I’m working on a project where I need to clean a dataset that will be used for a chatbot. The dataset includes text data, code snippets, and mathematical formulas, and I want to ensure that while cleaning the data, I do not alter or break the code snippets or formulas.

Specifically, I want to:

Redact sensitive data such as URLs, email addresses, and personal information. Standardize dates and remove any unnecessary special characters from the non-code parts of the dataset. Preserve the integrity of code snippets and formulas by keeping the special characters, indentation, and syntax intact. My challenges are:

The dataset has columns that contain code snippets and mathematical formulas . Removing special characters or changing the formatting would make code snippets and formulas useless. What would be the best approach to clean this dataset while preserving the structure of the code and formulas? How should I:

Detect code snippets and formulas? Clean non-code text while leaving the code and formulas intact? Handle specific elements like LaTeX, Python code, or other syntaxes? Here’s the approach I’m considering:

Identify the code and formula sections using markers (e.g., backticks for code, LaTeX syntax for formulas). Clean only the non-code sections for personal data and irrelevant special characters. Avoid altering any syntax or formatting in the code or formulas. Any guidance, suggestions, or code examples on how to achieve this would be greatly appreciated!

I’m working on a project where I need to clean a dataset that will be used for a chatbot. The dataset includes text data, code snippets, and mathematical formulas, and I want to ensure that while cleaning the data, I do not alter or break the code snippets or formulas.

Specifically, I want to:

Redact sensitive data such as URLs, email addresses, and personal information. Standardize dates and remove any unnecessary special characters from the non-code parts of the dataset. Preserve the integrity of code snippets and formulas by keeping the special characters, indentation, and syntax intact. My challenges are:

The dataset has columns that contain code snippets and mathematical formulas . Removing special characters or changing the formatting would make code snippets and formulas useless. What would be the best approach to clean this dataset while preserving the structure of the code and formulas? How should I:

Detect code snippets and formulas? Clean non-code text while leaving the code and formulas intact? Handle specific elements like LaTeX, Python code, or other syntaxes? Here’s the approach I’m considering:

Identify the code and formula sections using markers (e.g., backticks for code, LaTeX syntax for formulas). Clean only the non-code sections for personal data and irrelevant special characters. Avoid altering any syntax or formatting in the code or formulas. Any guidance, suggestions, or code examples on how to achieve this would be greatly appreciated!

• python
• data-cleaning
• data-preprocessing

• You are going to have to define very carefully the rules for processing each column and try to recognise as many exceptional cases as you can. Obvious keywords like ftp, http,mailto and wildcard matches on email addresses can be identified and zapped but with any large dataset there is always a risk of collateral damage. Things with an @ in need very careful consideration. You really should try to solve your problem and then ask for advice after presenting your best effort as an MRE. – Martin Brown Commented Feb 17 at 11:13

2 Answers 2

I think if you want to clean a dataset without messing up code snippets or mathematical formulas, the best way is to first identify them and temporarily replace them with unique placeholders.

You can use regex patterns like ... for code blocks or $...$ for LaTeX formulas. Once these are safely set aside, go ahead and clean the rest of the text—remove things like emails, URLs, and unnecessary special characters, and standardize dates.

After that, just put back the original code snippets and formulas by replacing the placeholders. This way, your dataset stays clean without breaking any important formatting or content.

However if you've have a lot of code snippets, replacing multiple code snippets one by one can be a tiring job. A better way to handle this is by using hashing. First, save each code snippet in a dictionary or map with a unique hash key. Before cleaning the text, replace the code blocks with placeholders like <CODE_HASH_123>.

Then, clean the remaining text in batches—removing unnecessary elements while keeping the placeholders as they are. Once the cleaning is done, bring back the original code snippets using the hash map.

If you say that code is usually represented in latex, code-blocks or ticks. you have the punctuations which you might need to ignore at the time of tokenization. Use libraries specific to each syntax (e.g., pyparsing for Python, pylatex for LaTeX) to parse and clean code snippets and formulas.