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How can I split csv files in python?

Because of the memory error, i have to split my csv files. I did research it. I found it from one of the stack overflow user who is Aziz Alto. This is his code.
csvfile = open('#', 'r').readlines()
filename = 1
for i in range(len(csvfile)):
if i % 10000000 == 0:
open(str(filename) + '.csv', 'w+').writelines(csvfile[i:i+10000000])
filename += 1
It works well but for second file, the code did not add header which is very important for me. My question is that How can I add header for second file?

import pandas as pd
rows = pd.read_csv("csvfile.csv", chunksize=5000000)
for i, chuck in enumerate(rows):
chuck.to_csv('out{}.csv'.format(i)) # i is for chunk number of each iteration
chucksize you specify how many rows you want- in excel you can have upto 1,048,576 rows.
This will save it as 5000000 and with header.
hope this Helps!!

On the 2nd till last file you have to always add the 1st line of your original file (the one containing the header):
# this loads the first file fully into memory
with open('#', 'r') as f:
csvfile = f.readlines()
linesPerFile = 1000000
filename = 1
# this is better then your former loop, it loops in 1000000 lines a peice,
# instead of incrementing 1000000 times and only write on the millionth one
for i in range(0,len(csvfile),linesPerFile):
with open(str(filename) + '.csv', 'w+') as f:
if filename > 1: # this is the second or later file, we need to write the
f.write(csvfile[0]) # header again if 2nd.... file
f.writelines(csvfile[i:i+linesPerFile])
filename += 1

