dataframely 2.0.0

A declarative, polars-native data frame validation library

dataframely — A declarative, 🐻‍❄️-native data frame validation library

🗂 Table of Contents

• Introduction
• Installation
• Usage

📖 Introduction

Dataframely is a Python package to validate the schema and content of polars data frames. Its purpose is to make data pipelines more robust by ensuring that data meets expectations and more readable by adding schema information to data frame type hints.

💿 Installation

You can install dataframely using your favorite package manager, e.g., pixi or pip:

pixi add dataframely
pip install dataframely

🎯 Usage

Defining a data frame schema

import dataframely as dy
import polars as pl

class HouseSchema(dy.Schema):
    zip_code = dy.String(nullable=False, min_length=3)
    num_bedrooms = dy.UInt8(nullable=False)
    num_bathrooms = dy.UInt8(nullable=False)
    price = dy.Float64(nullable=False)

    @dy.rule()
    def reasonable_bathroom_to_bedroom_ratio(cls) -> pl.Expr:
        ratio = pl.col("num_bathrooms") / pl.col("num_bedrooms")
        return (ratio >= 1 / 3) & (ratio <= 3)

    @dy.rule(group_by=["zip_code"])
    def minimum_zip_code_count(cls) -> pl.Expr:
        return pl.len() >= 2

Validating data against schema

import polars as pl

df = pl.DataFrame({
    "zip_code": ["01234", "01234", "1", "213", "123", "213"],
    "num_bedrooms": [2, 2, 1, None, None, 2],
    "num_bathrooms": [1, 2, 1, 1, 0, 8],
    "price": [100_000, 110_000, 50_000, 80_000, 60_000, 160_000]
})

# Validate the data and cast columns to expected types
validated_df: dy.DataFrame[HouseSchema] = HouseSchema.validate(df, cast=True)

See more advanced usage examples in the documentation.