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Summary of entries of Classes for bigframes.
Classes
Options
Global options affecting BigQuery DataFrames behavior.
BigQueryOptions
Encapsulates configuration for working with a session.
ComputeOptions
Encapsulates configuration for compute options.
DisplayOptions
Encapsulates configuration for displaying objects.
SamplingOptions
Encapsulates configuration for data sampling.
DataFrameGroupBy
Class for grouping and aggregating relational data.
SeriesGroupBy
Class for grouping and aggregating relational data.
AtDataFrameIndexer
API documentation for AtDataFrameIndexer
class.
AtSeriesIndexer
API documentation for AtSeriesIndexer
class.
ILocDataFrameIndexer
API documentation for ILocDataFrameIndexer
class.
IatDataFrameIndexer
API documentation for IatDataFrameIndexer
class.
IatSeriesIndexer
API documentation for IatSeriesIndexer
class.
IlocSeriesIndexer
API documentation for IlocSeriesIndexer
class.
LocDataFrameIndexer
API documentation for LocDataFrameIndexer
class.
LocSeriesIndexer
API documentation for LocSeriesIndexer
class.
Index
Immutable sequence used for indexing and alignment.
Window
Provide window calculations.
DataFrame
Two-dimensional, size-mutable, potentially heterogeneous tabular data.
KMeans
K-Means clustering.
ColumnTransformer
Applies transformers to columns of BigQuery DataFrames.
PCA
Principal component analysis (PCA).
RandomForestClassifier
A random forest classifier.
RandomForestRegressor
A random forest regressor.
XGBClassifier
XGBoost classifier model.
XGBRegressor
XGBoost regression model.
ARIMAPlus
Time Series ARIMA Plus model.
ONNXModel
Imported Open Neural Network Exchange (ONNX) model.
TensorFlowModel
Imported TensorFlow model.
XGBoostModel
Imported XGBoost model.
LinearRegression
Ordinary least squares Linear Regression.
LogisticRegression
Logistic Regression (aka logit, MaxEnt) classifier.
GeminiTextGenerator
Gemini text generator LLM model.
PaLM2TextEmbeddingGenerator
PaLM2 text embedding generator LLM model.
PaLM2TextGenerator
PaLM2 text generator LLM model.
Pipeline
Pipeline of transforms with a final estimator.
KBinsDiscretizer
Bin continuous data into intervals.
LabelEncoder
Encode target labels with value between 0 and n_classes-1.
MaxAbsScaler
Scale each feature by its maximum absolute value.
MinMaxScaler
Transform features by scaling each feature to a given range.
OneHotEncoder
Encode categorical features as a one-hot format.
StandardScaler
Standardize features by removing the mean and scaling to unit variance.
VertexAIModel
Remote model from a Vertex AI https endpoint. User must specify https endpoint, input schema and output schema. How to deploy a model in Vertex AI https://cloud.google.com/bigquery/docs/bigquery-ml-remote-model-tutorial#Deploy-Model-on-Vertex-AI.
DatetimeMethods
Accessor object for datetime-like properties of the Series values.
PlotAccessor
Make plots of Series or DataFrame with the matplotlib
backend.
StringMethods
Vectorized string functions for Series and Index.
StructAccessor
Accessor object for structured data properties of the Series values.
NamedAgg
NamedAgg(column, aggfunc)
option_context
Context manager to temporarily set options in the with
statement context.
You need to invoke as option_context(pat, val, [(pat, val), ...])
.
Series
N-dimensional analogue of DataFrame. Store multi-dimensional in a size-mutable, labeled data structure
Session
Establishes a BigQuery connection to capture a group of job activities related to DataFrames.
Modules
cluster
Clustering models. This module is styled after Scikit-Learn's cluster module: https://scikit-learn.org/stable/modules/clustering.html.
compose
Build composite transformers on heterogeneous data. This module is styled after scikit-Learn's compose module: https://scikit-learn.org/stable/modules/classes.html#module-sklearn.compose.
decomposition
Matrix Decomposition models. This module is styled after Scikit-Learn's decomposition module: https://scikit-learn.org/stable/modules/decomposition.html.
ensemble
Ensemble models. This module is styled after scikit-learn's ensemble module: https://scikit-learn.org/stable/modules/ensemble.html
forecasting
Forcasting models.
imported
Imported models.
linear_model
Linear models. This module is styled after scikit-learn's linear_model module: https://scikit-learn.org/stable/modules/linear_model.html.
llm
LLM models.
pairwise
API documentation for pairwise
module.
model_selection
Functions for test/train split and model tuning. This module is styled after scikit-learn's model_selection module: https://scikit-learn.org/stable/modules/classes.html#module-sklearn.model_selection.
pipeline
For composing estimators together. This module is styled after scikit-learn's pipeline module: https://scikit-learn.org/stable/modules/pipeline.html.
preprocessing
Transformers that prepare data for other estimators. This module is styled after scikit-learn's preprocessing module: https://scikit-learn.org/stable/modules/preprocessing.html.
remote
BigFrames general remote models.
datetimes
API documentation for datetimes
module.
plotting
API documentation for plotting
module.
strings
API documentation for strings
module.
structs
API documentation for structs
module.