The CodeUtility Matplotlib Executor allows you to write and run Python code that includes Matplotlib plotting commands - directly in your browser, with no setup or local installation required. It runs real Python environments with Matplotlib preinstalled, enabling instant visual output generation.

This tool uses the latest Matplotlib version and supports typical Python libraries used in data analysis and scientific computing, including NumPy and Pandas.

You can create 2D charts, scatter plots, histograms, or custom visualizations - the tool automatically captures your figure and displays it in a popup window when the code runs successfully.

It’s perfect for learners, data scientists, and engineers who want to quickly experiment with Python visualization code without needing Jupyter Notebook or a local Python setup.

More than a code runner

Use a multi-file editor, an isolated execution terminal, runtime-specific libraries, persistent workspaces, sharing, and real-time collaboration from the same executor.

How to use this executor?

Choose a runtime, open or create a file, write your code, and select Run. Output appears in the terminal, while signed-in users can keep files in a workspace, install libraries, and collaborate through shares.

Select a version and edit the current file or open another workspace file.
Run the code, provide input in the terminal when requested, and review saved run history.
Use libraries, workspace tools, sharing, and chat when your task needs more than one snippet.

Start coding

VersionSelect a runtime before running. Installed libraries belong to the selected language version.
Open files lists unopened text files. Select one to open it in an available editor tab.
Run executes the active file with its workspace available to the program.
Stop ends the current execution before its time limit.
Advanced Mode opens the IDE layout with a file explorer and movable, resizable terminal.

Workspace and libraries

New file creates a file in the selected parent directory and opens it in the editor.
New folder creates a directory, up to the supported nesting depth.
Upload files adds one or more safe files to a directory.
Download saves a file or a zipped directory to your device.
Copy duplicates a workspace file or directory.
Rename changes a file name and updates its open tab and history.
Move relocates an item; files and directories can also be dragged.
Delete removes an item after confirmation and closes its open editor tab.
Pre-installed Libs shows packages already included in this runtime.
Add Lib installs a private package for your account and selected runtime.

Terminal and assistance

Run history opens previous and next saved outputs.
Run with switches Python between the execution server and Browser Python. Browser Python synchronizes workspace files, while account-installed libraries and interactive terminal input require the execution server.
Copy output copies the current terminal text.
Clear output removes all saved run outputs after confirmation.
Explain summarizes the active code.
Recents opens recent explanations and results.
Settings changes fonts, themes, and visible toolbar buttons.

Sharing and collaboration

Share publishes the current file, a directory, or the whole workspace as a snapshot or live workspace. You control link access and who may save changes.
Logs records saved collaborator actions in a shared workspace.
Share chat lets signed-in participants message, mention people, share code blocks, and see online or typing members.
Live collaboration shows collaborator cursors and saved file updates. View-only users can load the latest version or keep temporary edits.

Quotas and limits

Execution time is limited to 20 seconds per server run. Use Stop when you want to end it sooner.
Workspace storage allows 100 files, 100 directories, and 10 MiB total for each signed-in user workspace and language.
Files and folders are limited to 1 MiB per file and 5 nested directory levels. Files generated by your code follow the same quota.
Private libraries are limited to 10 installed packages for each account and runtime.
Share snapshots can contain up to 100 files, 1 MiB per file, and 2 MiB total.
Share invitations support up to 25 people per share, with access and saving permissions managed separately.
Share chat allows 2,000 characters per message and 10,000 messages per share.
Quota enforcement also applies while code is running. A write that exceeds file count, size, storage, or nesting limits is rejected safely.

Good to know Only run code and install packages you trust. Execution is isolated and limited, but your program can still modify files inside a writable workspace up to its enforced quota.

📊 Introduction to Matplotlib

1. What is Matplotlib?

Matplotlib is a popular Python library used for creating static, animated, and interactive visualizations. It is especially useful in data science and scientific computing.

import matplotlib.pyplot as plt

2. Line Plot

A basic line chart using X and Y values.

import matplotlib.pyplot as plt

x = [1, 2, 3, 4]
y = [2, 4, 6, 8]

plt.plot(x, y)
plt.title("Line Plot")
plt.xlabel("X Axis")
plt.ylabel("Y Axis")
plt.show()

3. Bar Chart

Bar charts are useful for comparing discrete categories.

categories = ['A', 'B', 'C']
values = [5, 7, 3]

plt.bar(categories, values)
plt.title("Bar Chart")
plt.show()

4. Scatter Plot

Use scatter plots to show relationships between two numeric variables.

x = [1, 2, 3, 4]
y = [10, 20, 25, 30]

plt.scatter(x, y)
plt.title("Scatter Plot")
plt.show()

5. Pie Chart

Pie charts are used to show proportions.

labels = ['Apple', 'Banana', 'Cherry']
sizes = [30, 40, 30]

plt.pie(sizes, labels=labels, autopct='%1.1f%%')
plt.title("Fruit Distribution")
plt.show()

6. Histogram

Histograms help understand the distribution of numerical data.

import numpy as np

data = np.random.randn(1000)

plt.hist(data, bins=30)
plt.title("Histogram")
plt.show()

7. Multiple Plots

Plot multiple charts in one figure using subplots.

x = [1, 2, 3, 4]
y1 = [1, 4, 9, 16]
y2 = [2, 3, 5, 7]

plt.subplot(1, 2, 1)
plt.plot(x, y1)
plt.title("Plot 1")

plt.subplot(1, 2, 2)
plt.plot(x, y2)
plt.title("Plot 2")

plt.tight_layout()
plt.show()

8. Customize Styles

You can customize colors, styles, markers, and more.

plt.plot(x, y1, color='red', linestyle='--', marker='o')
plt.title("Styled Line")
plt.show()

9. Save Plot as Image

Save your plot to an image file using savefig().

plt.plot(x, y1)
plt.savefig("my_plot.png")

10. 3D Plot (Advanced)

Matplotlib also supports 3D plotting via mpl_toolkits.mplot3d.

from mpl_toolkits.mplot3d import Axes3D

fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')

x = [1, 2, 3]
y = [2, 3, 4]
z = [5, 6, 7]

ax.scatter(x, y, z)
plt.show()