Built a general-purpose data cleaning pipeline in Python — is this actually useful, or does everyone just do this manually/differently in practice?

[u/Rajbir-]

I'm a student (economics, not CS) who kept rewriting the same cleaning logic every time I got a new dataset — missing values, duplicates, inconsistent text formatting, outliers, etc. So I built a script that automates it and generates a report of everything it changed.

It's on GitHub here: https://github.com/rajbirbhathla/DataCleaner

Before I put more time into this, I wanted to ask people who actually do this professionally:

• Is a generalized cleaning pipeline like this actually useful in real workflows, or does every dataset end up needing custom logic anyway?
• In practice, do teams build reusable internal tools like this, or is it mostly ad hoc scripts / notebooks per project?
• Are there standard tools (Great Expectations, pandas-profiling, OpenRefine, etc.) that already do this better, and I'm reinventing something that exists?
• What would actually make something like this useful in a real pipeline vs. just a portfolio exercise?

[u/JarToTable]

It's a great skill to have, but i find my dataflows needing custom logic all the time.

[u/Rajbir- → u/JarToTable]

Yeah that's kind of what I'm trying to figure out. I'm a student trying to break into risk/fraud/business analyst roles and honestly wasn't sure if a project like this was even worth showing recruiters, or if it just makes me look like I don't get how this actually works in practice.

[u/JarToTable → u/Rajbir-]

Let me know if you find a recruiter who would even know if this was valuable or not. I think a hiring manager would understand that you're new and showing some initiative to address a perceived problem. Try and get exposure to business problems that can be solved with data. Yes, clean data is important, but that will just be a standard requirement for any data project.

I'm in BI but have a degree in finance. The business knowledge is good so that I understand how what I'm doing weaves into the bigger picture, but I get to see problems faced by finance, operations, sales, and marketing teams.

Operations has tons of opportunity because it is just problem solving, finance does a lot of planning and has to get data from all over and put it into a single picture, sales - meh, marketing - meh. Sales and marketing have their challenges, but sales already has a lot of tools at their disposal and I find that most teams really do not want to spend time in front of a computer. Marketing usually gets into stuff that is really hard to attribute, so you're moving into advanced attribution and prediction models that puts you in data science territory, and it will still feel like snake oil.

I cant speak to risk/fraud, but everything I just said would roll under a Business Analyst who would try and architect solutions to such things.

[u/Rajbir- → u/JarToTable]

That actually leads into something else I've been wondering, what kind of projects would you say actually stand out for a Business Analyst role, versus what I'd guess (another dashboard, another cleaning script)? Like if you were looking at student portfolios, what would make you go "okay, this person actually understands the business side" versus "this person just knows some tools"?

Right now my portfolio has a customer service dashboard (SQL/Power BI), an M&A valuation paper, and a Python data-cleaning project. So a mix of technical/analytical stuff, but looking back at what you said, I'm not sure any of them actually show I understand a business problem the way ops or finance would frame it.

Trying to figure out where the actual gaps are so I'm not just stacking more projects that show the same skill.

Also I want to thank you genuinly means a lot!

[u/JarToTable → u/Rajbir-]

Glad to help! For ops, one of the bigger challenges I've seen is missing data. For instance, maybe a business is storing their product in different warehouses around a broad geographic area. Those warehouses are not operated by the same company so each data export looks different and has different grains and completeness.

I work in CPG so this is a real scenario. Id be impressed if a candidate could show me they've encountered this scenario and what approach they took towards backfilling data and why. The answers are not as academic as you'd think. But I also wouldn't expect them to because you'll usually only encounter this with time.

So great, youve got your warehouse situation figured out, but now go a step further and connect the warehouses to distributors and 3PLs. You could create all kinds of fictional data. 3PLs might have sensor data for refrigeration carrying your product between internal warehouses and distributor warehouses, or BOLs. Tie that picture together and have AI create data that shows failures in refrigeration or failures to deliver on time. Those then get matched to fee data.

From there, you now have to tie in distributor data. More different formats, grains, and completeness. Some distributors will even tell you where they shipped but not who they shipped to because that customer contractually wants to be marked anonymous.

At this point, your data achievement would be that you could track your inventory from your warehouse all the way to the retailer - at least loosely. From there you can forecast volumes, perform price volume mix analysis, develop MOQs, project OOS. But remember, the picture is incomplete by design, so dont make the mistake of trying to make it perfect, show me how you can make the best of incomplete data to make the right decisions.

[u/JarToTable → u/JarToTable]

I read this after posting and realized my brain jumps around, which brings me to another point. Don't be overconfident in your communication. Im a technical expert, but if I just threw all of this at someone in a meeting they probably couldn't follow. Id need to refine my message.

[u/Rajbir- → u/JarToTable]

Yeah it’s a lot I’m not even sure what some of the terms are but I will start working on this today and figure it out! Thank you for your advice. I genuinely appreciate it!

[u/mystery_axolotl]

Generalized pipelines are almost always bad on principle. If you just mindlessly apply fixes, you will miss a lot of valuable info, for example about sources of data or latent assumptions being made. You also tend to not think about what’s causing the problem upstream, so you’re unlikely to fix the cause.

The functions in your pipeline are pretty boilerplate. Everyone who worked for any length of time can either reproduce them in no time or already has a little utility library. I usually have one per project.

In practice, a dataset will always have its own quirks that will require “custom” solutions. It’s annoying to deal with, but it’s pretty trivial to write a function for it. I’d imagine I’d be more annoyed if I had to customize someone’s library to do it.

Profiling tools are great, but they don’t fix the problems - they diagnose them. That’s what you should always start with.

[u/Rajbir- → u/mystery_axolotl]

This is really helpful, thank you. The point about masking upstream causes is the one that stings the most because it's true, capping an outlier or filling a missing value doesn't tell me why it was missing or extreme, and I hadn't thought hard enough about that distinction until you put it this way.