ntwrk
This library was created to simplify the concept of graphs by visualizing them in a perfect and clear way, especially for beginners in data structures.
Installation
You can install ntwrk via pip:
pip install ntwrk
Usage
For undirected unweighted graph
from ntwrk import graph
vertices = ["n", "e", "t", "w", "o", "r", "k"]
edges = ["ne", "nt", "nw", "no", "nk", "et", "eo", "er", "tw", "tk", "wo", "wr", "or", "ok", "rk"]
g = graph(vertices, edges)
print(g)
Output
Vertices : {'n', 'e', 't', 'w', 'o', 'r', 'k'}
Edges : {'ne', 'nt', 'nw', 'no', 'nk', 'et', 'eo', 'er', 'tw', 'tk', 'wo', 'wr', 'or', 'ok', 'rk'}
You can show all details (adjacency matrix, adjacency list)
from ntwrk import graph
vertices = ["n", "e", "t", "w", "o", "r", "k"]
edges = ["ne", "nt", "nw", "no", "nk", "et", "eo", "er", "tw", "tk", "wo", "wr", "or", "ok", "rk"]
g = graph(vertices, edges, detail=True)
print(g)
Output
Adjacency matrix :
----------------
╒═════════╤═════╤═════╤═════╤═════╤═════╤═════╤═════╕
│ Nodes │ n │ e │ t │ w │ o │ r │ k │
╞═════════╪═════╪═════╪═════╪═════╪═════╪═════╪═════╡
│ n │ 0 │ 1 │ 1 │ 1 │ 1 │ 0 │ 1 │
├─────────┼─────┼─────┼─────┼─────┼─────┼─────┼─────┤
│ e │ 1 │ 0 │ 1 │ 0 │ 1 │ 1 │ 0 │
├─────────┼─────┼─────┼─────┼─────┼─────┼─────┼─────┤
│ t │ 1 │ 1 │ 0 │ 1 │ 0 │ 0 │ 1 │
├─────────┼─────┼─────┼─────┼─────┼─────┼─────┼─────┤
│ w │ 1 │ 0 │ 1 │ 0 │ 1 │ 1 │ 0 │
├─────────┼─────┼─────┼─────┼─────┼─────┼─────┼─────┤
│ o │ 1 │ 1 │ 0 │ 1 │ 0 │ 1 │ 1 │
├─────────┼─────┼─────┼─────┼─────┼─────┼─────┼─────┤
│ r │ 0 │ 1 │ 0 │ 1 │ 1 │ 0 │ 1 │
├─────────┼─────┼─────┼─────┼─────┼─────┼─────┼─────┤
│ k │ 1 │ 0 │ 1 │ 0 │ 1 │ 1 │ 0 │
╘═════════╧═════╧═════╧═════╧═════╧═════╧═════╧═════╛
Adjacency list :
--------------
╒═════════╤══════════════════╕
│ Nodes │ Adjacent Nodes │
╞═════════╪══════════════════╡
│ n │ ╒═══╕ │
│ │ │ e │ │
│ │ ├───┤ │
│ │ │ t │ │
│ │ ├───┤ │
│ │ │ w │ │
│ │ ├───┤ │
│ │ │ o │ │
│ │ ├───┤ │
│ │ │ k │ │
│ │ ╘═══╛ │
├─────────┼──────────────────┤
│ e │ ╒═══╕ │
│ │ │ n │ │
│ │ ├───┤ │
│ │ │ t │ │
│ │ ├───┤ │
│ │ │ o │ │
│ │ ├───┤ │
│ │ │ r │ │
│ │ ╘═══╛ │
├─────────┼──────────────────┤
│ t │ ╒═══╕ │
│ │ │ n │ │
│ │ ├───┤ │
│ │ │ e │ │
│ │ ├───┤ │
│ │ │ w │ │
│ │ ├───┤ │
│ │ │ k │ │
│ │ ╘═══╛ │
├─────────┼──────────────────┤
│ w │ ╒═══╕ │
│ │ │ n │ │
│ │ ├───┤ │
│ │ │ t │ │
│ │ ├───┤ │
│ │ │ o │ │
│ │ ├───┤ │
│ │ │ r │ │
│ │ ╘═══╛ │
├─────────┼──────────────────┤
│ o │ ╒═══╕ │
│ │ │ n │ │
│ │ ├───┤ │
│ │ │ e │ │
│ │ ├───┤ │
│ │ │ w │ │
│ │ ├───┤ │
│ │ │ r │ │
│ │ ├───┤ │
│ │ │ k │ │
│ │ ╘═══╛ │
├─────────┼──────────────────┤
│ r │ ╒═══╕ │
│ │ │ e │ │
│ │ ├───┤ │
│ │ │ w │ │
│ │ ├───┤ │
