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Getting Started

Requirements

  • Python 3.10 or later
  • Pydantic v2

Installation

pip install md2pydantic

uv add md2pydantic

Optional Extras

pip install md2pydantic[yaml]    # YAML support (pyyaml)
pip install md2pydantic[pandas]  # DataFrame conversion

Complete Example

Define a Pydantic model whose field names match the table headers exactly (case-sensitive):

from pydantic import BaseModel
from md2pydantic import MDConverter

class Product(BaseModel):
    name: str
    price: float
    in_stock: bool

markdown = """
Here are the products currently available:

| name       | price | in_stock |
|------------|-------|----------|
| Widget     | 9.99  | Yes      |
| Gadget     | 24.50 | No       |
| Doohickey  | 3.75  | Yes      |
"""

products = MDConverter(Product).parse_tables(markdown)

Understanding the Output

parse_tables returns a list of validated model instances, one per table row:

for p in products:
    print(f"{p.name}: ${p.price} (in stock: {p.in_stock})")

# Widget: $9.99 (in stock: True)
# Gadget: $24.5 (in stock: False)
# Doohickey: $3.75 (in stock: True)

Type coercion happens automatically:

  • "9.99" (string in Markdown) becomes 9.99 (float)
  • "Yes" / "No" become True / False (bool)

Pydantic handles numeric coercion. md2pydantic handles boolean word mapping before passing data to Pydantic.

Next Steps