docs: add the first sample for the Single time-series forecasting from Google Analytics data tutorial #623
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| # Start by selecting the data you'll use for training. `read_gbq` accepts | ||
| # either a SQL query or a table ID. Since this example selects from multiple | ||
| # tables via a wildcard, use SQL to define this data. Watch issue | ||
| # https://github.com/googleapis/python-bigquery-dataframes/issues/169 |
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Wildcard tables are now supported. You aren't using SQL here.
| # [START bigquery_dataframes_single_timeseries_forecasting_model_tutorial] | ||
| import bigframes.pandas as bpd | ||
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| # Start by selecting the data you'll use for training. `read_gbq` accepts |
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In "Step two" (https://cloud.google.com/bigquery/docs/arima-single-time-series-forecasting-tutorial#step_two_optional_visualize_the_time_series_you_want_to_forecast) we aren't doing any training yet.
Instead, this sentence from the SQL version seems more applicable:
The
FROM bigquery-public-data.google_analytics_sample.ga_sessions_*clause indicates that you are querying thega_sessions_*tables in thegoogle_analytics_sampledataset.
Please rephrase that to apply to what you're doing here.
| 'bigquery-public-data.google_analytics_sample.ga_sessions_*' | ||
| ) | ||
| parsed_date = bpd.to_datetime(df.date, format= "%Y%m%d", utc = True) | ||
| total_visits = df.groupby(["date"])["parsed_date"].sum() |
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In our 1:1 we did a series groupby to calculate the number of visits per day. It is possible to do the same with a DataFrame groupby, but if so, you'll need to select just the "visits" field here before calling sum(), which is slightly more convoluted since visits is a subfield of a struct.
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BigQuery DataFrames sample for Single time-series forecasting from Google Analytics data, Step two (optional): Visualize the time series you want to forecast.
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Fixes #<issue_number_goes_here> 🦕