they are generally more seldom used in individual functions with lots of linear algebrait may be helpful to convert the function argument to matrix typethen cast back to regular arrays with np asarray (which does not copy any databefore returning them advanced array input and output in introduced you to np save and np load for storing arrays in binary format on disk there are number of additional options to consider for more sophisticated use in particularmemory maps have the additional benefit of enabling you to work with data sets that do not fit into ram memory-mapped files memory-mapped file is method for treating potentially very large binary data on disk as an in-memory array numpy implements memmap object that is ndarray-likeenabling small segments of large file to be read and written without reading the whole array into memory additionallya memmap has the same methods as an in-memory array and thus can be substituted into many algorithms where an ndarray would be expected to create new memmapuse the function np memmap and pass file pathdtypeshapeand file modein [ ]mmap np memmap('mymmap'dtype='float 'mode=' +'shape=( )in [ ]mmap out[ ]memmap([ ] ] ] ] ] ]]slicing memmap returns views on the data on diskin [ ]section mmap[: if you assign data to theseit will be buffered in memory (like python file object)but can be written to disk by calling flushin [ ]section[:np random randn( in [ ]mmap flush(in [ ]mmap out[ ]memmap([[- - - - - ] - - - - ][- - - - ]advanced array input and output www it-ebooks info