComponentType.line: [\"id\", \"p_from\"],  # line output columns id and p_from\n",
+    "        ComponentType.sym_load: ComponentAttributeFilterOptions.everything,  # all sym_load attributes as columns\n",
+    "    },\n",
+    ")\n",
+    "\n",
+    "print(\"List of component types in result dataset and associated data types\")\n",
+    "print({str(component_type): type(component_data) for component_type, component_data in output_data.items()})\n",
+    "print(\"------node result------\")\n",
+    "print(output_data[ComponentType.node].dtype.names)\n",
+    "print(\"------line result attributes------\")\n",
+    "print(list(output_data[ComponentType.line].keys()))\n",
+    "print(\"------sym_load result attributes------\")\n",
+    "print(list(output_data[ComponentType.sym_load].keys()))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "14a34c07",
+   "metadata": {},
+   "source": [
+    "Finally, it is possible to request columnar data for all attributes using the `ComponentAttributeFilterOptions.everything` as a shorthand."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "e89882f5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "List of component types in result dataset and associated data types\n",
+      "{'ComponentType.node': <class 'dict'>, 'ComponentType.line': <class 'dict'>, 'ComponentType.sym_load': <class 'dict'>, 'ComponentType.source': <class 'dict'>}\n",
+      "------node result------\n",
+      "['id', 'energized', 'u_pu', 'u', 'u_angle', 'p', 'q']\n",
+      "------line result attributes------\n",
+      "['id', 'energized', 'loading', 'p_from', 'q_from', 'i_from', 's_from', 'p_to', 'q_to', 'i_to', 's_to']\n",
+      "------sym_load result attributes------\n",
+      "['id', 'energized', 'p', 'q', 'i', 's', 'pf']\n"
+     ]
+    }
+   ],
+   "source": [
+    "output_data = model.calculate_power_flow(\n",
+    "    symmetric=True,\n",
+    "    error_tolerance=1e-8,\n",
+    "    max_iterations=20,\n",
+    "    calculation_method=CalculationMethod.newton_raphson,\n",
+    "    output_component_types=ComponentAttributeFilterOptions.everything,  # all attributes for all component types as columns\n",
+    ")\n",
+    "\n",
+    "print(\"List of component types in result dataset and associated data types\")\n",
+    "print({str(component_type): type(component_data) for component_type, component_data in output_data.items()})\n",
+    "print(\"------node result------\")\n",
+    "print(list(output_data[ComponentType.node].keys()))\n",
+    "print(\"------line result attributes------\")\n",
+    "print(list(output_data[ComponentType.line].keys()))\n",
+    "print(\"------sym_load result attributes------\")\n",
+    "print(list(output_data[ComponentType.sym_load].keys()))"
+   ]
+  },
   {
    "attachments": {},
    "cell_type": "markdown",
@@ -376,7 +561,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 13,
    "id": "ecbb8eeb",
    "metadata": {},
    "outputs": [
@@ -435,7 +620,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 14,
    "id": "1d035c65",
    "metadata": {},
    "outputs": [],
@@ -453,6 +638,31 @@
     "update_data = {ComponentType.sym_load: update_sym_load, ComponentType.line: update_line}"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "fcc57354",
+   "metadata": {},
+   "source": [
+    "Columnar data is also supported"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "aaf8fe64",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "line_update_dtype = power_grid_meta_data[DatasetType.update][ComponentType.line].dtype\n",
+    "columnar_update_line = {\n",
+    "    \"id\": np.array([3], dtype=line_update_dtype[\"id\"]),  # change line ID 3\n",
+    "    \"from_status\": np.array([0], dtype=line_update_dtype[\"from_status\"]),  # switch off at from side\n",
+    "}\n",
+    "# leave to-side swiching status the same, no need to specify\n",
+    "\n",
+    "update_data = {ComponentType.sym_load: update_sym_load, ComponentType.line: columnar_update_line}"
+   ]
+  },
   {
    "attachments": {},
    "cell_type": "markdown",
@@ -466,7 +676,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 16,
    "id": "63ea4cc3",
    "metadata": {},
    "outputs": [],
@@ -489,7 +699,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 17,
    "id": "188f6663",
    "metadata": {},
    "outputs": [],
@@ -511,7 +721,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 18,
    "id": "a93c1e16",
    "metadata": {},
    "outputs": [
@@ -600,7 +810,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 19,
    "id": "42d5cd8f",
    "metadata": {},
    "outputs": [],
@@ -634,7 +844,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 20,
    "id": "4e25006f",
    "metadata": {
     "scrolled": true
@@ -677,7 +887,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 21,
    "id": "afccf7a2",
    "metadata": {
     "scrolled": true
@@ -714,7 +924,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 22,
    "id": "9af1be38",
    "metadata": {
     "scrolled": true
@@ -757,7 +967,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 23,
    "id": "041368dc",
    "metadata": {},
    "outputs": [],
@@ -787,7 +997,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 24,
    "id": "34338ce3",
    "metadata": {},
    "outputs": [
@@ -819,7 +1029,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 25,
    "id": "04e56690",
    "metadata": {},
    "outputs": [],
@@ -851,7 +1061,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 26,
    "id": "0d5b94c2",
    "metadata": {},
    "outputs": [
@@ -934,7 +1144,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 27,
    "id": "b5f10bae",
    "metadata": {},
    "outputs": [
@@ -994,7 +1204,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 28,
    "id": "1a221507",
    "metadata": {},
    "outputs": [
@@ -1036,7 +1246,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 29,
    "id": "541af620",
    "metadata": {},
    "outputs": [
@@ -1084,7 +1294,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 30,
    "id": "20d8285c",
    "metadata": {},
    "outputs": [],
@@ -1095,7 +1305,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 31,
    "id": "b702eb15",
    "metadata": {},
    "outputs": [
@@ -1130,7 +1340,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 32,
    "id": "1ba71901",
    "metadata": {},
    "outputs": [
@@ -1139,16 +1349,16 @@
      "output_type": "stream",
      "text": [
       "Node data with invalid results\n",
-      "[[0.99940117 0.99268579 0.99452137]\n",
-      " [0.99934769 0.98622639 0.98935286]\n",
-      " [0.99928838 0.97965401 0.98409554]\n",
-      " [0.         0.         0.        ]\n",
-      " [0.99915138 0.96614948 0.97329879]\n",
-      " [0.99907317 0.95920586 0.96775071]\n",
-      " [0.9989881  0.95212621 0.96209647]\n",
-      " [0.         0.         0.        ]\n",
-      " [0.99879613 0.93753005 0.95044796]\n",
-      " [0.9986885  0.92999747 0.94444167]]\n",
+      "[[9.99401170e-001 9.92685785e-001 9.94521366e-001]\n",
+      " [9.99347687e-001 9.86226389e-001 9.89352855e-001]\n",
+      " [9.99288384e-001 9.79654011e-001 9.84095542e-001]\n",
+      " [0.00000000e+000 3.04369633e+101 3.41345331e+241]\n",
+      " [9.99151380e-001 9.66149483e-001 9.73298790e-001]\n",
+      " [9.99073166e-001 9.59205860e-001 9.67750710e-001]\n",
+      " [9.98988099e-001 9.52126208e-001 9.62096474e-001]\n",
+      " [1.66994188e-321 3.12878333e-312 9.75772002e+199]\n",
+      " [9.98796126e-001 9.37530046e-001 9.50447962e-001]\n",
+      " [9.98688504e-001 9.29997471e-001 9.44441670e-001]]\n",
