def execute_gremlin(client: NeptuneClient, query: str) -> pd.DataFrame:
    'Return results of a Gremlin traversal as pandas dataframe.\n\n    Parameters\n    ----------\n    client : neptune.Client\n        instance of the neptune client to use\n    traversal : str\n        The gremlin traversal to execute\n\n    Returns\n    -------\n    Union[pandas.DataFrame, Iterator[pandas.DataFrame]]\n        Results as Pandas DataFrame\n\n    Examples\n    --------\n    Run a Gremlin Query\n\n    >>> import awswrangler as wr\n        >>> client = wr.neptune.connect(neptune_endpoint, neptune_port, iam_enabled=False)\n    >>> df = wr.neptune.execute_gremlin(client, "g.V().limit(1)")\n    '
    results = client.read_gremlin(query)
    df = pd.DataFrame.from_records(results)
    return df
Return results of a Gremlin traversal as pandas dataframe.

Parameters
----------
client : neptune.Client
    instance of the neptune client to use
traversal : str
    The gremlin traversal to execute

Returns
-------
Union[pandas.DataFrame, Iterator[pandas.DataFrame]]
    Results as Pandas DataFrame

Examples
--------
Run a Gremlin Query

>>> import awswrangler as wr
    >>> client = wr.neptune.connect(neptune_endpoint, neptune_port, iam_enabled=False)
>>> df = wr.neptune.execute_gremlin(client, "g.V().limit(1)")
def execute_gremlin(client: NeptuneClient, query: str) -> pd.DataFrame:
    'Return results of a Gremlin traversal as pandas dataframe.\n\n    Parameters\n    ----------\n    client : neptune.Client\n        instance of the neptune client to use\n    traversal : str\n        The gremlin traversal to execute\n\n    Returns\n    -------\n    Union[pandas.DataFrame, Iterator[pandas.DataFrame]]\n        Results as Pandas DataFrame\n\n    Examples\n    --------\n    Run a Gremlin Query\n\n    >>> import awswrangler as wr\n        >>> client = wr.neptune.connect(neptune_endpoint, neptune_port, iam_enabled=False)\n    >>> df = wr.neptune.execute_gremlin(client, "g.V().limit(1)")\n    '
    results = client.read_gremlin(query)
    df = pd.DataFrame.from_records(results)
    return df