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More efficient way than zipping arrays for transposing a table in Python?

I have been trying to transpose my table of 2000000+ rows and 300+ columns on a cluster, but it seems that my Python script is getting killed due to lack of memory. I would just like to know if anyone has any suggestions on a more efficient way to store my table data other than using the array, as shown in my code below?
import sys
Seperator = "\t"
m = []
f = open(sys.argv[1], 'r')
data = f.read()
lines = data.split("\n")[:-1]
for line in lines:
m.append(line.strip().split("\t"))
for i in zip(*m):
for j in range(len(i)):
if j != len(i):
print(i[j] +Seperator)
else:
print(i[j])
print ("\n")
Thanks very much.

The first thing to note is that you've been careless with your variables. You're loading a large file into memory as a single string, and then a list of a strings, then a list of list of strings, before finally transposing said list. This will result in you storing all the data in the file three times before you even begin to transpose it.
If each individual string in the file is only about 10 characters long then you're going to need 18GB of memory just to store that (2e6 rows * 300 columns * 10 bytes * 3 duplicates). This is before you factor in all the overhead of python objects (~27 bytes per string object).
You have a couple of options options here.
create each new transposed row incrementally by reading over the file once for each old row and appending each new row one at a time (sacrifices time efficiency).
create one file for each new row and combine these row files at the end (sacrifices disk space efficiency, possibly problematic if you have a lot of columns in the initial file due to a limit of the number of open files a process may have).
Transposing with a limited number of open files
delimiter = ','
input_filename = 'file.csv'
output_filename = 'out.csv'
# find out the number of columns in the file
with open(input_filename) as input:
old_cols = input.readline().count(delimiter) + 1
temp_files = [
'temp-file-{}.csv'.format(i)
for i in range(old_cols)
]
# create temp files
for temp_filename in temp_files:
open(temp_filename, 'w') as output:
output.truncate()
with open(input_filename) as input:
for line in input:
parts = line.rstrip().split(delimiter)
assert len(parts) == len(temp_files), 'not enough or too many columns'
for temp_filename, cell in zip(temp_files, parts):
with open(temp_filename, 'a') as output:
output.write(cell)
output.write(',')
# combine temp files
with open(output_filename, 'w') as output:
for temp_filename in temp_files:
with open(temp_filename) as input:
line = input.read().rstrip()[:-1] + '\n'
output.write(line)

As the number of columns is far smaller than nuber of rows I would consider writing each column to separate file. And then combine them together.
import sys
Separator = "\t"
f = open(sys.argv[1], 'r')
for line in f:
for i, c in enumerate(line.strip().split("\t")):
dest = column_file[i] # you shoud open 300+ file handlers, one for each column
dest.write(c)
dest.write(Separator)
# all you need to do after than is combine the content of you "row" files

If you cannot store all of your file into memory, you can read it n times:
column_number = 4 # if necessary, read the first line of the file to calculate it
seperetor = '\t'
filename = sys.argv[1]
def get_nth_column(filename, n):
with open(filename, 'r') as file:
for line in file:
if line: # remove empty lines
yield line.strip().split('\t')[n]
for column in range(column_number):
print(seperetor.join(get_nth_column(filename, column)))
Note that an exception will be raised if the file does not have the right format. You could catch it if necessary.
When reading files : use with construct, to ensure that your file will be closed. And iterate directly on the file, instead of reading the content first. It is more readable and more efficient.

Adding each item in list to end of specific lines in FASTA file

I solved this in the comments below.
So essentially what I am trying to do is add each element of a list of strings to the end of specific lines in a different file.
Hard to explain but essentially I want to parse a FASTA file, and every time it reaches a header (line.startswith('>')) I want it to replace parts of that header with an element in a list I've already made.
For example:
File1:
">seq1 unwanted here
AATATTATA
ATATATATA
>seq2 unwanted stuff here
GTGTGTGTG
GTGTGTGTG
>seq3 more stuff I don't want
ACACACACAC
ACACACACAC"
I want it to keep ">seq#" but replace everything after with the next item in the list below:
List:
mylist = "['things1', '', 'things3', 'things4', '' 'things6', 'things7']"
Result (modified file1):
">seq1 things1
AATATTATA
ATATATATA
>seq2 # adds nothing here due to mylist[1] = ''
GTGTGTGTG
GTGTGTGTG
>seq3 things3
ACACACACAC
ACACACACAC
As you can see I want it to add even the blank items in the list.
So once again, I want it to parse this FASTA file, and every time it gets to a header (there are thousands), I want it to replace everything after the first word with the next item in the separate list I have made.

