I have a binary file that consists of consecutive binary subsequences of fixed and equal length. Each subsequence can be unpacked into the same number of values. I know the length of each subsequence and the binary format of the values.
How can I work through the binary file, chopping out the subsequences,
unpacking their content and write them out as csv as I go.
I know how to write out as csv. My problem is the reading from file and unpacking part. This is my non-working code.
import csv
import sys
import struct
writer = csv.writer(sys.stdout, delimiter=',', quoting=csv.QUOTE_NONE,escapechar='\\')
? rows = sys.stdin. ?
? header = id, time ....
? write the header with csv
i = 0
for row in rows:
unpacked_row = unpack('QqqqddiBIBcsbshlshhlQB',row)
writer.writerow(unpacked_row)
i += 1
Possible solution using Reading binary file in Python and looping over each byte and the answer of ignacio.
First calculate chunksize = struct.calcsize()
def bytes_from_file(filename, chunksize=8192):
with open(filename, "rb") as f:
while True:
chunk = f.read(chunksize)
if chunk:
yield chunk
else:
break
# example:
for chunk in bytes_from_file('filename'):
# row = unpack(chunk)
# write out row as csv
You need to calculate the size of the structure (Hint: struct.calcsize()) and read some multiple of that from the file at a time. You cannot directly iterate over the input as you can with a text file, since there is no delimiter as such.
You could use struct.Struct to unpack values from a file:
#!/usr/bin/env python
import csv
import sys
from struct import Struct
record = Struct('QqqqddiBIBcsbshlshhlQB')
with open('input_filename', 'rb') as file:
writer = csv.writer(sys.stdout, quoting=csv.QUOTE_NONE, escapechar='\\')
while True:
buf = file.read(record.size)
if len(buf) != record.size:
break
writer.writerow(record.unpack_from(buf))
You could also write the while-loop as:
from functools import partial
for buf in iter(partial(file.read, record.size), b''):
writer.writerow(record.unpack_from(buf))
Related
I am reading a CSV file through samba share. My CSV file format
hello;world
1;2;
Python code
import urllib
from smb.SMBHandler import SMBHandler
PATH = 'smb://myusername:mypassword#192.168.1.200/myDir/'
opener = urllib.request.build_opener(SMBHandler)
fh = opener.open(PATH + 'myFileName')
data = fh.read().decode('utf-8')
print(data) // This prints the data right
csvfile = csv.reader(data, delimiter=';')
for myrow in csvfile:
print(myrow) // This just prints ['h']. however it should print(hello;world)
break
fh.close()
The problem is that after decoding to utf-8, the rows are not the actual lines in the file
Desired output of a row after reading the file: hello;world
Current output of a row after reading the file: h
Any help is appreciated.
csv.reader takes an iterable that returns lines. Strings, when iterated, yield characters. The fix is simple:
csvfile = csv.reader(data.splitlines(), delimiter=';')
I am trying to convert a file containing more than 1 billion bytes into integers. Obviously, my machine cannot do this at once so I need to chunk my code. I was able to decode the first 50,000,000 bytes but I am wondering how to read the integers in the file that are between 50,000,001 and 100,000,000, 150,000,000 and 200,000,000 etc. The following is what I have now;the range function is not working with this.
import struct
with open(x, "rb") as f:
this_chunk = range(50000001, 100000000)
data = f.read(this_chunk)
ints1 = struct.unpack("I" * (this_chunk //4) , data)
print(ints1)
You can use f.seek(offset) to set the file pointer to start reading from a certain offset.
In your case, you'd want to skip 5000000 bytes, so you'd call
f.seek(50000000)
At this point, you'd want to read another 50000000 bytes, so you'd call f.read(50000000).
This would be your complete code listing, implementing f.seek and reading the whole file:
with open(x, "rb") as f:
f.seek(50000000) # omit if you don't want to skip this chunk
data = f.read(50000000)
while data:
... # do something
data = f.read(50000000)
Use f.read(50000000) in a loop at it will read the file in chunks of 50000000, e.g.:
In []:
from io import StringIO
s = '''hello'''
with StringIO(s) as f:
while True:
c = f.read(2)
if not c:
break
print(c)
Out[]:
he
ll
o
How can I read multiple CSV input files line by line, compare the characters in each line, write the line appearing first alphabetically to an output file, and then advance the pointer of the minimum value's file to continue the comparisons with all files until the end of all input files is reached. Here's some rough planning toward a solution.
buffer = []
for inFile in inFiles:
f = open(inFile, "r")
line = f.next()
buffer.append([line, inFile])
#find minimum value in buffer alphabetically...
