Create a k-means clustering model on the lengths and sex of penguins using the BigQuery DataFrames API.

## Explore further


For detailed documentation that includes this code sample, see the following:

- [Use ML and AI with BigQuery DataFrames](https://docs.cloud.google.com/bigquery/docs/dataframes-ml-ai)

## Code sample

### Python


Before trying this sample, follow the Python setup instructions in the
[BigQuery quickstart using
client libraries](https://docs.cloud.google.com/bigquery/docs/quickstarts/quickstart-client-libraries).


For more information, see the
[BigQuery Python API
reference documentation](https://docs.cloud.google.com/python/docs/reference/bigquery/latest).


To authenticate to BigQuery, set up Application Default Credentials.
For more information, see

[Set up authentication for client libraries](https://docs.cloud.google.com/bigquery/docs/authentication#client-libs).

    from bigframes.ml.cluster import KMeans
    import bigframes.pandas as bpd

    # Load data from BigQuery
    query_or_table = "bigquery-public-data.ml_datasets.penguins"
    bq_df = bpd.read_gbq(query_or_table)

    # Create the KMeans model
    cluster_model = KMeans(n_clusters=10)
    cluster_model.fit(bq_df["culmen_length_mm"], bq_df["sex"])

    # Predict using the model
    result = cluster_model.predict(bq_df)
    # Score the model
    score = cluster_model.score(bq_df)

## What's next


To search and filter code samples for other Google Cloud products, see the
[Google Cloud sample browser](https://docs.cloud.google.com/docs/samples?product=bigquery).