mining | programs for data mining | Data Mining library

You can use optional chaining to check if properties of object does exist:

Use melt to flatten your dataframe then pivot_table to reshape your dataframe:

We can use a lambda expression (~) to apply the pdf_text on the elements individually and then paste/str_c the first two elements (based on the expected output)

You could use a dictionary where the pair is the key and the number how often it occurs is the value. You'll need to make sure that you always generate the same key for (Author1,Author2) and (Author2, Author1) but you could choose alphabetic ordering for dealing with that.

Then you simply increment the number stored for the pair whenever you encounter it.

The hoverinfo parameter "text" needs to be quoted:

In the predicate filter for the for-each:

It's easier to consider base cases of what your callbacks may look like and then extend upon them. From my experience, the main reason I typically structure a callback is to handle a particular output caused by a particular input.

E.g.

If output O1 is an effect of input I1, then I make a callback C1(I1 → O1).

If output O1 is an effect of Inputs I1 and I2, then I make a callback C1([I1, I2] → O1)

If you happen to have multiple outputs that are effects of an input, then you combine those outputs in the same callback.

E.g.

If output O1 is an effect of input I1 and output O2 is an effect of input I1, then I make a callback C1(I1 → [O1, O2])

Also, keep in mind that Dash doesn't allow multiple callbacks to have the same output component (e.g., a particular component property can be associated with one and only one callback). In which case, if you have a particular output that requires to be updated simultaneously with another, but is not necessarily an effect of that same input, then you should still combine the outputs into the single callback.

E.g.

If output O1 is an effect of input I1 and output O2 must be updated with O1, then I make a callback C1(I1 → [O1, O2])

I'm not sure why in the comment you tried using State() instead of Input(), as State() is used in the event a property must still be read as input, but doesn't necessarily have to be triggered.

So using the above logic, e.g.,

if output O1 that is an effect of input I1 and is not an effect of Input (which we now call State) S1, but still requires S1, then I make a callback C1([I1, S1] → [O1])

It's normal to have large callbacks that have many inputs and outputs as long as the callback is not trying to do too many disjointed things at once (otherwise when you go back to look at your code you'll spend a lot of time trying to remember what you did believe me :)).

Given the mapper dictionary:

OK, I finally see the problem. You are extracting a subset of a dataframe and passing it to this file. So, data["Nama Golongan"][44] is referring to index 44, because the indicies get carried through with the subset.

The problem is that data.loc does NOT use the index. It's strictly row numbers. They're all going to start with 0. If you ONLY want the first 23 rows, you don't need your if sequence at all. Replace the whole thing with this: