Convert numpy type to python

It looks like you're correct:

>>> import numpy>>> import json>>> json.dumps(numpy.int32(685))Traceback (most recent call last):  File "<stdin>", line 1, in <module>  File "/usr/lib/python2.7/json/__init__.py", line 243, in dumps    return _default_encoder.encode(obj)  File "/usr/lib/python2.7/json/encoder.py", line 207, in encode    chunks = self.iterencode(o, _one_shot=True)  File "/usr/lib/python2.7/json/encoder.py", line 270, in iterencode    return _iterencode(o, 0)  File "/usr/lib/python2.7/json/encoder.py", line 184, in default    raise TypeError(repr(o) + " is not JSON serializable")TypeError: 685 is not JSON serializable

The unfortunate thing here is that numpy numbers' __repr__ doesn't give you any hint about what type they are. They're running around masquerading as ints when they aren't (gasp). Ultimately, it looks like json is telling you that an int isn't serializable, but really, it's telling you that this particular np.int32 (or whatever type you actually have) isn't serializable. (No real surprise there -- No np.int32 is serializable). This is also why the dict that you inevitably printed before passing it to json.dumps looks like it just has integers in it as well.

The easiest workaround here is probably to write your own serializer¹:

class MyEncoder(json.JSONEncoder):    def default(self, obj):        if isinstance(obj, numpy.integer):            return int(obj)        elif isinstance(obj, numpy.floating):            return float(obj)        elif isinstance(obj, numpy.ndarray):            return obj.tolist()        else:            return super(MyEncoder, self).default(obj)

You use it like this:

json.dumps(numpy.float32(1.2), cls=MyEncoder)json.dumps(numpy.arange(12), cls=MyEncoder)json.dumps({'a': numpy.int32(42)}, cls=MyEncoder)

etc.

^{1Or you could just write the default function and pass that as the defaut keyword argument to json.dumps. In this scenario, you'd replace the last line with raise TypeError, but ... meh. The class is more extensible :-)}

You could also convert the array to a python list (use the tolist method) and then convert the list to json.

You can use our fork of ujson to deal with NumPy int64. caiyunapp/ultrajson: Ultra fast JSON decoder and encoder written in C with Python bindings and NumPy bindings

pip install nujson

Then

>>> import numpy as np>>> import nujson as ujson>>> a = {"a": np.int64(100)}>>> ujson.dumps(a)'{"a":100}'>>> a["b"] = np.float64(10.9)>>> ujson.dumps(a)'{"a":100,"b":10.9}'>>> a["c"] = np.str_("12")>>> ujson.dumps(a)'{"a":100,"b":10.9,"c":"12"}'>>> a["d"] = np.array(list(range(10)))>>> ujson.dumps(a)'{"a":100,"b":10.9,"c":"12","d":[0,1,2,3,4,5,6,7,8,9]}'>>> a["e"] = np.repeat(3.9, 4)>>> ujson.dumps(a)'{"a":100,"b":10.9,"c":"12","d":[0,1,2,3,4,5,6,7,8,9],"e":[3.9,3.9,3.9,3.9]}'