       "Node data with only valid results\n",
       "[[0.99940117 0.99268579 0.99452137]\n",
       " [0.99934769 0.98622639 0.98935286]\n",
@@ -1165,7 +1375,6 @@
     "# we run the batch calculation with continue_on_batch_error=True,\n",
     "#    it will return the results with partially valid data\n",
     "\n",
-    "\n",
     "output_data = model.calculate_power_flow(update_data=time_series_mutation, continue_on_batch_error=True)\n",
     "\n",
     "# print node data for u_pu, note that the data is rubbish for scenario 3 and 7\n",
@@ -1191,7 +1400,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "venv",
    "language": "python",
    "name": "python3"
   },
@@ -1205,12 +1414,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.2"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "f2534ebf7a1a13ecc51c3a04ce741b49cf1feb97b5ca55170ed7b2036b4058c0"
-   }
+   "version": "3.13.0"
   }
  },
  "nbformat": 4,
diff --git a/docs/examples/Serialization Example.ipynb b/docs/examples/Serialization Example.ipynb
index 201324c0d..b718946da 100644
--- a/docs/examples/Serialization Example.ipynb	
+++ b/docs/examples/Serialization Example.ipynb	
@@ -30,7 +30,7 @@
     "    # suppress warning about pyarrow as future required dependency\n",
     "    from pandas import DataFrame\n",
     "\n",
-    "from power_grid_model import PowerGridModel, ComponentType\n",
+    "from power_grid_model import PowerGridModel, ComponentType, ComponentAttributeFilterOptions\n",
     "from power_grid_model.utils import json_deserialize, json_serialize"
    ]
   },
@@ -42,7 +42,9 @@
     "\n",
     "The data is in the `data/serialized-input.json` file.\n",
     "\n",
-    "### Load the JSON file"
+    "### Load the JSON file\n",
+    "\n",
+    "This is just for illustration purposes."
    ]
   },
   {
@@ -122,9 +124,19 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "components: dict_keys(['node', 'line', 'source', 'sym_load'])\n"
+      "components: [<ComponentType.node: 'node'>, <ComponentType.line: 'line'>, <ComponentType.source: 'source'>, <ComponentType.sym_load: 'sym_load'>]\n"
      ]
     },
+    {
+     "data": {
+      "text/plain": [
+       "array([(1, 10500.), (2, 10500.), (3, 10500.)],\n",
+       "      dtype={'names': ['id', 'u_rated'], 'formats': ['<i4', '<f8'], 'offsets': [0, 8], 'itemsize': 16, 'aligned': True})"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
     {
      "data": {
       "text/html": [
@@ -184,7 +196,8 @@
    "source": [
     "dataset = json_deserialize(data)\n",
     "\n",
-    "print(\"components:\", dataset.keys())\n",
+    "print(\"components:\", list(dataset.keys()))\n",
+    "display(dataset[ComponentType.node])\n",
     "display(DataFrame(dataset[ComponentType.node]))"
    ]
   },
@@ -326,9 +339,9 @@
       "      {\"id\": 5, \"energized\": 1, \"loading\": 0.1977047433812966, \"p_from\": 1242625.764367574, \"q_from\": -1613928.536947772, \"i_from\": 108.7376088597131, \"s_from\": 2036880.976553238, \"p_to\": -1019999.999999999, \"q_to\": -219999.9999999689, \"i_to\": 55.73199226981006, \"s_to\": 1043455.796859639}\n",
       "    ],\n",
       "    \"source\": [\n",
-      "      {\"id\": 15, \"energized\": 1, \"p\": -7838134.060668075, \"q\": -105359359.482604, \"i\": 5640.069564151436, \"s\": 105650513.3737531, \"pf\": -0.07418926619825886},\n",
-      "      {\"id\": 16, \"energized\": 1, \"p\": 10248883.05839188, \"q\": 102488830.5811948, \"i\": 5498.573991718331, \"s\": 102999999.989541, \"pf\": 0.09950371902361739},\n",
-      "      {\"id\": 17, \"energized\": 1, \"p\": -0.001808079734064212, \"q\": -0.01040918620434095, \"i\": 5.640069564151435e-07, \"s\": 0.01056505133737531, \"pf\": -0.1711378086415808}\n",
+      "      {\"id\": 15, \"energized\": 1, \"p\": -7836685.751732428, \"q\": -105348495.343833, \"i\": 5639.485452974508, \"s\": 105639571.7275539, \"pf\": -0.07418324046166491},\n",
+      "      {\"id\": 16, \"energized\": 1, \"p\": 10248883.05839198, \"q\": 102488830.5811954, \"i\": 5498.573991718361, \"s\": 102999999.9895415, \"pf\": 0.09950371902361781},\n",
+      "      {\"id\": 17, \"energized\": 1, \"p\": -0.001807829591278542, \"q\": -0.0104081191056169, \"i\": 5.639485452974507e-07, \"s\": 0.01056395717275539, \"pf\": -0.1711318553942043}\n",
       "    ],\n",
       "    \"sym_load\": [\n",
       "      {\"id\": 7, \"energized\": 1, \"p\": 1010000, \"q\": 210000, \"i\": 55.07135393955915, \"s\": 1031600.697944704, \"pf\": 0.979060989404389},\n",
@@ -367,7 +380,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{\"version\":\"1.0\",\"type\":\"sym_output\",\"is_batch\":false,\"attributes\":{\"node\":[\"id\",\"energized\",\"u_pu\",\"u\",\"u_angle\",\"p\",\"q\"],\"line\":[\"id\",\"energized\",\"loading\",\"p_from\",\"q_from\",\"i_from\",\"s_from\",\"p_to\",\"q_to\",\"i_to\",\"s_to\"],\"source\":[\"id\",\"energized\",\"p\",\"q\",\"i\",\"s\",\"pf\"],\"sym_load\":[\"id\",\"energized\",\"p\",\"q\",\"i\",\"s\",\"pf\"]},\"data\":{\"node\":[[1,1,1.030000000001025,10815.00000001077,-2.530316910142707e-14,2408997.839438867,-2863495.364674167],[2,1,1.029996969815606,10814.96818306386,-0.004397999804754745,-1009999.99999997,-210000.0000000655],[3,1,1.029483905569345,10809.58100847812,-0.006839956175380238,-1019999.999999999,-219999.9999999689]],\"line\":[[4,1,0.3995319091937107,2408997.839438867,-2863495.364674167,199.7659545968554,3742041.7279784,-2252625.764367544,1403928.536947823,141.6984332838951,2654305.591138465],[5,1,0.1977047433812966,1242625.764367574,-1613928.536947772,108.7376088597131,2036880.976553238,-1019999.999999999,-219999.9999999689,55.73199226981006,1043455.796859639]],\"source\":[[15,1,-7838134.060668075,-105359359.482604,5640.069564151436,105650513.3737531,-0.07418926619825886],[16,1,10248883.05839188,102488830.5811948,5498.573991718331,102999999.989541,0.09950371902361739],[17,1,-0.001808079734064212,-0.01040918620434095,5.640069564151435e-07,0.01056505133737531,-0.1711378086415808]],\"sym_load\":[[7,1,1010000,210000,55.07135393955915,1031600.697944704,0.979060989404389],[8,1,1020000,220000,55.73199226981046,1043455.796859647,0.9775210440823288]]}}\n"
+      "{\"version\":\"1.0\",\"type\":\"sym_output\",\"is_batch\":false,\"attributes\":{\"node\":[\"id\",\"energized\",\"u_pu\",\"u\",\"u_angle\",\"p\",\"q\"],\"line\":[\"id\",\"energized\",\"loading\",\"p_from\",\"q_from\",\"i_from\",\"s_from\",\"p_to\",\"q_to\",\"i_to\",\"s_to\"],\"source\":[\"id\",\"energized\",\"p\",\"q\",\"i\",\"s\",\"pf\"],\"sym_load\":[\"id\",\"energized\",\"p\",\"q\",\"i\",\"s\",\"pf\"]},\"data\":{\"node\":[[1,1,1.030000000001025,10815.00000001077,-2.530316910142707e-14,2408997.839438867,-2863495.364674167],[2,1,1.029996969815606,10814.96818306386,-0.004397999804754745,-1009999.99999997,-210000.0000000655],[3,1,1.029483905569345,10809.58100847812,-0.006839956175380238,-1019999.999999999,-219999.9999999689]],\"line\":[[4,1,0.3995319091937107,2408997.839438867,-2863495.364674167,199.7659545968554,3742041.7279784,-2252625.764367544,1403928.536947823,141.6984332838951,2654305.591138465],[5,1,0.1977047433812966,1242625.764367574,-1613928.536947772,108.7376088597131,2036880.976553238,-1019999.999999999,-219999.9999999689,55.73199226981006,1043455.796859639]],\"source\":[[15,1,-7836685.751732428,-105348495.343833,5639.485452974508,105639571.7275539,-0.07418324046166491],[16,1,10248883.05839198,102488830.5811954,5498.573991718361,102999999.9895415,0.09950371902361781],[17,1,-0.001807829591278542,-0.0104081191056169,5.639485452974507e-07,0.01056395717275539,-0.1711318553942043]],\"sym_load\":[[7,1,1010000,210000,55.07135393955915,1031600.697944704,0.979060989404389],[8,1,1020000,220000,55.73199226981046,1043455.796859647,0.9775210440823288]]}}\n"
