Skip to content

tests.system.test_read_gbq: test_default_dtypes[scalar-types-nullable-normal-range-True] failed #646

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom
Closed
flaky-bot bot opened this issue Jul 14, 2023 · 1 comment · Fixed by #655
Closed

tests.system.test_read_gbq: test_default_dtypes[scalar-types-nullable-normal-range-True] failed #646

flaky-bot bot opened this issue Jul 14, 2023 · 1 comment · Fixed by #655
Labels
api: bigquery Issues related to the googleapis/python-bigquery-pandas API. flakybot: flaky Tells the Flaky Bot not to close or comment on this issue. flakybot: issue An issue filed by the Flaky Bot. Should not be added manually. priority: p3 Desirable enhancement or fix. May not be included in next release. type: bug Error or flaw in code with unintended results or allowing sub-optimal usage patterns.

Comments

@flaky-bot
Copy link

flaky-bot bot commented Jul 14, 2023

Note: #536 was also for this test, but it was closed more than 10 days ago. So, I didn't mark it flaky.

commit: 7fab432
buildURL: Build Status, Sponge
status: failed

Test output
read_gbq = functools.partial(, project_id='precise-truck-742', credentials=)
query = '\nSELECT\n  bools.row_num AS row_num,\n  bool_col,\n  bytes_col,\n  date_col,\n  datetime_col,\n  float_col,\n  int64...ND `bools`.row_num = `times`.row_num\n  AND `bools`.row_num = `timestamps`.row_num\nORDER BY row_num ASC\n            '
expected =    row_num  bool_col  ...         time_col                    timestamp_col
0        1      True  ...              NaT...                 NaT
2        3        ...  23:59:59.999999 2018-04-11 23:59:59.999999+00:00

[3 rows x 11 columns]

