Notebook

Introduction to the JupyterLab and Jupyter Notebooks¶

This is a short introduction to two of the flagship tools created by the Jupyter Community.

Note: This interface is provided by the JupyterLite project, which embeds an entire JupyterLab interface, with many popular packages for scientific computing, in your browser. There may be some minor differences in behavior between JupyterLite and the JupyterLab you install locally.

JupyterLab 🧪¶

JupyterLab is a next-generation web-based user interface for Project Jupyter. It enables you to work with documents and activities such as Jupyter notebooks, text editors, terminals, and custom components in a flexible, integrated, and extensible manner. It is the interface that you're looking at right now.

For an overview of the JupyterLab interface, see the JupyterLab Welcome Tour on this page, by going to Help -> Welcome Tour and following the prompts.

See Also: For a more in-depth tour of JupyterLab with a full environment that runs in the cloud, see the JupyterLab introduction on Binder.

Jupyter Notebooks 📓¶

Jupyter Notebooks are a community standard for communicating and performing interactive computing. They are a document that blends computations, outputs, explanatory text, mathematics, images, and rich media representations of objects.

JupyterLab is one interface used to create and interact with Jupyter Notebooks.

For an overview of Jupyter Notebooks, see the JupyterLab Welcome Tour on this page, by going to Help -> Notebook Tour and following the prompts.

See Also: For a more in-depth tour of Jupyter Notebooks and the Classic Jupyter Notebook interface, see the Jupyter Notebook IPython tutorial on Binder.

An example: visualizing data in the notebook ✨¶

Below is an example of a code cell. We'll visualize some simple data using two popular packages in Python. We'll use NumPy to create some random data, and Matplotlib to visualize it.

Note how the code and the results of running the code are bundled together.

In [1]:

from matplotlib import pyplot as plt
import numpy as np

# Generate 100 random data points along 3 dimensions
x, y, scale = np.random.randn(3, 100)
fig, ax = plt.subplots()

# Map each onto a scatterplot we'll create with Matplotlib
ax.scatter(x=x, y=y, c=scale, s=np.abs(scale)*500)
ax.set(title="Some random data, created with JupyterLab!")
plt.show()

Next steps 🏃¶

This is just a short introduction to JupyterLab and Jupyter Notebooks. This demonstration contains a lot more that you can play around with. Here are some pointers to help you take the next step. Each of the items below corresponds to a file or folder in the file browser to the left.

python.ipynb is a Jupyter Notebook that shows off some basic Python functionality, including more visualizations, data structures, and scientific computing libraries.
pyolite/ is a folder that contains several Jupyter Notebooks that highlight many more things that you can do in JupyterLab / JupyterLite. Explore them for inspiration about what you'd like to do next.
p5.ipynb is a Jupyter Notebook that shows off computing with the p5 platform, which allows you to build visual experiences with Javascript.
javascript.ipynb is a Jupyter Notebook that shows off how you can run Javascript code within an ipynb file.