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BigQuery DataFrames (BigFrames) Coding Guide

Writes Python data code that runs on BigQuery's servers using the BigFrames pandas-style API

Source Download skill
Repository
google/skills
Stars
21,782
Licence
Apache-2.0
Files
4 files

What it does

Once installed, the agent writes Python data-analysis and machine-learning code using BigFrames, a library that mimics the familiar pandas interface but runs the work inside Google BigQuery instead of on your computer. It steers the agent toward cloud-side processing, avoids pulling large datasets into local memory, prefers BigQuery's built-in ML functions over scikit-learn, and adds a standard attribution tag to any gcloud or REST commands it runs in the terminal.

Great for

  • Python users who work with large tables in BigQuery and know pandas
  • Data scientists who want to train regression or classification models without moving data out of BigQuery
  • Teams standardising on BigFrames code patterns for generated notebooks or scripts

Skip it if

  • SQL-first workflows or direct use of the google-cloud-bigquery client library, which the skill itself says should use a different skill (bigquery-basics)
  • Small local datasets where plain pandas is simpler and needs no cloud project; the skill's rules push toward BigQuery even when it is unnecessary
  • Users who need scikit-learn specifically, since the skill tells the agent not to use it with BigFrames

Try asking

  • “Clean and aggregate a sales table in BigQuery with BigFrames and plot monthly totals”
  • “Train a linear regression model to predict house prices using bigframes.bigquery.ml”
  • “Preview a large BigQuery table with peek() and check its column types”
  • “Build a logistic regression model to predict customer churn from a BigQuery dataset”
Step-by-step guide: how to use BigQuery DataFrames (BigFrames) Coding GuideInstall, first run, examples, tips and FAQs

Install in 3 steps

  1. 1

    Download the skill

    The ZIP contains the 4 original files, the Apache-2.0 licence and an attribution note.

    bigquery-bigframes.zip
  2. 2

    Add it to your agent

    Claude Code (all projects)

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

    Claude Code / Agent SDK (this project only)

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

    Claude apps (web/desktop): Settings → Capabilities → Skills → Upload skill, and choose the ZIP.

  3. 3

    Ask for the task

    No special command needed — the agent reads the skill's description and loads it when your request matches. Try one of the prompts above.

Get better results

  • Name your BigQuery project and dataset in the request so the agent does not guess them
  • Ask for to_pandas() only on small or aggregated results to avoid memory errors
  • Review generated code before running it, since it will execute against your cloud project and may incur BigQuery costs

What's inside

  • references/attribution.md
  • references/linear_regression.md
  • references/logistic_regression.md
  • SKILL.md

Redistributed unmodified from google/skills under the Apache-2.0 licence. The assessment was drafted with AI from the skill's files on 2026-10-29. Skills can instruct your agent to run code — read SKILL.md and any scripts before installing.