│ │ │ o │ │
│ │ ├───┤ │
│ │ │ k │ │
│ │ ╘═══╛ │
├─────────┼──────────────────┤
│ k │ ╒═══╕ │
│ │ │ n │ │
│ │ ├───┤ │
│ │ │ t │ │
│ │ ├───┤ │
│ │ │ o │ │
│ │ ├───┤ │
│ │ │ r │ │
│ │ ╘═══╛ │
╘═════════╧══════════════════╛
You can add nodes with edges
from ntwrk import graph
vertices = ["n", "e", "t", "w", "o", "r", "k"]
edges = ["ne", "nt", "nw", "no", "nk", "et", "eo", "er", "tw", "tk", "wo", "wr", "or", "ok", "rk"]
g = graph(vertices, edges)
print(g)
g.add("x", edge="kx")
print(g)
Output
Vertices : {'n', 'e', 't', 'w', 'o', 'r', 'k'}
Edges : {'ne', 'nt', 'nw', 'no', 'nk', 'et', 'eo', 'er', 'tw', 'tk', 'wo', 'wr', 'or', 'ok', 'rk'}
Vertices : {'n', 'e', 't', 'w', 'o', 'r', 'k', 'x'}
Edges : {'ne', 'nt', 'nw', 'no', 'nk', 'et', 'eo', 'er', 'tw', 'tk', 'wo', 'wr', 'or', 'ok', 'rk', 'kx'}
You can add nodes and edges
from ntwrk import graph
vertices = ["n", "e", "t", "w", "o", "r", "k"]
edges = ["ne", "nt", "nw", "no", "nk", "et", "eo", "er", "tw", "tk", "wo", "wr", "or", "ok", "rk"]
g = graph(vertices, edges)
print(g)
g.add("x")
g.add_edge(node1="k", node2="x")
print(g)
Output
Vertices : {'n', 'e', 't', 'w', 'o', 'r', 'k'}
Edges : {'ne', 'nt', 'nw', 'no', 'nk', 'et', 'eo', 'er', 'tw', 'tk', 'wo', 'wr', 'or', 'ok', 'rk'}
Vertices : {'n', 'e', 't', 'w', 'o', 'r', 'k', 'x'}
Edges : {'ne', 'nt', 'nw', 'no', 'nk', 'et', 'eo', 'er', 'tw', 'tk', 'wo', 'wr', 'or', 'ok', 'rk', 'kx'}
You can delete nodes
from ntwrk import graph
vertices = ["n", "e", "t", "w", "o", "r", "k", "x"]
edges = ["ne", "nt", "nw", "no", "nk", "et", "eo", "er", "tw", "tk", "wo", "wr", "or", "ok", "rk", "kx"]
g = graph(vertices, edges)
print(g)
g.delete_node("x")
print(g)
Output
Vertices : {'n', 'e', 't', 'w', 'o', 'r', 'k', 'x'}
Edges : {'ne', 'nt', 'nw', 'no', 'nk', 'et', 'eo', 'er', 'tw', 'tk', 'wo', 'wr', 'or', 'ok', 'rk', 'kx'}
Vertices : {'n', 'e', 't', 'w', 'o', 'r', 'k'}
Edges : {'ne', 'nt', 'nw', 'no', 'nk', 'et', 'eo', 'er', 'tw', 'tk', 'wo', 'wr', 'or', 'ok', 'rk'}
You can delete edges
from ntwrk import graph
vertices = ["n", "e", "t", "w", "o", "r", "k", "x"]
edges = ["ne", "nt", "nw", "no", "nk", "et", "eo", "er", "tw", "tk", "wo", "wr", "or", "ok", "rk", "kx"]
g = graph(vertices, edges)
print(g)
g.delete_edge("k", "x")
print(g)
Output
Vertices : {'n', 'e', 't', 'w', 'o', 'r', 'k', 'x'}
Edges : {'ne', 'nt', 'nw', 'no', 'nk', 'et', 'eo', 'er', 'tw', 'tk', 'wo', 'wr', 'or', 'ok', 'rk', 'kx'}
Vertices : {'n', 'e', 't', 'w', 'o', 'r', 'k', 'x'}
Edges : {'ne', 'nt', 'nw', 'no', 'nk', 'et', 'eo', 'er', 'tw', 'tk', 'wo', 'wr', 'or', 'ok', 'rk'}
You can show how many nodes are in the graph
from ntwrk import graph
