MDAnalysis is a Python library for analyzing molecular dynamics (MD) simulation trajectories and protein structures. It provides a uniform, object-oriented interface that abstracts raw simulation data, enabling users to write portable and immediately usable code across biomolecular simulation communities. The library supports over 25 file formats and is widely used, forming the foundation for specialized biomolecular simulation tools. It is available under the GNU General Public License v2 and is written in Python and Cython, using NumPy arrays for interoperability with the scientific Python ecosystem. MDAnalysis allows users to perform complex analyses, such as calculating root mean square fluctuations (RMSF) for residues in proteins, and supports interactive use in Jupyter notebooks with visualization tools like nglview. It also includes a variety of analysis algorithms, including RMSD, RMSF, and lipid diffusion analysis, and provides a flexible framework for developing new analysis tools. The library's modular design and efficient data structures enable it to handle large simulations, including those with up to 10 million particles. Recent improvements include faster access to atom attributes and more efficient initialization of topology data structures, significantly reducing load times for large systems. MDAnalysis also supports parallelization and integrates with other tools like PyLOOS, mdtraj, and pytraj, which are used for similar purposes. MDAnalysis has an active international developer community and is widely adopted, with over 195 citations of its original paper. It is used in various research areas, including biophysics, chemistry, and materials science, and is essential for analyzing large-scale simulations. The library continues to evolve, with ongoing efforts to improve performance, introduce transparent parallelization, and integrate with high-performance data analytics libraries.MDAnalysis is a Python library for analyzing molecular dynamics (MD) simulation trajectories and protein structures. It provides a uniform, object-oriented interface that abstracts raw simulation data, enabling users to write portable and immediately usable code across biomolecular simulation communities. The library supports over 25 file formats and is widely used, forming the foundation for specialized biomolecular simulation tools. It is available under the GNU General Public License v2 and is written in Python and Cython, using NumPy arrays for interoperability with the scientific Python ecosystem. MDAnalysis allows users to perform complex analyses, such as calculating root mean square fluctuations (RMSF) for residues in proteins, and supports interactive use in Jupyter notebooks with visualization tools like nglview. It also includes a variety of analysis algorithms, including RMSD, RMSF, and lipid diffusion analysis, and provides a flexible framework for developing new analysis tools. The library's modular design and efficient data structures enable it to handle large simulations, including those with up to 10 million particles. Recent improvements include faster access to atom attributes and more efficient initialization of topology data structures, significantly reducing load times for large systems. MDAnalysis also supports parallelization and integrates with other tools like PyLOOS, mdtraj, and pytraj, which are used for similar purposes. MDAnalysis has an active international developer community and is widely adopted, with over 195 citations of its original paper. It is used in various research areas, including biophysics, chemistry, and materials science, and is essential for analyzing large-scale simulations. The library continues to evolve, with ongoing efforts to improve performance, introduce transparent parallelization, and integrate with high-performance data analytics libraries.