5 Best Ways to Convert Python Dict to JSON with DateTime Objects

💡 Problem Formulation: Converting Python dictionaries that contain datetime objects to JSON is a common task in web and API development. However, the json module in Python does not natively serialize datetime objects. The challenge is to convert a Python dictionary, like {"event": "conference", "date": datetime(2025, 6, 14, 12, 30)}, to a JSON-compliant string such as {"event": "conference", "date": "2025-06-14T12:30:00"}. This article goes through several methods to accomplish this task.

Method 1: Using a Custom Encoder

Define a custom encoder that subclasses json.JSONEncoder and overrides the default method to format datetime objects into strings. This method is flexible and easily customizable for different date-time formats.

Here’s an example:

import json
from datetime import datetime

class DateTimeEncoder(json.JSONEncoder):
    def default(self, obj):
        if isinstance(obj, datetime):
            return obj.isoformat()
        return super(DateTimeEncoder, self).default(obj)

data = {"event": "conference", "date": datetime(2025, 6, 14, 12, 30)}
json_data = json.dumps(data, cls=DateTimeEncoder)

The output of this code snippet:

{"event": "conference", "date": "2025-06-14T12:30:00"}

This code snippet creates a custom JSON encoder that checks if the object is a datetime instance; if so, it converts it to an ISO 8601 string using isoformat(). Otherwise, it uses the default serialization method. Then we use json.dumps() to serialize our dictionary, specifying our custom encoder with the cls argument.

Method 2: Using a Pre-Serialization Step

Before serialization, iterate over the dictionary and convert any datetime objects into strings. This method is manual but straightforward – ideal for simple use-cases or when you have a one-off conversion task.

Here’s an example:

import json
from datetime import datetime

def serialize_dates(d):
    for key, value in d.items():
        if isinstance(value, datetime):
            d[key] = value.isoformat()

data = {"event": "conference", "date": datetime(2025, 6, 14, 12, 30)}
serialize_dates(data)
json_data = json.dumps(data)

The output of this code snippet:

{"event": "conference", "date": "2025-06-14T12:30:00"}

In this method, we define a serialize_dates() function that converts datetime objects to strings within the dictionary. After running the dictionary through this function, it can be passed directly to json.dumps() without any issues.

Method 3: Using a Serialization Function

Create a serialization function that handles datetime objects and use the default parameter of json.dumps() to specify it. This method combines the customizability of a custom encoder with the simplicity of a one-off function.

Here’s an example:

import json
from datetime import datetime

def serialize_datetime(obj):
    if isinstance(obj, datetime):
        return obj.isoformat()

data = {"event": "conference", "date": datetime(2025, 6, 14, 12, 30)}
json_data = json.dumps(data, default=serialize_datetime)

The output of this code snippet:

{"event": "conference", "date": "2025-06-14T12:30:00"}

The function serialize_datetime() will be called by json.dumps() for any objects that are not natively serializable. It formats datetime objects to string and passes other types through unchanged.

Method 4: Utilizing Third-Party Libraries

Leverage popular third-party libraries such as arrow or pendulum that offer enhanced datetime manipulation and serialization to JSON. These libraries often have built-in JSON serialization support, easing the process.

Here’s an example:

import arrow
from datetime import datetime

data = {"event": "conference", "date": arrow.get(datetime(2025, 6, 14, 12, 30))}
json_data = data['date'].to('utc').for_json()

The output of this code snippet:

{"event": "conference", "date": "2025-06-14T12:30:00+00:00"}

Here we converted the datetime object to an arrow object that can be directly serialized to JSON. The method for_json() provided by the arrow library outputs a JSON-formatted string.

Bonus One-Liner Method 5: Using a lambda function with json.dumps

For a quick and dirty one-liner, use a lambda function as the default parameter in the json.dumps() method. This approach is suitable for small scripts or when adding a function or a class is overkill.

Here’s an example:

import json
from datetime import datetime

data = {"event": "conference", "date": datetime(2025, 6, 14, 12, 30)}
json_data = json.dumps(data, default=lambda x: x.isoformat() if isinstance(x, datetime) else x)

The output of this code snippet:

{"event": "conference", "date": "2025-06-14T12:30:00"}

This compact snippet employs a lambda function that checks each object and returns an ISO 8601 formatted string if the object is a datetime.

Summary/Discussion

• Method 1: Custom Encoder. Most flexible and customizable. Ideal for complex applications. Can be overkill for simple tasks.
• Method 2: Pre-Serialization Step. Straightforward and simple. Best for applications with a single serialization logic. Not suitable for multiple date formats.
• Method 3: Serialization Function. A blend of customizability and simplicity. Best when switchable serialization methods are needed. Requires additional functions.
• Method 4: Third-Party Libraries. Powerful features and easy integration. Recommended for applications already using those libraries. Adds external dependency.
• Method 5: Lambda One-Liner. Quick and concise. Good for one-time usage or small scripts. Least readable and maintainable.