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Python Online Compiler

Run Python code online with support for NumPy, Pandas, Matplotlib, library management and code sharing

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How to run Python code online

  1. 1

    Paste code or select an example

    Enter Python code in the editor, or quickly get started by selecting from preset examples like NumPy, Pandas, Matplotlib, etc.

  2. 2

    Choose Python version

    Switch between Python 3.8-3.12 versions as needed to verify different syntax or library compatibility.

  3. 3

    Load required libraries and run

    If your code uses third-party libraries, you can open "Library Manager" to load the required packages; if a package is missing and causes an error, you can install it with one click and run again.

  4. 4

    View results and share

    After running, view results, errors, or Matplotlib charts in the output area, and you can copy the output, restore history, or generate a sharing link.

Features Overview

A free Python online compiler and Python Playground. Run Python 3.8-3.12 code in your browser, with support for loading commonly used libraries like NumPy, Pandas, Matplotlib, Scikit-learn, and Requests on demand, without needing to install a local environment.

Tool Description

Free Python online compiler / online IDE. Supports Python 3.8-3.12, NumPy, Pandas, Matplotlib, Scikit-learn, Requests and custom .py modules, running Python code locally in the browser. Provides preset examples, history, one-click missing package installation and code sharing, data is not uploaded.

Capabilities Checklist

  • Supports switching between Python 3.8-3.12 versions, ideal for learning, example verification, and quick experiments.
  • Includes 15+ popular Python libraries with support for loading NumPy, Pandas, Matplotlib, SciPy, Scikit-learn, Requests, Beautiful Soup, and more.
  • Provides a library manager and quick preset combinations, automatically handling dependencies and displaying estimated load sizes.
  • Automatically detects missing packages and provides one-click install options, reducing troubleshooting costs after import errors.
  • Separates standard output, error output, and runtime warnings, with Matplotlib charts viewable directly in the results panel.
  • Offers 12+ preset examples, last 20 local history records, and sharing links for convenient teaching, debugging, and collaboration.
  • All code and data are executed locally in the browser without being uploaded to the server, making it suitable for privacy-sensitive scenarios.

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Frequently Asked Questions

How do I run Python code online?
Paste Python code into the editor, or select a preset example first, then click "Run". The tool executes code locally in your browser and displays output, errors, or charts in real-time.
Does this Python online compiler support NumPy, Pandas, and Matplotlib?
Yes. The tool can load 15+ popular libraries including NumPy, Pandas, Matplotlib, SciPy, Scikit-learn, Requests, Beautiful Soup, and more, suitable for scientific computing, data analysis, and visualization verification.
What should I do if I get a missing library error when importing?
When your code uses a library that hasn't been loaded, the tool automatically detects common missing packages and provides a one-click install option. You can also manually open "Library Manager" to search for and load the libraries you need.
How do I save or share my Python code?
The last 20 execution records are automatically saved in your browser's local history and can be restored, copied, or deleted anytime. To share, use "Share Code" to generate a link with code parameters.
Will my code be uploaded to the server?
No. Python code, output, and most processing happen locally in your browser and are not actively uploaded to the server, making it suitable for debugging sensitive scripts or handling private data workflows.
Can I upload custom Python modules?
Yes. The library manager supports uploading custom .py module files, suitable for importing your own utility functions or lightweight business logic, which can then be called directly in the current Playground.
What scenarios is this tool suitable for?
It's suitable for learning Python, verifying syntax, testing algorithms, teaching demonstrations, handling small-scale data, and quickly testing NumPy, Pandas, and Matplotlib code. For production deployment or long-running computation tasks, we recommend using a local or server environment.