Python: get all node descendants in a tree
0
$begingroup$
I have a CSV holding a "flat" table of a tree's edges (NOT binary, but a node cannot have two parents), ~1M edges:
node_id parent_id
1 0
2 1
3 1
4 2
...
The nodes are sorted in a way that a parent_id must always come before any of its children, so a parent_id will always be lower than node_id.
I wish, for each node_id, to get the set of all ancestor nodes (including itself, propagated until root which is node 0 here), and a set of all descendant nodes (including itself, propagated until leaves), and speed is crucial.
Currently what I do at high level:
- Read the CSV in pandas, call it
nodes_df
- Iterate once through
nodes_dfto getnode_ancestors, a{node_id: set(ancestors)}dict adding for each node's ancestors itself and its parent's ancestors (which I know I have seen all by then) - Iterate through
nodes_dfagain in reverse order to getnode_descendants, a{node_id: set(ancestors)}dict adding for each node's descendants itself and its child's descendants (which I know I have seen all by then)
Code:
import pandas as pd
from collections import defaultdict
# phase 1
nodes_df = pd.read_csv('input.csv')
# phase 2
node_ancestors = defaultdict(set)
node_ancestors[0] = set([0])
for id, ndata in nodes_df1.iterrows():
node_ancestors[ndata['node_id']].add(ndata['node_id'])
node_ancestors[ndata['node_id']].update(node_ancestors[ndata['parent_id']])
# phase 3
node_descendants = defaultdict(set)
node_descendants[0] = set([0])
for id, ndata in nodes_df1[::-1].iterrows():
node_descendants[ndata['node_id']].add(ndata['node_id'])
node_descendants[ndata['parent_id']].
update(node_descendants[ndata['node_id']])
So, this takes minutes on my laptop, which is ages for my application. How do I improve?
Plausible directions:
- Can I use pandas better? Can I get
node_ancestorsand/ornode_descendantsby some clever join which is out of my league? - Can I use a python graph library like
Networkxorigraph(which in my experience is faster on large graphs)? E.g. in both libraries I have aget_all_shortest_pathsmethods, which returns something like a{node_id: dist}dictionary, from which I could select the keys, but... I need this for every node, so again a long long loop - Parallelizing - no idea how to do this
python tree graph pandas
asked 4 mins ago
Giora SimchoniGiora Simchoni
1011
New contributor
Giora Simchoni is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
0
$begingroup$
I have a CSV holding a "flat" table of a tree's edges (NOT binary, but a node cannot have two parents), ~1M edges:
node_id parent_id
1 0
2 1
3 1
4 2
...
The nodes are sorted in a way that a parent_id must always come before any of its children, so a parent_id will always be lower than node_id.
I wish, for each node_id, to get the set of all ancestor nodes (including itself, propagated until root which is node 0 here), and a set of all descendant nodes (including itself, propagated until leaves), and speed is crucial.
Currently what I do at high level:
- Read the CSV in pandas, call it
nodes_df
- Iterate once through
nodes_dfto getnode_ancestors, a{node_id: set(ancestors)}dict adding for each node's ancestors itself and its parent's ancestors (which I know I have seen all by then) - Iterate through
nodes_dfagain in reverse order to getnode_descendants, a{node_id: set(ancestors)}dict adding for each node's descendants itself and its child's descendants (which I know I have seen all by then)
Code:
import pandas as pd
from collections import defaultdict
# phase 1
nodes_df = pd.read_csv('input.csv')
# phase 2
node_ancestors = defaultdict(set)
node_ancestors[0] = set([0])
for id, ndata in nodes_df1.iterrows():
node_ancestors[ndata['node_id']].add(ndata['node_id'])
node_ancestors[ndata['node_id']].update(node_ancestors[ndata['parent_id']])
# phase 3
node_descendants = defaultdict(set)
node_descendants[0] = set([0])
for id, ndata in nodes_df1[::-1].iterrows():
node_descendants[ndata['node_id']].add(ndata['node_id'])
node_descendants[ndata['parent_id']].
update(node_descendants[ndata['node_id']])
So, this takes minutes on my laptop, which is ages for my application. How do I improve?
Plausible directions:
- Can I use pandas better? Can I get
node_ancestorsand/ornode_descendantsby some clever join which is out of my league? - Can I use a python graph library like
Networkxorigraph(which in my experience is faster on large graphs)? E.g. in both libraries I have aget_all_shortest_pathsmethods, which returns something like a{node_id: dist}dictionary, from which I could select the keys, but... I need this for every node, so again a long long loop - Parallelizing - no idea how to do this
python tree graph pandas
asked 4 mins ago
Giora SimchoniGiora Simchoni
1011
New contributor
Giora Simchoni is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
0
0
0
$begingroup$
I have a CSV holding a "flat" table of a tree's edges (NOT binary, but a node cannot have two parents), ~1M edges:
node_id parent_id
1 0
2 1
3 1
4 2
...
