docs(notebook): Add description for expose view in anywidget_mode.ipynb

Author: shuoweil

notebooks/dataframes/anywidget_mode.ipynb

@@ -761,35 +761,35 @@
        "</div>[5 rows x 15 columns in total]"
       ],
       "text/plain": [
-       "                                              result  \\\n",
+       "                                              result  \\n",
        "0  {'application_number': None, 'class_internatio...   \n",
        "1  {'application_number': None, 'class_internatio...   \n",
        "2  {'application_number': None, 'class_internatio...   \n",
        "3  {'application_number': None, 'class_internatio...   \n",
        "4  {'application_number': None, 'class_internatio...   \n",
        "\n",
-       "                                            gcs_path issuer language  \\\n",
+       "                                            gcs_path issuer language  \\n",
        "0  gs://gcs-public-data--labeled-patents/espacene...     EU       DE   \n",
        "1  gs://gcs-public-data--labeled-patents/espacene...     EU       DE   \n",
        "2  gs://gcs-public-data--labeled-patents/espacene...     EU       DE   \n",
        "3  gs://gcs-public-data--labeled-patents/espacene...     EU       DE   \n",
        "4  gs://gcs-public-data--labeled-patents/espacene...     EU       DE   \n",
        "\n",
-       "  publication_date class_international class_us application_number  \\\n",
+       "  publication_date class_international class_us application_number  \\n",
        "0        29.08.018           E04H 6/12     <NA>         18157874.1   \n",
        "1       03.10.2018           H05B 6/12     <NA>         18165514.3   \n",
        "2       03.10.2018          H01L 21/20     <NA>         18166536.5   \n",
        "3       03.10.2018          A01K 31/00     <NA>         18171005.4   \n",
        "4       03.10.2018          G06F 11/30     <NA>         18157347.8   \n",
        "\n",
-       "  filing_date priority_date_eu          representative_line_1_eu  \\\n",
+       "  filing_date priority_date_eu          representative_line_1_eu  \\n",
        "0  21.02.2018       22.02.2017  Liedtke & Partner Patentanwälte   \n",
        "1  03.04.2018       30.03.2017                              <NA>   \n",
        "2  16.02.2016             <NA>            Scheider, Sascha et al   \n",
        "3  05.02.2015       05.02.2014    Stork Bamberger Patentanwälte   \n",
        "4  19.02.2018       31.03.2017                    Hoffmann Eitle   \n",
        "\n",
-       "            applicant_line_1     inventor_line_1  \\\n",
+       "            applicant_line_1     inventor_line_1  \\n",
        "0       SHB Hebezeugbau GmbH   VOLGER, Alexander   \n",
        "1       BSH Hausgeräte GmbH  Acero Acero, Jesus   \n",
        "2  EV Group E. Thallner GmbH       Kurz, Florian   \n",
@@ -815,15 +815,25 @@
     "bpd._read_gbq_colab(\"\"\"\n",
     "  SELECT\n",
     "    AI.GENERATE(\n",
-    "      prompt=>(\\\"Extract the values.\\\", OBJ.GET_ACCESS_URL(OBJ.FETCH_METADATA(OBJ.MAKE_REF(gcs_path, \\\"us.conn\\\")), \\\"r\\\")),\n",
+    "      prompt=>(\\\"Extract the values.\\\\", OBJ.GET_ACCESS_URL(OBJ.FETCH_METADATA(OBJ.MAKE_REF(gcs_path, \\\\"us.conn\\\\")), \\\\"r\\\\\")),\n",
     "      connection_id=>\\\"bigframes-dev.us.bigframes-default-connection\\\",\n",
-    "      output_schema=>\\\"publication_date string, class_international string, application_number string, filing_date string\\\") AS result,\n",
+    "      output_schema=>\\\"publication_date string, class_international string, application_number string, filing_date string\\\"\n",
+    "    ) AS result,\n",
     "    *\n",
     "  FROM `bigquery-public-data.labeled_patents.extracted_data`\n",
     "  LIMIT 5;\n",
     "\"\"\")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "expose-view-intro",
+   "metadata": {},
+   "source": [
+    "### Expose View for Nested Data\n",
+    "BigQuery DataFrames now supports an expose view for columns containing nested data structures, such as structs, arrays, and arrays of structs. This feature flattens the nested data, presenting each field as a distinct column for improved readability and easier data analysis. For example, a column of structs will be expanded so that each key in the struct becomes its own column. Similarly, arrays are unnested, with each element appearing in a new row, making it simple to work with complex, multi-level data directly within the table."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 11,
@@ -921,7 +931,7 @@
        "</div>[2 rows x 4 columns in total]"
       ],
       "text/plain": [
-       "   id                    struct_col   array_col  \\\n",
+       "   id                    struct_col   array_col  \\n",
        "0   1  {'name': 'Alice', 'age': 30}  [10 20 30]   \n",
        "1   2    {'name': 'Bob', 'age': 25}     [40 50]   \n",
        "\n",