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For example, in Microsoft Excel one must first select the entire data in the original table and then go to the Insert tab and select "Pivot Table" (or "Pivot Chart"). The user then has the option of either inserting the pivot table into an existing sheet or creating a new sheet to house the pivot table.
Once empty though it is very easy to delete all the empty cells, rows, and columns in the source editor. Switch over to it by clicking the arrow at the top right of the editing window. It may be even faster to use the visual editor to copy the parts you want from the table into a new blank table.
The number in cell B2 is not "the number of cars sold in January", but simply "the value in cell B2". The formula for calculating the average is based on the manipulation of the cells, in the form =C2/B2. As the spreadsheet is unaware of the user's desire for D to be an output column, the user copies that formula into all of the cells in D.
As an example, VBA code written in Microsoft Access can establish references to the Excel, Word and Outlook libraries; this allows creating an application that – for instance – runs a query in Access, exports the results to Excel and analyzes them, and then formats the output as tables in a Word document or sends them as an Outlook email.
According to PostgreSQL v.9 documentation, an SQL window function "performs a calculation across a set of table rows that are somehow related to the current row", in a way similar to aggregate functions. [7] The name recalls signal processing window functions. A window function call always contains an OVER clause.
A standard Sudoku contains 81 cells, in a 9×9 grid, and has 9 boxes, each box being the intersection of the first, middle, or last 3 rows, and the first, middle, or last 3 columns. Each cell may contain a number from one to nine, and each number can only occur once in each row, column, and box.
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In computer programming contexts, a data cube (or datacube) is a multi-dimensional ("n-D") array of values. Typically, the term data cube is applied in contexts where these arrays are massively larger than the hosting computer's main memory; examples include multi-terabyte/petabyte data warehouses and time series of image data.