cugraph.from_pandas_edgelist#

cugraph.from_pandas_edgelist(df, source='source', destination='destination', edge_attr=None, create_using=<class 'cugraph.structure.graph_classes.Graph'>, renumber=True)[source]#

Initialize a graph from the edge list. It is an error to call this method on an initialized Graph object. Source argument is source column name and destination argument is destination column name.

By default, renumbering is enabled to map the source and destination vertices into an index in the range [0, V) where V is the number of vertices. If the input vertices are a single column of integers in the range [0, V), renumbering can be disabled and the original external vertex ids will be used.

If weights are present, edge_attr argument is the weights column name.

Parameters:

dfpandas.DataFrame: A DataFrame that contains edge information
sourcestr or array-like, optional (default=’source’): source column name or array of column names
destinationstr or array-like, optional (default=’destination’): destination column name or array of column names
edge_attrstr or None, optional (default=None): the weights column name.
renumberbool, optional (default=True): Indicate whether or not to renumber the source and destination vertex IDs.
create_using: cugraph.Graph (instance or class), optional (default=Graph): Specify the type of Graph to create. Can pass in an instance to create a Graph instance with specified ‘directed’ attribute.

Returns:

Gcugraph.Graph: Graph containing edges from the pandas edgelist

Examples

>>> #  Download dataset from
>>> #  https://github.com/rapidsai/cugraph/datasets/...
>>> df = pd.read_csv(datasets_path / 'karate.csv', delimiter=' ',
...                  header=None, names=["0", "1", "2"],
...                  dtype={"0": "int32", "1": "int32", "2": "float32"})
>>> G = cugraph.Graph()
>>> G.from_pandas_edgelist(df, source='0', destination='1',
...                        edge_attr='2', renumber=False)