| Name | pytest-ditto JSON |
| Version |
0.1.2
JSON |
| download |
| home_page | None |
| Summary | Snapshot testing pytest plugin with minimal ceremony and flexible persistence formats. |
| upload_time | 2024-06-09 06:46:45 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.10 |
| license | None |
| keywords |
pytest
testing
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# pytest-ditto
[](https://badge.fury.io/py/pytest-ditto)
[](https://github.com/owlowlyowl/pytest-ditto/actions/workflows/ci.yml)
Snapshot testing pytest plugin with minimal ceremony and flexible persistence formats.
## Introduction
The `pytest-ditto` plugin is intended to be used snapshot/regression testing. There are
two key components: the `snapshot` fixture and the snapshot persistence formats.
### 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 the `fn` with the
value of `x` does not change.
```python
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 will load the file and use that value in the test to test the output of the
computed value.
By default, the snapshot data is converted and persisted using `pickle`; however, there
are a range of persistence formats that can be used.
### @ditto Marks
If the default persistence format, `pickle`, isn't appropriate different formats can be
specified per test by using `ditto` marks - customised `pytest` mark decorators.
The default persistence types are: `pickle`, `yaml` and `json`; however additional
plugins can be installed as per below:
- pandas via `pytest-ditto-pandas`
## Usage
### `pd.DataFrame`
```python
import pandas as pd
import ditto
def awesome_fn_to_test(df: pd.DataFrame):
df.loc[:, "a"] *= 2
return df
# The following test uses pandas.DataFrame.to_parquet to write the data snapshot to the
# `.ditto` directory with filename:
# `test_fn_with_parquet_dataframe_snapshot@ab_dataframe.pandas.parquet`.
@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"))
# The following test uses pandas.DataFrame.to_json(orient="table") to write the data
# snapshot to the `.ditto` directory with filename:
# `test_fn_with_json_dataframe_snapshot@ab_dataframe.pandas.json`.
@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"))
```
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