Okay, so today I decided to mess around with data validation, and you know, I’ve heard a lot about this library called “Great Expectations.” The name itself is pretty catchy, right? It’s like, setting the bar high! Anyway, I was working with some NBA player data, and who better to test this out on than Victor Wembanyama? That guy’s stats are crazy, so perfect for checking if my data is all good.
Setting Up
First things first, I gotta install the thing. Pretty standard, just used pip.
pip install great_expectations
It took a bit to download, so a great time for a quick snack break.
Getting the Data Ready
I already had some player data saved as a CSV. It’s just basic stuff: player names, points, rebounds, assists, you know, the usual. I loaded it into a pandas DataFrame. I love pandas. It is a great tool to tidy the data.
Creating Expectations
This is where the “Great Expectations” part comes in. I started by telling the library, “Hey, I got this DataFrame, and I want you to check it out.”
Then I started creating, what they are calling “expectations”. For example, I figured, “Wembanyama is tall, so his height column should definitely not have any missing values.” So, I added an expectation for that:
expect_column_values_to_not_be_null(column="height")
I also thought, “His points per game should probably be above a certain number,” so I added another one:
expect_column_mean_to_be_between(column="points", min_value=20, max_value=50)
I mean, I can expect the points to be in between the range, like I won’t expect he will get 60 points, right?
Just playing around with different expectations, really. You can check for unique values, ranges, all sorts of things. It’s like setting up rules for your data.
Validation Time!
After setting up a few expectations, I ran the validation. It’s like giving your data a test. Great Expectations spits out a report telling you if your data passed or failed. Pretty neat!
In my case, everything passed! Wembanyama’s stats were as expected (pun intended). If something was off, like a missing height value or his scoring average was way too low, the report would flag it.
Wrapping Up
Overall, my first try with Great Expectations was pretty smooth. It’s a simple way to make sure your data is making sense, especially when you’re dealing with a lot of it. I can see this being super useful for bigger projects, where you really need to trust your data. This is so cool, and I’m definitely going to use it more.
