How to insert the BYCOL formula in Google Sheets

Introduction
Google Sheets is a powerful tool that can make tasks like data management and analysis a breeze. One of the essential functions you’ll encounter is the BYCOL formula. In this article, we’ll break down the BYCOL formula in simple, student-friendly terms. We’ll cover what it is, how to use it, and provide practical examples.
This function is meant to be used with the LAMBDA function. The formula groups values by columns in a selected range or an array, allows the LAMBDA function to use the grouped values as input, and returns a computed value for each column in a row. In this article, you will learn what the BYCOL formula is and how to use it.
How to insert the BYCOL formula in Google Sheets:
There are few steps to follow for inserting the ‘BYCOL’ formula in Google sheets.
- Type “=BYCOL” or go to “Insert” → “Function” → “Array” → “BYCOL”.
- Select a range or an array to which you apply the LAMBDA by columns.
- Enter a LAMBDA function with a placeholder and logic.
- Press the “Enter” key.

Syntax:
BYCOL(array_or_range,LAMBDA)
- array_or_range: An array or range to be grouped by columns.
- LAMBDA: A LAMBDA that’s applied to each column in the given array or range to obtain its grouped value.
- Syntax: LAMBDA(name,formula_expression)
- Requirements:
- The LAMBDA must have exactly 1 name argument along with a formula_expression which uses that name. The name resolves to the current column being grouped when the LAMBDA is applied.
Example:
If you are a school teacher. You want to do a result analysis on performance based on the data set in the picture. The BYCOL formula allows you to show calculation results by columns
Step 1: Identify Your Data / Picking Our Data

Step 2: Insert the BYCOL(array_or_range,LAMBDA) Function

Step 3: After filling the formula in cell, click enter & get your answer

Here are the assumptions in the formula in cell H4.
Array_or_range: C2:F15 Lambda: LAMBDA(num,MAX(num))
Here are the assumptions in the formula in cell H5.
Array_or_range:C2:F15 Lambda: LAMBDA(num,AVERAGE(num))
Here are the assumptions in the formula in cell H6.
Array_or_range: C2:F15 Lambda: LAMBDA(num,MIN(num)
Here are the assumptions in the formula in cell H7.
Array_or_range: C2:F15 Lambda: LAMBDA(num,MEDIAN(num))
Real-World Applications
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BYCOL can be used to quickly calculate totals, averages, or other financial metrics for specific columns of financial data, making budgeting and financial analysis more efficient.
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Sales teams can use BYCOL to analyse sales data by region, product, or time period. This allows for targeted marketing efforts and better resource allocation.
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BYCOL can assist in managing inventory levels by providing instant calculations for quantities, values, or turnover rates for specific products or categories.
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Teachers can use BYCOL to calculate averages, highest scores, or lowest scores for specific assignments or exams. This helps in evaluating student performance more effectively.
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Researchers can utilise BYCOL to process experimental data by applying various functions to specific columns, enabling quicker analysis and interpretation.
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HR professionals can use BYCOL to calculate various metrics such as total working hours, average salaries, or overtime pay for specific employee groups or departments.
- E-commerce businesses can employ BYCOL to analyse sales data by product categories, customer demographics, or geographical regions to make informed marketing and stocking decisions.
- Project managers can use BYCOL to track and analyse project progress, budget utilisation, or resource allocation for specific tasks or phases of a project.
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Healthcare professionals can use BYCOL to analyse patient data, calculate averages, or track specific health metrics over time for different patient groups.
- BYCOL can be employed to calculate key performance indicators (KPIs) for specific aspects of the supply chain, such as order fulfilment rates, shipping costs, or inventory turnover.
- Organisations can use BYCOL to analyse survey responses, calculate averages, or identify trends and patterns in feedback data for specific questions or demographics.
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Before conducting more advanced analyses, data scientists can use BYCOL to quickly clean and preprocess datasets by applying various functions to specific columns.
Conclusion:
In conclusion, mastering the BYCOL formula in Google Sheets can greatly enhance your ability to efficiently handle and analyse data. Its power lies in its capacity to perform calculations on specific columns, saving you time and effort. Whether you’re organising data or performing complex mathematical operations, BYCOL proves to be an invaluable tool.
By understanding the syntax and practical examples provided, you now have the knowledge to confidently utilise this function in your own spreadsheets. Remember to double-check your selected range and experiment with different functions to best suit your needs.
Frequently Asked Questions (FAQs):
Yes, BYCOL can also perform operations on text data. For example, you can use it to find the maximum or minimum value within a column of text.