Hi, I have a C program which outputs large (~GB) files. It is a simple binary dump of an array of structure containing 9 doubles. You can see this as a double 1D array of size 9*Stot (Stot being the allocated size of the array of structure). The 1D array represents a 3D array (Sx * Sy * Sz = Stot) containing 9 values per cell. I want to read these files in the most efficient way possible, and I would like to have your insight on this. Right now, the fastest way I found was: imzeros = zeros((Sy,Sz),dtype=float64,order='C') imex = imshow(imzeros) f = open(filename, 'rb') data = numpy.fromfile(file=f, dtype=numpy.float64, count=9*Stot) mask_Ex = numpy.arange(6,9*Stot,9) Ex = data[mask].reshape((Sz,Sy,Sx), order='C').transpose() imex.set_array(squeeze(Ex3D[:,:,z])) The arrays will be big, so everything should be well optimized. I have multiple questions: 1) Should I change this: Ex = data[mask].reshape((Sz,Sy,Sx), order='C').transpose() imex.set_array(squeeze(Ex3D[:,:,z])) to: imex.set_array(squeeze(data[mask].reshape((Sz,Sy,Sx), order='C').transpose()[:,:,z])) I mean, is I don't use a temporary variable, will it be faster or less memory hungry? 2) If not, is the operation "Ex = " update the variable data or create another one? Ideally I would like to only update it. Maybe this would be better: Ex[:,:,:] = data[mask].reshape((Sz,Sy,Sx), order='C').transpose() Would it? 3) The machine where the code will be run might be big-endian. Is there a way for python to read the big-endian file and "translate" it automatically to little-endian? Something like "numpy.fromfile(file=f, dtype=numpy.float64, count=9*Stot, endianness='big')"? Thanx a lot! ;) Nicolas