Deep Mutational Scanning is a robust method for massive scale assessments of genotypic variants and is being applied to wide domains of research. dms2dfe (Deep Mutational Scanning to Distribution of Fitness Effects) is a comprehensive computational workflow designed to streamline analysis of such data on the basis of evolutionary principles. dms2dfe assists in contextualizing data from Deep Mutational Scanning experiment in terms of Distribution of Fitness Effects which is a powerful indicator of evolutionary dynamics. In addition to estimations of preferential enrichments of experimentally determined mutations, dms2dfe utilizes a novel application of robust random forest modeling to infer of preferential enrichments of mutants which are not empirically determined. This helps to deduce biologically relevant interpretations from population level dynamics of DFEs across different experimental conditions by solving normalization issue and sampling bias. dms2dfe is available at https://github.com/kc-lab/dms2dfe .