What problem does it solve? Writing pandas-style data analysis and machine learning code against BigQuery requires knowing BigFrames-specific idioms, and common mistakes like materializing data locally or using raw SQL break lazy execution and cause out-of-memory errors. ## Core Features & Use Cases - BigFrames DataFrame Best Practices: Enforces partial ordering mode, peek() previews, accessor-based transformations, and schema verification instead of local materialization. - BigQuery ML Integration: Guides model training, evaluation, and prediction through bigframes.bigquery.ml so computation stays in BigQuery rather than client memory. - Legacy BigFrames ML Support: Covers the legacy bigframes.ml package including ARIMA Plus forecasting, PCA, and model persistence with to_gbq(). - Use Case: In a notebook, ask for a logistic regression model trained on the public penguins dataset, and receive correct BigFrames code that trains and evaluates the model entirely inside BigQuery. ## Quick Start Write BigFrames code that loads the penguins dataset from BigQuery and trains a linear regression model to predict penguin body mass.
