Python pyarrow.int8方法代碼示例

# 需要導入模塊: import pyarrow [as 別名]
# 或者: from pyarrow import int8 [as 別名]
def test_as_spark_schema():
    """Try using 'as_spark_schema' function"""
    TestSchema = Unischema('TestSchema', [
        UnischemaField('int_field', np.int8, (), ScalarCodec(IntegerType()), False),
        UnischemaField('string_field', np.string_, (), ScalarCodec(StringType()), False),
        UnischemaField('string_field_implicit', np.string_, ()),
    ])

    spark_schema = TestSchema.as_spark_schema()
    assert spark_schema.fields[0].name == 'int_field'

    assert spark_schema.fields[1].name == 'string_field'
    assert spark_schema.fields[1].dataType == StringType()

    assert spark_schema.fields[2].name == 'string_field_implicit'
    assert spark_schema.fields[2].dataType == StringType()

    assert TestSchema.fields['int_field'].name == 'int_field'
    assert TestSchema.fields['string_field'].name == 'string_field' 

# 需要導入模塊: import pyarrow [as 別名]
# 或者: from pyarrow import int8 [as 別名]
def test_render_has_header_false(self):
        with tempfile_context("http") as http_path:
            httpfile.write(
                http_path,
                {"url": "https://blah"},
                "200 OK",
                [("content-type", "text/csv")],
                io.BytesIO(b"1,2\n3,4"),
            )
            with self.render(P(has_header=False), FetchResult(http_path)) as result:
                assert_arrow_table_equals(
                    result.table,
                    {
                        "Column 1": pyarrow.array([1, 3], pyarrow.int8()),
                        "Column 2": pyarrow.array([2, 4], pyarrow.int8()),
                    },
                )
                self.assertEqual(result.errors, []) 

# 需要導入模塊: import pyarrow [as 別名]
# 或者: from pyarrow import int8 [as 別名]
def test_dataframe_category_column(self):
        assert_arrow_table_equals(
            dataframe_to_arrow_table(
                pd.DataFrame({"A": ["A", "B", None, "A"]}, dtype="category"),
                [Column("A", ColumnType.TEXT())],
                self.path,
            ),
            arrow_table(
                {
                    "A": pyarrow.DictionaryArray.from_arrays(
                        pyarrow.array([0, 1, None, 0], type=pyarrow.int8()),
                        pyarrow.array(["A", "B"], type=pyarrow.string()),
                    )
                }
            ),
        ) 

# 需要導入模塊: import pyarrow [as 別名]
# 或者: from pyarrow import int8 [as 別名]
def test_dataframe_all_null_category_column(self):
        assert_arrow_table_equals(
            dataframe_to_arrow_table(
                pd.DataFrame({"A": [None]}, dtype=str).astype("category"),
                [Column("A", ColumnType.TEXT())],
                self.path,
            ),
            arrow_table(
                {
                    "A": pyarrow.DictionaryArray.from_arrays(
                        pyarrow.array([None], type=pyarrow.int8()),
                        pyarrow.array([], type=pyarrow.string()),
                    )
                }
            ),
        ) 

# 需要導入模塊: import pyarrow [as 別名]
# 或者: from pyarrow import int8 [as 別名]
def test_render_has_header_false(self):
        with tempfile_context(prefix="http-") as http_path:
            httpfile.write(
                http_path,
                {"url": "http://example.com/hello"},
                "200 OK",
                [("content-type", "text/csv")],
                io.BytesIO(b"1,2\n3,4"),
            )
            table, errors = call_render(P(has_header=False), FetchResult(http_path))
            assert_arrow_table_equals(
                table,
                {
                    "Column 1": pyarrow.array([1, 3], pyarrow.int8()),
                    "Column 2": pyarrow.array([2, 4], pyarrow.int8()),
                },
            )
            self.assertEqual(errors, []) 

# 需要導入模塊: import pyarrow [as 別名]
# 或者: from pyarrow import int8 [as 別名]
def test_index_normalize_during_init_warn_collision(collision, caplog):
    index_dct = {1: ["a", "c"], 2.0: ["d"]}
    if collision:
        index_dct["1"] = ["a", "b"]

    caplog.set_level(logging.DEBUG)
    ExplicitSecondaryIndex(column="col", dtype=pa.int8(), index_dct=index_dct)

    warn = [
        t[2]
        for t in caplog.record_tuples
        if t[0] == "kartothek.core.index" and t[1] == logging.WARN
    ]

    if collision:
        assert any(
            msg.startswith(
                "Value normalization for index column col resulted in 1 collision(s)."
            )
            for msg in warn
        )
    else:
        assert not any(
            msg.startswith("Value normalization for index column") for msg in warn
        ) 

