Polars on steroids!

This package provides a generic extension to Polars DataFrame, allowing data validation and typing goodies.

Features

• Generic DataFrame: Ensures type safety using Python's TypedDict.
• Data Validation: Checks that the DataFrame conforms to the expected schema.
• Custom Checks: Leverage the power of polars expression to add custom checks.
• Lightweight: No dependencies (except polars)!

Installation

pip install polaroids

Get Started

Defining a Schema

Schemas are defined using Python's TypedDict:

from typing import Annotated, TypedDict
from polaroids import DataFrame, Field
import polars as pl

class BasicSchema(TypedDict):
    a: Annotated[int, Field(
        sorted="ascending",
        coerce=True,
        unique=True,
        checks=[lambda d: d.ge(0)],
    )]
    b: int | None

df = pl.DataFrame({"a": [0.0, 1.0], "b": [None, 0]})

DataFrame[BasicSchema](df).validate()
# shape: (2, 2)
# ┌─────┬──────┐
# │ a   ┆ b    │
# │ --- ┆ ---  │
# │ i64 ┆ i64  │
# ╞═════╪══════╡
# │ 0   ┆ null │
# │ 1   ┆ 0    │
# └─────┴──────┘

Get typing goodies!

Get your TypedDict back when you leave polars ✅: