echartsy 0.3.1

A matplotlib-style fluent builder API for Apache ECharts in Python, Jupyter Notebooks, and Streamlit.

echartsy

Interactive charts in Python — the matplotlib workflow, the ECharts experience.

Build publication-quality interactive charts with a familiar
fig = figure() → fig.bar() → fig.show() workflow.
Works everywhere: Jupyter · Streamlit · standalone scripts

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Why echartsy?

• Feels like matplotlib — If you know plt.figure() / plt.show(), you already know 90% of the API. No JSON wrangling, no JavaScript.
• Interactive out of the box — Every chart ships with tooltips, legend toggling, zoom, and a built-in export toolbox. Zero configuration needed.
• One library, three engines — Write once, render in Jupyter notebooks, Streamlit apps, or plain Python scripts that pop open a browser.
• Composable & animated — Layer pies on bar charts, build dual-axis dashboards, or animate any chart across time with TimelineFigure.

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Installation

pip install echartsy

Need extras?

pip install echartsy[jupyter]     # Jupyter Notebook / JupyterLab
pip install echartsy[streamlit]   # Streamlit apps
pip install echartsy[scipy]       # KDE density plots
pip install echartsy[all]         # Everything

Requirements: Python 3.9+ · pandas ≥ 1.5 · numpy ≥ 1.23

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Quick Start

import pandas as pd
import echartsy as ec

ec.config(engine="jupyter")          # or "python" / "streamlit"

df = pd.DataFrame({
    "Fruit": ["Apples", "Bananas", "Cherries", "Dates", "Elderberries"],
    "Sales": [120, 95, 78, 42, 63],
})

fig = ec.figure()
fig.bar(df, x="Fruit", y="Sales", gradient=True, labels=True)
fig.title("Fruit Sales")
fig.show()

Three lines from DataFrame to interactive chart.

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Gallery

Cartesian Charts

Bar + Pie Overlay fig = ec.figure(height="500px") fig.bar(df, x="Dept", y="Budget", gradient=True, labels=True) fig.pie(df, names="Dept", values="Budget", center=["82%","25%"], radius=["18%","28%"]) fig.show()	Smooth Line fig = ec.figure() fig.plot(df, x="Month", y="Sales", smooth=True, area=True) fig.show()
Scatter Plot fig = ec.figure() fig.scatter(df, x="Height", y="Weight", color="Gender", size="Age") fig.show()	Grouped Bar fig = ec.figure() fig.bar(df, x="Quarter", y="Revenue", hue="Region") fig.show()
Stacked Bar fig = ec.figure() fig.bar(df, x="Month", y="Revenue", hue="Product", stack=True) fig.show()	Histogram fig = ec.figure() fig.hist(df, column="Score", bins=20) fig.show()
Area Chart fig = ec.figure() fig.plot(df, x="Month", y="Users", area=True, smooth=True) fig.show()	Horizontal Bar fig = ec.figure() fig.bar(df, x="Country", y="Population", orient="h") fig.show()
Dual Axis: Bar + Line fig = ec.figure() fig.bar(df, x="Month", y="Revenue") fig.plot(df, x="Month", y="Growth", smooth=True, axis=1) fig.ylabel("Revenue ($K)") fig.ylabel_right("Growth %") fig.show()	Gradient Bars fig = ec.figure() fig.bar(df, x="City", y="Temp", gradient=True, gradient_colors=["#83bff6","#188df0"]) fig.show()
Multi-Line with Area fig = ec.figure() fig.plot(df, x="Month", y="Value", hue="Series", smooth=True, area=True) fig.show()	Boxplot fig = ec.figure() fig.boxplot(df, x="Department", y="Salary") fig.show()

Standalone Charts

Donut / Pie fig = ec.figure() fig.pie(df, names="Browser", values="Share", inner_radius="40%") fig.show()	Radar fig = ec.figure() fig.radar(indicators, data, series_names=["Warrior","Mage"]) fig.show()
Heatmap fig = ec.figure() fig.heatmap(df, x="Day", y="Hour", value="Count") fig.show()	Funnel fig = ec.figure() fig.funnel(df, names="Stage", values="Count") fig.show()
Treemap fig = ec.figure() fig.treemap(df, path=["Category","SubCat"], value="Sales") fig.show()	Sankey Diagram fig = ec.figure() fig.sankey(df, levels=["Source","Channel","Outcome"], value="Users") fig.show()

Composite & Dashboard Charts

Bar + Pie (Dark)	Triple Composite
KPI Dashboard	Stacked + Trend + Pie
Side-by-Side Pies	Dark Theme

Every chart is fully interactive — hover for tooltips, click legend items to toggle series, use the toolbox to export. Open the HTML demos in assets/ for the live experience, or run python generate_demos.py yourself.

