Looker functions and operators

If your admin has granted you the permissions to create table calculations, you can use the following features to quickly perform common functions without needing to create Looker expressions:

  • Shortcut Calculations to quickly perform common calculations on numeric fields that are in an Explore's data table

If your admin has granted you the permissions to create custom fields, you can use the following features to quickly perform common functions without needing to create Looker expressions:

  • Custom groups to quickly group values under custom labels without needing to develop CASE WHEN logic in sql parameters or type: case fields

  • Custom bins to group numeric type dimensions in custom tiers without needing to develop type: tier LookML fields

Looker expressions (sometimes referred to as Lexp) are used to perform calculations for:

A major part of these expressions is the functions and operators that you can use in them. The functions and operators can be divided into a few basic categories:

Some functions are only available for table calculations

Looker expressions for custom filters and custom fields do not support Looker functions that convert datatypes, aggregate data from multiple rows, or refer to other rows or pivot columns. These functions are supported only for table calculations (including table calculations used in the expression parameter of a data test).

This page is organized to clarify which functions and operators are available, depending on where you are using your Looker expression.

Mathematical functions and operators

Mathematical functions and operators work in one of two ways:

  • Some mathematical functions perform calculations based on a single row. For example, rounding, taking a square root, multiplying, and similar functions can be used for values in a single row, returning a distinct value for each and every row. All mathematical operators, such as +, are applied one row at a time.
  • Other mathematical functions, like averages and running totals, operate over many rows. These functions take many rows and reduce them to a single number, then display that same number on every row.

Functions for any Looker expression

Function Syntax Purpose
abs abs(value) Returns the absolute value of value.

For an example, see the Standard Deviation and Simple Time Series Outlier Detection Using Table Calculations Community post.
ceiling ceiling(value) Returns the smallest integer greater than or equal to value.
exp exp(value) Returns e to the power of value.
floor floor(value) Returns the largest integer less than or equal to value.
ln ln(value) Returns the natural logarithm of value.
log log(value) Returns the base 10 logarithm of value.
mod mod(value, divisor) Returns the remainder of dividing value by divisor.
power power(base, exponent) Returns base raised to the power of exponent.

For an example, see the Standard Deviation and Simple Time Series Outlier Detection Using Table Calculations Community post.
rand rand() Returns a random number between 0 and 1.
round round(value, num_decimals) Returns value rounded to num_decimals decimal places.

For examples using round, see the Using pivot_index in table calculations and Standard Deviation and Simple Time Series Outlier Detection Using Table Calculations Community posts.
sqrt sqrt(value) Returns the square root of value.

For an example, see the Standard Deviation and Simple Time Series Outlier Detection Using Table Calculations Community post.

Functions for table calculations only

Many of these functions operate over many rows and will only consider the rows returned by your query.

Function Syntax Purpose
acos acos(value) Returns the inverse cosine of value.
asin asin(value) Returns the inverse sine of value.
atan atan(value) Returns the inverse tangent of value.
beta_dist beta_dist(value, alpha, beta, cumulative) Returns the position of value on the beta distribution with parameters alpha and beta. If cumulative = yes, returns the cumulative probability.
beta_inv beta_inv(probability, alpha, beta) Returns the position of probability on the inverse cumulative beta distribution with parameters alpha and beta.
binom_dist binom_dist(num_successes, num_tests, probability, cumulative) Returns the probability of getting num_successes successes in num_tests tests with the given probability of success. If cumulative = yes, returns the cumulative probability.
binom_inv binom_inv(num_tests, test_probability, target_probability) Returns the smallest number k such that binom(k, num_tests, test_probability, yes) >= target_probability.
chisq_dist chisq_dist(value, dof, cumulative) Returns the position of value on the gamma distribution with dof degrees of freedom. If cumulative = yes, returns the cumulative probability.
chisq_inv chisq_inv(probability, dof) Returns the position of probability on the inverse cumulative gamma distribution with dof degrees of freedom.
chisq_test chisq_test(actual, expected) Returns the probability for the chi-squared test for independence between actual and expected data. actual can be a column or a column of lists, and expected must be the same type.
combin combin(set_size, selection_size) Returns the number of ways of choosing selection_size elements from a set of size set_size.
confidence_norm confidence_norm(alpha, stdev, n) Returns half the width of the normal confidence interval at significance level alpha, standard deviation stdev, and sample size n.
confidence_t confidence_t(alpha, stdev, n) Returns half the width of the Student's t-distribution confidence interval at significance level alpha, standard deviation stdev, and sample size n.
correl correl(column_1, column_2) Returns the correlation coefficient of column_1 and column_2.
cos cos(value) Returns the cosine of value.
count count(expression) Returns the count of non-null values in the column defined by expression, unless expression defines a column of lists, in which case returns the count in each list.
count_distinct count_distinct(expression) Returns the count of distinct non-null values in the column defined by expression, unless expression defines a column of lists, in which case returns the count in each list.
covar_pop covar_pop(column_1, column_2) Returns the population covariance of column_1 and column_2.
covar_samp covar_samp(column_1, column_2) Returns the sample covariance of column_1 and column_2.
degrees degrees(value) Converts value from radians to degrees.
expon_dist expon_dist(value, lambda, cumulative) Returns the position of value on the exponential distribution with parameter lambda. If cumulative = yes, returns the cumulative probability.
f_dist f_dist(value, dof_1, dof_2, cumulative) Returns the position of value on the F distribution with parameters dof_1 and dof_2. If cumulative = yes, returns the cumulative probability.
f_inv f_inv(probability, dof_1, dof_2) Returns the position of probability on the inverse cumulative F distribution with parameters dof_1 and dof_2.
fact fact(value) Returns the factorial of value.
gamma_dist gamma_dist(value, alpha, beta, cumulative) Returns the position of value on the gamma distribution with parameters alpha and beta. If cumulative = yes, returns the cumulative probability.
gamma_inv gamma_inv(probability, alpha, beta) Returns the position of probability on the inverse cumulative gamma distribution with parameters alpha and beta.
geomean geomean(expression) Returns the geometric mean of the column created by expression unless expression defines a column of lists, in which case returns the geometric mean of each list.
hypgeom_dist hypgeom_dist(sample_successes, sample_size, population_successes, population_size, cumulative) Returns the probability of getting sample_successes from the given sample_size, number of population_successes, and population_size. If cumulative = yes, returns the cumulative probability.
intercept intercept(y_column, x_column) Returns the intercept of the linear regression line through the points determined by y_column and x_column.

