TypeError: Object of type int32 is not JSON serializable

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Borislav Hadzhiev

Last updated: Apr 20, 2022

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TypeError: Object of type int32 is not JSON serializable #

The Python "TypeError: Object of type int32 is not JSON serializable" occurs when we try to convert a numpy int32 object to a JSON string. To solve the error, convert the numpy int to a Python integer before converting it to JSON, e.g. int(my_numpy_int).

Here is an example of how the error occurs.

main.py
import json import numpy as np salary = np.power(50, 2, dtype=np.int32) # ⛔️ TypeError: Object of type int32 is not JSON serializable json_str = json.dumps({'salary': salary})

We tried passing a numpy int32 object to the json.dumps() method but the method doesn't handle numpy integers by default.

To solve the error, use the built-in int() (or float) constructor to convert the numpy int32 object to a native Python integer before serializing it.

main.py
import json import numpy as np salary = np.power(50, 2, dtype=np.int32) # ✅ convert to Python native int json_str = json.dumps({'salary': int(salary)}) print(json_str) # 👉️ '{"salary": 2500}' print(type(json_str)) # 👉️ <class 'str'>

The default JSON encoder handles int and float values, so we can use a native Python int instead of a numpy int32 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) salary = np.power(50, 2, dtype=np.int32) json_str = json.dumps({'salary': salary}, cls=NpEncoder) print(json_str) # 👉️ {"salary": 2500} 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 int32 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) salary = np.power(50, 2, dtype=np.int32) # ✅ provide cls keyword argument json_str = json.dumps({'salary': salary}, cls=NpEncoder) print(json_str) # 👉️ {"salary": 2500} print(type(json_str)) # 👉️ <class 'str'>

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

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