I define a method called dist, to calculate the distance between two points which I does it correctly when directly using the method. 
However, when I get a function to call it to calculate the distance between two points, I get
UnboundLocalError: local variable 'minkowski_distance' referenced before assignment
edit
sorry, I just realised, this function does work. However I have another method calling it that doesn't. I put the last method at the bottom
This is the method:
class MinkowskiDistance(Distance):
  def __init__(self, dist_funct_name_str = 'Minkowski distance', p=2):
    self.p = p
  def dist(self, obj_a, obj_b):
    distance_to_power_p=0
    p=self.p
    for i in range(len(obj_a)):
      distance_to_power_p += abs((obj_a[i]-obj_b[i]))**(p)
      minkowski_distance = (distance_to_power_p)**(1/p)
    return minkowski_distance
and this is the function: 
(it basically splits the tuples x and y into their number and string components and calculates the distance between the numeric part of x and y and then the distance between the string parts, then adds them.
def total_dist(x, y, p=2, q=2):
    jacard = QGramDistance(q=q)
    minkowski = MinkowskiDistance(p=p)
    x_num = []
    x_str = []
    y_num = []
    y_str = []
    #I am spliting each vector into its numerical parts and its string parts so that the distances
    #of each part can be found, then summed together.
    for i in range(len(x)):
        if type(x[i]) == float or type(x[i]) == int:
            x_num.append(x[i])
            y_num.append(y[i])
        else:
            x_str.append(x[i])
            y_str.append(y[i])
    num_dist = minkowski.dist(x_num,y_num)
    str_dist = I find using some more steps
    #I am simply adding the two types of distance to get the total distance:
    return num_dist + str_dist
class NearestNeighbourClustering(Clustering):
  def __init__(self, data_file,
               clust_algo_name_str='', strip_header = "no", remove = -1):
      self.data_file= data_file
      self.header_strip = strip_header
      self.remove_column = remove
  def run_clustering(self, max_dist, p=2, q=2):
      K = {}
      #dictionary of clusters
      data_points = self.read_data_file()
      K[0]=[data_points[0]]
      k=0
      #I added the first point in the data to the 0th cluster
      #k = number of clusters minus 1
      n = len(data_points)
      for i in range(1,n):
          data_point_in_a_cluster = "no"
          for c in range(k+1):
              distances_from_i = [total_dist(data_points[i],K[c][j], p=p, q=q) for j in range(len(K[c]))]
          d = min(distances_from_i)
          if d <= max_dist:
              K[c].append(data_points[i])
              data_point_in_a_cluster = "yes"
      if data_point_in_a_cluster == "no":
          k += 1
          K[k]=[data_points[i]]
  return K