marshmallow-code/marshmallow

★ 7,238PythonAudience · developerComplexity · 2/5LicenseSetup · easy

Mindmap

mindmap
  root((marshmallow))
    What it does
      Serialize objects
      Deserialize data
      Validate inputs
    Core Concepts
      Schema class
      Field types
      Validation rules
    Use Cases
      REST API output
      Request validation
      Data conversion
    Features
      Framework agnostic
      Nested schemas
      Extension ecosystem

mindmap root((marshmallow)) What it does Serialize objects Deserialize data Validate inputs Core Concepts Schema class Field types Validation rules Use Cases REST API output Request validation Data conversion Features Framework agnostic Nested schemas Extension ecosystem

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Things people build with this

USE CASE 1

Serialize Python objects to JSON for an API response without manually writing conversion code for each field.

USE CASE 2

Validate incoming API request data against your schema rules and return structured error messages when validation fails.

USE CASE 3

Define data transformation rules once in a schema class and reuse the same definition for serialization, deserialization, and validation.

USE CASE 4

Convert database query results into formatted dictionaries ready to be returned as JSON from a Flask or Django endpoint.

Tech stack

Python

Getting it running

Difficulty · easy Time to first run · 5min

Use freely for any purpose, including commercial use, as long as you keep the copyright notice (MIT License).

In plain English

Marshmallow is a Python library that helps you move data cleanly between your application and the outside world. When your app works with objects that have nested structures, dates, or custom types, marshmallow handles the translation into plain Python dictionaries and strings that can be sent as JSON or stored in a database. It also works in reverse, taking raw incoming data and converting it back into the objects your code expects. The core concept is a "schema," which is a class you write that describes the shape of your data. You define which fields exist, what types they should be, and any rules they must follow. Once you have a schema, you can use it to serialize (convert your objects to simple data), deserialize (convert incoming data to objects), and validate (check that the incoming data meets your rules). All three operations come from the same schema definition, so you only describe your data once. Marshmallow works with any Python web framework or database library. It does not care whether you are using Flask, Django, SQLAlchemy, or something else entirely. You define schemas as plain Python classes, and the library stays out of the way of your broader codebase. This makes it a common choice in HTTP API projects where data needs to be validated on the way in and formatted on the way out. Installing marshmallow takes one command via pip, and the full documentation is hosted at marshmallow.readthedocs.io. The project has an active ecosystem of third-party extensions listed in its GitHub wiki, covering integrations with popular frameworks and tools. The library is MIT licensed and maintained through open-source contributions, with professional support available via a Tidelift subscription for teams that need it.

Copy-paste prompts

Prompt 1

Using marshmallow, write a schema for a User object with email, name, and age fields, then validate a dictionary of incoming API data against it.

Prompt 2

Show me how to use marshmallow to serialize a list of SQLAlchemy model instances to JSON for a REST API response.

Prompt 3

How do I nest one marshmallow schema inside another to represent a User who has a list of Address objects?

Prompt 4

Using marshmallow, how do I add a custom validator to a field that rejects any email address not ending in a specific domain?

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