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Try BigQuery DataFramesUse this quickstart to perform the following analysis and machine learning (ML) tasks by using the BigQuery DataFrames API in a BigQuery notebook:
bigquery-public-data.ml_datasets.penguins public dataset.In the Google Cloud console, on the project selector page, select or create a Google Cloud project.
Note: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.In the Google Cloud console, on the project selector page, select or create a Google Cloud project.
Note: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.Verify that billing is enabled for your Google Cloud project.
Verify that the BigQuery API is enabled.
If you created a new project, the BigQuery API is automatically enabled.
To create and run notebooks, you need the following Identity and Access Management (IAM) roles:
roles/bigquery.user)roles/aiplatform.notebookRuntimeUser)roles/dataform.codeCreator)Follow the instructions in Create a notebook from the BigQuery editor to create a new notebook.
Try BigQuery DataFramesTry BigQuery DataFrames by following these steps:
Copy the following code and paste it into 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()
# 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 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)
Modify the bpd.options.bigquery.project = your_gcp_project_id line to specify your project, for example bpd.options.bigquery.project = "myproject".
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.
The easiest way to eliminate billing is to delete the project that you created for the tutorial.
To delete the project:
appspot.com URL, delete selected resources inside the project instead of deleting the whole project.If you plan to explore multiple architectures, tutorials, or quickstarts, reusing projects can help you avoid exceeding project quota limits.
Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. For details, see the Google Developers Site Policies. Java is a registered trademark of Oracle and/or its affiliates.
Last updated 2025-08-13 UTC.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Hard to understand","hardToUnderstand","thumb-down"],["Incorrect information or sample code","incorrectInformationOrSampleCode","thumb-down"],["Missing the information/samples I need","missingTheInformationSamplesINeed","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2025-08-13 UTC."],[[["This quickstart demonstrates how to use the BigQuery DataFrames API within a BigQuery notebook to perform data analysis and machine learning tasks on a public dataset of penguins."],["You'll learn how to create a DataFrame from a BigQuery table, calculate the average body mass of penguins, and build a linear regression model."],["The tutorial covers essential steps like creating and configuring a BigQuery notebook, setting up project-specific options, and handling data subsets, as well as cleaning up training data by removing null values and irrelevant columns."],["The quickstart guides you through fitting and scoring a linear regression model using the penguin dataset and provides instructions on how to clean up resources and delete the project to eliminate billing after completing the tutorial."],["Before creating and running the notebook, ensure that the BigQuery API is enabled for your project and that you have the proper IAM roles including BigQuery User, Notebook Runtime User and Code Creator."]]],[]]
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