how to find circulation in dataframe

Question

my goal is to find if the following df has a ‘circulation’

given:

df = pd.DataFrame({'From':['USA','UK','France','Italy','Russia','china','Japan','Australia','Russia','Italy'],
                  'to':['UK','France','Italy','Russia','china','Australia','New Zealand','Japan','USA','France']})
df

and if I graph it, it would look like this (eventually, note that the order on the df is different):

USA-->UK-->France-->Italy-->Russia-->China-->Australia-->Japan-->Australia
                      |       |
                      |       |
                    France   USA

The point is this: You cannot go backward, so Italy cannot go to France and Russia cannot go to USA.

Note: From can have multiple Tos

How can I find it in pandas so the end result would look like this:

I can solve it without pandas (I get df.to_dict('records') and then iterate to find the circulation and then go back to pandas) but I wish to stay on pandas.

Asked By: ProcolHarum

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Source

Answer 1

The logic is not fully clear, however you can approach your problem with a graph.

Your graph is the following:

Let us consider circulating nodes, those that have more than one destination.

You can obtain this with networkx:

import networkx as nx

G = nx.from_pandas_edgelist(df, source='From', target='to', create_using=nx.DiGraph)

circulating = {n for n in G if len(list(G.successors(n)))>1}

df['IS_CIRCULATING'] = df['From'].isin(circulating).astype(int)

output:

        From           to  IS_CIRCULATING
0        USA           UK               0
1         UK       France               0
2     France        Italy               0
3      Italy       Russia               1
4     Russia        china               1
5      china    Australia               0
6      Japan  New Zealand               0
7  Australia        Japan               0
8     Russia          USA               1
9      Italy       France               1

With pure pandas:

df['IS_CIRCULATING'] = df.groupby('From')['to'].transform('nunique').gt(1).astype(int)

Answered By: mozway

Answer 2

import networkx as nx

gg1=nx.DiGraph(df.apply(tuple,axis=1).tolist())

df11=pd.DataFrame(nx.dfs_edges(gg1),columns=df.columns)

df.merge(df11.assign(IS_CIRCULATING=0), how='left', on=['From','to']).fillna(1)

out

  From           to  IS_CIRCULATING
0        USA           UK             0.0
1         UK       France             0.0
2     France        Italy             0.0
3      Italy       Russia             0.0
4     Russia        china             0.0
5      china    Australia             0.0
6      Japan  New Zealand             0.0
7  Australia        Japan             0.0
8     Russia          USA             1.0
9      Italy       France             1.0

Answered By: G.G

how to find circulation in dataframe

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