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See how clean and simple that is? We import the `desc` function from `pyspark.sql.functions`, and then we call `orderBy` on our `sales_data` DataFrame, passing `desc("sales_amount")` as the argument. The result, `sorted_sales_data`, will be a new DataFrame with the rows sorted by `sales_amount` in descending order. The product with the highest sales will be at the top, followed by the next highest, and so on. This is incredibly useful for identifying top performers, analyzing sales trends, or simply getting a quick overview of your data. Remember, the original DataFrame remains unchanged; `orderBy` returns a new DataFrame with the sorted data. This is a common pattern in PySpark and ensures that you're always working with immutable data, which can help prevent unexpected side effects in your code. So, with this basic syntax under your belt, you're well on your way to mastering sorting in PySpark!
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