Landscape ecology is the study of how different land uses and natural areas are arranged across a region, and how these spatial patterns affect biodiversity, ecosystem health, and human impacts. To measure and track these patterns, ecologists are using a range of tools and metrics that capture features such as connectivity, fragmentation, and the balance between natural and developed land. One such method is the Landscape Mosaic (LM) approach which classifies land into categories based on the mix of agriculture, natural habitats, and developed areas (e.g., urban), providing an integrated view of how humans are influencing ecosystems. Until recently, LM was only available through a specialized software package (i.e., GuidosToolbox), which limits its flexibility, interaction with other tools, and integration in scientific workflows. To address this, we present PyLM, a Python-based implementation of the LM model, making it easier for researchers, planners, and conservationists to analyze land use/cover (LUC) maps, generate statistics, and embed results into broader environmental workflows. The applicability of PyLM is demonstrated through a use case based on a LUC dataset for Switzerland. This new implementation enhances accessibility, supports sustainability assessments, and strengthens the ability to monitor landscapes over time.