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#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import datetime
import decimal
from unittest import mock
import pytest
from google.cloud import bigquery
pandas = pytest.importorskip("pandas")
pyarrow = pytest.importorskip("pyarrow", minversion="3.0.0")
TEST_PATH = "/v1/project/test-proj/dataset/test-dset/table/test-tbl/data"
@pytest.fixture
def class_under_test():
from google.cloud.bigquery.table import RowIterator
return RowIterator
@pytest.mark.skipif(
pandas.__version__.startswith("2."),
reason="pandas 2.0 changes some default dtypes and we haven't update the test to account for those",
)
def test_to_dataframe_nullable_scalars(monkeypatch, class_under_test):
# See tests/system/test_arrow.py for the actual types we get from the API.
arrow_schema = pyarrow.schema(
[
pyarrow.field("bignumeric_col", pyarrow.decimal256(76, scale=38)),
pyarrow.field("bool_col", pyarrow.bool_()),
pyarrow.field("bytes_col", pyarrow.binary()),
pyarrow.field("date_col", pyarrow.date32()),
pyarrow.field("datetime_col", pyarrow.timestamp("us", tz=None)),
pyarrow.field("float64_col", pyarrow.float64()),
pyarrow.field("int64_col", pyarrow.int64()),
pyarrow.field("numeric_col", pyarrow.decimal128(38, scale=9)),
pyarrow.field("string_col", pyarrow.string()),
pyarrow.field("time_col", pyarrow.time64("us")),
pyarrow.field(
"timestamp_col", pyarrow.timestamp("us", tz=datetime.timezone.utc)
),
]
)
arrow_table = pyarrow.Table.from_pydict(
{
"bignumeric_col": [decimal.Decimal("123.456789101112131415")],
"bool_col": [True],
"bytes_col": [b"Hello,\x00World!"],
"date_col": [datetime.date(2021, 8, 9)],
"datetime_col": [datetime.datetime(2021, 8, 9, 13, 30, 44, 123456)],
"float64_col": [1.25],
"int64_col": [-7],
"numeric_col": [decimal.Decimal("-123.456789")],
"string_col": ["abcdefg"],
"time_col": [datetime.time(14, 21, 17, 123456)],
"timestamp_col": [
datetime.datetime(
2021, 8, 9, 13, 30, 44, 123456, tzinfo=datetime.timezone.utc
)
],
},
schema=arrow_schema,
)
nullable_schema = [
bigquery.SchemaField("bignumeric_col", "BIGNUMERIC"),
bigquery.SchemaField("bool_col", "BOOLEAN"),
bigquery.SchemaField("bytes_col", "BYTES"),
bigquery.SchemaField("date_col", "DATE"),
bigquery.SchemaField("datetime_col", "DATETIME"),
bigquery.SchemaField("float64_col", "FLOAT"),
bigquery.SchemaField("int64_col", "INT64"),
bigquery.SchemaField("numeric_col", "NUMERIC"),
bigquery.SchemaField("string_col", "STRING"),
bigquery.SchemaField("time_col", "TIME"),
bigquery.SchemaField("timestamp_col", "TIMESTAMP"),
]
mock_client = mock.create_autospec(bigquery.Client)
mock_client.project = "test-proj"
mock_api_request = mock.Mock()
mock_to_arrow = mock.Mock()
mock_to_arrow.return_value = arrow_table
rows = class_under_test(mock_client, mock_api_request, TEST_PATH, nullable_schema)
monkeypatch.setattr(rows, "to_arrow", mock_to_arrow)
df = rows.to_dataframe()
# Check for expected dtypes.
# Keep these in sync with tests/system/test_pandas.py
assert df.dtypes["bignumeric_col"].name == "object"
assert df.dtypes["bool_col"].name == "boolean"
assert df.dtypes["bytes_col"].name == "object"
assert df.dtypes["date_col"].name == "dbdate"
assert df.dtypes["datetime_col"].name == "datetime64[ns]"
assert df.dtypes["float64_col"].name == "float64"
assert df.dtypes["int64_col"].name == "Int64"
assert df.dtypes["numeric_col"].name == "object"
assert df.dtypes["string_col"].name == "object"
assert df.dtypes["time_col"].name == "dbtime"
assert df.dtypes["timestamp_col"].name == "datetime64[ns, UTC]"
# Check for expected values.
assert df["bignumeric_col"][0] == decimal.Decimal("123.456789101112131415")
assert df["bool_col"][0] # True
assert df["bytes_col"][0] == b"Hello,\x00World!"
