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
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
- Getting Started — quick introduction
- Usage Guide — comprehensive reference
- API Reference — decorator signatures
- Changelog — version history
Contributing
Issues and pull requests welcome on GitHub.
License
MIT
1 maintainer
Metadata
1 maintainer
Release files for daffy 3.1.0
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| daffy-3.1.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 100.8 kB
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