fix: to_gbq uses default_type for ambiguous array types and struc…

…t field types (#838) * fix: `to_gbq` uses `default_type` for ambiguous array types and struct field types * 🦉 Updates from OwlBot post-processor See https://github.com/googleapis/repo-automation-bots/blob/main/packages/owl-bot/README.md * fix arrow list(null) case too * 🦉 Updates from OwlBot post-processor See https://github.com/googleapis/repo-automation-bots/blob/main/packages/owl-bot/README.md * lint * Update pandas_gbq/schema/pandas_to_bigquery.py Co-authored-by: Chalmer Lowe <[email protected]> * Update pandas_gbq/schema/pandas_to_bigquery.py Co-authored-by: Chalmer Lowe <[email protected]> * 🦉 Updates from OwlBot post-processor See https://github.com/googleapis/repo-automation-bots/blob/main/packages/owl-bot/README.md * 🦉 Updates from OwlBot post-processor See https://github.com/googleapis/repo-automation-bots/blob/main/packages/owl-bot/README.md * remove redundant string check * Apply suggestions from code review Co-authored-by: Chalmer Lowe <[email protected]> * add docstrings and a few more test cases * use python 3.10 for docs github action --------- Co-authored-by: Owl Bot <gcf-owl-bot[bot]@users.noreply.github.com> Co-authored-by: Chalmer Lowe <[email protected]>
googleapis · Dec 19, 2024 · cf1aadd · cf1aadd
1 parent 5484a8c
commit cf1aadd
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diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml
@@ -12,7 +12,7 @@ jobs:
     - name: Setup Python
       uses: actions/setup-python@v5
       with:
-        python-version: "3.9"
+        python-version: "3.10"
     - name: Install nox
       run: |
         python -m pip install --upgrade setuptools pip wheel

diff --git a/owlbot.py b/owlbot.py
@@ -57,6 +57,7 @@
         "noxfile.py",
         "README.rst",
         # exclude this file as we have an alternate prerelease.cfg
+        ".github/workflows/docs.yml",
         ".kokoro/presubmit/prerelease-deps.cfg",
         ".kokoro/presubmit/presubmit.cfg",
     ],

diff --git a/pandas_gbq/schema/pandas_to_bigquery.py b/pandas_gbq/schema/pandas_to_bigquery.py
@@ -4,7 +4,7 @@
 
 import collections.abc
 import datetime
-from typing import Optional, Tuple
+from typing import Any, Optional, Tuple
 import warnings
 
 import db_dtypes
@@ -28,14 +28,21 @@
 # `docs/source/writing.rst`.
 _PANDAS_DTYPE_TO_BQ = {
     "bool": "BOOLEAN",
+    "boolean": "BOOLEAN",
     "datetime64[ns, UTC]": "TIMESTAMP",
+    "datetime64[us, UTC]": "TIMESTAMP",
     "datetime64[ns]": "DATETIME",
+    "datetime64[us]": "DATETIME",
     "float32": "FLOAT",
     "float64": "FLOAT",
     "int8": "INTEGER",
     "int16": "INTEGER",
     "int32": "INTEGER",
     "int64": "INTEGER",
+    "Int8": "INTEGER",
+    "Int16": "INTEGER",
+    "Int32": "INTEGER",
+    "Int64": "INTEGER",
     "uint8": "INTEGER",
     "uint16": "INTEGER",
     "uint32": "INTEGER",
@@ -103,7 +110,7 @@ def dataframe_to_bigquery_fields(
 
         # Try to automatically determine the type based on a few rows of the data.
         values = dataframe.reset_index()[column]
-        bq_field = values_to_bigquery_field(column, values)
+        bq_field = values_to_bigquery_field(column, values, default_type=default_type)
 
         if bq_field:
             bq_schema_out.append(bq_field)
@@ -114,7 +121,9 @@ def dataframe_to_bigquery_fields(
             arrow_value = pyarrow.array(values)
             bq_field = (
                 pandas_gbq.schema.pyarrow_to_bigquery.arrow_type_to_bigquery_field(
-                    column, arrow_value.type
+                    column,
+                    arrow_value.type,
+                    default_type=default_type,
                 )
             )
 