use_bqstorage_apis = {False, True}, use_bqstorage_api = True


@pytest.mark.parametrize(["use_bqstorage_api"], [(True,), (False,)])
@pytest.mark.parametrize(
    ["query", "expected", "use_bqstorage_apis"],
    [
        pytest.param(
            *QueryTestCase(
                query="""
SELECT
  bools.row_num AS row_num,
  bool_col,
  bytes_col,
  date_col,
  datetime_col,
  float_col,
  int64_col,
  numeric_col,
  string_col,
  time_col,
  timestamp_col
FROM
  UNNEST([
      STRUCT(1 AS row_num, TRUE AS bool_col),
      STRUCT(2 AS row_num, FALSE AS bool_col),
      STRUCT(3 AS row_num, TRUE AS bool_col) ]) AS `bools`
INNER JOIN
  UNNEST([
      STRUCT(1 AS row_num, CAST('C00010FF' AS BYTES FORMAT 'HEX') AS bytes_col),
      STRUCT(2 AS row_num, CAST('F1AC' AS BYTES FORMAT 'HEX') AS bytes_col),
      STRUCT(3 AS row_num, CAST('FFBADD11' AS BYTES FORMAT 'HEX') AS bytes_co) ]) AS `bytes`
INNER JOIN
  UNNEST([
      STRUCT(1 AS row_num, DATE(1998, 9, 4) AS date_col),
      STRUCT(2 AS row_num, DATE(2011, 10, 1) AS date_col),
      STRUCT(3 AS row_num, DATE(2018, 4, 11) AS date_col) ]) AS `dates`
INNER JOIN
  UNNEST([
      STRUCT(1 AS row_num, DATETIME('1998-09-04 12:34:56.789101') AS datetime_col),
      STRUCT(2 AS row_num, DATETIME('2011-10-01 00:01:02.345678') AS datetime_col),
      STRUCT(3 AS row_num, DATETIME('2018-04-11 23:59:59.999999') AS datetime_col) ]) AS `datetimes`
INNER JOIN
  UNNEST([
      STRUCT(1 AS row_num, 1.125 AS float_col),
      STRUCT(2 AS row_num, -2.375 AS float_col),
      STRUCT(3 AS row_num, 0.0 AS float_col) ]) AS `floats`
INNER JOIN
  UNNEST([
      -- 2 ^ 63 - 1, but in hex to avoid intermediate overlfow.
      STRUCT(1 AS row_num, 0x7fffffffffffffff AS int64_col),
      STRUCT(2 AS row_num, -1 AS in64_col),
      -- -2 ^ 63, but in hex to avoid intermediate overlfow.
      STRUCT(3 AS row_num, -0x8000000000000000 AS int64_col) ]) AS `ints`
INNER JOIN
  UNNEST([
      STRUCT(1 AS row_num, CAST('123.456789' AS NUMERIC) AS numeric_col),
      STRUCT(2 AS row_num, CAST('-123.456789' AS NUMERIC) AS numeric_col),
      STRUCT(3 AS row_num, CAST('999.999999' AS NUMERIC) AS numeric_col) ]) AS `numerics`
INNER JOIN
  UNNEST([
      STRUCT(1 AS row_num, 'abcdefghijklmnopqrstuvwxyz' AS string_col),
      STRUCT(2 AS row_num, 'ABCDEFGHIJKLMNOPQRSTUVWXYZ' AS string_col),
      STRUCT(3 AS row_num, 'こんにちは' AS string_col) ]) AS `strings`
INNER JOIN
  UNNEST([
      STRUCT(1 AS row_num, CAST('00:00:00.000000' AS TIME) AS time_col),
      STRUCT(2 AS row_num, CAST('09:08:07.654321' AS TIME) AS time_col),
      STRUCT(3 AS row_num, CAST('23:59:59.999999' AS TIME) AS time_col) ]) AS `times`
INNER JOIN
  UNNEST([
      STRUCT(1 AS row_num, TIMESTAMP('1998-09-04 12:34:56.789101') AS timestamp_col),
      STRUCT(2 AS row_num, TIMESTAMP('2011-10-01 00:01:02.345678') AS timestamp_col),
      STRUCT(3 AS row_num, TIMESTAMP('2018-04-11 23:59:59.999999') AS timestamp_col) ]) AS `timestamps`
WHERE
  `bools`.row_num = `dates`.row_num
  AND `bools`.row_num = `bytes`.row_num
  AND `bools`.row_num = `datetimes`.row_num
  AND `bools`.row_num = `floats`.row_num
  AND `bools`.row_num = `ints`.row_num
  AND `bools`.row_num = `numerics`.row_num
  AND `bools`.row_num = `strings`.row_num
  AND `bools`.row_num = `times`.row_num
  AND `bools`.row_num = `timestamps`.row_num
ORDER BY row_num ASC
                """,
                expected=pandas.DataFrame(
                    {
                        "row_num": pandas.Series([1, 2, 3], dtype="Int64"),
                        "bool_col": pandas.Series(
                            [True, False, True],
                            dtype="boolean"
                            if FEATURES.pandas_has_boolean_dtype
                            else "bool",
                        ),
                        "bytes_col": [
                            bytes.fromhex("C00010FF"),
                            bytes.fromhex("F1AC"),
                            bytes.fromhex("FFBADD11"),
                        ],
                        "date_col": pandas.Series(
                            [
                                datetime.date(1998, 9, 4),
                                datetime.date(2011, 10, 1),
                                datetime.date(2018, 4, 11),
                            ],
                            dtype=db_dtypes.DateDtype(),
                        ),
                        "datetime_col": pandas.Series(
                            [
                                "1998-09-04 12:34:56.789101",
                                "2011-10-01 00:01:02.345678",
                                "2018-04-11 23:59:59.999999",
                            ],
                            dtype="datetime64[ns]",
                        ),
                        "float_col": [1.125, -2.375, 0.0],
                        "int64_col": pandas.Series(
                            [(2**63) - 1, -1, -(2**63)], dtype="Int64"
                        ),
                        "numeric_col": [
                            decimal.Decimal("123.456789"),
                            decimal.Decimal("-123.456789"),
                            decimal.Decimal("999.999999"),
                        ],
