pytest-ditto-polars

Extension plugin for pytest-ditto for polars DataFrame snapshots.

Use the following marks for their associated recorder:

• @ditto.polars.parquet
• @ditto.polars.ipc
• @ditto.polars.csv
• @ditto.polars.ndjson

Each mark is shorthand for @ditto.record("polars.<format>").

Installation

pip install pytest-ditto[polars]

Usage

import polars as pl
import polars.testing

import ditto


def awesome_fn_to_test(df: pl.DataFrame) -> pl.DataFrame:
    return df.with_columns(pl.col("a") * 2)


# The following test uses polars.DataFrame.write_parquet to write the data snapshot
# to the `.ditto` directory with filename:
# `<module>.test_fn_with_parquet_dataframe_snapshot@ab_dataframe.polars.parquet`.


@ditto.polars.parquet
def test_fn_with_parquet_dataframe_snapshot(snapshot):
    input_data = pl.DataFrame({"a": [1, 2, 3], "b": [4, 5, 9]})
    result = awesome_fn_to_test(input_data)
    pl.testing.assert_frame_equal(result, snapshot(result, key="ab_dataframe"))

Format notes

Prefer @ditto.polars.parquet. It round-trips a DataFrame's values and dtypes. The other formats are available when you need them, with these limits:

IPC uses Polars' Arrow IPC writer. Polars documents write_ipc as unstable, so a snapshot recorded on one Polars version may fail to load or compare on another even if the DataFrame under test did not change.

CSV and NDJSON keep no type metadata, so dtypes are inferred again on load. Frames of ints, floats, and strings round-trip in the plugin tests. Do not use these formats for categoricals, enums, nested, or temporal columns.