      ]
     }
    ],
@@ -452,25 +465,25 @@
       "                    -219999.9999999689]],\n",
       "          'source': [[15,\n",
       "                      1,\n",
-      "                      -7838134.060668075,\n",
-      "                      -105359359.482604,\n",
-      "                      5640.069564151436,\n",
-      "                      105650513.3737531,\n",
-      "                      -0.07418926619825886],\n",
+      "                      -7836685.751732428,\n",
+      "                      -105348495.343833,\n",
+      "                      5639.485452974508,\n",
+      "                      105639571.7275539,\n",
+      "                      -0.07418324046166491],\n",
       "                     [16,\n",
       "                      1,\n",
-      "                      10248883.05839188,\n",
-      "                      102488830.5811948,\n",
-      "                      5498.573991718331,\n",
-      "                      102999999.989541,\n",
-      "                      0.09950371902361739],\n",
+      "                      10248883.05839198,\n",
+      "                      102488830.5811954,\n",
+      "                      5498.573991718361,\n",
+      "                      102999999.9895415,\n",
+      "                      0.0995037190236178],\n",
       "                     [17,\n",
       "                      1,\n",
-      "                      -0.001808079734064212,\n",
-      "                      -0.01040918620434095,\n",
-      "                      5.640069564151435e-07,\n",
-      "                      0.01056505133737531,\n",
-      "                      -0.1711378086415808]],\n",
+      "                      -0.001807829591278542,\n",
+      "                      -0.0104081191056169,\n",
+      "                      5.639485452974507e-07,\n",
+      "                      0.01056395717275539,\n",
+      "                      -0.1711318553942043]],\n",
       "          'sym_load': [[7,\n",
       "                        1,\n",
       "                        1010000,\n",
@@ -591,11 +604,147 @@
     "print(\"----Node result from compact data----\")\n",
     "print(DataFrame(output_data_compact[ComponentType.node]))"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Selective deserialization and dataset format\n",
+    "\n",
+    "To control the dataset returned by the deserialization functionality, you can use the `data_filter` argument.\n",
+    "\n",
+    "### Deserialization to columnar dataset\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "components: [<ComponentType.node: 'node'>, <ComponentType.line: 'line'>, <ComponentType.source: 'source'>, <ComponentType.sym_load: 'sym_load'>]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'id': array([1, 2, 3], dtype=int32),\n",
+       " 'u_rated': array([10500., 10500., 10500.])}"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dataset = json_deserialize(data, data_filter=ComponentAttributeFilterOptions.everything)\n",
+    "\n",
+    "print(\"components:\", list(dataset.keys()))\n",
+    "display(dataset[ComponentType.node])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Deserialized data format selection per component type\n",
+    "\n",
+    "To select specific components and data formats for the deserialized data, provide a dictionary of components and their desired output types to the `data_filter`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "components: [<ComponentType.node: 'node'>, <ComponentType.line: 'line'>, <ComponentType.source: 'source'>, <ComponentType.sym_load: 'sym_load'>]\n",
+      "node attributes: ['id', 'u_rated']\n",
+      "source attributes: ['id', 'node', 'status', 'u_ref', 'sk']\n",
+      "sym_load attributes: ['id', 'node', 'status', 'type', 'p_specified', 'q_specified']\n",
+      "line attributes: ['id', 'from_node', 'to_node', 'from_status', 'to_status', 'r1', 'x1', 'c1', 'tan1', 'i_n']\n"
+     ]
+    }
+   ],
+   "source": [
+    "dataset = json_deserialize(\n",
+    "    data,\n",
+    "    data_filter={\n",
+    "        ComponentType.node: None,  # nodes in a row-based data format\n",
+    "        ComponentType.source: [\"id\", \"node\", \"status\", \"u_ref\", \"sk\"],  # only specific attributes\n",
+    "        ComponentType.sym_load: ComponentAttributeFilterOptions.everything,  # all attributes as columns\n",
+    "        ComponentType.line: ComponentAttributeFilterOptions.relevant,  # only attributes that are not null/nan\n",
+    "    },\n",
+    ")\n",
+    "\n",
+    "print(\"components:\", list(dataset.keys()))\n",
+    "print(\"node attributes:\", list(dataset[ComponentType.node].dtype.names))\n",
+    "print(\"source attributes:\", list(dataset[ComponentType.source].keys()))\n",
+    "print(\"sym_load attributes:\", list(dataset[ComponentType.sym_load].keys()))\n",
+    "print(\"line attributes:\", list(dataset[ComponentType.line].keys()))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A columnar dataset can also be serialized again, as one would expect."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\n",
+      "  \"version\": \"1.0\",\n",
+      "  \"type\": \"sym_output\",\n",
+      "  \"is_batch\": false,\n",
+      "  \"attributes\": {},\n",
+      "  \"data\": {\n",
+      "    \"node\": [\n",
+      "      {\"id\": 1, \"energized\": 1, \"u_pu\": 1.030000000001025, \"u\": 10815.00000001077, \"u_angle\": -2.530316910142707e-14, \"p\": 2408997.839438867, \"q\": -2863495.364674167},\n",
+      "      {\"id\": 2, \"energized\": 1, \"u_pu\": 1.029996969815606, \"u\": 10814.96818306386, \"u_angle\": -0.004397999804754745, \"p\": -1009999.99999997, \"q\": -210000.0000000655},\n",
+      "      {\"id\": 3, \"energized\": 1, \"u_pu\": 1.029483905569345, \"u\": 10809.58100847812, \"u_angle\": -0.006839956175380238, \"p\": -1019999.999999999, \"q\": -219999.9999999689}\n",
+      "    ],\n",
+      "    \"line\": [\n",
+      "      {\"id\": 4, \"energized\": 1, \"loading\": 0.3995319091937107, \"p_from\": 2408997.839438867, \"q_from\": -2863495.364674167, \"i_from\": 199.7659545968554, \"s_from\": 3742041.7279784, \"p_to\": -2252625.764367544, \"q_to\": 1403928.536947823, \"i_to\": 141.6984332838951, \"s_to\": 2654305.591138465},\n",
+      "      {\"id\": 5, \"energized\": 1, \"loading\": 0.1977047433812966, \"p_from\": 1242625.764367574, \"q_from\": -1613928.536947772, \"i_from\": 108.7376088597131, \"s_from\": 2036880.976553238, \"p_to\": -1019999.999999999, \"q_to\": -219999.9999999689, \"i_to\": 55.73199226981006, \"s_to\": 1043455.796859639}\n",
+      "    ],\n",
+      "    \"source\": [\n",
+      "      {\"id\": 15, \"energized\": 1, \"p\": -7836685.751732428, \"q\": -105348495.343833, \"i\": 5639.485452974508, \"s\": 105639571.7275539, \"pf\": -0.07418324046166491},\n",
+      "      {\"id\": 16, \"energized\": 1, \"p\": 10248883.05839198, \"q\": 102488830.5811954, \"i\": 5498.573991718361, \"s\": 102999999.9895415, \"pf\": 0.09950371902361781},\n",
+      "      {\"id\": 17, \"energized\": 1, \"p\": -0.001807829591278542, \"q\": -0.0104081191056169, \"i\": 5.639485452974507e-07, \"s\": 0.01056395717275539, \"pf\": -0.1711318553942043}\n",
+      "    ],\n",
+      "    \"sym_load\": [\n",
+      "      {\"id\": 7, \"energized\": 1, \"p\": 1010000, \"q\": 210000, \"i\": 55.07135393955915, \"s\": 1031600.697944704, \"pf\": 0.979060989404389},\n",