Fast csv file splitting
If you have a very big file and you have to try different partitions (say to find the best way to split it) the above solutions are too slow to try.
Another way to solve this (and a very fast one) is to create an index file by record number. It takes about six minutes to create an index file of a csv file of 6867839 rows and 9 Gb, and an additional 2 minutes for joblib to store it on disk.
This method is particularly impressive if you are dealing with huge files, like 3 Gb or more.
Here's the code for creating the index file:
# Usage:
# creaidx.py filename.csv
# indexes a csv file by record number. This can be used to
# access any record directly or to split a file without the
# need of reading it all. The index file is joblib-stored as
# filename.index
# filename.csv is the file to create index for
import os,sys,joblib
BLKSIZE=512
def checkopen(s,m='r',bz=None):
if os.access(s,os.F_OK):
if bz==None:
return open(s,m) # returns open file
else:
return open(s,m,bz) # returns open file with buffer size
else:
return None
def get_blk():
global ix,off,blk,buff
while True: # dealing with special cases
if ix==0:
n=0
break
if buff[0]==b'\r':
n=2
off=0
break
if off==BLKSIZE-2:
n=0
off=0
break
if off==BLKSIZE-1:
n=0
off=1
break
n=2
off=buff.find(b'\r')
break
while (off>=0 and off<BLKSIZE-2):
idx.append([ix,blk,off+n])
# g.write('{},{},{}\n'.format(ix,blk,off+n))
print(ix,end='\r')
n=2
ix+=1
off= buff.find(b'\r',off+2)
def crea_idx():
global buff,blk
buff=f.read(BLKSIZE)
while len(buff)==BLKSIZE:
get_blk()
buff=f.read(BLKSIZE)
blk+=1
get_blk()
idx[-1][2]=-1
return
if len(sys.argv)==1:
sys.exit("Need to provide a csv filename!")
ix=0
blk=0
off=0
idx=[]
buff=b'0'
s=sys.argv[1]
f=checkopen(s,'rb')
idxfile=s.replace('.csv','.index')
if checkopen(idxfile)==None:
with open(idxfile,'w') as g:
crea_idx()
joblib.dump(idx,idxfile)
else:
if os.path.getctime(idxfile)<os.path.getctime(s):
with open(idxfile,'w') as g:
crea_idx()
joblib.dump(idx,idxfile)
f.close()
Let's use a toy example:
strings,numbers,colors
string1,1,blue
string2,2,red
string3,3,green
string4,4,yellow
The index file will be:
[[0, 0, 0],
[1, 0, 24],
[2, 0, 40],
[3, 0, 55],
[4, 0, 72],
[5, 0, -1]]
Note the -1 at the last index element to indicate end of index file in case of a sequential access. You can use a tool like this to access any individual row of the csv file:
def get_rec(n=1,binary=False):
n=1 if n<0 else n+1
s=b'' if binary else ''
if len(idx)==0:return ''
if idx[n-1][2]==-1:return ''
f.seek(idx[n-1][1]*BLKSIZE+idx[n-1][2])
buff=f.read(BLKSIZE)
x=buff.find(b'\r')
while x==-1:
s=s+buff if binary else s+buff.decode()
buff=f.read(BLKSIZE)
x=buff.find(b'\r')
return s+buff[:x]+b'\r\n' if binary else s+buff[:x].decode()
The first field of the index record is obviously unnecessary. It is kept there for debugging purposes. As a side note, if you substitute this field by any field in the csv record and you sort the index file by that field, then you have the csv file sorted by that field if you use the index field to access the csv file.
Now, once you have you index file created you just call the following program with the filename (the one which index was created already) and a number between 1 and 100 which will be the percentage the file will be split at as command line parameters:
start_time = time.time()
BLKSIZE=512
WSIZE=1048576 # pow(2,20) 1Mb for faster reading/writing
import sys
import joblib
from common import Drv,checkopen
ix=0
blk=0
off=0
idx=[]
buff=b'0'
if len(sys.argv)<3:
sys.exit('Argument missing!')
s=Drv+sys.argv[1]
if sys.argv[2].isnumeric():
pct=int(sys.argv[2])/100
else:
sys.exit('Bad percentage: '+sys.argv[2])
f=checkopen(s,'rb')
idxfile=s.replace('.csv','.index')
if checkopen(idxfile):
print('Loading index...')
idx=joblib.load(idxfile)
print('Done loading index.')
else:
sys.exit(idxfile+' does not exist.')
head=get_rec(0,True)
n=int(pct*(len(idx)-2))
off=idx[n+1][1]*BLKSIZE+idx[n+1][2]-len(head)-1
num=off//WSIZE
res=off%WSIZE
sout=s.replace('.csv','.part1.csv')
i=0
with open(sout,'wb') as g:
g.write(head)
f.seek(idx[1][1]*BLKSIZE+idx[1][2])
for x in range(num):
print(i,end='\r')
i+=1
buff=f.read(WSIZE)
g.write(buff)
buff=f.read(res)
g.write(buff)
print()
i=0
sout=s.replace('.csv','.part2.csv')
with open(sout,'wb') as g:
g.write(head)
f.seek(idx[n+1][1]*BLKSIZE+idx[n+1][2])
buff=f.read(WSIZE)
while len(buff)==WSIZE:
g.write(buff)
print(i,end='\r')
i+=1
buff=f.read(WSIZE)
g.write(buff)
end_time = time.time()
The file are created using blocks of 1048576 bytes. You can play with that figure to make file creation faster or to tailor it to machines with less memory resources.
The file is split only on two partitions, each of them having the header of the original file. It is not too difficult to change the code to make it
split files into more than two partitions.
Finally to put things in perspective, to split a csv file of 6867839 rows and 9 Gb by 50%, it took me roughly 6 minutes to create the index file and another 2 minutes for joblib to store it on disk. It took 3 additional minutes to split the file.

counting number with 3 csvs python

I have 3 csv that i will like to change one column to a running number that depends on the number on rows in the file.
For exmaple, file 1 got 400 rows, file 2 got 240, and file 3 got 100.
so the added column for file 1 will be running number from 1 to 400.
so the added column for file 2 will be running number from 401 to 640.
so the added column for file 3 will be running number from 641 to 741.
what I wrote is this
file1 = str(path) + "file1"
file2 = str(path) + "file2"
file3 = str(path) + "file3"
files = [file1, file2, file3]
class File_Editor():
def line_len(self):
for k in range(0,2):
file_name = open(files[k] + ".csv")
numline = len(file_name.readlines())
print (numline)
I am stuck with making the running number for each file by remembering the number of row that were on the file before.
Thanks Alot!
+++++EDIT+++++
#roganjosh Thanks alot, I used your code with a little fixed for the running_number = 1, I have put it inside the def, that both files will have the same running number.
One last thing, How can I add at the first row Index, for example, "Number"
and then from the 2nd row, run the running_number_in_csv.
Thanks