@@ -151,6 +160,19 @@ def dataframe_to_bigquery_fields(
 
 
 def dtype_to_bigquery_field(name, dtype) -> Optional[schema.SchemaField]:
+    """Infers the BigQuery schema field type from a pandas dtype.
+
+    Args:
+        name (str):
+            Name of the column/field.
+        dtype:
+            A pandas / numpy dtype object.
+
+    Returns:
+        Optional[schema.SchemaField]:
+            The schema field, or None if a type cannot be inferred, such as if
+            it is ambiguous like the object dtype.
+    """
     bq_type = _PANDAS_DTYPE_TO_BQ.get(dtype.name)
 
     if bq_type is not None:
@@ -164,9 +186,44 @@ def dtype_to_bigquery_field(name, dtype) -> Optional[schema.SchemaField]:
     return None
 
 
-def value_to_bigquery_field(name, value) -> Optional[schema.SchemaField]:
-    if isinstance(value, str):
-        return schema.SchemaField(name, "STRING")
+def value_to_bigquery_field(
+    name: str, value: Any, default_type: Optional[str] = None
+) -> Optional[schema.SchemaField]:
+    """Infers the BigQuery schema field type from a single value.
+
+    Args:
+        name:
+            The name of the field.
+        value:
+            The value to infer the type from. If None, the default type is used
+            if available.
+        default_type:
+            The default field type.  Defaults to None.
+
+    Returns:
+        The schema field, or None if a type cannot be inferred.
+    """
+
+    # Set the SchemaField datatype to the given default_type if the value
+    # being assessed is None.
+    if value is None:
+        return schema.SchemaField(name, default_type)
+
+    # Map from Python types to BigQuery types. This isn't super exhaustive
+    # because we rely more on pyarrow, which can check more than one value to
+    # determine the type.
+    type_mapping = {
+        str: "STRING",
+    }
+
+    # geopandas and shapely are optional dependencies, so only check if those
+    # are installed.
+    if _BaseGeometry is not None:
+        type_mapping[_BaseGeometry] = "GEOGRAPHY"
+
+    for type_, bq_type in type_mapping.items():
+        if isinstance(value, type_):
+            return schema.SchemaField(name, bq_type)
 
     # For timezone-naive datetimes, the later pyarrow conversion to try and
     # learn the type add a timezone to such datetimes, causing them to be
@@ -182,35 +239,51 @@ def value_to_bigquery_field(name, value) -> Optional[schema.SchemaField]:
         else:
             return schema.SchemaField(name, "DATETIME")
 
-    if _BaseGeometry is not None and isinstance(value, _BaseGeometry):
-        return schema.SchemaField(name, "GEOGRAPHY")
-
     return None
 
 
-def values_to_bigquery_field(name, values) -> Optional[schema.SchemaField]:
+def values_to_bigquery_field(
+    name: str, values: Any, default_type: str = "STRING"
+) -> Optional[schema.SchemaField]:
+    """Infers the BigQuery schema field type from a list of values.
+
+    This function iterates through the given values to determine the
+    corresponding schema field type.
+
+    Args:
+        name:
+            The name of the field.
+        values:
+            An iterable of values to infer the type from. If all the values
+            are None or the iterable is empty, the function returns None.
+        default_type:
+            The default field type to use if a specific type cannot be
+            determined from the values. Defaults to "STRING".
+
+    Returns:
+        The schema field, or None if a type cannot be inferred.
+    """
     value = pandas_gbq.core.pandas.first_valid(values)
 
-    # All NULL, type not determinable.
+    # All values came back as NULL, thus type not determinable by this method.
+    # Return None so we can try other methods.
     if value is None:
         return None
 
-    field = value_to_bigquery_field(name, value)
-    if field is not None:
+    field = value_to_bigquery_field(name, value, default_type=default_type)
+    if field:
         return field
 
-    if isinstance(value, str):
-        return schema.SchemaField(name, "STRING")
-
-    # Check plain ARRAY values here. Let STRUCT get determined by pyarrow,
-    # which can examine more values to determine all keys.
+    # Check plain ARRAY values here. Exclude mapping types to let STRUCT get
+    # determined by pyarrow, which can examine more values to determine all
+    # keys.
     if isinstance(value, collections.abc.Iterable) and not isinstance(
         value, collections.abc.Mapping
     ):
         # It could be that this value contains all None or is empty, so get the
         # first non-None value we can find.
         valid_item = pandas_gbq.core.pandas.first_array_valid(values)
-        field = value_to_bigquery_field(name, valid_item)
+        field = value_to_bigquery_field(name, valid_item, default_type=default_type)
 
         if field is not None:
             return schema.SchemaField(name, field.field_type, mode="REPEATED")

diff --git a/pandas_gbq/schema/pyarrow_to_bigquery.py b/pandas_gbq/schema/pyarrow_to_bigquery.py
@@ -37,7 +37,31 @@
 }
 
