- Matplotlib Tutorial
- Matplotlib - Home
- Matplotlib - Introduction
- Matplotlib - Environment Setup
- Matplotlib - Anaconda distribution
- Matplotlib - Jupyter Notebook
- Matplotlib - Pyplot API
- Matplotlib - Simple Plot
- Matplotlib - PyLab module
- Object-oriented Interface
- Matplotlib - Figure Class
- Matplotlib - Axes Class
- Matplotlib - Multiplots
- Matplotlib - Subplots() Function
- Matplotlib - Subplot2grid() Function
- Matplotlib - Grids
- Matplotlib - Formatting Axes
- Matplotlib - Setting Limits
- Setting Ticks and Tick Labels
- Matplotlib - Twin Axes
- Matplotlib - Bar Plot
- Matplotlib - Histogram
- Matplotlib - Pie Chart
- Matplotlib - Scatter Plot
- Matplotlib - Contour Plot
- Matplotlib - Quiver Plot
- Matplotlib - Box Plot
- Matplotlib - Violin Plot
- Three-dimensional Plotting
- Matplotlib - 3D Contour Plot
- Matplotlib - 3D Wireframe plot
- Matplotlib - 3D Surface plot
- Matplotlib - Working With Text
- Mathematical Expressions
- Matplotlib - Working with Images
- Matplotlib - Transforms

- Matplotlib Useful Resources
- Matplotlib - Quick Guide
- Matplotlib - Useful Resources
- Matplotlib - Discussion

- Selected Reading
- UPSC IAS Exams Notes
- Developer's Best Practices
- Questions and Answers
- Effective Resume Writing
- HR Interview Questions
- Computer Glossary
- Who is Who

# Matplotlib - Jupyter Notebook

Jupyter is a loose acronym meaning Julia, Python, and R. These programming languages were the first target languages of the Jupyter application, but nowadays, the notebook technology also supports many other languages.

In 2001, Fernando Pérez started developing Ipython. **IPython** is a command shell for interactive computing in multiple programming languages, originally developed for the Python.

Consider the following features provided by IPython −

Interactive shells (terminal and Qt-based).

A browser-based notebook with support for code, text, mathematical expressions, inline plots and other media.

Support for interactive data visualization and use of GUI toolkits.

Flexible, embeddable interpreters to load into one's own projects.

In 2014, Fernando Pérez announced a spin-off project from IPython called Project Jupyter. IPython will continue to exist as a Python shell and a kernel for Jupyter, while the notebook and other language-agnostic parts of IPython will move under the Jupyter name. Jupyter added support for Julia, R, Haskell and Ruby.

To start the Jupyter notebook, open Anaconda navigator (a desktop graphical user interface included in Anaconda that allows you to launch applications and easily manage Conda packages, environments and channels without the need to use command line commands).

Navigator displays the installed components in the distribution.

Launch Jupyter Notebook from the Navigator −

You will see the application opening in the web browser on the following address − **http://localhost:8888.**

You probably want to start by making a new notebook. You can easily do this by clicking on the "New button" in the "Files tab". You see that you have the option to make a regular text file, a folder, and a terminal. Lastly, you will also see the option to make a Python 3 notebook.