Multivariate Functional Shape Data Analysis in Python (MFSDA_Python) is a Python based package for statistical shape analysis. A multivariate varying coefficient model is introduced to build the association between the multivariate shape measurements and demographic information and other clinical, biological variables. Statistical inference, i.e., hypothesis testing, is also included in this package, which can be used in investigating whether some covariates of interest are significantly associated with the shape information. The hypothesis testing results are further used in clustering based analysis, i.e., significant suregion detection. This MFSDA package is developed by Chao Huang and Hongtu Zhu from the BIG-S2 lab.