Technical Lesson: Deserialization with Marshmallow

Introduction

We've seen how to use the marshmallow library to serialize object state using the dump() or dumps() methods, which respectively produce a dictionary and JSON-encoded string. In this lesson, we'll use the load() and loads() methods to perform the reverse process of deserialization, creating a Python class instance from input data formatted as either a dictionary or a JSON-encoded string.

Scenario

You’ve just joined the backend team at a small e-commerce platform. Customer and product data often comes in as JSON payloads from the website frontend or third-party integrations. However, your Python application needs to work with Python objects — not raw JSON.

The backend team needs a reliable, validated process to convert incoming JSON or dictionaries into structured, typed Python objects or dictionaries. Your task is to learn how to deserialize external data correctly and safely using Marshmallow.

In production systems, almost all API inputs (e.g., new user signups, order creation) arrive as JSON and must be deserialized before your code can process them.

Tools & Resources

• GitHub Repo
• marshmallow
• marshmallow quickstart

Setup

This lesson is a code-along, so fork and clone the repo.

Run pipenv install to install the dependencies and pipenv shell to enter your virtual environment before running your code.

$ pipenv install
$ pipenv shell

---

Instructions

Task 1: Define the Problem

When applications receive data from outside sources (like APIs, forms, or integrations), the data:

• Is often formatted as a JSON string or a Python dictionary.
• May be missing required fields or have wrong data types.
• Cannot be used directly to instantiate Python objects safely.

Problem:

Without a proper system, it’s difficult to trust or use external data because it may not match what your application expects.

Solution:

Use Marshmallow schemas to:

• Validate incoming data against expected types and structures.
• Deserialize dictionaries or JSON strings into Python dictionaries or Python objects.
• Safely integrate external data into your application’s flow.

Task 2: Determine the Design

We’ll approach deserialization methodically:

1. Schema Setup:
   • Define a Schema subclass describing the expected fields and their types.
2. Data Input:
   • Accept incoming data as either a dictionary or a JSON string.
3. Deserialization Methods:
   • Use:
     • load() for dictionary inputs
     • loads() for JSON string inputs
4. Result Handling:
   • Receive a dictionary by default.
   • Alternatively, configure the schema with @post_load to return a model instance.
5. Handling Collections:
   • Use many=True if deserializing a list of objects.
6. Advanced Field Control:
   • Apply load_only and dump_only for fields that should only appear during deserialization or serialization, respectively.
   • Add computed fields using fields.Function and fields.Method if needed.
7. Validation and Error Handling:
   • Marshmallow automatically checks that types match.
   • (Error handling will be discussed more deeply in validation-focused lessons.)

Task 3: Develop, Test, and Refine the Code

Step 1: Deserialization

Within the lib directory, you'll find the file deserialize.py that defines a marshmallow schema named HamsterSchema. The file does not include a hamster model class, since we won't be creating model objects just yet.

# lib/deserialize.py

from marshmallow import Schema, fields
from pprint import pprint

# schema

class HamsterSchema(Schema):
     name = fields.Str()
     breed = fields.Str()
     dob = fields.Date()

Schemas are the contract: they tell Marshmallow what input is valid and how to rebuild it.

The load() method validates and deserializes an input dictionary to an application-level data structure. By default, load() returns a dictionary of field names mapped to deserialized values. We'll explore what happens if there are validation errors in a separate lesson.

Update the code as shown below to create a schema instance, along with an input dictionary that contains key-value pairs for each field defined in the schema. We can then call the load() method on the schema, passing the input dictionary as a parameter. The load() method validates that the contents of the input dictionary (i.e. the dictionary key-value pairs correspond to the field types specified in the schema), and then returns a new dictionary as the method result.

# validate and deserialize an input dictionary to an output dictionary
# of field names mapped to deserialized values with the load() method.

hamster_schema = HamsterSchema()
hamster_dict = {"name" : "Fluffernutter", "breed": "Roborovski", "dob" : "2014-08-11"}
result = hamster_schema.load(hamster_dict)
print(type(result)) # => <class 'dict'>
pprint(result)      #
# => {'breed': 'Roborovski',
# =>  'dob': datetime.date(2014, 8, 11),
# =>  'name': 'Fluffernutter'}

The output should display the deserialized data in a dictionary format. The pretty-printing function pprint displays the dictionary across multiple lines if there is too much content to fit on one line. Notice the dob field has been converted from the datetime input string "2014-08-11 into an object datetime.date(2014, 8, 11).

