I've found two different approaches online when using the Elbow Method to determine the optimal number of clusters for K-Means.

One approach is to use the following code:

    distortions_2.append(sum(np.min(cdist(data
                                  , kmeanModel.cluster_centers_
                                  , 'euclidean')
                                  , axis = 1)) / data.shape[0])

Another is to use inertia_ from sklearn.cluster.KMeans:

    distortions_3.append(kmeanModel.inertia_)

When I plot the results (using the same random states) both give different results but I'm not sure what the differences are, can anyone help?