Natural Language to Code Generation in Interactive Data Science Notebooks

Computational notebooks, such as Jupyter notebooks, are interactive computing environments that are ubiquitous among data scientists to perform data wrangling and analytic tasks. To measure the performance of AI pair programmers that automatically synthesize programs for those tasks given natural language (NL) intents from users, we build ARCADE, a benchmark of 1,078 code generation problems using the pandas data analysis framework in data science notebooks. AR-CADE features multiple rounds of NL-to-code problems from the same notebook. It requires a model to understand rich multi-modal contexts, such as existing notebook cells and their execution states as well as previous turns of interaction. To establish a strong baseline on this challenging task, we develop PACH-INCO, a 62B code language model (LM) for Python computational notebooks, which significantly outperforms public code LMs. Finally, we explore few-shot prompting strategies to elicit better code with step-by-step decomposition and NL explanations, showing the potential to improve the diversity and explainability of model predictions. ARCADE is publicly available at https://github.com/ google-research/arcade-nl2code/ .