Knapsack greedy algorithm in Python

list comprehensions aren't too useful in that code. Well, I think I can help improve your code anyway:

valuePerWeight = sorted([[values[i] / weights[i], weights[i]] for i in range(numItems)], reverse=True)

is overcomplicated instead of the index and range. Use zip instead, which is faster and cleaner:

valuePerWeight = sorted([[v / w, w] for v,w in zip(values,weights)], reverse=True)

Same there: not very pythonic to work with indexes when you can use enumerate:

for i in range(numItems):
    if valuePerWeight[i][1] > 0 and maxi < valuePerWeight[i][0]:
        maxi = valuePerWeight[i][0]
        idx = i

could be:

for i,item in enumerate(valuePerWeight):
    if item [1] > 0 and maxi < item [0]:
        maxi = item [0]
        idx = i

clearer and faster right? and after having found idx you could save a lot of access-by-index time by creating:

v = valuePerWeight[idx][0]
w = valuePerWeight[idx][1]

and refer to v and w in the rest of the code: clearer and faster (double access by index is costly cpu-wise)

Last item: you're dividing and multiplying by the same value. Since they're float operations, you could simplfy:

if valuePerWeight[idx][1] > 0:
    value += (capacity / valuePerWeight[idx][1]) * valuePerWeight[idx][1] * valuePerWeight[idx][0]
    return value

by (using v and w as instructed above)

if w > 0:
    value += capacity * v
    return value

Also, you don't need numItems at all now. Just turn the while loop as:

while capacity > 0 and valuePerWeight:

(when valuePerWeight is empty, the loop ends)

So to sum it all up, here's my proposal for an improved code of yours:

def get_optimal_value(capacity, weights, values):
    value = 0.

    valuePerWeight = valuePerWeight = sorted([[v / w, w] for v,w in zip(values,weights)], reverse=True)
    while capacity > 0 and valuePerWeight:
        maxi = 0
        idx = None
        for i,item in enumerate(valuePerWeight):
            if item [1] > 0 and maxi < item [0]:
                maxi = item [0]
                idx = i

        if idx is None:
            return 0.

        v = valuePerWeight[idx][0]
        w = valuePerWeight[idx][1]

        if w <= capacity:
            value += v*w
            capacity -= w
        else:
            if w > 0:
                value += capacity * v
                return value
        valuePerWeight.pop(idx)

    return value

if __name__ == "__main__":
    n = 3
    capacity = 50
    values = [60, 100, 120]
    weights = [20, 50, 30]
    opt_value = get_optimal_value(capacity, weights, values)
    print("{:.10f}".format(opt_value)) # print 180.0000000000

tested and stil returns 180.0, fortunately.