https://github.com/lewagon/nbresult
Testing Library for Jupyter Notebooks
https://github.com/lewagon/nbresult
notebook-jupyter python testing
Last synced: 9 months ago
JSON representation
Testing Library for Jupyter Notebooks
- Host: GitHub
- URL: https://github.com/lewagon/nbresult
- Owner: lewagon
- License: mit
- Created: 2020-11-17T09:57:30.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2025-01-15T14:40:32.000Z (over 1 year ago)
- Last Synced: 2025-07-10T22:21:09.235Z (10 months ago)
- Topics: notebook-jupyter, python, testing
- Language: Python
- Homepage: https://pypi.org/project/nbresult/
- Size: 1.94 MB
- Stars: 8
- Watchers: 10
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE
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README
# nbresult
A simple package to test Jupyter notebook result for the Le Wagon's Data Science Bootcamp.
## 1. Installation
Installation with `pip` from [Pypi](https://pypi.org/):
```bash
pip install nbresult
```
## 2. Usage
### 2.1 Basic usage
Considering the default data challenge architecture:
```bash
.
├── challenge.ipynb
├── Makefile
├── README.md
├── data
│ └── data.csv
└── tests
└── __init__.py
```
If you want to test a variable `log_model_score` from the `challenge.ipynb` notebook with `pytest`:
Anywhere in the notebook you can add a cell with the following code:
```python
from nbresult import ChallengeResult
result = ChallengeResult('score',
score=log_model_score
)
result.write()
```
This outputs a `score.pickle` file in the `tests` directory:
```bash
.
├── challenge.ipynb
├── Makefile
├── README.md
├── data
│ └── data.csv
└── tests
├── __init__.py
└── score.pickle
```
Now you would like to write test on the `log_model_score` with `pytest`. Create a `test_score.py` file:
```python
# tests/test_score.py
from nbresult import ChallengeResultTestCase
class TestScore(ChallengeResultTestCase):
def test_score_is_above_82(self):
self.assertEqual(self.result.score > 0.82, True)
```
Finally you can run your tests with `pytest`:
```bash
pytest tests/test_score.py
```
OR
Run the tests with `make`:
- Setup a `Makefile`
```make
# Makefile
default: pytest
pytest:
PYTHONDONTWRITEBYTECODE=1 pytest -v --color=yes
```
- Run `make`
OR
Run the tests inside the notebook:
```python
from nbresult import ChallengeResult
result = ChallengeResult('score',
score=log_model_score
)
result.write()
print(result.check())
```
### 2.2 Advanced usage
For more advanced folder structure, you also can specify a `subdir` folder in which store & read pickle file
```python
from nbresult import ChallengeResult
result = ChallengeResult('score',
subdir='a', # This will store pickle in tests/a/score.pickle
score=log_model_score
)
result.write()
result.check()
```
Check out detailed example below
## Testing
Run `make`