How to use pantab - 10 common examples

def export_dataframe_tableau(dataframe, filename="dataframe.hyper"):
    """
    Export a Pandas dataframe to a Tableau file.

    :param dataframe: Pandas dataframe.
    :param filename: Python string.
    :return: None.
    """
    global result
    import pantab
    # TODO: Fix ModuleNotFoundError: No module named 'tableauhyperapi'
    result = dataframe.to_json(orient='split')
    pantab.frame_to_hyper(dataframe, filename)
    with open(filename, "rb") as binary_file:
        file_bin = binary_file.read()
    assert file_bin is not None
    result = file_bin
    resultMetadata.put("file.extension", ".hyper")
    resultMetadata.put("file.name", filename)
    resultMetadata.put("content.type", "application/octet-stream")

{
                "int16": np.int16,
                "int32": np.int32,
                "int64": np.int64,
                "float32": np.float32,
                "float64": np.float64,
                "bool": np.bool,
                "datetime64": "datetime64[ns]",
                "datetime64_utc": "datetime64[ns, UTC]",
                "timedelta64": "timedelta64[ns]",
                "object": "object",
            }
        )

        path = "test.hyper"
        pantab.frame_to_hyper(df, path, table="test")
        return df

def peakmem_write_frame(self):
        pantab.frame_to_hyper(self.df, "dummy.hyper", table="dummy")

def time_write_frame(self, df):
        pantab.frame_to_hyper(df, "dummy.hyper", table="dummy")

def time_write_frame(self):
        pantab.frame_to_hyper(self.df, "dummy.hyper", table="dummy")

def setup_cache(self):
        df = pd.DataFrame(np.ones((10_000_000, 1)), columns=["a"])
        path = "test.hyper"
        pantab.frame_to_hyper(df, path, table="test")

def _pandas_to_tableau_type(typ: str) -> pantab_types._ColumnType:
    try:
        return pantab_types._column_types[typ]
    except KeyError:
        raise TypeError("Conversion of '{}' dtypes not supported!".format(typ))

def _read_table(*, connection: tab_api.Connection, table: TableType) -> pd.DataFrame:
    if isinstance(table, str):
        table = tab_api.TableName(table)

    table_def = connection.catalog.get_table_definition(table)
    columns = table_def.columns

    dtypes: Dict[str, str] = {}
    for column in columns:
        column_type = pantab_types._ColumnType(column.type, column.nullability)
        try:
            dtypes[column.name.unescaped] = pantab_types._pandas_types[column_type]
        except KeyError as e:
            raise TypeError(
                f"Column {column.name} has unsupported datatype {column.type}"
            ) from e

    query = f"SELECT * from {table}"
    dtype_strs = tuple(dtypes.values())

    df = pd.DataFrame(libreader.read_hyper_query(connection._cdata, query, dtype_strs))

    df.columns = dtypes.keys()

    # TODO: remove this hackery...
    for k, v in dtypes.items():

def _read_table(*, connection: tab_api.Connection, table: TableType) -> pd.DataFrame:
    if isinstance(table, str):
        table = tab_api.TableName(table)

    table_def = connection.catalog.get_table_definition(table)
    columns = table_def.columns

    dtypes: Dict[str, str] = {}
    for column in columns:
        column_type = pantab_types._ColumnType(column.type, column.nullability)
        try:
            dtypes[column.name.unescaped] = pantab_types._pandas_types[column_type]
        except KeyError as e:
            raise TypeError(
                f"Column {column.name} has unsupported datatype {column.type}"
            ) from e

    query = f"SELECT * from {table}"
    dtype_strs = tuple(dtypes.values())

    df = pd.DataFrame(libreader.read_hyper_query(connection._cdata, query, dtype_strs))

    df.columns = dtypes.keys()

    # TODO: remove this hackery...
    for k, v in dtypes.items():
        if v == "date":
            dtypes[k] = "datetime64[ns]"

def _insert_frame(
    df: pd.DataFrame,
    *,
    connection: tab_api.Connection,
    table: pantab_types.TableType,
    table_mode: str,
) -> None:
    _validate_table_mode(table_mode)

    if isinstance(table, str):
        table = tab_api.TableName(table)

    # Populate insertion mechanisms dependent on column types
    column_types: List[pantab_types._ColumnType] = []
    columns: List[tab_api.TableDefinition.Column] = []
    for col_name, dtype in df.dtypes.items():
        column_type = _pandas_to_tableau_type(dtype.name)
        column_types.append(column_type)
        columns.append(
            tab_api.TableDefinition.Column(
                name=col_name,
                type=column_type.type_,
                nullability=column_type.nullability,
            )
        )

    # Sanity check for existing table structures
    if table_mode == "a" and connection.catalog.has_table(table):
        table_def = connection.catalog.get_table_definition(table)
        _assert_columns_equal(columns, table_def.columns)