BigFrames (BigQuery DataFrame) basics

BigFrames is a Python library that lets you take advantage of BigQuery data processing by using familiar Python APIs.

Generic Coding Guidelines

• Avoid .to_pandas(): You MUST NOT use .to_pandas() to download the entire dataset into memory. 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()
• Avoid read_gbq() for SQL: Do not write SQL queries and execute them with read_gbq(). 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. 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() or DataFrame.apply().
• Schema Verification: Do not assume schema of intermediate outputs. Check .dtypes after loading, and use display() 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 "plot" accessor. If that didn't work either, you MUST sample or aggregate your data to make it small enough before calling "to_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 a random_state argument when instantiating BigFrames ML models.
• Automatic Scaling: Do not use OneHotEncoder or StandardScaler unless explicitly requested (handled automatically).
• Hyperparameter Tuning: You must write custom loops (BigFrames lacks GridSearchCV or RandomizedSearchCV).
• 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.
• PCA: BigFrames' PCA class lacks simple transform() method. Use predict() instead.
• Model Persistence: To persist a model, use model.to_gbq(). To load a persisted model, use bpd.read_gbq_model().