From a Pandas Dataframe, build networkx chart or flow chart between different rows with common values in certain columns

I'm working with data that shows order flow across multiple rows, with each row being an independent stop/station. Sample data looks like this:

  Firm           event_type   id previous_id
0    A                 send  111            
1    B     receive and send  222         111
2    C  receive and execute  333         222
3    D  receive and execute  444         222
4    E   receive and cancel  123         100

The link here is decided by the two fields "id" and "previous_id". For instance, in the sample data, the previous_id of Firm B is the same as the id of Firm A, 111. Therefore order flows from Firm A to Firm B.

And for Firm E, since its previous_id doesn't match the id of any row, I intend it to be a standalone part in the flow.

Therefore what I want to achieve based on the sample data is something like this:

(Color is just for illustration purposes, not a must have).

I have been trying to work upon answer from @Dinari in this related question but couldn't get it to work. I would like the label of the networkx directed chart to be a column other than the columns with shared values.

Thanks.

# convert dataypes to ensure that dictionary access will work df['id'] = df['id'].astype(str) df['previous_id'] = df['previous_id'].astype(str) # create a mapping from ids to Firms replace_dict = dict(df[['id', 'Firm']].values) # apply that mapping. If no Firm can be found use placeholders 'no_source' and 'no_target' df['source'] = df['previous_id'].apply(lambda x: replace_dict.get(x) if replace_dict.get(x) else 'no_source' ) df['target'] = df['id'].apply(lambda x: replace_dict.get(x) if replace_dict.get(x) else 'no_target' ) #make the graph G = nx.from_pandas_edgelist(df, source='source', target='target') # drop all placeholder nodes G.remove_nodes_from(['no_source', 'no_target']) # draw graph nx.draw_networkx(G, node_shape='s')

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