code: def to_perseus(graphs):
      
      graph_attributes = []
      networks = {}
      for graph in graphs:
          attributes = dict(graph.graph)
          attributes.update({"Name" : attributes.get("Name", attributes.get("name", "networkx graph")),
              "GUID": attributes.get("GUID", str(uuid.uuid4()))})
          graph_attributes.append(attributes)
          if len(graph) > 0:
              edge_table = pd.DataFrame([dict(data, **{"Source": str(f), "Target": str(t)}) for f,t,data in graph.edges(data=True)])
              edge_table.columns.name = "Column Name"
              node_table = pd.DataFrame([dict(data, **{"Node": str(n)}) for n,data in graph.nodes(data=True)])
              node_table.columns.name = "Column Name"
          else:
              edge_table = pd.DataFrame(columns=pd.Index(['Source', 'Target'], name='Column Name'))
              node_table = pd.DataFrame(columns=pd.Index(['Node'], name='Column Name'))
          guid = attributes['GUID']
          networks[guid] = {
              'edge_table': edge_table,
              'node_table': node_table,
              'name': attributes['Name'],
              'guid': guid }
      network_table = pd.DataFrame(graph_attributes)
      network_table.columns.name = "Column Name"
      return network_table, networks
docs: Create a network table and the network dictionary for export to Perseus.
      
      :param graphs: Collection of networkx graphs
  
      >>> from perseuspy import nx
      >>> G = nx.random_graphs.barabasi_albert_graph(10, 3)
      >>> network_table, networks = nx.to_perseus([G])