Python, create a network with a given node and edges from pandas dataframe

Question

I'm working on a code snippet that generates a network graph.

def create_network(df, node, column_edge, column_edge1=None, column_edge2=None):

    #  select columns, remove NaN
    df_edge1 = df[[node, column_edge]].dropna(subset=[column_edge]).drop_duplicates()

    # To create connections between "node" who have the same "edge",
    # join data with itself on the "node" column.
    df_edge1 = df_edge1.merge(
                              df_edge1[[node, column_edge]].rename(columns={node:node+"_2"}), 
                              on=column_edge
                              )

    # By joining the data with itself, node will have a connection with themselves.
    # Remove self connections, to keep only connected nodes which are different.
    edge1 = df_edge1[~(df_edge1[node]==df_edge1[node+"_2"])].dropna()[[node, node +"_2", column_edge]]
        
    # To avoid counting twice the connections (person 1 connected to person 2 and person 2 connected to person 1)
    # we force the first ID to be "lower" then ID_2
    edge1.drop(edge1.loc[edge1[node+"_2"]<edge1[node]].index.tolist(), inplace=True)

    G = nx.from_pandas_edgelist(df=edge1, source=node, target=node + '_2', edge_attr=column_edge)

    G.add_nodes_from(nodes_for_adding=df[node].tolist())


    if column_edge1:


        df_edge2 = df[[node, column_edge1]].dropna(subset=[column_edge1]).drop_duplicates()

        df_edge2 = df_edge2.merge(
            df_edge2[[node, column_edge1]].rename(columns={node:node+"_2"}), 
            on=column_edge1
        )

        edge2 = df_edge2[~(df_edge2[node]==df_edge2[node+"_2"])].dropna()[[node, node+"_2", column_edge1]]

        edge2.drop(edge2.loc[edge2[node+"_2"]<edge2[node]].index.tolist(), inplace=True)

        # Create the connections in the graph
        links_attributes = {tuple(row[[node, node+"_2"]]): {column_edge1: row[column_edge1]} for i,row in edge2.iterrows()}

        # create the connection, without attribute.
        G.add_edges_from(links_attributes)
        # adds the attribute.
        nx.set_edge_attributes(G=G, values=links_attributes)


    if column_edge2:

        df_edge3 = df[[node, column_edge2]].dropna(subset=[column_edge2]).drop_duplicates()

        df_edge3 = df_edge3.merge(
                                  df_edge3[[node, column_edge2]].rename(columns={node:node+"_2"}), 
                                  on=column_edge2
                                  )


        edge3 = df_edge3[~(df_edge3[node]==df_edge3[node+"_2"])].dropna()[[node, node+"_2", column_edge2]]

        edge3.drop(edge3.loc[edge3[node+"_2"]<edge3[node]].index.tolist(), inplace=True)

        # Create the connections in the graph
        links_attributes2 = {tuple(row[[node, node+"_2"]]): {column_edge2: row[column_edge2]} for i,row in edge3.iterrows()}

        # create the connection, without attribute.
        G.add_edges_from(links_attributes2) 
        # adds the attribute.
        nx.set_edge_attributes(G=G, values=links_attributes2)
    
    return G

The above function takes dataframe, node, and one or more column edges and generates the graph using Networkx python package.

Is there a better / more elegant / more accurate way to do this?