`scipy.sparse.csgraph._traversal:connected_components`

Signature

   def     connected_components (    csgraph    ,    directed    =  True   ,    connection    =  weak   ,    return_labels    =  True     )

Analyze the connected components of a sparse graph

csgraph : array_like or sparse array or matrix: The N x N matrix representing the compressed sparse graph. The input csgraph will be converted to csr format for the calculation.
directed : bool, optional: If True (default), then operate on a directed graph: only move from point i to point j along paths csgraph[i, j]. If False, then find the shortest path on an undirected graph: the algorithm can progress from point i to j along csgraph[i, j] or csgraph[j, i].
connection : str, optional: ['weak'|'strong']. For directed graphs, the type of connection to use. Nodes i and j are strongly connected if a path exists both from i to j and from j to i. A directed graph is weakly connected if replacing all of its directed edges with undirected edges produces a connected (undirected) graph. If directed == False, this keyword is not referenced.
return_labels : bool, optional: If True (default), then return the labels for each of the connected components.

: n_components: int: The number of connected components.
: labels: ndarray: The length-N array of labels of the connected components.

from scipy.sparse import csr_array
from scipy.sparse.csgraph import connected_components

✓

graph = [
[0, 1, 1, 0, 0],
[0, 0, 1, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 1],
[0, 0, 0, 0, 0]
]
graph = csr_array(graph)

✓

print(graph)

✗

n_components, labels = connected_components(csgraph=graph, directed=False, return_labels=True)
n_components
labels

✓