vertices = ["n", "e", "t", "w", "o", "r", "k"]
edges = ["ne", "nt", "nw", "no", "nk", "et", "eo", "er", "tw", "tk", "wo", "wr", "or", "ok", "rk"]
g = graph(vertices, edges)
print(len(g))
Output
7
You can show how many edges are in the graph
from ntwrk import graph
vertices = ["n", "e", "t", "w", "o", "r", "k"]
edges = ["ne", "nt", "nw", "no", "nk", "et", "eo", "er", "tw", "tk", "wo", "wr", "or", "ok", "rk"]
g = graph(vertices, edges)
print(g.edge_count())
Output
15
You can show how many degrees are in the graph
from ntwrk import graph
vertices = ["n", "e", "t", "w", "o", "r", "k"]
edges = ["ne", "nt", "nw", "no", "nk", "et", "eo", "er", "tw", "tk", "wo", "wr", "or", "ok", "rk"]
g = graph(vertices, edges)
print(g.degree_count())
Output
30
You can show the node degree
from ntwrk import graph
vertices = ["n", "e", "t", "w", "o", "r", "k"]
edges = ["ne", "nt", "nw", "no", "nk", "et", "eo", "er", "tw", "tk", "wo", "wr", "or", "ok", "rk"]
g = graph(vertices, edges)
print(g.get_degree("n"))
Output
5
You can traverse the graph using different algorithms (DFS, BFS)
from ntwrk import graph
vertices = ["n", "e", "t", "w", "o", "r", "k"]
edges = ["ne", "nt", "nw", "no", "nk", "et", "eo", "er", "tw", "tk", "wo", "wr", "or", "ok", "rk"]
g = graph(vertices, edges)
print(g.traverse("n"))
print(g.traverse("n", algorithm="dfs"))
print(g.traverse("n", algorithm="bfs"))
Output
['n', 'e', 't', 'w', 'o', 'r', 'k']
['n', 'k', 'r', 'o', 'w', 't', 'e']
['n', 'e', 't', 'w', 'o', 'k', 'r']
You can find the path between two nodes
from ntwrk import graph
vertices = ["n", "e", "t", "w", "o", "r", "k"]
edges = ["ne", "nt", "nw", "no", "nk", "et", "eo", "er", "tw", "tk", "wo", "wr", "or", "ok", "rk"]
g = graph(vertices, edges)
print(g.find_path("n", "k"))
Output
['n', 'e', 't', 'w', 'o', 'r', 'k']
You can check whether the graph is connected or disconnected
from ntwrk import graph
vertices = ["n", "e", "t", "w", "o", "r", "k"]
edges = ["ne", "nt", "nw", "no", "nk", "et", "eo", "er", "tw", "tk", "wo", "wr", "or", "ok", "rk"]
g = graph(vertices, edges)
print(g.isConnected())
print(g.isDisconnected())
Output
True
False
You can create a complete graph
from ntwrk import graph
vertices = ["n", "e", "t", "w", "o", "r", "k"]
g = graph(vertices, complete=True)
print(g)
Output
Vertices : {'n', 'e', 't', 'w', 'o', 'r', 'k'}
Edges : {'ne', 'nt', 'nw', 'no', 'nr', 'nk', 'et', 'ew', 'eo', 'er', 'ek', 'tw', 'to', 'tr', 'tk', 'wo', 'wr', 'wk', 'or', 'ok', 'rk'}
For undirected weighted graph
from ntwrk import graph
vertices = ["n", "e", "t", "w", "o", "r", "k"]
