when should I use log scaling and standard scaling in python?
Yes you are right
No, not necessarily it makes data normally distributed but it makes all data unit free and within same location and scale i.e. mean 0 and variance 1. Therefore, many algorithms that are based on distance measures do not get more affected by high variance variable like house price as opposed to low variance variable like interest rate in a National Revenue model.
By normalizer if you meant scaling then yes

However, For tree based algorithms or any algorithm that do not use distances measures the scaling is not required
I am studying data analyzing and data science recently.
When I preprocess the money data(amount of money), I routinely use 'log' to lower the scale and normalize. However, there are some scalers in python like 'StandardScaler', 'Normalizer', 'MinScaler' and etc. I wonder when should I use log method and StandardScaler/Normalizer to scale data.
I have thought about this question and understand like below.

log scale lowers size and normalize data, but doesn't make the perfect Nomal distribution. Mostly used in monetary data.

StandardScaler makes the data perfect Normal distribution

I have to use Normalizer when I have different measure features together(like distance, dollars, weight etc). Use this to preprocess features in one scoop.


Am I understanding it right?