 
-def arrow_type_to_bigquery_field(name, type_) -> Optional[schema.SchemaField]:
+def arrow_type_to_bigquery_field(
+    name, type_, default_type="STRING"
+) -> Optional[schema.SchemaField]:
+    """Infers the BigQuery schema field type from an arrow type.
+
+    Args:
+        name (str):
+            Name of the column/field.
+        type_:
+            A pyarrow type object.
+
+    Returns:
+        Optional[schema.SchemaField]:
+            The schema field, or None if a type cannot be inferred, such as if
+            it is a type that doesn't have a clear mapping in BigQuery.
+
+            null() are assumed to be the ``default_type``, since there are no
+            values that contradict that.
+    """
+    # If a sub-field is the null type, then assume it's the default type, as
+    # that's the best we can do.
+    # https://github.com/googleapis/python-bigquery-pandas/issues/836
+    if pyarrow.types.is_null(type_):
+        return schema.SchemaField(name, default_type)
+
     # Since both TIMESTAMP/DATETIME use pyarrow.timestamp(...), we need to use
     # a special case to disambiguate them. See:
     # https://github.com/googleapis/python-bigquery-pandas/issues/450
@@ -52,22 +76,49 @@ def arrow_type_to_bigquery_field(name, type_) -> Optional[schema.SchemaField]:
         return schema.SchemaField(name, detected_type)
 
     if pyarrow.types.is_list(type_):
-        return arrow_list_type_to_bigquery(name, type_)
+        return arrow_list_type_to_bigquery(name, type_, default_type=default_type)
 
     if pyarrow.types.is_struct(type_):
         inner_fields: list[pyarrow.Field] = []
         struct_type = cast(pyarrow.StructType, type_)
         for field_index in range(struct_type.num_fields):
             field = struct_type[field_index]
-            inner_fields.append(arrow_type_to_bigquery_field(field.name, field.type))
+            inner_fields.append(
+                arrow_type_to_bigquery_field(
+                    field.name, field.type, default_type=default_type
+                )
+            )
 
         return schema.SchemaField(name, "RECORD", fields=inner_fields)
 
     return None
 
 
-def arrow_list_type_to_bigquery(name, type_) -> Optional[schema.SchemaField]:
-    inner_field = arrow_type_to_bigquery_field(name, type_.value_type)
+def arrow_list_type_to_bigquery(
+    name, type_, default_type="STRING"
+) -> Optional[schema.SchemaField]:
+    """Infers the BigQuery schema field type from an arrow list type.
+
+    Args:
+        name (str):
+            Name of the column/field.
+        type_:
+            A pyarrow type object.
+
+    Returns:
+        Optional[schema.SchemaField]:
+            The schema field, or None if a type cannot be inferred, such as if
+            it is a type that doesn't have a clear mapping in BigQuery.
+
+            null() are assumed to be the ``default_type``, since there are no
+            values that contradict that.
+    """
+    inner_field = arrow_type_to_bigquery_field(
+        name, type_.value_type, default_type=default_type
+    )
+
+    # If this is None, it means we got some type that we can't cleanly map to
+    # a BigQuery type, so bubble that status up.
     if inner_field is None:
         return None
 

diff --git a/tests/unit/schema/test_pandas_to_bigquery.py b/tests/unit/schema/test_pandas_to_bigquery.py
@@ -21,13 +21,34 @@ def module_under_test():
 def test_dataframe_to_bigquery_fields_w_named_index(module_under_test):
     df_data = collections.OrderedDict(
         [
+            ("str_index", ["a", "b"]),
             ("str_column", ["hello", "world"]),
             ("int_column", [42, 8]),
+            ("nullable_int_column", pandas.Series([42, None], dtype="Int64")),
+            ("uint_column", pandas.Series([7, 13], dtype="uint8")),
             ("bool_column", [True, False]),
+            ("boolean_column", pandas.Series([True, None], dtype="boolean")),
+            (
+                "datetime_column",
+                [
+                    datetime.datetime(1999, 12, 31, 23, 59, 59, 999999),
+                    datetime.datetime(2000, 1, 1, 0, 0, 0),
+                ],
+            ),
+            (
+                "timestamp_column",
+                [
+                    datetime.datetime(
+                        1999, 12, 31, 23, 59, 59, 999999, tzinfo=datetime.timezone.utc
+                    ),
+                    datetime.datetime(
+                        2000, 1, 1, 0, 0, 0, tzinfo=datetime.timezone.utc
+                    ),
+                ],
+            ),
         ]
     )
-    index = pandas.Index(["a", "b"], name="str_index")
-    dataframe = pandas.DataFrame(df_data, index=index)
+    dataframe = pandas.DataFrame(df_data).set_index("str_index", drop=True)
 
