Feature reduction by removing certain columns in dataframe
It depends, generally speaking it might be okay as those columns only seems to holds very few information. However there are some case, notably when the outcome is unbalanced, where some skewed features are usefull. Say you are trying to predict something that happen 1% of the time, a feature with only 2% of non-zero value but causally leading to the outcome, might be very predictive for your target.
So when removing those it is important to check (before) there is no clear dependency to the outcome and (after) the performance doesn’t drop too much when removing them.