How to Solve Python TypeError: Object of type dict_values is not JSON Serializable

by | Programming, Python, Tips

This error occurs when we try to serialize a dict_values object to a JSON string using the json.dumps() method. You can solve this error by converting the dict_values object to a list using the built-in list() method. For example,

import json 

my_dict = {'particle':'electron', 'mass':0.511, 'charge': -1}

vals = list(my_dict.values())

json_str = json.dumps(vals)

This tutorial will go through the error in detail and how to solve it with code examples.


TypeError: Object of type dict_values is not JSON Serializable

Let’s break up the error message to understand what the error means. TypeError occurs whenever you attempt to use an illegal operation for a specific data type. The part “Object of type dict_values” tells us the error is due to an illegal operation with an object of the dict_values class. dict_values is a view object that displays all of the values in the given dictionary. The view object changes as the dictionary changes.

Serialization in Python refers to converting a Python object into a transmittable format that we can recreate when needed using deserialization. JSON serialization returns a human-readable string form called a JSON string. The JSON encoder json.dump() and json.dumps() can only serialize certain object types like dictionaries, lists, or strings.

is not JSON serializable” informs us that the JSON serialization is an illegal operation for the dict_values type.

Example

Let’s look at an example of serializing a dict_values view object. First, we will import the json module and then define a dictionary.

import json 

my_dict = {'particle':'electron', 'mass':0.511, 'charge': -1}

The above dictionary contains information about the electron. Next, we will call the values method on the dictionary object to get the dict_values view object.

vals = my_dict.values()

Next, we will attempt to serialize the dict_values object to a JSON string by passing it to the json.dumps() method.

json_str = json.dumps(vals)

print(json_str)

Let’s run the code to see what happens:

TypeError: Object of type dict_values is not JSON serializable

The error occurs because vals is a dict_values object, which is not JSON serializable. We can check the type of an object by using the built-in type() method as follows:

print(type(vals))
<class 'dict_values'>

Solution #1: Convert dict_values to list

The simplest way to solve this error is to convert the dict_values object to a list using the built-in list() method. The list data type is JSON serializable.

Let’s look at the revised code:

import json 

my_dict = {'particle':'electron', 'mass':0.511, 'charge': -1}

vals = list(my_dict.values())

json_str = json.dumps(vals)

print(json_str)

Let’s run the code to get the JSON string containing the list of dictionary values:

["electron", 0.511, -1]

Solution #2: Define a custom function for the default kwarg

We can also solve this error by defining a custom function that converts a dict_values object to a list. We can then pass the function as the default argument for the json.dumps() method. The function we set default to gets called for objects that are not JSON serializable. Let’s look at the custom function:

import json

from collections import abc

def serialize_dict_values(obj):

    if isinstance(obj, abc.ValuesView):

        return list(obj)

    raise TypeError ("Type %s is not serializable" % type(obj))

To check if an object is an instance of dict_values we can use the collections.abc module, which provides abstract base classes to test whether a class provides a particular interface.

In this case, the Abstract Base Class (ABC) we need is dictionary values views.

Note that if the object obj is not an instance of ValuesView the function will raise a TypeError. Let’s set the default keyword argument to our custom function and run the code:

my_dict = {'particle':'electron', 'mass':0.511, 'charge': -1}

vals = my_dict.values()

json_str = json.dumps(vals, default=serialize_dict_values)

print(json_str)

Let’s run the code to see the JSON string:

["electron", 0.511, -1]

We can also set default to list. Let’s look at the revised code:

import json 

my_dict = {'particle':'electron', 'mass':0.511, 'charge': -1}

vals = my_dict.values()

json_str = json.dumps(vals, default=list)

print(json_str)

Let’s run the code to see the JSON string:

["electron", 0.511, -1]

Solution #3: Define a JSONEncoder subclass for the cls kwarg

We can also solve this error by building a custom JSONEncoder subclass. This subclass will override the default method to serialize additional types.

import json

from collections import abc

class dict_values_encoder(json.JSONEncoder):

    def default(self, obj):

        if isinstance(obj, abc.ValuesView):

            return list(obj)

        return json.JSONEncoder.default(self, obj)

Similar to the custom function, the default method of the dict_values_encoder class checks if the object is an instance of dict_values using the ValuesView ABC and converts it to a list and returns it.

We have to specify the custom JSONEncoder subclass with the cls keyword argument. Otherwise, JSONEncoder is used. Let’s look at the updated code:

my_dict = {'particle':'electron', 'mass':0.511, 'charge': -1}

vals = my_dict.values()

json_str = json.dumps(vals, cls=dict_values_encoder)

print(json_str)

Let’s run the code to see the result:

["electron", 0.511, -1]

Below is the collection of objects that the JSONEncoder class supports by default, and their JSON equivalent.

PythonJSON
dictobject
list, tuplearray
strstring
int, float, int- & float- derived Enumsnumber
Truetrue
Falsefalse
Nonenull
JSONEncoder Supported objects and types by default

Summary

Congratulations on reading to the end of this tutorial!

For further reading on “not JSON serializable” TypeErrors, go to the article:

Go to the online courses page on Python to learn more about Python for data science and machine learning.

Have fun and happy researching!

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Suf is a senior advisor in data science with deep expertise in Natural Language Processing, Complex Networks, and Anomaly Detection. Formerly a postdoctoral research fellow, he applied advanced physics techniques to tackle real-world, data-heavy industry challenges. Before that, he was a particle physicist at the ATLAS Experiment of the Large Hadron Collider. Now, he’s focused on bringing more fun and curiosity to the world of science and research online.

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