TypeError: Object of type ndarray is not JSON serializable

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Borislav Hadzhiev

Last updated: Apr 20, 2022

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TypeError: Object of type ndarray is not JSON serializable #

The Python "TypeError: Object of type ndarray is not JSON serializable" occurs when we try to convert a numpy ndarray object to a JSON string. To solve the error, use the tolist() method on the array to convert it to a Python list before serializing it to JSON.

Here is an example of how the error occurs.

main.py
import json import numpy as np arr = np.array([1, 2, 3, 4]) # ⛔️ TypeError: Object of type ndarray is not JSON serializable json_str = json.dumps({'nums': arr})

We tried passing a numpy ndarray object to the json.dumps() method but the method doesn't handle numpy arrays by default.

To solve the error, use the tolist() method on the array to convert it to a Python list.

main.py
import json import numpy as np arr = np.array([1, 2, 3, 4]) # ✅ used tolist() json_str = json.dumps({'nums': arr.tolist()}) print(json_str) # 👉️ {"nums": [1, 2, 3, 4]} print(type(json_str)) # 👉️ <class 'str'>

We can use a native Python list instead of a numpy ndarray when serializing to JSON.

The json.dumps method converts a Python object to a JSON formatted string.

Alternatively, you can extend from the JSONEncoder class and handle the conversions in a default method.

main.py
import json import numpy as np class NpEncoder(json.JSONEncoder): def default(self, obj): if isinstance(obj, np.integer): return int(obj) if isinstance(obj, np.floating): return float(obj) if isinstance(obj, np.ndarray): return obj.tolist() return json.JSONEncoder.default(self, obj) arr = np.array([1, 2, 3, 4]) json_str = json.dumps({'nums': arr}, cls=NpEncoder) print(json_str) # 👉️ {"nums": [1, 2, 3, 4]} print(type(json_str)) # 👉️ <class 'str'>

We extended from the JSONEncoder class.

The JSONEncoder class supports the following objects and types by default.

PythonJSON
dictobject
list, tuplearray
strstring
int, float, int and float derived Enumsnumber
Truetrue
Falsefalse
Nonenull

Notice that the JSONEncoder class doesn't support numpy ndarray to JSON conversion by default.

We can handle this by extending from the class and implementing a default() method that returns a serializable object.

main.py
import json import numpy as np class NpEncoder(json.JSONEncoder): def default(self, obj): if isinstance(obj, np.integer): return int(obj) if isinstance(obj, np.floating): return float(obj) if isinstance(obj, np.ndarray): return obj.tolist() return json.JSONEncoder.default(self, obj)

If the passed in object is an instance of np.integer, we convert the object to a Python int and return the result.

If the passed in object is an instance of np.floating, we convert it to a Python float and return the result.

If the object is an instance of np.ndarray, we convert it to a Python list and return the result.

In all other cases, we let the base classes' default method do the serialization.

To use a custom JSONEncoder, specify it with the cls keyword argument in your call to the json.dumps() method.

main.py
import json import numpy as np class NpEncoder(json.JSONEncoder): def default(self, obj): if isinstance(obj, np.integer): return int(obj) if isinstance(obj, np.floating): return float(obj) if isinstance(obj, np.ndarray): return obj.tolist() return json.JSONEncoder.default(self, obj) arr = np.array([1, 2, 3, 4]) # ✅ provide cls keyword argument json_str = json.dumps({'nums': arr}, cls=NpEncoder) print(json_str) # 👉️ {"nums": [1, 2, 3, 4]} print(type(json_str)) # 👉️ <class 'str'>

If you don't provide the cls kwarg, the default JSONEncoder is used.

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