                        "string_col": [
                            "abcdefghijklmnopqrstuvwxyz",
                            "ABCDEFGHIJKLMNOPQRSTUVWXYZ",
                            "こんにちは",
                        ],
                        "time_col": pandas.Series(
                            ["00:00:00.000000", "09:08:07.654321", "23:59:59.999999"],
                            dtype=db_dtypes.TimeDtype(),
                        ),
                        "timestamp_col": pandas.Series(
                            [
                                "1998-09-04 12:34:56.789101",
                                "2011-10-01 00:01:02.345678",
                                "2018-04-11 23:59:59.999999",
                            ],
                            dtype="datetime64[ns]",
                        ).dt.tz_localize(datetime.timezone.utc),
                    }
                ),
            ),
            id="scalar-types-nonnull-normal-range",
        ),
        pytest.param(
            *QueryTestCase(
                query="""
SELECT
  bools.row_num AS row_num,
  bool_col,
  bytes_col,
  date_col,
  datetime_col,
  float_col,
  int64_col,
  numeric_col,
  string_col,
  time_col,
  timestamp_col
FROM
  UNNEST([
      STRUCT(1 AS row_num, TRUE AS bool_col),
      STRUCT(2 AS row_num, FALSE AS bool_col),
      STRUCT(3 AS row_num, NULL AS bool_col) ]) AS `bools`
INNER JOIN
  UNNEST([
      STRUCT(1 AS row_num, NULL AS bytes_col),
      STRUCT(2 AS row_num, CAST('F1AC' AS BYTES FORMAT 'HEX') AS bytes_col),
      STRUCT(3 AS row_num, CAST('' AS BYTES FORMAT 'HEX') AS bytes_co) ]) AS `bytes`
INNER JOIN
  UNNEST([
      STRUCT(1 AS row_num, DATE(1970, 1, 1) AS date_col),
      STRUCT(2 AS row_num, NULL AS date_col),
      STRUCT(3 AS row_num, DATE(2018, 4, 11) AS date_col) ]) AS `dates`
INNER JOIN
  UNNEST([
      STRUCT(1 AS row_num, DATETIME('1970-01-01 00:00:00.000000') AS datetime_col),
      STRUCT(2 AS row_num, DATETIME('2011-10-01 00:01:02.345678') AS datetime_col),
      STRUCT(3 AS row_num, NULL AS datetime_col) ]) AS `datetimes`
INNER JOIN
  UNNEST([
      STRUCT(1 AS row_num, NULL AS float_col),
      STRUCT(2 AS row_num, -2.375 AS float_col),
      STRUCT(3 AS row_num, 0.0 AS float_col) ]) AS `floats`
INNER JOIN
  UNNEST([
      STRUCT(1 AS row_num, -1 AS int64_col),
      STRUCT(2 AS row_num, NULL AS int64_col),
      STRUCT(3 AS row_num, 0 AS int64_col) ]) AS `int64s`
INNER JOIN
  UNNEST([
      STRUCT(1 AS row_num, CAST('123.456789' AS NUMERIC) AS numeric_col),
      STRUCT(2 AS row_num, NULL AS numeric_col),
      STRUCT(3 AS row_num, CAST('999.999999' AS NUMERIC) AS numeric_col) ]) AS `numerics`
INNER JOIN
  UNNEST([
      STRUCT(1 AS row_num, '' AS string_col),
      STRUCT(2 AS row_num, 'こんにちは' AS string_col),
      STRUCT(3 AS row_num, NULL AS string_col) ]) AS `strings`
INNER JOIN
  UNNEST([
      STRUCT(1 AS row_num, NULL AS time_col),
      STRUCT(2 AS row_num, CAST('00:00:00.000000' AS TIME) AS time_col),
      STRUCT(3 AS row_num, CAST('23:59:59.999999' AS TIME) AS time_col) ]) AS `times`
INNER JOIN
  UNNEST([
      STRUCT(1 AS row_num, TIMESTAMP('1970-01-01 00:00:00.000000') AS timestamp_col),
      STRUCT(2 AS row_num, NULL AS timestamp_col),
      STRUCT(3 AS row_num, TIMESTAMP('2018-04-11 23:59:59.999999') AS timestamp_col) ]) AS `timestamps`
WHERE
  `bools`.row_num = `dates`.row_num
  AND `bools`.row_num = `bytes`.row_num
  AND `bools`.row_num = `datetimes`.row_num
  AND `bools`.row_num = `floats`.row_num
  AND `bools`.row_num = `int64s`.row_num
  AND `bools`.row_num = `numerics`.row_num
  AND `bools`.row_num = `strings`.row_num
  AND `bools`.row_num = `times`.row_num
  AND `bools`.row_num = `timestamps`.row_num
ORDER BY row_num ASC
            """,
                expected=pandas.DataFrame(
                    {
                        "row_num": pandas.Series([1, 2, 3], dtype="Int64"),
                        "bool_col": pandas.Series(
                            [True, False, None],
                            dtype="boolean"
                            if FEATURES.pandas_has_boolean_dtype
                            else "object",
                        ),
                        "bytes_col": [None, bytes.fromhex("F1AC"), b""],
                        "date_col": pandas.Series(
                            [
                                datetime.date(1970, 1, 1),
                                None,
                                datetime.date(2018, 4, 11),
                            ],
                            dtype=db_dtypes.DateDtype(),
                        ),
                        "datetime_col": pandas.Series(
                            [
                                "1970-01-01 00:00:00.000000",
                                "2011-10-01 00:01:02.345678",
                                None,
                            ],
                            dtype="datetime64[ns]",
                        ),
                        "float_col": [None, -2.375, 0.0],
                        "int64_col": pandas.Series([-1, None, 0], dtype="Int64"),
                        "numeric_col": [
                            decimal.Decimal("123.456789"),
                            None,
                            decimal.Decimal("999.999999"),
