How to view which rows comprise a topic

[ecksteing]

Is it possible to view the rows that comprise a topic? If so how?
For example, say I have a dataframe with a column called "ID" that is unique for each row, and the text to model is in a column titled "TEXT". How do I view which rows are deemed to constitute each topic?
I'm more familiar with PyCaret and .head shows the dataframe columns and assigned topic number. Any help appreciated :)

[MaartenGr]

The package follows, to a certain extent, sklearn's API in that whenever you use transform on a set of documents, it will return the topics in the same order. Let's say you have the following code:

from bertopic import BERTopic
from sklearn.datasets import fetch_20newsgroups

docs = fetch_20newsgroups(subset='all',  remove=('headers', 'footers', 'quotes'))['data']

topic_model = BERTopic()
topics, probs = topic_model.fit_transform(docs)

Here, docs is a list of documents on which you train the model. Running .fit_transform(docs) will return the variable topics. In topics, you will find the topics that belong to each documents. The topic in topics[0] corresponds to the document in docs[0], topics[1] to docs[1], etc.

You can use that structure to extract the documents under a certain topic by using, for example, the following:

import pandas as pd
results = pd.DataFrame({"Doc": docs, "Topic": topics})

The results variable can then be extended with whatever metadata you have, like the ID of each document.

If you already have a data frame, df, with TEXT and ID columns, then you would only need to do the following:

df["Topic"] = topics

[ecksteing]

Thank you Maarten. Worked perfectly and very much appreciated!