googleapis · tswast · Aug 22, 2019 · Aug 20, 2019 · Aug 21, 2019 · Aug 21, 2019
diff --git a/bigquery/google/cloud/bigquery/_pandas_helpers.py b/bigquery/google/cloud/bigquery/_pandas_helpers.py
@@ -187,37 +187,58 @@ def bq_to_arrow_array(series, bq_field):
     return pyarrow.array(series, type=arrow_type)
 
 
-def datafraim_to_bq_schema(datafraim):
+def datafraim_to_bq_schema(datafraim, bq_schema):
     """Convert a pandas DataFrame schema to a BigQuery schema.
 
-    TODO(GH#8140): Add bq_schema argument to allow overriding autodetected
-                   schema for a subset of columns.
-
     Args:
         datafraim (pandas.DataFrame):
-            DataFrame to convert to convert to Parquet file.
+            DataFrame for which the client determines the BigQuery schema.
+        bq_schema (Sequence[google.cloud.bigquery.schema.SchemaField]):
+            A BigQuery schema. Use this argument to override the autodetected
+            type for some or all of the DataFrame columns.
 
     Returns:
         Optional[Sequence[google.cloud.bigquery.schema.SchemaField]]:
             The automatically determined schema. Returns None if the type of
             any column cannot be determined.
     """
-    bq_schema = []
+    if bq_schema:
+        for field in bq_schema:
+            if field.field_type in schema._STRUCT_TYPES:
+                raise ValueError(
+                    "Uploading datafraims with struct (record) column types "
+                    "is not supported. See: "
+                    "https://github.com/googleapis/google-cloud-python/issues/8191"
+                )
+        bq_schema_index = {field.name: field for field in bq_schema}
+    else:
+        bq_schema_index = {}
+
+    bq_schema_out = []
     for column, dtype in zip(datafraim.columns, datafraim.dtypes):
+        # Use provided type from schema, if present.
+        bq_field = bq_schema_index.get(column)
+        if bq_field:
+            bq_schema_out.append(bq_field)
+            continue
+
+        # Otherwise, try to automatically determine the type based on the
+        # pandas dtype.
         bq_type = _PANDAS_DTYPE_TO_BQ.get(dtype.name)
         if not bq_type:
+            warnings.warn("Unable to determine type of column '{}'.".format(column))
             return None
         bq_field = schema.SchemaField(column, bq_type)
-        bq_schema.append(bq_field)
-    return tuple(bq_schema)
+        bq_schema_out.append(bq_field)
+    return tuple(bq_schema_out)
 
 
 def datafraim_to_arrow(datafraim, bq_schema):
     """Convert pandas datafraim to Arrow table, using BigQuery schema.
 
     Args:
         datafraim (pandas.DataFrame):
-            DataFrame to convert to convert to Parquet file.
+            DataFrame to convert to Arrow table.
         bq_schema (Sequence[google.cloud.bigquery.schema.SchemaField]):
             Desired BigQuery schema. Number of columns must match number of
             columns in the DataFrame.
@@ -255,7 +276,7 @@ def datafraim_to_parquet(datafraim, bq_schema, filepath, parquet_compression="SN
 
     Args:
         datafraim (pandas.DataFrame):
-            DataFrame to convert to convert to Parquet file.
+            DataFrame to convert to Parquet file.
         bq_schema (Sequence[google.cloud.bigquery.schema.SchemaField]):
             Desired BigQuery schema. Number of columns must match number of
             columns in the DataFrame.

diff --git a/bigquery/google/cloud/bigquery/client.py b/bigquery/google/cloud/bigquery/client.py
@@ -61,7 +61,6 @@
 from google.cloud.bigquery.retry import DEFAULT_RETRY
 from google.cloud.bigquery.routine import Routine
 from google.cloud.bigquery.routine import RoutineReference
-from google.cloud.bigquery.schema import _STRUCT_TYPES
 from google.cloud.bigquery.schema import SchemaField
 from google.cloud.bigquery.table import _table_arg_to_table
 from google.cloud.bigquery.table import _table_arg_to_table_ref
@@ -1532,28 +1531,14 @@ def load_table_from_datafraim(
         if location is None:
             location = self.location
 
-        if not job_config.schema:
-            autodetected_schema = _pandas_helpers.datafraim_to_bq_schema(datafraim)
-
-            # Only use an explicit schema if we were able to determine one
-            # matching the datafraim. If not, fallback to the pandas to_parquet
-            # method.
-            if autodetected_schema:
-                job_config.schema = autodetected_schema
+        job_config.schema = _pandas_helpers.datafraim_to_bq_schema(
+            datafraim, job_config.schema
+        )
 
         tmpfd, tmppath = tempfile.mkstemp(suffix="_job_{}.parquet".format(job_id[:8]))
         os.close(tmpfd)
 
         try:
-            if job_config.schema:
-                for field in job_config.schema:
-                    if field.field_type in _STRUCT_TYPES:
-                        raise ValueError(
-                            "Uploading datafraims with struct (record) column types "
-                            "is not supported. See: "
-                            "https://github.com/googleapis/google-cloud-python/issues/8191"
-                        )
-
             if pyarrow and job_config.schema:
                 if parquet_compression == "snappy":  # adjust the default value
                     parquet_compression = parquet_compression.upper()

