Grist has a powerful data engine to calculate the cells of your tables using formulas. If you’ve used spreadsheets before, or database expressions, you’ll be on familiar territory - but there are some wrinkles you’ll want to know about, so hang around.

Let’s start with a classic use of spreadsheets. Suppose you have a list of products you’ve ordered, the quantity you ordered, and the unit price of each. You’ve made a column to show the quantity times the unit price, but want the computer to do that part for you.


Just select a cell in the column you want to fill, and hit = key to tell Grist you want to enter a formula, rather than a value.


Did you notice, when you did that, the labels of the columns changed a little? “Product” became “$Product”, and “Unit Price” became “$Unit_Price”. This is Grist telling you how to refer to those columns in your formula. Just type $Quantity * $Unit_Price. You’ll find an auto-complete feature ready to help you. Or if you don’t like typing, click on the Quantity column, type the multiplication symbol, and then click on the Unit Price column. Your formula should look like this:


To control the column ID, like “$Unit_Price”, that’s used in formulas, see Renaming columns.

Press Enter, and your formula is applied to all cells in the column.


Grist formulas are written in Python, the most popular language for data science. The entirety of Python’s standard library is available to you. For those with a spreadsheet background, we’ve also added a suite of Excel-like functions, with all-uppercase names. Here’s the full list of functions.

If you’ve worked with spreadsheets before, you may be surprised that you don’t need to specify row numbers, like B1 * C1. In Grist, a single formula applies to a whole column. You don’t have to worry about filling it in for all rows, and can refer to values in the same row without fuss.

Formulas that operate over many rows#

If you are a spreadsheet user, you may find yourself wanting to have some special rows at the end of your table that have formulas different to the rest. In Grist, we’d like you to consider adding a widget to your page instead. For common use cases, Summary tables may be exactly what you need. Or if you want to set things up yourself, you can add an extra table widget like this (see Page widgets for details):


This is just another table, giving us a place to put formulas outside of the structure of the Materials table. For example, if we wanted to count how many products there are in that table, we could use this formula:


Every table in your document is available by its name in formulas, as a UserTable. This formula uses the all method to access the rows of the table, but doesn’t do anything with them but count them.

Here’s a formula to compute the average price, using the Excel-like function AVERAGE:


The all method returns a RecordSet, which supports iterating over individual columns this way. Equivalently, we could use a Python list comprehension:

AVERAGE(material.Price for material in Materials.all)

If you are not familiar with Python, it is worth following a tutorial. There are thousands online, including this official one. Python will be useful to you for all sorts of data work, not just Grist.

List comprehension is useful once we’re doing anything nuanced. For example, here’s a formula to list the names of products with a quantity greater than 80:

[m.Product for m in Materials.all if m.Quantity > 80]

This is a list comprehension, but now with a conditional. The result is a list, which is rendered as text in a cell.

Python can help in other ways in your search for rows. For example, here’s a formula to find the name of the product with the highest quantity:

max(Materials.all, key=lambda m: m.Quantity).Product

Formulas are case-sensitive, with Excel-like functions being all-caps (MAX), and regular Python generally all lowercase (max).

For exact matches, there is a shortcut to avoid iteration called lookupRecords, or lookupOne for single matches. Just pass the the values of columns you require to be matched. For example, here is a formula to look up the product name of a material with a quantity of 52:


For very large tables, it is wise to use lookups as much as you can, rather than iterating through rows.

Returning to our example document, you can now see how we calculated the Total Spent, Average Quantity, and Most Ordered Product columns:

Column Formula
Total Spent SUM(Materials.all.Price)
Average Quantity AVERAGE(Materials.all.Quantity)
Most Ordered Product max(Materials.all, key=lambda m: m.Quantity).Product

Separating out calculations like this from the body of your data can take some getting used to, but working this way can help keep your document more organized. And it brings other advantages. For example we could switch the formatting of the summary widget via the side panel:


Varying formula by row#

Having a formula apply to all rows is convenient and reduces the changes of mistakes.

If you need to have a column change its behavior on different rows, it is possible using a conditional in the formula. For example, here is a replacement for the Materials.Price formula that ignores the price and shows zero for products whose name ends in “(Sample)”:

if $Product.endswith("(Sample)"):
  return 0
  return $Quantity * $Unit_Price

Code viewer#

Once you have a lot of formulas, or if you have been invited to a document and want to get an overview of its formulas, there is a code viewer available with a pure Python summary of the document.


Special values available in formulas#

For those familiar with Python, here are the extra values available to you in Grist:

  • rec is the current row. The $column syntax is shorthand for rec.column. The rec variable is of type Record.
  • table is the current table, and is of type UserTable.
  • Tables in your document are available by their name, and are also of type UserTable.
  • Many extra spreadsheet functions are available, see the full function list.

If your table or column has a space in its name, or other characters that are awkward in Python, those characters are replaced with an underscore. Auto-complete may help you if you’re not sure. You can also control the “ids” of columns and tables in the right side panel.

