title: Enumerate through 2D array in numpy and append to new array
body: <p>I'm trying to enumerate through a 2D numpy array of shape (512, 512), which holds the pixel values of an image. So basically it's an array representing width and height in pixel values for the image. I'm trying to enumerate through each element to output: [y_index, x_index, pixel_value]. I need these 3 output values to be stored in an array (either the existing one or a new one, whichever is more efficient to execute). 
  So the input array would have a shape of (512, 512), and if I'm not mistaken,  the output array would have an array shape (262144, 3). 262144 is the total number of pixels in a 512x512 matrix. And 3 because there are 3 columns, for 3 pieces of data that I need to extract: <code>pixel value</code>, <code>y coordinate</code>, <code>x coordinate</code>. So basically I want to have an array of pixel values and their y, x coordinates.     </p>
  
  <p>In the code below, I used <code>ndenumerate</code> to enumerate through the numpy array (<code>img</code>) of pixel values (512, 512). But I'm struggling on how to store the output values in an array. I created <code>coordinates</code> array to store the output values, but my attempt at it with the last line is clearly incorrect to achieve the desired effect. So how to solve this?  </p>
  
  <pre><code>img = as_np_array[:, :, 0]
  img = img.reshape(512, 512)
  coordinates = np.empty([262,144, 3])
  for index, x in np.ndenumerate(img):
      coordinates = np.append(coordinates, [[x], index[0], index[1]])
  </code></pre>
  
  <p>Also the other challenge I'm facing is, to execute this code, my Intel Core i7 2.7GHz (4 Cores) processor takes about 7-10 minutes (possibly more at times) to execute. Is there a more efficient code that can execute faster?</p>
  
  <p>Any help would be greatly appreciated.</p>