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Advanced Dataclass Features – Fields, Metadata, Post-Init, and Validation

In Lesson 3, you learned how @dataclass eliminates boilerplate by auto-generating __init__(), __repr__(), and __eq__(). But real-world data models need more control: mutable defaults without gotchas, validation on creation, computed fields, metadata for serialization. That's where advanced dataclass features come in.

In this lesson, you'll master the tools that let dataclasses handle production complexity while staying clean and readable. We'll explore field() for customization, __post_init__() for validation, InitVar for temporary data, and practical JSON serialization. By the end, you'll build dataclasses that enforce their own correctness and integrate seamlessly with APIs.

The Challenge: Default Values and Mutable Objects​

Before diving into solutions, let's see why basic dataclass defaults can be dangerous. In Python, if you write this:

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This mutable default attempt fails immediately in Python 3.10+. If it were allowed, all instances would share the same list object:

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This mutable default gotcha is the most common dataclass mistake. The solution: default_factory.

💬 AI Colearning Prompt​

"Explain why mutable default arguments in Python are dangerous. Why does default=[] cause shared state between instances?"

The Solution: field() and default_factory​

The field() function gives you fine control over each dataclass field. Here's the production-ready pattern:

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Now each instance gets its own mutable containers:

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Why this matters: In production APIs and databases, shared mutable state causes subtle bugs that only appear when you create multiple instances. Using default_factory is non-negotiable for production dataclasses.

🎓 Expert Insight​

In AI-native development, you don't memorize Python's mutable default gotcha—you recognize "mutable type as default?" and immediately reach for default_factory. The pattern becomes automatic.

Code Example 1: Using default_factory for Mutable Defaults​

Let's see field() in action. This is the foundation for all advanced dataclass features.

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Validation Step: Run this with type checking:

python script.py
mypy --strict script.py  # Should pass type check

Specification Reference: This example demonstrates Spec Example 1 from the plan: "Using default_factory for mutable defaults (list, dict)"

AI Prompts Used:

  • "Create a dataclass for a Product with name, price, and mutable fields (tags, metadata). Use field() with default_factory for mutable types."
  • Validate: Run the code, confirm each instance has its own list/dict

Field Customization: More Control Over Individual Fields​

Beyond default_factory, field() offers other parameters for controlling how fields behave:

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Why each parameter matters:

  • init=False: Field won't appear in __init__() signature (good for computed fields)
  • repr=False: Field excluded from string representation (good for secrets)
  • compare=False: Field excluded from equality comparisons
  • metadata: Arbitrary data attached to field (for validators, serialization hints, documentation)
  • doc: NEW in Python 3.14 – Field documentation string (accessible via introspection)

🤝 Practice Exercise​

Ask your AI: "Create a User dataclass with name, email, created_at. Add metadata to the email field for validation. Then explain what metadata is and how you'd use it for validation."

Expected Outcome: You'll understand that metadata is arbitrary data you attach to fields for use in custom validation functions.

Code Example 2: Field with Metadata and init/repr Control​

Here's a realistic example showing how these parameters work together:

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Specification Reference: Spec Example 2: "Field with metadata (for serialization, validation)"

Validation: Run code, verify field behavior (email not in repr, created_at not compared, etc.)

Validation After Creation: post_init()​

The __post_init__() method runs immediately after __init__() completes. It's perfect for validation and computed fields that depend on other fields.

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Why post_init() is essential:

  1. Validation happens at creation time (fail fast)
  2. Invalid states are impossible to create
  3. Cleaner than manual validation after instantiation
  4. Computed fields can depend on constructor parameters

💬 AI Colearning Prompt​

"What happens if I try to create an Order with amount=0? How would I handle that differently than amount=-50?"

Code Example 3: post_init() for Validation and Computed Fields​

Here's a practical example combining validation with computed attributes:

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Specification Reference: Spec Example 3: "post_init() for validation and computed fields"

Validation: Run code, verify validation works, check computed fields are set correctly

InitVar: Temporary Data for Initialization​

Sometimes you need to pass data to __post_init__() for processing, but don't want to store it as an instance field. That's where InitVar comes in:

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Key insight: InitVar fields appear in __init__() signature but NOT as instance fields. They're for data needed during initialization but not afterwards.

Code Example 4: InitVar for Post-Init Processing Without Storage​

Here's a more complex example showing InitVar's power:

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Specification Reference: Spec Example 4: "InitVar for post-init processing without storage"

Validation: Run code, verify discount_percent is not a stored field, verify final_price is computed correctly

Serialization: Converting Dataclasses to JSON and Dicts​

Real-world applications need to convert dataclasses to JSON (for APIs) and back. Python 3.10+ has asdict() and astuple() built in:

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Specification Reference: Spec Example 5: "Dataclass with JSON serialization (to_dict/from_dict)"

Code Example 6: Real-World API Model with All Advanced Features​

Here's a production-ready example combining everything: validation, computed fields, field customization, and serialization:

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Specification Reference: Spec Example 6: "Real-world API model (combining all features)"

Validation Steps:

  1. Run the code successfully
  2. Check JSON serialization handles nested datetime
  3. Verify password_hash is not shown in repr
  4. Confirm validation catches invalid email and short password
  5. Verify asdict() includes all fields except InitVar

Common Mistakes to Avoid​

You now understand the tools. Here are the pitfalls to watch for:

Mistake 1: Forgetting default_factory for Mutable Defaults​

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Mistake 2: Complex Logic in post_init()​

__post_init__() should validate and compute simple fields. Complex logic belongs in methods:

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Mistake 3: Not Validating Field Metadata​

Metadata is inert—it doesn't auto-validate. You must write validation logic:

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Mistake 4: Comparing Instances When You Shouldn't​

By default, __eq__() compares all fields. Use compare=False for fields that shouldn't affect equality:

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Part 1: Discover Validation by Building Broken Code First​

Your Role: Active experimenter discovering why validation matters

Before learning __post_init__(), experience what happens without validation.

Discovery Exercise: Invalid States Without Validation​

Step 1: Create invalid instances easily

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Problem you'll notice: Dataclasses accept any data without validation. Invalid states silently propagate through your code.

Step 2: What we want instead

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Deliverable: Document problems with unvalidated data:

  • Invalid states accepted silently
  • Bugs appear far from the source
  • Hard to debug

Part 2: AI Teaches __post_init__() Validation​

Your Role: Student learning from AI Teacher

Now ask AI to teach you how __post_init__() enables validation.

AI Teaching Prompt​

Ask your AI companion:

"I want to add validation to a dataclass so invalid instances can't be created. Explain:

  1. What is __post_init__() and when does it run?
  2. Show me how to validate fields in __post_init__() (raise ValueError if invalid)
  3. What's the difference between default and default_factory?
  4. What is InitVar and when would you use it?
  5. Show me a complete Product dataclass with validation, defaults, and InitVar"

What You'll Learn from AI​

Expected AI Response (summary):

  • __post_init__(): Runs after __init__(), perfect for validation
  • Validation pattern: Raise ValueError/TypeError with clear messages
  • default: For immutable types (int, str, tuple)
  • default_factory: For mutable types (list, dict)
  • InitVar: Temporary fields passed to __post_init__() but not stored

Convergence Activity​

After AI explains, test your understanding:

Ask AI: "Create a Product dataclass with:

  1. name (required, non-empty string)
  2. price (required, positive float)
  3. discount_percent (InitVar, optional, 0-100)
  4. final_price (computed field, set in post_init)
  5. tags (optional, default empty list)

Show post_init() that validates all inputs and computes final_price."

