# Sklearn: MaxAbsScaler mandatory when your dataframe has a lot of dummy variables?

I have a dataset with the following variables:

price (numerical, min 350, max 25400)
model_age_days (numerical, min 423,  max 3405)
factor_1 (dummy)
factor_2 (dummy)
factor_3 (dummy)
(...)
factor_58 (dummy)


So the model has 60 columns, 58 age of which are One Hot Encoded dummy variables (created using pandas its get_dummies feature). So for each row, most columns will have a value of 0

I am training a classifier to predict the factor (but will likely perform other types of analysis later) , using a 'One vs the Rest' approach for each factor and am now trying to decide how to preprocess the data.

My question is as follows:

Am I correct in assuming that this dataset could be considered sparse and that, following the Sklearn documentation the MaxAbsScaler is thus almost mandatory?

MaxAbsScaler and maxabs_scale were specifically designed for

I'm asking this since I would like to create a 2d graph (using PCA) later, but the tutorials / documentation seem to suggest that the StandardScaler is preferred for PCA.