Pandas vs SQL vs Polars: First Data Job Tool Choice

You'll Interview With All Three

Here's what nobody tells you about your first data analyst job: you won't get to pick your tool. The interview will test SQL. The take-home assignment might be Pandas. The team uses Polars because someone read a benchmark thread on Reddit. You need all three.

But for learning — when you're building that portfolio project or cleaning your first real dataset — the choice matters. I've watched too many beginners wrestle with Polars syntax when Pandas would've gotten them to insights in half the time. And I've seen others write 200-line Pandas scripts for tasks SQL handles in 8 lines.

Let's run the same analysis in all three tools and see where each one falls apart.

Photo by Stefan G on Pexels

The Test: Messy E-Commerce Data

We're analyzing a fictional online store's transactions. The dataset has everything wrong with it:

• Missing customer IDs (about 3% of rows)
• Duplicate orders from a payment retry bug
• Timestamps in two different formats (some ISO, some MM/DD/YYYY HH:MM)

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