Resolving "TypeError: Object of type 'int64' is not JSON serializable" in Python (NumPy and JSON)

Understanding the Error:

• JSON Serialization: When you want to transmit or store data in JavaScript Object Notation (JSON) format, you use the json.dumps function in Python. JSON has a specific set of data types it can handle, including strings, numbers (floats), booleans, lists, dictionaries, and null.
• NumPy int64 Type: NumPy, a popular library for numerical computing in Python, provides its own data types like int64 for integers. While Python itself has a built-in int type, NumPy's int64 can store larger integers.

The Conflict:

The core of the error is that the json.dumps function in Python's standard library cannot serialize (convert into JSON format) NumPy's int64 data type directly. This is because JSON doesn't inherently support NumPy's specific data type extensions.

There are two main approaches to address this error:

1. Convert NumPy Integer to Python Integer:

   • You can convert the NumPy int64 object to a regular Python int object using the int() function. Python's built-in int type is compatible with JSON serialization.

   import numpy as np
   import json
   
   # Create a NumPy integer
   data = np.int64(10)
   
   # Convert to Python int before serialization
   data = int(data)
   json_data = json.dumps(data)
   print(json_data)  # Output: 10
   

2. Create a Custom JSON Encoder (for more complex scenarios):

Here's a basic example:

import numpy as np
import json

class NumpyEncoder(json.JSONEncoder):
    def default(self, obj):
        if isinstance(obj, np.integer):
            return int(obj)
        return super().default(obj)

# Create a NumPy integer
data = np.int64(10)

# Use the custom encoder
json_data = json.dumps(data, cls=NumpyEncoder)
print(json_data)  # Output: 10

By converting the NumPy integer to a standard Python integer or using a custom encoder, you can ensure successful JSON serialization of your data.

import numpy as np
import json

# Create a NumPy integer
data = np.int64(100)

# Convert to Python int before serialization (recommended for simple cases)
data = int(data)
json_data = json.dumps(data)
print(json_data)  # Output: 100

Create a Custom JSON Encoder:

import numpy as np
import json

class NumpyEncoder(json.JSONEncoder):
    def default(self, obj):
        if isinstance(obj, np.integer):
            return int(obj)  # Convert NumPy int to Python int
        return super().default(obj)

# Create a NumPy integer
data = np.int64(200)

# Use the custom encoder (useful for complex data structures)
json_data = json.dumps(data, cls=NumpyEncoder)
print(json_data)  # Output: 200

These examples demonstrate how to handle the "TypeError: Object of type 'int64' is not JSON serializable" error. Choose the method that best suits your specific situation. The first approach is simpler for basic cases, while the custom encoder is more flexible for handling various NumPy data types within complex data structures.

• You can convert the NumPy integer int64 object to a Python string using the str() function. JSON can handle strings, and you can convert the string back to an integer later if needed.

import numpy as np
import json

# Create a NumPy integer
data = np.int64(300)

# Convert to string for JSON serialization
data_str = str(data)
json_data = json.dumps(data_str)
print(json_data)  # Output: "300" (as a string)

# Convert back to integer if needed (after deserialization)
data_back = int(json_data)
print(data_back)  # Output: 300 (as an integer)

Use a Third-Party Library (for specific needs):

• If you're working with extensive NumPy data and require more advanced functionalities, consider libraries like ujson or orjson. These libraries offer faster JSON serialization and often have built-in support for NumPy data types.

Choosing the Right Method:

The best method depends on your specific use case and data complexity:

• For simple cases where you only need to serialize basic NumPy integers, converting to Python integers is straightforward.
• If you're dealing with complex data structures containing NumPy objects, a custom encoder provides more control over the serialization process.
• String conversion is a viable option if you need to represent the data as a string within JSON and can convert back to integers later.
• Third-party libraries are useful for performance-critical scenarios or when dealing with very large NumPy datasets.

Remember that converting to strings might lose information about the original NumPy data type (e.g., precision) if you plan to convert back to integers later. Choose the method that balances ease of use, performance, and data fidelity for your situation.