
Echarts
- 319 installs
- 15 repo stars
- Updated June 16, 2026
- eng0ai/eng0-template-skills
echarts is a Claude Code skill that builds interactive charts and dashboards using Apache ECharts and pyecharts for developers who need bar, line, and themed visualizations with tooltips and responsive data binding.
About
echarts is a frontend development skill from eng0ai/eng0-template-skills covering Apache ECharts charting and its pyecharts Python wrapper. It documents chart creation patterns—including bar charts with add_xaxis/add_yaxis, global title and tooltip options, and axis configuration—plus self-contained HTML report generation with embedded JavaScript output. Developers reach for echarts when building web dashboards, analytics reports, or data exploration UIs that need interactive tooltips, themes, and responsive chart configs without hand-writing raw ECharts option objects from scratch. The skill bridges Python data workflows and browser-rendered visualizations, making it useful for report generation pipelines and SaaS analytics screens alike.
- ECharts option schema guidance
- Responsive chart layouts
- Theme and palette customization
- Tooltip and legend patterns
- Dynamic data update handling
Echarts by the numbers
- 319 all-time installs (skills.sh)
- Ranked #732 of 2,245 Frontend Development skills by installs in the Skillselion catalog
- Data as of Jul 30, 2026 (Skillselion catalog sync)
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| Installs | 319 |
|---|---|
| repo stars | ★ 15 |
| Last updated | June 16, 2026 |
| Repository | eng0ai/eng0-template-skills ↗ |
How do you build interactive ECharts dashboards?
Build interactive charts and dashboards in web apps using Apache ECharts configs, themes, tooltips, and responsive data-binding patterns.
Who is it for?
Developers building Python-driven reports or web dashboards that need Apache ECharts interactive charts with themes and tooltips.
Skip if: Teams standardized on D3.js, Plotly, or Chart.js who do not want ECharts-specific option schemas.
When should I use this skill?
A developer asks to create bar charts, dashboards, or HTML visualization reports with pyecharts or Apache ECharts.
What you get
Apache ECharts chart configs, pyecharts Python code, and self-contained HTML reports with embedded JS visualizations.
- ECharts chart configs
- Self-contained HTML visualization reports
Files
ECharts Skill
Technology Stack
- pyecharts: Python wrapper for Apache ECharts
- Apache ECharts: JavaScript charting library
- Output: Self-contained HTML with embedded JS
Chart Types Reference
Bar Charts
from pyecharts.charts import Bar
from pyecharts import options as opts
chart = Bar()
chart.add_xaxis(labels)
chart.add_yaxis("Series Name", values)
chart.set_global_opts(
title_opts=opts.TitleOpts(title="Chart Title"),
tooltip_opts=opts.TooltipOpts(trigger="axis"),
xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=45)),
)Line Charts
from pyecharts.charts import Line
chart = Line()
chart.add_xaxis(dates)
chart.add_yaxis("Actual", values, is_smooth=True)
chart.add_yaxis("7-Day MA", moving_avg_7, is_smooth=True, linestyle_opts=opts.LineStyleOpts(type_="dashed"))Pie Charts
from pyecharts.charts import Pie
chart = Pie()
chart.add("", list(zip(labels, values)))
chart.set_global_opts(legend_opts=opts.LegendOpts(orient="vertical", pos_left="left"))Heatmaps
from pyecharts.charts import HeatMap
chart = HeatMap()
chart.add_xaxis(x_labels)
chart.add_yaxis("", y_labels, value=[[x, y, val], ...])
chart.set_global_opts(
visualmap_opts=opts.VisualMapOpts(min_=0, max_=max_val),
)Scatter Plots (for anomalies)
from pyecharts.charts import Scatter
chart = Scatter()
chart.add_xaxis(dates)
chart.add_yaxis("Cost", costs, symbol_size=10)
# Add anomaly markers with different color/sizeCritical: Browser Compatibility
Always convert to lists for JavaScript:
# CORRECT
chart.add_xaxis(df['column'].tolist())
chart.add_yaxis("Label", df['values'].tolist())
# WRONG - causes rendering issues
chart.add_xaxis(df['column'].values) # numpy array
chart.add_xaxis(df['column']) # pandas SeriesTheme Options
Available themes in pyecharts:
macarons(default) - Colorful, professionalshine- Bright colorsroma- Muted, elegantvintage- Retro feeldark- Dark backgroundlight- Light, minimal
Usage:
from pyecharts.globals import ThemeType
chart = Bar(init_opts=opts.InitOpts(theme=ThemeType.MACARONS))HTML Report Structure
def generate_html_report(self, output_path: str, top_n: int = 10) -> str:
# Create all charts
charts = [
self.create_cost_by_service_chart(top_n),
self.create_cost_by_account_chart(),
# ... more charts
]
# Combine into page
page = Page(layout=Page.SimplePageLayout)
for chart in charts:
page.add(chart)
# Render to file
page.render(output_path)
return output_pathFormatting Numbers
# Currency formatting in tooltips
tooltip_opts=opts.TooltipOpts(
trigger="axis",
formatter="{b}: ${c:,.2f}"
)
# Axis label formatting
yaxis_opts=opts.AxisOpts(
axislabel_opts=opts.LabelOpts(formatter="${value:,.0f}")
)Common Issues & Solutions
Empty Charts
1. Check browser console for JS errors 2. Verify .tolist() on all data 3. Hard refresh (Ctrl+Shift+R) 4. Check data exists in HTML source
Chart Too Small
init_opts=opts.InitOpts(width="100%", height="400px")Labels Overlapping
xaxis_opts=opts.AxisOpts(
axislabel_opts=opts.LabelOpts(rotate=45, interval=0)
)Legend Too Long
legend_opts=opts.LegendOpts(
type_="scroll",
orient="horizontal",
pos_bottom="0%"
)Testing Visualizations
# Test chart creation
uv run pytest tests/test_visualizer.py -v
# Regenerate example report
uv run pytest tests/test_examples.py -v -s
# View in browser
open examples/example_report.htmlRelated skills
How it compares
Choose echarts when the stack is Apache ECharts or pyecharts and you need self-contained HTML chart output rather than a React-specific charting component library.
FAQ
What libraries does the echarts skill use?
The echarts skill covers pyecharts as a Python wrapper for Apache ECharts, the JavaScript charting library. Output is self-contained HTML with embedded JS, suitable for reports and web dashboards.
What chart types does echarts document?
The echarts skill documents bar chart creation with pyecharts, including xaxis/yaxis setup, global title and tooltip options, and axis label configuration. It also covers visualization best practices for HTML report generation.