edges = [("ne", 9), ("nt", 6), ("nw", 9), ("no", 1), ("nk", 3), ("et", 15), ("eo", 10), ("er", 13), ("tw", 3), ("tk", 9), ("wo", 8), ("wr", 5), ("or", 3), ("ok", 4), ("rk", 7)]
g = graph(vertices, edges, weighted=True)
print(g)
Output
Vertices : {'n', 'e', 't', 'w', 'o', 'r', 'k'}
Edges : {['ne', 9], ['nt', 6], ['nw', 9], ['no', 1], ['nk', 3], ['et', 15], ['eo', 10], ['er', 13], ['tw', 3], ['tk', 9], ['wo', 8], ['wr', 5], ['or', 3], ['ok', 4], ['rk', 7]}
You can show all details (adjacency matrix, adjacency list)
from ntwrk import graph
vertices = ["n", "e", "t", "w", "o", "r", "k"]
edges = [("ne", 9), ("nt", 6), ("nw", 9), ("no", 1), ("nk", 3), ("et", 15), ("eo", 10), ("er", 13), ("tw", 3), ("tk", 9), ("wo", 8), ("wr", 5), ("or", 3), ("ok", 4), ("rk", 7)]
g = graph(vertices, edges, weighted=True, detail=True)
print(g)
Output
Adjacency matrix :
----------------
╒═════════╤═════╤═════╤═════╤═════╤═════╤═════╤═════╕
│ Nodes │ n │ e │ t │ w │ o │ r │ k │
╞═════════╪═════╪═════╪═════╪═════╪═════╪═════╪═════╡
│ n │ 0 │ 9 │ 6 │ 9 │ 1 │ 0 │ 3 │
├─────────┼─────┼─────┼─────┼─────┼─────┼─────┼─────┤
│ e │ 9 │ 0 │ 15 │ 0 │ 10 │ 13 │ 0 │
├─────────┼─────┼─────┼─────┼─────┼─────┼─────┼─────┤
│ t │ 6 │ 15 │ 0 │ 3 │ 0 │ 0 │ 9 │
├─────────┼─────┼─────┼─────┼─────┼─────┼─────┼─────┤
│ w │ 9 │ 0 │ 3 │ 0 │ 8 │ 5 │ 0 │
├─────────┼─────┼─────┼─────┼─────┼─────┼─────┼─────┤
│ o │ 1 │ 10 │ 0 │ 8 │ 0 │ 3 │ 4 │
├─────────┼─────┼─────┼─────┼─────┼─────┼─────┼─────┤
│ r │ 0 │ 13 │ 0 │ 5 │ 3 │ 0 │ 7 │
├─────────┼─────┼─────┼─────┼─────┼─────┼─────┼─────┤
│ k │ 3 │ 0 │ 9 │ 0 │ 4 │ 7 │ 0 │
╘═════════╧═════╧═════╧═════╧═════╧═════╧═════╧═════╛
Adjacency list :
--------------
╒═════════╤════════════════════════════════╕
│ Nodes │ ╒════════════════╤═════════╕ │
│ │ │ Adjacent Nodes │ Weights │ │
│ │ ╘════════════════╧═════════╛ │
╞═════════╪════════════════════════════════╡
│ n │ ╒═══╤═══╕ │
│ │ │ e │ 9 │ │
│ │ ├───┼───┤ │
│ │ │ t │ 6 │ │
│ │ ├───┼───┤ │
│ │ │ w │ 9 │ │
│ │ ├───┼───┤ │
│ │ │ o │ 1 │ │
│ │ ├───┼───┤ │
│ │ │ k │ 3 │ │
│ │ ╘═══╧═══╛ │
├─────────┼────────────────────────────────┤
│ e │ ╒═══╤════╕ │
│ │ │ n │ 9 │ │
│ │ ├───┼────┤ │
│ │ │ t │ 15 │ │
│ │ ├───┼────┤ │
│ │ │ o │ 10 │ │
│ │ ├───┼────┤ │
│ │ │ r │ 13 │ │
│ │ ╘═══╧════╛ │
├─────────┼────────────────────────────────┤
│ t │ ╒═══╤════╕ │
│ │ │ n │ 6 │ │
│ │ ├───┼────┤ │
│ │ │ e │ 15 │ │
│ │ ├───┼────┤ │
│ │ │ w │ 3 │ │
│ │ ├───┼────┤ │
│ │ │ k │ 9 │ │
│ │ ╘═══╧════╛ │
├─────────┼────────────────────────────────┤
│ w │ ╒═══╤═══╕ │
│ │ │ n │ 9 │ │
│ │ ├───┼───┤ │
│ │ │ t │ 3 │ │
│ │ ├───┼───┤ │
│ │ │ o │ 8 │ │
│ │ ├───┼───┤ │
│ │ │ r │ 5 │ │
│ │ ╘═══╧═══╛ │
├─────────┼────────────────────────────────┤
│ o │ ╒═══╤════╕ │
│ │ │ n │ 1 │ │
