Bigquery Bigframes

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Tool integration
Data and AI
Apache-2.0
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From the author

Generates Python code using BigQuery DataFrames (BigFrames). Use by default for any Python data task involving BigQuery, including data processing, analysis, and machine learning. Don't use for SQL-first workflows or the google-cloud-bigquery client library — use bigquery-basics.

— the description by Google

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

BigFrames is a Python library that enables scalable data processing and machine learning directly within BigQuery using familiar Pandas-like APIs. This skill provides essential patterns for efficient data manipulation, performance optimization, and model training while ensuring computations remain in the cloud to avoid memory constraints.

What it does

Provides best practices for using BigFrames to perform data cleaning and transformation.
Outlines performance optimization techniques like partial ordering and efficient data previewing.
Guides users on leveraging BigQuery's scalable ML engine via the bigframes.bigquery.ml package.
Instructs on avoiding local data materialization to prevent Out of Memory errors.
Defines proper usage of built-in accessors over custom UDFs for better performance.
Includes instructions for mandatory usage attribution when executing terminal commands.

When to use it

When performing data analysis or processing tasks on BigQuery data using Python.
When you need to train machine learning models directly on BigQuery datasets.
When you want to leverage BigQuery's distributed compute power while using a Pandas-like interface.
When you need to optimize existing BigFrames code for better performance and scalability.

Advantages

Maintains data processing within the cloud for maximum scalability.
Reduces the risk of client-side memory issues by avoiding unnecessary data downloads.
Leverages BigQuery's native ML engine for efficient model training.
Provides a familiar Pandas-like API for complex BigQuery operations.

Usage examples

“How do I preview data in BigFrames efficiently?”: The agent will recommend using the peek(n) method instead of head(n) to randomly sample rows, which is faster and compatible with partial ordering mode.
“How should I train a linear regression model on BigQuery data?”: The agent will provide code using the bigframes.bigquery.ml package, demonstrating how to load data, clean it, and use bbq.ml.create_model for training.
“Why should I avoid using to_pandas() in my script?”: The agent will explain that to_pandas() downloads all data to local memory, which bypasses BigQuery's distributed computation and risks causing Out of Memory errors.
“How do I perform string operations on a column in BigFrames?”: The agent will advise using built-in accessors like df.col.str.upper() instead of custom lambdas or UDFs to ensure better performance.

Install

Download the skill as a ZIP:
https://autogineer.com/agent-skills/google/bigquery-bigframes/download

Unzip it into the cross-client skills folder of your project:

mkdir -p .agents/skills && unzip bigquery-bigframes.zip -d .agents/skills

Documentation by Google

BigFrames (BigQuery DataFrame) basics

BigFrames is a Python library that lets you take advantage of BigQuery data processing by using familiar Python APIs.

Attribution

MUST READ: Before executing any gcloud, REST API, or MCP command, you MUST read Usage Attribution. For commands executed directly in the terminal (never in generated scripts, Terraform, or user code), prefix gcloud with CLOUDSDK_METRICS_REQUEST_ATTRIBUTION="agent-skills/1.0 (skill:bigquery-bigframes)" and append agent-skills/1.0 (skill:bigquery-bigframes) to User-Agent headers.

Dataframe API best practices

Stay in the Cloud: Perform data cleaning, transformation, and analysis via BigFrames methods to leverage BigQuery's scale rather than downloading data.
Prefer partial ordering mode: Enable partial ordering mode right after importing BigFrames. This speeds up data processing significantly by relaxing row-sequence constraints.
```
import bigframes.pandas as bpd
bpd.options.bigquery.ordering_mode = 'partial'
```
Use peek() for data preview: Use peek(n) to preview data instead of head(n). peek(n) randomly samples n rows and is significantly faster. head(n) returns rows in strict order and fails in partial ordering mode unless the DataFrame has been explicitly sorted.
Avoid materializing data locally: Methods like to_pandas() download all data to client memory, bypassing BigQuery’s distributed computation and risking Out of Memory (OOM) errors. Do not materialize data locally unless:
- The dataset is small enough to fit safely in memory.
- An error message explicitly requires local materialization.
Prefer Dataframe API over SQL queries: Do not write raw SQL queries via read_gbq() if a DataFrame/Series method achieves the same result, as it breaks the Pandas abstraction and prevents lazy query execution.
Accessors over UDFs/Lambdas:
- Use built-in accessors (e.g., df.col.str.*, df.col.dt.*) instead of remote User Defined Functions (UDFs). UDFs require extra resources and time to deploy.
- Do not use lambdas with Series.map() or DataFrame.apply(). These methods do not accept functions without udf or remote_function decorators.
```
# Avoid:
df["upper"] = df["name"].map(lambda x: x.upper())

# Prefer:
df["upper"] = df["name"].str.upper()
```
Schema Verification: Do not assume the schema of intermediate outputs. Proactively verify schemas using .dtypes and inspect sample records using display() with .peek().
Visualization: Plot directly from the BigFrames DataFrame/Series when possible. BigFrames is compatible with Matplotlib and Seaborn. If direct plotting fails, use the .plot accessor. If the dataset is too large to plot, aggregate or sample the data before calling .to_pandas() to plot locally.

Machine Learning

Use bigframes.bigquery.ml package: Do not use Scikit-learn or other ML libraries with BigQuery DataFrames. Standard Scikit-learn models require bringing data into local client memory, whereas bigframes.bigquery.ml delegates training directly to BigQuery's scalable ML engine. Import functions from bigframes.bigquery.ml.

Reference Directory

Linear Regression: Train a linear regression model to predict numerical values.
Logistic Regression: Train a logistic regression model to predict boolean values.

BigFrames ML (Legacy)

The BigFrames ML package (bigframes.ml) is a legacy package that mimics the scikit-learn API but is no longer recommended for new projects. Only use this package if the user explicitly requests BigFrames ML.

Legacy Imports: When legacy BigFrames ML is requested, import tools and classes from bigframes.ml instead of bigframes.bigquery.ml.
DataFrame Return on Prediction: Unlike Scikit-learn, BigFrames' predict() method always returns a DataFrame containing both predictions and features, rather than a single series of predictions.
No random_state: Do not pass a random_state argument when instantiating BigFrames ML models, as this parameter is not supported in the BigFrames ML package.
Automatic Scaling: Do not use OneHotEncoder or StandardScaler unless explicitly requested, as scaling is handled automatically.
Hyperparameter Tuning: Write custom loops for hyperparameter tuning, as BigFrames lacks GridSearchCV or RandomizedSearchCV.
ARIMA Plus (Forecasting):
- Import from bigframes.ml.forecasting.
- Sort data chronologically and split around a timepoint before training.
- Ensure the prediction horizon is less than or equal to the training horizon.
PCA: BigFrames' PCA class lacks a transform() method. Use predict() instead.
Model Persistence: To persist a model, use model.to_gbq(). To load a persisted model, use bpd.read_gbq_model().

Files

ATTRIBUTION.mdAdded by AutoGineer854 B
LICENSELicence file added by AutoGineer11.1 KiB
SKILL.md5.4 KiB
references/attribution.md1.8 KiB
references/linear_regression.md1.4 KiB
references/logistic_regression.md1.1 KiB

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Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Needs Python

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Attribution and licence

Published by

Google

Publisher

Google (Official)

Source

google/skills · skills/cloud/bigquery-bigframes · commit 2194d3b

Licence

Apache License 2.0 (redistribution allowed)

LICENSE (added by AutoGineer from LICENSE)
ATTRIBUTION.md (written by AutoGineer)

Synced

2026-11-09

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Download ZIP

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