+      "      {\"id\": 8, \"energized\": 1, \"p\": 1020000, \"q\": 220000, \"i\": 55.73199226981046, \"s\": 1043455.796859647, \"pf\": 0.9775210440823288}\n",
+      "    ]\n",
+      "  }\n",
+      "}\n"
+     ]
+    }
+   ],
+   "source": [
+    "serialized_output = json_serialize(output)\n",
+    "\n",
+    "print(serialized_output)"
+   ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "venv",
    "language": "python",
    "name": "python3"
   },
@@ -609,7 +758,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.2"
+   "version": "3.13.0"
   }
  },
  "nbformat": 4,
diff --git a/docs/examples/Short Circuit Example.ipynb b/docs/examples/Short Circuit Example.ipynb
index 4fd209f1e..bf3479305 100644
--- a/docs/examples/Short Circuit Example.ipynb	
+++ b/docs/examples/Short Circuit Example.ipynb	
@@ -327,7 +327,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "venv",
    "language": "python",
    "name": "python3"
   },
@@ -341,12 +341,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.2"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "f2534ebf7a1a13ecc51c3a04ce741b49cf1feb97b5ca55170ed7b2036b4058c0"
-   }
+   "version": "3.13.0"
   }
  },
  "nbformat": 4,
diff --git a/docs/examples/State Estimation Example.ipynb b/docs/examples/State Estimation Example.ipynb
index 8bec41a6e..5066ac316 100644
--- a/docs/examples/State Estimation Example.ipynb	
+++ b/docs/examples/State Estimation Example.ipynb	
@@ -1089,7 +1089,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "venv",
    "language": "python",
    "name": "python3"
   },
@@ -1103,7 +1103,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.2"
+   "version": "3.13.0"
   }
  },
  "nbformat": 4,
diff --git a/docs/examples/Transformer Examples.ipynb b/docs/examples/Transformer Examples.ipynb
index 147dd1e38..e949cc0da 100644
--- a/docs/examples/Transformer Examples.ipynb	
+++ b/docs/examples/Transformer Examples.ipynb	
@@ -1722,7 +1722,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "venv",
    "language": "python",
    "name": "python3"
   },
@@ -1736,12 +1736,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.14"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "f2534ebf7a1a13ecc51c3a04ce741b49cf1feb97b5ca55170ed7b2036b4058c0"
-   }
+   "version": "3.13.0"
   }
  },
  "nbformat": 4,
diff --git a/docs/examples/Validation Examples.ipynb b/docs/examples/Validation Examples.ipynb
index c041efeb7..6b09acf8c 100644
--- a/docs/examples/Validation Examples.ipynb	
+++ b/docs/examples/Validation Examples.ipynb	
@@ -113,8 +113,8 @@
       "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[1;31mIDWrongType\u001b[0m                               Traceback (most recent call last)",
       "Cell \u001b[1;32mIn[2], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m# Without validation\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m model \u001b[38;5;241m=\u001b[39m \u001b[43mPowerGridModel\u001b[49m\u001b[43m(\u001b[49m\u001b[43merror_data\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m      3\u001b[0m output_data \u001b[38;5;241m=\u001b[39m model\u001b[38;5;241m.\u001b[39mcalculate_state_estimation(symmetric\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n",
-      "File \u001b[1;32m~\\Documents\\power-grid-model\\src\\power_grid_model\\core\\power_grid_model.py:114\u001b[0m, in \u001b[0;36mPowerGridModel.__init__\u001b[1;34m(self, input_data, system_frequency)\u001b[0m\n\u001b[0;32m    112\u001b[0m prepared_input \u001b[38;5;241m=\u001b[39m prepare_input_view(input_data)\n\u001b[0;32m    113\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_model_ptr \u001b[38;5;241m=\u001b[39m pgc\u001b[38;5;241m.\u001b[39mcreate_model(system_frequency, input_data\u001b[38;5;241m=\u001b[39mprepared_input\u001b[38;5;241m.\u001b[39mget_dataset_ptr())\n\u001b[1;32m--> 114\u001b[0m \u001b[43massert_no_error\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    115\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_all_component_count \u001b[38;5;241m=\u001b[39m {k: v \u001b[38;5;28;01mfor\u001b[39;00m k, v \u001b[38;5;129;01min\u001b[39;00m prepared_input\u001b[38;5;241m.\u001b[39mget_info()\u001b[38;5;241m.\u001b[39mtotal_elements()\u001b[38;5;241m.\u001b[39mitems() \u001b[38;5;28;01mif\u001b[39;00m v \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m}\n",
-      "File \u001b[1;32m~\\Documents\\power-grid-model\\src\\power_grid_model\\core\\error_handling.py:164\u001b[0m, in \u001b[0;36massert_no_error\u001b[1;34m(batch_size, decode_error)\u001b[0m\n\u001b[0;32m    162\u001b[0m error \u001b[38;5;241m=\u001b[39m find_error(batch_size\u001b[38;5;241m=\u001b[39mbatch_size, decode_error\u001b[38;5;241m=\u001b[39mdecode_error)\n\u001b[0;32m    163\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m error \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m--> 164\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m error\n",
+      "File \u001b[1;32m~\\Documents\\power-grid-model\\src\\power_grid_model\\core\\power_grid_model.py:125\u001b[0m, in \u001b[0;36mPowerGridModel.__init__\u001b[1;34m(self, input_data, system_frequency)\u001b[0m\n\u001b[0;32m    123\u001b[0m prepared_input \u001b[38;5;241m=\u001b[39m prepare_input_view(_map_to_component_types(input_data))\n\u001b[0;32m    124\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_model_ptr \u001b[38;5;241m=\u001b[39m pgc\u001b[38;5;241m.\u001b[39mcreate_model(system_frequency, input_data\u001b[38;5;241m=\u001b[39mprepared_input\u001b[38;5;241m.\u001b[39mget_dataset_ptr())\n\u001b[1;32m--> 125\u001b[0m \u001b[43massert_no_error\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    126\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_all_component_count \u001b[38;5;241m=\u001b[39m {k: v \u001b[38;5;28;01mfor\u001b[39;00m k, v \u001b[38;5;129;01min\u001b[39;00m prepared_input\u001b[38;5;241m.\u001b[39mget_info()\u001b[38;5;241m.\u001b[39mtotal_elements()\u001b[38;5;241m.\u001b[39mitems() \u001b[38;5;28;01mif\u001b[39;00m v \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m}\n",
+      "File \u001b[1;32m~\\Documents\\power-grid-model\\src\\power_grid_model\\core\\error_handling.py:169\u001b[0m, in \u001b[0;36massert_no_error\u001b[1;34m(batch_size, decode_error)\u001b[0m\n\u001b[0;32m    167\u001b[0m error \u001b[38;5;241m=\u001b[39m find_error(batch_size\u001b[38;5;241m=\u001b[39mbatch_size, decode_error\u001b[38;5;241m=\u001b[39mdecode_error)\n\u001b[0;32m    168\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m error \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m--> 169\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m error\n",
       "\u001b[1;31mIDWrongType\u001b[0m: Wrong type for object with id 4\n\nTry validate_input_data() or validate_batch_data() to validate your data.\n"
      ]
     }
@@ -133,14 +133,14 @@
    "outputs": [
     {
      "ename": "ValidationException",
-     "evalue": "There are 5 validation errors in input_data:\n   1. Fields line.id and sym_power_sensor.id are not unique for 2 lines/sym_power_sensors.\n   2. Field 'to_node' does not contain a valid node id for 1 line.\n   3. Field 'power_sigma' is not greater than zero for 2 sym_power_sensors.\n   4. Field 'measured_object' does not contain a valid line/transformer id for 1 sym_power_sensor. (measured_terminal_type=branch_from)\n   5. Field 'measured_object' does not contain a valid source id for 1 sym_power_sensor. (measured_terminal_type=source)",
