What problem does it solve? Writing pandas-style data analysis and machine learning code against BigQuery requires knowing BigFrames-specific APIs and pitfalls, such as partial ordering mode, avoiding local materialization, and using BigQuery-native ML functions instead of scikit-learn. ## Core Features & Use Cases - DataFrame Best Practices: Enforces partial ordering mode, peek() over head(), accessor methods over UDFs and lambdas, and schema verification with .dtypes. - BigQuery-Native ML: Guides model training, evaluation, and prediction through bigframes.bigquery.ml functions like create_model, evaluate, and predict instead of local scikit-learn. - Reference Examples: Includes ready linear regression and logistic regression examples training models on the public penguins dataset. - Use Case: A data scientist in a notebook wants to train a logistic regression model on a large BigQuery table without downloading data locally; the Skill produces BigFrames code that delegates training to BigQuery's ML engine. ## Quick Start Write BigFrames Python code that loads a BigQuery table, cleans it with DataFrame methods, and trains a linear regression model using bigframes.bigquery.ml.