Looking at your previous questions that are left open, the common theme is fundamental issue with understanding in how to use functions in Python that isn't being addressed. I will try and unpick part of this to prevent similar questions arising. I'm assuming you come from a scientific background like me so I'll stick to that.
You never pass arguments to your functions, only self. Instead you try to reference globals from within the function, but there is no need and it is confusing. For example, I might have the equation y = x^2 + 3x + 5 that is both a mathematical function and can be a python function.
def quadratic(value_of_x):
y = (value_of_x **2) + (3*value_of_x) + 5
return y
eg_1 = quadratic(5)
print (eg_1)
eg_2 = quadratic(3)
print (eg_2)
# But this will fail
#print (y)
y exists only within the Python function as a local variable and is destroyed once you leave the def / return block. In this case, eg_1, eg_2 assume the value of y at the end of the function and value_of_x assumes the value that I put in brackets on the function call (the argument/variable). That's the point of functions, they can be used over and over.
I can also pass multiple arguments to the function.
def new_quadratic(value_of_x, coefficient):
y = coefficient*(value_of_x **2) + (3*value_of_x) + 5
return y
eg_3 = new_quadratic(5, 2)
print (eg_3)
Not only can I not get a value for y outside of the scope of a function, but a function does nothing unless it's called. This does nothing; it's the equivalent of knowing the formula in your head but never running a number through it - you're just defining it as something that your script could use.
starting_number = 5
def modify_starting_number(starting_number):
starting_number = starting_number * 2
return starting_number
print (starting_number)
Whereas this does what you expected it to do. You call the function i.e. pass the number through the formula.
starting_number = 5
def modify_starting_num(starting_num):
starting_num = starting_num * 2
return starting_num
starting_number = modify_starting_num(starting_number) # Calling the function
print (starting_number)
With that out of the way, on to your question.
import csv
files = ['file_1', 'file_2']
def running_number_in_csv(filename_list):
""" running_number resets every time the function is called, but is
remembered within the function itself"""
running_number = 1
for individual_file in filename_list:
new_rows = [] # Make something to hold row + extra column
# Read contents of each row and append the running number to the list
with open(individual_file + '.csv', 'r') as infile:
reader = csv.reader(infile)
for row in reader:
row.append(running_number)
new_rows.append(row)
running_number += 1 # Increments every row, regardless of file name number
# Write the list containing the extra column for running number
with open(individual_file + '.csv', 'w') as outfile: # Might need 'wb' in Windows
writer = csv.writer(outfile)
writer.writerows(new_rows)
get_running_number = running_number_in_csv(files) # CALL THE FUNCTION :)

#roganjosh I have fixed my code.
I know what is the lenght on the file, now i need to add a column with running numbers like:
file1
1 to 400
file2
401 to 641
file 3
642 to 742
Thanks alot!

Writing a random amount of random numbers to a file and returning their squares

So, I'm trying to write a random amount of random whole numbers (in the range of 0 to 1000), square these numbers, and return these squares as a list. Initially, I started off writing to a specific txt file that I had already created, but it didn't work properly. I looked for some methods I could use that might make things a little easier, and I found the tempfile.NamedTemporaryFile method that I thought might be useful. Here's my current code, with comments provided:
# This program calculates the squares of numbers read from a file, using several functions
# reads file- or writes a random number of whole numbers to a file -looping through numbers
# and returns a calculation from (x * x) or (x**2);
# the results are stored in a list and returned.
# Update 1: after errors and logic problems, found Python method tempfile.NamedTemporaryFile:
# This function operates exactly as TemporaryFile() does, except that the file is guaranteed to have a visible name in the file system, and creates a temprary file that can be written on and accessed
# (say, for generating a file with a list of integers that is random every time).
import random, tempfile
# Writes to a temporary file for a length of random (file_len is >= 1 but <= 100), with random numbers in the range of 0 - 1000.
def modfile(file_len):
with tempfile.NamedTemporaryFile(delete = False) as newFile:
for x in range(file_len):
newFile.write(str(random.randint(0, 1000)))
print(newFile)
return newFile
# Squares random numbers in the file and returns them as a list.
def squared_num(newFile):
output_box = list()
for l in newFile:
exp = newFile(l) ** 2
output_box[l] = exp
print(output_box)
return output_box
print("This program reads a file with numbers in it - i.e. prints numbers into a blank file - and returns their conservative squares.")
file_len = random.randint(1, 100)
newFile = modfile(file_len)
output = squared_num(file_name)
print("The squared numbers are:")
print(output)
Unfortunately, now I'm getting this error in line 15, in my modfile function: TypeError: 'str' does not support the buffer interface. As someone who's relatively new to Python, can someone explain why I'm having this, and how I can fix it to achieve the desired result? Thanks!
EDIT: now fixed code (many thanks to unutbu and Pedro)! Now: how would I be able to print the original file numbers alongside their squares? Additionally, is there any minimal way I could remove decimals from the outputted float?