What you have will work, but there are a few unnecessary lines so I've edited down to use a few less lines. Also, an important note is that you don't close your file handles. This could result in errors, specifically when writing to file, either way it's bad practice. code:
#!/usr/bin/python
import sys
# gets list of annotations
def get_annos(infile):
with open(infile, 'r') as fh: # makes sure the file is closed properly
annos = []
for line in fh:
annos.append( line.split('\t')[5] ) # added tab as separator
return annos
# replaces extra info on each header with correct annotation
def add_annos(infile1, infile2, outfile):
annos = get_annos(infile1) # contains list of annos
with open(infile2, 'r') as f2, open(outfile, 'w') as output:
for line in f2:
if line.startswith('>'):
line_split = list(line.split()[0]) # split line on whitespace and store first element in list
line_split.append(annos.pop(0)) # append data of interest to current id line
output.write( ' '.join(line_split) + '\n' ) # join and write to file with a newline character
else:
output.write(line)
anno = sys.argv[1]
seq = sys.argv[2]
out = sys.argv[3]
add_annos(anno, seq, out)
get_annos(anno)
This is not perfect but it cleans things up a bit. I'd might veer away from using pop() to associate the annotation data with the sequence IDs unless you are certain the files are in the same order every time.

There is a great library in python for Fasta and other DNA file parsing. It is totally helpful in Bioinformatics. You can also manipulate any data according to your need.
Here is a simple example extracted from the library website:
from Bio import SeqIO
for seq_record in SeqIO.parse("ls_orchid.fasta", "fasta"):
print(seq_record.id)
print(repr(seq_record.seq))
print(len(seq_record))
You should get something like this on your screen:
gi|2765658|emb|Z78533.1|CIZ78533
Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGATGAGACCGTGG...CGC', SingleLetterAlphabet())
740
...
gi|2765564|emb|Z78439.1|PBZ78439
Seq('CATTGTTGAGATCACATAATAATTGATCGAGTTAATCTGGAGGATCTGTTTACT...GCC', SingleLetterAlphabet())
592

***********EDIT*********
I solved this before anyone could help. This is my code, can anyone tell me if I have any bad practices? Is there a way to do this without writing everything to a new file? Seems like it would take a long time/lots of memory.
#!/usr/bin/python
# Script takes unedited FASTA file, removed seq length and
# other header info, adds annotation after sequence name
# run as: $ python addanno.py testanno.out testseq.fasta out.txt
import sys
# gets list of annotations
def get_annos(infile):
f = open(infile)
list2 = []
for line in f:
columns = line.strip().split('\t')
list2.append(columns[5])
return list2
# replaces extra info on each header with correct annotation
def add_annos(infile1, infile2, outfile):
mylist = get_annos(infile1) # contains list of annos
f2 = open(infile2, 'r')
output = open(out, 'w')
for line in f2:
if line.startswith('>'):
l = line.partition(" ")
list3 = list(l)
del list3[1:]
list3.append(' ')
list3.append(mylist.pop(0))
final = ''.join(list3)
line = line.replace(line, final)
output.write(line)
output.write('\n')
else:
output.write(line)
anno = sys.argv[1]
seq = sys.argv[2]
out = sys.argv[3]
add_annos(anno, seq, out)
get_annos(anno)

python: adding a zero if my value is less then 3 digits long

I have a csv file that needs to add a zero in front of the number if its less than 4 digits.
I only have to update a particular row:
import csv
f = open('csvpatpos.csv')
csv_f = csv.reader(f)
for row in csv_f:
print row[5]
then I want to parse through that row and add a 0 to the front of any number that is shorter than 4 digits. And then input it into a new csv file with the adjusted data.

You want to use string formatting for these things:
>>> '{:04}'.format(99)
'0099'
Format String Syntax documentation

When you think about parsing, you either need to think about regex or pyparsing. In this case, regex would perform the parsing quite easily.
But that's not all, once you are able to parse the numbers, you need to zero fill it. For that purpose, you need to use str.format for padding and justifying the string accordingly.
Consider your string
st = "parse through that row and add a 0 to the front of any number that is shorter than 4 digits."
In the above lines, you can do something like
Implementation
parts = re.split(r"(\d{0,3})", st)
''.join("{:>04}".format(elem) if elem.isdigit() else elem for elem in parts)
Output
'parse through that row and add a 0000 to the front of any number that is shorter than 0004 digits.'

