Add header to CSV without loading CSV

Question

Is there a way to add a header row to a CSV without loading the CSV into memory in python? I have an 18GB CSV I want to add a header to, and all the methods I\'ve seen requi

Answer 1

Just use the fact that csv module iterates on the rows, so it never loads the whole file in memory

import csv

with open("huge_csv.csv") as fr, open("huge_output.csv","w",newline='') as fw:
    cr = csv.reader(fr)
    cw = csv.writer(fw)
    cw.writerow(["title1","title2","title3"])
    cw.writerows(cr)

using writerows ensure a very good speed. The memory is spared here. Everything is done line-by-line. Since the data is properly processed, you could even change the separator and/or the quoting in the output file.

Answer 2

Here is a comparison of the three suggested solutions for a ~200 MB CSV file with 10^6 rows and 10 columns (n=50). The ratio stays approximately the same for larger and smaller files (10 MB to 8 GB).

cp:shutil:csv_reader 1:10:55

i.e. using the builtin cp function is approximately 55 times faster than using Python's csv module.

Computer:

• regular HDD
• Python 3.5.2 64-bit
• Ubuntu 16.04
• i7-3770

---

import csv
import random
import shutil
import time
import subprocess

rows = 1 * 10**3
cols = 10
repeats = 50

shell_script = '/tmp/csv.sh'
input_csv = '/tmp/temp.csv'
output_csv = '/tmp/huge_output.csv'
col_titles = ['titles_' + str(i) for i in range(cols)]

with open(shell_script, 'w') as f:
    f.write("#!/bin/bash\necho '{0}' > {1}\ncat {2} >> {1}".format(','.join(col_titles), output_csv, input_csv))

with open(shell_script, 'w') as f:
    f.write("echo '{0}' > {1}\ncat {2} >> {1}".format(','.join(col_titles), output_csv, input_csv))
subprocess.call(['chmod', '+x', shell_script])

run_times = dict([
    ('csv_writer', list()),
    ('external', list()),
    ('shutil', list())
])

def random_csv():
    with open(input_csv, 'w') as csvfile:
        csv_writer = csv.writer(csvfile, delimiter=',')
        for i in range(rows):
            csv_writer.writerow([str(random.random()) for i in range(cols)])
    with open(output_csv, 'w'):
        pass

for r in range(repeats):
    random_csv()
    #http://stackoverflow.com/a/41982368/2776376
    start_time = time.time()
    with open(input_csv) as fr, open(output_csv, "w", newline='') as fw:
        cr = csv.reader(fr)
        cw = csv.writer(fw)
        cw.writerow(col_titles)
        cw.writerows(cr)
    run_times['csv_writer'].append(time.time() - start_time)

    random_csv()
    #http://stackoverflow.com/a/41982383/2776376
    start_time = time.time()
    subprocess.call(['bash', shell_script])
    run_times['external'].append(time.time() - start_time)

    random_csv()
    #http://stackoverflow.com/a/41982383/2776376
    start_time = time.time()
    with open('header.txt', 'w') as header_file:
        header_file.write(','.join(col_titles))

    with open(output_csv, 'w') as new_file:
        with open('header.txt', 'r') as header_file, open(input_csv, 'r') as main_file:
            shutil.copyfileobj(header_file, new_file)
            shutil.copyfileobj(main_file, new_file)
    run_times['shutil'].append(time.time() - start_time)

    print('#'*20)
    for key in run_times:
        print('{0}: {1:.2f} seconds'.format(key, run_times[key][-1]))

print('#'*20)
print('Averages')
for key in run_times:
    print('{0}: {1:.2f} seconds'.format(key, sum(run_times[key])/len(run_times[key])))

---

If you really want to do it in Python, you could create the header file first and then merge it with your 2nd file via shutil.copyfileobj.

import shutil
with open('header.txt', 'w') as header_file:
    header_file.write('col1;col2;col3')

with open('new_file.csv', 'w') as new_file:
    with open('header.txt', 'r') as header_file, open('main.csv', 'r') as main_file:
        shutil.copyfileobj(header_file, new_file)
        shutil.copyfileobj(main_file, new_file)

Answer 3

You will need to rewrite the whole file. Simplest is not to use python

echo 'col1, col2, col2,... ' > out.csv
cat in.csv >> out.csv

Python based solutions will work at much higher levels and will be a lot slower. 18GB is a lot of data after all. Better to work with operating system functionality, which will be the fastest.