feat: support CSV format in load_table_from_dataframe pandas connector

google/cloud/bigquery/client.py

@@ -2113,7 +2113,8 @@ def load_table_from_dataframe(
 
             Due to the way REPEATED fields are encoded in the ``parquet`` file
             format, a mismatch with the existing table schema can occur, and
-            100% compatibility cannot be guaranteed for REPEATED fields.
+            100% compatibility cannot be guaranteed for REPEATED fields when
+            using the parquet format.
 
             https://github.com/googleapis/python-bigquery/issues/17
 
@@ -2153,6 +2154,14 @@ def load_table_from_dataframe(
                 column names matching those of the dataframe. The BigQuery
                 schema is used to determine the correct data type conversion.
                 Indexes are not loaded. Requires the :mod:`pyarrow` library.
+
+                By default, this method uses the parquet source format. To
+                override this, supply a value for
+                :attr:`~google.cloud.bigquery.job.LoadJobConfig.source_format`
+                with the format name. Currently only
+                :attr:`~google.cloud.bigquery.job.SourceFormat.CSV` and
+                :attr:`~google.cloud.bigquery.job.SourceFormat.PARQUET` are
+                supported.
             parquet_compression (Optional[str]):
                  [Beta] The compression method to use if intermittently
                  serializing ``dataframe`` to a parquet file.
@@ -2181,10 +2190,6 @@ def load_table_from_dataframe(
                 If ``job_config`` is not an instance of :class:`~google.cloud.bigquery.job.LoadJobConfig`
                 class.
         """
-        if pyarrow is None:
-            # pyarrow is now the only supported  parquet engine.
-            raise ValueError("This method requires pyarrow to be installed")
-
         job_id = _make_job_id(job_id, job_id_prefix)
 
         if job_config:
@@ -2197,15 +2202,20 @@ def load_table_from_dataframe(
         else:
             job_config = job.LoadJobConfig()
 
-        if job_config.source_format:
-            if job_config.source_format != job.SourceFormat.PARQUET:
-                raise ValueError(
-                    "Got unexpected source_format: '{}'. Currently, only PARQUET is supported".format(
-                        job_config.source_format
-                    )
-                )
-        else:
+        supported_formats = {job.SourceFormat.CSV, job.SourceFormat.PARQUET}
+        if job_config.source_format is None:
+            # default value
             job_config.source_format = job.SourceFormat.PARQUET
+        if job_config.source_format not in supported_formats:
+            raise ValueError(
+                "Got unexpected source_format: '{}'. Currently, only PARQUET and CSV are supported".format(
+                    job_config.source_format
+                )
+            )
+
+        if pyarrow is None and job_config.source_format == job.SourceFormat.PARQUET:
+            # pyarrow is now the only supported  parquet engine.
+            raise ValueError("This method requires pyarrow to be installed")
 
         if location is None:
             location = self.location
@@ -2245,27 +2255,43 @@ def load_table_from_dataframe(
                 stacklevel=2,
             )
 
-        tmpfd, tmppath = tempfile.mkstemp(suffix="_job_{}.parquet".format(job_id[:8]))
+        tmpfd, tmppath = tempfile.mkstemp(
+            suffix="_job_{}.{}".format(job_id[:8], job_config.source_format.lower())
+        )
         os.close(tmpfd)
 
         try:
-            if job_config.schema:
-                if parquet_compression == "snappy":  # adjust the default value
-                    parquet_compression = parquet_compression.upper()
 
-                _pandas_helpers.dataframe_to_parquet(
-                    dataframe,
-                    job_config.schema,
+            if job_config.source_format == job.SourceFormat.PARQUET:
+
+                if job_config.schema:
+                    if parquet_compression == "snappy":  # adjust the default value
+                        parquet_compression = parquet_compression.upper()
+
+                    _pandas_helpers.dataframe_to_parquet(
+                        dataframe,
+                        job_config.schema,
+                        tmppath,
+                        parquet_compression=parquet_compression,
+                    )
+                else:
+                    dataframe.to_parquet(tmppath, compression=parquet_compression)
+
+            else:
+
+                dataframe.to_csv(
                     tmppath,
-                    parquet_compression=parquet_compression,
+                    index=False,
+                    header=False,
+                    encoding="utf-8",
+                    float_format="%.17g",
+                    date_format="%Y-%m-%d %H:%M:%S.%f",
                 )
-            else:
-                dataframe.to_parquet(tmppath, compression=parquet_compression)
 
-            with open(tmppath, "rb") as parquet_file:
+            with open(tmppath, "rb") as tmpfile:
                 file_size = os.path.getsize(tmppath)
                 return self.load_table_from_file(
-                    parquet_file,
+                    tmpfile,
                     destination,
                     num_retries=num_retries,
                     rewind=True,

tests/system.py

@@ -1165,6 +1165,140 @@ def test_load_table_from_json_basic_use(self):
         self.assertEqual(tuple(table.schema), table_schema)
         self.assertEqual(table.num_rows, 2)
 
