diff --git a/samples/snippets/create_single_timeseries_forecasting_model_test.py b/samples/snippets/create_single_timeseries_forecasting_model_test.py
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+# Copyright 2024 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (t
+# you may not use this file except in compliance wi
+# 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
+# distributed under the License is distributed on a
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, eit
+# See the License for the specific language governi
+# limitations under the License.
+
+
+def test_create_single_timeseries():
+
+    # [START bigquery_dataframes_single_timeseries_forecasting_model_tutorial]
+    import bigframes.pandas as bpd
+
+    # Start by loading the historical data from BigQuerythat you want to analyze and forecast.
+    # This clause indicates that you are querying the ga_sessions_* tables in the google_analytics_sample dataset.
+    # Read and visualize the time series you want to forecast.
+    df = bpd.read_gbq("bigquery-public-data.google_analytics_sample.ga_sessions_*")
+    parsed_date = bpd.to_datetime(df.date, format="%Y%m%d", utc=True)
+    visits = df["totals"].struct.field("visits")
+    total_visits = visits.groupby(parsed_date).sum()
+
+    # Expected output: total_visits.head()
+    # date
+    # 2016-08-01 00:00:00+00:00    1711
+    # 2016-08-02 00:00:00+00:00    2140
+    # 2016-08-03 00:00:00+00:00    2890
+    # 2016-08-04 00:00:00+00:00    3161
+    # 2016-08-05 00:00:00+00:00    2702
+    # Name: visits, dtype: Int64
+
+    total_visits.plot.line()
+
+    # [END bigquery_dataframes_single_timeseries_forecasting_model_tutorial]