MD simulations have been widely used in the study of soft matter self-assembly. MD simulations generate a large amount of data from which it is typically challenging to extract meaningful predictive understanding. This difficulty arises not only from the high dimensionality of the output data, but also from the fast and complex dynamics that are intrinsic to soft matter. Interpreting the molecular mechanisms present in MD simulations typically requires bespoke computational tools to quantify such complex behavior. As a result, it is often not possible to replicate experimental findings or to reproduce quantifiable results. The computational soft matter community has invested significant effort in simplifying the creation of inputs for soft matter simulations, as exemplified by tools such as PySoftK, Polymer Structure Predictor, Radonpy 20 and MoSDeF. However, a comprehensive package for analyzing soft matter material properties has not yet been developed. To address this issue, PySoftK (version 1.0) now includes a toolkit designed for analysis of soft matter simulations, providing a unified computational framework in which modelling and analysis can be streamlined under modern software development standards. This feature supports data provenance and reproducibility of results. In line with the design commitment of PySoftK to minimize user inputs and provide highly efficient code, PySoftK v1.0 enables the analysis of large-scale soft matter systems.