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commit: 1dca732
buildURL: Build Status, Sponge
status: failed
Test output
read_gbq = functools.partial(, project_id='precise-truck-742', credentials=) query = '\nSELECT\n dates.row_num AS row_num,\n date_col,\n datetime_col,\n timestamp_col\nFROM\n UNNEST([\n STRUCT(...dates`.row_num = `datetimes`.row_num\n AND `dates`.row_num = `timestamps`.row_num\nORDER BY row_num ASC\n ' expected = row_num ... timestamp_col 0 1 ... 0001-01-01 00:00:00+00:00 1 2 ... 9999-12-31 23:59:59.999999+00:00 2 3 ... 2262-04-11 23:47:16.854776+00:00[3 rows x 4 columns]
use_bqstorage_apis = {False, True}, use_bqstorage_api = False@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[:, 1] (column name="date_col") are different
E
E Attribute "dtype" are different
E [left]: dbdate
E [right]: objecttests/system/test_read_gbq.py:557: 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.Important issue which blocks shipping the next release. Will be fixed prior to next release.Error or flaw in code with unintended results or allowing sub-optimal usage patterns.