     returned_schema = module_under_test.dataframe_to_bigquery_fields(
         dataframe, [], index=True
@@ -37,27 +58,37 @@ def test_dataframe_to_bigquery_fields_w_named_index(module_under_test):
         schema.SchemaField("str_index", "STRING", "NULLABLE"),
         schema.SchemaField("str_column", "STRING", "NULLABLE"),
         schema.SchemaField("int_column", "INTEGER", "NULLABLE"),
+        schema.SchemaField("nullable_int_column", "INTEGER", "NULLABLE"),
+        schema.SchemaField("uint_column", "INTEGER", "NULLABLE"),
         schema.SchemaField("bool_column", "BOOLEAN", "NULLABLE"),
+        schema.SchemaField("boolean_column", "BOOLEAN", "NULLABLE"),
+        schema.SchemaField("datetime_column", "DATETIME", "NULLABLE"),
+        schema.SchemaField("timestamp_column", "TIMESTAMP", "NULLABLE"),
     )
     assert returned_schema == expected_schema
 
 
 def test_dataframe_to_bigquery_fields_w_multiindex(module_under_test):
     df_data = collections.OrderedDict(
         [
+            ("str_index", ["a", "a"]),
+            ("int_index", [0, 0]),
+            (
+                "dt_index",
+                [
+                    datetime.datetime(1999, 12, 31, 23, 59, 59, 999999),
+                    datetime.datetime(2000, 1, 1, 0, 0, 0),
+                ],
+            ),
             ("str_column", ["hello", "world"]),
             ("int_column", [42, 8]),
             ("bool_column", [True, False]),
         ]
     )
-    index = pandas.MultiIndex.from_tuples(
-        [
-            ("a", 0, datetime.datetime(1999, 12, 31, 23, 59, 59, 999999)),
-            ("a", 0, datetime.datetime(2000, 1, 1, 0, 0, 0)),
-        ],
-        names=["str_index", "int_index", "dt_index"],
+    dataframe = pandas.DataFrame(df_data).set_index(
+        ["str_index", "int_index", "dt_index"],
+        drop=True,
     )
-    dataframe = pandas.DataFrame(df_data, index=index)
 
     returned_schema = module_under_test.dataframe_to_bigquery_fields(
         dataframe, [], index=True

diff --git a/tests/unit/schema/test_pyarrow_to_bigquery.py b/tests/unit/schema/test_pyarrow_to_bigquery.py
@@ -42,16 +42,14 @@ def test_arrow_type_to_bigquery_field_scalar_types(pyarrow_type, bigquery_type):
 
 
 def test_arrow_type_to_bigquery_field_unknown():
-    assert (
-        pyarrow_to_bigquery.arrow_type_to_bigquery_field("test_name", pyarrow.null())
-        is None
-    )
+    assert pyarrow_to_bigquery.arrow_type_to_bigquery_field(
+        "test_name", pyarrow.null(), default_type="DEFAULT_TYPE"
+    ) == bigquery.SchemaField("test_name", "DEFAULT_TYPE")
 
 
 def test_arrow_type_to_bigquery_field_list_of_unknown():
-    assert (
-        pyarrow_to_bigquery.arrow_type_to_bigquery_field(
-            "test_name", pyarrow.list_(pyarrow.null())
-        )
-        is None
-    )
+    assert pyarrow_to_bigquery.arrow_type_to_bigquery_field(
+        "test_name",
+        pyarrow.list_(pyarrow.null()),
+        default_type="DEFAULT_TYPE",
+    ) == bigquery.SchemaField("test_name", "DEFAULT_TYPE", mode="REPEATED")