GlyphX

A Better, Faster, and Simpler Python Visualization Library

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GlyphX is a modern alternative to matplotlib.pyplot with interactive, SVG-based charts that automatically display in:

• Jupyter notebooks
• CLI environments
• IDEs

It provides simplicity, high-quality rendering, built-in tooltips, zoom/pan, and export options — without ever needing plt.show().

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Features

| Feature | GlyphX | Matplotlib | |----------------------------|------------|------------| | Auto-display | ✅ | ❌ | | Interactive tooltips | ✅ | ❌ | | Zoom / pan (in browser) | ✅ | ❌ | | Built-in export buttons | ✅ SVG/PNG/JPG | ❌ | | Multi-plot grid layout | ✅ | ✅ | | Seaborn-style charts | ✅ (lmplot, pairplot, etc.) | Partial | | Hover highlighting | ✅ | ❌ | | Colorblind-friendly mode | ✅ | ❌ | | Shared axes support | ✅ | ✅ | | Font & theme customization | ✅ | ✅ |

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Installation

pip install glyphx

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Quick Example

from glyphx import plot

fig = plot(x=[1, 2, 3], y=[2, 4, 6], kind="line", label="Demo")
# No need for fig.show(); it auto-displays in Jupyter or saves via fig.save()

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Chart Types

• Line chart
• Bar chart (including grouped bars)
• Scatter plot
• Pie / Donut chart
• Box plot
• Histogram
• Swarm plot
• Violin plot
• Count plot
• lmplot, jointplot, pairplot
• Faceted charts (FacetGrid, facet_plot)

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Interactivity

All charts support:

• Mouseover tooltips
• Zoom / pan (mouse wheel + drag)
• Click-to-download buttons (SVG, PNG, JPG)

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Export Options

fig.save("my_chart.png")
fig.save("my_chart.svg")

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Grid Layout

from glyphx.layout import grid

charts = [plot(...), plot(...), plot(...)]
html = grid(charts, cols=2)

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Theming

from glyphx.themes import themes
theme = themes["dark"]

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Comparison with Matplotlib

📈 Line Plot

<table> <tr><th>Matplotlib</th><th>GlyphX</th></tr> <tr> <td><img src="images/matplotlib_line.png" width="300"/></td> <td><img src="images/glyphx_line.png" width="300"/></td> </tr> <tr> <td><pre><code class="language-python">import matplotlib.pyplot as plt

plt.plot([1, 2, 3], [4, 5, 6]) plt.title("Simple Line Plot") plt.xlabel("X Axis") plt.ylabel("Y Axis") plt.show()</code></pre></td> <td><pre><code class="language-python">from glyphx import plot

plot(x=[1, 2, 3], y=[4, 5, 6], kind="line", title="Simple Line Plot", xlabel="X Axis", ylabel="Y Axis")</code></pre></td>

</tr> </table>

📊 Bar Chart

<table> <tr><th>Matplotlib</th><th>GlyphX</th></tr> <tr> <td><img src="images/matplotlib_bar.png" width="300"/></td> <td><img src="images/glyphx_bar.png" width="300"/></td> </tr> <tr> <td><pre><code class="language-python">import matplotlib.pyplot as plt

plt.bar(["A", "B", "C"], [5, 3, 7]) plt.title("Bar Chart") plt.xlabel("Categories") plt.ylabel("Values") plt.show()</code></pre></td> <td><pre><code class="language-python">from glyphx import plot

plot(x=["A", "B", "C"], y=[5, 3, 7], kind="bar", title="Bar Chart", xlabel="Categories", ylabel="Values")</code></pre></td>

</tr> </table>

🔵 Scatter Plot

<table> <tr><th>Matplotlib</th><th>GlyphX</th></tr> <tr> <td><img src="images/matplotlib_scatter.png" width="300"/></td> <td><img src="images/glyphx_scatter.png" width="300"/></td> </tr> <tr> <td><pre><code class="language-python">import matplotlib.pyplot as plt

plt.scatter([1, 2, 3, 4], [4, 1, 3, 5]) plt.title("Scatter Plot") plt.xlabel("X Axis") plt.ylabel("Y Axis") plt.show()</code></pre></td> <td><pre><code class="language-python">from glyphx import plot

plot(x=[1, 2, 3, 4], y=[4, 1, 3, 5], kind="scatter", title="Scatter Plot", xlabel="X Axis", ylabel="Y Axis")</code></pre></td>

</tr> </table>

🥧 Pie Chart

<table> <tr><th>Matplotlib</th><th>GlyphX</th></tr> <tr> <td><img src="images/matplotlib_pie.png" width="300"/></td> <td><img src="images/glyphx_pie.png" width="300"/></td> </tr> <tr> <td><pre><code class="language-python">import matplotlib.pyplot as plt

labels = ["A", "B", "C"] sizes = [30, 45, 25] plt.pie(sizes, labels=labels) plt.title("Pie Chart") plt.show()</code></pre></td> <td><pre><code class="language-python">from glyphx import plot

plot(data=[30, 45, 25], kind="pie", labels=["A", "B", "C"], title="Pie Chart")</code></pre></td>

</tr> </table>

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Subplot Grid Example

<table> <tr> <td colspan="2"><img src="images/glyphx_sublpot.png" width="600"/></td> </tr> <tr> <td colspan="2"><pre><code class="language-python">from glyphx import Figure, series, themes

fig = Figure(rows=2, cols=2, theme=themes["dark"])

ax1 = fig.add_axes(0, 0) ax1.add(series.LineSeries([1, 2], [3, 4], label="Line")) ax1.legend_pos = "right"

ax2 = fig.add_axes(1, 0) ax2.add(series.ScatterSeries([1, 2, 3, 4], [4, 1, 3, 5], label="Scatter")) ax2.legend_pos = "right"

ax3 = fig.add_axes(0, 1) ax3.add(series.BarSeries(x=["A", "B", "C"], y=[5, 3, 7], label="Bar")) ax3.legend_pos = "right"

ax4 = fig.add_axes(1, 1) ax4.add(series.PieSeries(values=[30, 45, 25], labels=["A", "B", "C"]))

fig.plot()</code></pre></td>

</tr> </table> <table> <tr> <td colspan="2"><img src="images/matplotlib_sublpot.png" width="600"/></td> </tr> <tr> <td colspan="2"><pre><code class="language-python">import matplotlib.pyplot as plt import numpy as np

fig, axs = plt.subplots(2, 2, figsize=(10, 8))

axs[0, 0].plot([1, 2], [3, 4]) axs[0, 0].set_title("Line")

axs[1, 0].scatter([1, 2, 3, 4], [4, 1, 3, 5]) axs[1, 0].set_title("Scatter")

axs[0, 1].bar(["A", "B", "C"], [5, 3, 7]) axs[0, 1].set_title("Bar")

axs[1, 1].pie([30, 45, 25], labels=["A", "B", "C"]) axs[1, 1].set_title("Pie")

plt.tight_layout() plt.show()</code></pre></td>

</tr> </table>

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License

MIT License
(c) 2025 GlyphX contributors