Thank you for your help. :) I used this : try: NData[ (NData==transparent)[:len(OData)] ] = OData[ (NData==transparent)[:len(OData)] ] except: pass That means overwrite all "transparent" data from NData with valid data from OData. I am sure it's not the best method yet, but it's the only one that works. --- On Mon, 5/18/09, Cristi Constantin wrote: From: Cristi Constantin Subject: [Numpy-discussion] Overlap arrays with "transparency" To: "Numpy Discussion" Date: Monday, May 18, 2009, 5:37 AM Good day. I am working on this algorithm for a few weeks now, so i tried almost everything... I want to overlap / overwrite 2 matrices, but completely ignore some values (in this case ignore 0) Let me explain: a = [ [1, 2, 3, 4, 5], [9,7], [0,0,0,0,0], [5,5,5] ] b = [ [0,0,9,9], [1,1,1,1], [2,2,2,2] ] Then, we have: a over b = [ [1,2,3,4,5], [9,7,1,1], [1,1,1,1,0], [5,5,5,2] ] b over a = [ [0,0,9,9,5], 1,1,1,1], 2,2,2,2,0], 5,5,5] ] That means, completely overwrite one list of arrays over the other, not matter what values one has, not matter the size, just ignore 0 values on overwriting. I checked the documentation, i just need some tips. TempA = [[]] # One For Cicle in here to get the Element data...     Data = vElem.data                 # This is a list of numpy ndarrays.     #     for nr_row in range( len(Data) ): # For each numpy ndarray (row) in Data.         #         NData = Data[nr_row]                   # New data, to be written over old data.         OData = TempA[nr_row:nr_row+1] or [[]] # This is old data. Can be numpy ndarray, or empty list.         OData = OData[0]         #         # NData must completely eliminate transparent pixels... here comes the algorithm... No algorithm yet.         #         if len(NData) >= len(OData):             # If new data is longer than old data, old data will be completely overwritten.             TempA[nr_row:nr_row+1] = [NData]         else: # Old data is longer than new data ; old data cannot be null.             TempB = np.copy(OData)             TempB.put( range(len(NData)), NData )             #TempB[0:len(NData)-1] = NData # This returns "ValueError: shape mismatch: objects cannot be broadcast to a single shape"             TempA[nr_row:nr_row+1] = [TempB]             del TempB         #     # # The result is stored inside TempA as list of numpy arrays. I would use 2D arrays, but they are slower than Python Lists containing Numpy arrays. I need to do this overwrite in a very big loop and every delay is very important. I tried to create a masked array where all "zero" values are ignored on overlap, but it doesn't work. Masked or not, the "transparent" values are still overwritten. Please, any suggestion is useful. Thank you.