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Understanding student practice behavior and its connection to their learning is essential for effective recommender systems that provide personalized learning support. In this study, we apply a sequential pattern mining approach to analyze student practice behavior in a practice system for introductory Python programming. Our goal is to identify different types of practice behavior and connect them to student performance. We examine two types of practice sequences: (1) by login session and (2) by learning topic. For each sequence type, we use SPAM (Sequential PAttern Mining) to identify the most frequent micro-patterns and build behavior profiles of individual learners as vectors of micro-pattern frequencies observed in their behavior. We confirm that these vectors are stable for both sequence types (p < 0.03 for session sequences and p < 0.003 for topic sequences). Using the vectors, we perform K-means clustering where we identify two practice behaviors: example explorers and persistent finishers. We repeat this experiment using different coding approaches for student sequences and obtain similar clusters. Our results suggest that example explorers and persistent finishers might represent two typical types of divergent student behaviors in a programming practice system. Finally, to better understand the relationship between students' background knowledge, learning outcomes, and practice behavior, we perform statistical analyses to assess the significance of the associations among pre-test scores, cluster assignments, and final course grades.