Daffy - DataFrame Column Validator

Description
Working with DataFrames often means passing them through multiple transformation functions, making it easy to lose track of their structure over time. Daffy adds runtime validation and documentation to your DataFrame operations through simple decorators. By declaring the expected columns and types in your function definitions, you can:
@df_in(columns=["price", "bedrooms", "location"])
@df_out(columns=["price_per_room", "price_category"])
def analyze_housing(houses_df):
return analyzed_df
Like type hints for DataFrames, Daffy helps you catch structural mismatches early and keeps your data pipeline documentation synchronized with the code. Compatible with both Pandas and Polars.
Key Features
- Validate DataFrame columns at function entry and exit points
- Support regex patterns for matching column names (e.g.,
"r/column_\d+/"
)
- Check data types of columns
- Control strictness of validation (allow or disallow extra columns)
- Works with both Pandas and Polars DataFrames
- Project-wide configuration via pyproject.toml
- Integrated logging for DataFrame structure inspection
- Enhanced type annotations for improved IDE and type checker support
Documentation
Installation
Install with your favorite Python dependency manager:
pip install daffy
Quick Start
from daffy import df_in, df_out
@df_in(columns=["Brand", "Price"])
@df_out(columns=["Brand", "Price", "Discount"])
def apply_discount(cars_df):
cars_df = cars_df.copy()
cars_df["Discount"] = cars_df["Price"] * 0.1
return cars_df
License
MIT