Supported Jupyter Notebook Libraries

The Jupyter notebook provided by Nexis® Data Lab are prebuilt with the following Python libraries:

 

LexisNexis Packages

lexisnexis.news.dataset This library connects to your workspace and allows you to access documents which you exported using the UI. Many of the examples here show how to use it.

 

Machine Learning Packages

Library Description Link to Library

Pandas

Pandas is an open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools for the Python

https://pandas.pydata.org/

NumPy NumPy is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays. https://numpy.org/
SciKit Learn Scikit-learn (formerly scikits.learn and also known as sklearn) is a free software machine learning library for the Python programming language. It features various classification, regression and clustering algorithms including support vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy. https://scikit-learn.org/stable/
Natural Language Toolkit   https://www.nltk.org/

Spacy NLP

 

https://spacy.io/

Matplotlib

 

https://matplotlib.org/

Gluon NLP

 

https://gluon-nlp.mxnet.io/

 

Visualization packages:

Seaborn

Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics.

It uses matplotlib to draw plots. Many tasks can be accomplished with only seaborn functions, but further customization might require using matplotlib directly.

https://seaborn.pydata.org/

Altair

Another visualization tool, one main feature is the user interactivity - not provided by seaborn packages. It comes with abilities to export, select, zoom, pan or even bind multiple plots to reflect the changes with user selection.

https://altair-viz.github.io/

Wordcloud

 

http://amueller.github.io/word_cloud/

pyLDAvis

It is a Python library specific to interactive topic model visualization. It lets you visualize the different clusters of topics, and see the top words associated along with the inter topic distancing and other choice of other user metrics.

https://pypi.org/project/pyLDAvis/