Daffy — Validate pandas & Polars DataFrames with Python Decorators

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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

Installation

pip install daffy

or with conda:

conda install -c conda-forge daffy

Works with whatever DataFrame library you already have installed. Python 3.10–3.14.

Quickstart

from daffy import df_in, df_out

@df_in(["price", "bedrooms", "location"])
@df_out(["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.

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

Examples

Column validation

from daffy import df_in, df_out

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

Value constraints

Vectorized checks with zero row iteration overhead:

@df_in({
    "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 Also supported: notin, str_startswith, str_endswith, str_contains, str_length

Nullability and uniqueness

@df_in({
    "user_id": {"unique": True, "nullable": False},  # user_id must be unique and not null
    "email": {"nullable": False},  # email cannot be null
    "age": {"dtype": "int64"},
})
def clean_users(df):
    return df

Row validation with Pydantic

For complex, cross-field validation:

pip install 'daffy[pydantic]'

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

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 ✅

Configuration

Configure Daffy project-wide via pyproject.toml:

[tool.daffy]
strict = true

Documentation

Full documentation available at daffy.readthedocs.io

Contributing

Issues and pull requests welcome on GitHub.

License

MIT

Project links

Data verified by PyPI on Aug 7, 2026
Data provided by the project maintainers, verified at the time the release was uploaded to PyPI.

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  • Released:
Newer release available (3.1.0)

1 maintainer

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Avatar for vertti from gravatar.com vertti

Credits

Author: Janne Sinivirta

GitHub Statistics

Data verified by PyPI on Aug 7, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License expression

MIT
View SPDX License List

Requires

Python >=3.10

Provides Extra

pydantic

Tags

column-validation data-pipeline data-quality data-validation dataframe dataframe-schema dataframe-validator decorator modin narwhals pandas pandas-validation polars polars-validation pyarrow pydantic row-validation runtime-validation schema typing validate-dataframe validation

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