ibis-vega-transform 1.1.0

Evaluate Vega transforms using Ibis expressions

ibis-vega-transform

Python evaluation of Vega transforms using Ibis expressions.

For inspiration, see https://github.com/jakevdp/altair-transform

Getting started

pip install ibis-vega-transform
jupyter labextension  install ibis-vega-transform

Then in a notebook, import the Python package and pass in an ibis expression to a Altair chart:

import altair as alt
import ibis_vega_transform
import ibis
import pandas as pd


source = pd.DataFrame({
    'a': ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I'],
    'b': [28, 55, 43, 91, 81, 53, 19, 87, 52]
})

connection = ibis.pandas.connect({'source': source })
table = connection.table('source')

alt.Chart(table).mark_bar().encode(
    x='a',
    y='b'
)

Check out the notebooks in the [./examples/](./examples/] directory to see some options using interactive charts and the OmniSci backend.

Development

To install from source, run the following in a terminal:

git clone [e-mail]:Quansight/ibis-vega-transform.git

cd ibis-vega-transform
conda env create -f binder/environment.yml
conda activate ibis-vega-transform

pip install -e .[dev]
jlpm
jupyter labextension install . --no-build


jupyter lab --watch
jlpm run build:watch

To format all the files:

black ibis_vega_transform
jlpm run prettier

Releasing

First create a test environment:

conda create -n tmp -c conda-forge nodejs
conda activate tmp

Then bump the Python version in setup.py and upload a test version:

pip install --upgrade setuptools wheel twine
rm -rf dist/
python setup.py sdist bdist_wheel
twine upload --repository-url https://test.pypi.org/legacy/ dist/*

Install the test version in your new environment:

pip install --index-url https://test.pypi.org/simple/ --extra-index-url https://pypi.org/simple ibis-vega-transform

Now bump the version for the Javascript package in package.json. The run a build, create a tarball, and install it as a JupyterLab extension:

yarn run build
yarn pack --filename out.tgz
jupyter labextension install out.tgz

Now open JupyterLab and run through all the notebooks in examples to make sure they still render correctly.

Now you can publish the Python package:

twine upload dist/*

And publish the node package:

npm publish out.tgz

And add a git tag for the release and push:

git tag <new version>
git push
git push --tags