React - Convert some csv columns to array using papaparse

Question

I'm using papaparse to read csv file and then converting that file to array of objects. I'm using react-dropzone to upload file and then then converting it into array of objects. But I need all of my headers to be in lower case, no blank space in between some columns to have data in form of array.

Here is my csv file

| Name | Age | Data one | Data two |
-------|-----|----------|----------|
| John | 23  | A, B     | C        |
-------|-----|----------|----------|
| Jane | 40  | E, F     | G, H     |
-------|-----|----------|----------|

Here is my code:

import React from "react";
import Dropzone from "react-dropzone";
import Papa from 'papaparse';

const App = () => {
 const handleOnDrop = (acceptedFiles) => {

  // acceptedFiles is the actual file uploaded by user
  Papa.parse(acceptedFiles[0], {
     header: true,
     complete: (results) => {
       console.log(results)
     }
  })
 }
}

OutPut:
[
 {
  Name: "John",
  Age: "23",
  Data one: "A, B",
  Data two: "C"
 },
 {
  Name: "Jane",
  Age: "40",
  Data one: "E, F",
  Data two: "G, H"
 }
]

The output that I need is:

[
 {
  name: "John",
  age: "23",
  dataone: ["A", "B"], // Each individual data should be a string
  datatwo: ["C"]
 },
 {
  name: "Jane",
  age: "40",
  dataone: ["E", "F"],
  datatwo: ["G", "H"]
 }
]

I'm new to papaparse and not sure how it's done

Answer 1

In the second parameter of parse you can provide a function to the key transformHeader to convert the column names. E.g. (headerName) => headerName.toLowerCase().replaceAll(' ', '').

The second problem is unrelated to papaparse. You can use results.map(result => { ...result, dataone: result.dataone.split(', ') }) and do the same for datatwo. Are the two column names known upfront or does it need to work dynamically and it needs to be applied to all strings with a comma?

Use the following code when the column names are in an array.

results.map(result => {
  for(const column of columnsToconvertToArray) {
    result[column] = result[column].split(', ');
  }
  return result;
});