How to hash a large object (dataset) in Python?

Question

Thanks to John Montgomery I think I have found a solution, and I think it has less overhead than converting every number in possibly huge arrays to strings:

I can create a byte-view of the arrays and use these to update the hash. And somehow this seems to give the same digest as directly updating using the array:

>>> import hashlib
>>> import numpy
>>> a = numpy.random.rand(10, 100)
>>> b = a.view(numpy.uint8)
>>> print a.dtype, b.dtype # a and b have a different data type
float64 uint8
>>> hashlib.sha1(a).hexdigest() # byte view sha1
'794de7b1316b38d989a9040e6e26b9256ca3b5eb'
>>> hashlib.sha1(b).hexdigest() # array sha1
'794de7b1316b38d989a9040e6e26b9256ca3b5eb'

What's the format of the data in the arrays? Couldn't you just iterate through the arrays, convert them into a string (via some reproducible means) and then feed that into your hash via update?

e.g.

import hashlib
m = hashlib.md5() # or sha1 etc
for value in array: # array contains the data
    m.update(str(value))

Don't forget though that numpy arrays won't provide __hash__() because they are mutable. So be careful not to modify the arrays after your calculated your hash (as it will no longer be the same).

There is a package for memoizing functions that use numpy arrays as inputs joblib. Found from this question.

Here is how I do it in jug (git HEAD at the time of this answer):

e = some_array_object
M = hashlib.md5()
M.update('np.ndarray')
M.update(pickle.dumps(e.dtype))
M.update(pickle.dumps(e.shape))
try:
    buffer = e.data
    M.update(buffer)
except:
    M.update(e.copy().data)

The reason is that e.data is only available for some arrays (contiguous arrays). Same thing with a.view(np.uint8) (which fails with a non-descriptive type error if the array is not contiguous).