EDDI | Conversation Management Middleware for Conversational AI | Chat library

Parsing CSV file is an old topic. You will find at least 100 answers with code examples here on stackoverflow. Very often you will find a solution with the function std::getline. Please read the documentation here. It can read characters from a std::ifstream until or up to a delimiter (a comma , in our case), then store the result, without the comma, in a string and discard the comma from the stream. So, throwing that away. The characters will be stored in a std::string. If numbers or other types are needed, we need to convert the string to the other type, using the appropriate function.

Example: We have a string consisting of characters ‘1’, ‘2’ and ‘3’, so, “123”. The quotes indicate the string type. If we want to convert this string into an integer, we can use for example the function std::stoi.

In your case, you have 2 double values. So, we would split the input of the file into strings and then convert the 2 strings with the double values in it, using the function std::stod.

What you need to know additionally is, that often a 2 step approach is used. This is done to prevent potential problems arising from extracting all the string parts from one csv line. So,

• we first read a complete line,
• then put that line into a std::istringstream, and finally
• read input and split the CSV from there.

Then, the rest is simple. Just use std::getline to al the data that you need.

Last but not least, to read the file, we will simply open it, read line by line, create an “EmployeeAccount” and push that into a std::vector

At the end we do some debug output.

Please see below one of may potential implementation proposals:

You can groupby your id and name columns and apply a function to the category one like this:

A better approach would be to use set intersection (assuming you're trying to count unique matches, i.e., you're not interested in how many times "apple" is mentioned in a review, only that it is mentioned, period).

This should get you what you want, again, assuming you want to count unique matches and assuming your lemmatized column values are indeed lists of strings:

You can use exclusions on the dependency to avoid the download of the transitive dependencies.

But usually, these transitive dependencies are needed to run your JAR, so you should be really be sure you don't need them.

The problem is that you have an empty line between GET and the query, so there's no query being sent, hence it's equivalent to a match_all query:

First, the loop version seems to be more appropriate for this task which aggregates over two fields (sum by height and weight), and modifies the state of empty name fields while traversing the input collection because it ensures only one pass over the entire input.

Therefore, "stateful" stream operation as forEach should be used for this entire task but this does not make significant difference with usual for-each loop. Generally, use of such side-effect operations is NOT recommended:

_Side-effects in behavioral parameters to stream operations are, in general, discouraged, as they can often lead to unwitting violations of the statelessness requirement, as well as other thread-safety hazards. _

So, if the task is split into two separate subtasks, resolving each task separately using Stream API would be more appropriate.

1. Aggregate the multiple fields using a container for the aggregated fields (the container can be implemented as a separate object/record, or array/collection of the fields).

Example using Java 16+ record and Stream::reduce(BinaryOperator accumulator):

First of all, assuming your data is in excell file, you should be able to read that. Install openpyxl: pip install openpyxl
Now Here is my solution to print similar values in console. I assume that in Sheet2, all names are in this format:
last_name, first_name
and in Sheet1, all names are in this format:
first_name last_name
So here is a pythonic solution to do what you want:

As the commenters mentioned, triggers are not the right tool for preventing duplicates. You want a unique constraint for multiple columns.

UNCACHE TABLE removes the entries and associated data from the in-memory and/or on-disk cache for a given table or view, not drop the table. So you can still query it.