                        ],
                        "string_col": ["", "こんにちは", None],
                        "time_col": pandas.Series(
                            [None, "00:00:00", "23:59:59.999999"],
                            dtype=db_dtypes.TimeDtype(),
                        ),
                        "timestamp_col": pandas.Series(
                            [
                                "1970-01-01 00:00:00.000000",
                                None,
                                "2018-04-11 23:59:59.999999",
                            ],
                            dtype="datetime64[ns]",
                        ).dt.tz_localize(datetime.timezone.utc),
                    }
                ),
            ),
            id="scalar-types-nullable-normal-range",
        ),
        pytest.param(
            *QueryTestCase(
                query="""
SELECT
  bools.row_num AS row_num,
  bool_col,
  bytes_col,
  date_col,
  datetime_col,
  float_col,
  int64_col,
  numeric_col,
  string_col,
  time_col,
  timestamp_col
FROM
  UNNEST([
      STRUCT(1 AS row_num, CAST(NULL AS BOOL) AS bool_col) ]) AS `bools`
INNER JOIN
  UNNEST([
      STRUCT(1 AS row_num, CAST(NULL AS BYTES) AS bytes_col) ]) AS `bytes`
INNER JOIN
  UNNEST([
      STRUCT(1 AS row_num, CAST(NULL AS DATE) AS date_col) ]) AS `dates`
INNER JOIN
  UNNEST([
      STRUCT(1 AS row_num, CAST(NULL AS DATETIME) AS datetime_col) ]) AS `datetimes`
INNER JOIN
  UNNEST([
      STRUCT(1 AS row_num, CAST(NULL AS FLOAT64) AS float_col) ]) AS `floats`
INNER JOIN
  UNNEST([
      STRUCT(1 AS row_num, CAST(NULL AS INT64) AS int64_col) ]) AS `int64s`
INNER JOIN
  UNNEST([
      STRUCT(1 AS row_num, CAST(NULL AS NUMERIC) AS numeric_col) ]) AS `numerics`
INNER JOIN
  UNNEST([
      STRUCT(1 AS row_num, CAST(NULL AS STRING) AS string_col) ]) AS `strings`
INNER JOIN
  UNNEST([
      STRUCT(1 AS row_num, CAST(NULL AS TIME) AS time_col) ]) AS `times`
INNER JOIN
  UNNEST([
      STRUCT(1 AS row_num, CAST(NULL AS TIMESTAMP) AS timestamp_col) ]) AS `timestamps`
WHERE
  `bools`.row_num = `dates`.row_num
  AND `bools`.row_num = `bytes`.row_num
  AND `bools`.row_num = `datetimes`.row_num
  AND `bools`.row_num = `floats`.row_num
  AND `bools`.row_num = `int64s`.row_num
  AND `bools`.row_num = `numerics`.row_num
  AND `bools`.row_num = `strings`.row_num
  AND `bools`.row_num = `times`.row_num
  AND `bools`.row_num = `timestamps`.row_num
ORDER BY row_num ASC
            """,
                expected=pandas.DataFrame(
                    {
                        "row_num": pandas.Series([1], dtype="Int64"),
                        "bool_col": pandas.Series(
                            [None],
                            dtype="boolean"
                            if FEATURES.pandas_has_boolean_dtype
                            else "object",
                        ),
                        "bytes_col": [None],
                        "date_col": pandas.Series(
                            [None],
                            dtype=db_dtypes.DateDtype(),
                        ),
                        "datetime_col": pandas.Series(
                            [None],
                            dtype="datetime64[ns]",
                        ),
                        "float_col": pandas.Series([None], dtype="float64"),
                        "int64_col": pandas.Series([None], dtype="Int64"),
                        "numeric_col": [None],
                        "string_col": [None],
                        "time_col": pandas.Series(
                            [None],
                            dtype=db_dtypes.TimeDtype(),
                        ),
                        "timestamp_col": pandas.Series(
                            [None],
                            dtype="datetime64[ns]",
                        ).dt.tz_localize(datetime.timezone.utc),
                    }
                ),
            ),
            id="scalar-types-null",
        ),
        pytest.param(
            *QueryTestCase(
                query="""
SELECT
  bignumerics.row_num AS row_num,
  bignumeric_col,
  nullable_col,
  null_col
FROM
  UNNEST([
      STRUCT(1 AS row_num, CAST('123456789.123456789' AS BIGNUMERIC) AS bignumeric_col),
      STRUCT(2 AS row_num, CAST('-123456789.123456789' AS BIGNUMERIC) AS bignumeric_col),
      STRUCT(3 AS row_num, CAST('987654321.987654321' AS BIGNUMERIC) AS bignumeric_col) ]) AS `bignumerics`
INNER JOIN
  UNNEST([
      STRUCT(1 AS row_num, CAST('123456789.123456789' AS BIGNUMERIC) AS nullable_col),
      STRUCT(2 AS row_num, NULL AS nullable_col),
      STRUCT(3 AS row_num, CAST('987654321.987654321' AS BIGNUMERIC) AS nullable_col) ]) AS `nullables`
INNER JOIN
  UNNEST([
      STRUCT(1 AS row_num, CAST(NULL AS BIGNUMERIC) AS null_col),
      STRUCT(2 AS row_num, CAST(NULL AS BIGNUMERIC) AS null_col),
      STRUCT(3 AS row_num, CAST(NULL AS BIGNUMERIC) AS null_col) ]) AS `nulls`
WHERE
  `bignumerics`.row_num = `nullables`.row_num
  AND `bignumerics`.row_num = `nulls`.row_num
ORDER BY row_num ASC
            """,
                expected=pandas.DataFrame(
                    {
                        "row_num": pandas.Series([1, 2, 3], dtype="Int64"),
