Hello, numpy arrays are great for interfacing python with libraries that expect continuous memory buffers for data passing. However, libraries interfacing to data acquisition hardware often use those buffers as ring buffers where, once the buffer has been filled with data, new data will be written overwriting the data at the beginning of the buffer. The application is supposed to have read this data meanwhile. Efficiently processing the data in the ring buffer requires addressing efficiently the content of the ring buffer. In C or Cython this is very easy, just compute the wrap around when accessing the single elements of the array: buffer = np.empty(size) adc.setup(buffer) start = 0 for n in adc.read(): # data processing for i in range(start, start + n): element = buffer[start % size] .... start += n My current approach to do the same thing in Python is to use the np.roll() function to "linearize" the buffer access: buffer = np.empty(size) adc.setup(buffer) start = 0 for n in adc.read(): data = np.roll(buffer, -start)[:n] start += n # data processing process(data) Since np.roll() returns a view on the array i suppose this is very efficient. Does anyone have a better idea on how to do this? Thank you. Cheers, Daniele