+     "evalue": "There are 5 validation errors in input_data:\n   1. Fields line.id and sym_power_sensor.id are not unique for 2 lines/sym_power_sensors.\n   2. Field 'to_node' does not contain a valid node id for 1 line.\n   3. Field 'power_sigma' is not greater than zero for 2 sym_power_sensors.\n   4. Field 'measured_object' does not contain a valid line/generic_branch/transformer id for 1 sym_power_sensor. (measured_terminal_type=branch_from)\n   5. Field 'measured_object' does not contain a valid source id for 1 sym_power_sensor. (measured_terminal_type=source)",
      "output_type": "error",
      "traceback": [
       "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[1;31mValidationException\u001b[0m                       Traceback (most recent call last)",
       "Cell \u001b[1;32mIn[3], line 4\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpower_grid_model\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mvalidation\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m assert_valid_input_data\n\u001b[0;32m      3\u001b[0m \u001b[38;5;66;03m# Assert valid data\u001b[39;00m\n\u001b[1;32m----> 4\u001b[0m \u001b[43massert_valid_input_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43merror_data\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msymmetric\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[0;32m      5\u001b[0m model \u001b[38;5;241m=\u001b[39m PowerGridModel(error_data)\n\u001b[0;32m      6\u001b[0m output_data \u001b[38;5;241m=\u001b[39m model\u001b[38;5;241m.\u001b[39mcalculate_state_estimation(symmetric\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n",
       "File \u001b[1;32m~\\Documents\\power-grid-model\\src\\power_grid_model\\validation\\assertions.py:57\u001b[0m, in \u001b[0;36massert_valid_input_data\u001b[1;34m(input_data, calculation_type, symmetric)\u001b[0m\n\u001b[0;32m     53\u001b[0m validation_errors \u001b[38;5;241m=\u001b[39m validate_input_data(\n\u001b[0;32m     54\u001b[0m     input_data\u001b[38;5;241m=\u001b[39minput_data, calculation_type\u001b[38;5;241m=\u001b[39mcalculation_type, symmetric\u001b[38;5;241m=\u001b[39msymmetric\n\u001b[0;32m     55\u001b[0m )\n\u001b[0;32m     56\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m validation_errors:\n\u001b[1;32m---> 57\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m ValidationException(validation_errors, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124minput_data\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
-      "\u001b[1;31mValidationException\u001b[0m: There are 5 validation errors in input_data:\n   1. Fields line.id and sym_power_sensor.id are not unique for 2 lines/sym_power_sensors.\n   2. Field 'to_node' does not contain a valid node id for 1 line.\n   3. Field 'power_sigma' is not greater than zero for 2 sym_power_sensors.\n   4. Field 'measured_object' does not contain a valid line/transformer id for 1 sym_power_sensor. (measured_terminal_type=branch_from)\n   5. Field 'measured_object' does not contain a valid source id for 1 sym_power_sensor. (measured_terminal_type=source)"
+      "\u001b[1;31mValidationException\u001b[0m: There are 5 validation errors in input_data:\n   1. Fields line.id and sym_power_sensor.id are not unique for 2 lines/sym_power_sensors.\n   2. Field 'to_node' does not contain a valid node id for 1 line.\n   3. Field 'power_sigma' is not greater than zero for 2 sym_power_sensors.\n   4. Field 'measured_object' does not contain a valid line/generic_branch/transformer id for 1 sym_power_sensor. (measured_terminal_type=branch_from)\n   5. Field 'measured_object' does not contain a valid source id for 1 sym_power_sensor. (measured_terminal_type=source)"
      ]
     }
    ],
@@ -163,11 +163,11 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "MultiComponentNotUniqueError ['line', 'sym_power_sensor'] : [('line', 6), ('sym_power_sensor', 6)]\n",
-      "InvalidIdError line : [6]\n",
-      "NotGreaterThanError sym_power_sensor : [6, 7]\n",
-      "InvalidIdError sym_power_sensor : [6]\n",
-      "InvalidIdError sym_power_sensor : [7]\n"
+      "MultiComponentNotUniqueError [<ComponentType.line: 'line'>, <ComponentType.sym_power_sensor: 'sym_power_sensor'>] : [(<ComponentType.line: 'line'>, np.int32(6)), (<ComponentType.sym_power_sensor: 'sym_power_sensor'>, np.int32(6))]\n",
+      "InvalidIdError ComponentType.line : [6]\n",
+      "NotGreaterThanError ComponentType.sym_power_sensor : [6, 7]\n",
+      "InvalidIdError ComponentType.sym_power_sensor : [6]\n",
+      "InvalidIdError ComponentType.sym_power_sensor : [7]\n"
      ]
     }
    ],
@@ -198,7 +198,7 @@
       "   1. Fields line.id and sym_power_sensor.id are not unique for 2 lines/sym_power_sensors.\n",
       "   2. Field 'to_node' does not contain a valid node id for 1 line.\n",
       "   3. Field 'power_sigma' is not greater than zero for 2 sym_power_sensors.\n",
-      "   4. Field 'measured_object' does not contain a valid line/transformer id for 1 sym_power_sensor. (measured_terminal_type=branch_from)\n",
+      "   4. Field 'measured_object' does not contain a valid line/generic_branch/transformer id for 1 sym_power_sensor. (measured_terminal_type=branch_from)\n",
       "   5. Field 'measured_object' does not contain a valid source id for 1 sym_power_sensor. (measured_terminal_type=source)\n"
      ]
     }
@@ -230,7 +230,7 @@
       "\tFields line.id and sym_power_sensor.id are not unique for 2 lines/sym_power_sensors.\n",
       "\t\tcomponent: line/sym_power_sensor\n",
       "\t\tfield: line.id and sym_power_sensor.id\n",
-      "\t\tids: [('line', 6), ('sym_power_sensor', 6)]\n",
+      "\t\tids: [(<ComponentType.line: 'line'>, np.int32(6)), (<ComponentType.sym_power_sensor: 'sym_power_sensor'>, np.int32(6))]\n",
       "\n",
       "\tField 'to_node' does not contain a valid node id for 1 line.\n",
       "\t\tcomponent: line\n",
@@ -245,11 +245,11 @@
       "\t\tids: [6, 7]\n",
       "\t\tref_value: zero\n",
       "\n",
-      "\tField 'measured_object' does not contain a valid line/transformer id for 1 sym_power_sensor. (measured_terminal_type=branch_from)\n",
+      "\tField 'measured_object' does not contain a valid line/generic_branch/transformer id for 1 sym_power_sensor. (measured_terminal_type=branch_from)\n",
       "\t\tcomponent: sym_power_sensor\n",
       "\t\tfield: 'measured_object'\n",
       "\t\tids: [6]\n",
-      "\t\tref_components: line/transformer\n",
+      "\t\tref_components: line/generic_branch/transformer\n",
       "\t\tfilters: (measured_terminal_type=branch_from)\n",
       "\n",
       "\tField 'measured_object' does not contain a valid source id for 1 sym_power_sensor. (measured_terminal_type=source)\n",
@@ -271,7 +271,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "venv",
    "language": "python",
    "name": "python3"
   },
@@ -285,12 +285,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.2"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "39f035c5e58f53e973c1999fdd4393d6db2dd3f04fe05f524988f90f4bbdb576"
-   }
+   "version": "3.13.0"
   }
  },
  "nbformat": 4,
diff --git a/src/power_grid_model/validation/errors.py b/src/power_grid_model/validation/errors.py
index a956810a5..58bc84649 100644
--- a/src/power_grid_model/validation/errors.py
+++ b/src/power_grid_model/validation/errors.py
@@ -58,14 +58,26 @@ def component_str(self) -> str:
         """
         A string representation of the component to which this error applies
         """
-        return str(self.component)
+        if self.component is None:
+            return str(None)
+        if isinstance(self.component, list):
+            return "/".join(component.value for component in self.component)
+        return self.component.value
 
