The BigQuery DataFrames (BigFrames) API Reference documents the pandas-compatible and scikit-learn-compatible Python interfaces powered by BigQuery's distributed compute engine.
Designed to support the modern data stack, these APIs empower:
- Data Analysts to write familiar pandas code for scalable data exploration, cleaning, and aggregation without hitting memory limits.
- Data Engineers to build robust big data pipelines, leveraging advanced geospatial, array, and JSON functions native to BigQuery.
- Data Scientists to train, evaluate, and deploy machine learning models directly on BigQuery using the ML modules, or integrate Generative AI via BigQuery ML and Gemini.
Use this reference to discover the classes, methods, and functions that make up the BigQuery DataFrames ecosystem.
.. autosummary::
:toctree: api
bigframes._config
bigframes.bigquery
bigframes.bigquery.ai
bigframes.bigquery.ml
bigframes.bigquery.obj
bigframes.enums
bigframes.exceptions
bigframes.geopandas
bigframes.pandas
bigframes.pandas.api.typing
bigframes.streaming
BigQuery DataFrames provides extensions to pandas DataFrame objects.
.. autosummary::
:toctree: api
bigframes.extensions.core.dataframe_accessor.BigQueryDataFrameAccessor
bigframes.extensions.core.dataframe_accessor.AIAccessor
BigQuery DataFrames provides many machine learning modules, inspired by scikit-learn, enabling data scientists to quickly build, train, and deploy models on large datasets natively within BigQuery.
.. autosummary::
:toctree: api
bigframes.ml
bigframes.ml.cluster
bigframes.ml.compose
bigframes.ml.decomposition
bigframes.ml.ensemble
bigframes.ml.forecasting
bigframes.ml.imported
bigframes.ml.impute
bigframes.ml.linear_model
bigframes.ml.llm
bigframes.ml.metrics
bigframes.ml.model_selection
bigframes.ml.pipeline
bigframes.ml.preprocessing
bigframes.ml.remote