Data scaling before PCA: how to deal with categorical values?
You can not use PCA, or at least it is not recommended, for mixed data. It is best to use Factor analysis of mixed data. You are lucky that Prince is a Python package that covers all data scenarios, borrowing from its explanation:


  
  All your variables are numeric: use principal component analysis (prince.PCA)
  You have a contingency table: use correspondence analysis (prince.CA)
  You have more than 2 variables and they are all categorical: use multiple correspondence analysis (prince.MCA)
  You have groups of categorical or numerical variables: use multiple factor analysis (prince.MFA)
  You have both categorical and numerical variables: use factor analysis of mixed data (prince.FAMD)
  


Check also this question/answers in stats.stackexchange or discussion in researchgate.