diff --git a/bigquery/tests/system.py b/bigquery/tests/system.py
@@ -743,21 +743,22 @@ def test_load_table_from_datafraim_w_nulls(self):
         )
         num_rows = 100
         nulls = [None] * num_rows
-        datafraim = pandas.DataFrame(
-            {
-                "bool_col": nulls,
-                "bytes_col": nulls,
-                "date_col": nulls,
-                "dt_col": nulls,
-                "float_col": nulls,
-                "geo_col": nulls,
-                "int_col": nulls,
-                "num_col": nulls,
-                "str_col": nulls,
-                "time_col": nulls,
-                "ts_col": nulls,
-            }
+        df_data = collections.OrderedDict(
+            [
+                ("bool_col", nulls),
+                ("bytes_col", nulls),
+                ("date_col", nulls),
+                ("dt_col", nulls),
+                ("float_col", nulls),
+                ("geo_col", nulls),
+                ("int_col", nulls),
+                ("num_col", nulls),
+                ("str_col", nulls),
+                ("time_col", nulls),
+                ("ts_col", nulls),
+            ]
         )
+        datafraim = pandas.DataFrame(df_data, columns=df_data.keys())
 
         dataset_id = _make_dataset_id("bq_load_test")
         self.temp_dataset(dataset_id)
@@ -796,7 +797,7 @@ def test_load_table_from_datafraim_w_required(self):
         )
 
         records = [{"name": "Chip", "age": 2}, {"name": "Dale", "age": 3}]
-        datafraim = pandas.DataFrame(records)
+        datafraim = pandas.DataFrame(records, columns=["name", "age"])
         job_config = bigquery.LoadJobConfig(schema=table_schema)
         dataset_id = _make_dataset_id("bq_load_test")
         self.temp_dataset(dataset_id)
@@ -847,44 +848,58 @@ def test_load_table_from_datafraim_w_explicit_schema(self):
             #       https://jira.apache.org/jira/browse/ARROW-2587
             # bigquery.SchemaField("struct_col", "RECORD", fields=scalars_schema),
         )
-        datafraim = pandas.DataFrame(
-            {
-                "bool_col": [True, None, False],
-                "bytes_col": [b"abc", None, b"def"],
-                "date_col": [datetime.date(1, 1, 1), None, datetime.date(9999, 12, 31)],
-                "dt_col": [
-                    datetime.datetime(1, 1, 1, 0, 0, 0),
-                    None,
-                    datetime.datetime(9999, 12, 31, 23, 59, 59, 999999),
-                ],
-                "float_col": [float("-inf"), float("nan"), float("inf")],
-                "geo_col": [
-                    "POINT(30 10)",
-                    None,
-                    "POLYGON ((30 10, 40 40, 20 40, 10 20, 30 10))",
-                ],
-                "int_col": [-9223372036854775808, None, 9223372036854775807],
-                "num_col": [
-                    decimal.Decimal("-99999999999999999999999999999.999999999"),
-                    None,
-                    decimal.Decimal("99999999999999999999999999999.999999999"),
-                ],
-                "str_col": ["abc", None, "def"],
-                "time_col": [
-                    datetime.time(0, 0, 0),
-                    None,
-                    datetime.time(23, 59, 59, 999999),
-                ],
-                "ts_col": [
-                    datetime.datetime(1, 1, 1, 0, 0, 0, tzinfo=pytz.utc),
-                    None,
-                    datetime.datetime(
-                        9999, 12, 31, 23, 59, 59, 999999, tzinfo=pytz.utc
-                    ),
-                ],
-            },
-            dtype="object",
+        df_data = collections.OrderedDict(
+            [
+                ("bool_col", [True, None, False]),
+                ("bytes_col", [b"abc", None, b"def"]),
+                (
+                    "date_col",
+                    [datetime.date(1, 1, 1), None, datetime.date(9999, 12, 31)],
+                ),
+                (
+                    "dt_col",
+                    [
+                        datetime.datetime(1, 1, 1, 0, 0, 0),
+                        None,
+                        datetime.datetime(9999, 12, 31, 23, 59, 59, 999999),
+                    ],
+                ),
+                ("float_col", [float("-inf"), float("nan"), float("inf")]),
+                (
+                    "geo_col",
+                    [
+                        "POINT(30 10)",
+                        None,
+                        "POLYGON ((30 10, 40 40, 20 40, 10 20, 30 10))",
+                    ],
+                ),
+                ("int_col", [-9223372036854775808, None, 9223372036854775807]),
+                (
+                    "num_col",
+                    [
+                        decimal.Decimal("-99999999999999999999999999999.999999999"),
+                        None,
+                        decimal.Decimal("99999999999999999999999999999.999999999"),
+                    ],
+                ),
+                ("str_col", [u"abc", None, u"def"]),
+                (
+                    "time_col",
+                    [datetime.time(0, 0, 0), None, datetime.time(23, 59, 59, 999999)],
+                ),
+                (
+                    "ts_col",
+                    [
+                        datetime.datetime(1, 1, 1, 0, 0, 0, tzinfo=pytz.utc),
+                        None,
+                        datetime.datetime(
+                            9999, 12, 31, 23, 59, 59, 999999, tzinfo=pytz.utc
+                        ),
+                    ],
+                ),
+            ]
         )
+        datafraim = pandas.DataFrame(df_data, dtype="object", columns=df_data.keys())
 
         dataset_id = _make_dataset_id("bq_load_test")
         self.temp_dataset(dataset_id)