By default tempfile.NamedTemporaryFile creates a binary file (mode='w+b'). To open the file in text mode and be able to write text strings (instead of byte strings), you need to change the temporary file creation call to not use the b in the mode parameter (mode='w+'):
tempfile.NamedTemporaryFile(mode='w+', delete=False)

You need to put newlines after each int, lest they all run together creating a huge integer:
newFile.write(str(random.randint(0, 1000))+'\n')
(Also set the mode, as explained in PedroRomano's answer):
with tempfile.NamedTemporaryFile(mode = 'w+', delete = False) as newFile:
modfile returns a closed filehandle. You can still get a filename out of it, but you can't read from it. So in modfile, just return the filename:
return newFile.name
And in the main part of your program, pass the filename on to the squared_num function:
filename = modfile(file_len)
output = squared_num(filename)
Now inside squared_num you need to open the file for reading.
with open(filename, 'r') as f:
for l in f:
exp = float(l)**2 # `l` is a string. Convert to float before squaring
output_box.append(exp) # build output_box with append
Putting it all together:
import random, tempfile
def modfile(file_len):
with tempfile.NamedTemporaryFile(mode = 'w+', delete = False) as newFile:
for x in range(file_len):
newFile.write(str(random.randint(0, 1000))+'\n')
print(newFile)
return newFile.name
# Squares random numbers in the file and returns them as a list.
def squared_num(filename):
output_box = list()
with open(filename, 'r') as f:
for l in f:
exp = float(l)**2
output_box.append(exp)
print(output_box)
return output_box
print("This program reads a file with numbers in it - i.e. prints numbers into a blank file - and returns their conservative squares.")
file_len = random.randint(1, 100)
filename = modfile(file_len)
output = squared_num(filename)
print("The squared numbers are:")
print(output)
PS. Don't write lots of code without running it. Write little functions, and test that each works as expected. For example, testing modfile would have revealed that all your random numbers were being concatenated. And printing the argument sent to squared_num would have shown it was a closed filehandle.
Testing the pieces gives you firm ground to stand on and lets you develop in an organized way.

Quickly find differences between two large text files

I have two 3GB text files, each file has around 80 million lines. And they share 99.9% identical lines (file A has 60,000 unique lines, file B has 80,000 unique lines).
How can I quickly find those unique lines in two files? Is there any ready-to-use command line tools for this? I'm using Python but I guess it's less possible to find a efficient Pythonic method to load the files and compare.
Any suggestions are appreciated.

If order matters, try the comm utility. If order doesn't matter, sort file1 file2 | uniq -u.

I think this is the fastest method (whether it's in Python or another language shouldn't matter too much IMO).
Notes:
1.I only store each line's hash to save space (and time if paging might occur)
2.Because of the above, I only print out line numbers; if you need actual lines, you'd just need to read the files in again
3.I assume that the hash function results in no conflicts. This is nearly, but not perfectly, certain.
4.I import hashlib because the built-in hash() function is too short to avoid conflicts.
import sys
import hashlib
file = []
lines = []
for i in range(2):
# open the files named in the command line
file.append(open(sys.argv[1+i], 'r'))
# stores the hash value and the line number for each line in file i
lines.append({})
# assuming you like counting lines starting with 1
counter = 1
while 1:
# assuming default encoding is sufficient to handle the input file
line = file[i].readline().encode()
if not line: break
hashcode = hashlib.sha512(line).hexdigest()
lines[i][hashcode] = sys.argv[1+i]+': '+str(counter)
counter += 1
unique0 = lines[0].keys() - lines[1].keys()
unique1 = lines[1].keys() - lines[0].keys()
result = [lines[0][x] for x in unique0] + [lines[1][x] for x in unique1]

With 60,000 or 80,000 unique lines you could just create a dictionary for each unique line, mapping it to a number. mydict["hello world"] => 1, etc. If your average line is around 40-80 characters this will be in the neighborhood of 10 MB of memory.
Then read each file, converting it to an array of numbers via the dictionary. Those will fit easily in memory (2 files of 8 bytes * 3GB / 60k lines is less than 1 MB of memory). Then diff the lists. You could invert the dictionary and use it to print out the text of the lines that differ.
EDIT:
In response to your comment, here's a sample script that assigns numbers to unique lines as it reads from a file.
#!/usr/bin/python
class Reader:
def __init__(self, file):
self.count = 0
self.dict = {}
self.file = file
def readline(self):
line = self.file.readline()
if not line:
return None
if self.dict.has_key(line):
return self.dict[line]
else:
self.count = self.count + 1
self.dict[line] = self.count
return self.count
if __name__ == '__main__':
print "Type Ctrl-D to quit."
import sys
r = Reader(sys.stdin)
result = 'ignore'
while result:
result = r.readline()
print result

