Pattern mining is essential for uncovering valuable patterns hidden in big data. While software such as WEKA, Mahout, SPMF, and Knime offer some capabilities, they are often limited in algorithms or integration. To overcome these limitations, researchers at the University of Aizu have developed the pattern mining (PAMI) package. This open-source Python package, available on GitHub and distributed through the Python Package Index, offers over 80 algorithms to identify user interest-based patterns in various databases across multiple computing environments. This chapter introduces the architecture and systematic organization of the algorithms in PAMI. It provides detailed guidance on the installation, maintenance, and execution of the algorithms in PAMI, both from the terminal and within Python programs. Additionally, the chapter explains the input and output requirements for the algorithms, including how they report runtime and memory usage. Through practical examples and instructions, this chapter aims to help users effectively utilize the PAMI package for pattern mining tasks.