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Powering My Way Through 3 Different Data Automation Projects

by Mike Levin

Thursday, November 03, 2022

Sometimes the new work is mentally fatiguing even before you begin, but it has to get done. And in those times you’ve got to trick yourself into finding the love. Of course after you find the love for the work at hand and dive into it you’re not tricking yourself anymore. You really have gotten into the flow or the zone. It’s just a getting started thing. And that’s where the tricks of conjuring love-worthiness come into play.

Trick #1: Nicknames. Or a good label. Sometimes all you have to do is give a good nickname to a project. Project? Blech! Sounds too big. Remember, Every Little Thing gets done! Project is too big a way of thinking of it. That’s disheartening. Instead, think of it a little thing. It’s a little thing to make a new folder on your computer with a well-chosen nickname. That’s what you’re going to cal your repository. Just made a repo called semrushautomation.

Yup. I’m going to automate certain functions inside a website so that I don’t have to use the API and use up api-tokens. So the folder is made and now I make my first Jupyter Notebook in that folder and give it the same name as the folder. Reinforce the nickname. And now I’m copy/pasting the best starting-point code over to this notebook from a similar project.

Okay, since it’s browser automation on a site that requires login, just pop the site up in Chrome at all and take note that you’re actually already logged in. The way I use browser automation in Microsoft Playwright and Puppeteer is with regular Google Chrome (not chromium) and so I can give it my whole browser user profile including the “keep me logged in” status of various websites. I’m using that to avoid putting in unnecessary login code. Okay, confirmed!

Alright now instead of just the homepage put in the URL of the tool within the site that you’re going to be using. Okay now some particular thing is making it seem more difficult than it is. It’s a hurdle that must be overcome. And then another little hurdle like it is likely to follow. And then another. And so the strategy is to just go get the first one done then come back here and think out loud again… okay, done. Success assured.

Beware distractions. They can be terribly powerful and you feel like you should indulge in one as a reward for getting one of the above steps done. If you do, set a time-limit. Indulge in say Google Discover Homepage which gives you science news stories galore. Read one. Return! Okay, I have what amounts to a single test-run with sample data of what will be going into a loop for many runs with real data. So I have an outer loop context to think about and getting the real data ready to be used. Next step!

Proving the viability of a project is part of the fun. Getting that “success assured” moment is a thing. You don’t know what you don’t know until you try, so try as soon as possible. Make discoveries and note things. Watch closely so you can let your subconscious take things in you may not notice at first. Getting access to the real data come sometimes be a hurdle in itself, or at least getting the real data where you need it and how you need it as the input for some process.

Now it’s time to slice the Jupyter Notebook. What was once a test is now going to be a real job, so the Python imports and setting of initial configuration variables need not be re-run all the time. And I need a code block between the imports and what is to soon become one iteration in a loop. But before I get to loopifying the code, I need to load the data as part of the outer-context to the loop. The input data (list of keywords) will control the loop. While not quite a 1-liner, I find this line pretty magical:

with open("keywords.csv") as fh:
    keywords = [x.rstrip() for x in fh.readlines()]

Now we’ve got a list of keywords as a Python list in memory in our Jupyter Notebook. Our old friend modulo is chomping at the bit to get started. Outer-loop time! I need to chop my long keyword list into batches of 100. We will build it up. First step:

for i, keyword in enumerate(keywords):
    print(i, keyword)

If you’re not familiar with modulo, think to yourself what technique must be used in spreadsheets and other grids to alternate the white to light-grey row backgrounds. Rows often alternate between 2 shades for better readability and clearly if the rows were numbered, you could use such rules as if the number is even shade it and if the number is odd don’t shade it. See? That’s how modulo is used. That would be row_number % 2. The percentage sign in this context is the modulo math operator. If you wanted to identify every 3rd line it would be row_number % 3 and so on. Here we’re using 100. Also I’m trying to make my convention for modulo results of a counter the underscore-version of that counter name.

for i, keyword in enumerate(keywords):
    _i = i % 100
    if not _i:
        print(i, keyword)

Alright now there’s multiple ways of going about the steps to follow. One might say there’s radically different philosophies and preferences. Each approach has its own strengths and weaknesses. I’m shifting towards a new philosophy that keeps each step as discrete and readable as possible. And so I’m going to want to step through ready-made chunked (in groups of 100 keywords) data. And so we chunk it:

tables = []
for i, keyword in enumerate(keywords):
    _i = i % 100
    if not _i:
        if i:
            tables.append(table)
        table = []
        print(i, keyword)
    table.append(keyword)

At the end you have nested tables and you can use a for table in tables loop to unpack and use each chunk. So the above step is really in preparation for an outer loop.

Fast forward to end of work day. Pshwew! Marathon session. Not only did I finish that Chrome automation project that this journal entry started out with, but I also ran a 2000+ keyword SERP-scraping job with various transforms to make it useful in the deliverable, and I’m on to the 3rd item for today which is sort of like reverse-tagging. I’m taking keyword cluster terms which are essentially category or topic keywords and matching them back to the content they were inferred from.

And I’m finishing up that last project too. Woo! Today went from automating data-pulls against the SEMRush web UI to performing actual Google search result scraping to a fuzzy matching project. Most days don’t have this quantity of deliverables. It’s all really one deliverable ultimately but with a lot of facets.

Wow, what a day. In fact, it’s tomorrow by the time I’m writing this paragraph but I don’t want to start a new blog post. Just watch The Shape of Water. Wow. Been awhile since I saw a movie as moving as that. Gotta get to sleep. But mind racing. Looking forward to editing that email I auto-send to myself. Don’t let myself overlook it!