$ python lib/deserialize.py
<class 'dict'>
{'breed': 'Roborovski',
 'dob': datetime.date(2014, 8, 11),
 'name': 'Fluffernutter'}
<class 'dict'>

We can use the loads() method to deserialize using as input a JSON-encoded string. Update the code to add the following, noticing the variable hamster_json is assigned to a string, not a dictionary:

# validate and deserialize a JSON-encoded string to an output dictionary
# of field names mapped to deserialized values with the loads() method.

hamster_json = '{"name": "Wiggles", "breed": "Siberian", "dob": "2020-01-30"}'
result_2 = hamster_schema.loads(hamster_json)
print(type(result_2))  # => <class 'dict'>
pprint(result_2)
# => {'breed': 'Siberian',
# =>  'dob': datetime.date(2020, 1, 30),
# =>  'name': 'Wiggles'}

The loads() method returns a dictionary as a result as seen in the output.

$ python lib/deserialize.py
...
<class 'dict'>
{'breed': 'Siberian',
 'dob': datetime.date(2020, 1, 30),
 'name': 'Wiggles'}

Step 2: Deserializing a collection

A collection can be deserialized by passing many=True, either when instantiating the schema or as an extra parameter passed to the load() or loads() methods. The result of deserialization is a list of dictionaries.

Update the code to deserialize a collection as shown below by passing a list of dictionaries along with many=True as parameters to the load(): method.

# deserialize a list of dictionaries

hamster_1 = {"name": "Nibbles", "breed": "European",  "dob": "2018-04-30"}
hamster_2 = {"name": "Snuggles", "breed": "Chinese", "dob": "2023-10-07"}
hamster_list = [hamster_1, hamster_2]
result_3 = hamster_schema.load(hamster_list, many = True)
print(type(result_3))  # => <class 'list'>
pprint(result_3)       # list of dictionaries
# => [{'breed': 'European', 'dob': datetime.date(2018, 4, 30), 'name': 'Nibbles'},
# =>  {'breed': 'Chinese', 'dob': datetime.date(2023, 10, 7), 'name': 'Snuggles'}]

We can also deserialize a JSON-encoded string containing an array of objects using the loads() method. Let's pass the parameter many=True when creating the schema to demonstrate that option. Add the following code and confirm the output:

# deserialize a JSON array

hamster_1 = '{"name": "Honey", "breed": "Turkish", "dob": "2009-06-03"}'
hamster_2 = '{"name": "Squeaky", "breed": "Winter White", "dob": "2022-12-31"}'
hamsters  = f'[{hamster_1}, {hamster_2}]'   #string contains JSON array of objects
hamster_schema_many = HamsterSchema(many=True)
result_4 = hamster_schema_many.loads(hamsters)
print(type(result_4))  # => <class 'list'>
pprint(result_4)       # list of dictionaries
# => [{'breed': 'Turkish', 'dob': datetime.date(2009, 6, 3),'name': 'Honey'},
# =>  {'breed': 'Winter White', 'dob': datetime.date(2022, 12, 31), 'name': 'Squeaky'}]

Step 3: Deserializing to objects (post-load)

To deserialize to an object, we need to define a method of the schema and decorate it with @post_load. The method receives a dictionary of deserialized data. This is often the moment when business logic (e.g., auto-setting timestamps, calculating values) is introduced during deserialization.

Open lib/post_load.py. The file defines a model class named Dog, along a schema named DogSchema. If you run the current version of the code, the loads method returns a dictionary, not a Dog instance, since by default the method returns a dictionary data structure. The call to pprint displays the dictionary contents.

# lib/post_load.py

from marshmallow import Schema, fields, post_load
from pprint import pprint

# model

class Dog:
    def __init__(self, name, breed, tail_wagging = False):
        self.name = name
        self.breed = breed
        self.tail_wagging = tail_wagging

    def give_treat(self):
        self.tail_wagging = True

    def scold(self):
        self.tail_wagging = False

# schema

class DogSchema(Schema):
    name = fields.Str()
    breed = fields.Str()
    tail_wagging = fields.Boolean()

# deserialize

dog_schema = DogSchema()
dog_json = '{"name": "Snuggles", "breed": "Beagle", "tail_wagging": true}'
dog = dog_schema.loads(dog_json)
print(type(dog))             # => <class 'dict'>
print(isinstance(dog, Dog))  # => False
pprint(dog)                  # => {'breed': 'Beagle', 'name': 'Snuggles', 'tail_wagging': True}