#write it to an output file...
#how do I advance one line in the file with the min value?
#and then continue the line-by-line comparisons in input files?
You can use heapq.merge:
import heapq
import contextlib
files = [open(fn) for fn in inFiles]
with contextlib.nested(*files):
with open('output', 'w') as f:
f.writelines(heapq.merge(*files))
In Python 3.x (3.3+):
import heapq
import contextlib
with contextlib.ExitStack() as stack:
files = [stack.enter_context(open(fn)) for fn in inFiles]
with open('output', 'w') as f:
f.writelines(heapq.merge(*files))
I am reading some value for file and wants to write modified value into file. My file is .ktx format [binary packed format].
I am using struct.pack() but seems that something is going wrong with that:
bytes = file.read(4)
bytesAsInt = struct.unpack("l",bytes)
number=1+(bytesAsInt[0])
number=hex(number)
no=struct.pack("1",number)
outfile.write(no)
I want to write in both ways little-endian and big-endian.
no_little =struct.pack(">1",bytesAsInt)
no_big =struct.pack("<1",bytesAsInt) # i think this is default ...
again you can check the docs and see the format characters you need
https://docs.python.org/3/library/struct.html
>>> struct.unpack("l","\x05\x04\x03\03")
(50529285,)
>>> struct.pack("l",50529285)
'\x05\x04\x03\x03'
>>> struct.pack("<l",50529285)
'\x05\x04\x03\x03'
>>> struct.pack(">l",50529285)
'\x03\x03\x04\x05'
also note that it is a lowercase L , not a one (as also covered in the docs)
I haven't tested this but the following function should solve your problem. At the moment it reads the file contents completely, creates a buffer and then writes out the updated contents. You could also modify the file buffer directly using unpack_from and pack_into but it might be slower (again, not tested). I'm using the struct.Struct class since you seem to want to unpack the same number many times.
import os
import struct
from StringIO import StringIO
def modify_values(in_file, out_file, increment=1, num_code="i", endian="<"):
with open(in_file, "rb") as file_h:
content = file_h.read()
num = struct.Struct(endian + num_code)
buf = StringIO()
try:
while len(content) >= num.size:
value = num.unpack(content[:num.size])[0]
value += increment
buf.write(num.pack(value))
content = content[num.size:]
except Exception as err:
# handle
else:
buf.seek(0)
with open(out_file, "wb") as file_h:
file_h.write(buf.read())
An alternative is to use the array which makes it quite easy. I don't know how to implement endianess with an array.
def modify_values(filename, increment=1, num_code="i"):
with open(filename, "rb") as file_h:
arr = array("i", file_h.read())
for i in range(len(arr)):
arr[i] += increment
with open(filename, "wb") as file_h:
arr.tofile(file_h)
I have a 7GB csv file which I'd like to split into smaller chunks, so it is readable and faster for analysis in Python on a notebook. I would like to grab a small set from it, maybe 250MB, so how can I do this?
You don't need Python to split a csv file. Using your shell:
$ split -l 100 data.csv
Would split data.csv in chunks of 100 lines.
I had to do a similar task, and used the pandas package:
for i,chunk in enumerate(pd.read_csv('bigfile.csv', chunksize=500000)):
chunk.to_csv('chunk{}.csv'.format(i), index=False)
Here is a little python script I used to split a file data.csv into several CSV part files. The number of part files can be controlled with chunk_size (number of lines per part file).
The header line (column names) of the original file is copied into every part CSV file.
It works for big files because it reads one line at a time with readline() instead of loading the complete file into memory at once.
#!/usr/bin/env python3
def main():
chunk_size = 9998 # lines
def write_chunk(part, lines):
with open('data_part_'+ str(part) +'.csv', 'w') as f_out:
f_out.write(header)
f_out.writelines(lines)
with open('data.csv', 'r') as f:
count = 0
header = f.readline()
lines = []
for line in f:
count += 1
lines.append(line)
if count % chunk_size == 0:
write_chunk(count // chunk_size, lines)
lines = []
# write remainder
if len(lines) > 0:
write_chunk((count // chunk_size) + 1, lines)
if __name__ == '__main__':
main()
This graph shows the runtime difference of the different approaches outlined by other posters (on an 8 core machine when splitting a 2.9 GB file with 11.8 million rows of data into ~290 files).
