from_pandas_edgelist(df, source='source', target='target', edge_attr=None, create_using=None, edge_key=None)
The Pandas DataFrame should contain at least two columns of node names and zero or more columns of edge attributes. Each row will be processed as one edge instance.
Note: This function iterates over DataFrame.values, which is not guaranteed to retain the data type across columns in the row. This is only a problem if your row is entirely numeric and a mix of ints and floats. In that case, all values will be returned as floats. See the DataFrame.iterrows documentation for an example.
An edge list representation of a graph
A valid column name (string or integer) for the source nodes (for the directed case).
A valid column name (string or integer) for the target nodes (for the directed case).
A valid column name (str or int) or iterable of column names that are used to retrieve items and add them to the graph as edge attributes. If :None:None:`True`, all of the remaining columns will be added. If :None:None:`None`, no edge attributes are added to the graph.
Graph type to create. If graph instance, then cleared before populated.
A valid column name for the edge keys (for a MultiGraph). The values in this column are used for the edge keys when adding edges if create_using is a multigraph.
Returns a graph from Pandas DataFrame containing an edge list.
Simple integer weights on edges:
>>> import pandas as pd
... pd.options.display.max_columns = 20
... import numpy as np
... rng = np.random.RandomState(seed=5)
... ints = rng.randint(1, 11, size=(3, 2))
... a = ["A", "B", "C"]
... b = ["D", "A", "E"]
... df = pd.DataFrame(ints, columns=["weight", "cost"])
... df[0] = a
... df["b"] = b
... df[["weight", "cost", 0, "b"]] weight cost 0 b 0 4 7 A D 1 7 1 B A 2 10 9 C E
>>> G = nx.from_pandas_edgelist(df, 0, "b", ["weight", "cost"])
... G["E"]["C"]["weight"] 10
>>> G["E"]["C"]["cost"] 9
>>> edges = pd.DataFrame(
... {
... "source": [0, 1, 2],
... "target": [2, 2, 3],
... "weight": [3, 4, 5],
... "color": ["red", "blue", "blue"],
... }
... )
... G = nx.from_pandas_edgelist(edges, edge_attr=True)
... G[0][2]["color"] 'red'
Build multigraph with custom keys:
>>> edges = pd.DataFrame(See :
... {
... "source": [0, 1, 2, 0],
... "target": [2, 2, 3, 2],
... "my_edge_key": ["A", "B", "C", "D"],
... "weight": [3, 4, 5, 6],
... "color": ["red", "blue", "blue", "blue"],
... }
... )
... G = nx.from_pandas_edgelist(
... edges,
... edge_key="my_edge_key",
... edge_attr=["weight", "color"],
... create_using=nx.MultiGraph(),
... )
... G[0][2] AtlasView({'A': {'weight': 3, 'color': 'red'}, 'D': {'weight': 6, 'color': 'blue'}})
The following pages refer to to this document either explicitly or contain code examples using this.
networkx.convert_matrix.from_pandas_edgelist
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