bigframes.pandas

BigQuery DataFrames provides a DataFrame API backed by the BigQuery engine.

Functions

clean_up_by_session_id(session_id[, ...])	Searches through BigQuery tables and routines and deletes the ones created during the session with the given session id.
close_session()	Start a fresh session the next time a function requires a session.
concat()	Concatenate BigQuery DataFrames objects along a particular axis.
crosstab(index, columns[, values, rownames, ...])	Compute a simple cross tabulation of two (or more) factors.
cut(x, bins, *[, right, labels, session])	Bin values into discrete intervals.
deploy_remote_function(func, **kwargs)	Orchestrates the creation of a BigQuery remote function that deploys immediately.
deploy_udf(func, **kwargs)	Orchestrates the creation of a BigQuery UDF that deploys immediately.
from_glob_path(path, *[, connection, name])	Create a BigFrames DataFrame that contains a BigFrames Blob column from a global wildcard path.
get_default_session_id()	Gets the session id that is used whenever a custom session has not been provided.
get_dummies(data[, prefix, prefix_sep, ...])	Convert categorical variable into dummy/indicator variables.
get_global_session()	Gets the global session.
merge(left, right[, how, on, left_on, ...])	Merge DataFrame objects with a database-style join.
qcut(x, q, *[, labels, duplicates])	Quantile-based discretization function.
read_arrow(pa_table)	Load a PyArrow Table to a BigQuery DataFrames DataFrame.
read_csv(filepath_or_buffer, *[, sep, ...])	Loads data from a comma-separated values (csv) file into a DataFrame.
read_gbq([columns, configuration, ...])	Loads a DataFrame from BigQuery.
read_gbq_function(function_name[, ...])	Loads a BigQuery function from BigQuery.
read_gbq_model(model_name)	Loads a BigQuery ML model from BigQuery.
read_gbq_object_table(object_table, *[, name])	Read an existing object table to create a BigFrames Blob DataFrame.
read_gbq_query([columns, configuration, ...])	Turn a SQL query into a DataFrame.
read_gbq_table([columns, max_results, ...])	Turn a BigQuery table into a DataFrame.
read_json(path_or_buf, *[, orient, dtype, ...])	Convert a JSON string to DataFrame object.
read_pandas()	Loads DataFrame from a pandas DataFrame.
read_parquet(path, *[, engine, write_engine])	Load a Parquet object from the file path (local or Cloud Storage), returning a DataFrame.
read_pickle(filepath_or_buffer[, ...])	Load pickled BigFrames object (or any object) from file.
remote_function([input_types, output_type, ...])	Decorator to turn a user defined function into a BigQuery remote function.
reset_session()	Start a fresh session the next time a function requires a session.
to_datetime()	This function converts a scalar, array-like or Series to a datetime object.
to_timedelta(arg[, unit, session])	Converts a scalar or Series to a timedelta object.
udf(*[, input_types, output_type, ...])	Decorator to turn a Python user defined function (udf) into a [BigQuery managed user-defined function](https://cloud.google.com/bigquery/docs/user-defined-functions-python).

Classes

DataFrame([data, index, columns, dtype, ...])	Two-dimensional, size-mutable, potentially heterogeneous tabular data.
DatetimeIndex([data, dtype, name, session])	Immutable sequence used for indexing and alignment with datetime-like values
Index([data, dtype, name, session])	Immutable sequence used for indexing and alignment.
MultiIndex([data, dtype, name, session])	A multi-level, or hierarchical, index object for pandas objects.
NamedAgg(column, aggfunc)	Create new instance of NamedAgg(column, aggfunc)
Series([data, index, dtype, name, copy, session])

Module Attributes

pandas.NA = <NA>

pandas.BooleanDtype = <class 'pandas.core.arrays.boolean.BooleanDtype'>

pandas.Float64Dtype = <class 'pandas.core.arrays.floating.Float64Dtype'>

pandas.Int64Dtype = <class 'pandas.core.arrays.integer.Int64Dtype'>

pandas.StringDtype = <class 'pandas.core.arrays.string_.StringDtype'>

pandas.ArrowDtype = <class 'pandas.core.dtypes.dtypes.ArrowDtype'>

pandas.options = <bigframes._config.global_options.Options object>

pandas.option_context = <class 'bigframes_vendored.pandas._config.config.option_context'>