Home/ Questions/The complete guide to the ''typeerror object of type ndarray is not json serializable'' issue.
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James O'Kelly
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Asked: 2022-05-18T17:58:23+00:00 In:

The complete guide to the ”typeerror object of type ndarray is not json serializable” issue.

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Hi everyone, I’m learning about python. While working, I try load the webpage after creating a NumPy array, and saving it as a Django context variable. As a result, I get the message:

array([ 0, 239, 479, 717, 952, 1192, 1432, 1667], dtype=int64) is not JSON serializable

What can I do about the “typeerror object of type ndarray is not json serializable” issue? Is there a better approach?

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2 Answers

  1. Best Answer
    Expert

    The cause:

    The error happens because you are attempting to change a NumPy ndarray object into a JSON string but the default function which is called for objects can not be serialized, so it raises an error.

    Solution:

    You can solve this problem by applying “.tolist()” method for the first arrays as the below:

    import numpy as np
import codecs, json

a = np.arange(10).reshape(2,5) # a 2 by 5 array
b = a.tolist() # nested lists with the similar data, indices
file_path = "/path.json" ## your path variable
json.dump(b, codecs.open(file_path, 'w', encoding='utf-8'),
separators=(',', ':'),
sort_keys=True,
indent=4) ### the array is saved in .json format by this

    Next, let’s use these following commands to “unjsonify” the array:

    obj_text = codecs.open(file_path, 'r', encoding='utf-8').read()
b_new = json.loads(obj_text)
a_new = np.array(b_new)
    • 0

  2. Save as JSON a numpy.ndarray and any other nested-list component. 

    class NumpyEncoder(json.JSONEncoder):
 def default(self, obj):
 if isinstance(obj, np.ndarray):
 return obj.tolist()
 return json.JSONEncoder.default(self, obj)

a = np.array([[1, 2, 3], [4, 5, 6]])
print(a.shape)
json_dump = json.dumps({'a': a, 'aa': [2, (2, 3, 4), a], 'bb': [2]}, 
 cls=NumpyEncoder)
print(json_dump)

    What will output look like? 

    (2, 3)
{"a": [[1, 2, 3], [4, 5, 6]], "aa": [2, [2, 3, 4], [[1, 2, 3], [4, 5, 6]]], "bb": [2]}

    To restore using JSON: 

    json_load = json.loads(json_dump)
a_restored = np.asarray(json_load["a"])
print(a_restored)
print(a_restored.shape)

    What will output look like? 

    [[1 2 3]
 [4 5 6]]
(2, 3)
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