│ │ ├───┼────┤ │
│ │ │ e │ 10 │ │
│ │ ├───┼────┤ │
│ │ │ w │ 8 │ │
│ │ ├───┼────┤ │
│ │ │ r │ 3 │ │
│ │ ├───┼────┤ │
│ │ │ k │ 4 │ │
│ │ ╘═══╧════╛ │
├─────────┼────────────────────────────────┤
│ r │ ╒═══╤════╕ │
│ │ │ e │ 13 │ │
│ │ ├───┼────┤ │
│ │ │ w │ 5 │ │
│ │ ├───┼────┤ │
│ │ │ o │ 3 │ │
│ │ ├───┼────┤ │
│ │ │ k │ 7 │ │
│ │ ╘═══╧════╛ │
├─────────┼────────────────────────────────┤
│ k │ ╒═══╤═══╕ │
│ │ │ n │ 3 │ │
│ │ ├───┼───┤ │
│ │ │ t │ 9 │ │
│ │ ├───┼───┤ │
│ │ │ o │ 4 │ │
│ │ ├───┼───┤ │
│ │ │ r │ 7 │ │
│ │ ╘═══╧═══╛ │
╘═════════╧════════════════════════════════╛
For directed unweighted graph
from ntwrk import graph
vertices = ["n", "e", "t", "w", "o", "r", "k"]
edges = ["ne", "nt", "nw", "no", "nk", "et", "eo", "er", "tw", "tk", "wo", "wr", "or", "ok", "rk"]
g = graph(vertices, edges, directed=True)
print(g)
Output
Vertices : {'n', 'e', 't', 'w', 'o', 'r', 'k'}
Edges : {'ne', 'nt', 'nw', 'no', 'nk', 'et', 'eo', 'er', 'tw', 'tk', 'wo', 'wr', 'or', 'ok', 'rk'}
You can show all details (adjacency matrix, adjacency list)
from ntwrk import graph
vertices = ["n", "e", "t", "w", "o", "r", "k"]
edges = ["ne", "nt", "nw", "no", "nk", "et", "eo", "er", "tw", "tk", "wo", "wr", "or", "ok", "rk"]
g = graph(vertices, edges, directed=True, detail=True)
print(g)
Output
Adjacency matrix :
----------------
╒═════════╤═════╤═════╤═════╤═════╤═════╤═════╤═════╕
│ Nodes │ n │ e │ t │ w │ o │ r │ k │
╞═════════╪═════╪═════╪═════╪═════╪═════╪═════╪═════╡
│ n │ 0 │ 1 │ 1 │ 1 │ 1 │ 0 │ 1 │
├─────────┼─────┼─────┼─────┼─────┼─────┼─────┼─────┤
│ e │ 0 │ 0 │ 1 │ 0 │ 1 │ 1 │ 0 │
├─────────┼─────┼─────┼─────┼─────┼─────┼─────┼─────┤
│ t │ 0 │ 0 │ 0 │ 1 │ 0 │ 0 │ 1 │
├─────────┼─────┼─────┼─────┼─────┼─────┼─────┼─────┤
│ w │ 0 │ 0 │ 0 │ 0 │ 1 │ 1 │ 0 │
├─────────┼─────┼─────┼─────┼─────┼─────┼─────┼─────┤
│ o │ 0 │ 0 │ 0 │ 0 │ 0 │ 1 │ 1 │
├─────────┼─────┼─────┼─────┼─────┼─────┼─────┼─────┤
│ r │ 0 │ 0 │ 0 │ 0 │ 0 │ 0 │ 1 │
├─────────┼─────┼─────┼─────┼─────┼─────┼─────┼─────┤
│ k │ 0 │ 0 │ 0 │ 0 │ 0 │ 0 │ 0 │
╘═════════╧═════╧═════╧═════╧═════╧═════╧═════╧═════╛
Adjacency list :
--------------
╒═════════╤══════════════════╕
│ Nodes │ Adjacent Nodes │
╞═════════╪══════════════════╡
│ n │ ╒═══╕ │
│ │ │ e │ │
│ │ ├───┤ │
│ │ │ t │ │
│ │ ├───┤ │
│ │ │ w │ │
│ │ ├───┤ │
│ │ │ o │ │
│ │ ├───┤ │
│ │ │ k │ │
│ │ ╘═══╛ │
├─────────┼──────────────────┤
│ e │ ╒═══╕ │
│ │ │ t │ │
│ │ ├───┤ │
│ │ │ o │ │
│ │ ├───┤ │
│ │ │ r │ │
│ │ ╘═══╛ │
├─────────┼──────────────────┤
│ t │ ╒═══╕ │
│ │ │ w │ │
│ │ ├───┤ │
│ │ │ k │ │
│ │ ╘═══╛ │
├─────────┼──────────────────┤
│ w │ ╒═══╕ │
│ │ │ o │ │
│ │ ├───┤ │
│ │ │ r │ │
