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bigquery-bigframes
GitHub生成基于BigQuery DataFrames (BigFrames) 的Python代码，适用于Pandas/scikit-learn风格的DataFrame和ML任务。禁止使用to_pandas()、SQL或本地库，强调云端计算、BigFrames ML包及特定开发规范。
Trigger Scenarios
编写BigFrames代码
对BigQuery进行Pandas风格的数据框操作
在Notebook中进行大数据分析
Install
npx skills add google/skills --skill bigquery-bigframes -g -y
SKILL.md
Frontmatter
{
"name": "bigquery-bigframes",
"metadata": {
"category": "BigDataAndAnalytics"
},
"description": "Generates Python code using BigQuery DataFrames (BigFrames), the pandas\/scikit-learn-style API over BigQuery. Use when writing BigFrames code or doing pandas-style dataframe\/ML work against BigQuery (e.g. in a notebook). Don't use for SQL-first workflows or the google-cloud-bigquery client library — use bigquery-basics."
}
BigFrames Development Standards
- Avoid
.to_pandas(): You MUST NOT use.to_pandas()to download the entire dataset into memory as this downloads all data to the client's memory, bypassing BigQuery's distributed computation and risking Out of Memory (OOM) errors. There are some exceptions:- An error message explicitly requests you to use
to_pandas() - You are going to visualize the data, and the visualization library does not accept BigFrames Dataframe/Series instances. In this case, reduce the amount of data you are going to download before calling
.to_pandas()
- An error message explicitly requests you to use
- Avoid
read_gbq()for SQL: Do not write SQL queries and execute them withread_gbq()to maintain the Pandas-like DataFrame abstraction and allow lazy executions. Use BigFrames Dataframe/Series methods instead. - Use BigFrames ML package for Machine Learning Tasks: Do not use
Scikit-learn or other ML libraries with BigFrames dataframes because
standard Scikit-learn models require bringing data into local client memory,
whereas bigframes.ml delegates training directly to BigQuery's scalable ML
engine. Import your tools/classes from
bigframes.ml. - Stay in the Cloud: Perform data cleaning, transformation, and analysis via BigFrames methods to leverage BigQuery's scale.
- Accessors over UDFs/Lambdas:
- Prefer built-in accessors (e.g.,
df.col.str.*,df.col.dt.*) over remote UDFs. - Do not use lambdas with
Series.map()orDataFrame.apply().
- Prefer built-in accessors (e.g.,
- Schema Verification: Do not assume schema of intermediate outputs. Check
.dtypesafter loading, and usedisplay()with.head()or.peek(). - Visualization: BigFrames Dataframe mostly works directly with
Matplotlib, Seaborn, and other plotting libraries. If your attempt didn't
work, try using the
plotaccessor. If that didn't work either, you MUST sample or aggregate your data to make it small enough before callingto_pandas().
Model Development
- Unlike Scikit-learn: BigFrames'
predict()method always returns a DataFrame containing both predictions and features (not just a series of predictions). - No
random_state: Do not pass arandom_stateargument when instantiating BigFrames ML models, because this parameter is not supported in the BigFrames ML package. - Automatic Scaling: Do not use
OneHotEncoderorStandardScalerunless explicitly requested (handled automatically). - Hyperparameter Tuning: You must write custom loops (BigFrames lacks
GridSearchCVorRandomizedSearchCV). - ARIMA Plus (Forecasting):
- Import from
bigframes.ml.forecasting. - Sort data chronologically and split around a timepoint before training.
- Prediction horizon must be less than or equal to training horizon.
- Import from
- PCA: BigFrames' PCA class lacks simple
transform()method. Usepredict()instead. - Model Persistence: To persist a model, use
model.to_gbq(). To load a persisted model, usebpd.read_gbq_model().
Version History
- aabe37a Current 2026-07-05 15:28
