Other answers already adressed why this fails, here is a convenient solution that sets a default for if the key is not already present, such that your appending does not fail. The way I read it, you want a dictionary with lists of other dictionaries as values. Imagining a situation such as

Copysomedict = {}
somevar = 0
somevar_name = str(somevar)

key1 = "oh"
value1 = 1

You can do

Copysomedict.setdefault(somevar_name,[]).append({key1,value1})

This will evaluate to

{'0': [{'oh', 1}]}

In other words, change lines of this sort

Copysomedict[some_variables_name] += [{ 'somekey': somevalue }]

Into:

Copysomedict.setdefault(some_variables_name,[]).append({'somekey':somevalue})

I hope this answers your question.

How about:

def add_element(dict, key, value):
    if key not in dict:
        dict[key] = []
    dict[key].append(value)

d = {}
add_element(d, 'foo', 'val1')
add_element(d, 'foo', 'val2')
add_element(d, 'bar', 'val3')

Look at your dicitonary:

In [21]: dd = dict(zip(numpy.arange(5), numpy.arange(5)*10))
In [22]: dd
Out[22]: {0: 0, 1: 10, 2: 20, 3: 30, 4: 40}

the keys are numbers. The same thing can be produced without numpy:

In [23]: dict(zip(range(5), range(0,50,10)))
Out[23]: {0: 0, 1: 10, 2: 20, 3: 30, 4: 40}

Your vectorize array access:

In [29]: y = np.vectorize(lambda x: dd[x], otypes=[int])
In [30]: y([0,1,3])
Out[30]: array([ 0, 10, 30])
In [31]: y([])
Out[31]: array([], dtype=int64)

I added the otypes so that the [] works.

I don't see why you want to add an entry that has an array key. None of the other keys are arrays. And as you found out an array can't be a key.

dictionary[np.array([], dtype='int64')]

vectorize passes scalar values from the argument to your function. It does not pass an array. So there's no point to having an array, empty or not, as a key.

Whether np.vectorize is the best tool for using this dictionary is another question. Usually it doesn't improve speed over iterative access. Using a dictionary might the underlying problem, since it can only be accessed on key at a time.

===

Without the otypes, vectorize raises an error

ValueError: cannot call `vectorize` on size 0 inputs unless `otypes` is set

vectorize makes a trial call to the function to determine the return dtype.

===

Here's a more robust version of your y, one that won't choke on a missing key:

In [32]: y = np.vectorize(lambda x: dd.get(x,-100), otypes=[int])
In [33]: y([1,2,3,10])
Out[33]: array([  10,   20,   30, -100])