Alright, let’s talk about my little experiment with Feather and Scissors 5.5. I’ve been meaning to try this out for ages, and finally got around to it last week.
So, first things first, I grabbed the Feather framework. Downloaded it, you know, the usual. Then I started poking around, trying to figure out how it all works. Honestly, the documentation was a bit… sparse. Had to do some serious digging.
Next up, I pulled out my trusty Scissors 5.5. I’ve been using it for years, so I’m pretty comfortable with it. The goal was to see how well Feather could integrate with Scissors for some basic data processing.
I decided to start simple. Wrote a little script to load some data from a CSV file using Scissors. Pretty straightforward stuff. Then, I tried to pass that data into a Feather dataframe. That’s where things got a bit tricky.
Turns out, Feather has some quirks about data types. I spent a good chunk of time wrestling with that. Had to do some conversions, some casting, you name it. Ended up using Pandas as a middleman for some of the transformations. Not ideal, but it worked.
Once I got the data into Feather, I started playing around with its features. Serialization was super fast, like, ridiculously fast. That’s definitely a win. I experimented with some basic filtering and aggregation operations too. Everything seemed pretty snappy.
The biggest hurdle was definitely the initial data conversion. If I were doing this regularly, I’d probably write some helper functions to streamline the process. But for a one-off experiment, it was manageable.
Overall, I’d say it was a worthwhile experience. Feather is definitely fast for certain things, but it’s not a magic bullet. You still need to understand your data and how to work with it. And Scissors, well, it’s still my old reliable. Good tools, both of them.
