reading and parsing a TSV file, then manipulating it for saving as CSV (*efficiently*)

My source data is in a TSV file, 6 columns and greater than 2 million rows.

Here's what I'm trying to accomplish:

1. I need to read the data in 3 of the columns (3, 4, 5) in this source file
2. The fifth column is an integer. I need to use this integer value to duplicate a row entry with using the data in the third and fourth columns (by the number of integer times).
3. I want to write the output of #2 to an output file in CSV format.

Below is what I came up with.

My question: is this an efficient way to do it? It seems like it might be intensive when attempted on 2 million rows.

First, I made a sample tab separate file to work with, and called it 'sample.txt'. It's basic and only has four rows:

Row1_Column1    Row1-Column2    Row1-Column3    Row1-Column4    2   Row1-Column6
Row2_Column1    Row2-Column2    Row2-Column3    Row2-Column4    3   Row2-Column6
Row3_Column1    Row3-Column2    Row3-Column3    Row3-Column4    1   Row3-Column6
Row4_Column1    Row4-Column2    Row4-Column3    Row4-Column4    2   Row4-Column6

then I have this code:

import csv 

with open('sample.txt','r') as tsv:
    AoA = [line.strip().split('\t') for line in tsv]

for a in AoA:
    count = int(a[4])
    while count > 0:
        with open('sample_new.csv', 'a', newline='') as csvfile:
            csvwriter = csv.writer(csvfile, delimiter=',')
            csvwriter.writerow([a[2], a[3]])
        count = count - 1