If I understand correctly, you want the lines of these files without duplicates. This does the job:
uniqA = set(open('fileA', 'r'))

Python has difflib which claims to be quite competitive with other diff utilities see:
http://docs.python.org/library/difflib.html

What is the lightest way of doing this task?

I have a file whose contents are of the form:
.2323 1
.2327 1
.3432 1
.4543 1
and so on some 10,000 lines in each file.
I have a variable whose value is say a=.3344
From the file I want to get the row number of the row whose first column is closest to this variable...for example it should give row_num='3' as .3432 is closest to it.
I have tried in a method of loading the first columns element in a list and then comparing the variable to each element and getting the index number
If I do in this method it is very much time consuming and slow my model...I want a very quick method as this need to to called some 1000 times minimum...
I want a method with least overhead and very quick can anyone please tell me how can it be done very fast.
As the file size is maximum of 100kb can this be done directly without loading into any list of anything...if yes how can it be done.
Any method quicker than the method mentioned above are welcome but I am desperate to improve the speed -- please help.
def get_list(file, cmp, fout):
ind, _ = min(enumerate(file), key=lambda x: abs(x[1] - cmp))
return fout[ind].rstrip('\n').split(' ')
#root = r'c:\begpython\wavnk'
header = 6
for lst in lists:
save = database_index[lst]
#print save
index, base,abs2, _ , abs1 = save
using_data[index] = save
base = 'C:/begpython/wavnk/'+ base.replace('phone', 'text')
fin, fout = base + '.pm', base + '.mcep'
file = open(fin)
fout = open(fout).readlines()
[next(file) for _ in range(header)]
file = [float(line.partition(' ')[0]) for line in file]
join_cost_index_end[index] = get_list(file, float(abs1), fout)
join_cost_index_strt[index] = get_list(file, float(abs2), fout)
this is the code i was using..copying file into a list.and all please give better alternarives to this

Building on John Kugelman's answer, here's a way you might be able to do a binary search on a file with fixed-length lines:
class SubscriptableFile(object):
def __init__(self, file):
self._file = file
file.seek(0,0)
self._line_length = len(file.readline())
file.seek(0,2)
self._len = file.tell() / self._line_length
def __len__(self):
return self._len
def __getitem__(self, key):
self._file.seek(key * self._line_length)
s = self._file.readline()
if s:
return float(s.split()[0])
else:
raise KeyError('Line number too large')
This class wraps a file in a list-like structure, so that now you can use the functions of the bisect module on it:
def find_row(file, target):
fw = SubscriptableFile(file)
i = bisect.bisect_left(fw, target)
if fw[i + 1] - target < target - fw[i]:
return i + 1
else:
return i
Here file is an open file object and target is the number you want to find. The function returns the number of the line with the closest value.
I will note, however, that the bisect module will try to use a C implementation of its binary search when it is available, and I'm not sure if the C implementation supports this kind of behavior. It might require a true list, rather than a "fake list" (like my SubscriptableFile).

Is the data in the file sorted in numerical order? Are all the lines of the same length? If not, the simplest approach is best. Namely, reading through the file line by line. There's no need to store more than one line in memory at a time.
Code:
def closest(num):
closest_row = None
closest_value = None
for row_num, row in enumerate(file('numbers.txt')):
value = float(row.split()[0])
if closest_value is None or abs(value - num) < abs(closest_value - num):
closest_row = row
closest_row_num = row_num
closest_value = value
return (closest_row_num, closest_row)
print closest(.3344)
Output for sample data:
(2, '.3432 1\n')
If the lines are all the same length and the data is sorted then there are some optimizations that will make this a very fast process. All the lines being the same length would let you seek directly to particular lines (you can't do this in a normal text file with lines of different length). Which would then enable you to do a binary search.
A binary search would be massively faster than a linear search. A linear search will on average have to read 5,000 lines of a 10,000 line file each time, whereas a binary search would on average only read log2 10,000 ≈ 13 lines.

Load it into a list then use bisect.