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

Question

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?

Answer 1

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.

Answer 2

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))