The nodes are sorted in a way that a parent_id must always come before any of its children, so a parent_id will always be lower than node_id.
I wish, for each node_id, to get the set of all ancestor nodes (including itself, propagated until root which is node 0 here), and a set of all descendant nodes (including itself, propagated until leaves), and speed is crucial.
Currently what I do at high level:
- Read the CSV in pandas, call it
nodes_df
- Iterate once through
nodes_dfto getnode_ancestors, a{node_id: set(ancestors)}dict adding for each node's ancestors itself and its parent's ancestors (which I know I have seen all by then) - Iterate through
nodes_dfagain in reverse order to getnode_descendants, a{node_id: set(ancestors)}dict adding for each node's descendants itself and its child's descendants (which I know I have seen all by then)
Code:
import pandas as pd
from collections import defaultdict
# phase 1
nodes_df = pd.read_csv('input.csv')
# phase 2
node_ancestors = defaultdict(set)
node_ancestors[0] = set([0])
for id, ndata in nodes_df1.iterrows():
node_ancestors[ndata['node_id']].add(ndata['node_id'])
node_ancestors[ndata['node_id']].update(node_ancestors[ndata['parent_id']])
# phase 3
node_descendants = defaultdict(set)
node_descendants[0] = set([0])
for id, ndata in nodes_df1[::-1].iterrows():
node_descendants[ndata['node_id']].add(ndata['node_id'])
node_descendants[ndata['parent_id']].
update(node_descendants[ndata['node_id']])
So, this takes minutes on my laptop, which is ages for my application. How do I improve?
Plausible directions:
- Can I use pandas better? Can I get
node_ancestorsand/ornode_descendantsby some clever join which is out of my league? - Can I use a python graph library like
Networkxorigraph(which in my experience is faster on large graphs)? E.g. in both libraries I have aget_all_shortest_pathsmethods, which returns something like a{node_id: dist}dictionary, from which I could select the keys, but... I need this for every node, so again a long long loop - Parallelizing - no idea how to do this
python tree graph pandas
asked 4 mins ago
Giora SimchoniGiora Simchoni
1011
New contributor
Giora Simchoni is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
I have a CSV holding a "flat" table of a tree's edges (NOT binary, but a node cannot have two parents), ~1M edges:
node_id parent_id
1 0
2 1
3 1
4 2
...
The nodes are sorted in a way that a parent_id must always come before any of its children, so a parent_id will always be lower than node_id.
I wish, for each node_id, to get the set of all ancestor nodes (including itself, propagated until root which is node 0 here), and a set of all descendant nodes (including itself, propagated until leaves), and speed is crucial.
Currently what I do at high level:
- Read the CSV in pandas, call it
nodes_df
- Iterate once through
nodes_dfto getnode_ancestors, a{node_id: set(ancestors)}dict adding for each node's ancestors itself and its parent's ancestors (which I know I have seen all by then) - Iterate through
nodes_dfagain in reverse order to getnode_descendants, a{node_id: set(ancestors)}dict adding for each node's descendants itself and its child's descendants (which I know I have seen all by then)
Code:
import pandas as pd
from collections import defaultdict
# phase 1
nodes_df = pd.read_csv('input.csv')
# phase 2
node_ancestors = defaultdict(set)
node_ancestors[0] = set([0])
for id, ndata in nodes_df1.iterrows():
node_ancestors[ndata['node_id']].add(ndata['node_id'])
node_ancestors[ndata['node_id']].update(node_ancestors[ndata['parent_id']])
# phase 3
node_descendants = defaultdict(set)
node_descendants[0] = set([0])
for id, ndata in nodes_df1[::-1].iterrows():
node_descendants[ndata['node_id']].add(ndata['node_id'])
node_descendants[ndata['parent_id']].
update(node_descendants[ndata['node_id']])
So, this takes minutes on my laptop, which is ages for my application. How do I improve?
Plausible directions:
- Can I use pandas better? Can I get
node_ancestorsand/ornode_descendantsby some clever join which is out of my league? - Can I use a python graph library like
Networkxorigraph(which in my experience is faster on large graphs)? E.g. in both libraries I have aget_all_shortest_pathsmethods, which returns something like a{node_id: dist}dictionary, from which I could select the keys, but... I need this for every node, so again a long long loop - Parallelizing - no idea how to do this
python tree graph pandas
python tree graph pandas
asked 4 mins ago
Giora SimchoniGiora Simchoni
1011
New contributor
Giora Simchoni is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 4 mins ago
Giora SimchoniGiora Simchoni
1011
New contributor
Giora Simchoni is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 4 mins ago
Giora SimchoniGiora Simchoni
1011
New contributor
Giora Simchoni is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 4 mins ago
Giora SimchoniGiora Simchoni
1011
asked 4 mins ago
Giora SimchoniGiora Simchoni
1011
1011
New contributor
Giora Simchoni is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
Giora Simchoni is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
Giora Simchoni is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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