render codelab

Author: tswast

bigquery-dataframes-iowa-liquor-sales/codelab.json

@@ -3,7 +3,7 @@
   "format": "html",
   "prefix": "https://storage.googleapis.com",
   "mainga": "UA-49880327-14",
-  "updated": "2024-12-04T09:50:13-06:00",
+  "updated": "2024-12-04T11:11:22-06:00",
   "id": "bigquery-dataframes-iowa-liquor-sales",
   "duration": 0,
   "title": "Exploratory data analysis of Iowa liquor sales using the BigQuery DataFrames package",

bigquery-dataframes-iowa-liquor-sales/index.html

@@ -459,10 +459,10 @@ <h2 is-upgraded>Setup your project for function deployment</h2>
 iam_admin_client = iam_admin_v1.IAMClient()
 request = types.CreateServiceAccountRequest()
 
+account_id = &#34;bigframes-no-permissions&#34;
 request.account_id = account_id
 request.name = f&#34;projects/{project_id}&#34;
 
-account_id = &#34;bigframes-no-permissions&#34;
 display_name = &#34;bigframes remote function (no permissions)&#34;
 service_account = types.ServiceAccount()
 service_account.display_name = display_name
@@ -480,6 +480,9 @@ <h2 is-upgraded>Setup your project for function deployment</h2>
 bqclient.create_dataset(dataset, exists_ok=True)
 </code></pre>
 <h2 is-upgraded>Deploying a remote function</h2>
+<p>Enable the Cloud Functions API if not yet already enabled.</p>
+<pre><code>!gcloud services enable cloudfunctions.googleapis.com
+</code></pre>
 <p>Now, deploy your function to the dataset you just created. Add a <code>@bpd.remote_function</code> decorator to the function you created in the previous steps.</p>
 <pre><code>import bigframes.pandas as bpd
 
@@ -494,7 +497,7 @@ <h2 is-upgraded>Deploying a remote function</h2>
     # TODO: Replace this with your version of nltk.
     packages=[&#34;nltk==3.9.1&#34;],
     # Replace this with your service account email.
-    cloud_function_service_account=&#34;bigframes-no-permissions@your-project-id.iam.gserviceaccount.com&#34;,
+    cloud_function_service_account=f&#34;bigframes-no-permissions@{project_id}.iam.gserviceaccount.com&#34;,
     cloud_function_ingress_settings=&#34;internal-only&#34;,
 )
 def lemmatize(word: str) -&gt; str:
@@ -525,7 +528,7 @@ <h2 is-upgraded>Using the remote functions</h2>
 bpd.options.bigquery.ordering_mode = &#34;partial&#34;
 bpd.options.display.repr_mode = &#34;deferred&#34;
 
-lemmatize = bpd.read_gbq_function(&#34;swast-scratch.functions.lemmatize&#34;)
+lemmatize = bpd.read_gbq_function(f&#34;{project_id}.functions.lemmatize&#34;)
 
 words = bpd.Series([&#34;whiskies&#34;, &#34;whisky&#34;, &#34;whiskey&#34;, &#34;vodkas&#34;, &#34;vodka&#34;])
 words.apply(lemmatize).to_pandas()