+    @unittest.skipIf(pandas is None, "Requires `pandas`")
+    def test_load_table_from_dataframe_w_explicit_schema_source_format_csv(self):
+        from google.cloud.bigquery.job import SourceFormat
+
+        table_schema = (
+            bigquery.SchemaField("bool_col", "BOOLEAN"),
+            bigquery.SchemaField("bytes_col", "BYTES"),
+            bigquery.SchemaField("date_col", "DATE"),
+            bigquery.SchemaField("dt_col", "DATETIME"),
+            bigquery.SchemaField("float_col", "FLOAT"),
+            bigquery.SchemaField("geo_col", "GEOGRAPHY"),
+            bigquery.SchemaField("int_col", "INTEGER"),
+            bigquery.SchemaField("num_col", "NUMERIC"),
+            bigquery.SchemaField("str_col", "STRING"),
+            bigquery.SchemaField("time_col", "TIME"),
+            bigquery.SchemaField("ts_col", "TIMESTAMP"),
+        )
+        df_data = collections.OrderedDict(
+            [
+                ("bool_col", [True, None, False]),
+                ("bytes_col", ["abc", None, "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
+                        ),
+                    ],
+                ),
+            ]
+        )
+        dataframe = pandas.DataFrame(df_data, dtype="object", columns=df_data.keys())
+
+        dataset_id = _make_dataset_id("bq_load_test")
+        self.temp_dataset(dataset_id)
+        table_id = "{}.{}.load_table_from_dataframe_w_explicit_schema_csv".format(
+            Config.CLIENT.project, dataset_id
+        )
+
+        job_config = bigquery.LoadJobConfig(
+            schema=table_schema, source_format=SourceFormat.CSV
+        )
+        load_job = Config.CLIENT.load_table_from_dataframe(
+            dataframe, table_id, job_config=job_config
+        )
+        load_job.result()
+
+        table = Config.CLIENT.get_table(table_id)
+        self.assertEqual(tuple(table.schema), table_schema)
+        self.assertEqual(table.num_rows, 3)
+
+    @unittest.skipIf(pandas is None, "Requires `pandas`")
+    def test_load_table_from_dataframe_w_explicit_schema_source_format_csv_floats(self):
+        from google.cloud.bigquery.job import SourceFormat
+
+        table_schema = (bigquery.SchemaField("float_col", "FLOAT"),)
+        df_data = collections.OrderedDict(
+            [
+                (
+                    "float_col",
+                    [
+                        0.14285714285714285,
+                        0.51428571485748,
+                        0.87128748,
+                        1.807960649,
+                        2.0679610649,
+                        2.4406779661016949,
+                        3.7148514257,
+                        3.8571428571428572,
+                        1.51251252e40,
+                    ],
+                ),
+            ]
+        )
+        dataframe = pandas.DataFrame(df_data, dtype="object", columns=df_data.keys())
+
+        dataset_id = _make_dataset_id("bq_load_test")
+        self.temp_dataset(dataset_id)
+        table_id = "{}.{}.load_table_from_dataframe_w_explicit_schema_csv".format(
+            Config.CLIENT.project, dataset_id
+        )
+
+        job_config = bigquery.LoadJobConfig(
+            schema=table_schema, source_format=SourceFormat.CSV
+        )
+        load_job = Config.CLIENT.load_table_from_dataframe(
+            dataframe, table_id, job_config=job_config
+        )
+        load_job.result()
+
+        table = Config.CLIENT.get_table(table_id)
+        rows = self._fetch_single_page(table)
+        floats = [r.values()[0] for r in rows]
+        self.assertEqual(tuple(table.schema), table_schema)
+        self.assertEqual(table.num_rows, 9)
+        self.assertEqual(floats, df_data["float_col"])
+
     def test_load_table_from_json_schema_autodetect(self):
         json_rows = [
             {"name": "John", "age": 18, "birthday": "2001-10-15", "is_awesome": False},

tests/unit/test_client.py

@@ -8366,6 +8366,56 @@ def test_load_table_from_dataframe_w_invaild_job_config(self):
         err_msg = str(exc.value)
         assert "Expected an instance of LoadJobConfig" in err_msg
 
+    @unittest.skipIf(pandas is None, "Requires `pandas`")
+    def test_load_table_from_dataframe_with_csv_source_format(self):
+        from google.cloud.bigquery.client import _DEFAULT_NUM_RETRIES
+        from google.cloud.bigquery import job
+        from google.cloud.bigquery.schema import SchemaField
+
+        client = self._make_client()
+        records = [{"id": 1, "age": 100}, {"id": 2, "age": 60}]
+        dataframe = pandas.DataFrame(records)
+        job_config = job.LoadJobConfig(
+            write_disposition=job.WriteDisposition.WRITE_TRUNCATE,
+            source_format=job.SourceFormat.CSV,
+        )
+
+        get_table_patch = mock.patch(
+            "google.cloud.bigquery.client.Client.get_table",
+            autospec=True,
+            return_value=mock.Mock(
+                schema=[SchemaField("id", "INTEGER"), SchemaField("age", "INTEGER")]
+            ),
+        )
+        load_patch = mock.patch(
+            "google.cloud.bigquery.client.Client.load_table_from_file", autospec=True
+        )
+        with load_patch as load_table_from_file, get_table_patch:
+            client.load_table_from_dataframe(
+                dataframe, self.TABLE_REF, job_config=job_config
+            )
+
+        load_table_from_file.assert_called_once_with(
+            client,
+            mock.ANY,
+            self.TABLE_REF,
+            num_retries=_DEFAULT_NUM_RETRIES,
+            rewind=True,
+            size=mock.ANY,
+            job_id=mock.ANY,
+            job_id_prefix=None,
+            location=None,
+            project=None,
+            job_config=mock.ANY,
+            timeout=None,
+        )
+
+        sent_file = load_table_from_file.mock_calls[0][1][1]
+        assert sent_file.closed
+
+        sent_config = load_table_from_file.mock_calls[0][2]["job_config"]
+        assert sent_config.source_format == job.SourceFormat.CSV
+
     def test_load_table_from_json_basic_use(self):
         from google.cloud.bigquery.client import _DEFAULT_NUM_RETRIES
         from google.cloud.bigquery import job