fastest method to dump numpy array into string

You should definitely use numpy.save, you can still do it in-memory:

>>> import io
>>> import numpy as np
>>> import zlib
>>> f = io.BytesIO()
>>> arr = np.random.rand(100, 100)
>>> np.save(f, arr)
>>> compressed = zlib.compress(f.getvalue())

And to decompress, reverse the process:

>>> np.load(io.BytesIO(zlib.decompress(compressed)))
array([[ 0.80881898,  0.50553303,  0.03859795, ...,  0.05850996,
         0.9174782 ,  0.48671767],
       [ 0.79715979,  0.81465744,  0.93529834, ...,  0.53577085,
         0.59098735,  0.22716425],
       [ 0.49570713,  0.09599001,  0.74023709, ...,  0.85172897,
         0.05066641,  0.10364143],
       ...,
       [ 0.89720137,  0.60616688,  0.62966729, ...,  0.6206728 ,
         0.96160519,  0.69746633],
       [ 0.59276237,  0.71586014,  0.35959289, ...,  0.46977027,
         0.46586237,  0.10949621],
       [ 0.8075795 ,  0.70107856,  0.81389246, ...,  0.92068768,
         0.38013495,  0.21489793]])
>>>

Which, as you can see, matches what we saved earlier:

>>> arr
array([[ 0.80881898,  0.50553303,  0.03859795, ...,  0.05850996,
         0.9174782 ,  0.48671767],
       [ 0.79715979,  0.81465744,  0.93529834, ...,  0.53577085,
         0.59098735,  0.22716425],
       [ 0.49570713,  0.09599001,  0.74023709, ...,  0.85172897,
         0.05066641,  0.10364143],
       ...,
       [ 0.89720137,  0.60616688,  0.62966729, ...,  0.6206728 ,
         0.96160519,  0.69746633],
       [ 0.59276237,  0.71586014,  0.35959289, ...,  0.46977027,
         0.46586237,  0.10949621],
       [ 0.8075795 ,  0.70107856,  0.81389246, ...,  0.92068768,
         0.38013495,  0.21489793]])
>>>

THe default pickle method provides a pure ascii output. To get (much) better performance, use the latest version available. Versions 2 and above are binary and, if memory serves me right, allows numpy arrays to dump their buffer directly into the stream without addtional operations.

To select version to use, add the optional argument while pickling (no need to specify it while unpickling), for instance pkl.dumps(data, 2). To pick the latest possible version, use pkl.dumps(data, -1)

Note that if you use different python versions, you need to specify the lowest supported version. See Pickle documentation for details on the different versions