docs: add sample for getting started with BQML

samples/snippets/bqml_getting_started_test.py

@@ -14,14 +14,23 @@
 
 
 def test_bqml_getting_started():
+<<<<<<< HEAD
     # [start bigquery_getting_Started_bqml_tutorial]
     import bigframes.pandas as bpd
+=======
+    # [START bigquery_getting_Started_bqml_tutorial]
+>>>>>>> 9ec139e7e275e8082379022e3eb1ce06e9664c2e
     from bigframes.ml.linear_model import LogisticRegression
+    import bigframes.pandas as bpd
 
+<<<<<<< HEAD
     # Read_gbq loads a DataFrame from BiqQuery and gives an unordered, 
     # unindexed data source. The default DataFrame will have an arbitary 
     # index and ordering. 
 
+=======
+    # EXPLANATION - REFERENCE GBQ DOCS!
+>>>>>>> 9ec139e7e275e8082379022e3eb1ce06e9664c2e
     df = bpd.read_gbq(
     # Generate_UUID produces a random universally uniquee identifier
     # as a STRING value.
@@ -34,18 +43,18 @@ def test_bqml_getting_started():
         """,
         index_col="rowindex",
     )
-    
+
     # Extract the total number of transactions within
     # the Google Analytics session.
     #
     # Because the totals column is a STRUCT data type, we need to call
-    # Series.struct.field("transactions") to extract the transactions field. 
-    # See the reference documentation below: 
+    # Series.struct.field("transactions") to extract the transactions field.
+    # See the reference documentation below:
     # https://cloud.google.com/python/docs/reference/bigframes/latest/bigframes.operations.structs.StructAccessor#bigframes_operations_structs_StructAccessor_field
-    transactions = df['totals'].struct.field("transactions")
+    transactions = df["totals"].struct.field("transactions")
 
-    # If the number of transactions is NULL, the value in the label 
-    # column is set to 0. Otherwise, it is set to 1. These values 
+    # If the number of transactions is NULL, the value in the label
+    # column is set to 0. Otherwise, it is set to 1. These values
     # represent the possible outcomes.
     label = transactions.notnull().map({True: 1, False: 0})
 
@@ -59,14 +68,20 @@ def test_bqml_getting_started():
     # Extract where the visitors country of origin is. 
     country = df["geoNetwork"].struct.field("country").fillna("")
 
+<<<<<<< HEAD
     # Extract the total pageviews from the totals column.
     pageviews = df['totals'].struct.field("pageviews").fillna(0)
+=======
+    # Total number of pageviews within the session.
+    pageviews = df["totals"].struct.field("pageviews").fillna(0)
+>>>>>>> 9ec139e7e275e8082379022e3eb1ce06e9664c2e
 
     # Selecting values to represent data in columns in DataFrames.
     features = bpd.DataFrame(
         {"os": operatingSystem, "is_mobile": isMobile, "pageviews": pageviews}
     )
 
+<<<<<<< HEAD
     # Logistic Regression model splits data into two classes,giving the
     # probablity the data is in one of the classes. 
     model = LogisticRegression() 
@@ -76,3 +91,13 @@ def test_bqml_getting_started():
     # 
     model.to_gbq("bqml_tutorial.sample_model", replace=True) 
     # [END bigquery_getting_started_bqml_tutorial]
+=======
+    # Logistic Regression model splits data into two classes, giving the
+    # probablity the data is in one of the classes.
+    model = LogisticRegression()
+    model.fit(features, label)
+
+    # When writing a DataFrame to a BigQuery table, include destinaton table
+    # and parameters, index defaults to "True".
+    model.to_gbq("bqml_tutorial.sample_model", replace=True)
+>>>>>>> 9ec139e7e275e8082379022e3eb1ce06e9664c2e