The following code will read in the given csv file, iterate through each row and each item in each row, and output it to a new csv file.
import csv
import os
f = open('csvpatpos.csv')
# open temp .csv file for output
out = open('csvtemp.csv','w')
csv_f = csv.reader(f)
for row in csv_f:
# create a temporary list for this row
temp_row = []
# iterate through all of the items in the row
for item in row:
# add the zero filled value of each temporary item to the list
temp_row.append(item.zfill(4))
# join the current temporary list with commas and write it to the out file
out.write(','.join(temp_row) + '\n')
out.close()
f.close()
Your results will be in csvtemp.csv. If you want to save the data with the original filename, just add the following code to the end of the script
# remove original file
os.remove('csvpatpos.csv')
# rename temp file to original file name
os.rename('csvtemp.csv','csvpatpos.csv')
Pythonic Version
The code above is is very verbose in order to make it understandable. Here is the code refactored to make it more Pythonic
import csv
new_rows = []
with open('csvpatpos.csv','r') as f:
csv_f = csv.reader(f)
for row in csv_f:
row = [ x.zfill(4) for x in row ]
new_rows.append(row)
with open('csvpatpos.csv','wb') as f:
csv_f = csv.writer(f)
csv_f.writerows(new_rows)

Will leave you with two hints:
s = "486"
s.isdigit() == True
for finding what things are numbers.
And
s = "486"
s.zfill(4) == "0486"
for filling in zeroes.

Extracting oddly arranged data from csv and converting to another csv file using python

I have a odd csv file thas has data with header value and its corresponding data in a manner as below:
,,,Completed Milling Job,,,,,, # row 1
,,,,Extended Report,,,,,
,,Job Spec numerical control,,,,,,,
Job Number,3456,,,,,, Operator Id,clipper,
Coder Machine Name,Caterpillar,,,,,,Job Start time,3/12/2013 6:22,
Machine type,Stepper motor,,,,,,Job end time,3/12/2013 9:16,
I need to extract the data from this strucutre create another csv file as per the structure below:
Status,Job Number,Coder Machine Name,Machine type, Operator Id,Job Start time,Job end time,,, # header
Completed Milling Job,3456,Caterpillar,Stepper motor,clipper,3/12/2013 6:22,3/12/2013 9:16,,, # data row
If you notice, there is a new header column added called 'status" but the value is in the first row of the csv file. rest of the column names in output file are extracted from the original file.
Any thoughts will be greatly appreciated - thanks

Assuming the files are all exactly like that (at least in terms of caps) this should work, though I can only guarantee it on the exact data you have supplied:
#!/usr/bin/python
import glob
from sys import argv
g=open(argv[2],'w')
g.write("Status,Job Number,Coder Machine Name,Machine type, Operator Id,Job Start time,Job end time\n")
for fname in glob.glob(argv[1]):
with open(fname) as f:
status=f.readline().strip().strip(',')
f.readline()#extended report not needed
f.readline()#job spec numerical control not needed
s=f.readline()
job_no=s.split('Job Number,')[1].split(',')[0]
op_id=s.split('Operator Id,')[1].strip().strip(',')
s=f.readline()
machine_name=s.split('Coder Machine Name,')[1].split(',')[0]
start_t=s.split('Job Start time,')[1].strip().strip(',')
s=f.readline()
machine_type=s.split('Machine type,')[1].split(',')[0]
end_t=s.split('Job end time,')[1].strip().strip(',')
g.write(",".join([status,job_no,machine_name,machine_type,op_id,start_t,end_t])+"\n")
g.close()
It takes a glob argument (like Job*.data) and an output filename and should construct what you need. Just save it as 'so.py' or something and run it as python so.py <data_files_wildcarded> output.csv

Here is a solution that should work on any CSV files that follow the same pattern as what you showed. That is a seriously nasty format.
I got interested in the problem and worked on it during my lunch break. Here's the code:
COMMA = ','
NEWLINE = '\n'
def _kvpairs_from_line(line):
line = line.strip()
values = [item.strip() for item in line.split(COMMA)]
i = 0
while i < len(values):
if not values[i]:
i += 1 # advance past empty value
else:
# yield pair of values
yield (values[i], values[i+1])
i += 2 # advance past pair
def kvpairs_by_column_then_row(lines):
"""
Given a series of lines, where each line is comma-separated values
organized as key/value pairs like so:
key_1,value_1,key_n+1,value_n+1,...
key_2,value_2,key_n+2,value_n+2,...
...
key_n,value_n,key_n+n,value_n+n,...
Yield up key/value pairs taken from the first column, then from the second column
and so on.
"""
pairs = [_kvpairs_from_line(line) for line in lines]
done = [False for _ in pairs]
while not all(done):
for i in range(len(pairs)):
if not done[i]:
try:
key_value_tuple = next(pairs[i])
yield key_value_tuple
except StopIteration:
done[i] = True
STATUS = "Status"
columns = [STATUS]
d = {}
with open("data.csv", "rt") as f:
# get an iterator that lets us pull lines conveniently from file
itr = iter(f)
# pull first line and collect status
line = next(itr)
lst = line.split(COMMA)
d[STATUS] = lst[3]
# pull next lines and make sure the file is what we expected
line = next(itr)
assert "Extended Report" in line
line = next(itr)
assert "Job Spec numerical control" in line
# pull all remaining lines and save in a list
lines = [line.strip() for line in f]
for key, value in kvpairs_by_column_then_row(lines):
columns.append(key)
d[key] = value
with open("output.csv", "wt") as f:
# write column headers line
line = COMMA.join(columns)
f.write(line + NEWLINE)
# write data row
line = COMMA.join(d[key] for key in columns)
f.write(line + NEWLINE)

