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VS Code Jupyter extension

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Quick Overview

The microsoft/vscode-jupyter repository is an extension for Visual Studio Code that provides support for Jupyter notebooks and interactive Python programming. It allows users to create, edit, and run Jupyter notebooks directly within VS Code, offering a seamless integration of data science workflows into the popular code editor.

Pros

  • Seamless integration with VS Code, providing a familiar environment for developers
  • Support for multiple kernels, including Python, R, and Julia
  • Interactive debugging capabilities for notebook cells
  • Rich IntelliSense and code completion features

Cons

  • Occasional performance issues with large notebooks or datasets
  • Some advanced Jupyter features may not be fully supported
  • Learning curve for users transitioning from traditional Jupyter environments
  • Dependency on VS Code, which may not be preferred by all data scientists

Getting Started

  1. Install Visual Studio Code from https://code.visualstudio.com/
  2. Open VS Code and navigate to the Extensions view (Ctrl+Shift+X)
  3. Search for "Jupyter" and install the "Jupyter" extension by Microsoft
  4. Open or create a new .ipynb file to start working with Jupyter notebooks in VS Code
  5. Use the "Select Kernel" button in the top-right corner to choose your preferred kernel

For more detailed instructions and advanced features, refer to the official documentation at https://code.visualstudio.com/docs/datascience/jupyter-notebooks

Competitor Comparisons

jupyter logo

notebook

11,556

Jupyter Interactive Notebook

Pros of Notebook

  • Native web-based interface, accessible from any browser without additional software
  • Simpler, more focused user interface for data science and interactive computing
  • Easier to share and collaborate on notebooks through platforms like JupyterHub

Cons of Notebook

  • Limited integrated development environment (IDE) features compared to VS Code
  • Less extensible and customizable than VS Code's Jupyter extension
  • Fewer language support options outside of Python and R

Code Comparison

Notebook (Python cell):

import pandas as pd
df = pd.read_csv('data.csv')
df.head()

vscode-jupyter (Python file with cell markers):

#%%
import pandas as pd
df = pd.read_csv('data.csv')
df.head()

Summary

Notebook provides a streamlined, web-based experience for interactive computing, while vscode-jupyter integrates Jupyter functionality into a full-featured IDE. The choice between them depends on user preferences, development needs, and the desired balance between simplicity and advanced features.

jupyterlab logo

jupyterlab

14,069

JupyterLab computational environment.

Pros of JupyterLab

  • Native support for interactive widgets and rich outputs
  • Integrated file browser and terminal
  • Extensible architecture with a wide range of plugins

Cons of JupyterLab

  • Steeper learning curve for new users
  • Less seamless integration with version control systems
  • Limited language support compared to VS Code's extensive ecosystem

Code Comparison

JupyterLab (Python cell):

%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(0, 10, 100)
plt.plot(x, np.sin(x))
plt.show()

VS Code Jupyter (Python cell):

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(0, 10, 100)
plt.plot(x, np.sin(x))
plt.show()

The code examples are similar, but JupyterLab uses the %matplotlib inline magic command by default, while VS Code Jupyter handles this automatically. Both environments support interactive plotting, but JupyterLab's native support for widgets may provide a more seamless experience for certain visualizations.

nteract logo

nteract

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📘 The interactive computing suite for you! ✨

Pros of nteract

  • Standalone desktop application, not tied to a specific editor
  • Supports multiple programming languages beyond Python
  • More flexible and customizable UI with composable components

Cons of nteract

  • Smaller community and less frequent updates
  • Fewer built-in features compared to VS Code's extensive ecosystem
  • May require more setup and configuration for advanced workflows

Code Comparison

nteract:

import { ContentRef } from "@nteract/core";
import { NotebookApp } from "@nteract/notebook-app-component";

<NotebookApp contentRef={ContentRef} />

vscode-jupyter:

import { NotebookEditor } from './notebookEditor';
import { INotebookModel } from './notebookModel';

class JupyterNotebookProvider implements vscode.NotebookContentProvider {
    async openNotebook(uri: vscode.Uri): Promise<INotebookModel> {
        // Implementation details
    }
}

Summary

nteract offers a standalone, multi-language notebook experience with a flexible UI, while vscode-jupyter integrates tightly with VS Code's extensive features. nteract may appeal to users seeking a dedicated notebook application, while vscode-jupyter benefits from VS Code's ecosystem and frequent updates. The choice between them depends on individual preferences and workflow requirements.