                        # TODO: Support a special (nullable) dtype for decimal data.
                        # https://github.com/googleapis/python-db-dtypes-pandas/issues/49
                        "bignumeric_col": [
                            decimal.Decimal("123456789.123456789"),
                            decimal.Decimal("-123456789.123456789"),
                            decimal.Decimal("987654321.987654321"),
                        ],
                        "nullable_col": [
                            decimal.Decimal("123456789.123456789"),
                            None,
                            decimal.Decimal("987654321.987654321"),
                        ],
                        "null_col": [None, None, None],
                    }
                ),
            ),
            id="bignumeric-normal-range",
            marks=pytest.mark.skipif(
                not FEATURES.bigquery_has_bignumeric,
                reason="BIGNUMERIC not supported in this version of google-cloud-bigquery",
            ),
        ),
        pytest.param(
            *QueryTestCase(
                query="""
SELECT
  dates.row_num AS row_num,
  date_col,
  datetime_col,
  timestamp_col
FROM
  UNNEST([
      STRUCT(1 AS row_num, DATE(1, 1, 1) AS date_col),
      STRUCT(2 AS row_num, DATE(9999, 12, 31) AS date_col),
      STRUCT(3 AS row_num, DATE(2262, 4, 12) AS date_col) ]) AS `dates`
INNER JOIN
  UNNEST([
      STRUCT(1 AS row_num, DATETIME('0001-01-01 00:00:00.000000') AS datetime_col),
      STRUCT(2 AS row_num, DATETIME('9999-12-31 23:59:59.999999') AS datetime_col),
      STRUCT(3 AS row_num, DATETIME('2262-04-11 23:47:16.854776') AS datetime_col) ]) AS `datetimes`
INNER JOIN
  UNNEST([
      STRUCT(1 AS row_num, TIMESTAMP('0001-01-01 00:00:00.000000') AS timestamp_col),
      STRUCT(2 AS row_num, TIMESTAMP('9999-12-31 23:59:59.999999') AS timestamp_col),
      STRUCT(3 AS row_num, TIMESTAMP('2262-04-11 23:47:16.854776') AS timestamp_col) ]) AS `timestamps`
WHERE
  `dates`.row_num = `datetimes`.row_num
  AND `dates`.row_num = `timestamps`.row_num
ORDER BY row_num ASC
            """,
                expected=pandas.DataFrame(
                    {
                        "row_num": pandas.Series([1, 2, 3], dtype="Int64"),
                        "date_col": pandas.Series(
                            [
                                datetime.date(1, 1, 1),
                                datetime.date(9999, 12, 31),
                                datetime.date(2262, 4, 12),
                            ],
                            dtype="object",
                        ),
                        "datetime_col": pandas.Series(
                            [
                                datetime.datetime(1, 1, 1, 0, 0, 0, 0),
                                datetime.datetime(9999, 12, 31, 23, 59, 59, 999999),
                                # One microsecond more than pandas.Timestamp.max.
                                datetime.datetime(2262, 4, 11, 23, 47, 16, 854776),
                            ],
                            dtype="object",
                        ),
                        "timestamp_col": pandas.Series(
                            [
                                datetime.datetime(
                                    1, 1, 1, 0, 0, 0, 0, tzinfo=datetime.timezone.utc
                                ),
                                datetime.datetime(
                                    9999,
                                    12,
                                    31,
                                    23,
                                    59,
                                    59,
                                    999999,
                                    tzinfo=datetime.timezone.utc,
                                ),
                                # One microsecond more than pandas.Timestamp.max.
                                datetime.datetime(
                                    2262,
                                    4,
                                    11,
                                    23,
                                    47,
                                    16,
                                    854776,
                                    tzinfo=datetime.timezone.utc,
                                ),
                            ],
                            dtype="object",
                        ),
                    }
                ),
                use_bqstorage_apis={True, False}
                if FEATURES.bigquery_has_accurate_timestamp
                else {True},
            ),
            id="issue365-extreme-datetimes",
        ),
    ],
)
def test_default_dtypes(
    read_gbq, query, expected, use_bqstorage_apis, use_bqstorage_api
):
    if use_bqstorage_api not in use_bqstorage_apis:
        pytest.skip(f"use_bqstorage_api={use_bqstorage_api} not supported.")
    # the parameter useQueryCache=False is used in the following function call
    # to avoid a failing test due to cached data that may be out of order.
    result = read_gbq(
        query,
        use_bqstorage_api=use_bqstorage_api,
        configuration={"query": {"useQueryCache": False}},
    )





