Modern Python applications consume massive amounts of memory in data centers. Emerging memory technologies such as CXL have emerged as a pivotal interconnect for memory expansion. Prior efforts in memory tiering that relied on OS page or hardware counters information incurred notable overhead and lacked awareness of fine-grained object access patterns. Moreover, these tiering configurations cannot be tailored to individual Python applications, limiting their applicability in QoS-sensitive environments. In this paper, we introduce Memory Tiering in Python VM (MTP), an extension module built atop the popular CPython interpreter to support memory tiering in Python applications. MTP leverages reference count changes from garbage collection to infer object temperatures and reduces unnecessary migration overhead through a software-defined page temperature table with adaptive lazy demotion. To the best of our knowledge, MTP is the first framework to offer portability, easy deployment, and per-application tiering customization for Python workloads.