We can deserialize to an object by defining a method in the schema and decorating it with @post_load. The method receives a dictionary of deserialized data. Update the DogSchema as shown to add this method:

class DogSchema(Schema):
    name = fields.Str()
    breed = fields.Str()
    tail_wagging = fields.Boolean()

    # take a dictionary of deserialized data and return a model instance
    @post_load
    def make_dog(self, data, **kwargs):
        return Dog(**data)

If you run the code, you'll see the output reflects that a Dog instance was returned from loading:

$ python lib/post_load.py
<class '__main__.Dog'>
True
<__main__.Dog object at 0x102e1c910>

Let's update the comments to reflect the correct output.

print(type(dog))             # => <class 'dict'>
print(isinstance(dog, Dog))  # => True
pprint(dog)                  # => <models.Dog object at 0x102c28d90>

Now that we've deserialized to a Dog object, we can invoke the methods scold() and give_treat() to change the object's state. We'll also use the dumps() method to serialize the object and confirm the state changes.

# lib/post_load.py

from marshmallow import Schema, fields, post_load
from pprint import pprint

# model

class Dog:
    def __init__(self, name, breed, tail_wagging = False):
        self.name = name
        self.breed = breed
        self.tail_wagging = tail_wagging

    def give_treat(self):
        self.tail_wagging = True

    def scold(self):
        self.tail_wagging = False

# schema

class DogSchema(Schema):
    name = fields.Str()
    breed = fields.Str()
    tail_wagging = fields.Boolean()

    # take a dictionary of deserialized data and return a model instance
    @post_load
    def make_dog(self, data, **kwargs):
        return Dog(**data)

# deserialize to a class instance by defining a @post_load method in the schema

dog_schema = DogSchema()
dog_json = '{"name": "Snuggles", "breed": "Beagle", "tail_wagging": true}'
dog = dog_schema.loads(dog_json)
print(type(dog))             # => <class 'dict'>
print(isinstance(dog, Dog))  # => True
pprint(dog)                  # => <models.Dog object at 0x102c28d90>

# call instance methods to change object state, deserialize to JSON using dumps()
pprint(dog_schema.dumps(dog))   # => '{"name": "Snuggles", "breed": "Beagle", "tail_wagging": true}'
dog.scold()
pprint(dog_schema.dumps(dog))   # => '{"name": "Snuggles", "breed": "Beagle", "tail_wagging": false}'
dog.give_treat()
pprint(dog_schema.dumps(dog))   # => '{"name": "Snuggles", "breed": "Beagle", "tail_wagging": true}'

Step 4: Load-only and Dump-Only Fields

We may want define fields that are "load-only" (used during deserialization) or "dump_only" (used during serialization).

For example, consider the User model show below.
The constructor takes parameters for the name and password. However, created_at is computed as the current timestamp and not passed as a parameter.

class User:
    def __init__(self, name, password):
        self.name = name
        self.password = password
        self.created_at = datetime.now()

We want values for name and password when we deserialize input data to create a User instance, but we don't need a value for created_at since it is computed. We probably do not want to include password when we serialize the object state since that value should not be shared.

The parameter load_only=True is used to indicate a schema field should be used during deserialization but not serialization, while dump_only=True indicates the field should be serialized but not used during deserialization. By default, a field is defined as load_only=False and dump_only=False, meaning it will be used for serialization and deserialization.

Consider the code in lib/load_only_dump_only.py.

• The name field is used for both serialization and deserialization.
• The password field is loaded/deserialized, but is not dumped/serialized.
• The created_at field is not loaded/deserialized, but is dumped/serialized.

# lib/load_only_dump_only.py

from marshmallow import Schema, fields, post_load
from datetime import datetime
from pprint import pprint

# model

class User:
    def __init__(self, name, password):
        self.name = name
        self.password = password
        self.created_at = datetime.now()

# schema

class UserSchema(Schema):
    name = fields.Str()
    password = fields.Str(load_only=True)         #read-only/deserialized
    created_at = fields.DateTime(dump_only=True)  #write-only/serialized

    @post_load
    def make_user(self, data, **kwargs):
        return User(**data)

user_schema = UserSchema()

user_data = {"name": "Lua",  "password": "secret"}   # created_at is dump_only

user =  user_schema.load( user_data  )

pprint(user_schema.dumps(user))                      # password is load_only
# => '{"name": "Lua", "created_at": "2023-11-11T10:56:55.898190"}'

Run the code to confirm the output

$ python lib/load_only_dump_only.py
'{"name": "Lua", "created_at": "2023-11-15T07:30:30.660156"}'

Step 5: Custom Fields

You can create a custom field beyond the builtin field types. This is often done during serialization to compute a value from other fields. We will look at two options for creating a custom field (1) defining a function field, and (2) defining a method field.

