streamlit-echarts 0.4.0
Echarts custom component for Streamlit
Requires Python: >=3.6

# Streamlit - ECharts

[![Streamlit App](https://static.streamlit.io/badges/streamlit_badge_black_white.svg)](https://share.streamlit.io/andfanilo/streamlit-echarts-demo/master/app.py)

A Streamlit component to display [ECharts](https://echarts.apache.org/en/index.html).

![](./img/demo.gif)

## Install

```shell script
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](https://share.streamlit.io/andfanilo/streamlit-echarts-demo/master/app.py) and [source code](https://github.com/andfanilo/streamlit-echarts-demo) for more examples.

**st_echarts example**

```python
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**

```python
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](https://echarts.apache.org/en/tutorial.html#ECharts%20Basic%20Concepts%20Overview).
  For example the basic line chart in JS :

```javascript
// 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
# 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](https://echarts.apache.org/en/tutorial.html#Overview%20of%20Style%20Customization).
  You can specify built-int themes or pass over style configuration as a Python dict.
- **events** : Python dictionary which maps an [event](https://echarts.apache.org/en/tutorial.html#Events%20and%20Actions%20in%20ECharts) to a Js function as string.
  For example :

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

will get mapped to :

```javascript
myChart.on("click", function (params) {
  console.log(params.name);
});
```

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

```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" }
    ],
}
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

```python
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](https://github.com/echarts-maps/echarts-countries-js/tree/master/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

```python
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

```shell script
cd frontend
npm install
```

- Python side

```shell script
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

```shell script
cd frontend
npm run start
```

- Python side

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

```shell script
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).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() + '%';}"
            ),
        )
    )
```

**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()`.

```python
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.

![](./img/randomize.gif)

## Credits

- It's really a wrapper around [echarts-for-react](https://github.com/hustcc/echarts-for-react).

# Support me

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