fix!: remove out-of-date BigQuery ML protocol buffers (#1178)

deps!: BigQuery Storage and pyarrow are required dependencies (#776)

fix!: use nullable `Int64` and `boolean` dtypes in `to_dataframe` (#786) 

feat!: destination tables are no-longer removed by `create_job` (#891)

feat!: In `to_dataframe`, use `dbdate` and `dbtime` dtypes from db-dtypes package for BigQuery DATE and TIME columns (#972)

fix!: automatically convert out-of-bounds dates in `to_dataframe`, remove `date_as_object` argument (#972)

feat!: mark the package as type-checked (#1058)

feat!: default to DATETIME type when loading timezone-naive datetimes from Pandas (#1061)

feat: add `api_method` parameter to `Client.query` to select `INSERT` or `QUERY` API (#967)

fix: improve type annotations for mypy validation (#1081)

feat: use `StandardSqlField` class for `Model.feature_columns` and `Model.label_columns` (#1117)

docs: Add migration guide from version 2.x to 3.x (#1027)

Release-As: 3.0.0

.coveragerc

@@ -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

README.rst

@@ -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
@@ -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
-
-
-

UPGRADING.md

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

docs/bigquery/legacy_proto_types.rst

@@ -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:

docs/bigquery_v2/types.rst → docs/bigquery/standard_sql.rst

@@ -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:

docs/conf.py

@@ -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

docs/index.rst

@@ -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

docs/reference.rst

@@ -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

docs/snippets.py

@@ -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

docs/usage/pandas.rst

@@ -14,12 +14,12 @@ First, ensure that the :mod:`pandas` library is installed by running:
 
    pip install --upgrade pandas
 
-Alternatively, you can install the BigQuery python client library with
+Alternatively, you can install the BigQuery Python 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`:
 
@@ -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
 ------------------------------------------------------------
@@ -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 BigQuery python client library with :mod:`pandas` and
+install the BigQuery Python client library with :mod:`pandas` and
 :mod:`pyarrow` by running:
 
 .. code-block:: bash