rerun example

Signed-off-by: Nitish Bharambe <[email protected]>

docs/examples/arrow_example.ipynb

@@ -117,7 +117,7 @@
    "metadata": {},
    "source": [
     "The primitive types of each attribute in the arrow tables need to match to make the operation efficient.\n",
-    "Zero-copy conversion is not guaranteed if the data types provided by the PGM via `power_grid_meta_data` are not used.\n",
+    "Zero-copy conversion is not guaranteed if the data types provided via the PGM via `power_grid_meta_data` are not used.\n",
     "Note that the asymmetric type of attribute in power-grid-model has a shape of `(3,)` along with a specific type. These represent the 3 phases of electrical system.\n",
     "Hence, special care is required when handling asymmetric attributes. \n",
     "\n",
@@ -143,10 +143,10 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "-------node schema-------\n",
+      "-------node scehma-------\n",
       "id: int32\n",
       "u_rated: double\n",
-      "-------asym load schema-------\n",
+      "-------asym load scehma-------\n",
       "id: int32\n",
       "node: int32\n",
       "status: int8\n",
@@ -173,9 +173,9 @@
     "    return pa.schema(schemas)\n",
     "\n",
     "\n",
-    "print(\"-------node schema-------\")\n",
+    "print(\"-------node scehma-------\")\n",
     "print(pgm_schema(DatasetType.input, ComponentType.node))\n",
-    "print(\"-------asym load schema-------\")\n",
+    "print(\"-------asym load scehma-------\")\n",
     "print(pgm_schema(DatasetType.input, ComponentType.asym_load))"
    ]
   },
@@ -188,12 +188,12 @@
     "The [power-grid-model documentation on Components](https://power-grid-model.readthedocs.io/en/stable/user_manual/components.html) provides documentation on which components are required and which ones are optional.\n",
     "\n",
     "Construct the Arrow data as a table with the correct headers and data types. \n",
-    "The creation and initialization of arrays and combining the data in a RecordBatch is up to the user."
+    "The creation of arrays and combining it in a RecordBatch as well as the method of initializing that RecordBatch is up to the user."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -213,7 +213,6 @@
     }
    ],
    "source": [
-    "# create the individual columns with the correct data type\n",
     "nodes_schema = pgm_schema(DatasetType.input, ComponentType.node)\n",
     "nodes = pa.record_batch(\n",
     "    [\n",
@@ -223,7 +222,6 @@
     "    names=(\"id\", \"u_rated\"),\n",
     ")\n",
     "\n",
-    "# or convert directly using the schema\n"
     "lines = pa.record_batch(\n",
     "    {\n",
     "        \"id\": [4, 5],\n",
@@ -369,7 +367,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -792,7 +790,6 @@
     "    data: SingleColumnarData, dataset_type: DatasetType, component_type: ComponentType\n",
     ") -> pa.RecordBatch:\n",
     "    \"\"\"Convert NumPy data to Arrow data.\"\"\"\n",
-    "    # pa.record_batch.from_arrays(data, schema=pgm_schema(DatasetType.result, ComponentType.node))\n",
     "    component_pgm_schema = pgm_schema(dataset_type, component_type, data.keys())\n",
     "    pa_columns = {}\n",
     "    for attribute, data in data.items():\n",
@@ -820,7 +817,7 @@
     {
      "data": {
       "text/plain": [
-       "<pyarrow.lib.DoubleArray object at 0x000001A81FF94A00>\n",
+       "<pyarrow.lib.DoubleArray object at 0x00000184F527A680>\n",
        "[\n",
        "  1,\n",
        "  0.01,\n",