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Supported Chart Types

Category	Method	Description
Line	fig.plot()	Smooth/straight lines, multi-series via hue, optional filled area
Bar	fig.bar()	Vertical/horizontal, grouped (hue), stacked, gradient fills
Scatter	fig.scatter()	Color and size encoding, numeric axes
Histogram	fig.hist()	Auto-binned frequency distribution
Boxplot	fig.boxplot()	Five-number statistical summary
KDE	fig.kde()	Kernel density estimation (requires scipy)
Pie / Donut	fig.pie()	Pie, donut (inner_radius), rose charts, side-by-side multiples
Radar	fig.radar()	Multi-indicator polygon charts
Heatmap	fig.heatmap()	Matrix visualisation with colour mapping
Sankey	fig.sankey()	Multi-level flow diagrams
Treemap	fig.treemap()	Hierarchical area charts
Funnel	fig.funnel()	Stage-based conversion funnels

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Rendering Engines

echartsy writes your chart once; ec.config() controls where it renders.

Engine	Use case	Install
"python"	Standalone scripts — opens the default browser	No extra deps
"jupyter"	Jupyter Notebook / JupyterLab inline widgets	pip install echartsy[jupyter]
"streamlit"	Streamlit applications	pip install echartsy[streamlit] (or just pip install streamlit)

ec.config(engine="jupyter")

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Adaptive Dark Mode

Charts automatically respond to the user's OS or browser prefers-color-scheme setting.

ec.config(engine="jupyter", adaptive="auto")     # auto-detect (default)
ec.config(engine="jupyter", adaptive="dark")     # force dark
ec.config(engine="jupyter", adaptive="light")    # force light

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Style Presets & Palettes

Apply a pre-built visual theme in one argument:

fig = ec.figure(style=ec.StylePreset.CLINICAL)         # Clean defaults
fig = ec.figure(style=ec.StylePreset.DASHBOARD_DARK)    # Dark background
fig = ec.figure(style=ec.StylePreset.KPI_REPORT)        # Warm rusty tones
fig = ec.figure(style=ec.StylePreset.MINIMAL)            # Minimal & light

Or set a custom palette at any time:

fig.palette(["#667eea", "#764ba2", "#f093fb", "#f5576c", "#4facfe"])
fig.palette(ec.PALETTE_RUSTY)
fig.palette(ec.PALETTE_CLINICAL)

Build your own StylePreset for full control over fonts, grid lines, tooltip style, and more:

my_style = ec.StylePreset(
    palette=("#264653", "#2a9d8f", "#e9c46a", "#f4a261", "#e76f51"),
    bg="#fefae0",
    font_family="Georgia",
    title_font_size=20,
)
fig = ec.figure(style=my_style)

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Emphasis (Hover Highlighting)

Control what happens when users hover over chart elements — highlight, dim, scale, or restyle — using typed Python dataclasses instead of raw dicts.

Every chart method accepts an optional emphasis parameter with a chart-specific type:

Chart method	Emphasis class	Extra fields
bar(), hist(), boxplot(), heatmap()	Emphasis	—
plot(), kde()	LineEmphasis	line_style, area_style, end_label
scatter()	ScatterEmphasis	scale
pie()	PieEmphasis	scale, scale_size, label_line
radar()	RadarEmphasis	line_style, area_style
sankey()	SankeyEmphasis	line_style, focus supports "adjacency"
funnel()	FunnelEmphasis	label_line
treemap()	TreemapEmphasis	label_line, upper_label

All classes share these common fields: disabled, focus ("none" / "self" / "series"), blur_scope, item_style, and label.

from echartsy import Figure, Emphasis, ItemStyle

# Highlight the hovered series, dim everything else
fig = Figure()
fig.bar(df, x="Month", y="Revenue", hue="Region",
        emphasis=Emphasis(
            focus="series",
            item_style=ItemStyle(shadow_blur=10, shadow_color="rgba(0,0,0,0.3)"),
        ))
fig.show()

from echartsy import Figure, LineEmphasis, LineStyle, LabelStyle

# Bold the hovered line and show value labels
fig = Figure()
fig.plot(df, x="Date", y="Price", hue="Stock",
         emphasis=LineEmphasis(
             focus="series",
             line_style=LineStyle(width=4),
             label=LabelStyle(show=True, formatter="{c}"),
         ))
fig.show()

from echartsy import Figure, PieEmphasis, ItemStyle

# Scale and shadow on hover
fig = Figure()
fig.pie(df, names="Category", values="Amount",
        emphasis=PieEmphasis(
            scale=True, scale_size=15,
            item_style=ItemStyle(shadow_blur=20),
        ))
fig.show()

When emphasis is omitted, each chart keeps its existing default behaviour (backward compatible).

