Welcome to Data Cleaning! Real-world data is often messy. Imagine a survey where some people didn't answer every question. Those blank spots are "missing values," a common problem we need to fix before any analysis.

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Why do you think missing data could be a problem for analysis?

In Pandas, missing data is often represented by NaN, which stands for "Not a Number". When you see NaN in a DataFrame, it's a placeholder for a value that doesn't exist or is unknown. It's our primary target.

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Why do you think Pandas needs a special symbol like NaN for missing data?

So, how do we find these NaN values systematically? Pandas gives us a powerful tool: the .isnull() method. You can apply it to your entire DataFrame to check every single cell for missing data all at once.

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What do you think this method returns as its output?

When you run .isnull(), it doesn't show your original data. Instead, it returns a DataFrame of the same shape, but filled with only True or False values. This is often called a "boolean mask."

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