17 may

Count how many lines are in a CSV Python?

I'm using python (Django Framework) to read a CSV file. I pull just 2 lines out of this CSV as you can see. What I have been trying to do is store in a variable the total number of rows the CSV also.

How can I get the total number of rows?

file = object.myfilePath
fileObject = csv.reader(file)
for i in range(2):

I have tried:



Mithilesh Gupta

row_count = sum(1 for line in open(filename)) worked for me.

Note : sum(1 for line in csv.reader(filename)) seems to calculate the length of first line

David Martínez

I think we can improve the best answer a little bit, I'm using:

len = sum(1 for _ in reader)

Moreover, we shouldnt forget pythonic code not always have the best performance in the project. In example: If we can do more operations at the same time in the same data set Its better to do all in the same bucle instead make two or more pythonic bucles.

Ruben Romo


data = pd.read_csv("data.csv")

and in the output you can see something like (aa,bb) where aa is the # of rows


import csv
count = 0
with open('filename.csv', 'rb') as count_file:
    csv_reader = csv.reader(count_file)
    for row in csv_reader:
        count += 1

print count

sam collins

To do it you need to have a bit of code like my example here:

file = open("Task1.csv")
numline = len(file.readlines())
print (numline)

I hope this helps everyone.

Martijn Pieters

You need to count the number of rows:

row_count = sum(1 for row in fileObject)  # fileObject is your csv.reader

Using sum() with a generator expression makes for an efficient counter, avoiding storing the whole file in memory.

If you already read 2 rows to start with, then you need to add those 2 rows to your total; rows that have already been read are not being counted.


This works for csv and all files containing strings in Unix-based OSes:

import os

numOfLines = int(os.popen('wc -l < file.csv').read()[:-1])

In case the csv file contains a fields row you can deduct one from numOfLines above:

numOfLines = numOfLines - 1


First you have to open the file with open

input_file = open("nameOfFile.csv","r+")

Then use the csv.reader for open the csv

reader_file = csv.reader(input_file)

At the last, you can take the number of row with the instruction 'len'

value = len(list(reader_file))

The total code is this:

input_file = open("nameOfFile.csv","r+")
reader_file = csv.reader(input_file)
value = len(list(reader_file))

Remember that if you want to reuse the csv file, you have to make a input_file.fseek(0), because when you use a list for the reader_file, it reads all file, and the pointer in the file change its position

Alex Troush

numline = len(file_read.readlines())


might want to try something as simple as below in the command line:

sed -n '$=' filename or wc -l filename

Old Bald Guy

Several of the above suggestions count the number of LINES in the csv file. But some CSV files will contain quoted strings which themselves contain newline characters. MS CSV files usually delimit records with \r\n, but use \n alone within quoted strings.

For a file like this, counting lines of text (as delimited by newline) in the file will give too large a result. So for an accurate count you need to use csv.reader to read the records.


Use "list" to fit a more workably object.

You can then count, skip, mutate till your heart's desire:

list(fileObject) #list values

len(list(fileObject)) # get length of file lines

list(fileObject)[10:] # skip first 10 lines


2018-10-29 EDIT

Thank you for the comments.

I tested several kinds of code to get the number of lines in a csv file in terms of speed. The best method is below.

with open(filename) as f:
    sum(1 for line in f)

Here is the code tested.

import timeit
import csv
import pandas as pd

filename = './sample_submission.csv'

def talktime(filename, funcname, func):
    print(f"# {funcname}")
    t = timeit.timeit(f'{funcname}("{filename}")', setup=f'from __main__ import {funcname}', number = 100) / 100
    print('Elapsed time : ', t)
    print('n = ', func(filename))

def sum1forline(filename):
    with open(filename) as f:
        return sum(1 for line in f)
talktime(filename, 'sum1forline', sum1forline)

def lenopenreadlines(filename):
    with open(filename) as f:
        return len(f.readlines())
talktime(filename, 'lenopenreadlines', lenopenreadlines)

def lenpd(filename):
    return len(pd.read_csv(filename)) + 1
talktime(filename, 'lenpd', lenpd)

def csvreaderfor(filename):
    cnt = 0
    with open(filename) as f:
        cr = csv.reader(f)
        for row in cr:
            cnt += 1
    return cnt
talktime(filename, 'csvreaderfor', csvreaderfor)

def openenum(filename):
    cnt = 0
    with open(filename) as f:
        for i, line in enumerate(f,1):
            cnt += 1
    return cnt
talktime(filename, 'openenum', openenum)

The result was below.

# sum1forline
Elapsed time :  0.6327946722068599
n =  2528244

# lenopenreadlines
Elapsed time :  0.655304473598555
n =  2528244

# lenpd
Elapsed time :  0.7561274056295324
n =  2528244

# csvreaderfor
Elapsed time :  1.5571560935772661
n =  2528244

# openenum
Elapsed time :  0.773000013928679
n =  2528244

In conclusion, sum(1 for line in f) is fastest. But there might not be significant difference from len(f.readlines()).

sample_submission.csv is 30.2MB and has 31 million characters.




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