Group Data into Clusters Based on DataFrame Columns

Question

I have a dataframe (df) similar to this:

Node_Start	Node_End
1.0	208.0
1.0	911.0
800.0	1.0
3.0	800.0
2.0	511.0
700.0	3.0
200.0	4.0

I would like to add a column that shows a related cluster based upon the values in columns 'Node_Start' and 'Node_End':

Node_Start	Node_End	Group
1.0	208.0	1
1.0	911.0	1
800.0	1.0	1
3.0	800.0	1
2.0	511.0	2
700.0	3.0	1
200.0	4.0	3

In other words, since 1.0 is in both 'Node_Start' & 'Node_End' it gets assigned to Group 1. Since 800.0 is connected to both 1.0 and 3.0, those rows also get assigned to Group 1. 2.0 and 511.0 are not related to any other row values and get assigned to Group 2. 200.0 and 4.0 are not related to any other rows and get assigned to Group 3. And so on...

The following code accomplishes the desired results but is a bit clunky and will not work on my entire dataset as it is too big (over 500,000 rows) and my kernel crashes before completing the job

def consolidate(sets):
    # http://rosettacode.org/wiki/Set_consolidation#Python:_Iterative
    setlist = [s for s in sets if s]
    for i, s1 in enumerate(setlist):
        if s1:
            for s2 in setlist[i+1:]:
                intersection = s1.intersection(s2)
                if intersection:
                    s2.update(s1)
                    s1.clear()
                    s1 = s2
    return [s for s in setlist if s]

def group_ids(pairs):
    groups = consolidate(map(set, pairs))
    d = {}
    for i, group in enumerate(sorted(groups)):
        for elem in group:
            d[elem] = i
    return d