Jupyter notebooks are mostly known for their web-based user interface, such as JupyterLab or the Classic Notebook.

Python has incredibly scalable options for exploring data. With Pandas or Dask, you can scale Jupyter up to big data. But what about small data? Personal data? Private data? [JupyterLab and Jupyter Notebook provide a great environment to scrutinize my laptop-based life.

A guide to document clustering with Python

pyLDAvis is a python library for interactive topic model visualization. It is a port of the fabulous R package by Carson Sievert and Kenny Shirley. They did the hard work of crafting an effective visualization. pyLDAvis makes it easy to use the visualiziation from Python and, in particular, Jupyter notebooks.

To learn more about the method behind the visualization, it is possible to read the original paper explaining it.

This notebook provides a quick overview of how to use pyLDAvis.

Jupyter notebooks as Markdown documents, Julia, Python or R scripts.

A iPython notebook that introduces how to use the topicmodels module for implementing Latent Dirichlet Allocation using the collapsed Gibbs sampling algorithm of Griffiths and Steyvers (2004). The module contains three classes: one for processing raw text, another for implementing LDA, and another for querying. This tutorial will go through the main features of each, for full details see the documented source code.