PyDMD is a Python package designed to implement dynamic mode decomposition (DMD) and its variants, making it a powerful tool for analyzing dynamical systems. DMD is a data-driven technique that reveals coherent spatiotemporal patterns from data, and its linear algebra-based formulation allows for various optimizations and extensions. The package includes several cutting-edge DMD methods and tools, such as optimized DMD for noise suppression, coherent spatiotemporal scale separation (CoSTS) for multiscale measurements, parametric DMD for parameterized systems, randomized DMD for data compression, and physics-informed DMD for enforcing model constraints. These features are particularly useful for handling noisy, high-dimensional, and strongly nonlinear dynamics. The PyDMD package is modular and user-friendly, with each DMD variant having its own module. It supports a wide range of functionalities, including parameter handling, data preprocessing, and visualization. The package also provides Jupyter Notebook tutorials and comprehensive documentation to assist users in applying DMD to real-world data. The article provides an overview of the features available in PyDMD version 1.0, along with a brief explanation of the DMD algorithm and practical tips for using DMD effectively. It emphasizes the importance of selecting the appropriate DMD variant based on the nature of the data and the specific requirements of the problem. The package is open-source and available on GitHub, making it accessible to researchers and practitioners from various scientific disciplines.PyDMD is a Python package designed to implement dynamic mode decomposition (DMD) and its variants, making it a powerful tool for analyzing dynamical systems. DMD is a data-driven technique that reveals coherent spatiotemporal patterns from data, and its linear algebra-based formulation allows for various optimizations and extensions. The package includes several cutting-edge DMD methods and tools, such as optimized DMD for noise suppression, coherent spatiotemporal scale separation (CoSTS) for multiscale measurements, parametric DMD for parameterized systems, randomized DMD for data compression, and physics-informed DMD for enforcing model constraints. These features are particularly useful for handling noisy, high-dimensional, and strongly nonlinear dynamics. The PyDMD package is modular and user-friendly, with each DMD variant having its own module. It supports a wide range of functionalities, including parameter handling, data preprocessing, and visualization. The package also provides Jupyter Notebook tutorials and comprehensive documentation to assist users in applying DMD to real-world data. The article provides an overview of the features available in PyDMD version 1.0, along with a brief explanation of the DMD algorithm and practical tips for using DMD effectively. It emphasizes the importance of selecting the appropriate DMD variant based on the nature of the data and the specific requirements of the problem. The package is open-source and available on GitHub, making it accessible to researchers and practitioners from various scientific disciplines.