Consider the code in lib/custom_field.py. A cat has fields name, dob, and favorite_toys.

# lib/custom_field.py

from marshmallow import Schema, fields, post_load
from datetime import date
from pprint import pprint

# model

class Cat:
    def __init__(self, name, dob, favorite_toys = []):
        self.name = name
        self.dob = dob
        self.favorite_toys = favorite_toys

# schema

class CatSchema(Schema):
    name = fields.Str(required=True, error_messages={"required": "Name is required."})
    dob = fields.Date(format="%Y-%m-%d")
    favorite_toys = fields.List(fields.Str())

    @post_load
    def make_cat(self, data, **kwargs):
        return Cat(**data)

schema = CatSchema()

#deserialize
cat_1 = schema.load({"name": "Meowie", "dob": "2020-11-28", "favorite_toys": ["ball", "squeaky mouse"]})
cat_2 = schema.load({"name": "Whiskers", "dob": "2015-4-15", "favorite_toys": []})

#serialize
pprint(schema.dump(cat_1))
# => {'age': 2,
# =>  'dob': '2020-11-28',
# =>  'favorite_toys': ['ball', 'squeaky mouse']}

pprint(schema.dump(cat_2))
# => {'age': 8,
# => 'dob': '2015-04-15',
# => 'favorite_toys': []}

Running the code produces the expected output:

{'dob': '2020-11-28',
 'favorite_toys': ['ball', 'squeaky mouse'],
 'name': 'Meowie'}
{'dob': '2015-04-15', 'favorite_toys': [], 'name': 'Whiskers'}

Suppose we would like to include in the serialized output two more fields:

• likes_toys : a boolean that is true if the list of favorite toys is not empty
• age : an integer calculated using the date of birth and the current date.

These fields will not be included during loading, since they can be calculated from the other fields. While we could update the model class to calculate them, we can also just update the schema to compute them during serialization.

We can use define likes_toys using the class fields.Function as shown below. We pass a callable from which to compute the value. The function must take a single argument obj which is the object to be serialized.

likes_toys = fields.Function(lambda obj : len(obj.favorite_toys) > 0, dump_only = True)

The calculation for the age field doesn't necessarily work as a simple lambda expression, so we'll implement that field using the class fields.Method as shown:

    age = fields.Method("calculate_age", dump_only = True)

    def calculate_age(self, obj):
        today = date.today()
        return today.year - obj.dob.year - ((today.month, today.day) < (obj.dob.month, obj.dob.day))

Update the code to add the likes_toys function and age method fields to the schema:

# lib/custom_field.py

from marshmallow import Schema, fields, post_load
from datetime import date
from pprint import pprint

# model

class Cat:
    def __init__(self, name, dob, favorite_toys = []):
        self.name = name
        self.dob = dob
        self.favorite_toys = favorite_toys

# schema

class CatSchema(Schema):
    name = fields.Str(required=True, error_messages={"required": "Name is required."})
    dob = fields.Date(format="%Y-%m-%d")
    favorite_toys = fields.List(fields.Str())
    likes_toys = fields.Function(lambda obj : len(obj.favorite_toys) > 0, dump_only = True)
    age = fields.Method("calculate_age", dump_only = True)

    def calculate_age(self, obj):
        today = date.today()
        return today.year - obj.dob.year - ((today.month, today.day) < (obj.dob.month, obj.dob.day))

    @post_load
    def make_cat(self, data, **kwargs):
        return Cat(**data)

schema = CatSchema()

#deserialize
cat_1 = schema.load({"name": "Meowie", "dob": "2020-11-28", "favorite_toys": ["ball", "squeaky mouse"]})
cat_2 = schema.load({"name": "Whiskers", "dob": "2015-4-15", "favorite_toys": []})