  pandas.testing.assert_frame_equal(result, expected)





E       AssertionError: Attributes of DataFrame.iloc[:, 4] (column name="datetime_col") are different

E

E       Attribute "dtype" are different

E       [left]:  datetime64[us]

E       [right]: datetime64[ns]


tests/system/test_read_gbq.py:557: AssertionError
@flaky-bot flaky-bot bot added flakybot: issue An issue filed by the Flaky Bot. Should not be added manually. priority: p1 Important issue which blocks shipping the next release. Will be fixed prior to next release. type: bug Error or flaw in code with unintended results or allowing sub-optimal usage patterns. labels Jul 14, 2023
@product-auto-label product-auto-label bot added the api: bigquery Issues related to the googleapis/python-bigquery-pandas API. label Jul 14, 2023
@flaky-bot flaky-bot bot added the flakybot: flaky Tells the Flaky Bot not to close or comment on this issue. label Jul 14, 2023
@flaky-bot
Copy link
Author

flaky-bot bot commented Jul 14, 2023

Looks like this issue is flaky. 😟

I'm going to leave this open and stop commenting.

A human should fix and close this.

When run at the same commit (7fab432), this test passed in one build (Build Status, Sponge) and failed in another build (Build Status, Sponge).

@chalmerlowe chalmerlowe added priority: p3 Desirable enhancement or fix. May not be included in next release. and removed priority: p1 Important issue which blocks shipping the next release. Will be fixed prior to next release. labels Jul 14, 2023
@chalmerlowe chalmerlowe mentioned this issue Jul 20, 2023
Merged
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Assignees
No one assigned
Labels
api: bigquery Issues related to the googleapis/python-bigquery-pandas API. flakybot: flaky Tells the Flaky Bot not to close or comment on this issue. flakybot: issue An issue filed by the Flaky Bot. Should not be added manually. priority: p3 Desirable enhancement or fix. May not be included in next release. type: bug Error or flaw in code with unintended results or allowing sub-optimal usage patterns.
Projects
None yet
Milestone
No milestone
Development

Successfully merging a pull request may close this issue.

fix: edit units for time in tests googleapis/python-bigquery-pandas
1 participant
@chalmerlowe