We've checked our data's size and previewed some rows. Now, let's get a quick summary of the numbers in our dataset. This helps us understand the data's characteristics at a glance.
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What patterns might you miss if you only looked at individual data points instead of a summary?
This summary is called "descriptive statistics." It includes common measures like the average, the minimum value, and the maximum value, giving you a high-level overview of your numerical columns.
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What could the average price of a car tell you?
Pandas has a fantastic method for this: .describe(). When you call df.describe(), it automatically calculates key statistics for all numerical columns in your DataFrame. It smartly ignores text columns.
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What kind of columns do you think .describe() would analyze in an employee dataset?
Let's see what this looks like in practice. Here's the output from running .describe() on a car dataset:
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What does a small standard deviation tell you about the data?