spyder-ide logo

spyder

8,185

Official repository for Spyder - The Scientific Python Development Environment

Pros of Spyder

  • Integrated scientific environment with built-in variable explorer and data viewer
  • More focused on scientific computing and data analysis workflows
  • Includes a powerful debugger and profiler specifically designed for scientific Python code

Cons of Spyder

  • Less extensible compared to VS Code's plugin ecosystem
  • Slower development cycle and fewer frequent updates
  • Limited support for non-Python languages and general-purpose development tasks

Code Comparison

Spyder:

# In Spyder, you can run cells directly
# %% Cell 1
import numpy as np
data = np.random.rand(100, 100)

# %% Cell 2
import matplotlib.pyplot as plt
plt.imshow(data)
plt.show()

VS Code Jupyter:

# In VS Code, you use Jupyter-style cell markers
# %%
import numpy as np
data = np.random.rand(100, 100)

# %%
import matplotlib.pyplot as plt
plt.imshow(data)
plt.show()

Both environments support cell-based execution, but Spyder uses its own cell markers, while VS Code Jupyter adheres to the standard Jupyter notebook format. Spyder's integrated environment may provide a more seamless experience for scientific workflows, while VS Code offers greater flexibility and extensibility for various development tasks.

fastai logo

nbdev

4,882

Create delightful software with Jupyter Notebooks

Pros of nbdev

  • Designed for literate programming, allowing seamless integration of code, documentation, and tests in notebooks
  • Supports two-way sync between notebooks and source code files
  • Facilitates easier creation of Python packages directly from notebooks

Cons of nbdev

  • Steeper learning curve for users not familiar with the fastai ecosystem
  • More limited language support compared to vscode-jupyter (primarily focused on Python)

Code Comparison

nbdev:

# %% [markdown]
# # My Module
# This is a documentation cell

# %% [code]
def my_function(x):
    """A sample function"""
    return x * 2

vscode-jupyter:

# %%
# No special syntax for documentation
def my_function(x):
    """A sample function"""
    return x * 2

Key Differences

  • nbdev focuses on creating Python packages from notebooks, while vscode-jupyter is a more general-purpose Jupyter notebook extension for VS Code
  • nbdev uses special cell markers for documentation and code organization, whereas vscode-jupyter relies on standard Jupyter notebook conventions
  • vscode-jupyter offers broader language support and integration with VS Code features, while nbdev is more specialized for Python package development

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README

Jupyter Extension for Visual Studio Code

A Visual Studio Code extension that provides basic notebook support for language kernels that are supported in Jupyter Notebooks today, and allows any Python environment to be used as a Jupyter kernel. This is NOT a Jupyter kernel--you must have Python environment in which you've installed the Jupyter package, though many language kernels will work with no modification. To enable advanced features, modifications may be needed in the VS Code language extensions.

NotebookUI

LinkDescription
File an issueReport problems and suggest enhancements
Go to docsJupyter extension and data science in VS Code documentation, tutorials, and more
DiscussionsPost questions, and engage in community discussions

Work in the browser

Editing Jupyter notebooks in VS Code can also be done on the browser in two ways.

  1. The Jupyter extension has support for a web based interface provided by vscode.dev (which includes github.dev, available by typing '.' when viewing a repo on github.com)
  2. The Jupyter extension can be installed on VS Code in GitHub Codespaces the same way it is done locally (or sync your VS Code settings to have your extensions installed automatically on Codespaces).

Installed extensions

The Jupyter Extension will automatically install the following extensions by default to provide enhanced Jupyter notebook experiences in VS Code.

You can also install the Jupyter PowerToys extension to try out experimental features (not installed by default). Extensions installed through the marketplace are subject to the Marketplace Terms of Use, and any or all of these extensions can be disabled or uninstalled.

Working with Python

Quick Start

  • Step 1. Install VS Code
  • Step 2. Install Anaconda/Miniconda or another Python environment in which you've installed the Jupyter package
  • Since not working with Python, make sure to have a Jupyter Kernel that corresponds to the language you would like to use installed on your machine.
  • Step 3. Install the Jupyter Extension and the Python Extension
  • Step 4. Open or create a notebook file by opening the Command Palette (Ctrl+Shift+P) and select Jupyter: Create New Jupyter Notebook.
  • Step 5. Select your kernel by clicking on the kernel picker in the top right of the notebook or by invoking the Notebook: Select Notebook Kernel command and start coding!

