The Reality of Real‑World Data Over the past few days, we have been working with perfect, pristine datasets. I built those datasets specifically so we could focus on learning commands like filter() and groupby() without any errors. However, out in the real world, data is incredibly messy. Humans make typos when entering data, sensors go offline and miss readings, and database migrations often corrupt text. When Pandas encounters an empty cell in a CSV file, it fills it with a special marker called NaN (Not a Number). If you try to run mathematical operations on a column filled with NaNs, your analysis will either crash or, even worse, return mathematically incorrect results that could lead to terrible business decisions. Today, I am going to teach you how to identify and clean this messy data professionally. Note: Because our interactive workspace acts just like a real Jupyter Notebook, we only need to load our data in the very first cell. The remaining cells will remember the variables!…