Suppose I have mixed data and (python) code which is capable of doing PCA (principal component analysis) on continuous predictors and MCA (multiple correspondence analysis) on nominal predictors. Is it possible to combine results from PCA and MCA into one?
You may want to use Factor analysis of mixed data.
It allows you to do dimension reduction on a complete data set.
A R implementation could be found in the FactoMineR package. But this function struggle when you have a high number of data/columns.
I am not aware of the existence of the equivalent in python.
You can find FAMD implementation for Python here.
I was looking for the same thing in Python and I came the prince package that has FAMD implemented.
I don't have enough points to comment so answering here @Edo prince Package has only CA, MCA and PCA PAckages in it. I do not see any FAMD in here.
dir(prince) ['CA', 'MCA', 'PCA', '__builtins__', '__cached__', '__doc__', '__file__', '__loader__', '__name__', '__package__', '__path__', '__spec__', '__version__', 'ca', 'mca', 'pca', 'plot', 'svd']