Which one data load method is the best for perfomance?
The method entirely depends on the requirements.
If there is only one client reading and modifying the properties, this is a rather simple problem. When modifying data, just change the instance attributes in your current Python program and -- at the same time -- keep the DB in sync while keeping your program responsive. To that end, you should outsource blocking calls to another thread or make use of greenlets. If there is only one client, there definitely is no need to fetch a property from the DB on each value lookup.
If there are multiple clients reading the data and only one client modifying the data, you have to think about which level of synchronization you need. If you need 100 % synchronization, you will have to fetch data from the DB on each value lookup.
If there are multiple clients changing the data in the database you better look into a rock-solid industry standard solution rather than writing your own DB cache/mapper.
Your distinction between (2) and (3) does not really make sense. If you fetch data on every lookup, there is no need to 'store' data. You see, if there can be multiple clients involved these things quickly become quite complex and it's really hard to get it right.
For example, I have object user stored in database (Redis)
It has several fields: 

String nick
String password
String email
List posts
List comments
Set followers
and so on...

In Python programm I have class (User) with same fields for this object. Instances of this class maps to object in database. The question is how to get data from DB for best performance:

Load values for each field on instance creating and initialize fields with it.
Load field value each time on field value requesting.
As second one but after value load replace field property by loaded value.

p.s. redis runs in localhost