Run Matplotlib Online – Free Matplotlib Online Compiler

Run a Matplotlib plot and charts in your browser with our free online Matplotlib compiler. No installation or signup required – Try It Now.

Start Plotting Now →See the Code

Try This Matplotlib Example

import matplotlib.pyplot as plt
import numpy as np

# Generate data
x = np.linspace(0, 2 * np.pi, 200)
y_sin = np.sin(x)
y_cos = np.cos(x)

# Create figure with two subplots
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 4))

# Sine wave
ax1.plot(x, y_sin, linewidth=2)
ax1.set_title('Sine Wave')
ax1.set_xlabel('x')
ax1.set_ylabel('sin(x)')
ax1.grid(True, alpha=0.3)

# Cosine wave
ax2.plot(x, y_cos, linewidth=2)
ax2.set_title('Cosine Wave')
ax2.set_xlabel('x')
ax2.set_ylabel('cos(x)')
ax2.grid(True, alpha=0.3)

plt.suptitle('Trigonometric Functions', fontsize=14, fontweight='bold')
plt.tight_layout()
plt.savefig('trig_functions.png', dpi=100, bbox_inches='tight')
plt.show()
print("Chart rendered successfully!")

Open in full editor →Loads instantly in your browser. No install.

What You Can Do With Matplotlib Online

Line, Bar & Scatter Charts

Create publication-quality plots using plt.plot(), plt.bar(), plt.scatter(), and plt.subplots(). Customise titles, labels, colours, and grids.

Charts Render Inline

Matplotlib output renders as an image directly in the output panel. No file downloads needed — see your chart the moment the code runs.

Full NumPy Integration

Use NumPy to generate and transform your data, then pass it straight to Matplotlib. Both libraries are pre-loaded and ready to use together.

How to Plot Your First Chart Online

Using our browser-based Python environment, you can generate data and visualize it instantly without setting up a local IDE. Here is a quick guide to getting started with the Matplotlib online compiler:

Import the library: Start with import matplotlib.pyplot as plt. Pull in numpy as np too — you almost always want it for generating or reshaping data.
Create a figure and axes: Use fig, ax = plt.subplots(figsize=(10, 6)). The object-oriented interface (working with ax directly) scales much better than the global plt calls once your charts get serious.
Plot the data: Call ax.plot(x, y), ax.bar(...), ax.scatter(...), or whichever chart type fits your story. Pass color, linewidth, alpha, and label as you go.
Customise titles, labels, and the legend: A chart without labels is a riddle. ax.set_title(...), ax.set_xlabel(...), ax.set_ylabel(...), then ax.legend() to surface the series names you set.
Run tight_layout: Wrap up styling with plt.tight_layout() so long axis labels, rotated ticks, and titles never collide at the edges of the figure.
Save vs show: Use plt.savefig("chart.png", dpi=300, bbox_inches="tight") when you need a crisp file, and plt.show() to render inline in the output panel. You can do both in the same script.
Go multi-panel with subplots: When one chart isn't enough, scale to fig, axes = plt.subplots(2, 2, figsize=(12, 8)) and index axes[0, 1].plot(...) per cell. Add plt.suptitle(...) for a figure-wide title.

For advanced charting options, 3D plots, and custom styling, be sure to check out the official Matplotlib documentation.

Frequently Asked Questions

Can I run Matplotlib online without installing Python?

Yes. PythonHere runs Python in your browser via WebAssembly (Pyodide). Matplotlib is pre-loaded — just import matplotlib.pyplot as plt and run.

Will my charts actually display in the browser?

Yes. Matplotlib charts render as inline images in the output panel. You can see your plots immediately after running the code.

Is it free?

100% free, forever. No account required, no time limit, no credit card.

Can I use NumPy and pandas with Matplotlib?

Yes. NumPy, pandas, and Matplotlib are all pre-installed and available simultaneously. Build your data pipeline and visualise it in one script.

Which Matplotlib version runs in the browser?

PythonHere ships Matplotlib 3.x via Pyodide — the same modern release you'd install with pip. Every plotting API you know (pyplot, OO interface, animations, 3D toolkit, mplot3d) works exactly as documented on matplotlib.org.

