Now that we can find and count missing values, what's the simplest way to fix them? The most direct approach is to just remove the rows or columns that contain them. It's a quick way to get a completely clean dataset.
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What might you lose by choosing this quick and simple approach?
Pandas gives us the .dropna() method to do this. By default, it scans your DataFrame and removes any row that contains at least one NaN value. It's a powerful and fast way to eliminate missing data points from your analysis.
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Why do you think removing the entire row is the default behavior?
Let's see it in action. Imagine a row for a user who has a name and email, but their age is NaN. Running .dropna() would remove that entire user's record from the DataFrame, even though some of the data was valid.
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How does this example illustrate the potential downside of dropping rows?
Dropping rows is best when you have a large dataset and only a few rows have missing values. If you drop them, it won't significantly impact your overall analysis. But if many rows have NaNs, you could lose too much valuable data.
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What's the key factor that determines whether dropping rows is appropriate?
What if an entire column is mostly empty and not useful? You can also drop columns by specifying the axis: . This command removes any that contains one or more values.
