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Streamlit - ECharts

Streamlit App

A Streamlit component to display ECharts.

Install

pip install streamlit-echarts

Usage

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 out the demo and source code for more examples.

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)

API

st_echarts API

st_echarts(
    options: Dict
    theme: Union[str, Dict]
    events: Dict[str, str]
    height: str
    width: str
    renderer: str
    map: Map
    key: str
)
  • options : Python dictionary that resembles the JSON counterpart of echarts options. For example the basic line chart in JS :
// JS code
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 :

# Python code
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 Python 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);
});

Return values from events are sent back to Python, for example:

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" }
    ],
}
events = {
    "click": "function(params) { console.log(params.name); return params.name }",
    "dblclick":"function(params) { return [params.type, params.name, params.value] }"
}
value = st_echarts(option, events=events)
st.write(value)  # shows name on bar click and type+name+value on bar double click

The JS code needs to be a one-liner. You can use Javascript minifiers like https://javascript-minifier.com/ or https://www.minifier.org/ to transform your Javascript code to a one-liner.

  • height / width : size of the div wrapper
  • map : register a map using the dedicated Map class
from streamlit_echarts import Map
with open("USA.json", "r") as f:
    map = Map(
        "USA",
        json.loads(f.read()),
        {
            "Alaska": {"left": -131, "top": 25, "width": 15},
            "Hawaii": {"left": -110, "top": 28, "width": 5},
            "Puerto Rico": {"left": -76, "top": 26, "width": 2},
        },
    )
options = {...}
st_echarts(options, map=map)

You'll find a lot of GeoJSON data inside the source code of echarts-countries-js.

  • 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.

st_pyecharts API

def st_pyecharts(
    chart: Base
    theme: Union[str, Dict]
    events: Dict[str, str]
    height: str
    width: str
    renderer: str
    map: Map
    key: str
)
  • chart : Pyecharts instance

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

Development

Install

  • JS side
cd frontend
npm install
  • Python side
conda create -n streamlit-echarts python=3.7
conda activate streamlit-echarts
pip install -e .

Run

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

  • JS side
cd frontend
npm run start
  • Python side

Demo example is on https://github.com/andfanilo/streamlit-echarts-demo.

git clone https://github.com/andfanilo/streamlit-echarts-demo
cd streamlit-echarts-demo/
streamlit run app.py

Caveats

Theme definition

  • 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).

On Javascript functions

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

Note: you need the JS string to be on one-line. You can use Javascript minifiers like https://javascript-minifier.com/ or https://www.minifier.org/ to transform your Javascript code to a one-liner.

st_pyecharts VS using pyecharts with components.html

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.

Credits

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