pytest-ditto 1.0.0

Snapshot testing pytest plugin with minimal ceremony and flexible persistence formats.

pytest-ditto

Snapshot testing pytest plugin with minimal ceremony and flexible recorders.

Introduction

The pytest-ditto plugin is intended to be used for snapshot/regression testing. There are two key components: the snapshot fixture and snapshot recorders.

The snapshot Fixture

In the following basic example, the function to test is fn, the test is using the snapshot fixture and it is asserting that the result of calling fn with the value of x does not change.

import ditto


def fn(x: int) -> int:
    return x + 1  # original implementation
    # return x + 2  # new implementation


def test_fn(snapshot) -> None:
    x = 1
    result = fn(x)
    assert result == snapshot(result, key="fn")

The first time the test is run, the snapshot fixture takes the data passed to it and persists it to a .ditto directory in the same location as the test module. Subsequent test runs load the stored file and use that value for comparison.

By default, snapshot data is persisted using pickle; however, a range of recorders can be selected per test using ditto marks.

@ditto Marks

If the default recorder (pickle) isn't appropriate, a different recorder can be specified per test using ditto marks — customised pytest mark decorators.

The built-in recorders are:

Mark	Recorder	File extension
@ditto.pickle	pickle	.pkl
@ditto.yaml	yaml	.yaml
@ditto.json	json	.json
@ditto.record("name")	any registered recorder	varies

@ditto.pickle, @ditto.yaml, and @ditto.json are convenience shorthands for @ditto.record("pickle"), @ditto.record("yaml"), and @ditto.record("json") respectively.

Additional recorders can be installed via plugins:

Recorders	Plugin	Marks
pandas	pytest-ditto-pandas	@ditto.pandas.parquet @ditto.pandas.json @ditto.pandas.csv
pyarrow	pytest-ditto-pyarrow	@ditto.pyarrow.parquet @ditto.pyarrow.feather @ditto.pyarrow.csv

Usage

pd.DataFrame

Install pytest-ditto[pandas] to make pandas recorders available.

import pandas as pd

import ditto


def awesome_fn_to_test(df: pd.DataFrame):
    df.loc[:, "a"] *= 2
    return df


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


@ditto.pandas.json
def test_fn_with_json_dataframe_snapshot(snapshot):
    input_data = pd.DataFrame({"a": [1, 2, 3], "b": [4, 5, 9]})
    result = awesome_fn_to_test(input_data)
    pd.testing.assert_frame_equal(result, snapshot(result, key="ab_dataframe"))

For the above example the snapshot files would be found in the following locations:

• .ditto/test_fn_with_parquet_dataframe_snapshot@ab_dataframe.pandas.parquet
• .ditto/test_fn_with_json_dataframe_snapshot@ab_dataframe.pandas.json

pyarrow.Table

Install pytest-ditto[pyarrow] to make pyarrow recorders available.

import pyarrow as pa
import pyarrow.compute as pc
import ditto
import pytest


@pytest.fixture
def table() -> pa.Table:
    return pa.table(
        [
            [1, 2, 3, 4],
            [4.5, 5.2, 6.8, 3.5],
            [7, 8.5, None, None],
            [True, False, True, True],
            ["a", "b", "c", "x"],
        ],
        names=list("abcde"),
    )


def fn(x: pa.Table):
    even_filter = (pc.bit_wise_and(pc.field("a"), pc.scalar(1)) == pc.scalar(0))
    return x.filter(even_filter)


@ditto.pyarrow.parquet
def test_fn_with_pyarrow_parquet_snapshot(snapshot, table):
    result = fn(table)
    assert result.equals(snapshot(result, key="filtered"))

For the above example the snapshot files would be found in the following location:

• .ditto/[e-mail]

unittest.TestCase

DittoTestCase provides the snapshot fixture as a cached_property for use with unittest.TestCase:

import unittest
from ditto import DittoTestCase


def fn(x: int) -> int:
    return x + 1


class TestFn(DittoTestCase):
    def test_fn(self):
        result = fn(1)
        assert result == self.snapshot(result, key="fn")

Snapshot files are placed in a .ditto directory adjacent to the test file, using the fully-qualified test method name as the group name.

Custom Recorders

A Recorder is a frozen dataclass pairing a file extension with save and load functions. Plugin packages register Recorder instances via the ditto_recorders entry point group.

from pathlib import Path
from ditto.recorders import Recorder


def _save(data: MyType, filepath: Path) -> None:
    ...  # write data to filepath


def _load(filepath: Path) -> MyType:
    ...  # read and return data from filepath


my_recorder: Recorder[MyType] = Recorder(
    extension="myformat",
    save=_save,
    load=_load,
)

Register it in pyproject.toml:

[project.entry-points.ditto_recorders]
my_recorder = "my_package.recorders:my_recorder"

Once registered, the recorder is available by name via @ditto.record("my_recorder"). Plugin marks (e.g. @ditto.myplugin.myformat) can also be registered via the ditto_marks entry point group.