Daffy — Validate pandas & Polars DataFrames with Python Decorators

Validate your pandas and Polars DataFrames at runtime with simple Python decorators. Daffy catches missing columns, wrong data types, and invalid values before they cause downstream errors in your data pipeline.

Also supports Modin and PyArrow DataFrames.

• ✅ Column & dtype validation — lightweight, minimal overhead
• ✅ Value constraints — nullability, uniqueness, range checks
• ✅ Row validation with Pydantic — when you need deeper checks
• ✅ Works with pandas, Polars, Modin, PyArrow — no lock-in

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Installation

pip install daffy

or with conda:

conda install -c conda-forge daffy

Works with whatever DataFrame library you already have installed. Python 3.9–3.14.

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Quickstart

from daffy import df_in, df_out

@df_in(columns=["price", "bedrooms", "location"])
@df_out(columns=["price_per_room", "price_category"])
def analyze_housing(houses_df):
    # Transform raw housing data into price analysis
    return analyzed_df

If a column is missing, has wrong dtype, or violates a constraint — Daffy fails fast with a clear error message at the function boundary.

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Why Daffy?

Most DataFrame validation tools are schema-first (define schemas separately) or pipeline-wide (run suites over datasets). Daffy is decorator-first: validate inputs and outputs where transformations happen.

Non-intrusive	Just add decorators — no refactoring, no custom DataFrame types, no schema files
Easy to adopt	Add in 30 seconds, remove just as fast if needed
In-process	No external stores, orchestrators, or infrastructure
Pay for what you use	Column validation is essentially free; opt into row validation when needed

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Examples

Column validation

from daffy import df_in, df_out

@df_in(columns=["Brand", "Price"])
@df_out(columns=["Brand", "Price", "Discount"])
def apply_discount(df):
    df = df.copy()
    df["Discount"] = df["Price"] * 0.1
    return df

Regex column matching

Match dynamic column names with regex patterns:

@df_in(columns=["id", "r/feature_\\d+/"])
def process_features(df):
    return df

Value constraints

Vectorized checks with zero row iteration overhead:

@df_in(columns={
    "price": {"checks": {"gt": 0, "lt": 10000}},
    "status": {"checks": {"isin": ["active", "pending", "closed"]}},
    "email": {"checks": {"str_regex": r"^[^@]+@[^@]+\.[^@]+$"}},
})
def process_orders(df):
    return df

Available checks: gt, ge, lt, le, between, eq, ne, isin, notnull, str_regex

Nullability and uniqueness

@df_in(
    columns=["user_id", "email", "age"],
    nullable={"email": False},  # email cannot be null
    unique=["user_id"],         # user_id must be unique
)
def clean_users(df):
    return df

Row validation with Pydantic

For complex, cross-field validation (requires pydantic>=2.4.0):

from pydantic import BaseModel, Field
from daffy import df_in

class Product(BaseModel):
    name: str
    price: float = Field(gt=0)
    stock: int = Field(ge=0)

@df_in(row_validator=Product)
def process_inventory(df):
    return df

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Daffy vs Alternatives

Use Case	Daffy	Pandera	Great Expectations
Function boundary guardrails	✅ Primary focus	⚠️ Possible	❌ Not designed for
Quick column/type checks	✅ Lightweight	⚠️ Requires schemas	⚠️ Requires setup
Complex statistical checks	⚠️ Limited	✅ Extensive	✅ Extensive
Pipeline/warehouse QA	❌ Not designed for	⚠️ Some support	✅ Primary focus
Multi-backend support	✅	⚠️ Varies	✅

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Configuration

Configure Daffy project-wide via pyproject.toml:

[tool.daffy]
strict = true

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Documentation

Full documentation available at daffy.readthedocs.io

• Getting Started — quick introduction
• Usage Guide — comprehensive reference
• API Reference — decorator signatures
• Changelog — version history

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Contributing

Issues and pull requests welcome on GitHub.

License

MIT