     @property
     def field_str(self) -> str:
         """
         A string representation of the field to which this error applies
         """
-        return f"'{self.field}'"
+
+        def _unpack(field: str | tuple[ComponentType, str]) -> str:
+            if isinstance(field, str):
+                return f"'{field}'"
+            return ".".join(field)
+
+        if isinstance(self.field, list):
+            return self._delimiter.join(_unpack(field) for field in self.field)
+        return _unpack(self.field) if self.field else str(self.field)
 
     def get_context(self, id_lookup: Optional[list[str] | dict[int, str]] = None) -> dict[str, Any]:
         """
@@ -155,10 +167,6 @@ def __init__(self, component: ComponentType, fields: list[str], ids: list[int]):
         if len(self.field) < 2:
             raise ValueError(f"{type(self).__name__} expects at least two fields: {self.field}")
 
-    @property
-    def field_str(self) -> str:
-        return self._delimiter.join(f"'{field}'" for field in self.field)
-
 
 class MultiComponentValidationError(ValidationError):
     """
@@ -187,15 +195,6 @@ def __init__(self, fields: list[tuple[ComponentType, str]], ids: list[tuple[Comp
         if len(self.component) < 2:
             raise ValueError(f"{type(self).__name__} expects at least two components: {self.component}")
 
-    @property
-    def component_str(self) -> str:
-        str_components = [str(component) for component in self.component]
-        return "/".join(str_components)
-
-    @property
-    def field_str(self) -> str:
-        return self._delimiter.join(f"{component}.{field}" for component, field in self.field)
-
 