Can I save plots as PNG, SVG, or PDF?

Yes. plt.savefig("chart.png", dpi=300) works, and so do .svg and .pdf extensions. The file is written to the in-browser virtual filesystem — handy for chaining into a download or showing the rendered bytes back in the output panel.

Does plt.show() work in the browser?

Yes. plt.show() is the natural endpoint of any script and our editor intercepts it to render the figure inline. You can also skip plt.show() entirely — the figure auto-renders when the cell finishes executing.

Can I use Seaborn here?

Yes. Seaborn is available via Pyodide. Pop import micropip; await micropip.install("seaborn") at the top, or simply import seaborn as sns — the loader pulls it on demand. Once imported, sns.heatmap, sns.pairplot, and the full statistical chart catalogue are at your fingertips.

Why does my plot look pixelated?

The default DPI is low. Bump it with plt.figure(figsize=(10, 6), dpi=120) for the on-screen render, or plt.savefig("out.png", dpi=300) for crisp exports. For reports, prefer SVG or PDF — they stay sharp at any zoom level.

How do I make subplots or multi-panel figures?

Use fig, axes = plt.subplots(rows, cols, figsize=(w, h)). axes is a NumPy array — index it like axes[0, 1].plot(...). Wrap up with plt.tight_layout() so titles and tick labels never collide.

Can I make interactive plots — pan and zoom?

Pyodide ships a patched WebAgg backend for Matplotlib that supports pan, zoom, and figure toolbar interactions. For richer browser-native interactivity (hover tooltips, drill-down, linked brushing), Plotly or Bokeh tend to be the better tool — both are pip-installable in PythonHere.

The 7 Plot Types You'll Actually Use

Matplotlib exposes hundreds of chart types, but in practice you'll reach for the same handful again and again. Here's the working set every data person should keep in muscle memory — copy any snippet straight into the editor and tweak from there.

Line plot — plt.plot

plt.plot(x, y, linewidth=2, color='#1B6CA8')
plt.xlabel('Date')
plt.ylabel('Revenue')
plt.show()

When to use it: time series, trends, anything continuous along an axis.

Bar chart — plt.bar

categories = ['A', 'B', 'C', 'D']
values = [12, 19, 7, 15]
plt.bar(categories, values, color='#f7c948')
plt.show()

When to use it: categorical comparisons — products, regions, campaigns.

Scatter plot — plt.scatter

plt.scatter(height, weight, alpha=0.6, s=40)
plt.xlabel('Height (cm)')
plt.ylabel('Weight (kg)')
plt.show()

When to use it: relationships and correlations between two numeric variables.

Histogram — plt.hist

plt.hist(samples, bins=30, edgecolor='black')
plt.xlabel('Value')
plt.ylabel('Frequency')
plt.show()

When to use it: distributions — see where the mass of your data lives.

Box plot — plt.boxplot

plt.boxplot([group_a, group_b, group_c],
            labels=['A', 'B', 'C'])
plt.ylabel('Score')
plt.show()

When to use it: distributions plus outliers, side-by-side across groups.

Heatmap — plt.imshow / sns.heatmap

import seaborn as sns
sns.heatmap(corr_matrix, annot=True, cmap='viridis')
plt.show()

When to use it: matrix data, correlation grids, confusion matrices.

Subplots — plt.subplots

fig, axes = plt.subplots(2, 2, figsize=(10, 8))
axes[0, 0].plot(x, y)
axes[0, 1].bar(cats, vals)
plt.tight_layout(); plt.show()

When to use it: dashboards — multiple related views in a single figure.

Matplotlib vs Seaborn vs Plotly vs Bokeh

Four big names in the Python plotting world, and they don't all do the same job. Here's how to pick without overthinking it.

Matplotlib — the foundation

Low-level, infinitely controllable, the bedrock the rest of the ecosystem stands on. If you need a publication-grade static figure with pixel-perfect control, this is your tool. The trade-off: you'll write more boilerplate than with the convenience layers above it.

