numline = len(file_read.readlines())

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.

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

sed -n '$=' filename

or

wc -l filename

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

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))
    print('n')

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.

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 rn, 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.

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

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

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

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

try

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

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

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.

After iterating the whole file with csv.reader() method, you have the total number of lines read, via instance variable line_num:

import csv
with open('csv_path_file') as f:
    csv_reader = csv.reader(f)
    for row in csv_reader:
        pass
    print(csv_reader.line_num)

Quoting the official documentation:

csvreader.line_num

The number of lines read from the source iterator.

Small caveat:

• total number of lines, includes the header, if the CSV has.

import pandas as pd
data = pd.read_csv('data.csv') 
totalInstances=len(data)

You can also use a classic for loop:

import pandas as pd
df = pd.read_csv('your_file.csv')

count = 0
for i in df['a_column']:
    count = count + 1

print(count)

If you are working on a Unix system, the fastest method is the following shell command

cat FILE_NAME.CSV | wc -l

From Jupyter Notebook or iPython, you can use it with a !:

! cat FILE_NAME.CSV | wc -l

If you have to parse the CSV (e.g., because of the presence of line breaks in the fields or commented out lines) but the CSV is too large to fit the memory all at once, you might parse the CSV piece-by-piece:

import pandas as pd
import os
import sys

csv.field_size_limit(sys.maxsize)  # increase the maximal line length in pd.read_csv()

cnt = 0
for chunk in pd.read_csv(filepath, chunksize=10**6):
    cnt += len(chunk)
print(cnt)

I think mine will be the simplest approach here:

import csv
file = open(filename, 'r')
csvfile = csv.reader(file)
file.close
print("row", len(list(csvfile)))

With pyarrow lib, is almost 6 times faster than dixhom suggested method.

Used: csv with 3,921,865 rows and 927MB file size

Standard

sum(1 for _ in open(file_path))

# result: 3.57 s ± 90.7 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

With pyarrow

import pyarrow.csv as csv

sum([len(chunk) for chunk in csv.open_csv(file_path)])

# result: 854 ms ± 4.88 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

import pandas as pd
import csv

filename = 'data.csv'

row_count = sum(1 for line in open(filename))

# count no of lines 
print("Number of records : - ",row_count)

The result was : Number of records : – 163210690