BigFrames (BigQuery DataFrame) basics

Dataframe API best practices

• Stay in the Cloud: Perform data cleaning, transformation, and analysis via BigFrames methods to leverage BigQuery's scale rather than downloading data.
• Prefer partial ordering mode: Enable partial ordering mode right after importing BigFrames. This speeds up data processing significantly by relaxing row-sequence constraints.

  import bigframes.pandas as bpd
  bpd.options.bigquery.ordering_mode = 'partial'

• Use peek() for data preview: Use peek(n) to preview data instead of head(n). peek(n) randomly samples n rows and is significantly faster. head(n) returns rows in strict order and fails in partial ordering mode unless the DataFrame has been explicitly sorted.
• Avoid materializing data locally: Methods like to_pandas() download all data to client memory, bypassing BigQuery’s distributed computation and risking Out of Memory (OOM) errors. Do not materialize data locally unless:
  • The dataset is small enough to fit safely in memory.
  • An error message explicitly requires local materialization.
• Prefer Dataframe API over SQL queries: Do not write raw SQL queries via read_gbq() if a DataFrame/Series method achieves the same result, as it breaks the Pandas abstraction and prevents lazy query execution.
• Accessors over UDFs/Lambdas:
  • Use built-in accessors (e.g., df.col.str.*, df.col.dt.*) instead of remote User Defined Functions (UDFs). UDFs require extra resources and time to deploy.
  • Do not use lambdas with Series.map() or DataFrame.apply(). These methods do not accept functions without udf or remote_function decorators.

  # Avoid:
  df["upper"] = df["name"].map(lambda x: x.upper())
  
  # Prefer:
  df["upper"] = df["name"].str.upper()

• Schema Verification: Do not assume the schema of intermediate outputs. Proactively verify schemas using .dtypes and inspect sample records using display() with .peek().
• Visualization: Plot directly from the BigFrames DataFrame/Series when possible. BigFrames is compatible with Matplotlib and Seaborn. If direct plotting fails, use the .plot accessor. If the dataset is too large to plot, aggregate or sample the data before calling .to_pandas() to plot locally.

Machine Learning

• Use bigframes.bigquery.ml package: Do not use Scikit-learn or other ML libraries with BigQuery DataFrames. Standard Scikit-learn models require bringing data into local client memory, whereas bigframes.bigquery.ml delegates training directly to BigQuery's scalable ML engine. Import functions from bigframes.bigquery.ml.

Reference Directory

• Linear Regression [blocked]: Train a linear regression model to predict numerical values.
• Logistic Regression [blocked]: Train a logistic regression model to predict boolean values.

BigFrames ML (Legacy)

• Legacy Imports: When legacy BigFrames ML is requested, import tools and classes from bigframes.ml instead of bigframes.bigquery.ml.
• DataFrame Return on Prediction: Unlike Scikit-learn, BigFrames' predict() method always returns a DataFrame containing both predictions and features, rather than a single series of predictions.
• No random_state: Do not pass a random_state argument when instantiating BigFrames ML models, as this parameter is not supported in the BigFrames ML package.
• Automatic Scaling: Do not use OneHotEncoder or StandardScaler unless explicitly requested, as scaling is handled automatically.
• Hyperparameter Tuning: Write custom loops for hyperparameter tuning, as BigFrames lacks GridSearchCV or RandomizedSearchCV.
• ARIMA Plus (Forecasting):
  • Import from bigframes.ml.forecasting.
  • Sort data chronologically and split around a timepoint before training.
  • Ensure the prediction horizon is less than or equal to the training horizon.
• PCA: BigFrames' PCA class lacks a 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().