How to convert regex to python code

I need to convert the following regular expression to the python code:

find /bin -type f -size +150000000c -exec ls -al {} \; | sort -k 5 -nr | sed 's/ \+/\t/g' | cut -f 5,9| more

Any help will be greatly appreciated
Thanks

@Klaus , Welcome.

If I understand correctly, you want to replace space characters by tabs ?
in python, use the replace function

print( 'This has spaces but they will be changed to tabs'.replace(" ", "\t"))
This	has	spaces	but	they	will	be	changed	to	tabs

if more 'complex' then import the regular expression module (re)

import re
print(re.sub(r'\s+', '\t', "This has spaces but will be replaced by tabs"))
This	has	spaces	but	will	be	replaced	by	tabs

I have presumed you are familiar with python.

If you need more than this - please elaborate .

Thanks a lot munkeHoller

np, we'll mark this as completed. if you have more Q's post in a new thread, tks

Optimized using find -exec + and ls -S and awk:

find /bin/ -type f -size +150000000c -exec ls -Sl {} + | awk '{print $5"\t"$9}' | more

Here's the GNU find alternative:

find /bin/ -type f -size +150000000c -printf '%s %p\n'

you can pipe it through sort -nk1,1 to get it sorted.

To convert a regular expression (regex) into Python code, you'll use the re module, which is part of Python's standard library. The re module provides support for working with regular expressions.

Here's a basic outline of how you can convert a regex pattern into Python code:

Step 1: Import the re Module

Make sure to import the re module at the beginning of your Python script or in the interpreter session:

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import re

Step 2: Define Your Regular Expression Pattern

Define your regex pattern as a string. For example, if you have a regex pattern to match an email address:

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regex_pattern = r'\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b'

Step 3: Compile the Regex Pattern (Optional but Recommended)

You can compile the regex pattern into a regular expression object for better performance if you plan to use it multiple times:

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regex = re.compile(regex_pattern)

Step 4: Using the Regex

a. Matching:

To find the first occurrence of the pattern in a string, use re.search():

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result = re.search(regex_pattern, your_text_here)
if result:
    print("Found match:", result.group())
else:
    print("No match")

If you compiled the pattern earlier, you'd use the regex object:

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result = regex.search(your_text_here)

b. Finding All Matches:

To find all occurrences of the pattern in a string, use re.findall():

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results = re.findall(regex_pattern, your_text_here)
for result in results:
    print("Found match:", result)

With the compiled regex:

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results = regex.findall(your_text_here)

c. Splitting:

To split a string based on the regex pattern, use re.split():

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parts = re.split(regex_pattern, your_text_here)
print(parts)

With the compiled regex:

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parts = regex.split(your_text_here)
print(parts)

d. Substitution:

To replace matches in a string with a new substring, use re.sub():

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new_string = re.sub(regex_pattern, replacement_string, your_text_here)
print(new_string)

With the compiled regex:

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new_string = regex.sub(replacement_string, your_text_here)
print(new_string)

Example:

Here's an example that combines the above steps:

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import re

# Define regex pattern for matching email addresses
regex_pattern = r'\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b'

# Compile the regex pattern
regex = re.compile(regex_pattern)

# Test text
text = "Contact us at email@example.com or support@website.com for assistance."

# Search for the first email match
result = regex.search(text)
if result:
    print("Found match:", result.group())
else:
    print("No match")

# Find all email addresses in the text
results = regex.findall(text)
for result in results:
    print("Found match:", result)

# Split the text by email addresses
parts = regex.split(text)
print(parts)

# Replace email addresses with a generic label
new_text = regex.sub("EMAIL", text)
print(new_text)

This example demonstrates searching for email addresses in a piece of text using regex, finding all matches, splitting the text based on email addresses, and then replacing the email addresses with a generic label.

@audreyshura , your previous post(s) also using LLM generated output, you were asked to stamp that as such , so, just a reminder to declare at such.

Thanks a lot. This was a great update.

@Klaus , to be clear, @audreyshura has not created this, it was one of the LLM (AI) such as gemini or chatgpt !, take its fidelity with a pinch of salt before using.

You cannot trust coded generated by generative AI using LLMs.

This kind of generated code is dangerous because the LLMs do not check for bugs and since you do not understand the code which was AI generated, you cannot maintain it, especially for large code blocks.

@audreyshura has been silenced for a while for posting chatbot AI replies without referencing the generative AI or LLM used.

All users posting here must declare their generative AI postings and the exact AI and LLM version.

No exceptions.

Using generative AI to generate code is dangerous. I know. Why. Because I have refactored all lot of code using ChatGPT and Bard. Both are very buggy and use inconsistent design patterns. Every line must be checked carefully and AI generated code, when it does work, can be difficult to maintain because it was not written by "you" but generated by an algorithm which has zero "idea" what it is doing. It's only generating text and it not really coding, debugging and has zero "clue" to what it is doing. It's a huge "internet parrot" so to speak and no substitute for actually learning to write and debug and maintain code yourself.

This new member has been silenced.:

Screenshot 2024-04-03 at 8.16.35 AM2168×256 33 KB

and will be banned if they continue to post chatbot generated code without full attribution.