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Composite Charts

Overlay pies on cartesian charts, or combine bar + line on dual axes — all on one figure.

fig = ec.figure(height="550px")

# Primary axis: bars
fig.bar(df, x="Month", y="Revenue", labels=True, border_radius=4)

# Secondary axis: trend line
fig.plot(df, x="Month", y="Growth", smooth=True, axis=1, line_width=3)

# Inset pie
fig.pie(df_mix, names="Plan", values="Share",
        center=["25%", "32%"], radius=["15%", "25%"])

fig.ylabel("Revenue ($K)")
fig.ylabel_right("Growth %")
fig.legend(top=40, left=350)
fig.show()

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Timeline Animations

Animate any chart across a time dimension with TimelineFigure. It mirrors the Figure API; every series method gains one extra parameter: time_col.

fig = ec.TimelineFigure(height="500px", interval=1.5)
fig.bar(df, x="Country", y="GDP", time_col="Year", labels=True)
fig.title("GDP by Country")
fig.ylabel("GDP (Trillion USD)")
fig.legend(top=30)
fig.show()

TimelineFigure features:

Feature	API
Playback control	TimelineFigure(interval=2.0, autoplay=True, loop=True)
Adjust after creation	fig.playback(interval=1.0, rewind=True)
Fixed axis ranges	fig.xlim(0, 5000), fig.ylim(0, 15) — consistent scales across frames
Smart frame sorting	Parses years, quarters (Q1 2024), months (Jan 2024), ISO dates, fiscal years
Supported series	bar(), plot(), scatter(), pie()
Diagnose format	ec.detect_time_format(df["Year"])

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Chart Configuration Reference

Every Figure and TimelineFigure supports these configuration methods:

# Titles
fig.title("Main Title", subtitle="Sub-title")

# Axes
fig.xlabel("X Label", rotate=30)
fig.ylabel("Y Label")
fig.ylabel_right("Secondary Y")
fig.xlim(0, 100)
fig.ylim(0, 500)

# Layout
fig.legend(orient="vertical", left="right", top=40)
fig.margins(left=100, right=120, top=40)
fig.grid(show=True)

# Interactivity
fig.datazoom(start=0, end=80)
fig.toolbox(download=True, zoom=True)
fig.tooltip(trigger="axis")

# Export
fig.save(name="my_chart", fmt="png", dpi=3)
fig.to_html("my_chart.html")

# Palette
fig.palette(["#5470C6", "#91CC75", "#FAC858"])

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Exporting

# Standalone HTML file (fully interactive, no server needed)
fig.to_html("report.html")

# Raw ECharts option dict (for debugging or custom renderers)
option = fig.to_option()

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API at a Glance

ec.config(engine, adaptive="auto")

Set the global rendering engine ("python", "jupyter", "streamlit") and theme adaptation mode ("auto", "light", "dark").

ec.figure(**kwargs) / ec.Figure(**kwargs)

Create a chart canvas. Key keyword arguments:

Parameter	Default	Description
height	"400px"	CSS height of the chart container
width	None	CSS width (defaults to full container)
renderer	"canvas"	"canvas" or "svg"
style	StylePreset.CLINICAL	A StylePreset instance

ec.TimelineFigure(**kwargs) / ec.timeline_figure(**kwargs)

Same as Figure but adds timeline animation. Extra parameters:

Parameter	Default	Description
interval	2.0	Seconds between animation frames
autoplay	True	Start playing automatically
loop	True	Loop back to the first frame

ec.StylePreset

Frozen dataclass bundling visual defaults: palette, bg, font_family, title_font_size, axis_label_font_size, grid_line_color, and more.

ec.Emphasis, ec.LineEmphasis, ec.PieEmphasis, ...

Frozen dataclasses configuring hover-highlight behaviour per series. Common fields: disabled, focus, blur_scope, item_style (ItemStyle), label (LabelStyle). Chart-specific subclasses add fields like line_style, area_style, scale, scale_size, and label_line. See the Emphasis section for the full mapping.

Sub-style dataclasses

Class	Key fields
ec.ItemStyle	color, border_color, border_width, border_radius, shadow_blur, shadow_color, opacity
ec.LabelStyle	show, position, formatter, font_size, font_weight, color
ec.LineStyle	color, width, type ("solid" / "dashed" / "dotted"), shadow_blur, opacity
ec.AreaStyle	color, opacity
ec.LabelLineStyle	show, length, length2

ec.detect_time_format(series)

Diagnostic helper that inspects a pandas Series and reports how well TimelineFigure will parse its values.

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Generating Showcase Images

To regenerate the demo screenshots shown above:

# 1. Generate the interactive HTML demos
python generate_demos.py

# 2. Capture PNG screenshots (requires Playwright)
pip install playwright && playwright install chromium
python capture_screenshots.py

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What's New in v0.3.0

• Fixed: xlabel(rotate=...) no longer raises KeyError when called after heatmap() or other methods that reset the x-axis.
• Fixed: _auto_key() collisions — multiple charts with the same height, series count, and mode no longer overwrite each other in Streamlit.
• Added: TimelineFigure.xlim() and TimelineFigure.ylim() — lock axis ranges across animation frames for consistent comparisons.
• Changed: Streamlit rendering now uses st.components.v1.html() with ECharts from CDN instead of the streamlit-echarts third-party component. This fixes silent empty renders on modern Streamlit versions and removes the external dependency.

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Contributing

Contributions, bug reports, and feature requests are welcome. Please open an issue or submit a pull request on GitHub.

License

MIT — Jigar, 2026