The shell approach is from Thomas Orozco, Python approach s from Roberto, Pandas approach is from Quentin Febvre and here's the Dask snippet:
ddf = dd.read_csv("../nyc-parking-tickets/Parking_Violations_Issued_-_Fiscal_Year_2015.csv", blocksize=10000000, dtype=dtypes)
ddf.to_csv("../tmp/split_csv_dask")
I'd recommend Dask for splitting files, even though it's not the fastest, because it's the most flexible solution (you can write out different file formats, perform processing operations before writing, easily modify compression formats, etc.). The Pandas approach is almost as flexible, but cannot perform processing on the entire dataset (like sorting the entire dataset before writing).
Bash / native Python filesystem operations are clearly quicker, but that's not what I'm typically looking for when I have a large CSV. I'm typically interested in splitting large CSVs into smaller Parquet files, for performant, production data analyses. I don't usually care if the actually splitting takes a couple minutes more. I'm more interested in splitting accurately.
I wrote a blog post that discusses this in more detail. You can probably Google around and find the post.
See the Python docs on file objects (the object returned by open(filename) - you can choose to read a specified number of bytes, or use readline to work through one line at a time.
Maybe something like this?
#!/usr/local/cpython-3.3/bin/python
import csv
divisor = 10
outfileno = 1
outfile = None
with open('big.csv', 'r') as infile:
for index, row in enumerate(csv.reader(infile)):
if index % divisor == 0:
if outfile is not None:
outfile.close()
outfilename = 'big-{}.csv'.format(outfileno)
outfile = open(outfilename, 'w')
outfileno += 1
writer = csv.writer(outfile)
writer.writerow(row)
I agree with #jonrsharpe readline should be able to read one line at a time even for big files.
If you are dealing with big csv files might I suggest using pandas.read_csv. I often use it for the same purpose and always find it awesome (and fast). Takes a bit of time to get used to idea of DataFrames. But once you get over that it speeds up large operations like yours massively.
Hope it helps.
here is my code which might help
import os
import pandas as pd
import uuid
class FileSettings(object):
def __init__(self, file_name, row_size=100):
self.file_name = file_name
self.row_size = row_size
class FileSplitter(object):
def __init__(self, file_settings):
self.file_settings = file_settings
if type(self.file_settings).__name__ != "FileSettings":
raise Exception("Please pass correct instance ")
self.df = pd.read_csv(self.file_settings.file_name,
chunksize=self.file_settings.row_size)
def run(self, directory="temp"):
try:os.makedirs(directory)
except Exception as e:pass
counter = 0
while True:
try:
file_name = "{}/{}_{}_row_{}_{}.csv".format(
directory, self.file_settings.file_name.split(".")[0], counter, self.file_settings.row_size, uuid.uuid4().__str__()
)
df = next(self.df).to_csv(file_name)
counter = counter + 1
except StopIteration:
break
except Exception as e:
print("Error:",e)
break
return True
def main():
helper = FileSplitter(FileSettings(
file_name='sample1.csv',
row_size=10
))
helper.run()
main()
In the case of wanting to split by rough boundaries in bytes, the newest datapoints being the bottom-most ones and wanting to put the newest datapoints in the first file:
from pathlib import Path
TEN_MB = 10000000
FIVE_MB = 5000000
def split_file_into_chunks(path, chunk_size=TEN_MB):
path = str(path)
output_prefix = path.rpartition('.')[0]
output_ext = path.rpartition('.')[-1]
with open(path, 'rb') as f:
seek_positions = []
for x, line in enumerate(f):
if not x:
header = line
seek_positions.append(f.tell())
part = 0
last_seek_pos = seek_positions[-1]
for seek_pos in reversed(seek_positions):
if last_seek_pos-seek_pos >= chunk_size:
with open(f'{output_prefix}.arch.{part}.{output_ext}', 'wb') as f_out:
f.seek(seek_pos)
f_out.write(header)
f_out.write(f.read(last_seek_pos-seek_pos))
last_seek_pos = seek_pos
part += 1
with open(f'{output_prefix}.arch.{part}.{output_ext}', 'wb') as f_out:
f.seek(0)
f_out.write(f.read(last_seek_pos))
Path(path).rename(path+'~')
Path(f'{output_prefix}.arch.0.{output_ext}').rename(path)
Path(path+'~').unlink()