Open source software (OSS) communities are often able to produce high quality software comparable to proprietary software. The success of an open source software development (OSSD) community is often attributed to the underlying governance model, and a key component of these models is the decisionmaking (DM) process. While there have been studies on the decision-making processes publicized by OSS communities (e.g., through published process diagrams), little has been done to study decision-making processes that can be extracted using a bottom up, data-driven approach, which can then be used to assess whether the publicized processes conform to the extracted processes. To bridge this gap, we undertook a large-scale data driven study to understand how decisions are made in an OSSD community, using the case study of Python Enhancement Proposals (PEPs), which embody decisions made during the evolution of the Python language. Our main contributions are: (a) the design and development of a framework using information retrieval and natural language processing techniques to analyze the Python email archives (comprising 1.48 million emails), and (b) the extraction of decision-making processes that reveal activities that are neither explicitly mentioned in documentation published by the Python community nor identified in prior research work. Our results provide insights into the actual decision-making process employed by the Python community.