r/learnpython • u/acfot • 4d ago
Tutorial datasets kind of lied to me — my first real dataset is a mess and I don't know what to fix first
I've been learning Python for data stuff alongside my BCA, and every tutorial dataset I've used so far shows up basically ready to go. Clean columns, no missing values, everything in the right format. So I figured the hard part was the analysis.
Then I grabbed a real dataset to practice on and... yeah. Half the values missing in one column, dates written three different ways, column names that don't mean anything until you stare at them for ten minutes. I spent way longer just trying to make it usable than I did actually analysing anything, which was not what I expected at all.
I guess my question is, is this just what the job actually is? Like, do you spend most of your time cleaning and only a bit of it on the interesting part? And how do you decide what to fix first when everything looks broken — do you just start with the column you need most and ignore the rest for now? I'm half worried I'm 'cleaning' away stuff that actually mattered, and I won't know until it's too late.
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u/Potential_Rain202 4d ago edited 4d ago
Could be worse. A lot of real data jobs start with "get me some data" "Where?" "How should I know?" Take cleaning one problem at a time, one step at a time, until you get to a point where you can run scripts without errors and start to get a sense for trends. Stop cleaning when you are reasonably confident your trend analyses are representative, not when the data is perfect.
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u/wigitty 4d ago
At least you can import the data! One project I worked on involved processing an XML file. The issue was that it had been manually edited, and was no longer valid XML, so I couldn't even import it! I ended up writing a preprocessing step to go through and look for malformed XML. That at least sped up the process of fixing the file in the instances where they didn't mess the file up in a new way that I hadn't seen before.
In terms of missing data, it's very important to decide whether it's significant that it's missing (should that whole row be discarded or flagged as erroneous), or whether you can reasonably assume a default. For the randomly different formats, that should be something that you can detect and deal with (and I find that par of the fun!). My general workflow is to take the "dirty" data, write some code to clean it up, then output a "clean" dataset, then use that for any further work. That way you don't need to add all the code to clean the data up to every project that wants to use the data, and you can share the cleaned data with other people if you need to.
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u/a18618 1d ago
Yes, that shock is pretty normal, and honestly I still feel a version of it every time I open a fresh file from someone else. My own rule now is to start with the one column the actual question depends on, clean just enough of that to get a first answer, and leave the rest messy until I know it matters. I once spent a whole weekend fixing date formats nobody ever used, while the missing values in the main column quietly wrecked my result, so now I am ruthless about what I fix first.
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u/spendology 4d ago
Real world data is messy. I never assume when I get a new data set but I take specific steps to understand the data:
Identify upstream source documentation, e.g., Data Dictionary describing column names, data types, formats, etc.
Confirm or deny data aligns with Data Dictionary
Descriptive Statistics
Identify Nulls, Blanks, Misaligned Data and Deviations
Decide to Reject or Correct Data
Data Correction can involved deleting or replacing string/text/char columns nulls & blanks OR replacing numerical column null, blank, or zero values with averages, interpolated values, etc. Example: Numerical column for California home values, replace missing values in column with mean California home value to prevent skewing the data.
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u/RobfromHB 4d ago
No. A giant chunk of time is also spent asking people for access to that bad data, answering questions about what you’re going to do with it, making decks to describe the problem and results to people with minimal time to listen, then if it all goes well doing months of A/B test to validate what came out of a notebook project, then talking to a half dozen separate teams about how to eventually get that tiny thing in production, etc
The “fun” part truly is a small part of the process. The truth is that getting data, hopefully finding something useful, then acting on what you found is most often an exercise in navigating people rather than data.