Try BigQuery DataFrames

Use this quickstart to perform the following analysis and machine learning (ML) tasks by using the BigQuery DataFrames API in a BigQuery notebook:

  • Create a DataFrame over the bigquery-public-data.ml_datasets.penguins public dataset.
  • Calculate the average body mass of a penguin.
  • Create a linear regression model.
  • Create a DataFrame over a subset of the penguin data to use as training data.
  • Clean up the training data.
  • Set the model parameters.
  • Fit the model.
  • Score the model.

Before you begin

  1. Sign in to your Google Cloud account. If you're new to Google Cloud, create an account to evaluate how our products perform in real-world scenarios. New customers also get $300 in free credits to run, test, and deploy workloads.
  2. In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

    Go to project selector

  3. In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

    Go to project selector

  4. Make sure that billing is enabled for your Google Cloud project.

  5. Ensure that the BigQuery API is enabled.

    Enable the API

    If you created a new project, the BigQuery API is automatically enabled.

Required permissions

To create and run notebooks, you need the following Identity and Access Management (IAM) roles:

Create a notebook

Follow the instructions in Create a notebook from the BigQuery editor to create a new notebook.

Try BigQuery DataFrames

Try BigQuery DataFrames by following these steps:

  1. Create a new code cell in the notebook.
  2. Copy the following code and paste it into the code cell:

    import bigframes.pandas as bpd
    # Set BigQuery DataFrames options
    bpd.options.bigquery.project = your_gcp_project_id
    bpd.options.bigquery.location = "us"
    # Create a DataFrame from a BigQuery table
    query_or_table = "bigquery-public-data.ml_datasets.penguins"
    df = bpd.read_gbq(query_or_table)
    # Use the DataFrame just as you would a pandas DataFrame, but calculations
    # happen in the BigQuery query engine instead of the local system.
    average_body_mass = df["body_mass_g"].mean()
    print(f"average_body_mass: {average_body_mass}")
    # Create the Linear Regression model
    from import LinearRegression
    # Filter down to the data we want to analyze
    adelie_data = df[df.species == "Adelie Penguin (Pygoscelis adeliae)"]
    # Drop the columns we don't care about
    adelie_data = adelie_data.drop(columns=["species"])
    # Drop rows with nulls to get our training data
    training_data = adelie_data.dropna()
    # Pick feature columns and label column
    X = training_data[
    y = training_data[["body_mass_g"]]
    model = LinearRegression(fit_intercept=False), y)
    model.score(X, y)
  3. Modify the bpd.options.bigquery.project = your_gcp_project_id line to specify your project, for example bpd.options.bigquery.project = "myproject".

  4. Run the code cell.

    The code cell returns the average body mass for the penguins in the dataset, and then returns the evaluation metrics for the model.

Clean up

The easiest way to eliminate billing is to delete the project that you created for the tutorial.

To delete the project:

  1. In the Google Cloud console, go to the Manage resources page.

    Go to Manage resources

  2. In the project list, select the project that you want to delete, and then click Delete.
  3. In the dialog, type the project ID, and then click Shut down to delete the project.

What's next

Try the Getting Started with BigQuery DataFrames notebook.