Mar 29, 2022 · Jul 27, 2021 · Jul 28, 2021 · Aug 5, 2021 · Aug 12, 2021 · Aug 16, 2021
diff --git a/.coveragerc b/.coveragerc
 show_missing = True
 omit =
    google/cloud/bigquery/__init__.py
    google/cloud/bigquery_v2/*  # Legacy proto-based types.
 exclude_lines =
    # Re-enable the standard pragma
    pragma: NO COVER
diff --git a/README.rst b/README.rst
 Python Client for Google BigQuery
 =================================

 |GA| |pypi| |versions|
 |GA| |pypi| |versions|

 Querying massive datasets can be time consuming and expensive without the
 right hardware and infrastructure. Google `BigQuery`_ solves this problem by

 .. _OpenTelemetry documentation: https://opentelemetry-python.readthedocs.io
 .. _Cloud Trace: https://cloud.google.com/trace



diff --git a/UPGRADING.md b/UPGRADING.md
 limitations under the License.
 -->

 # 3.0.0 Migration Guide

 ## New Required Dependencies

 Some of the previously optional dependencies are now *required* in `3.x` versions of the
 library, namely
 [google-cloud-bigquery-storage](https://pypi.org/project/google-cloud-bigquery-storage/)
 (minimum version `2.0.0`) and [pyarrow](https://pypi.org/project/pyarrow/) (minimum
 version `3.0.0`).

 The behavior of some of the package "extras" has thus also changed:
 * The `pandas` extra now requires the [db-types](https://pypi.org/project/db-dtypes/)
   package.
 * The `bqstorage` extra has been preserved for comaptibility reasons, but it is now a
   no-op and should be omitted when installing the BigQuery client library.

   **Before:**
   ```
   $ pip install google-cloud-bigquery[bqstorage]
   ```

   **After:**
   ```
   $ pip install google-cloud-bigquery
   ```

 * The `bignumeric_type` extra has been removed, as `BIGNUMERIC` type is now
   automatically supported. That extra should thus not be used.

   **Before:**
   ```
   $ pip install google-cloud-bigquery[bignumeric_type]
   ```

   **After:**
   ```
   $ pip install google-cloud-bigquery
   ```


 ## Type Annotations

 The library is now type-annotated and declares itself as such. If you use a static
 type checker such as `mypy`, you might start getting errors in places where
 `google-cloud-bigquery` package is used.

 It is recommended to update your code and/or type annotations to fix these errors, but
 if this is not feasible in the short term, you can temporarily ignore type annotations
 in `google-cloud-bigquery`, for example by using a special `# type: ignore` comment:

 ```py
 from google.cloud import bigquery  # type: ignore
 ```

 But again, this is only recommended as a possible short-term workaround if immediately
 fixing the type check errors in your project is not feasible.

 ## Re-organized Types

 The auto-generated parts of the library has been removed, and proto-based types formerly
 found in `google.cloud.bigquery_v2` have been replaced by the new implementation (but
 see the [section](#legacy-types) below).

 For example, the standard SQL data types should new be imported from a new location:

 **Before:**
 ```py
 from google.cloud.bigquery_v2 import StandardSqlDataType
 from google.cloud.bigquery_v2.types import StandardSqlField
 from google.cloud.bigquery_v2.types.standard_sql import StandardSqlStructType
 ```

 **After:**
 ```py
 from google.cloud.bigquery import StandardSqlDataType
 from google.cloud.bigquery.standard_sql import StandardSqlField
 from google.cloud.bigquery.standard_sql import StandardSqlStructType
 ```

 The `TypeKind` enum defining all possible SQL types for schema fields has been renamed
 and is not nested anymore under `StandardSqlDataType`:


 **Before:**
 ```py
 from google.cloud.bigquery_v2 import StandardSqlDataType

 if field_type == StandardSqlDataType.TypeKind.STRING:
    ...
 ```

 **After:**
 ```py

 from google.cloud.bigquery import StandardSqlTypeNames

 if field_type == StandardSqlTypeNames.STRING:
    ...
 ```


 ## Issuing queries with `Client.create_job` preserves destination table

 The `Client.create_job` method no longer removes the destination table from a
 query job's configuration. Destination table for the query can thus be
 explicitly defined by the user.


 ## Changes to data types when reading a pandas DataFrame

 The default dtypes returned by the `to_dataframe` method have changed.

