How can data be reshaped by converting columns to rows?

Data can be reshaped by converting columns to rows through the "Unpivot" process. Unpivoting is a transformation technique often used in data manipulation and analysis that allows users to take columns that represent different categories of data and convert them into rows. This is particularly useful in scenarios where data is organized in a wide format, making it difficult to analyze trends or patterns across different categories.

For example, if you have a dataset where each column represents sales for different months (e.g., January, February, March), unpivoting this data would create a row for each month, consolidating the sales information into a more elongated format. This makes it easier to perform time series analysis or aggregate the data based on different criteria.

The other options serve different purposes: "Pivot" transforms data by rotating it, creating columns from unique values of a specified variable instead. "Join" is used to combine two datasets based on a common field. "Filter" is a process for narrowing down the dataset based on defined criteria, which does not involve reshaping the structure of the data itself. Understanding these distinctions is crucial for effective data manipulation and analysis.