Saving dictionary of numpy arrays

When saving a dictionary with numpy, the dictionary is encoded into an array. To have what you need, you can do as in this example:

my_dict = {'a' : np.array(range(3)), 'b': np.array(range(4))}

np.save('my_dict.npy',  my_dict)    

my_dict_back = np.load('my_dict.npy')

print(my_dict_back.item().keys())    
print(my_dict_back.item().get('a'))

So you are probably missing .item() for the reloaded dictionary. Check this out:

for key, key_d in data2.item().items():
    print key, key_d

The comparison my_dict == my_dict_back.item() works only for dictionaries that does not have lists or arrays in their values.

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EDIT: for the item() issue mentioned above, I think it is a better option to save dictionaries with the library pickle rather than with numpy.

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SECOND EDIT: if not happy with pickle, and all the types in the dictionary are compatible with , json is an option as well.

I really liked the deepdish (it saves them in HDF5 format):

>>> import deepdish as dd
>>> d = {'foo': np.arange(10), 'bar': np.ones((5, 4, 3))}
>>> dd.io.save('test.h5', d)

$ ddls test.h5
/bar                       array (5, 4, 3) [float64]
/foo                       array (10,) [int64]

>>> d = dd.io.load('test.h5')

for my experience, it seems to be partially broken for large datasets, though :(

Let's look at a small example:

In [819]: N
Out[819]: 
array([[  0.,   1.,   2.,   3.],
       [  4.,   5.,   6.,   7.],
       [  8.,   9.,  10.,  11.]])

In [820]: data={'N':N}

In [821]: np.save('temp.npy',data)

In [822]: data2=np.load('temp.npy')

In [823]: data2
Out[823]: 
array({'N': array([[  0.,   1.,   2.,   3.],
       [  4.,   5.,   6.,   7.],
       [  8.,   9.,  10.,  11.]])}, dtype=object)

np.save is designed to save numpy arrays. data is a dictionary. So it wrapped it in a object array, and used pickle to save that object. Your data2 probably has the same character.

You get at the array with:

In [826]: data2[()]['N']
Out[826]: 
array([[  0.,   1.,   2.,   3.],
       [  4.,   5.,   6.,   7.],
       [  8.,   9.,  10.,  11.]])