title: Inverse Distance Weighted interpolation in a grid(Python)
body: <p>My problem set up is the following: 
  I have a gird made of cells,every cells have the same dimensions.
  Only 4 cells are labeled(contain a measure) and are indicated in the image by some points.
  In my python program i have a dataframe, in which each row represent a grid cell.Each cell has its coordinated and a label(that is None for all the cells excluding the 4 cells that contain a measure).</p>
  
  <p><a href="https://i.stack.imgur.com/JhMOt.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/JhMOt.png" alt="enter image description here"></a></p>
  
  <p><a href="https://i.stack.imgur.com/6WS38.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/6WS38.png" alt="enter image description here"></a></p>
  
  <p>What i want to do is the following: Use the inverse weighted interpolation in order to obtain a measure(label) for each unlabeled cell in my grid.
  Do you have any suggestion on how to face this problem, or you know if there similar examples of code that do the same thing. Any help is appreciated.
  Thank you!</p>