LS-Land-Issue-LS-Magazine-LS-Models-LS-Dreams-Reallola-and-BD-Company-Video-Series-f5.1.txt UPDATED
LS-Land-Issue-LS-Magazine-LS-Models-LS-Dreams-Reallola-and-BD-Company-Video-Series-f5.1.txt
I’m trying to obtain the model names, e.g. “LS-Land-Issue-LS-Magazine-LS-Models-LS-Dreams-Reallola-and-BD-Company-Video-Series-f5.1.txt” with the sub string “LS-Land-Issue-LS-Magazine-LS-Models-LS-Dreams-Reallola-and-BD-Company-Video-Series-f5.1.txt” at the end. Any ideas?
A:
I’ve used the code from the answer by this user:
import re
my_str = ‘LS-Land-Issue-LS-Magazine-LS-Models-LS-Dreams-Reallola-and-BD-Company-Video-Series-f5.1.txt’
pattern = re.compile(r'(\w+)$’, re.IGNORECASE)
match = pattern.search(my_str)
print(match.group(1))
But of course, if you were actually doing anything useful with a match, you’d want to do something else.
that fits the bill. It offers the wonderful combination of size, resilience, and mobility. It’s built to last.
It’s all part of the service. We keep our fingers crossed that repairs will not be needed, but we are ready if they are. We will be without a motor in the boat until repair can be carried out. [Our] chances are good, but you can never say what will happen,” he added.
At some time over the last 12 months or so, Mr Duncan Smith’s boat is also to be overhauled to make it more comfortable for a full-time kayaker.
The ground has already been cleared. “I’ve got a carpenter making an accommodation berth and a ‘long box’ and parts are being put together,” he said.
“The carpenter has done the work on similar boats and knows how to make it very comfortable for four people, including sleeping. I’ve also had a builder with me who has done the work on the hull, which is in very good shape.
“We could spend weeks making her as comfortable as possible, but the call is to be ready to go.”
I have thousands of.txt files and I would like to process them to build up a single.csv file where each line is a new.txt file, and each column is a column of the corresponding.txt file.
My current approach is to read each.txt file in the directory, do some basic manipulations, write the.txt file name to a list, and use that to create the output.csv file.
f_name = []
for root, dirs, files in os.walk(join(current_dir, ‘test’)):
if ‘
‘ in root:
continue
for file_name in files:
f_name.append(file_name)
for i in f_name:
print(i)
This works for the first few thousand.txt files, but after this is runs out of memory. Any tips?
A:
readlines() is a list-like object, so it can be used for “lazy” concatenation of text. For example:
with open(“file.txt”) as f:
next(f) # discard the first line
f.readlines()
# note that `f.readlines()` returns itself so there’s no need for `[]`
# the right-hand side of `.readlines()` is a generator
So you could modify your code like this:
f_name = []
with open(“file.txt”) as f:
next(f) # discard the first line
while True:
for name in f:
f_name.append(name)
# if `f` returned an empty iterator
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