Python - How to preprocess data for large areas of null values?

Question

For missing values, there are basically methods such as deleting, filling, and reconstructing variables, but how are the specific operations performed? For example, when you don’t know the meaning of each field:

1. How to determine that this field has nothing to do with the model and what proportion of missing values ​​can it delete?

2. When is missing value considered a useful status?

3. When is data filling required?

Answer 1

There are still many methods.
I found a few links, attached below, which should be helpful to you.

Some features in the training set contain missing values. How to deal with them?
How to determine whether the missing values ​​are randomly missing?

Answer 2

Please tell me how to use it.