  Diverse presentation formats play a pivotal role in effectively conveying
code and analytical processes during data analysis. One increasingly popular
format is tutorial videos, particularly those based on Jupyter notebooks, which
offer an intuitive interpretation of code and vivid explanations of analytical
procedures. However, creating such videos requires a diverse skill set and
significant manual effort, posing a barrier for many analysts. To bridge this
gap, we introduce an innovative tool called NotePlayer, which connects notebook
cells to video segments and incorporates a computational engine with language
models to streamline video creation and editing. Our aim is to make the process
more accessible and efficient for analysts. To inform the design of NotePlayer,
we conducted a formative study and performed content analysis on a corpus of 38
Jupyter tutorial videos. This helped us identify key patterns and challenges
encountered in existing tutorial videos, guiding the development of NotePlayer.
Through a combination of a usage scenario and a user study, we validated the
effectiveness of NotePlayer. The results show that the tool streamlines the
video creation and facilitates the communication process for data analysts.