 * Now, the BigQuery `BOOLEAN` data type maps to the pandas `boolean` dtype.
  Previously, this mapped to the pandas `bool` dtype when the column did not
  contain `NULL` values and the pandas `object` dtype when `NULL` values are
  present.
 * Now, the BigQuery `INT64` data type maps to the pandas `Int64` dtype.
  Previously, this mapped to the pandas `int64` dtype when the column did not
  contain `NULL` values and the pandas `float64` dtype when `NULL` values are
  present.
 * Now, the BigQuery `DATE` data type maps to the pandas `dbdate` dtype, which
  is provided by the
  [db-dtypes](https://googleapis.dev/python/db-dtypes/latest/index.html)
  package. If any date value is outside of the range of
  [pandas.Timestamp.min](https://pandas.pydata.org/docs/reference/api/pandas.Timestamp.min.html)
  (1677-09-22) and
  [pandas.Timestamp.max](https://pandas.pydata.org/docs/reference/api/pandas.Timestamp.max.html)
  (2262-04-11), the data type maps to the pandas `object` dtype. The
  `date_as_object` parameter has been removed.
 * Now, the BigQuery `TIME` data type maps to the pandas `dbtime` dtype, which
  is provided by the
  [db-dtypes](https://googleapis.dev/python/db-dtypes/latest/index.html)
  package.


 ## Changes to data types loading a pandas DataFrame

 In the absence of schema information, pandas columns with naive
 `datetime64[ns]` values, i.e. without timezone information, are recognized and
 loaded using the `DATETIME` type.  On the other hand, for columns with
 timezone-aware `datetime64[ns, UTC]` values, the `TIMESTAMP` type is continued
 to be used.

 ## Changes to `Model`, `Client.get_model`, `Client.update_model`, and `Client.list_models`

 The types of several `Model` properties have been changed.

 - `Model.feature_columns` now returns a sequence of `google.cloud.bigquery.standard_sql.StandardSqlField`.
 - `Model.label_columns` now returns a sequence of `google.cloud.bigquery.standard_sql.StandardSqlField`.
 - `Model.model_type` now returns a string.
 - `Model.training_runs` now returns a sequence of dictionaries, as recieved from the [BigQuery REST API](https://cloud.google.com/bigquery/docs/reference/rest/v2/models#Model.FIELDS.training_runs).

 <a name="legacy-protobuf-types"></a>
 ## Legacy Protocol Buffers Types

 For compatibility reasons, the legacy proto-based types still exists as static code
 and can be imported:

 ```py
 from google.cloud.bigquery_v2 import Model  # a sublcass of proto.Message
 ```

 Mind, however, that importing them will issue a warning, because aside from
 being importable, these types **are not maintained anymore**. They may differ
 both from the types in `google.cloud.bigquery`, and from the types supported on
 the backend.

 ### Maintaining compatibility with `google-cloud-bigquery` version 2.0

 If you maintain a library or system that needs to support both
 `google-cloud-bigquery` version 2.x and 3.x, it is recommended that you detect
 when version 2.x is in use and convert properties that use the legacy protocol
 buffer types, such as `Model.training_runs`, into the types used in 3.x.

 Call the [`to_dict`
 method](https://proto-plus-python.readthedocs.io/en/latest/reference/message.html#proto.message.Message.to_dict)
 on the protocol buffers objects to get a JSON-compatible dictionary.

 ```py
 from google.cloud.bigquery_v2 import Model

 training_run: Model.TrainingRun = ...
 training_run_dict = training_run.to_dict()
 ```

 # 2.0.0 Migration Guide

 from google.cloud.bigquery_v2 import types

 distance_type = types.Model.DistanceType.COSINE
 ```
 ```
diff --git a/docs/bigquery/legacy_proto_types.rst b/docs/bigquery/legacy_proto_types.rst
 Legacy proto-based Types for Google Cloud Bigquery v2 API
 =========================================================

 .. warning::
    These types are provided for backward compatibility only, and are not maintained
    anymore. They might also differ from the types uspported on the backend. It is
    therefore strongly advised to migrate to the types found in :doc:`standard_sql`.

    Also see the :doc:`3.0.0 Migration Guide<../UPGRADING>` for more information.