Freeze a formula column#

If you’d like to save the output of your formula as plain values, you can simply turn off the formula. First open the column options in the side panel:


Now click on the ACTIONS menu and select Convert to data column option to turn it off:


Notice that there is no = sign in the column cells any more, showing that it is no longer a formula. The cells will no longer change if other cells they used to depend on change.


The original formula is saved but stays inactive. It may come useful again if you wish to convert the column back to a formula column, or use it as a Trigger Formula.

The side panel has lots of other handy settings, such as cell formatting (number of digits after decimal point, color, etc). The options apply just as much to formula columns as to regular columns.


Grist functions lookupOne and lookupRecords are useful for enumerating subsets of your data. For example, suppose we added a Category column to our Materials table, and wished to list all products belonging to a specific category. We can do this using TABLE.lookupRecords, where TABLE is the table of interest, and supplying it with the column values to match. For example, Materials.lookupRecords(Category='Ship'), as here:


If you are following on, see Adding a field for details of how to add a new field to a card. If you care about the order of results, lookupRecords takes an optional sort_by parameter. For example, we could use this formula to sort by the product name itself:

list(Materials.lookupRecords(Category='Ship', sort_by='Product').Product)

If you want to sort by multiple columns, remember that you can create a hidden formula column that combines data in any way you like, and then sort by that.

The order of records returned by lookupRecords may not match the order of rows you see in a table. To get that order, use sort_by='manualSort'. This is an internal column that is updated with the manually established sort order of rows.

If you find yourself doing a lot of look-ups, please consider whether Summary tables and Summary formulas might be what you are looking for.


Lookups are handy for recursive formulas. Suppose we have a table counting how many events we have per day, and want to add a cumulative sum of those event counts. One way to do that is with a formula like this:

yesterday = Events.lookupOne(date=$date - datetime.timedelta(days=1))
$events + (yesterday.cumulative or 0)


For clarity, we’ve split this formula into two lines. The first line makes a variable pointing to the row of the day before. The second line computes the value we want in the cell. Python note: the value of the last line is automatically returned (you could prefix it with return if you like).

Notice the yesterday.cumulative or 0. For the earliest row in the table, there will be no yesterday. In this case, lookupOne returns a special empty record, for which yesterday.cumulative will be None.

If you’d like to simplify this formula, or find yourself using the same lookup in multiple formulas, it would be worth making yesterday a reference column. Simply add a reference column, and give a formula for it that matches how we defined yesterday here.

To actually enter this formula in a cell, you’d use Shift + Enter to divide the lines. For longer formulas, you may prefer to use the side panel, where a simple Enter gives you a new line. Click on the column header, select “Column Options” and edit the Formula field.

Trigger Formulas#

Formula columns are great for calculated values – those determined by other data in the document. It may also be useful to store independent data in a column, but still use a formula to calculate it in some situations. This is exactly what Trigger Formulas offer. It is a very powerful feature that allows you to create a Timestamp or Authorship column, recalculate your data based on a set of conditions that you decide , clean data when a new value is entered, or provide sensible default value for a column.

Each data column may have an Optional formula that gets triggered on certain conditions. This formula is available in the creator panel, under the DATA COLUMN section.

Optional formula

When you have an empty column, you first need to convert it a data column by clicking Make into data column option under the ACTIONS menu. For a formula column, you need first to convert it to a Data column by clicking the Convert to data column option under the same ACTIONS menu:

Convert to data column

To control when the formula is evaluated, use the two checkbox options below:

a Created-At column

  • Apply to new records triggers the formula only when a new record is created (a default cell value).
  • Apply on record changes triggers the formula when a record is updated.

Applying to new records is self-explanatory, the formula will be evaluated only once when you add a new record. It is a perfect solution to provide default values to the empty cells. Second option allows you to fine grain the conditions and specify which columns, when updated, will trigger the evaluation:

an Updated-At column

You probably noticed the first option Current field. At first glance, you probably wonder: “Why would I want to trigger the column on its own change?”. This option allows you to react to a value that is being entered into the column, just before it is saved!

In the formula editor, you have access to two variables that are not available to regular formulas:

  • value which is the value that a user wants to enter,
  • user which represents a user object that is making the change (you will also see this in the Access rules section).

This allows you to make your application even smarter, track when a record was updated, or see who made the last change to a row. Simple examples:

  1. Ensure that the value in a column is always written in capital letters: data cleanup - uppercase With the trigger formula of value.upper(), the value typed into this column will be converted to upper case automatically.

  2. Format a value that the user enters to sanitize the data before saving: data cleanup - format With the formula like value if value.startswith("SK") else "SK" + value, the value typed into this column will always be prefixed with “SK”.

  3. Overwrite a default value from a referenced table: data cleanup - reference You can use a formula like value or $Client.Phone, to provide a default value from a referenced table, but still allow the user to type a new one.

In each of these examples, when the user tries to modify a cell, Grist (before updating the record) will evaluate the formula and store its result in the column instead of the value provided by the user.

For a detailed, real-life example read our guide on how to create time and user stamps.