Create interactive charts and data visualizations using pyecharts (Python) and Apache ECharts. Use when user needs charts, graphs, or data visualizations rendered as HTML. Triggers include "create chart", "make a graph", "visualize data", "bar chart", "line chart", "pie chart", "scatter plot", "heatmap", "data visualization", "plot this data", "chart this". Do NOT use for static images or matplotlib-style charts.
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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)),
)
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"))
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"))
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),
)
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/size
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 Series
Available themes in pyecharts:
macarons (default) - Colorful, professionalshine - Bright colorsroma - Muted, elegantvintage - Retro feeldark - Dark backgroundlight - Light, minimalUsage:
from pyecharts.globals import ThemeType
chart = Bar(init_opts=opts.InitOpts(theme=ThemeType.MACARONS))
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_path
# 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}")
)
.tolist() on all datainit_opts=opts.InitOpts(width="100%", height="400px")
xaxis_opts=opts.AxisOpts(
axislabel_opts=opts.LabelOpts(rotate=45, interval=0)
)
legend_opts=opts.LegendOpts(
type_="scroll",
orient="horizontal",
pos_bottom="0%"
)
Create ECharts visualizations with these requirements:
Use 100% width and height (not fixed px):
init_opts=opts.InitOpts(width="100%", height="400px")
Use a professional scheme:
colors = ['#5470c6', '#91cc75', '#fac858', '#ee6666', '#73c0de']
grid_opts=opts.GridOpts(pos_left="60px", pos_right="40px", pos_top="60px", pos_bottom="40px")
title_opts=opts.TitleOpts(
title="Chart Title",
title_textstyle_opts=opts.TextStyleOpts(font_size=18, font_weight="bold", color="#333")
)
Enable tooltip with shadow and add toolbox:
tooltip_opts=opts.TooltipOpts(trigger="axis", axis_pointer_type="shadow")
toolbox_opts=opts.ToolboxOpts(
feature=opts.ToolBoxFeatureOpts(
save_as_image=opts.ToolBoxFeatureSaveAsImageOpts(title="Save as Image"),
data_zoom=opts.ToolBoxFeatureDataZoomOpts(zoom_title="Zoom", back_title="Reset Zoom"),
restore=opts.ToolBoxFeatureRestoreOpts(title="Restore"),
)
)
Smooth entrance animation (enabled by default in pyecharts)
<div id="chart" style="width:100%;height:100%;min-height:400px;"></div>
<script>
var chart = echarts.init(document.getElementById('chart'));
chart.setOption({
color: ['#5470c6', '#91cc75', '#fac858', '#ee6666', '#73c0de'],
grid: { left: 60, right: 40, top: 60, bottom: 40 },
tooltip: { trigger: 'axis', axisPointer: { type: 'shadow' } },
toolbox: { feature: { saveAsImage: { title: 'Save as Image' }, dataZoom: { title: { zoom: 'Zoom', back: 'Reset Zoom' } }, restore: { title: 'Restore' } } },
// ... chart-specific options
});
window.addEventListener('resize', () => chart.resize());
</script>
After generating chart HTML files, upload them to the Artifact Store so the user can access them.
# 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.html
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