assign each point to the cluster with the closest centroid python

def kmeans(X, k, maxiter, seed = None):
    """
    specify the number of clusters k and
    the maximum iteration to run the algorithm
    """
    n_row, n_col = X.shape

    # randomly choose k data points as initial centroids
    if seed is not None:
        np.random.seed(seed)
    
    rand_indices = np.random.choice(n_row, size = k)
    centroids = X[rand_indices]

    for itr in range(maxiter):
        # compute distances between each data point and the set of centroids
        # and assign each data point to the closest centroid
        distances_to_centroids = pairwise_distances(X, centroids, metric = 'euclidean')
        cluster_assignment = np.argmin(distances_to_centroids, axis = 1)

        # select all data points that belong to cluster i and compute
        # the mean of these data points (each feature individually)
        # this will be our new cluster centroids
        new_centroids = np.array([X[cluster_assignment == i].mean(axis = 0) for i in range(k)])
        
        # if the updated centroid is still the same,
        # then the algorithm converged
        if np.all(centroids == new_centroids):
            break
        
        centroids = new_centroids
    
    return centroids, cluster_assignment