Why does json.dumps(list(np.arange(5))) fail while json.dumps(np.arange(5).tolist()) works

I noticed this problem when a computer running Ubuntu was updated recently and the default version of Python changed to 2.7.

import json
import numpy as np

json.dumps(list(np.arange(5))) # Fails, throws a "TypeError: 0 is not JSON serializable"
json.dumps(np.arange(5).tolist()) # Works 

Is there a difference between list() and the tolist() methd of a numpy array?

Answers


It looks like the tolist() method turns the numpy int32 (or whatever size you have) back into an int, which JSON knows what to do with:

>>> list(np.arange(5))
[0, 1, 2, 3, 4]
>>> type(list(np.arange(5)))
<type 'list'>
>>> type(list(np.arange(5))[0])
<type 'numpy.int32'>
>>> np.arange(5).tolist()
[0, 1, 2, 3, 4]
>>> type(np.arange(5).tolist())
<type 'list'>
>>> type(np.arange(5).tolist()[0])
<type 'int'>

As the docs say for tolist():

Return the array as a (possibly nested) list.

Return a copy of the array data as a (nested) Python list. Data items are converted to the nearest compatible Python type.

The last line makes the difference here.


Because the elements of a NumPy array are not native ints, but of NUmPy's own types:

>>> type(np.arange(5)[0])
<type 'numpy.int64'>

You can use a custom JSONEncoder to support the ndarray type returned by arange:

import numpy as np
import json

class NumPyArangeEncoder(json.JSONEncoder):
    def default(self, obj):
        if isinstance(obj, np.ndarray):
            return obj.tolist() # or map(int, obj)
        return json.JSONEncoder.default(self, obj)

print(json.dumps(np.arange(5), cls=NumPyArangeEncoder))

The problem is that with the first you don't get an int. You get a numpy.int64. That cannot be serialized.


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