ipecharts
v1.0.8
Published
Apache Echarts Jupyter Widget.
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ipecharts
brings interactive widgets based on Apache ECharts charting library to the Jupyter ecosystem. By using the Jupyter Widget protocol, ipecharts
is fully compatible with other widget libraries and tools in the Jupyter ecosystem.
https://github.com/trungleduc/ipecharts/assets/4451292/c6e73b4d-61ef-4098-a274-92233d0801b0
[!NOTE]
pyecharts
also supports using Echarts in the notebook, but they are not using Jupyter Widget likeipecharts
. In this library, HTML code is injected into the notebook to render the chart.
Try it online!
You can try it online by clicking on this badge:
Documentation
You can read the documentation following this link: https://ipecharts.readthedocs.io/
Installation
To install the extension, execute:
pip install ipecharts
or with conda:
conda install -c conda-forge ipecharts
Usage
ipecharts
widgets are generated automatically from ECharts 5.5.0
. It provides two high-level widgets to create charts in notebooks: EChartsRawWidget
and EChartsWidget
.
Create charts using EChartsRawWidget
EChartsRawWidget
is a simple widget to render ECharts
option dictionary. It is fully compatible with the JavaScript version of ECharts
. Here is an example of converting the following JS example:
import * as echarts from 'echarts';
var chartDom = document.getElementById('main');
var myChart = echarts.init(chartDom);
var option;
option = {
xAxis: {
type: 'category',
boundaryGap: false,
data: ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun']
},
yAxis: {
type: 'value'
},
series: [
{
data: [820, 932, 901, 934, 1290, 1330, 1320],
type: 'line',
areaStyle: {}
}
]
};
option && myChart.setOption(option);
into using EChartsRawWidget
:
from ipecharts import EChartsRawWidget
option = {
'xAxis': {
'type': 'category',
'boundaryGap': False,
'data': ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun']
},
'yAxis': {
'type': 'value'
},
'series': [
{
'data': [820, 932, 901, 934, 1290, 1330, 1320],
'type': 'line',
'areaStyle': {}
}
]
}
EChartsRawWidget(option=option)
Create charts using EChartsWidget
While the raw widget can render the charts correctly, it lacks the interactivity of a Jupyter widget. ipecharts
provides EChartsWidget
and configuration classes for nearly all available options of ECharts to correct this issue.
Here is the equivalent of the above chart but using EChartsWidget
:
from ipecharts import EChartsWidget
from ipecharts.option import Option, XAxis, YAxis
from ipecharts.option.series import Line
line = Line(data=[820, 932, 901, 934, 1290, 1330, 1320], areaStyle={})
option = Option(
xAxis=XAxis(
type="category",
boundaryGap=False,
data=["Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun"],
),
yAxis=YAxis(type="value"),
series=[line],
)
EChartsWidget(option=option)
While it looks more verbose, the advantage is the reactivity. We can update the line data and have the chart updated automatically.
Configure EChartsWidget
with traitlets
Each key in the option dictionary of ECharts has an equivalent configuration class with the same name. These classes contain traits with the same name as the corresponding ECharts option. Any change to these traits will be propagated to the top-level widget, and the chart will be updated automatically.
For instance, you can compare the scatter option of ECharts at https://echarts.apache.org/en/option.html#series-scatter.type and the equivalent Scatter class in the ipecharts documentation. The Python class is generated automatically from the ECharts option.
By using Traitlets to configure your widget, you can use EChartsWidget with other widgets in the Jupyter ecosystem. Here is an example of controlling the chart with an ipywidgets Button:
from ipecharts.option import Option, XAxis, YAxis
from ipecharts.option.series import Line
from ipywidgets.widgets import Button
line = Line(smooth=True, areaStyle={}, data=numpy.random.rand(10).tolist())
option = Option(
xAxis=XAxis(type="category"),
yAxis=YAxis(type="value"),
series=[line],
)
chart = EChartsWidget(option=option)
button = Button(description="Generate data")
def on_button_clicked(b):
data = numpy.random.rand(10).tolist()
line.data = data
button.on_click(on_button_clicked)
display(button, chart)
Customize the chart container style
Both EChartsWidget
and EChartsRawWidget
classes allow you to customize the style of the chart container by setting the style attribute. The style attribute accepts a dictionary where keys are CSS property names in camelCase or kebab-case (as strings), and values are the corresponding CSS values.
Example: 'backgroundColor': '#f0f0f0' or 'background-color': '#f0f0f0'
from ipecharts import EChartsWidget
from ipecharts.option import Option, XAxis, YAxis
from ipecharts.option.series import Line
# Define the data for the line series
line = Line(
data=[820, 932, 901, 934, 1290, 1330, 1320],
areaStyle={}
)
# Create the option object with xAxis, yAxis, and series
option = Option(
xAxis=XAxis(
type="category",
boundaryGap=False,
data=["Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun"]
),
yAxis=YAxis(type="value"),
series=[line]
)
# Define the style for the widget
style = {
'width': '450px',
'height': '300px',
'border': '5px solid #ccc'
}
# Create the EChartsWidget with the option and style
chart = EChartsWidget(option=option, style=style)
# Display the chart
chart
After the widget has been created and displayed, you can update its style by modifying the style attribute.
# Update the style of the chart
chart.style = {
'width': '800px',
'height': '600px',
'border': '2px solid #000'
}
# The widget will automatically update to reflect the new styles.
https://github.com/user-attachments/assets/e4245101-8dff-40a9-a4d4-1f75a06b88c4
Contributing
Development install
Note: You will need markdownify
, autodoc-traits
, sphinx
to generate the code
The jlpm
command is JupyterLab's pinned version of
yarn that is installed with JupyterLab. You may use
yarn
or npm
in lieu of jlpm
below.
# Clone the repo to your local environment
# Change directory to the ipecharts directory
# Install package in development mode
pip install -e "."
# Link your development version of the extension with JupyterLab
jupyter labextension develop . --overwrite
# Rebuild extension Typescript source after making changes
jlpm build
You can watch the source directory and run JupyterLab at the same time in different terminals to watch for changes in the extension's source and automatically rebuild the extension.
# Watch the source directory in one terminal, automatically rebuilding when needed
jlpm watch
# Run JupyterLab in another terminal
jupyter lab
With the watch command running, every saved change will immediately be built locally and available in your running JupyterLab. Refresh JupyterLab to load the change in your browser (you may need to wait several seconds for the extension to be rebuilt).
By default, the jlpm build
command generates the source maps for this extension to make it easier to debug using the browser dev tools. To also generate source maps for the JupyterLab core extensions, you can run the following command:
jupyter lab build --minimize=False
Development uninstall
pip uninstall ipecharts
In development mode, you will also need to remove the symlink created by jupyter labextension develop
command. To find its location, you can run jupyter labextension list
to figure out where the labextensions
folder is located. Then you can remove the symlink named ipecharts
within that folder.
Packaging the extension
See RELEASE