My source data is in a TSV file, 6 columns and greater than 2 million rows.
Here's what I'm trying to accomplish:
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
This question is related to
python
file
csv
tab-delimited-text
You should use the csv module to read the tab-separated value file. Do not read it into memory in one go. Each row you read has all the information you need to write rows to the output CSV file, after all. Keep the output file open throughout.
import csv
with open('sample.txt', newline='') as tsvin, open('new.csv', 'w', newline='') as csvout:
tsvin = csv.reader(tsvin, delimiter='\t')
csvout = csv.writer(csvout)
for row in tsvin:
count = int(row[4])
if count > 0:
csvout.writerows([row[2:4] for _ in range(count)])
or, using the itertools module to do the repeating with itertools.repeat():
from itertools import repeat
import csv
with open('sample.txt', newline='') as tsvin, open('new.csv', 'w', newline='') as csvout:
tsvin = csv.reader(tsvin, delimiter='\t')
csvout = csv.writer(csvout)
for row in tsvin:
count = int(row[4])
if count > 0:
csvout.writerows(repeat(row[2:4], count))
Source: Stackoverflow.com