You are working in the Power Query Editor with a table that contains product sales data. The table has a 'ProductName' column, followed by twelve columns representing each month's sales (e.g., 'Jan_Sales', 'Feb_Sales', 'Mar_Sales'). To create a time-series analysis, you need to restructure this data so that you have one column for the month and another for the sales amount, while retaining the product name for each row. Which single transformation should you apply to the twelve month columns to achieve this?
The correct transformation is 'Unpivot Columns'. This operation is designed to take columns and turn them into rows, creating attribute-value pairs. In this scenario, selecting the twelve month columns and applying 'Unpivot Columns' will create an 'Attribute' column (containing the month names like 'Jan_Sales') and a 'Value' column (containing the sales figures). This is the standard method for converting data from a wide format to a long, normalized format suitable for analysis in Power BI.
'Pivot Columns' performs the opposite action, turning rows into columns, and is used to summarize data into a wider format.
'Transpose' swaps the entire table's rows and columns, which would not produce the desired structure of retaining the product name alongside the new month and sales columns.
'Group By' is used to aggregate data, such as calculating a sum or average for each product, but it does not restructure the columns into rows as required.
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