For an example, see the How to Forecast in Looker with Table Calculations Community post.
kurtosis kurtosis(expression) Returns the sample excess kurtosis of the column created by expression unless expression defines a column of lists, in which case returns the sample excess kurtosis of each list.
large large(expression, k) Returns the kth largest value of the column created by expression unless expression defines a column of lists, in which case returns the kth largest value of each list.
match match(value, expression) Returns the row number of the first occurrence of value in the column created by expression unless expression defines a column of lists, in which case returns the position of value in each list.
max max(expression) Returns the max of the column created by expression unless expression defines a column of lists, in which case returns the max of each list.

For examples using max, see the Using lists in table calculations and Grouping by a dimension in table calculations Community posts.
mean mean(expression) Returns the mean of the column created by expression unless expression defines a column of lists, in which case returns the mean of each list.

For examples using mean, see the Calculating Moving Averages Community post and the Standard Deviation and simple time series outlier detection using Table Calculations Community post.
median median(expression) Returns the median of the column created by expression unless expression defines a column of lists, in which case returns the median of each list.
min min(expression) Returns the min of the column created by expression unless expression defines a column of lists, in which case returns the min of each list.
mode mode(expression) Returns the mode of the column created by expression unless expression defines a column of lists, in which case returns the mode of each list.
multinomial multinomial(value_1, value_2, ...) Returns the factorial of the sum of the arguments divided by the product of each of their factorials.
negbinom_dist negbinom_dist(num_failures, num_successes, probability, cumulative) Returns the probability of getting num_failures failures before getting num_successes successes, with the given probability of success. If cumulative = yes, returns the cumulative probability.
norm_dist norm_dist(value, mean, stdev, cumulative) Returns the position of value on the normal distribution with the given mean and stdev. If cumulative = yes, returns the cumulative probability.
norm_inv norm_inv(probability, mean, stdev) Returns the position of probability on the inverse normal cumulative distribution.
norm_s_dist norm_s_dist(value, cumulative) Returns the position of value on the standard normal distribution. If cumulative = yes, returns the cumulative probability.
norm_s_inv norm_s_inv(probability) Returns the position of probability on the inverse standard normal cumulative distribution.
percent_rank percent_rank(column, value) Returns the rank of value in column as a percentage from 0 to 1 inclusive, where column is the column, field, list, or range containing the dataset to consider; and value is the column with the value for which the percentage rank will be determined.

Sample Usage:

percent_rank(${view_name.field_1}, ${view_name.field_1})

percent_rank(list(1, 2, 3), ${view_name.field_1})

percent_rank(list(1, 2, 3), 2)
percentile percentile(value_column, percentile_value) Returns the value from the column created by expression corresponding to the given percentile_value, unless expression defines a column of lists, in which case returns the percentile value for each list. percentile_value must be between 0 and 1; otherwise returns null.
pi pi() Returns the value of pi.
poisson_dist poisson_dist(value, lambda, cumulative) Returns the position of value on the poisson distribution with parameter lambda. If cumulative = yes, returns the cumulative probability.
product product(expression) Returns the product of the column created by expression unless expression defines a column of lists, in which case returns the product of each list.
radians radians(value) Converts value from degrees to radians.
rank rank(value, expression) Returns the rank of value in the column created by expression. For example, if you want to rank orders by their total sale price, you could use rank(${order_items.total_sale_price},${order_items.total_sale_price}), which gives a rank for each value of order_items.total_sale_price in your query when comparing it to the entire column of order_items.total_sale_price in your query. In the case where the expression defines multiple lists, this function returns the relative size of the value in each list.