Working with other Languages

The Jupyter Extension supports other languages in addition to Python such as Julia, R, and C#.

Quick Start

  • Step 1. Install VS Code
  • Step 2. Since not working with Python, make sure to have a Jupyter kernelspec that corresponds to the language you would like to use installed on your machine.
  • Step 3. Install the Jupyter Extension
  • Step 4. Open or create a notebook file and start coding!

Notebook support

The Jupyter Extension uses the built-in notebook support from VS Code. This UI gives a number of advantages to users of notebooks:

  • Out of the box support for VS Code's vast array of basic code editing features like hot exit, find & replace, and code folding.
  • Editor extensions like VIM, bracket colorization, linters and many more are available while editing a cell.
  • Deep integration with general workbench and file-based features in VS Code like outline view (Table of Contents), breadcrumbs and other operations.
  • Fast load times for Jupyter notebook (.ipynb) files. Any notebook file is loaded and rendered as quickly as possible, while execution-related operations are initialized behind the scenes.
  • Includes a notebook-friendly diff tool, making it much easier to compare and see differences between code cells, output and metadata.
  • Extensibility beyond what the Jupyter extension provides. Extensions can now add their own language or runtime-specific take on notebooks, such as the .NET Interactive Notebooks and Gather
  • While the Jupyter extension comes packaged with a large set of the most commonly used renderers for output, the marketplace supports custom installable renderers to make working with your notebooks even more productive. To get started writing your own, see VS Code's renderer api documentation.

Useful commands

Open the Command Palette (Command+Shift+P on macOS and Ctrl+Shift+P on Windows/Linux) and type in one of the following commands:

CommandDescription
Jupyter: Create New Jupyter NotebookCreates a new Jupyter Notebook
Notebook: Select Notebook KernelSelect or switch kernels within your notebook
Notebook: Change Cell LanguageChange the language of the cell currently in focus
Jupyter: Export to HTML Jupyter: Export to PDFCreate a presentation-friendly version of your notebook in HTML or PDF

To see all available Jupyter Notebook commands, open the Command Palette and type Jupyter or Notebook.

Context Keys for Key bindings

You can use the extension's context keys in 'when' clauses. Here's an example:

  {
    "key": "ctrl+i",
    "command": "jupyter.runAndDebugCell",
    "when": "!jupyter.webExtension"
  }

That keybinding states the jupyter.runAndDebugCell command should map to CTRL+I when not in the jupyter.webExtension.

The full list of context keys can be found here: https://github.com/microsoft/vscode-jupyter/wiki/Extensibility-for-other-extensions#context-keys-for-keybindings

Feature details

Learn more about the rich features of the Jupyter extension:

  • IntelliSense: Edit your code with auto-completion, code navigation, syntax checking and more.

  • Jupyter Notebooks: Create and edit Jupyter Notebooks, add and run code/markdown cells, render plots, create presentation-friendly versions of your notebook by exporting to HTML or PDF and more.

Supported locales

The extension is available in multiple languages: de, en, es, fa, fr, it, ja, ko-kr, nl, pl, pt-br, ru, tr, zh-cn, zh-tw

Questions, issues, feature requests, and contributions

  • If you have a question about how to accomplish something with the extension, please ask on Discussions. Our wiki can be a source of information as well.

  • Any and all feedback is appreciated and welcome! If you come across a problem or bug with the extension, please file an issue.

    • If someone has already filed an issue that encompasses your feedback, please leave a 👍/👎 reaction on the issue.

  • Contributions are always welcome, so please see our contributing guide for more details.

  • If you're interested in the development of the extension, you can read about our development process

Data and telemetry

The Microsoft Jupyter Extension for Visual Studio Code collects usage data and sends it to Microsoft to help improve our products and services. Read our privacy statement to learn more. This extension respects the telemetry.telemetryLevel setting which you can learn more about at https://code.visualstudio.com/docs/supporting/faq#_how-to-disable-telemetry-reporting.

Trademarks

This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft's Trademark & Brand Guidelines. Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party's policies.