title: Using sparse matrix instead of numpy distance matrix
body: <p>I have a list of car ids and a list of x,y coordinates.
  I want to calculate the distance between each of the coordinates.</p>
  
  <p>The problem is ,after trying for weeks now, the distance matrix has limits and I'm dealing with Gigas of files with a resulting matrix of millions of rows and columns.
  Can this be done using sparse to make it more effecient?</p>
  
  <pre><code>list_coordinates = []
  for line in coordinates.readlines():
      list_coordinates.append(line.strip().split(','))
  
  list_coordinates_int = [list(map(float, x)) for x in list_coordinates]
  
  list_car_id = []
  for line in car_ids.readlines():
      list_car_id.append(line.strip().split(' '))
  
  df = pd.DataFrame(list_coordinates_int, columns=['xcord', 'ycord'], index=list_car_id)
  
  df2=pd.DataFrame(distance_matrix(df.values, df.values), index=df.index, columns=df.index)
  
  list_coordinates : [['875.88', '588.26'], ['751.49', '656.55']]     
  list_coordinates_int : [[875.88, 588.26], [751.49, 656.55]]    
  list_car_id : [['car.0', 'car2.0', 'car.0', 'car2.0', 'car.0']]
  
  </code></pre>
  
  <p>the resulting <strong>df2</strong> is like this:</p>
  
  <pre><code>              car.0      car2.0       car.4                  
     car.0     0.000000  141.902770    0.702140            
     car2.0   141.902770    0.000000  141.205831   
     car.4    141.902770    0.702140   0.000000  
  </code></pre>
  
  <p>is there a way i could get the same df2 using sparse or any other mehtod than distance matrix?</p>