Deliverable: Write a 3-paragraph explanation:

  1. How __post_init__() enables fail-fast validation
  2. The difference between default and default_factory
  3. When and why you'd use InitVar

Part 3: Student Challenges AI with Edge Cases​

Your Role: Student teaching AI about validation subtleties

Test AI's understanding of dataclass validation patterns.

Challenge 1: Mutable Defaults in __post_init__()​

Your prompt to AI:

"Here's code with a bug:

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If I do this:

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Predict: Will c2.items be shared across instances? Why or why not?"

Expected learning: default_factory=list creates a NEW list each time, so no sharing. AI should explain why this is essential.

Challenge 2: Validation After Nested Object Creation​

Your prompt to AI:

"I have nested dataclasses:

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If I create Person('Alice', Address('INVALID')), which error appears first and why?"

Expected learning: Address validation runs first (in Address's __post_init__()), so that error appears before Person's validation.

Challenge 3: Computing Derived Fields​

Your prompt to AI:

"Show me how to use InitVar to pass a discount_percent, then compute final_price in post_init():

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Explain: What happens to discount_percent? Why is final_price set to field(init=False)?"

Deliverable: Document three edge cases and verify AI's predictions through testing.

Part 4: Build Advanced Dataclass Patterns Reference​

Your Role: Knowledge synthesizer creating production patterns

Your Advanced Dataclass Patterns Reference​

Create a file called advanced_dataclass_patterns.md:

# Advanced Dataclass Patterns and Validation
*Chapter 31, Lesson 4*

## Pattern 1: Basic Validation in `__post_init__()`

```python
from dataclasses import dataclass

@dataclass
class User:
    name: str
    age: int

    def __post_init__(self):
        if len(self.name) < 2:
            raise ValueError("Name must be at least 2 characters")
        if self.age < 0 or self.age > 150:
            raise ValueError("Age must be between 0 and 150")

# Valid
u = User("Alice", 30)

# Invalid - raises ValueError immediately
try:
    u = User("A", 25)  # Error: Name must be at least 2 characters
except ValueError as e:
    print(f"Validation failed: {e}")

Pattern 2: Using field() with Defaults​

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Pattern 3: Using InitVar for Temporary Initialization Data​

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Pattern 4: Field Metadata for Documentation​

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Pattern 5: Serialization Methods​

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Pattern 6: Nested Dataclasses​

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Validation Best Practices​

  1. Fail fast: Validate in __post_init__(), not later
  2. Clear messages: Always include what was wrong in ValueError
  3. Type hints first: Use @dataclass with full type hints
  4. Immutable when possible: Use frozen=True for config objects
  5. Test invalid creation: Always test that invalid inputs raise errors

Common Gotchas​

Gotcha 1: InitVar not accessible outside __post_init__()​

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Gotcha 2: Validation doesn't prevent mutation​

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Gotcha 3: Field metadata is not enforced​

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**Guide Requirements**:
1. **Six practical patterns** — Basic validation through nested dataclasses
2. **Validation best practices** — 5+ guidelines
3. **Common gotchas** — 3-4 with fixes

**Deliverable**: Complete `advanced_dataclass_patterns.md` as your production reference.

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## Summary: Bidirectional Learning Pattern

**Part 1 (Student explores)**: You experienced problems with unvalidated dataclasses
**Part 2 (AI teaches)**: AI explained `__post_init__()`, `InitVar`, and field()
**Part 3 (Student teaches)**: You challenged AI with mutable defaults, nested validation, and InitVar semantics
**Part 4 (Knowledge synthesis)**: You built production-ready validation patterns

### What You've Built

1. Documentation of validation problems
2. Understanding of `__post_init__()`, `InitVar`, and `field()` (in your own words)
3. Edge case testing with AI
4. `advanced_dataclass_patterns.md` — Production patterns

### Next Steps

You've now mastered dataclasses. Future chapters will show you Pydantic (which automates even more validation), and you'll understand why Pydantic is sometimes worth adding as a dependency.