The paper introduces yt, an open-source, community-developed toolkit for analyzing and visualizing astrophysical simulation data. yt is designed to address the challenges of reproducibility, parallelization, and the increasing complexity and size of astrophysical simulations. It is oriented towards physically relevant quantities rather than native simulation codes, making it versatile for multiple simulation platforms. yt supports various simulation methods and codes, including Enzo, Orion, RAMSES, and FLASH. The toolkit includes methods for reading, handling, and visualizing data, such as projections, multivariate volume rendering, multi-dimensional histograms, halo finding, light cone generation, and topologically-connected isocontour identification. yt is primarily written in Python with C routines for fast computation and utilizes the NumPy library. It is designed to be modular, allowing for reusable components and parallelization of analysis tasks. The paper also discusses the underlying algorithms, parallelization strategies, and the integration of yt into running simulations. Additionally, it covers the pre-packaged analysis modules, future directions, and community engagement.The paper introduces yt, an open-source, community-developed toolkit for analyzing and visualizing astrophysical simulation data. yt is designed to address the challenges of reproducibility, parallelization, and the increasing complexity and size of astrophysical simulations. It is oriented towards physically relevant quantities rather than native simulation codes, making it versatile for multiple simulation platforms. yt supports various simulation methods and codes, including Enzo, Orion, RAMSES, and FLASH. The toolkit includes methods for reading, handling, and visualizing data, such as projections, multivariate volume rendering, multi-dimensional histograms, halo finding, light cone generation, and topologically-connected isocontour identification. yt is primarily written in Python with C routines for fast computation and utilizes the NumPy library. It is designed to be modular, allowing for reusable components and parallelization of analysis tasks. The paper also discusses the underlying algorithms, parallelization strategies, and the integration of yt into running simulations. Additionally, it covers the pre-packaged analysis modules, future directions, and community engagement.