I have a large .csv file that is well over 300 gb. I would like to chunk it into smaller files of 100,000,000 rows each (each row has approximately 55-60 bytes).
I wrote the following code:
import pandas as pd
df = pd.read_csv('/path/to/really/big.csv',header=None,chunksize=100000000)
count = 1
for chunk in df:
name = '/output/to/this/directory/file_%s.csv' %s count
chunk.to_csv(name,header=None,index=None)
print(count)
count+=1
This code works fine, and I have plenty of memory on disk to store the approximate 5.5-6 gb at a time, but it's slow.
Is there a better way?
EDIT
I have written the following iterative solution:
with open('/path/to/really/big.csv', 'r') as csvfile:
read_rows = csv.reader(csvfile)
file_count = 1
row_count = 1
f = open('/output/to/this/directory/file_%s.csv' %s count,'w')
for row in read_rows:
f.write(''.join(row))
row_count+=1
if row_count % 100000000 == 0:
f.close()
file_count += 1
f = open('/output/to/this/directory/file_%s.csv' %s count,'w')
EDIT 2
I would like to call attention to Vor's comment about using a Unix/Linux split command, this is the fastest solution I have found.
To split a big binary file in multiple files, you should first read the file by the size of chunk you want to create, then write that chunk to a file, read the next chunk and repeat until you reach the end of original file.
there is an existing tool for this in Unix/Linux.
split -l 100000 -d source destination
will add two digit numerical suffix to destination prefix for the chunks.
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