# object is used by default, but we can use "datetime64[ns]" automatically
# when data is within the supported range.
# https://github.com/googleapis/python-bigquery/issues/861
assert df["date_col"][0] == datetime.date(2021, 8, 9)
assert df["datetime_col"][0] == pandas.to_datetime("2021-08-09 13:30:44.123456")
assert df["float64_col"][0] == 1.25
assert df["int64_col"][0] == -7
assert df["numeric_col"][0] == decimal.Decimal("-123.456789")
assert df["string_col"][0] == "abcdefg"
# Pandas timedelta64 might be a better choice for pandas time columns. Then
# they can more easily be combined with date columns to form datetimes.
# https://github.com/googleapis/python-bigquery/issues/862
assert df["time_col"][0] == datetime.time(14, 21, 17, 123456)
assert df["timestamp_col"][0] == pandas.to_datetime("2021-08-09 13:30:44.123456Z")
def test_to_dataframe_nullable_scalars_with_custom_dtypes(
monkeypatch, class_under_test
):
"""Passing in explicit dtypes is merged with default behavior."""
arrow_schema = pyarrow.schema(
[
pyarrow.field("int64_col", pyarrow.int64()),
pyarrow.field("other_int_col", pyarrow.int64()),
]
)
arrow_table = pyarrow.Table.from_pydict(
{"int64_col": [1000], "other_int_col": [-7]},
schema=arrow_schema,
)
nullable_schema = [
bigquery.SchemaField("int64_col", "INT64"),
bigquery.SchemaField("other_int_col", "INT64"),
]
mock_client = mock.create_autospec(bigquery.Client)
mock_client.project = "test-proj"
mock_api_request = mock.Mock()
mock_to_arrow = mock.Mock()
mock_to_arrow.return_value = arrow_table
rows = class_under_test(mock_client, mock_api_request, TEST_PATH, nullable_schema)
monkeypatch.setattr(rows, "to_arrow", mock_to_arrow)
df = rows.to_dataframe(dtypes={"other_int_col": "int8"})
assert df.dtypes["int64_col"].name == "Int64"
assert df["int64_col"][0] == 1000
assert df.dtypes["other_int_col"].name == "int8"
assert df["other_int_col"][0] == -7
def test_to_dataframe_arrays(monkeypatch, class_under_test):
arrow_schema = pyarrow.schema(
[pyarrow.field("int64_repeated", pyarrow.list_(pyarrow.int64()))]
)
arrow_table = pyarrow.Table.from_pydict(
{"int64_repeated": [[-1, 0, 2]]},
schema=arrow_schema,
)
nullable_schema = [
bigquery.SchemaField("int64_repeated", "INT64", mode="REPEATED"),
]
mock_client = mock.create_autospec(bigquery.Client)
mock_client.project = "test-proj"
mock_api_request = mock.Mock()
mock_to_arrow = mock.Mock()
mock_to_arrow.return_value = arrow_table
rows = class_under_test(mock_client, mock_api_request, TEST_PATH, nullable_schema)
monkeypatch.setattr(rows, "to_arrow", mock_to_arrow)
df = rows.to_dataframe()
assert df.dtypes["int64_repeated"].name == "object"
assert tuple(df["int64_repeated"][0]) == (-1, 0, 2)
def test_to_dataframe_with_jobs_query_response(class_under_test):
resource = {
"kind": "bigquery#queryResponse",
"schema": {
"fields": [
{"name": "name", "type": "STRING", "mode": "NULLABLE"},
{"name": "number", "type": "INTEGER", "mode": "NULLABLE"},
]
},
"jobReference": {
"projectId": "test-project",
"jobId": "job_ocd3cb-N62QIslU7R5qKKa2_427J",
"location": "US",
},
"totalRows": "9",
"rows": [
{"f": [{"v": "Tiarra"}, {"v": "6"}]},
{"f": [{"v": "Timothy"}, {"v": "325"}]},
{"f": [{"v": "Tina"}, {"v": "26"}]},
{"f": [{"v": "Tierra"}, {"v": "10"}]},
{"f": [{"v": "Tia"}, {"v": "17"}]},
{"f": [{"v": "Tiara"}, {"v": "22"}]},
{"f": [{"v": "Tiana"}, {"v": "6"}]},
{"f": [{"v": "Tiffany"}, {"v": "229"}]},
{"f": [{"v": "Tiffani"}, {"v": "8"}]},
],
"totalBytesProcessed": "154775150",
"jobComplete": True,
"cacheHit": False,
"queryId": "job_ocd3cb-N62QIslU7R5qKKa2_427J",
}
rows = class_under_test(
client=None,
api_request=None,
path=None,
schema=[
bigquery.SchemaField.from_api_repr(field)
for field in resource["schema"]["fields"]
],
first_page_response=resource,
)
df = rows.to_dataframe()
assert list(df.columns) == ["name", "number"]
assert list(df["name"]) == [
"Tiarra",
"Timothy",
"Tina",
"Tierra",
"Tia",
"Tiara",
"Tiana",
"Tiffany",
"Tiffani",
]
assert list(df["number"]) == [6, 325, 26, 10, 17, 22, 6, 229, 8]
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