- Jupyter Tutorial
- Jupyter - Home
- IPython - Introduction
- IPython - Installation
- IPython - Getting Started
- Running & Editing Python Script
- IPython - History Command
- IPython - System Commands
- IPython - Command Line Options
- Dynamic Object Introspection
- IPython - IO Caching
- Setting IPython as Default Python Environment
- Importing Python Shell Code
- IPython - Embedding IPython
- IPython - Magic Commands
- Project Jupyter - Overview
- Jupyter Notebook - Introduction
- Working With Jupyter Online
- Installation and Getting Started
- Jupyter Notebook - Dashboard
- Jupyter Notebook - User Interface
- Jupyter Notebook - Types of Cells
- Jupyter Notebook - Editing
- Jupyter Notebook - Markdown Cells
- Cell Magic Functions
- Jupyter Notebook - Plotting
- Converting Notebooks
- Jupyter Notebook - IPyWidgets
- QtConsole - Getting Started
- QtConsole - Multiline Editing
- QtConsole - Inline Graphics
- QtConsole - Save to Html
- QtConsole - Multiple Consoles
- Connecting to Jupyter Notebook
- Using github and nbviewer
- JupyterLab - Overview
- Installation & Getting Started
- JupyterLab - Interface
- JupyterLab - Installing R Kernel
JupyterLab - Overview
Project Jupyter describes JupyterLab as a next generation web based user interfaces for all products under the Jupyter ecosystem. It enables you to work seamlessly with notebook, editors and terminals in an extensible manner.
Some of the important features of JupyterLab are discussed below −
Code Console acts as scratchpad for running code interactively. It has full support for rich output and can be linked to a notebook kernel to log notebook activity.
Any text file (Markdown, Python, R, LaTeX, etc.) can be run interactively in any Jupyter kernel.
Notebook cell output can be shown into its own tab, or along with the notebook, enabling simple dashboards with interactive controls backed by a kernel.
Live editing of document reflects in other viewers such as editors or consoles. It is possible to have live preview of Markdown, Delimiter-separated Values, or Vega/Vega-Lite documents.
JupyterLab can handle many file formats (images, CSV, JSON, Markdown, PDF etc.). It also displays rich output in these formats. JupyterLab provides customizable keyboard shortcuts uses key maps from many well-known text editors.
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