This student is asking the wrong question. The focus on memorizing Pandas syntax for interview coding sessions misses the larger point about what actually matters in data work. The real challenge isn't recalling whether it's `groupby` or `agg` first, it's knowing *why* you would reach for either tool in the first place.
The student has a statistics degree and a CS minor. They understand probability theory, hypothesis testing, and the mathematical underpinnings of machine learning. That is the hard part. That is the part that takes years to build and cannot be looked up in five seconds. Pandas syntax, by contrast, is a reference. It changes between versions. It varies across teams. It is the kind of thing that every practicing data scientist searches for multiple times a day, even after years of experience. The cheat sheet the student already built is exactly the right tool for the job. Memorizing it would be like memorizing the menu of a restaurant you visit weekly, you might feel clever, but it doesn't make the food taste better.
What the student should practice instead is the *logic* of data manipulation. How do you reshape a table so that a column of dates becomes a time series index? When do you merge on a key versus concatenate rows? What happens to null values when you aggregate? These are the conceptual decisions that live coding sessions test, not whether you remembered `axis=1` or `axis=0`. Interviewers who ask Pandas questions are looking for fluency in thinking, not fluency in typing. They want to see that you can break a messy real-world problem into clean, reproducible steps. That is a skill that transfers across tools, Pandas, SQL, Polars, or whatever comes next.
So here is the concrete advice: stop drilling syntax. Spend that time instead on a single project that forces you to clean, merge, and transform a dataset from scratch without a safety net. If you get stuck, look up the syntax, write it down, and move on. By the third or fourth project, the common patterns will stick naturally. The cheat sheet will shrink. And when the interviewer asks you to filter a DataFrame by date range, you will answer by explaining your logic first, then typing the code second. That is what separates a statistician who can code from someone who just memorized a library.