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Multiple edits on multiple rows without server round trips; Client-side column and row resizing; Load on demand, which loads data from the server as needed for viewing; Native Microsoft Excel import and export; In-cell editing; Multiple edits on multiple rows without server round trips; Client-side column and row resizing; Multiple sheets ...
A typical cell reference in "A1" style consists of one or two case-insensitive letters to identify the column (if there are up to 256 columns: A–Z and AA–IV) followed by a row number (e.g., in the range 1–65536). Either part can be relative (it changes when the formula it is in is moved or copied), or absolute (indicated with $ in front ...
To use column-major order in a row-major environment, or vice versa, for whatever reason, one workaround is to assign non-conventional roles to the indexes (using the first index for the column and the second index for the row), and another is to bypass language syntax by explicitly computing positions in a one-dimensional array.
The non-clustered index tree contains the index keys in sorted order, with the leaf level of the index containing the pointer to the record (page and the row number in the data page in page-organized engines; row offset in file-organized engines). In a non-clustered index, The physical order of the rows is not the same as the index order.
Row labels are used to apply a filter to one or more rows that have to be shown in the pivot table. For instance, if the "Salesperson" field is dragged on this area then the other output table constructed will have values from the column "Salesperson", i.e., one will have a number of rows equal to the number of "Sales Person". There will also ...
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If multiple users are using multiple replicas of the table, then it is likely that they will end up assigning the same values to AutoNumber fields in new rows that they add, causing replication conflicts when the replicas are merged. [2] This problem is addressed in two ways. First, it is possible to use Replication IDs for such AutoNumbers. [2]
Oppositions between rows and columns are then maximized, in order to uncover the underlying dimensions best able to describe the central oppositions in the data. As in factor analysis or principal component analysis , the first axis is the most important dimension, the second axis the second most important, and so on, in terms of the amount of ...
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