How to use pymapd - 10 common examples

def test_session_logon_success(self):
        conn = connect(user='admin', password='HyperInteractive',
                       host='localhost', dbname='omnisci')
        sessionid = conn._session
        connnew = connect(sessionid=sessionid, host='localhost')
        assert connnew._session == sessionid

def test_bad_protocol(self, mock_client):
        with pytest.raises(ValueError) as m:
            connect(user='user', host='localhost', dbname='dbname',
                    protocol='fake-proto')
        assert m.match('fake-proto')

def _connect(self):

        return connect(user="admin",
                       password='HyperInteractive',
                       host='localhost',
                       port=6274, protocol='binary', dbname='omnisci')

data = pd.DataFrame({
            "boolean_": [True, False],
            "smallint_": np.array([0, 1], dtype=np.int16),
            "int_": np.array([0, 1], dtype=np.int32),
            "bigint_": np.array([0, 1], dtype=np.int64),
            "float_": np.array([0, 1], dtype=np.float32),
            "double_": np.array([0, 1], dtype=np.float64),
            "varchar_": ["a", "b"],
            "text_": ['a', 'b'],
            "time_": [datetime.time(0, 11, 59), datetime.time(13)],
            "timestamp_": [pd.Timestamp("2016"), pd.Timestamp("2017")],
            "date_": [datetime.date(2016, 1, 1), datetime.date(2017, 1, 1)],
        }, columns=['boolean_', 'smallint_', 'int_', 'bigint_', 'float_',
                    'double_', 'varchar_', 'text_', 'time_', 'timestamp_',
                    'date_'])
        result = _pandas_loaders.build_input_columnar(data,
                                                      preserve_index=False)

        nulls = [False, False]
        expected = [
            TColumn(TColumnData(int_col=[True, False]), nulls=nulls),
            TColumn(TColumnData(int_col=np.array([0, 1], dtype=np.int16)), nulls=nulls),  # noqa
            TColumn(TColumnData(int_col=np.array([0, 1], dtype=np.int32)), nulls=nulls),  # noqa
            TColumn(TColumnData(int_col=np.array([0, 1], dtype=np.int64)), nulls=nulls),  # noqa
            TColumn(TColumnData(real_col=np.array([0, 1], dtype=np.float32)), nulls=nulls),  # noqa
            TColumn(TColumnData(real_col=np.array([0, 1], dtype=np.float64)), nulls=nulls),  # noqa
            TColumn(TColumnData(str_col=['a', 'b']), nulls=nulls),
            TColumn(TColumnData(str_col=['a', 'b']), nulls=nulls),
            TColumn(TColumnData(int_col=[719, 46800]), nulls=nulls),
            TColumn(TColumnData(int_col=[1451606400, 1483228800]), nulls=nulls),  # noqa
            TColumn(TColumnData(int_col=[1451606400, 1483228800]), nulls=nulls)
        ]

def test_build_table_columnar(self):

        from pymapd._pandas_loaders import build_input_columnar

        data = pd.DataFrame({"a": [1, 2, 3], "b": [1.1, 2.2, 3.3]})
        nulls = [False] * 3
        result = build_input_columnar(data, preserve_index=False)
        expected = [
            TColumn(TColumnData(int_col=[1, 2, 3]), nulls=nulls),
            TColumn(TColumnData(real_col=[1.1, 2.2, 3.3]), nulls=nulls)
        ]
        assert_columnar_equal(result[0], expected)

# unreliable since if there is no number outside the int32
            # bounds in a column with nulls then we will be assuming int
            "int_": np.array([0, 1, None], dtype=np.object),
            "bigint_": np.array([0, 9223372036854775807, None],
                                dtype=np.object),
            "double_": np.array([0, 1, None], dtype=np.float64),
            "varchar_": ["a", "b", None],
            "text_": ['a', 'b', None],
            "time_": [datetime.time(0, 11, 59), datetime.time(13), None],
            "timestamp_": [pd.Timestamp("2016"), pd.Timestamp("2017"), None],
            "date_": [datetime.date(1001, 1, 1), datetime.date(2017, 1, 1),
                      None],
        }, columns=['boolean_', 'int_', 'bigint_',
                    'double_', 'varchar_', 'text_', 'time_', 'timestamp_',
                    'date_'])
        result = _pandas_loaders.build_input_columnar(data,
                                                      preserve_index=False)

