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commit: 439d588
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 ... 23:59:58.999271 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.") result = read_gbq(query, use_bqstorage_api=use_bqstorage_api)pandas.testing.assert_frame_equal(result, expected)tests/system/test_read_gbq.py:551:
.nox/prerelease/lib/python3.8/site-packages/pandas/_testing/asserters.py:851: in assert_extension_array_equal
_testing.assert_almost_equal(
pandas/_libs/testing.pyx:52: in pandas._libs.testing.assert_almost_equal
???
???
E AssertionError: ExtensionArray are different
E
E ExtensionArray values are different (50.0 %)
E [index]: [0, 1, 2]
E [left]: [00:00:00, 23:59:59.999999]
E [right]: [00:00:00, 23:59:58.999271]pandas/_libs/testing.pyx:167: AssertionError
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Issues related to the googleapis/python-bigquery-pandas API.Tells the Flaky Bot not to close or comment on this issue.An issue filed by the Flaky Bot. Should not be added manually.Moderately-important priority. Fix may not be included in next release.Error or flaw in code with unintended results or allowing sub-optimal usage patterns.