# `easydatamodel` - The easiest way to create type-safe dataclasses in Python
Just annotate your fields and you're good to go.
```python
from easydatamodel import Model
class Person(Model):
name: str
age: int
```
Now you have a completely type-safe model that will validate your data for you, every time.
```python
Person(name="John Doe", age=1) # ✅ OK
Person(name="John Doe", age="timeless") # ❌ InvalidModelError
# easydatamodel also validates new value assignments
person = Person(name="John Doe", age=1)
person.age = "is but a number" # ❌ raises a TypeError
```
## Install
```
pip install easydatamodel
```
## Requirements
* Python 3.11+
## `easydatamodel` vs. `pydantic` and `dataclasses`
| Feature | `easydatamodel` | `pydantic` | `dataclasses` |
| -------------------------------------- | --------------- | -------------- | ------------- |
| **Validates data on instantiation** | ✅ | ✅ | ❌ |
| **Validates data on assignment** | ✅ | Off by default | ❌ |
| **`ClassVar` validation** | ✅ | ❌ | ❌ |
| **Automagic type coercion by default** | ❌ | ✅ | ❌ |
### Should you use `easydatamodel`?
`easydatamodel` is perfect for simple, type-safe dataclasses with minimal effort and low overhead.
However, you should consider using [`pydantic`](https://docs.pydantic.dev/) if you need more advanced features.
### `easydatamodel` as a meta-programming resource
Given the size of the `easydatamodel` codebase, **`easydatamodel` is a fantastic resource for intermediate and advanced Python developers looking to learn how Python metaprogramming works.**
This codebase demonstrates how only a few files of Python code can create a powerful library with an ergonomic syntax.
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