Computational notebooks, such as Jupyter, have been widely adopted by data scientists to write code for analyzing and visualizing data. Despite their growing adoption and popularity, few studies have been found to understand Jupyter development challenges from the practitioners’ point of view. This article presents a systematic study of bugs and challenges that Jupyter practitioners face through a large-scale empirical investigation. We mined 14,740 commits from 105 GitHub open source projects with Jupyter Notebook code. Next, we analyzed 30,416 StackOverflow posts, which gave us insights into bugs that practitioners face when developing Jupyter Notebook projects. Next, we conducted 19 interviews with data scientists to uncover more details about Jupyter bugs and to gain insight into Jupyter developers’ challenges. Finally, to validate the study results and proposed taxonomy, we conducted a survey with 91 data scientists. We highlight bug categories, their root causes, and the challenges that Jupyter practitioners face.