Seaborn — statistical convenience on top of Matplotlib

Seaborn is a thin, opinionated layer that compiles to Matplotlib under the hood. One-liners like sns.heatmap, sns.pairplot, and sns.violinplot save you 20 lines apiece. Best for exploratory analysis where you want sane defaults out of the box.

Plotly — interactive, web-native

Renders to HTML/SVG with hover tooltips, pan, zoom, and linked legends out of the box. The right pick when your output is a web dashboard or a Jupyter notebook someone else will explore. Heavier payload than a static PNG, though.

Bokeh — also interactive, different model

Bokeh leans into streaming data and big-dataset interactivity, with a server component for live dashboards. Pick it when you're building a custom analytics app rather than a one-off chart for a report.

Rule of thumb: static report → Matplotlib or Seaborn. Interactive web dashboard → Plotly or Bokeh. Exploring a fresh dataset in five minutes → Seaborn nearly always wins.

10 Styling Tips That Make Your Plots Look Professional

The gap between a default Matplotlib chart and one that looks like it belongs in The Economist is mostly small, repeatable choices. Steal these.

1.Set a sensible figure size

plt.figure(figsize=(10, 6))

The default canvas is square and small. (10, 6) reads better in blog posts and slide decks.

2.Apply a built-in style

plt.style.use('seaborn-v0_8-whitegrid')

Modern Matplotlib renamed the seaborn styles — use the seaborn-v0_8-* variants for the polished, light-grid look.

3.Title and axis labels — always

plt.title('Daily Active Users'); plt.xlabel('Date'); plt.ylabel('DAU')

A chart without labels is a riddle. Three lines of code and your future self will thank you.

4.Use tight_layout to stop clipping

plt.tight_layout()

Auto-adjusts padding so long axis labels and rotated ticks never get cut off at the edges.

5.Crank DPI for crisp exports

plt.savefig('report.png', dpi=300, bbox_inches='tight')

Screen DPI is fine at 100 — for print, decks, or retina screens, save at 300+ for that pin-sharp finish.

6.Set defaults via rcParams

plt.rcParams.update({'font.size': 12, 'axes.spines.top': False})

Tweak once at the top of your script and every chart inherits the look. Great for consistent reports.

7.Annotate the points that matter

plt.annotate('Launch day', xy=(launch_x, launch_y), xytext=(10, 20), textcoords='offset points', arrowprops={'arrowstyle': '->'})

A chart that points at the insight beats a chart the reader has to decode.

8.Use color-blind-safe palettes

plt.plot(x, y, color='#0072B2')  # or cmap='cividis'

Roughly 8% of men have some form of color vision deficiency. Cividis, viridis, and the Okabe-Ito palette are safe bets.

9.Keep the palette tight — 2 to 3 colors

colors = ['#1B6CA8', '#f7c948', '#888888']

Highlight one or two series, mute the rest. Rainbow plots overwhelm — restraint is what makes a chart look pro.

10.Save vector formats for reports

plt.savefig('figure.svg')  # or .pdf

SVG and PDF stay razor sharp at any zoom level — perfect for print, papers, and design hand-off.

From Static to Interactive — When to Move Beyond Matplotlib

Matplotlib is incredible at static figures — the kind that ship in a PDF report, a research paper, or a quarterly slide deck. But there's a clear point where the brief flips and you need something else.

Reach for Plotly, Altair, or Bokeh when your readers need to interact: hover tooltips that reveal exact values, drill-down from an overview into a single segment, linked brushing across multiple panels, or filtering by a dropdown. Web dashboards, exec-facing analytics tools, and embedded notebook explorations all live in this world.

The other clean signal is scale. Matplotlib renders every point as a Python-side primitive — beyond ~100k points it starts to chug. Bokeh's datashader pipeline and Plotly's WebGL traces (scattergl) are built for millions of rows.

The honest truth: you'll keep Matplotlib forever for one-off explorations and printed deliverables. You'll add an interactive library to the toolkit the day a stakeholder asks "can I click on it?"

Plot Your First Chart in the Browser

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