 class NotIdenticalError(SingleFieldValidationError):
     """
diff --git a/tests/unit/validation/test_errors.py b/tests/unit/validation/test_errors.py
index 5c0d87968..bb76d64bc 100644
--- a/tests/unit/validation/test_errors.py
+++ b/tests/unit/validation/test_errors.py
@@ -6,6 +6,7 @@
 
 import pytest
 
+from power_grid_model import ComponentType
 from power_grid_model.validation.errors import (
     ComparisonError,
     InvalidAssociatedEnumValueError,
@@ -21,65 +22,91 @@
 def test_validation_error():
     error = ValidationError()
     assert str(error) == "An unknown validation error occurred."
+    assert error.component_str == str(None)
+    assert error.field_str == str(None)
+    assert not error.ids
 
 
 def test_single_field_validation_error():
-    error = SingleFieldValidationError(component="alpha", field="bravo", ids=[0, 1, 1, 2, 3, 5])
-    assert error.component_str == "alpha"
+    error = SingleFieldValidationError(component=ComponentType.node, field="bravo", ids=[0, 1, 1, 2, 3, 5])
+    assert error.component_str == "node"
     assert error.field_str == "'bravo'"
-    assert str(error) == "Field 'bravo' is not valid for 6 alphas."
+    assert str(error) == "Field 'bravo' is not valid for 6 nodes."
 
 
 def test_multi_field_validation_error():
-    error = MultiFieldValidationError(component="charlie", fields=["delta", "echo"], ids=[0, 1, 1, 2, 3, 5])
-    assert error.component_str == "charlie"
+    error = MultiFieldValidationError(component=ComponentType.node, fields=["delta", "echo"], ids=[0, 1, 1, 2, 3, 5])
+    assert error.component_str == "node"
     assert error.field_str == "'delta' and 'echo'"
-    assert str(error) == "Combination of fields 'delta' and 'echo' is not valid for 6 charlies."
+    assert str(error) == "Combination of fields 'delta' and 'echo' is not valid for 6 nodes."
 
     with pytest.raises(ValueError, match="at least two fields"):
-        MultiFieldValidationError(component="charlie", fields=["delta"], ids=[])
+        MultiFieldValidationError(component=ComponentType.node, fields=["delta"], ids=[])
 
 
 def test_multi_component_validation_error():
     error = MultiComponentValidationError(
-        fields=[("foxtrot", "golf"), ("hotel", "india")],
-        ids=[("foxtrot", 0), ("foxtrot", 1), ("foxtrot", 1), ("hotel", 2), ("hotel", 3), ("hotel", 5)],
+        fields=[(ComponentType.node, "golf"), (ComponentType.line, "india")],
+        ids=[
+            (ComponentType.node, 0),
+            (ComponentType.node, 1),
+            (ComponentType.node, 1),
+            (ComponentType.line, 2),
+            (ComponentType.line, 3),
+            (ComponentType.line, 5),
+        ],
     )
-    assert error.component_str == "foxtrot/hotel"
-    assert error.field_str == "foxtrot.golf and hotel.india"
-    assert str(error) == "Fields foxtrot.golf and hotel.india are not valid for 6 foxtrots/hotels."
+    assert error.component_str == "line/node"
+    assert error.field_str == "line.india and node.golf"
+    assert str(error) == "Fields line.india and node.golf are not valid for 6 lines/nodes."
 
     with pytest.raises(ValueError, match="at least two fields"):
-        MultiComponentValidationError(fields=[("foxtrot", "golf")], ids=[])
+        MultiComponentValidationError(fields=[(ComponentType.node, "golf")], ids=[])
 
     with pytest.raises(ValueError, match="at least two components"):
-        MultiComponentValidationError(fields=[("foxtrot", "golf"), ("foxtrot", "india")], ids=[])
+        MultiComponentValidationError(fields=[(ComponentType.node, "golf"), (ComponentType.node, "india")], ids=[])
 
 
 def test_invalid_enum_value_error():
     class CustomType(IntEnum):
         pass
 
