docs: add sample for getting started with BQML

samples/snippets/bqml_getting_started_test.py

@@ -1,4 +1,3 @@
-
 # Copyright 2023 Google LLC
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -15,57 +14,58 @@
 
 
 def test_bqml_getting_started():
-    #[START bigquery_getting_Started_bqml_tutorial]
-    #DataFrame created from a BigQuery table:
-    import bigframes.pandas as bpd 
-    import bigframes 
+    # [START bigquery_getting_Started_bqml_tutorial]
+    # DataFrame created from a BigQuery table:
+    import bigframes
+    import bigframes.pandas as bpd
 
-    # Original sql query from tutorial, translated to Python using BigQuery BigFrames dataframes 
-    df = bpd.read_gbq('''
+    # Original sql query from tutorial, translated to Python using BigQuery BigFrames dataframes
+    df = bpd.read_gbq(
+        """
     SELECT GENERATE_UUID() AS rowindex, *
     FROM
     `bigquery-public-data.google_analytics_sample.ga_sessions_*`
     WHERE
     _TABLE_SUFFIX BETWEEN '20160801' AND '20170630'
-    ''',
-    index_col='rowindex')
+    """,
+        index_col="rowindex",
+    )
 
-    #Printing dataframe, setting totals value 
-    totals = df['totals']
+    # Printing dataframe, setting totals value
+    totals = df["totals"]
 
-    #Using totals, selecting id for transaction example 
-    totals['0000fb2c-2861-40be-9c6c-309afd7e7883']
+    # Using totals, selecting id for transaction example
+    totals["0000fb2c-2861-40be-9c6c-309afd7e7883"]
     transactions = totals.struct.field("transactions")
-    #Columns to indicate whether there was purchase 
+    # Columns to indicate whether there was purchase
     label = transactions.notnull().map({True: 1, False: 0})
 
-    #Operating systems of users, extracting child fields of a struct as a Series
-    operatingSystem = df['device'].struct.field("operatingSystem")
+    # Operating systems of users
+    operatingSystem = df["device"].struct.field("operatingSystem")
     operatingSystem = operatingSystem.fillna("")
 
-    #Indicates whether the users devices are mobile 
-    isMobile = df['device'].struct.field("isMobile")
+    # Indicates whether the users devices are mobile
+    isMobile = df["device"].struct.field("isMobile")
 
-    #Country from which the sessions originate, IP address based 
-    country = df['geoNetwork'].struct.field("country").fillna("")
+    # Country from which the sessions originate, IP address based
+    country = df["geoNetwork"].struct.field("country").fillna("")
 
-    #Total number of pageviews within the session,
+    # Total number of pageviews within the session
     pageviews = totals.struct.field("pageviews").fillna(0)
 
-    #Setting features for dataframe, 
-    features = bpd.DataFrame({
-        'os': operatingSystem,
-        'is_mobile': isMobile,
-        'pageviews': pageviews
-    })
+    # Setting features for dataframe,
+    features = bpd.DataFrame(
+        {"os": operatingSystem, "is_mobile": isMobile, "pageviews": pageviews}
+    )
+
+    # Printing out the dataframe
+    df
 
-    #Printing out the dataframe 
-    df 
-
-    #Creating a logistics regression model - 
+    # Creating a logistics regression model -
     from bigframes.ml.linear_model import LogisticRegression
+
     model = LogisticRegression()
-    #Model training parameters, 
+    # Model training parameters,
     model.fit(features, label)
-    #Write a DataFRame to a BigQuery table- 
-    model.to_gbq('bqml_tutorial.sample_model', replace = True)
+    # Write a DataFRame to a BigQuery table-
+    model.to_gbq("bqml_tutorial.sample_model", replace=True)