│ │ ╘═══╛ │
├─────────┼──────────────────┤
│ o │ ╒═══╕ │
│ │ │ r │ │
│ │ ├───┤ │
│ │ │ k │ │
│ │ ╘═══╛ │
├─────────┼──────────────────┤
│ r │ k │
╘═════════╧══════════════════╛
You can check whether the graph is strongly connected or weakly disconnected
from ntwrk import graph
vertices = ["n", "e", "t", "w", "o", "r", "k"]
edges = ["ne", "nt", "nw", "no", "nk", "et", "eo", "er", "tw", "tk", "wo", "wr", "or", "ok", "rk"]
g = graph(vertices, edges, directed=True, detail=True)
print(g.isStronglyConnected())
print(g.isWeaklyConnected())
Output
False
True
You can show the node indegree, outdegree
from ntwrk import graph
vertices = ["n", "e", "t", "w", "o", "r", "k"]
edges = ["ne", "nt", "nw", "no", "nk", "et", "eo", "er", "tw", "tk", "wo", "wr", "or", "ok", "rk"]
g = graph(vertices, edges, directed=True)
print(g.get_indegree("n"))
print(g.get_outdegree("n"))
Output
0
5
For directed weighted graph
from ntwrk import graph
vertices = ["n", "e", "t", "w", "o", "r", "k"]
edges = [("ne", 9), ("nt", 6), ("nw", 9), ("no", 1), ("nk", 3), ("et", 15), ("eo", 10), ("er", 13), ("tw", 3), ("tk", 9), ("wo", 8), ("wr", 5), ("or", 3), ("ok", 4), ("rk", 7)]
g = graph(vertices, edges, directed=True, weighted=True)
print(g)
Output
Vertices : {'n', 'e', 't', 'w', 'o', 'r', 'k'}
Edges : {['ne', 9], ['nt', 6], ['nw', 9], ['no', 1], ['nk', 3], ['et', 15], ['eo', 10], ['er', 13], ['tw', 3], ['tk', 9], ['wo', 8], ['wr', 5], ['or', 3], ['ok', 4], ['rk', 7]}
You can show all details (adjacency matrix, adjacency list)
from ntwrk import graph
vertices = ["n", "e", "t", "w", "o", "r", "k"]
edges = [("ne", 9), ("nt", 6), ("nw", 9), ("no", 1), ("nk", 3), ("et", 15), ("eo", 10), ("er", 13), ("tw", 3), ("tk", 9), ("wo", 8), ("wr", 5), ("or", 3), ("ok", 4), ("rk", 7)]
g = graph(vertices, edges, directed=True, weighted=True, detail=True)
print(g)
Output
Adjacency matrix :
----------------
╒═════════╤═════╤═════╤═════╤═════╤═════╤═════╤═════╕
│ Nodes │ n │ e │ t │ w │ o │ r │ k │
╞═════════╪═════╪═════╪═════╪═════╪═════╪═════╪═════╡
│ n │ 0 │ 9 │ 6 │ 9 │ 1 │ 0 │ 3 │
├─────────┼─────┼─────┼─────┼─────┼─────┼─────┼─────┤
│ e │ 0 │ 0 │ 15 │ 0 │ 10 │ 13 │ 0 │
├─────────┼─────┼─────┼─────┼─────┼─────┼─────┼─────┤
│ t │ 0 │ 0 │ 0 │ 3 │ 0 │ 0 │ 9 │
├─────────┼─────┼─────┼─────┼─────┼─────┼─────┼─────┤
│ w │ 0 │ 0 │ 0 │ 0 │ 8 │ 5 │ 0 │
├─────────┼─────┼─────┼─────┼─────┼─────┼─────┼─────┤
│ o │ 0 │ 0 │ 0 │ 0 │ 0 │ 3 │ 4 │
├─────────┼─────┼─────┼─────┼─────┼─────┼─────┼─────┤
│ r │ 0 │ 0 │ 0 │ 0 │ 0 │ 0 │ 7 │
├─────────┼─────┼─────┼─────┼─────┼─────┼─────┼─────┤
│ k │ 0 │ 0 │ 0 │ 0 │ 0 │ 0 │ 0 │
╘═════════╧═════╧═════╧═════╧═════╧═════╧═════╧═════╛
