How to Solve Python TypeError: Object of type DataFrame is not JSON serializable

by | Programming, Python, Tips

This error occurs when you try to serialize a DataFrame object to a JSON string using the json.dumps() method. You can solve this error by converting the DataFrame to a JSON string using the DataFrame to_json() method. For example,

json_str = my_dataframe.to_json()

You can also convert the DataFrame to a dictionary using the to_dict() method and pass the converted dictionary to the json.dumps() method.

json_str = json.dumps(df.to_dict())

This tutorial will go through how to solve the error with code examples.


TypeError: Object of type DataFrame 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 Decimal” tells us the error is due to an illegal operation with a DataFrame object.

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 DataFrame type.

Example

Let’s look at an example of JSON serializing a DataFrame object. The DataFrame contains the names of five pizzas and their respective prices.

We will attempt to serialize the DataFrame using the json.dumps() method.

import pandas as pd
import json

df = pd.DataFrame(
    {
        "pizza": ['margherita', 'pepperoni', 'hawaiian', 'marinara', 'four cheese'],
        "price":[8.99, 9.99, 10.99, 7.99, 11.99]
    }
)

json_str = json.dumps(df)
print(json_str)

Let’s run the code to see what happens:

TypeError: Object of type DataFrame is not JSON serializable

The error occurs because DataFrame is not a serializable data type.

Solution #1: Use to_json()

Fortunately, the DataFrame class has the to_json() method to convert the DataFrame to a JSON string. We can call the to_json() method directly on the DataFrame. Let’s look at the revised code:

import pandas as pd
import json

df = pd.DataFrame(
    {
        "pizza": ['margherita', 'pepperoni', 'hawaiian', 'marinara', 'four cheese'],
        "price":[8.99, 9.99, 10.99, 7.99, 11.99]
    }
)

json_str = df.to_json()
print(json_str)

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

{"pizza":{"0":"margherita","1":"pepperoni","2":"hawaiian","3":"marinara","4":"four cheese"},"price":{"0":8.99,"1":9.99,"2":10.99,"3":7.99,"4":11.99}}

Solution #2: Use to_dict()

We can also solve this error by converting the DataFrame to a dictionary using the to_dict() method and passing the dictionary to the json.dumps() method. Dictionary is a JSON serializable type.

Let’s look at the revised code:

import pandas as pd
import json

df = pd.DataFrame(
    {
        "pizza": ['margherita', 'pepperoni', 'hawaiian', 'marinara', 'four cheese'],
        "price":[8.99, 9.99, 10.99, 7.99, 11.99]
    }
)
df_dict = df.to_dict()
json_str = json.dumps(df_dict)
print(json_str)

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

{"pizza": {"0": "margherita", "1": "pepperoni", "2": "hawaiian", "3": "marinara", "4": "four cheese"}, "price": {"0": 8.99, "1": 9.99, "2": 10.99, "3": 7.99, "4": 11.99}}

Solution #3: Define a custom function for default kwarg

We can also solve this error by defining a custom function that converts a DataFrame object to a dictionary. Let’s look at the custom function:

import pandas as pd
import json

def serialize_dataframe(obj):

    if isinstance(obj, pd.DataFrame):

        return obj.to_dict()

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

The function checks if the object is a DataFrame and converts it to a dictionary. Otherwise, the function raises a TypeError.

Next, we will set the default keyword argument of the json.dumps() method to this function to convert the DataFrame to a dictionary.

df = pd.DataFrame(
    {
        "pizza": ['margherita', 'pepperoni', 'hawaiian', 'marinara', 'four cheese'],
        "price":[8.99, 9.99, 10.99, 7.99, 11.99]
    }
)

json_str = json.dumps(df, default=serialize_dataframe)
print(json_str)

Let’s run the code to see the result:

{"pizza": {"0": "margherita", "1": "pepperoni", "2": "hawaiian", "3": "marinara", "4": "four cheese"}, "price": {"0": 8.99, "1": 9.99, "2": 10.99, "3": 7.99, "4": 11.99}}

Solution #4: 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 pandas as pd
import json

class dataframe_encoder(json.JSONEncoder):

    def default(self, obj):

        if isinstance(obj, pd.DataFrame):

            return obj.to_dict()

        return json.JSONEncoder.default(self, obj)

Similar to the custom function, the default method of the dataframe_encoder class checks if the object is of type DataFrame, converts it to a dictionary 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:

df = pd.DataFrame(
    {
        "pizza": ['margherita', 'pepperoni', 'hawaiian', 'marinara', 'four cheese'],
        "price":[8.99, 9.99, 10.99, 7.99, 11.99]
    }
)

json_str = json.dumps(df, cls=dataframe_encoder)
print(json_str)

Let’s run the code to see the result:

{"pizza": {"0": "margherita", "1": "pepperoni", "2": "hawaiian", "3": "marinara", "4": "four cheese"}, "price": {"0": 8.99, "1": 9.99, "2": 10.99, "3": 7.99, "4": 11.99}}

Below is the collection of objects that the JSONEncoder class supports 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 errors involving JSON serialization, go to the articles:

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

Have fun and happy researching!

Research Scientist at Moogsoft | + posts

Suf is a research scientist at Moogsoft, specializing in Natural Language Processing and Complex Networks. Previously he was a Postdoctoral Research Fellow in Data Science working on adaptations of cutting-edge physics analysis techniques to data-intensive problems in industry. In another life, he was an experimental particle physicist working on the ATLAS Experiment of the Large Hadron Collider. His passion is to share his experience as an academic moving into industry while continuing to pursue research. Find out more about the creator of the Research Scientist Pod here and sign up to the mailing list here!

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