docs: add samples using SQL methods via the bigframes.bigquery module (#1358)

* docs: add bigquery_modeules_test.py file with bigframes.bigquery examples

* add asserts

* snippet prefix

* rename and space

* fix snippet names

* fix snippet names

* edit sninppet groups

* Update samples/snippets/bigquery_modules_test.py

* Update samples/snippets/bigquery_modules_test.py

---------

Co-authored-by: Tim Sweña (Swast) <[email protected]>

Author: rey-esp

samples/snippets/bigquery_modules_test.py

@@ -0,0 +1,69 @@
+# Copyright 2023 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+def test_bigquery_dataframes_examples() -> None:
+    # [START bigquery_dataframes_bigquery_methods_struct]
+    import bigframes.bigquery as bbq
+    import bigframes.pandas as bpd
+
+    # Load data from BigQuery
+    query_or_table = "bigquery-public-data.ml_datasets.penguins"
+    bq_df = bpd.read_gbq(query_or_table)
+
+    # Create a new STRUCT Series with subfields for each column in a DataFrames.
+    lengths = bbq.struct(
+        bq_df[["culmen_length_mm", "culmen_depth_mm", "flipper_length_mm"]]
+    )
+
+    lengths.peek()
+    # 146	{'culmen_length_mm': 51.1, 'culmen_depth_mm': ...
+    # 278	{'culmen_length_mm': 48.2, 'culmen_depth_mm': ...
+    # 337	{'culmen_length_mm': 36.4, 'culmen_depth_mm': ...
+    # 154	{'culmen_length_mm': 46.5, 'culmen_depth_mm': ...
+    # 185	{'culmen_length_mm': 50.1, 'culmen_depth_mm': ...
+    # dtype: struct[pyarrow]
+    # [END bigquery_dataframes_bigquery_methods_struct]
+
+    # [START bigquery_dataframes_bigquery_methods_scalar]
+    import bigframes.bigquery as bbq
+    import bigframes.pandas as bpd
+
+    # Load data from BigQuery
+    query_or_table = "bigquery-public-data.ml_datasets.penguins"
+
+    # The sql_scalar function can be used to inject SQL syntax that is not supported
+    # or difficult to express with the bigframes.pandas APIs.
+    bq_df = bpd.read_gbq(query_or_table)
+    shortest = bbq.sql_scalar(
+        "LEAST({0}, {1}, {2})",
+        columns=[
+            bq_df["culmen_depth_mm"],
+            bq_df["culmen_length_mm"],
+            bq_df["flipper_length_mm"],
+        ],
+    )
+
+    shortest.peek()
+    #         0
+    # 149	18.9
+    # 33	16.3
+    # 296	17.2
+    # 287	17.0
+    # 307	15.0
+    # dtype: Float64
+    # [END bigquery_dataframes_bigquery_methods_scalar]
+    assert bq_df is not None
+    assert lengths is not None
+    assert shortest is not None