t

net = Network(select_menu=True) net.from_nx(G) neighbor_map = net.get_adj_list() for node in net.nodes: x, y = pos[node["id"]] node["x"] = x*10000 node["y"] = y*10000 node["title"] += " Neighbors:\n" + "\n".join(neighbor_map[node["id"]]) node["value"] = len(neighbor_map[node["id"]]) net.toggle_physics(False) net.save_graph("trc_graph_select.html")

sin

net = Network()

Now that we have our NetworkX Graph, we can create a PyVis Network class.

net.save_graph("simple_graph.html") Copy to clipboard from IPython.display import HTML HTML(filename="simple_graph.html")

theo

cna

A lot of what I will cover here, can be found in the BookNLP repository, specifically in the Google Colab Jupyter Notebook.

2,326

tlel

It is important to remember that some of these are, of course

99400

3031

If you are interested in Pandas, though, I have a free textbook on it entitled Introduction to Pandas.

Mr. Mr.

bee

input_file="../data/harry_potter_cleaned.txt" output_directory="../data/harry_potter" book_id="harry_potter"

filefile

README.md file on the official repository for BookNLP as well as the official Google Colab Notebook.

Now that we have our data, we can iterate them all simultaneously with the example provided in the Top2Vec README on GitHub.

In the previous chapter, we learned how to flatten data with PCA.

This document vector is similar to the word vector that we met in Part Three of this textbook.

pip install umap-learn

pip install sentence_transformers

nltk.download('stopwords')

pip install pyldavis

pip install gensim

unidcode

by Key Camps

in 01.03: Rules-Based NER,

Possible variations are accounted for with a *

We have already met the Matcher in 01.03: Rules-Based Matching

with synthesize

Treblinka LOC

after

after

after