        nulls = [False, False, True]
        bool_na = -128
        int_na = -2147483648
        bigint_na = -9223372036854775808
        ns_na = -9223372037
        double_na = 0

        expected = [
            TColumn(TColumnData(int_col=[1, 0, bool_na]), nulls=nulls),
            TColumn(TColumnData(int_col=np.array([0, 1, int_na], dtype=np.int32)), nulls=nulls),  # noqa
            TColumn(TColumnData(int_col=np.array([0, 9223372036854775807, bigint_na], dtype=np.int64)), nulls=nulls),  # noqa
            TColumn(TColumnData(real_col=np.array([0, 1, double_na], dtype=np.float64)), nulls=nulls),  # noqa
            TColumn(TColumnData(str_col=['a', 'b', '']), nulls=nulls),
            TColumn(TColumnData(str_col=['a', 'b', '']), nulls=nulls),

TColumnType(col_name='qty',
                        col_type=TTypeInfo(type=1, encoding=0, nullable=True,
                                           is_array=False, precision=0,
                                           scale=0, comp_param=0),
                        is_reserved_keyword=False, src_name=''),
            TColumnType(col_name='price',
                        col_type=TTypeInfo(type=3, encoding=0, nullable=True,
                                           is_array=False, precision=0,
                                           scale=0, comp_param=0),
                        is_reserved_keyword=False, src_name=''),
            TColumnType(col_name='vol',
                        col_type=TTypeInfo(type=3, encoding=0, nullable=True,
                                           is_array=False, precision=0,
                                           scale=0, comp_param=0),
                        is_reserved_keyword=False, src_name='')]
        result = _extract_column_details(data)

        expected = [
            ColumnDetails(name='date_', type='STR', nullable=True, precision=0,
                          scale=0, comp_param=32, encoding='DICT',
                          is_array=False),
            ColumnDetails(name='trans', type='STR', nullable=True, precision=0,
                          scale=0, comp_param=32, encoding='DICT',
                          is_array=False),
            ColumnDetails(name='symbol', type='STR', nullable=True,
                          precision=0, scale=0, comp_param=32,
                          encoding='DICT',
                          is_array=False),
            ColumnDetails(name='qty', type='INT', nullable=True, precision=0,
                          scale=0, comp_param=0, encoding='NONE',
                          is_array=False),
            ColumnDetails(name='price', type='FLOAT', nullable=True,

def test_nonexistant_table_raises(self, nonexistant_table):
        result = _translate_exception(nonexistant_table)
        assert isinstance(result, DatabaseError)
        assert "Exception occurred: Table" in result.args[0]

data = pd.DataFrame({
            "boolean_": [True, False],
            "smallint_": np.array([0, 1], dtype=np.int16),
            "int_": np.array([0, 1], dtype=np.int32),
            "bigint_": np.array([0, 1], dtype=np.int64),
            "float_": np.array([0, 1], dtype=np.float32),
            "double_": np.array([0, 1], dtype=np.float64),
            "varchar_": ["a", "b"],
            "text_": ['a', 'b'],
            "time_": [datetime.time(0, 11, 59), datetime.time(13)],
            "timestamp_": [pd.Timestamp("2016"), pd.Timestamp("2017")],
            "date_": [datetime.date(2016, 1, 1), datetime.date(2017, 1, 1)],
        }, columns=['boolean_', 'smallint_', 'int_', 'bigint_', 'float_',
                    'double_', 'varchar_', 'text_', 'time_', 'timestamp_',
                    'date_'])
        result = _pandas_loaders.build_row_desc(data)
        expected = [
            TColumnType(col_name='boolean_',
                        col_type=TTypeInfo(type=10),
                        is_reserved_keyword=None),
            TColumnType(col_name='smallint_',
                        col_type=TTypeInfo(type=0),
                        is_reserved_keyword=None),
            TColumnType(col_name='int_',
                        col_type=TTypeInfo(type=1),
                        is_reserved_keyword=None),
            TColumnType(col_name='bigint_',
                        col_type=TTypeInfo(type=2)),
            TColumnType(col_name='float_',
                        col_type=TTypeInfo(type=3)),
            TColumnType(col_name='double_',
                        col_type=TTypeInfo(type=5)),

def test_create_non_pandas_raises(self):
        with pytest.raises(TypeError) as m:
            _pandas_loaders.build_row_desc([(1, 'a'), (2, 'b')])

        assert m.match('is not supported for type ')