Handling categorical variables in large df I have a df with nearly 40 million rows and ~20 columns (total size is 2.2+GB). 15 of my features are categorical. I figured that the most reasonable way to go about this problem without making the df any bigger would be fit/transform each with LabelEncoder and then convert each feature to category data type.

The only thing is that none of the categorical features are ordinal. Should I fit/transform them with StandardScaler or is that unnecessary?
No. If, as you said, the variables are categorical, performing a scaling does not make any sense. Plain LabelEncoder already does what you want.