sequent | CQRS & event sourcing framework for Ruby | Microservice library

It,s look like i want to convert multiple row to single row in animal column. But, it only conditional if they met sequential number and lowercase alphabet. after that, it restart an index to become sequent

So, i have a dataframe like :

My situation is: having .csv file with some columns e.g. name, surname, age, activity, and others I want to create objects in my relational database with row from this file being single object. Column names correspond to model fields and with others being multiple columns defined by user who creates .csv (those others land in model's filed comment separated with commas). Having relational db, base on model_1's object I create model_2 and model_3, all with the info from this .csv file.

My project is based on django rest framework and react.

Up till now my solution for this was to analize .csv file on with react using FileReader and readAsArrayBuffer which did it's job most of the time.

With react I not only have to analyze the file, but for every row make at least 3 x POSTing with axios. Sequentional posting is not ideal all the time.

My question is: > should files be analyzed on front- or back-end side ?

Heaving it done on the backend side with python seems a lot easier, but there might be a better solution of which I can't think of.

EDIT: The database I use is postgresql.

Csv file size could go up to a 6000 rows. With this many rows I could just completely ram up on posts from react. Also making a single post with file to handle the work to django could also result in a overtime for response from server.

EDIT2:

Example .csv file, might be a 6k rows long or more.

I am trying to use https://github.com/dominique-unruh/scala-isabelle for loading and parsing https://isabelle.in.tum.de/library/HOL/HOL-Imperative_HOL/Imperative_Quicksort.html - Imperative_Quicksort.thy. I am using scala-isabelle code from IntelliJ (path to source is C:\Workspace-IntelliJ\scala-isabelle):

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Consider the excel file as given:

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Given the following dataset