#serialize
pprint(schema.dump(cat_1))
# => {'age': 2,
# =>  'dob': '2020-11-28',
# =>  'favorite_toys': ['ball', 'squeaky mouse'],
# =>  'likes_toys': True,
# =>  'name': 'Meowie'}

pprint(schema.dump(cat_2))
# => {'age': 8,
# => 'dob': '2015-04-15',
# => 'favorite_toys': [],
# => 'likes_toys': False,
# => 'name': 'Whiskers'}

Now we see age and likes_toys included in the serialized output:

{'age': 2,
 'dob': '2020-11-28',
 'favorite_toys': ['ball', 'squeaky mouse'],
 'likes_toys': True,
 'name': 'Meowie'}
{'age': 8,
 'dob': '2015-04-15',
 'favorite_toys': [],
 'likes_toys': False,
 'name': 'Whiskers'}

Step 6: Verify your Code

Solution Code:

# lib/deserialize.py

from marshmallow import Schema, fields
from pprint import pprint

# schema

class HamsterSchema(Schema):
     name = fields.Str()
     breed = fields.Str()
     dob = fields.Date()


# validate and deserialize an input dictionary to an output dictionary
# of field names mapped to deserialized values with the load() method.

hamster_schema = HamsterSchema()
hamster_dict = {"name" : "Fluffernutter", "breed": "Roborovski", "dob" : "2014-08-11"}
result = hamster_schema.load(hamster_dict)
print(type(result)) # => <class 'dict'>
pprint(result)      #
# => {'breed': 'Roborovski dwarf',
# =>  'dob': datetime.date(2014, 8, 11),
# =>  'name': 'Fluffernutter'}

# validate and deserialize a JSON-encoded string to an output dictionary
# of field names mapped to deserialized values with the loads() method.

hamster_json = '{"name": "Wiggles", "breed": "Siberian", "dob": "2020-01-30"}'
result_2 = hamster_schema.loads(hamster_json)
print(type(result_2))  # => <class 'dict'>
pprint(result_2)
# => {'breed': 'Siberian',
# =>  'dob': datetime.date(2020, 1, 30),
# =>  'name': 'Wiggles'}

# deserialize a list of dictionaries

hamster_1 = {"name": "Nibbles", "breed": "European",  "dob": "2018-04-30"}
hamster_2 = {"name": "Snuggles", "breed": "Chinese", "dob": "2023-10-07"}
hamster_list = [hamster_1, hamster_2]
result_3 = hamster_schema.load(hamster_list, many = True)
print(type(result_3))  # => <class 'list'>
pprint(result_3)       # list of dictionaries
# => [{'breed': 'European', 'dob': datetime.date(2018, 4, 30), 'name': 'Nibbles'},
# =>  {'breed': 'Chinese', 'dob': datetime.date(2023, 10, 7), 'name': 'Snuggles'}]

# deserialize a JSON array

hamster_1 = '{"name": "Honey", "breed": "Turkish", "dob": "2009-06-03"}'
hamster_2 = '{"name": "Squeaky", "breed": "Winter White", "dob": "2022-12-31"}'
hamsters  = f'[{hamster_1}, {hamster_2}]'   #string contains JSON array of objects
hamster_schema_many = HamsterSchema(many=True)
result_4 = hamster_schema_many.loads(hamsters)
print(type(result_4))  # => <class 'list'>
pprint(result_4)       # list of dictionaries
# => [{'breed': 'Turkish', 'dob': datetime.date(2009, 6, 3),'name': 'Honey'},
# =>  {'breed': 'Winter White', 'dob': datetime.date(2022, 12, 31), 'name': 'Squeaky'}]

# lib/post_load.py

from marshmallow import Schema, fields, post_load
from pprint import pprint

# model

class Dog:
    def __init__(self, name, breed, tail_wagging = False):
        self.name = name
        self.breed = breed
        self.tail_wagging = tail_wagging

    def give_treat(self):
        self.tail_wagging = True

    def scold(self):
        self.tail_wagging = False

# schema

class DogSchema(Schema):
    name = fields.Str()
    breed = fields.Str()
    tail_wagging = fields.Boolean()

    # take a dictionary of deserialized data and return a model instance
    @post_load
    def make_dog(self, data, **kwargs):
        return Dog(**data)

# deserialize to a class instance by defining a @post_load method in the schema

dog_schema = DogSchema()
dog_json = '{"name": "Snuggles", "breed": "Beagle", "tail_wagging": true}'
dog = dog_schema.loads(dog_json)
print(type(dog))             # => <class 'dict'>
print(isinstance(dog, Dog))  # => True
pprint(dog)                  # => <models.Dog object at 0x102c28d90>

# call instance methods to change object state, deserialize to JSON using dumps()
pprint(dog_schema.dumps(dog))   # => '{"name": "Snuggles", "breed": "Beagle", "tail_wagging": true}'
dog.scold()
pprint(dog_schema.dumps(dog))   # => '{"name": "Snuggles", "breed": "Beagle", "tail_wagging": false}'
dog.give_treat()
pprint(dog_schema.dumps(dog))   # => '{"name": "Snuggles", "breed": "Beagle", "tail_wagging": true}'

# lib/load_only_dump_only.py

from marshmallow import Schema, fields, post_load
from datetime import datetime
from pprint import pprint

# model

class User:
    def __init__(self, name, password):
        self.name = name
        self.password = password
        self.created_at = datetime.now()

# schema

class UserSchema(Schema):
    name = fields.Str()
    password = fields.Str(load_only=True)         #read-only/deserialized
    created_at = fields.DateTime(dump_only=True)  #write-only/serialized

    @post_load
    def make_user(self, data, **kwargs):
        return User(**data)

user_schema = UserSchema()

user_data = {"name": "Lua",  "password": "secret"}   # created_at is dump_only

user =  user_schema.load( user_data  )

pprint(user_schema.dumps(user))                      # password is load_only
# => '{"name": "Lua", "created_at": "2023-11-11T10:56:55.898190"}'

# lib/custom_field.py

from marshmallow import Schema, fields, post_load
from datetime import date
from pprint import pprint

# model

class Cat:
    def __init__(self, name, dob, favorite_toys = []):
        self.name = name
        self.dob = dob
        self.favorite_toys = favorite_toys

# schema

class CatSchema(Schema):
    name = fields.Str(required=True, error_messages={"required": "Name is required."})
    dob = fields.Date(format="%Y-%m-%d")
    favorite_toys = fields.List(fields.Str())
    likes_toys = fields.Function(lambda obj : len(obj.favorite_toys) > 0, dump_only = True)
    age = fields.Method("calculate_age", dump_only = True)

    def calculate_age(self, obj):
        today = date.today()
        return today.year - obj.dob.year - ((today.month, today.day) < (obj.dob.month, obj.dob.day))

    @post_load
    def make_cat(self, data, **kwargs):
        return Cat(**data)

schema = CatSchema()

#deserialize
cat_1 = schema.load({"name": "Meowie", "dob": "2020-11-28", "favorite_toys": ["ball", "squeaky mouse"]})
cat_2 = schema.load({"name": "Whiskers", "dob": "2015-4-15", "favorite_toys": []})

#serialize
pprint(schema.dump(cat_1))
# => {'age': 2,
# =>  'dob': '2020-11-28',
# =>  'favorite_toys': ['ball', 'squeaky mouse'],
# =>  'likes_toys': True,
# =>  'name': 'Meowie'}

pprint(schema.dump(cat_2))
# => {'age': 8,
# => 'dob': '2015-04-15',
# => 'favorite_toys': [],
# => 'likes_toys': False,
# => 'name': 'Whiskers'}

Step 7: Commit and Push Git History

Commit and push your final code to GitHub.

git add .
git commit -m "final solution"
git push

If you used a feature branch, remember to open a PR and merge to main.

Task 4: Document and Maintain

Best Practice documentation steps:

• Add comments to the code to explain purpose and logic, clarifying intent and functionality of your code to other developers.
• Update README text to reflect the functionality of the application following https://makeareadme.com.
  • Add screenshot of completed work included in Markdown in README.
• Delete any stale branches on GitHub
• Remove unnecessary/commented out code
• If needed, update git ignore to remove sensitive data

Considerations

When building deserialization logic for real applications, consider:

Validation First

Always validate external data before trusting or using it.

Security

Restrict deserialization to only the fields your application expects - no extra fields!

Missing or Incorrect Data

Be prepared to handle missing required fields or invalid formats gracefully (using Marshmallow’s built-in validation).

Custom Transformations

Use @post_load, fields.Function, and fields.Method to enrich or modify the incoming data before using it.

API Contracts

Frontend teams will expect consistent field names and types; changes to the schema must be communicated carefully.

Extensibility

As your app grows, you can modularize and nest schemas (e.g., an OrderSchema that includes nested UserSchema and ProductSchema).