docs: add snippets for visualizing a time series and creating a time series model for the Limit forecasted values in time series model tutorial (#1310)

* merge main * temp snippet * fix typo * Delete demo.ipynb * Update samples/snippets/limit_single_timeseries_forecasting_model_test.py Co-authored-by: Tim Sweña (Swast) <swast@google.com> * Update samples/snippets/limit_single_timeseries_forecasting_model_test.py Co-authored-by: Tim Sweña (Swast) <swast@google.com> * Update samples/snippets/limit_single_timeseries_forecasting_model_test.py Co-authored-by: Tim Sweña (Swast) <swast@google.com> * Update samples/snippets/limit_single_timeseries_forecasting_model_test.py Co-authored-by: Tim Sweña (Swast) <swast@google.com> * Update samples/snippets/limit_single_timeseries_forecasting_model_test.py Co-authored-by: Tim Sweña (Swast) <swast@google.com> * add plot line for visualization * change bar chart to line chart --------- Co-authored-by: Tim Sweña (Swast) <swast@google.com>

Author: rey-esp

samples/snippets/limit_single_timeseries_forecasting_model_test.py

@@ -0,0 +1,64 @@
+# 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_limit_single_timeseries(random_model_id: str) -> None:
+ your_model_id = random_model_id
+
+ # [START bigquery_dataframes_bqml_limit_forecast_visualize]
+ import bigframes.pandas as bpd
+
+ df = bpd.read_gbq("bigquery-public-data.new_york.citibike_trips")
+
+ features = bpd.DataFrame(
+ {
+ "num_trips": df.starttime,
+ "date": df["starttime"].dt.date,
+ }
+ )
+ num_trips = features.groupby(["date"]).count()
+
+ num_trips.plot.line()
+ # [END bigquery_dataframes_bqml_limit_forecast_visualize]
+
+ # [START bigquery_dataframes_bqml_limit_forecast_create]
+ from bigframes.ml import forecasting
+ import bigframes.pandas as bpd
+
+ df = bpd.read_gbq("bigquery-public-data.new_york.citibike_trips")
+
+ features = bpd.DataFrame(
+ {
+ "start_station_id": df["start_station_id"],
+ "num_trips": df.starttime,
+ "date": df["starttime"].dt.date,
+ }
+ )
+ num_trips = features.groupby(["date", "start_station_id"], as_index=False).count()
+ model = forecasting.ARIMAPlus()
+
+ X = num_trips[["date"]]
+ y = num_trips[["num_trips"]]
+ id_col = num_trips[["start_station_id"]]
+
+ model.fit(X, y, id_col=id_col)
+
+ model.to_gbq(
+ your_model_id, # For example: "bqml_tutorial.nyc_citibike_arima_model",
+ replace=True,
+ )
+ # [END bigquery_dataframes_bqml_limit_forecast_create]
+ assert df is not None
+ assert features is not None
+ assert num_trips is not None