NetworkX Pandas Edge List Conversion: Missing Edge Key Column in MultiGraph

NetworkX Pandas Edge List Conversion: Missing Edge Key Column in MultiGraph

The `to_pandas_edgelist` function is defined to return a DataFrame that includes a column named “key” for MultiGraph and MultiDiGraph objects. This column uniquely identifies parallel edges. In practice the column may be missing when the graph has no parallel edges or when the function is called on a regular Graph instead of a MultiGraph, which matches the reported issue.

Likely Explanation

The missing “key” column is most often caused by calling `to_pandas_edgelist` on a Graph that lacks parallel edges, or by using a NetworkX version prior to 2.0 where the column was not guaranteed. Version‑dependent behavior also means that some releases suppress the column when the input graph contains only a single edge between any node pair.

Confirmed Facts

NetworkX 2.0 and newer always emit a “key” column for MultiGraph/MultiDiGraph instances that have at least two edges between the same node pair. The column is omitted if the graph is a plain Graph or if there are no duplicate edges. NaN values in node attribute columns are copied verbatim into the DataFrame; however, round‑trip conversion with `from_pandas_edgelist` can treat NaN differently across releases, so it is advisable to verify that attributes are retained after conversion. If the DataFrame index is non‑unique or unsorted, the adjacency matrix conversion may drop or reorder nodes, which can be avoided by ensuring a unique, sorted index before the conversion.

Steps to Resolve

1. Check the installed NetworkX version with `nx.__version__` and confirm it is 2.0 or later.
2. Run `nx.to_pandas_edgelist(multi_graph)` on a MultiGraph known to contain parallel edges and verify that the resulting DataFrame includes a column named “key”.
3. Inspect the DataFrame for NaN values in attribute columns and, if needed, perform a round‑trip conversion to ensure attributes are preserved.
4. Before any adjacency‑matrix operation, ensure the DataFrame index is unique and sorted (e.g., `df = df.set_index('source').sort_index().reset_index(drop=False)`).