# 需要導入模塊: import pyarrow [as 別名]
# 或者: from pyarrow import int8 [as 別名]
def _get_numeric_byte_size_test_cases():
  result = []
  for array_type, sizeof in [
      (pa.int8(), 1),
      (pa.uint8(), 1),
      (pa.int16(), 2),
      (pa.uint16(), 2),
      (pa.int32(), 4),
      (pa.uint32(), 4),
      (pa.int64(), 8),
      (pa.uint64(), 8),
      (pa.float32(), 4),
      (pa.float64(), 8),
  ]:
    result.append(
        dict(
            testcase_name=str(array_type),
            array=pa.array(range(9), type=array_type),
            slice_offset=2,
            slice_length=3,
            expected_size=(_all_false_null_bitmap_size(2) + sizeof * 9),
            expected_sliced_size=(_all_false_null_bitmap_size(1) + sizeof * 3)))
  return result 

# 需要導入模塊: import pyarrow [as 別名]
# 或者: from pyarrow import int8 [as 別名]
def test_iterate_over_int8_chunk():
    random.seed(datetime.datetime.now())
    column_meta = [
            {"logicalType": "FIXED", "precision": "3", "scale": "0"},
            {"logicalType": "FIXED", "precision": "3", "scale": "0"}
    ]

    def int8_generator():
        return random.randint(-128, 127)

    iterate_over_test_chunk([pyarrow.int8(), pyarrow.int8()],
                            column_meta, int8_generator) 

# 需要導入模塊: import pyarrow [as 別名]
# 或者: from pyarrow import int8 [as 別名]
def get_pyarrow_types():
    return {
        'bool': PA_BOOL,
        'float32': PA_FLOAT32,
        'float64': PA_FLOAT64,
        'int8': PA_INT8,
        'int16': PA_INT16,
        'int32': PA_INT32,
        'int64': PA_INT64,
        'string': PA_STRING,
        'timestamp': PA_TIMESTAMP,
        'base64': PA_BINARY
    }

# pylint: disable=too-many-branches,too-many-statements 

# 需要導入模塊: import pyarrow [as 別名]
# 或者: from pyarrow import int8 [as 別名]
def test_argparse_types():
    _map = {}
    csv2parquet.main_with_args(capture_args(_map), ['foo.csv', '--type', '0=string', '0=int8?'])
    assert _map['raw_types'] == [('0', pa.string(), False), ('0', pa.int8(), True)] 

# 需要導入模塊: import pyarrow [as 別名]
# 或者: from pyarrow import int8 [as 別名]
def test_fields():
    """Try using 'fields' getter"""
    TestSchema = Unischema('TestSchema', [
        UnischemaField('int_field', np.int8, (), ScalarCodec(IntegerType()), False),
        UnischemaField('string_field', np.string_, (), ScalarCodec(StringType()), False),
    ])

    assert len(TestSchema.fields) == 2
    assert TestSchema.fields['int_field'].name == 'int_field'
    assert TestSchema.fields['string_field'].name == 'string_field' 

# 需要導入模塊: import pyarrow [as 別名]
# 或者: from pyarrow import int8 [as 別名]
def test_as_spark_schema_unspecified_codec_type_for_non_scalars_raises():
    """Do not currently support choosing spark type automatically for non-scalar types."""
    TestSchema = Unischema('TestSchema', [
        UnischemaField('int_vector_unspecified_codec', np.int8, (1,)),
    ])

    with pytest.raises(ValueError, match='has codec set to None'):
        TestSchema.as_spark_schema() 

# 需要導入模塊: import pyarrow [as 別名]
# 或者: from pyarrow import int8 [as 別名]
def test_create_schema_view_fails_validate():
    """ Exercises code paths unischema.create_schema_view ValueError, and unischema.__str__."""
    TestSchema = Unischema('TestSchema', [
        UnischemaField('int_field', np.int8, (), ScalarCodec(IntegerType()), False),
        UnischemaField('string_field', np.string_, (), ScalarCodec(StringType()), False),
    ])
    with pytest.raises(ValueError, match='does not belong to the schema'):
        TestSchema.create_schema_view([UnischemaField('id', np.int64, (), ScalarCodec(LongType()), False)]) 

# 需要導入模塊: import pyarrow [as 別名]
# 或者: from pyarrow import int8 [as 別名]
def test_create_schema_view_using_invalid_type():
    """ Exercises code paths unischema.create_schema_view ValueError, and unischema.__str__."""
    TestSchema = Unischema('TestSchema', [
        UnischemaField('int_field', np.int8, (), ScalarCodec(IntegerType()), False),
        UnischemaField('string_field', np.string_, (), ScalarCodec(StringType()), False),
    ])
    with pytest.raises(ValueError, match='must be either a string'):
        TestSchema.create_schema_view([42]) 

# 需要導入模塊: import pyarrow [as 別名]
# 或者: from pyarrow import int8 [as 別名]
def test_create_schema_view_using_regex():
    TestSchema = Unischema('TestSchema', [
        UnischemaField('int_field', np.int8, (), ScalarCodec(IntegerType()), False),
        UnischemaField('string_field', np.string_, (), ScalarCodec(StringType()), False),
    ])
    view = TestSchema.create_schema_view(['int.*$'])
    assert set(view.fields.keys()) == {'int_field'}

    view = TestSchema.create_schema_view([u'int.*$'])
    assert set(view.fields.keys()) == {'int_field'}