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Raw rows arrive in no useful order. Sorting puts the most interesting records on top so patterns jump out at a glance. 🔎

In pandas you reorder rows with sort_values. You name the column to sort by, and pandas hands back a freshly ordered DataFrame.

By default sort_values goes smallest to largest. So numbers climb upward and text sorts alphabetically from A to Z.

Set ascending=False when you want the biggest values first, like the top-selling products or the highest scores.

Pass a list of columns and pandas sorts by the first, then breaks ties using the next, and so on down the list.

Give ascending a list too, one flag per column. That lets you sort region A to Z while sales go high to low.

By default sort_values returns a sorted copy and leaves the original untouched, so your source data stays safe.

Add inplace=True to rewrite the original DataFrame instead of returning a copy. Use it only when you truly want that.

Sorting keeps each row label glued to its data, so the index looks shuffled afterward. That is expected, not a bug.

Want tidy 0,1,2 labels after sorting? Chain reset_index with drop=True to renumber rows from the top.

Missing values sink to the bottom by default. Set na_position to first if you would rather see them up top.

You want the highest sales on top. How do you sort?

You now sort with sort_values, flip with ascending, break ties using a column list, and tidy labels with reset_index. Nicely done! 🎉