In our last unit, we used .isnull() to create a map of True and False values. This showed us where data was missing. But for large datasets, a giant map isn't practical. We need a summary.

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Why is a quick count often more useful than a huge map of missing values?

To get a count, we simply need to add up all the True values in each column of our boolean mask. Remember, True marks a missing value, so counting the Trues gives us the total number of missing entries per column.

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What advantage does counting missing values give us over just seeing where they are?

Here's a fantastic trick in Pandas: when you use the .sum() method on boolean values, it treats True as 1 and False as 0. So, summing a column of Trues and Falses is a super-fast way to count the s.