Matrix Transformation & Data Restructuring Tool

Learn to use :
ROW, COLUMN, TOROW, TOCOL

Matrix Transformation & Data Restructuring Tool
Function Focus:ROW, COLUMN, TOROW, TOCOL
Scenario:
DataBridge Analytics receives client data in inconsistent formats. Build a universal transformation tool that converts between row-based, column-based, and matrix formats.
Dataset Structure:
• Quarterly sales data in 3D format: Products (rows) x Quarters (columns) x Regions (sheets)
• Need to transform to: Flat table, Pivot-ready format, and Summary matrices
Tasks:
1.Use TOROW() to convert a 2D product x quarter matrix into a single row for charting
2.Apply TOCOL() to flatten multi-region data into a database-ready format
3.Utilize ROW() and COLUMN() to create dynamic coordinates for data validation
4.Build a bidirectional converter that can reshape data on demand
Expected Output:
Flexible data transformer that handles 2D->1D and 1D->2D conversions with dynamic positioning.
Evaluation Criteria:Correct TOROW/TOCOL syntax, ROW/COLUMN coordinate mapping, bidirectional transformation

Product Q1 Q2 Q3 Q4
Widget A 100 150 200 250
Widget B 110 160 210 260
Widget C 120 170 220 270

Product Q1 Q2 Q3 Q4
Widget A 300 350 400 450
Widget B 310 360 410 460
Widget C 320 370 420 470

Product Q1 Q2 Q3 Q4
Widget A 500 550 600 650
Widget B 510 560 610 660
Widget C 520 570 620 670



Flat_Data
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ow the Functions Meet the Evaluation Criteria
1. Correct TOROW/TOCOL Syntax
TOROW(array, [ignore], [scan_by_column]): In Task 1, we use TOROW(..., 1, TRUE). The 1 ignores blanks, and TRUE forces it to read down the columns first (Widget A Q1-Q4, then Widget B Q1-Q4) instead of left-to-right.
TOCOL(array, [ignore]): In Task 2, TOCOL(..., 1) is critical. Because our "3D" data has blank rows between regions, the 1 seamlessly drops the blanks and stitches all 3 regions into one continuous column.
2. ROW/COLUMN Coordinate Mapping
In Task 3, MAKEARRAY requires LAMBDA(r, c, ...) where r and c act exactly like ROW() and COLUMN(). We concatenate them to generate dynamic text coordinates (R1C1, R1C2, etc.).
In Task 4, we use pure math to map 1D index positions to 2D coordinates: (c - 1) * target_rows + r. This calculates exactly which item from the 1D list belongs in which row/column intersection of the new 2D matrix.
3. Bidirectional Transformation
2D ➔ 1D: Tasks 1 & 2 demonstrate collapsing grids into lines.
1D ➔ 2D: Task 4 demonstrates expanding a line back into a grid. By changing target_rows to 4 and target_cols to 3, the formula instantly transposes the reshaped output, proving true bidirectional flexibility.