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⭐️ Highlight

Pandera now supports Ibis 🦩! You can now validate data on all available ibis backends using the pandera.ibis module.

In-memory table example:

:::python
import ibis
import pandera.ibis as pa

class Schema(pa.DataFrameModel):
    state: str
    city: str
    price: int = pa.Field(in_range={"min_value": 5, "max_value": 20})

t = ibis.memtable(
    {
        'state': ['FL','FL','FL','CA','CA','CA'],
        'city': [
            'Orlando',
            'Miami',
            'Tampa',
            'San Francisco',
            'Los Angeles',
            'San Diego',
        ],
        'price': [8, 12, 10, 16, 20, 18],
    }
)
Schema.validate(t).execute()

Sqlite example:

:::python
con = ibis.sqlite.connect()
t = con.create_table(
    "table",
    schema=ibis.schema(dict(state="string", city="string", price="int64"))
)

con.insert(
    "table",
    obj=[
        ("FL", "Orlando", 8),
        ("FL", "Miami", 12),
        ("FL", "Tampa", 10),
        ("CA", "San Francisco", 16),
        ("CA", "Los Angeles", 20),
        ("CA", "San Diego", 18),
    ]
)

Schema.validate(t).execute()

What does this mean?

This release unlocks in database validation in some of the most widely used data platforms, including PostGres, Snowflake, BigQuery, MySQL, and more ✨. It means that you can validate data at scale, on your database/data framework of your choice, before fetching it for downstream analysis/modeling work.

Naturally, this also means that you can develop your schemas locally on a duckdb or sqlite backend and then use the same schemas in production on a remote database like postgres.

Learn more about the integration here.

What's Changed

New Contributors

Full Changelog: https://github.com/unionai-oss/pandera/compare/v0.24.0...v0.25.0

Source: README.md, updated 2025-07-08