mickey | Django package to create a blog site | Content Management System library

Rule of thumb for optimizing bash scripts:
The size of the input shouldn't affect how often a program has to run.

Your script is slow because bash has to run the function 20k times, which involves starting grep and awk. Just starting programs takes a hefty amount of time. Therefore, try an approach where the number of program starts is constant.

Here is an approach:

1. Process the second file, such that for every name only the line with the maximal mark remains.
   _{Can be done with sort and awk, or sort and uniq -f + Schwartzian transform.}
   
2. Then keep only those lines whose names appear in names.txt.
   _{Easy with grep -f}

You have to initialize each of your array item before you can use the properties

To give an overview which tools you can use and hw they work, have a look at this code

finally i found the answer, its really easy:

In Voyager Admin go to Tools->MenuBuilder->Builder (on the menu-type you wanna sort)

Here you can sort your Menu-Items per Drag&Drop

Greetings Mickey

Try:

There are two fundamental problems that are preventing you from doing this.

First, a std::multimap doesn't even have an overloaded [] operator. A std::multimap, by definition, can have multiple values for the same key. Therefore, if your multimap has two values for a "name", then which ["name"] do you want? You don't know. Neither does your multimap. Only a map has an overloaded [] operator.

Second, when all is said and done you wind up with a std::variant for a value, and there does not seem to be suitable << overload for a variant and a std::ostream. Fortunately, looks like it's easy to solve this problem, in this case. The main problem is that you cannot use a multimap:

Is this what you mean?

What about something like:

Welcome to SO, Père Noël. Pacakge {sentimenter}'s get_sentences() breaks the text input into sentences by default, as its name implies. To reconstruct the original text input as the defining key in your final data frame, you need to group and summarize the sentence-based output produced by sentiment(). In this example, I will simply average the sentiment scores, and append sentences by their element_id.