 .. automodule:: google.cloud.bigquery_v2.types
    :members:
    :undoc-members:
    :show-inheritance:
diff --git a/docs/bigquery_v2/types.rst → docs/bigquery/standard_sql.rst b/docs/bigquery_v2/types.rst → docs/bigquery/standard_sql.rst
 Types for Google Cloud Bigquery v2 API
 ======================================

 .. automodule:: google.cloud.bigquery_v2.types
 .. automodule:: google.cloud.bigquery.standard_sql
    :members:
    :undoc-members:
    :show-inheritance:
diff --git a/docs/conf.py b/docs/conf.py
 # List of patterns, relative to source directory, that match files and
 # directories to ignore when looking for source files.
 exclude_patterns = [
    "google/cloud/bigquery_v2/**",  # Legacy proto-based types.
    "_build",
    "**/.nox/**/*",
    "samples/AUTHORING_GUIDE.md",
    "samples/CONTRIBUTING.md",
    "samples/snippets/README.rst",
    "bigquery_v2/services.rst",  # generated by the code generator
 ]

 # The reST default role (used for this markup: `text`) to use for all
diff --git a/docs/index.rst b/docs/index.rst
 Migration Guide
 ---------------

 See the guide below for instructions on migrating to the 2.x release of this library.
 See the guides below for instructions on migrating from older to newer *major* releases
 of this library (from ``1.x`` to ``2.x``, or from ``2.x`` to ``3.x``).

 .. toctree::
    :maxdepth: 2
diff --git a/docs/reference.rst b/docs/reference.rst
 Additional Types
 ================

 Protocol buffer classes for working with the Models API.
 Helper SQL type classes.

 .. toctree::
    :maxdepth: 2

    bigquery_v2/types
    bigquery/standard_sql


 Legacy proto-based Types (deprecated)
 =====================================

 The legacy type classes based on protocol buffers.

 .. deprecated:: 3.0.0
    These types are provided for backward compatibility only, and are not maintained
    anymore.

 .. toctree::
    :maxdepth: 2

    bigquery/legacy_proto_types
diff --git a/docs/snippets.py b/docs/snippets.py
    import pandas
 except (ImportError, AttributeError):
    pandas = None
 try:
    import pyarrow
 except (ImportError, AttributeError):
    pyarrow = None

 from google.api_core.exceptions import InternalServerError
 from google.api_core.exceptions import ServiceUnavailable
diff --git a/docs/usage/pandas.rst b/docs/usage/pandas.rst

   pip install --upgrade pandas

 Alternatively, you can install the BigQuerypython client library with
 Alternatively, you can install the BigQueryPython client library with
 :mod:`pandas` by running:

 .. code-block:: bash

   pip install --upgrade google-cloud-bigquery[pandas]
   pip install --upgrade'google-cloud-bigquery[pandas]'

 To retrieve query results as a :class:`pandas.DataFrame`:

   :start-after: [START bigquery_list_rows_dataframe]
   :end-before: [END bigquery_list_rows_dataframe]

 The following data types are used when creating a pandas DataFrame.

 .. list-table:: Pandas Data Type Mapping
   :header-rows: 1

   * - BigQuery
     - pandas
     - Notes
   * - BOOL
     - boolean
     -
   * - DATETIME
     - datetime64[ns], object
     - The object dtype is used when there are values not representable in a
       pandas nanosecond-precision timestamp.
   * - DATE
     - dbdate, object
     - The object dtype is used when there are values not representable in a
       pandas nanosecond-precision timestamp.

       Requires the ``db-dtypes`` package. See the `db-dtypes usage guide
       <https://googleapis.dev/python/db-dtypes/latest/usage.html>`_
   * - FLOAT64
     - float64
     -
   * - INT64
     - Int64
     -
   * - TIME
     - dbtime
     - Requires the ``db-dtypes`` package. See the `db-dtypes usage guide
       <https://googleapis.dev/python/db-dtypes/latest/usage.html>`_

 Retrieve BigQuery GEOGRAPHY data as a GeoPandas GeoDataFrame
 ------------------------------------------------------------
 to load data from a :class:`pandas.DataFrame` to a
 :class:`~google.cloud.bigquery.table.Table`. To use this function, in addition
 to :mod:`pandas`, you will need to install the :mod:`pyarrow` library. You can
 install the BigQuerypython client library with :mod:`pandas` and
 install the BigQueryPython client library with :mod:`pandas` and
 :mod:`pyarrow` by running:

 .. code-block:: bash
Original file line number	Diff line number	Diff line change
Expand Up		@@ -6,6 +6,7 @@ fail_under = 100
		show_missing = True
		omit =
		google/cloud/bigquery/__init__.py
		google/cloud/bigquery_v2/* # Legacy proto-based types.
		exclude_lines =
		# Re-enable the standard pragma
		pragma: NO COVER
Expand Down
Original file line number	Diff line number	Diff line change
		@@ -1,7 +1,7 @@
		Python Client for Google BigQuery
		=================================

		\|GA\| \|pypi\| \|versions\|
		\|GA\| \|pypi\| \|versions\|

		Querying massive datasets can be time consuming and expensive without the
		right hardware and infrastructure. Google `BigQuery`_ solves this problem by
Expand DownExpand Up		@@ -140,6 +140,3 @@ In this example all tracing data will be published to the Google

		.. _OpenTelemetry documentation: https://opentelemetry-python.readthedocs.io
		.. _Cloud Trace: https://cloud.google.com/trace
Original file line number	Diff line number	Diff line change
Expand Up		@@ -11,6 +11,190 @@ See the License for the specific language governing permissions and
		limitations under the License.
		-->

		# 3.0.0 Migration Guide

		## New Required Dependencies

		Some of the previously optional dependencies are now required in `3.x` versions of the
		library, namely
		[google-cloud-bigquery-storage](https://pypi.org/project/google-cloud-bigquery-storage/)
		(minimum version `2.0.0`) and [pyarrow](https://pypi.org/project/pyarrow/) (minimum
		version `3.0.0`).

		The behavior of some of the package "extras" has thus also changed:
		* The `pandas` extra now requires the [db-types](https://pypi.org/project/db-dtypes/)
		package.
		* The `bqstorage` extra has been preserved for comaptibility reasons, but it is now a
		no-op and should be omitted when installing the BigQuery client library.

		Before:
		```
		$ pip install google-cloud-bigquery[bqstorage]
		```

		After:
		```
		$ pip install google-cloud-bigquery
		```

		* The `bignumeric_type` extra has been removed, as `BIGNUMERIC` type is now
		automatically supported. That extra should thus not be used.

		Before:
		```
		$ pip install google-cloud-bigquery[bignumeric_type]
		```

		After:
		```
		$ pip install google-cloud-bigquery
		```


		## Type Annotations

		The library is now type-annotated and declares itself as such. If you use a static
		type checker such as `mypy`, you might start getting errors in places where
		`google-cloud-bigquery` package is used.

		It is recommended to update your code and/or type annotations to fix these errors, but
		if this is not feasible in the short term, you can temporarily ignore type annotations
		in `google-cloud-bigquery`, for example by using a special `# type: ignore` comment:

		```py
		from google.cloud import bigquery # type: ignore
		```

		But again, this is only recommended as a possible short-term workaround if immediately
		fixing the type check errors in your project is not feasible.

		## Re-organized Types

		The auto-generated parts of the library has been removed, and proto-based types formerly
		found in `google.cloud.bigquery_v2` have been replaced by the new implementation (but
		see the [section](#legacy-types) below).

		For example, the standard SQL data types should new be imported from a new location:

		Before:
		```py
		from google.cloud.bigquery_v2 import StandardSqlDataType
		from google.cloud.bigquery_v2.types import StandardSqlField
		from google.cloud.bigquery_v2.types.standard_sql import StandardSqlStructType
		```

		After:
		```py
		from google.cloud.bigquery import StandardSqlDataType
		from google.cloud.bigquery.standard_sql import StandardSqlField
		from google.cloud.bigquery.standard_sql import StandardSqlStructType
		```

		The `TypeKind` enum defining all possible SQL types for schema fields has been renamed
		and is not nested anymore under `StandardSqlDataType`:


		Before:
		```py
		from google.cloud.bigquery_v2 import StandardSqlDataType

		if field_type == StandardSqlDataType.TypeKind.STRING:
		...
		```

		After:
		```py

		from google.cloud.bigquery import StandardSqlTypeNames

		if field_type == StandardSqlTypeNames.STRING:
		...
		```


		## Issuing queries with `Client.create_job` preserves destination table

		The `Client.create_job` method no longer removes the destination table from a
		query job's configuration. Destination table for the query can thus be
		explicitly defined by the user.


		## Changes to data types when reading a pandas DataFrame

		The default dtypes returned by the `to_dataframe` method have changed.

