Streamlit Vega-Lite

🐉 Here be dragons. This is a proof of concept.

Making Vega-Lite selection created by user interactions available in Python. Works with Altair.

For examples, see https://github.com/domoritz/streamlit-vega-lite/blob/master/streamlit_vega_lite/__init__.py. You can also try the demo at https://github.com/domoritz/streamlit-vega-lite-demo.

Documentation

Installation

pip install streamlit-vega-lite

Usage

There are two functions available. vega_lite_component expects a Vega-Lite specification as a dictionary and any named datasets as keyword arguments. The datasets will be transferred as efficient Arrow tables. altair_component supports Altair charts and automatically extracts all datasets and transfers them as Arrow dataframes.

Example

import altair as alt
import streamlit as st
import pandas as pd
import numpy as np

from streamlit_vega_lite import vega_lite_component, altair_component

hist_data = pd.DataFrame(np.random.normal(42, 10, (200, 1)), columns=["x"])

@st.cache
def altair_histogram():
    brushed = alt.selection_interval(encodings=["x"], name="brushed")

    return (
        alt.Chart(hist_data)
        .mark_bar()
        .encode(alt.X("x:Q", bin=True), y="count()")
        .add_selection(brushed)
    )

event_dict = altair_component(altair_chart=altair_histogram())

r = event_dict.get("x")
if r:
    filtered = hist_data[(hist_data.x >= r[0]) & (hist_data.x < r[1])]
    st.write(filtered)

Dev Setup

Open two terminals in the dev container using VSCode's Remote Containers Extension.

In the first terminal, run:

# Install python module in editable mode
pip install -e .

# Launch streamlit app
streamlit run streamlit_vega_lite/__init__.py

In the second terminal:

# Switch to location of frontend code
cd streamlit_vega_lite/frontend
# Install dependencies
yarn
# Launch frontend assets
yarn start

Then open http://localhost:8501/.

Style

Run Black for Python formatting.

black . -l 120

Run Prettier for other formatting in the frontend directory.

yarn format

Publish

See https://docs.streamlit.io/en/stable/publish_streamlit_components.html.

Make sure that _RELEASE is set to True.

pushd streamlit_vega_lite/frontend
yarn build
popd
python setup.py sdist bdist_wheel
python3 -m twine upload --repository pypi dist/*