bigquery-bigframes

SKILL.md

BigFrames (BigQuery DataFrame) basics

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

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.

```python
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: Train a linear
regression model to predict numerical values.

Logistic Regression: Train a logistic
regression model to predict boolean values.

BigFrames ML (Legacy)

The BigFrames ML package (bigframes.ml) is a legacy package that mimics the
scikit-learn API but is no longer recommended for new projects. Only use this
package if the user explicitly requests BigFrames ML.

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().

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