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.

    Roles required to select or create a project

    • Select a project: Selecting a project doesn't require a specific IAM role—you can select any project that you've been granted a role on.
    • Create a project: To create a project, you need the Project Creator (roles/resourcemanager.projectCreator), which contains the resourcemanager.projects.create permission. Learn how to grant roles.

    Go to project selector

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

    Roles required to select or create a project

    • Select a project: Selecting a project doesn't require a specific IAM role—you can select any project that you've been granted a role on.
    • Create a project: To create a project, you need the Project Creator (roles/resourcemanager.projectCreator), which contains the resourcemanager.projects.create permission. Learn how to grant roles.

    Go to project selector

    4.

  4. Verify that billing is enabled for your Google Cloud project.

  5. Verify 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. Add the following code to the code cell:

    import bigframes.pandas as bpd # Set BigQuery DataFrames options # Note: The project option is not required in all environments. # On BigQuery Studio, the project ID is automatically detected. bpd.options.bigquery.project = your_gcp_project_id # Use "partial" ordering mode to generate more efficient queries, but the # order of the rows in DataFrames may not be deterministic if you have not # explictly sorted it. Some operations that depend on the order, such as # head() will not function until you explictly order the DataFrame. Set the # ordering mode to "strict" (default) for more pandas compatibility. bpd.options.bigquery.ordering_mode = "partial" # Create a DataFrame from a BigQuery table query_or_table = "bigquery-public-data.ml_datasets.penguins" df = bpd.read_gbq(query_or_table) # Efficiently preview the results using the .peek() method. df.peek()

  3. Modify the bpd.options.bigquery.project = your_gcp_project_id line to specify your Google Cloud project ID. For example, bpd.options.bigquery.project = "myProjectID".

  4. Run the code cell.

    The code returns a DataFrame object with data about penguins.

  5. Create a new code cell in the notebook and add the following code:

    # 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}")

  6. Run the code cell.

    The code calculates the average body mass of the penguins and prints it to the Google Cloud console.

  7. Create a new code cell in the notebook and add the following code:

    # Create the Linear Regression model from bigframes.ml.linear_model 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[ [ "island", "culmen_length_mm", "culmen_depth_mm", "flipper_length_mm", "sex", ] ] y = training_data[["body_mass_g"]] model = LinearRegression(fit_intercept=False) model.fit(X, y) model.score(X, y)

  8. Run the code cell.

    The code returns the model's evaluation metrics.

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