  At present, code recommendation tools have gained greater importance to many
software developers in various areas of expertise. Having code recommendation
tools has enabled better productivity and performance in developing the code in
software and made it easier for developers to find code examples and learn from
them.
  This paper proposes Typhon, an approach to automatically recommend relevant
code cells in Jupyter notebooks. Typhon tokenizes developers' markdown
description cells and looks for the most similar code cells from the database
using text similarities such as the BM25 ranking function or CodeBERT, a
machine-learning approach. Then, the algorithm computes the similarity distance
between the tokenized query and markdown cells to return the most relevant code
cells to the developers.
  We evaluated the Typhon tool on Jupyter notebooks from Kaggle competitions
and found that the approach can recommend code cells with moderate accuracy.
The approach and results in this paper can lead to further improvements in code
cell recommendations in Jupyter notebooks.
