def to_dataframe(self, selector, vertex_range=None):
"""Select some elements of the graph and output as a pandas.DataFrame
Args:
selector (dict): Select some portions of graph.
vertex_range (dict, optional): Slice vertices. Defaults to None.
Returns:
`pandas.DataFrame`
"""
check_argument(self.graph_type == types_pb2.ARROW_PROPERTY)
self._ensure_loaded()
self._check_unmodified()
check_argument(
isinstance(selector, Mapping),
"selector of to_vineyard_dataframe must be a dict",
)
selector = {
key:
utils.transform_labeled_vertex_property_data_selector(self, value)
for key, value in selector.items()
}
selector = json.dumps(selector)
vertex_range = utils.transform_vertex_range(vertex_range)
op = dag_utils.graph_to_dataframe(self, selector, vertex_range)
ret = op.eval()
return utils.decode_dataframe(ret)
def to_dataframe(self, selector, vertex_range=None):
"""Return results as a pandas DataFrame
Args:
selector: dict
The key is column name in dataframe, and the value describes how to select values of context.
See more details in derived context class.
vertex_range: dict, optional, default to None.
Works as slicing. The expression {'begin': m, 'end': n} select a portion
of vertices from `m` to, but not including `n`. Type of `m`, `n` must be identical with vertices'
oid type.
Only the sub-ranges of vertices data will be retrieved.
Note the comparision is not based on numeric order, but on alphabetic order.
Returns:
pandas.DataFrame
"""
self._check_unmodified()
check_argument(isinstance(selector, Mapping),
"selector of to_dataframe must be a dict")
selector = {
key: self._transform_selector(value)
for key, value in selector.items()
}
selector = json.dumps(selector)
vertex_range = utils.transform_vertex_range(vertex_range)
op = dag_utils.context_to_dataframe(self, selector, vertex_range)
raw_values = op.eval()
return decode_dataframe(raw_values)