Adjacency list :
--------------
╒═════════╤════════════════════════════════╕
│ Nodes │ ╒════════════════╤═════════╕ │
│ │ │ Adjacent Nodes │ Weights │ │
│ │ ╘════════════════╧═════════╛ │
╞═════════╪════════════════════════════════╡
│ n │ ╒═══╤═══╕ │
│ │ │ e │ 9 │ │
│ │ ├───┼───┤ │
│ │ │ t │ 6 │ │
│ │ ├───┼───┤ │
│ │ │ w │ 9 │ │
│ │ ├───┼───┤ │
│ │ │ o │ 1 │ │
│ │ ├───┼───┤ │
│ │ │ k │ 3 │ │
│ │ ╘═══╧═══╛ │
├─────────┼────────────────────────────────┤
│ e │ ╒═══╤════╕ │
│ │ │ t │ 15 │ │
│ │ ├───┼────┤ │
│ │ │ o │ 10 │ │
│ │ ├───┼────┤ │
│ │ │ r │ 13 │ │
│ │ ╘═══╧════╛ │
├─────────┼────────────────────────────────┤
│ t │ ╒═══╤═══╕ │
│ │ │ w │ 3 │ │
│ │ ├───┼───┤ │
│ │ │ k │ 9 │ │
│ │ ╘═══╧═══╛ │
├─────────┼────────────────────────────────┤
│ w │ ╒═══╤═══╕ │
│ │ │ o │ 8 │ │
│ │ ├───┼───┤ │
│ │ │ r │ 5 │ │
│ │ ╘═══╧═══╛ │
├─────────┼────────────────────────────────┤
│ o │ ╒═══╤═══╕ │
│ │ │ r │ 3 │ │
│ │ ├───┼───┤ │
│ │ │ k │ 4 │ │
│ │ ╘═══╧═══╛ │
├─────────┼────────────────────────────────┤
│ r │ ╒═══╤═══╕ │
│ │ │ k │ 7 │ │
│ │ ╘═══╧═══╛ │
╘═════════╧════════════════════════════════╛
Note
You can use all the methods we use for undirected unweighted graph with other types of graphs as well.
License
This project is licensed under the MIT LICENSE - see the LICENSE for more details.
1 maintainer
Metadata
1 maintainer
Release files for ntwrk 1.0.2
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | Actions |
|---|---|---|---|---|
| ntwrk-1.0.2.tar.gz | 15.3 kB | Without provenance | Details for ntwrk-1.0.2.tar.gz |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Actions Reset |
|---|---|---|---|---|
| ntwrk-1.0.2-py3-none-any.whl | Python 3 | none | any | Details for ntwrk-1.0.2-py3-none-any.whl |
Total release size: 26.8 kB
Release files / ntwrk-1.0.2.tar.gz
| Download URL | ntwrk-1.0.2.tar.gz |
|---|---|
| Size | 15.3 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
8a05add3dcaefe525cc269128ef2bf4cfe9c320254335a38b6c62af49562bef4
|
|
BLAKE2b-256 checksum How to use checksums |
af76a3b3524a0f08331666a646b2e9acc4d6ae7656b231db7b3f02df985fd14f
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/5.1.1 CPython/3.11.2
|
Release files / ntwrk-1.0.2-py3-none-any.whl
| Download URL | ntwrk-1.0.2-py3-none-any.whl |
|---|---|
| Size | 11.4 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
aeff4c7b07ed233b088658b40c90fad9885f2bfb7d3a7952b2542b69c8fc230d
|
|
BLAKE2b-256 checksum How to use checksums |
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| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/5.1.1 CPython/3.11.2
|
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