Okay, so today I’m gonna talk about something I messed around with recently: “mexco.” Yeah, I know, sounds kinda weird, but bear with me.
First off, what is it? Well, “mexco” isn’t some official thing. It’s just a little project I cooked up to try and automate some stuff I was doing with Mexican company data. I was pulling data from different government websites, cleaning it up, and then using it for some analysis. Super tedious stuff, and I figured there had to be a better way.
How’d I start? Okay, so the first thing I did was figure out where the data was coming from. There are a few key government sites in Mexico that have company info. Some are easier to scrape than others. I spent a good chunk of time just inspecting the HTML of these sites to see how the data was structured. Annoying, but necessary.
Tools of the trade: I ended up using Python for pretty much everything. Specifically, I used:
- Requests: For fetching the HTML from the websites. Simple and effective.
- Beautiful Soup: For parsing the HTML and extracting the data I needed. This was a lifesaver because the HTML was often a mess.
- Pandas: For cleaning and organizing the data into a nice table format. Essential for any kind of data analysis.
- Selenium: I had to use this for a couple of sites that used JavaScript to load their data. Selenium lets you automate a web browser, so you can basically simulate a person clicking around and loading the data. A bit slower than just using Requests, but sometimes you gotta do what you gotta do.
The process: Here’s the basic flow I ended up with:
- Scrape the websites: Write Python scripts using Requests, Beautiful Soup, and Selenium to pull the data from the target websites. This involved figuring out the right URLs to request, identifying the HTML elements containing the data, and extracting that data.
- Clean the data: The raw data was often messy, with inconsistent formatting, missing values, and other issues. I used Pandas to clean up the data, standardize the formatting, and handle missing values. This step was super important to ensure the accuracy of the analysis.
- Combine the data: I was pulling data from multiple sources, so I needed to combine it into a single dataset. I used Pandas to merge the dataframes based on common identifiers like company names or tax IDs.
- Analyze the data: Once I had a clean, combined dataset, I could finally start doing some analysis. I used Pandas and other Python libraries to calculate summary statistics, identify trends, and create visualizations.
Challenges I faced: Oh man, there were plenty. Some of the websites would randomly change their HTML structure, which would break my scraping scripts. I had to constantly monitor the scripts and update them whenever the websites changed. Also, some of the websites had anti-scraping measures in place, which I had to work around. I tried to be respectful and not overload the servers with requests, but it was still a challenge.
What I learned: This project was a pain in the butt, but I learned a lot. I got a lot better at web scraping, data cleaning, and data analysis. I also learned the importance of being adaptable and persistent. Things rarely go according to plan, so you have to be able to troubleshoot problems and find creative solutions. Plus, I learned a bit more about Mexican business, which is always useful.
Did it work? Yeah, eventually. I now have a system that automatically pulls data from the relevant websites, cleans it up, and generates some basic reports. It saves me a ton of time, and it’s also more accurate than doing it manually. It’s still a work in progress, but I’m happy with how it turned out.
Future improvements: There’s always room for improvement. I’d like to add more data sources, improve the accuracy of the data cleaning, and develop more sophisticated analysis techniques. I’m also thinking about turning it into a web application so that other people can use it.
Final thoughts: “mexco” was a fun and challenging project. It taught me a lot about web scraping, data analysis, and problem-solving. If you’re interested in learning these skills, I highly recommend trying a similar project. Just be prepared for some frustration along the way, but the end result is worth it.
