k-means not clustering correct in python

I'm trying to clusterize a dataset using k-means. When I run my algorithm with just one iteration returns random clusters, as it should, but when I try multiple iterations, it returns only 0s. The matrix I'm using is a 50k x 140 binary matrix. Each row represents a user and each column represents an item.

 def clusterizator(matriz, nDeClusters, it=10): # matrix, number of clusters, number of iterations

    nOfLines = matriz.shape[0] # number of lines (users)
    nOfColumns = matriz.shape[1] # number of columns (items)
    clusterCurrently = np.zeros((nOfLines, 1)) # currently cluster assigned to each user
    listOfCurrentlyAssigneds = [] # list with numberOfClusters size, each element is a list of currently elements assigned to this cluster
    clusterCentroid = [] # centroid of each cluster

    clusterCentroid = np.random.randint(2, size=(nDeClusters, nOfColumns)) # starts with randoms centroids

    for repeat in xrange(it): # number of iterations
        listOfCurrentlyAssigneds = [[] for i in xrange(nDeClusters)] # create empty lists for each cluster

        for i in xrange(nOfLines): # for each user
            closestCentroid = clusterMaisProximo(matriz[i], clusterCentroid) # calculates the closest centroid
            clusterCurrently[i] = closestCentroid                            # assign the user to closest centroid
            listOfCurrentlyAssigneds[closestCentroid].append(matriz[i])      # put user on that centroid list

        for i in xrange(nDeClusters): # for each cluster
            if listOfCurrentlyAssigneds[i] != []: # if the list is not empty
                clusterCentroid[i] = centeroidnp(listOfCurrentlyAssigneds[i])   # calculates the new centroid




    return clusterCurrently    # return 1-column matrix with user x cluster

def distanciaEucl(elemento1, elemento2):
    return np.linalg.norm(elemento2-elemento1)  #calculates the distance between to items (or one user and one cluster)



def clusterMaisProximo(elemento, listaDeClusters):  # receive one user and the cluster's centroids list, return the closest one
    closest = 0
    closestDist = distanciaEucl(elemento, listaDeClusters[0]) # starts with the cluster[0]

    for i in xrange(len(listaDeClusters)-1): # for each cluster
        dist = distanciaEucl(elemento, listaDeClusters[i+1]) # get the distance to currently cluster's centroid
        if dist < closestDist: # if it is closer to the element
            closest = i+1 # update new closest element
            closestDist = dist # update new closest distance

    return closest   # return closest

# from https://stackoverflow.com/questions/23020659/fastest-way-to-calculate-the-centroid-of-a-set-of-coordinate-tuples-in-python-wi
# by Retozi (adapted)
def centeroidnp(lista):   # get a list of elements (number of elements x items)
    shape = list(lista[0].shape)
    shape[:0] = [len(lista)]
    arr = np.concatenate(lista).reshape(shape)   # get an array from the list
    length = arr.shape[0]
    somas = np.zeros(arr.shape[1])
    for i in xrange(arr.shape[1]): # for each item (dimension)
        somas[i] = (np.sum(arr[:, i]))/length   # sum all elements and divide by number of elements
    return somas  # return array that will be the new centroid position

I commented everything to try to make clear what each row is doing, some comments are dumb because at first my variables were written in Portuguese, then I translated to make it more clear.

I'm running it like this:

clust = clusterizator(train, 10, 2)

Example matrix:

train = [[0, 1, 1, 0], [1, 0, 0, 0], [0, 1, 1, 1], [1, 0, 0, 1], [1, 0, 0, 0]]