Marshmallow

4.2.3 verified Tue May 12 auth: no python install: verified quickstart: verified

Marshmallow is a lightweight library for converting complex datatypes to and from native Python datatypes. The current version is 4.2.3, released on March 28, 2026. It follows a regular release cadence, with updates approximately every few months.

pip install marshmallow

Common errors

error marshmallow.exceptions.ValidationError: {'field_name': ['Unknown field.']} ↓

cause This error occurs when the input data contains fields that are not defined in the Marshmallow schema.

fix

To fix this, either define the 'unknown' option in your Schema's Meta class to EXCLUDE or INCLUDE, or ensure your input data only contains fields explicitly defined in the schema.

error marshmallow.exceptions.ValidationError: {'field_name': ['Missing data for required field.']} ↓

cause This error indicates that a required field, explicitly marked with `required=True` in your Marshmallow schema, was not provided in the input data during deserialization (`load` method).

fix

Ensure that all fields marked as required=True in your schema are present in the data being loaded.

error TypeError: Object of type X is not JSON serializable ↓

cause This error often occurs when trying to `jsonify` or directly JSON-encode a Python object (like a SQLAlchemy model instance or a `datetime` object) that Python's default `json` module or a framework's `jsonify` function doesn't know how to convert. Marshmallow's role is to serialize these into JSON-serializable types, but if the output of `schema.dump()` is not used or if non-serializable objects are passed directly to `jsonify` before Marshmallow processes them, this error will appear.

fix

Ensure you are using your Marshmallow schema's .dump() method to serialize complex Python objects into a dictionary of JSON-serializable types (like strings, numbers, booleans, lists, and dicts) before attempting to JSON-encode them with jsonify or json.dumps(). Alternatively, configure custom JSON encoders for specific types if not using Marshmallow for that particular object.

error TypeError: 'type' object is not subscriptable ↓

cause This error typically arises in Marshmallow when attempting to define a nested schema within a `fields.List` using `fields.List(MySchema)` instead of `fields.List(fields.Nested(MySchema))`. The `fields.List` expects a Field instance, and `fields.Nested` creates that instance from a Schema class.

fix

When defining a list of nested objects in a Marshmallow schema, wrap the nested Schema class with fields.Nested(). For example, use my_list_field = fields.List(fields.Nested(MyNestedSchema)).

Warnings

breaking In Marshmallow 4.x, the 'load' method now returns a dictionary instead of an OrderedDict by default. To maintain the previous behavior, set the 'ordered' option to True in the schema's Meta class. ↓

fix Add 'ordered = True' in the Meta class of your schema.

gotcha The 'load' method in Marshmallow 4.x raises a ValidationError if it encounters unknown fields by default. To change this behavior, set the 'unknown' option in the schema's Meta class. ↓

fix Add 'unknown = INCLUDE' in the Meta class of your schema to include unknown fields.

Install compatibility verified last tested: 2026-05-12

python os / libc status wheel install import disk

3.10 alpine (musl) - - 0.46s 18.7M

3.10 slim (glibc) - - 0.31s 19M

3.11 alpine (musl) - - 0.99s 20.1M

3.11 slim (glibc) - - 0.81s 21M

3.12 alpine (musl) - - 0.70s 11.9M

3.12 slim (glibc) - - 0.73s 12M

3.13 alpine (musl) - - 0.68s 11.6M

3.13 slim (glibc) - - 0.67s 12M

3.9 alpine (musl) - - 0.10s 18.2M

3.9 slim (glibc) - - 0.10s 19M

Imports

Schema
```
from marshmallow import Schema
```
Ensure correct import path to avoid ImportError.
fields
```
from marshmallow import fields
```
Import fields from marshmallow to access various field types.

Quickstart verified last tested: 2026-04-23

A simple example demonstrating how to define a schema and load data using Marshmallow.

from marshmallow import Schema, fields

class UserSchema(Schema):
    name = fields.Str()
    email = fields.Email()
    created_at = fields.DateTime()

user_data = {
    'name': 'Monty',
    'email': 'monty@python.org',
    'created_at': '2014-08-17T14:54:16.049594+00:00'
}

schema = UserSchema()
result = schema.load(user_data)
print(result)