Python- Read from Multiple Files

I have 125 data files containing two columns and 21 rows of data. Please see the image below:
and I'd like to import them into a single .csv file (as 250 columns and 21 rows).
I am fairly new to python but this what I have been advised, code wise:
import glob
Results = [open(f) for f in glob.glob("*.data")]
fout = open("res.csv", 'w')
for row in range(21):
for f in Results:
fout.write( f.readline().strip() )
fout.write(',')
fout.write('\n')
fout.close()
However, there is slight problem with the code as I only get 125 columns, (i.e. the force and displacement columns are written in one column) Please refer to the image below:
I'd very much appreciate it if anyone could help me with this !

import glob
results = [open(f) for f in glob.glob("*.data")]
sep = ","
# Uncomment if your Excel formats decimal numbers like 3,14 instead of 3.14
# sep = ";"
with open("res.csv", 'w') as fout:
for row in range(21):
iterator = (f.readline().strip().replace("\t", sep) for f in results)
line = sep.join(iterator)
fout.write("{0}\n".format(line))
So to explain what went wrong with your code, your source files use tab as a field separator, but your code uses comma to separate the lines it reads from those files. If your excel uses period as a decimal separator, it uses comma as a default field separator. The whitespace is ignored unless enclosed in quotes, and you see the result.
If you use the text import feature of Excel (Data ribbon => From Text) you can ask it to consider both comma and tab as valid field separators, and then I'm pretty sure your original output would work too.
In contrast, the above code should produce a file that will open correctly when double clicked.

You don't need to write your own program to do this, in python or otherwise. You can use an existing unix command (if you are in that environment):
paste *.data > res.csv

Try this:
import glob, csv
from itertools import cycle, islice, count
def roundrobin(*iterables):
"roundrobin('ABC', 'D', 'EF') --> A D E B F C"
# Recipe credited to George Sakkis
pending = len(iterables)
nexts = cycle(iter(it).next for it in iterables)
while pending:
try:
for next in nexts:
yield next()
except StopIteration:
pending -= 1
nexts = cycle(islice(nexts, pending))
Results = [open(f).readlines() for f in glob.glob("*.data")]
fout = csv.writer(open("res.csv", 'wb'), dialect="excel")
row = []
for line, c in zip(roundrobin(Results), cycle(range(len(Results)))):
splitline = line.split()
for item,currItem in zip(splitline, count(1)):
row[c+currItem] = item
if count == len(Results):
fout.writerow(row)
row = []
del fout
It should loop over each line of your input file and stitch them together as one row, which the csv library will write in the listed dialect.

I suggest to get used to csv module. The reason is that if the data is not that simple (simple strings in headings, and then numbers only) it is difficult to implement everything again. Try the following:
import csv
import glob
import os
datapath = './data'
resultpath = './result'
if not os.path.isdir(resultpath):
os.makedirs(resultpath)
# Initialize the empty rows. It does not check how many rows are
# in the file.
rows = []
# Read data from the files to the above matrix.
for fname in glob.glob(os.path.join(datapath, '*.data')):
with open(fname, 'rb') as f:
reader = csv.reader(f)
for n, row in enumerate(reader):
if len(rows) < n+1:
rows.append([]) # add another row
rows[n].extend(row) # append the elements from the file
# Write the data from memory to the result file.
fname = os.path.join(resultpath, 'result.csv')
with open(fname, 'wb') as f:
writer = csv.writer(f)
for row in rows:
writer.writerow(row)
The with construct for opening a file can be replaced by the couple:
f = open(fname, 'wb')
...
f.close()
The csv.reader and csv.writer are simply wrappers that parse or compose the line of the file. The doc says that they require to open the file in the binary mode.