The MAP function in Google Sheets is a powerful tool that allows users to transform arrays by applying a specified LAMBDA function to each individual element. This function is particularly useful for advanced data manipulation, enabling the creation of dynamic outputs based on the initial dataset.

Syntax

MAP(array1, array2, ..., LAMBDA)

• array1: The first range of values to be processed.
• array2: The second range of values to be processed (optional).
• LAMBDA: A custom function defined using the LAMBDA syntax, which specifies how each value will be transformed.

Example #1

=MAP(A1:A5, LAMBDA(x, x2))

This function doubles each value in the range A1:A5. For example, if A1 is 3, the output for that cell will be 6. Result: 6, 8, 10, 12, 14 (assuming A1:A5 contains 3, 4, 5, 6, 7).

Example #2

=MAP(B1:B5, LAMBDA(x, x&" units"))

This function appends the string ” units” to each value in the range B1:B5. If B1 is 10, the output will be “10 units”. Result: “10 units”, “20 units”, “30 units”, “40 units”, “50 units” (assuming B1:B5 contains 10, 20, 30, 40, 50).

Example #3

=MAP(C1:C5, LAMBDA(x, IF(x>100, "High", "Low")))

This function checks each value in the range C1:C5, returning “High” if the value is greater than 100, and “Low” otherwise. If C1 is 120, the output for that cell will be “High”. Result: “High”, “Low”, “High”, “Low”, “Low” (assuming C1:C5 contains 120, 80, 150, 90, 60).

Error handling

• ERR: N/A: This error occurs when the function encounters an invalid operation, such as trying to perform calculations on non-numeric data.
• ERR: VALUE!: This indicates that one or more input arrays have mismatched dimensions, preventing the function from executing properly.
• ERR: REF!: This error happens when the function references a range that is invalid or no longer exists.

Conclusion

In summary, the MAP function is a highly versatile tool in Google Sheets, allowing users to apply custom transformations to data arrays flexibly and efficiently. By leveraging the LAMBDA function, users can create dynamic and personalized outputs, enhancing their data processing capabilities significantly.