-    error = InvalidEnumValueError(component="kilo", field="lima", ids=[1, 2, 3], enum=CustomType)
-    assert error.component == "kilo"
+    error = InvalidEnumValueError(component=ComponentType.node, field="lima", ids=[1, 2, 3], enum=CustomType)
+    assert error.component == "node"
     assert error.field == "lima"
     assert error.ids == [1, 2, 3]
     assert error.enum is CustomType
-    assert str(error) == "Field 'lima' contains invalid CustomType values for 3 kilos."
+    assert str(error) == "Field 'lima' contains invalid CustomType values for 3 nodes."
 
 
 def test_invalid_id_error():
-    error = InvalidIdError(component="mike", field="november", ids=[1, 2, 3], ref_components=["oscar", "papa"])
-    assert error.component == "mike"
+    error = InvalidIdError(ComponentType.node, field="november", ids=[1, 2, 3], ref_components=["oscar", "papa"])
+    assert error.component == "node"
     assert error.field == "november"
     assert error.ids == [1, 2, 3]
     assert error.ref_components == ["oscar", "papa"]
-    assert str(error) == "Field 'november' does not contain a valid oscar/papa id for 3 mikes."
+    assert str(error) == "Field 'november' does not contain a valid oscar/papa id for 3 nodes."
+
+
+def test_invalid_id_error_with_filters():
+    error = InvalidIdError(
+        ComponentType.node,
+        field="november",
+        ids=[1, 2, 3],
+        ref_components=["oscar", "papa"],
+        filters={"foo": "bar", "baz": ComponentType.node},
+    )
+    assert error.component == "node"
+    assert error.field == "november"
+    assert error.ids == [1, 2, 3]
+    assert error.ref_components == ["oscar", "papa"]
+    assert error.filters_str == "(foo=bar, baz=node)"
+    assert str(error) == "Field 'november' does not contain a valid oscar/papa id for 3 nodes. (foo=bar, baz=node)"
 
 
 def test_comparison_error():
-    error = ComparisonError(component="quebec", field="romeo", ids=[1, 2, 3], ref_value=0)
-    assert error.component == "quebec"
+    error = ComparisonError(component=ComponentType.node, field="romeo", ids=[1, 2, 3], ref_value=0)
+    assert error.component == "node"
     assert error.field == "romeo"
     assert error.ids == [1, 2, 3]
     assert error.ref_value == 0
@@ -93,10 +120,10 @@ def test_comparison_error():
 
 
 def test_error_context():
-    error = ComparisonError(component="sierra", field="tango", ids=[1, 2, 3], ref_value=0)
+    error = ComparisonError(component=ComponentType.node, field="tango", ids=[1, 2, 3], ref_value=0)
     context = error.get_context()
     assert len(context) == 4
-    assert context["component"] == "sierra"
+    assert context["component"] == "node"
     assert context["field"] == "'tango'"
     assert context["ids"] == [1, 2, 3]
     assert context["ref_value"] == "zero"
@@ -109,20 +136,24 @@ def test_error_context():
 
 
 def test_error_context_tuple_ids():
-    error = MultiComponentValidationError(fields=[("a", "x"), ("b", "y")], ids=[("a", 1), ("b", 2), ("a", 3)])
+    error = MultiComponentValidationError(
+        fields=[(ComponentType.node, "x"), ("b", "y")], ids=[(ComponentType.node, 1), ("b", 2), (ComponentType.node, 3)]
+    )
     context = error.get_context(id_lookup={1: "Victor", 3: "Whiskey"})
-    assert context["ids"] == {("a", 1): "Victor", ("b", 2): None, ("a", 3): "Whiskey"}
+    assert context["ids"] == {("node", 1): "Victor", ("b", 2): None, ("node", 3): "Whiskey"}
 
 
 def test_invalid_associated_enum_value_error():
     class CustomType(IntEnum):
         pass
 
-    error = InvalidAssociatedEnumValueError(component="foo", fields=["bar", "baz"], ids=[1, 2], enum=[CustomType])
-    assert error.component == "foo"
+    error = InvalidAssociatedEnumValueError(
+        component=ComponentType.node, fields=["bar", "baz"], ids=[1, 2], enum=[CustomType]
+    )
+    assert error.component == "node"
     assert error.field == ["bar", "baz"]
     assert error.ids == [1, 2]
     assert len(error.enum) == 1
     for actual, expected in zip(error.enum, [CustomType]):
         assert actual is expected
-    assert str(error) == "The combination of fields 'bar' and 'baz' results in invalid CustomType values for 2 foos."
+    assert str(error) == "The combination of fields 'bar' and 'baz' results in invalid CustomType values for 2 nodes."
diff --git a/tests/unit/validation/test_rules.py b/tests/unit/validation/test_rules.py
index 6f258826e..ff22676e0 100644
--- a/tests/unit/validation/test_rules.py
+++ b/tests/unit/validation/test_rules.py
@@ -7,7 +7,7 @@
 import numpy as np
 import pytest
 
-from power_grid_model import LoadGenType, initialize_array, power_grid_meta_data
+from power_grid_model import ComponentType, LoadGenType, initialize_array, power_grid_meta_data
 from power_grid_model.enum import Branch3Side, BranchSide, FaultPhase, FaultType
 from power_grid_model.validation.errors import (
     ComparisonError,
@@ -304,14 +304,16 @@ def test_all_cross_unique(cross_only):
         "node": np.array([(1, 0.1), (2, 0.2), (3, 0.3)], dtype=[("id", "i4"), ("foo", "f8")]),
         "line": np.array([(4, 0.4), (5, 0.5), (6, 0.6), (7, 0.7)], dtype=[("id", "i4"), ("bar", "f8")]),
     }
-    errors = all_cross_unique(valid, [("node", "foo"), ("line", "bar")], cross_only=cross_only)
+    errors = all_cross_unique(valid, [(ComponentType.node, "foo"), (ComponentType.line, "bar")], cross_only=cross_only)
     assert not errors
 
     invalid = {
         "node": np.array([(1, 0.1), (2, 0.2), (3, 0.2)], dtype=[("id", "i4"), ("foo", "f8")]),
         "line": np.array([(4, 0.2), (5, 0.1), (6, 0.3), (7, 0.3)], dtype=[("id", "i4"), ("bar", "f8")]),
     }
-    errors = all_cross_unique(invalid, [("node", "foo"), ("line", "bar")], cross_only=cross_only)
+    errors = all_cross_unique(
+        invalid, [(ComponentType.node, "foo"), (ComponentType.line, "bar")], cross_only=cross_only
+    )
     assert len(errors) == 1
     error = errors.pop()
     assert isinstance(error, MultiComponentNotUniqueError)