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()
- An error message explicitly requests you to use
- Avoid
read_gbq()for SQL: Do not write SQL queries and execute them withread_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()orDataFrame.apply().
- Prefer built-in accessors (e.g.,
- Schema Verification: Do not assume schema of intermediate outputs.
Check
.dtypesafter loading, and usedisplay()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 arandom_stateargument when instantiating BigFrames ML models. - Automatic Scaling: Do not use
OneHotEncoderorStandardScalerunless explicitly requested (handled automatically). - Hyperparameter Tuning: You must write custom loops (BigFrames lacks
GridSearchCVorRandomizedSearchCV). - 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.
- Import from
- PCA: BigFrames' PCA class lacks simple
transform()method. Usepredict()instead. - Model Persistence: To persist a model, use
model.to_gbq(). To load a persisted model, usebpd.read_gbq_model().