		* Now, the BigQuery `BOOLEAN` data type maps to the pandas `boolean` dtype.
		Previously, this mapped to the pandas `bool` dtype when the column did not
		contain `NULL` values and the pandas `object` dtype when `NULL` values are
		present.
		* Now, the BigQuery `INT64` data type maps to the pandas `Int64` dtype.
		Previously, this mapped to the pandas `int64` dtype when the column did not
		contain `NULL` values and the pandas `float64` dtype when `NULL` values are
		present.
		* Now, the BigQuery `DATE` data type maps to the pandas `dbdate` dtype, which
		is provided by the
		[db-dtypes](https://googleapis.dev/python/db-dtypes/latest/index.html)
		package. If any date value is outside of the range of
		[pandas.Timestamp.min](https://pandas.pydata.org/docs/reference/api/pandas.Timestamp.min.html)
		(1677-09-22) and
		[pandas.Timestamp.max](https://pandas.pydata.org/docs/reference/api/pandas.Timestamp.max.html)
		(2262-04-11), the data type maps to the pandas `object` dtype. The
		`date_as_object` parameter has been removed.
		* Now, the BigQuery `TIME` data type maps to the pandas `dbtime` dtype, which
		is provided by the
		[db-dtypes](https://googleapis.dev/python/db-dtypes/latest/index.html)
		package.


		## Changes to data types loading a pandas DataFrame

		In the absence of schema information, pandas columns with naive
		`datetime64[ns]` values, i.e. without timezone information, are recognized and
		loaded using the `DATETIME` type. On the other hand, for columns with
		timezone-aware `datetime64[ns, UTC]` values, the `TIMESTAMP` type is continued
		to be used.

		## Changes to `Model`, `Client.get_model`, `Client.update_model`, and `Client.list_models`

		The types of several `Model` properties have been changed.

		- `Model.feature_columns` now returns a sequence of `google.cloud.bigquery.standard_sql.StandardSqlField`.
		- `Model.label_columns` now returns a sequence of `google.cloud.bigquery.standard_sql.StandardSqlField`.
		- `Model.model_type` now returns a string.
		- `Model.training_runs` now returns a sequence of dictionaries, as recieved from the [BigQuery REST API](https://cloud.google.com/bigquery/docs/reference/rest/v2/models#Model.FIELDS.training_runs).

		<a name="legacy-protobuf-types"></a>
		## Legacy Protocol Buffers Types

		For compatibility reasons, the legacy proto-based types still exists as static code
		and can be imported:

		```py
		from google.cloud.bigquery_v2 import Model # a sublcass of proto.Message
		```

		Mind, however, that importing them will issue a warning, because aside from
		being importable, these types are not maintained anymore. They may differ
		both from the types in `google.cloud.bigquery`, and from the types supported on
		the backend.

		### Maintaining compatibility with `google-cloud-bigquery` version 2.0

		If you maintain a library or system that needs to support both
		`google-cloud-bigquery` version 2.x and 3.x, it is recommended that you detect
		when version 2.x is in use and convert properties that use the legacy protocol
		buffer types, such as `Model.training_runs`, into the types used in 3.x.

		Call the [`to_dict`
		method](https://proto-plus-python.readthedocs.io/en/latest/reference/message.html#proto.message.Message.to_dict)
		on the protocol buffers objects to get a JSON-compatible dictionary.

		```py
		from google.cloud.bigquery_v2 import Model

		training_run: Model.TrainingRun = ...
		training_run_dict = training_run.to_dict()
		```

		# 2.0.0 Migration Guide

Expand DownExpand Up		@@ -56,4 +240,4 @@ distance_type = enums.Model.DistanceType.COSINE
		from google.cloud.bigquery_v2 import types

		distance_type = types.Model.DistanceType.COSINE
		```
		```
Original file line number	Diff line number	Diff line change
		@@ -0,0 +1,14 @@
		Legacy proto-based Types for Google Cloud Bigquery v2 API
		=========================================================

		.. warning::
		These types are provided for backward compatibility only, and are not maintained
		anymore. They might also differ from the types uspported on the backend. It is
		therefore strongly advised to migrate to the types found in :doc:`standard_sql`.

		Also see the :doc:`3.0.0 Migration Guide<../UPGRADING>` for more information.

