pandasnoir is a TUI based game built with Textual and inspired by sqlnoir. The cases and the data are the same, but you can now test your pandas' skills on them.
The pixel "art" (I'm no professional, I know) was drawn by me with Aseprite. For it to render, your monitor should be bigger than a laptop's one or your terminal should be more zoomed out than mine.
If you find any value in this project please leave a star and consider to offer me a coffee (Paypal or Github sponsor).
[!IMPORTANT] For the Textual experience to fully express itself you should run the program on a modern terminal emulator (tested on Kitty only, but any other popular option should work fine).
[!IMPORTANT] Assets and output enrichment are mainly suited to Textual's default dark theme. Other themes – especially light ones – may not allow a good UI experience.
Once you launch the game the directory ~/.pandasnoir/ is created to store progress and make your work persistent. If you want to reset everything, just delete the directory.
Installation
The software can be installed through PyPi:
# using pip/pipx
pip install pandasnoir
pipx install pandasnoir # pipx upgrade to update it
# or using uv
uv tool install pandasnoir
You can also install the software from source:
git clone https://github.com/ndrscalia/pandasnoir
cd pandasnoir
pip install -e .
Then you can simply run pandasnoir.
The software can also be used without installing it through uv:
uvx --from pandasnoir pandasnoir
Contributing
Any contribution is welcome. Open an issue for bugs / qol proposals or use pull requests if you wrote a new case that you would like to be added.
Testing
To test the repo install the test dependencies
cd pandasnoir
pip install -e ".[test]" # or pip install -e ".[dev]"
pytest -v
The test suite was built with claude-code and then reviewed by me.
Metadata
Release files for pandasnoir 1.0.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| pandasnoir-1.0.0.tar.gz | 126.7 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| pandasnoir-1.0.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 258.5 kB
Release files / pandasnoir-1.0.0.tar.gz
| Download URL | pandasnoir-1.0.0.tar.gz |
|---|---|
| Size | 126.7 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
fbde29189f86dc84990639c94324b2ed66867b775ee9dc01ed8d33a93aa94015
|
|
BLAKE2b-256 checksum How to use checksums |
fbad5f56bce7a0961162d69b91941b7886da0600f88ad6d9fc5248e991a4eeca
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/6.1.0 CPython/3.13.7
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Apr 7, 2026.
Transparency logRelease files / pandasnoir-1.0.0-py3-none-any.whl
| Download URL | pandasnoir-1.0.0-py3-none-any.whl |
|---|---|
| Size | 131.8 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
861833fa8b811250fab78e502fa39249081a897efcfbb9eea7e5479722403755
|
|
BLAKE2b-256 checksum How to use checksums |
d9f847d7ca678505deb4029939b45bc64e87631742a9a299620075967801bb11
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/6.1.0 CPython/3.13.7
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Apr 7, 2026.
Transparency log