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Replace decimal with comma in multiple text files

I have 10 TAB delimited txt files in a folder. It has three columns (with numbers only) preceeded by a 21 line header (text and numbers). In order to process them further, I would like to :
Choose the second column from all text files (starting after the 21 line header; i attached figure with arrow), convert the comma into decimal and stack each of these columns from the 10 files into a new tab delimited/csv file. Once all files.
I know very little scripting. I have Rstudio and Python and have tried to fiddle around a bit. But I have really no clue what to do. Since I have to process multiple folders, my work would be really simplified if it could be possible.
Reference figure

From your requirements it sounds like this Python code should do the trick:
import os
import glob
DIR = "path/to/your/directory"
OUTPUT_FILE = "path/to/your/output.csv"
HEADER_SIZE = 21
input_files = glob.glob(os.path.join(DIR, "*.txt"))
for input_file in input_files:
print("Now processing", input_file)
# read the file
with open(input_file, "r") as h:
contents = h.readlines()
# drop header
contents = contents[HEADER_SIZE:]
# grab the 2nd column
column = []
for row in contents:
# stop at the footer
if "####" in row:
break
split = row.split("\t")
if len(split) >= 2:
column.append(split[1])
# replace the comma
column_replaced = [row.replace(",", ".") for row in column]
# append to the output file
with open(OUTPUT_FILE, "a") as h:
h.write("\n".join(column_replaced))
h.write("\n") # end on a newline
Note that this will discard everything that wasn't part of the second column in the output file.

The code below is not an exact solution but if you follow along its lines you will be close to what you need.
output <- "NewFileName.txt"
old_dir <- setwd("your/folder")
files <- list.files("\\.txt")
df_list <- lapply(files, read.table, skip = 21, sep = "\t")
x <- lapply(df_list, '[[', 2)
x <- gsub(",", ".", unlist(x))
write.table(x, output, row.names = FALSE, col.names = FALSE)
setwd(old_dir)

list =[]
filename = "my_text"
file = open(filename, "r")
for line in file:
res=line.replace(",", ".")
list.append(res)
print(res)
f = open(filename, "w")
for item in list:
f.write(item)`enter code here`

Remove 1000's separator from column in CSV?

I have a Python script where I'm importing a csv that has commas in values over 1000. These values are strings in the csv. I need to remove the commas from the values, and convert the strings to rounded floats inside the csv before it's imported into Python.
I've tried appending all the new values to a list to use the csv.writer, but I haven't been able to figure out how to have the writer only replace the values in the column that have commas. Here's what I have so far. :
import csv
RoomReport = r'path_to_csv'
new_values_list = []
f = open(RoomReport, "r")
reader = csv.reader(f)
writer = csv.writer(f)
for row in reader:
useable_area = row[7]
if "," in useable_area:
useable_area_no_comma = useable_area.replace(",","")
useable_area_rounded = int(round(float(useable_area_no_comma)))
new_values_list.append(useable_area_rounded)
f.close()

As I mentioned in a comment, this can only be done if the input csv file is formatted in a way that will allow the commas in the numbers to be differentiated from the commas between each one of them.
Here's an example of one way it could be done (by quoting all the values):
"0","1","2","3","4","5","6","7,123.6","8","9"
"0","1","2","3","4","5","6","1,000","8","9"
"0","1","2","3","4","5","6","20,000","8","9"
Here's code that will do what you want. It uses the locale.atof function to simplify cleaning up the number:
import csv
import locale
# Set local to someplace that uses a comma for the thousands separator.
locale.setlocale(locale.LC_ALL, 'English_US.1252')
RoomReport = r'RoomReport.csv'
cleaned_report = r'RoomReport_cleaned.csv'
new_values_list = []
with open(RoomReport, "r", newline='') as inp:
for row in csv.reader(inp):
if "," in row[7]:
row[7] = int(round(locale.atof(row[7])))
new_values_list.append(row)
# Create cleaned-up output file.
with open(cleaned_report, "w", newline='') as outp:
csv.writer(outp, quoting=csv.QUOTE_ALL).writerows(new_values_list)
The RoomReport_cleaned.csv it creates from the example input will contain this:
"0","1","2","3","4","5","6","7124","8","9"
"0","1","2","3","4","5","6","1000","8","9"
"0","1","2","3","4","5","6","20000","8","9"
Note that since the values in the output no longer have commas embedded in them, the quoting all fields is not longer necessary—so could be left out by not specifying csv.QUOTE_ALL.

maybe something like this?
import re
from sys import stdout
isnum = re.compile('^[0-9, ]+$')
non = re.compile('[, ]')
fd = StringIO()
out = csv.writer(fd)
out.writerow(['foo','1,000,000',19])
out.writerow(['bar','1,234,567',20])
fd.seek(0)
inp = csv.reader(fd)
out = csv.writer(stdout)
for row in inp:
for i, x in enumerate(row):
if isnum.match(x):
row[i] = float(non.sub('', x))
out.writerow(row)

Python removing substrings from strings

I'm trying to remove some substrings from a string in a csv file.
import csv
import string
input_file = open('in.csv', 'r')
output_file = open('out.csv', 'w')
data = csv.reader(input_file)
writer = csv.writer(output_file,quoting=csv.QUOTE_ALL)# dialect='excel')
specials = ("i'm", "hello", "bye")
for line in data:
line = str(line)
new_line = str.replace(line,specials,'')
writer.writerow(new_line.split(','))
input_file.close()
output_file.close()
So for this example:
hello. I'm obviously over the moon. If I am being honest I didn't think I'd get picked, so to get picked is obviously a big thing. bye.
I'd want the output to be:
obviously over the moon. If I am being honest I didn't think I'd get picked, so to get picked is obviously a big thing.
This however only works when im searching for a single word. So that specials = "I'm" for example. Do I need to add my words to a list or an array?

It looks like you aren't iterating through specials, since it's a tuple rather than a list, so it's only grabbing one of the values. Try this:
specials = ["i'm, "hello", "bye"]
for line in data:
new_line = str(line)
for word in specials:
new_line = str.replace(new_line, word, '')
writer.writerow(new_line.split(','))

It seems like you're already splitting the input via the csv.reader, but then you're throwing away all that goodness by turning the split line back into a string. It's best not to do this, but to keep working with the lists that are yielded from the csv reader. So, it becomes something like this:
for row in data:
new_row = [] # A place to hold the processed row data.
# look at each field in the row.
for field in row:
# remove all the special words.
new_field = field
for s in specials:
new_field = new_field.replace(s, '')
# add the sanitized field to the new "processed" row.
new_row.append(new_field)
# after all fields are processed, write it with the csv writer.
writer.writerow(new_row)

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)