There's a quiet kind of genius in the question being asked here, because it's not really about 35 CSV files. It's about the gap between what a program gives you and what you actually need. The user has a third-party tool that spits out files named BASE_VAR.csv, each with two columns: a shared date column and one variable column. They need to do this 100 times, with different BASE values, and end up with an Excel workbook where each sheet is named for BASE, the first column holds the dates, and the remaining columns hold each VAR. That's not a niche problem. That's the daily reality of anyone who works with data that comes from systems you can't control.
What stands out here is how clear the user is about the workflow. They've already broken it down: create a sheet, import the first CSV fully, then bring in only the second column from the remaining 34 files. That's not a fuzzy request. That's a spec. And yet, the instinct to ask "anyone have ideas on how to automate this?" is understandable, because the jump from "I can do this manually" to "I can do this with a script" feels like a leap. But it's not. It's a few lines of Python with pandas, or even a well-structured loop in Excel Power Query, and the repetition disappears.
The practical takeaway is this: the hard part isn't the automation. It's recognizing that the repetitive task is the signal, not the work. When you find yourself doing something 100 times, you're not being paid to do it 100 times. You're being paid to notice that it's the same task with a different label. The naming convention is consistent. The structure is consistent. The only thing changing is the BASE value. That's the kind of pattern that should trigger an automation instinct, not because you're lazy, but because you're efficient.
So here's the concrete point: write a script that loops through the BASE directories, reads the first CSV to get the dates, then reads the other 34 files and merges the second columns by the date column. Name the sheet after BASE, and save the whole thing as one Excel file. If you're in Python, pandas makes this trivial. If you're in Excel, Power Query can handle it with a folder import and a bit of M code. Either way, the solution is within reach, and once you build it once, you've built it for all 100. That's the real win: not just saving time, but removing the mental load of doing the same thing over and over. That's what automation is for.