You know, sometimes you just stumble upon old digital stuff and it just hits you, right? Like finding an old journal but it’s full of code and weird data files. That happened to me just the other week. I was digging through an external hard drive, looking for some photos from way back, and then bam, there it was. A folder labeled “Virgo_Project_2017.”
Man, 2017. What a year that was for me. I was kinda at a crossroads, you know? My old job, well, let’s just say it wasn’t going anywhere fast, and I was starting to feel this real itch to figure things out for myself. I wasn’t just gonna sit around and wait for opportunities; I wanted to go out and grab ’em. But to do that, I realized I needed some raw information, the kind you don’t just find summarized on a blog post or some fancy report. I needed to see the ground truth, especially about the job market in my area, and maybe even a bit about local living costs.
So, Virgo. That was my little secret mission back then. I actually started it because I felt completely lost trying to navigate online job boards. Everything felt so… generic. I wanted to see patterns, real trends in what companies were looking for, what skills were getting buzz, and what pay ranges were actually realistic. I figured, if I could just collect enough raw data, I could probably make more sense of it than any recruiter could tell me.
My first step? Honestly, it was just grabbing my trusty old laptop and firing up a text editor. I think it was Sublime Text 3 back then, a real workhorse. I didn’t have a grand plan, just this burning idea to pull data from a bunch of different public websites. I remembered messing around a bit with Python a few years before, so I thought, “Hey, why not give it a real shot?”
I started small. I literally just typed `import requests` and `from bs4 import BeautifulSoup` at the top of a blank file. My first target was a local job board that wasn’t super popular but had a lot of direct postings. I spent an entire afternoon just trying to figure out how to even get the webpage content. I remember just sitting there, copying bits of HTML from my browser’s ‘inspect element’ tool, trying to match it to what Python was seeing. It felt like learning a whole new language, bit by bit.
My Raw Process: Hacking It Together
- First, the Fetch: I used `*()` to pull down the page. Simple enough, right? But then sometimes websites would block me, or change their layout, and my script would just cough and die. I had to learn about user agents, about adding delays so I didn’t hammer their server too hard.
- Then, the Parse: Once I had the HTML, BeautifulSoup was my best friend. I’d sit there staring at the page source, trying to find the unique ‘div’ or ‘span’ tags that contained the job title, the company name, the salary range (if it was even there), and the description. It was like a puzzle, every single time.
- Next, the Extract: After parsing, I had to pull out the actual text. This was often messy. Lots of extra spaces, newlines, weird characters. I used basic string methods – `strip()`, `replace()`, things like that – just hacking away at it until it looked clean enough.
- Finally, the Store: At first, I just printed everything to the console. That got old fast. So then I started writing it to a CSV file. Just a simple comma-separated list of values. I figured I could open that in Excel later and do some basic sorting.
I distinctly remember one time, after a week of on-and-off tinkering, I finally got it to run for about fifty pages of listings without crashing. And when I opened that CSV file, man, it was just a raw dump of hundreds of job titles, companies, and locations. It wasn’t pretty, but it was mine. I could finally see, for example, that there were way more roles for ‘Junior Web Developer’ than ‘Senior Data Analyst’ in my city, and what the typical skill demands were. It gave me a much clearer picture of what I actually needed to learn, fast.
Fast forward to now. Why did I even think about “Virgo” again? Well, a buddy of mine was complaining about the exact same problem – feeling overwhelmed by the job market, not knowing where to focus his learning. And it just reminded me of that period in 2017. So I thought, “What the heck, let’s see what good old Virgo looks like now.”
I opened that folder, and man, some of those scripts were a mess! Variable names like `x`, `y`, `temp_list`. No comments. Just pure, unadulterated code-spaghetti. But you know what? With a bit of squinting and remembering, I could follow the logic. It was crude, sure, but it worked. I even tried running a couple of the old scripts, just to see. Of course, most of the websites had changed their structures completely, so it just threw errors left and right. But a few still managed to pull some fragmented data, which was actually kinda wild.
Looking back at Virgo today, it’s not just about the code. It was about taking control when I felt like I had none. It was about teaching myself to dig for answers instead of just waiting for them to appear. That whole period, learning to build that little scraper, it gave me a whole new sense of confidence in tackling unknowns. It showed me that even with basic tools and a lot of stubbornness, you can really build something useful, something that helps you see through the noise.