		.. automodule:: google.cloud.bigquery_v2.types
		:members:
		:undoc-members:
		:show-inheritance:
Original file line number	Diff line number	Diff line change
		@@ -1,7 +1,7 @@
		Types for Google Cloud Bigquery v2 API
		======================================

		.. automodule:: google.cloud.bigquery_v2.types
		.. automodule:: google.cloud.bigquery.standard_sql
		:members:
		:undoc-members:
		:show-inheritance:
Original file line number	Diff line number	Diff line change
Expand Up		@@ -109,12 +109,12 @@
		# List of patterns, relative to source directory, that match files and
		# directories to ignore when looking for source files.
		exclude_patterns = [
		"google/cloud/bigquery_v2/**", # Legacy proto-based types.
		"_build",
		"/.nox//*",
		"samples/AUTHORING_GUIDE.md",
		"samples/CONTRIBUTING.md",
		"samples/snippets/README.rst",
		"bigquery_v2/services.rst", # generated by the code generator
		]

		# The reST default role (used for this markup: `text`) to use for all
Expand Down
Original file line number	Diff line number	Diff line change
Expand Up		@@ -30,7 +30,8 @@ API Reference
		Migration Guide
		---------------

		See the guide below for instructions on migrating to the 2.x release of this library.
		See the guides below for instructions on migrating from older to newer major releases
		of this library (from ``1.x`` to ``2.x``, or from ``2.x`` to ``3.x``).

		.. toctree::
		:maxdepth: 2
Expand Down
Original file line number	Diff line number	Diff line change
Expand Up		@@ -202,9 +202,24 @@ Encryption Configuration
		Additional Types
		================

		Protocol buffer classes for working with the Models API.
		Helper SQL type classes.

		.. toctree::
		:maxdepth: 2

		bigquery_v2/types
		bigquery/standard_sql


		Legacy proto-based Types (deprecated)
		=====================================

		The legacy type classes based on protocol buffers.

		.. deprecated:: 3.0.0
		These types are provided for backward compatibility only, and are not maintained
		anymore.

		.. toctree::
		:maxdepth: 2

		bigquery/legacy_proto_types
Original file line number	Diff line number	Diff line change
Expand Up		@@ -30,10 +30,6 @@
		import pandas
		except (ImportError, AttributeError):
		pandas = None
		try:
		import pyarrow
		except (ImportError, AttributeError):
		pyarrow = None

		from google.api_core.exceptions import InternalServerError
		from google.api_core.exceptions import ServiceUnavailable
Expand Down
Original file line number	Diff line number	Diff line change
Expand Up		@@ -14,12 +14,12 @@ First, ensure that the :mod:`pandas` library is installed by running:

		pip install --upgrade pandas

		Alternatively, you can install the BigQuerypython client library with
		Alternatively, you can install the BigQueryPython client library with
		:mod:`pandas` by running:

		.. code-block:: bash

		pip install --upgrade google-cloud-bigquery[pandas]
		pip install --upgrade'google-cloud-bigquery[pandas]'

		To retrieve query results as a :class:`pandas.DataFrame`:

Expand All		@@ -37,6 +37,38 @@ To retrieve table rows as a :class:`pandas.DataFrame`:
		:start-after: [START bigquery_list_rows_dataframe]
		:end-before: [END bigquery_list_rows_dataframe]

		The following data types are used when creating a pandas DataFrame.

		.. list-table:: Pandas Data Type Mapping
		:header-rows: 1

		* - BigQuery
		- pandas
		- Notes
		* - BOOL
		- boolean
		-
		* - DATETIME
		- datetime64[ns], object
		- The object dtype is used when there are values not representable in a
		pandas nanosecond-precision timestamp.
		* - DATE
		- dbdate, object
		- The object dtype is used when there are values not representable in a
		pandas nanosecond-precision timestamp.

		Requires the ``db-dtypes`` package. See the `db-dtypes usage guide
		<https://googleapis.dev/python/db-dtypes/latest/usage.html>`_
		* - FLOAT64
		- float64
		-
		* - INT64
		- Int64
		-
		* - TIME
		- dbtime
		- Requires the ``db-dtypes`` package. See the `db-dtypes usage guide
		<https://googleapis.dev/python/db-dtypes/latest/usage.html>`_

		Retrieve BigQuery GEOGRAPHY data as a GeoPandas GeoDataFrame
		------------------------------------------------------------
Expand All		@@ -60,7 +92,7 @@ As of version 1.3.0, you can use the
		to load data from a :class:`pandas.DataFrame` to a
		:class:`~google.cloud.bigquery.table.Table`. To use this function, in addition
		to :mod:`pandas`, you will need to install the :mod:`pyarrow` library. You can
		install the BigQuerypython client library with :mod:`pandas` and
		install the BigQueryPython client library with :mod:`pandas` and
		:mod:`pyarrow` by running:

		.. code-block:: bash
Expand Down