oskar | Lightweight on-screen keyboard | Keyboard library

Replace this

I think you have wrong the included versions, also normally you should put the Javascript scripts at the end of the body tag:

This is an interesting question which is hard to have a best way to mock since the lazy(() => import('path/to/file')) takes a function as argument so we can't detect the value of anonymous function.

But I think I have a solution for you but it's not best to test all cases but a specific case it would work. You would mock as following:

This doesn't address rows where there are multiple countries in the "country" field, but the lines below should work for the other parts of the question:

Create initial dataframe:

Trying to be generic over the Executor trait is a bit overkill. You should probably just use a pool of size 1 in your test and manually callBegin and ROLLBACK.

As Aplet123 said:

Your lines probably all end in ;

Try this to fix:

replace

When you type Osakar python interpreter expects a variable which is not defined in your case.

Please note that Oskar is different from 'Oscar'. The first is a variable (that must be defined first). While the latter is a string.

Even if you use sting 'Oscar' you cannot use it with your function. Because the + does not work between a string and an integer.

The prePersist and other @createTimeStamp hooks works if you are making inserts from within the application by using the JPA or native hibernate methods. If you simply run inserts from outside your application context, there is no way hibernate would come to know what DMLs are running and hence the hooks will never be called. If you want that even from outside when you run the inserts, the timestamp for create should auto populate, you would need to add table level DDL instructions. Eg: ALTER TABLE mytable ALTER COLUMN datecreated SET DEFAULT now();

Now as to auto DDL ability of hibernate, the xml property create generates DDL from entity definitions but not DMLs. So, you can also embed the DEFAULT timestamp value in column definition of the concerned entity as so or similar:

The problem seems to be arise when a name has multiple movements in exactly the same Deposit/Withdrawal dates (hence the duplicates). Dataframe .pivot method cannot handle duplicate indexes, it's just not designed for that. For the purpose of your analysis .pivot_table will do the trick, the main difference being that this one can apply an aggregation function to handle duplicate indexes (sum in this case). https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.pivot_table.html

I personally tend to use .groupby with any problems of the sort because it offers the functionality of grouping not only by any combination of columns in df, but can also include exogenous series, calculations, index or levels of index of self or other, masks, etc.

So my code for this would be: