Daffy¶

Validate pandas and Polars DataFrames with Python decorators.

Daffy catches missing columns, wrong data types, and invalid values at runtime — before they cause errors downstream in your data pipeline. Just add decorators to your functions.

Also supports Modin and PyArrow DataFrames.

Quick Example¶

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.

Installation¶

pip install daffy

conda install -c conda-forge daffy

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

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.

Next Steps¶