streamlit-echarts 0.5.0

A Streamlit component to display ECharts.

streamlit-echarts

A Streamlit component to display ECharts.

Installation instructions

uv pip install streamlit-echarts

Development install (editable)

When developing locally, install in editable mode so Streamlit picks up Python code changes without rebuilding a wheel:

uv pip install -e . --force-reinstall

For frontend (TypeScript/React) changes, you still need to rebuild. Use Vite's watch mode for automatic rebuilds on save:

cd streamlit_echarts/frontend
npm run dev

Usage instructions

import streamlit as st
from streamlit_echarts import st_echarts

options = {
    "xAxis": {
        "type": "category",
        "data": ["Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun"],
    },
    "yAxis": {"type": "value"},
    "series": [{"data": [820, 932, 901, 934, 1290, 1330, 1320], "type": "bar"}],
}

st_echarts(options=options, height="400px")

Using with PyECharts

The v2 version of streamlit-echarts intentionally drops the pyecharts and simplejson dependencies to keep the package lightweight. You can still easily render PyECharts figures by dumping their configuration to JSON and passing it as a dictionary:

import json
import streamlit as st
from pyecharts import options as opts
from pyecharts.charts import Bar
from streamlit_echarts import st_echarts

b = (
    Bar()
    .add_xaxis(["Microsoft", "Amazon", "IBM", "Oracle", "Google", "Alibaba"])
    .add_yaxis(
        "2017-2018 Revenue in (billion $)", [21.2, 20.4, 10.3, 6.08, 4, 2.2]
    )
    .set_global_opts(
        title_opts=opts.TitleOpts(
            title="Top cloud providers 2018", subtitle="2017-2018 Revenue"
        )
    )
)

# Render by converting the PyECharts instance to a JSON dictionary
st_echarts(options=json.loads(b.dump_options()), height="500px")

Demo Application

A comprehensive demo application containing dozens of ECharts and PyECharts examples is available in the root of the repository. You can use it as an integration test to verify the component's functionality:

Note: You will need extra dependencies installed to run all examples in the demo:

uv pip install pyecharts pandas faker
streamlit run demo_app.py

Build and Publish

To package this component for distribution:

1. Build the frontend assets (from streamlit_echarts/frontend):

   npm i --legacy-peer-deps
   npm run build
   

2. Build the Python package (from the project root):

   uv build
   

3. Publish to PyPI:

   uv publish
   

   You will need a PyPI API token. You can pass it via --token or set the UV_PUBLISH_TOKEN environment variable.

   To bump the version, edit version in pyproject.toml before building.

Requirements

• Python >= 3.10
• Node.js >= 24 (LTS)

Expected output

• dist/streamlit_echarts-0.5.0-py3-none-any.whl
• dist/streamlit_echarts-0.5.0.tar.gz