In your main algorithm iteration loop, for each data point, calculate the squared distance between each point and the centroid to which it belongs, and sum all of these squared distances code example

Example: 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