medoids | K-medoids implementation

I need to get the following list filtered by the the index number 1 from each tuple inside the list of the list of each exterior tuple, As for instance... I have the following list

According to the Sklearn_extra documentation on KMedoids, KMedoids should have the following parameters: n_clusters, metric, method, init, max_iter and random_state. The method parameter determines which algorithm to use: alternate or pam. According to sklearn_extra's user guide these methods are inherently different from each other. For my specific application I want to use the PAM version of K-medoids. However, the method parameter seems to have disappeared. When I run an inspect on the KMedoids function:

I am using the fpc package for determining the optimal number of clusters. The pamk() function takes a dissimilarity matrix as an argument and does not require the user to specify k. According to the documentation:

pamk() This calls pam and clara for the partitioning around medoids clustering method (Kaufman and Rouseeuw, 1990) and includes two different ways of estimating the number of clusters.

but when I input two very similar matricies - foo and bar (data below), the function errors out on the second matrix (bar)

I have array consist of 3 vectors that represent 3 objects

I have x and y arrays, x consists of three arrays and y consists of three arrays that consist of seven values

i'm trying to build k-medoids algorithm in python. i have difficulties in computing the cost. I'm using 3 cluster. For example i have S (the distance matrix for every point to each cluster).

I'm looking for a way to apply the cluster solution from k- medoids algorithm (I'm using PAM) from one sample to another. I think this could be done for k-means algorithm: for data1, get the centroid from the clustering result; then in data2, for each observation, calculate the distance to each centroid and then assign each observation to its closest centroid. By doing this, we applied the clustering solution from data1 to data2. However, k- medoids algorithm (for example, PAM) uses medoids as cluster centers instead of means. In this case, it is not clear to me how to apply the clustering solution from one sample to another. Could anyone help answering this question? Many thanks!

I am reading data from a dataset containing points in a plane. Each point has x and y co-ordinate.

I'm following an excellent medium article: https://towardsdatascience.com/k-medoids-clustering-on-iris-data-set-1931bf781e05 to implement kmedoids from scratch. There is a place in the code where each pixel's distance to the medoid centers is calculated and it is VERY slow. It has numpy.linalg.norm inside a loop. Is there a way to optimize this with numpy.linalg.norm or with numpy broadcasting or scipy.spatial.distance.cdist and np.argmin to do the same thing?