A custom component to run Echarts in Streamlit session.

It's basically a Streamlit wrapper over echarts-for-react.

pip install streamlit-echarts

This library provides 2 functions to display echarts :

• st_echarts to display charts from echarts json options as Python dicts
• st_pyecharts to display charts from Pyecharts instances

Check the examples/ folder of the project for a more thourough quick start.

st_echarts example

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": "line"}
    ],
}
st_echarts(options=options)

st_pyecharts example

from pyecharts import options as opts
from pyecharts.charts import Bar
from streamlit_echarts import st_pyecharts

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"
        ),
        toolbox_opts=opts.ToolboxOpts(),
    )
)
st_pyecharts(b)

st_echarts(
    options: Dict
    theme: Union[str, Dict]
    events: Dict[str, str]
    height: str
    width: str
    renderer: str
    key: str
)

• options : Python dictionary that resembles the JSON counterpart of echarts options. For example the basic line chart in JS :

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

is represented in Python :

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

• theme : echarts theme. You can specify built-int themes or pass over style configuration as a Pythcon dict.
• events : Python dictionary which maps an event to a Js function as string. For example :

{
    "click": "function(params) { console.log(params.name) }"
}

will get mapped to :

myChart.on('click', function (params) {
    console.log(params.name);
});

• height / width : size of the div wrapper
• renderer : SVG or canvas
• key : assign a fixed identity if you want to change its arguments over time and not have it be re-created.

def st_pyecharts(
    chart: Base
    theme: Union[str, Dict]
    events: Dict[str, str]
    height: str
    width: str
    renderer: str
    key: str
)

• chart : Pyecharts instance

The docs for the remaining inputs are the same as its st_echarts counterpart.

• JS side

cd frontend
npm install

• Python side

conda create -n streamlit-echarts python=3.7
conda activate streamlit-echarts
pip install -e .

Both webpack dev server and Streamlit need to run for development mode.

• JS side

cd frontend
npm run start

• Python side

streamlit run examples/app.py

• Defining the theme in Pyecharts when instantiating chart like Bar(init_opts=opts.InitOpts(theme=ThemeType.LIGHT)) does not work, you need to call theme in st_pyecharts(c, theme=ThemeType.LIGHT).

• For now only china map is loaded. Need to find a way how to load json maps or from URL.

This library also provides the JsCode util class directly from pyecharts.

This class is used to indicate javascript code by wrapping it with a specific placeholder. On the custom component side, we parse every value in options looking for this specific placeholder to determine whether a value is a JS function.

As such, if you want to pass JS functions as strings in your options, you should use the corresponding JsCode module to wrap code with this placeholder :

• In Python dicts representing the JSON option counterpart, wrap any JS string function with streamlit_echarts.JsCode by calling JsCode(function).jscode. It's a smaller version of pyecharts.commons.utils.JsCode so you don't need to install pyecharts to use it.

series: [
    {
        type: 'scatter', // this is scatter chart
        itemStyle: {
            opacity: 0.8
        },
        symbolSize: JsCode("function (val) { return val[2] * 40;}").js_code,
        data: [["14.616","7.241","0.896"],["3.958","5.701","0.955"],["2.768","8.971","0.669"],["9.051","9.710","0.171"],["14.046","4.182","0.536"],["12.295","1.429","0.962"],["4.417","8.167","0.113"],["0.492","4.771","0.785"],["7.632","2.605","0.645"],["14.242","5.042","0.368"]]
    }
]

• In Pyecharts, use pyecharts.commons.utils.JsCode directly, JsCode automatically calls .jscode when dumping options.

.set_series_opts(
        label_opts=opts.LabelOpts(
            position="right",
            formatter=JsCode(
                "function(x){return Number(x.data.percent * 100).toFixed() + '%';}"
            ),
        )
    )

While this package provides a st_pyecharts method, if you're using pyecharts you can directly embed your pyecharts visualization inside st.html by passing the output of the chart's .render_embed().

from pyecharts.charts import Bar
from pyecharts import options as opts
import streamlit.components.v1 as components

c = (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"),
                     toolbox_opts=opts.ToolboxOpts())
    .render_embed() # generate a local HTML file
)
components.html(c, width=1000, height=1000)

Using st_pyecharts is still something you would want if you need to change data regularly without remounting the component, check for examples examples/app_pyecharts.py for Chart with randomization example.