Neb | Parser with a nebulous purpose | Parser library

That particular server requires you to specify a user agent:

I propose the following solution starting from the string you got after extracting text from the PDF:

You need to add extra field into $group and $project stages.

You need to use $max operator for time field and accumulatetests field time:tests values. In the last stage, we $reduce tests field taking highest value

As per the comments , I changed else 'valuenum end' as 'valuenum' to else 'valuenum' end and it worked.

It looks like the problem here is the async execution of your code. In this example here's what's happening:

1. Run s3.listObjectsV2() and when finished, fire callback (but not now, in the future)
2. Run first exec() and when finished, fire callback (but not now, in the future)
3. Run second exec() and when finished, fire callback (but not now, in the future)

And those three steps are fired immediately, one by one. And each of them has its own callback which fires in the future. Ok, but when is the future? - exactly! You don't know. In your case probably those two callbacks in exec()'s are fired before the callback from s3 and this is why it does not work.

The solution here is to make sure that those exec()s are fired after s3.listObjects. So you have two options: first is to make a promise out of the s3, like this: s3.listObjectsV2(params).promise() and await for it, then in .then((data) => {}) you have your data and in .catch((error) => {}) you have your error. Or you can simply put those exec()s in the callback of the s3 call.

Your code should look like this according to the solution 2 (from the comments):

We can use strsplit and split on "at" which will give us 2 parts of string and from every part we remove "#" followed by number and put it in a dataframe.

You can loop over parent div and find the child items and then add the class over there.

Here in the example, I am getting $('.featured-block__item-inner') as a parent and then finding items inside it.

For performance implications of the below solutions, see Pandas groupby.size vs series.value_counts vs collections.Counter with multiple series. They are presented below with best performance first.

You can create a series of counts with (Name, Surname) tuple indices using GroupBy.size: