Save Numpy Array using Pickle

You should use numpy.save and numpy.load.

I have no problems using pickle:

In [126]: arr = np.zeros((1000,2))In [127]: with open('test.pkl','wb') as f:     ...:     pickle.dump(arr, f)     ...:     In [128]: with open('test.pkl','rb') as f:     ...:     x = pickle.load(f)     ...:     print(x.shape)     ...:          ...:     (1000, 2)

pickle and np.save/load have a deep reciprocity. Like I can load this pickle with np.load:

In [129]: np.load('test.pkl').shapeOut[129]: (1000, 2)

If I open the pickle file in the wrong I do get your error:

In [130]: with open('test.pkl','wb') as f:     ...:     x = pickle.load(f)     ...:     print(x.shape)     ...:    UnsupportedOperation: read

But that shouldn't be surprising - you can't read a freshly opened write file. It will be empty.

np.save/load is the usual pair for writing numpy arrays. But pickle uses save to serialize arrays, and save uses pickle to serialize non-array objects (in the array). Resulting file sizes are similar. Curiously in timings the pickle version is faster.

It's been a bit but if you're finding this, Pickle completes in a fraction of the time.

with open('filename','wb') as f: pickle.dump(arrayname, f)with open('filename','rb') as f: arrayname1 = pickle.load(f)numpy.array_equal(arrayname,arrayname1) #sanity check

On the other hand, by default numpy compress took my 5.2GB down to .4GB and Pickle went to 1.7GB.