For an example, see the Ranks with Table Calculations Community post.
rank_avg rank_avg(value, expression) Returns the average rank of value in the column created by expression unless expression defines a column of lists, in which case returns the average rank of value in each list.
running_product running_product(value_column) Returns a running product of the values in value_column.
running_total running_total(value_column) Returns a running total of the values in value_column.

For an example, see the Creating a Running Total Down Columns with Table Calculations Best Practices page.
sin sin(value) Returns the sine of value.
skew skew(expression) Returns the sample skewness of the column created by expression unless expression defines a column of lists, in which case returns the sample skewness of each list.
slope slope(y_column, x_column) Returns the slope of the linear regression line through points determined by y_column and x_column.

For an example, see the How to Forecast in Looker with Table Calculations Community post.
small small(expression, k) Returns the kth smallest value of the column created by expression unless expression defines a column of lists, in which case returns the kth smallest value of each list.
stddev_pop stddev_pop(expression) Returns the standard deviation (population) of the column created by expression unless expression defines a column of lists, in which case returns the standard deviation (population) of each list.
stddev_samp stddev_samp(expression) Returns the standard deviation (sample) of the column created by expression unless expression defines a column of lists, in which case returns the standard deviation (sample) of each list.
sum sum(expression) Returns the sum of the column created by expression unless expression defines a column of lists, in which case returns the sum of each list.

For examples using sum, see the Aggregating Across Rows (Row Totals) in Table Calculations and How to Calculate Percent-of-Total Best Practices pages.
t_dist t_dist(value, dof, cumulative) Returns the position of value on the Student's t-distribution with dof degrees of freedom. If cumulative = yes, returns the cumulative probability.
t_inv t_inv(probability, dof) Returns the position of probability on the inverse normal cumulative distribution with dof degrees of freedom.
t_test t_test(column_1, column_2, tails, type) Returns the result of a Student's t-test on the data from column_1 and column_2, using 1 or 2 tails. type: 1 = paired, 2 = homoscedastic, 3 = heteroscedastic.
tan tan(value) Returns the tangent of value.
var_pop var_pop(expression) Returns the variance (population) of the column created by expression unless expression defines a column of lists, in which case returns the variance (population) of each list.
var_samp var_samp(expression) Returns the variance (sample) of the column created by expression unless expression defines a column of lists, in which case returns the variance (sample) of each list.
weibull_dist weibull_dist(value, shape, scale, cumulative) Returns the position of value on the Weibull distribution with parameters shape and scale. If cumulative = yes, returns the cumulative probability.
z_test z_test(data, value, stdev) Returns the one-tailed p-value of the z-test using the existing data and stdev on the hypothesized mean value.

Operators for any Looker expression

You can use the following standard mathematical operators:

Operator Syntax Purpose
+ value_1 + value_2 Adds value_1 and value_2.
- value_1 - value_2 Subtracts value_2 from value_1.
* value_1 * value_2 Multiplies value_1 and value_2.
/ value_1 / value_2 Divides value_1 by value_2.

String functions

String functions operate on sentences, words, or letters, which are collectively called "strings." You can use string functions to capitalize words and letters, extract parts of a phrase, check to see if a word or letter is in a phrase, or replace elements of a word or phrase. String functions can also be used to format the data returned in the table.

Functions for any Looker expression

Function Syntax Purpose
concat concat(value_1, value_2, ...) Returns value_1, value_2, ..., value_n joined as one string.
contains contains(string, search_string) Returns Yes if string contains search_string, and No otherwise. The contains function is case-sensitive.
length length(string) Returns the number of characters in string.
lower lower(string) Returns string with all characters converted to lowercase.
position position(string, search_string) Returns the start index of search_string in string if it exists, and 0 otherwise.
replace replace(string, old_string, new_string) Returns string with all occurrences of old_string replaced with new_string.
substring substring(string, start_position, length) Returns the substring of string, beginning at start_position, consisting of length characters. The start_position starts at 1, with 1 indicating the first character in the string, 2 indicating the second character in the string, and so on.
upper upper(string) Returns string with all characters converted to uppercase.

Functions for table calculations only

Function Syntax Purpose
split split(string, delimeter) Returns a list of strings in string broken up by delimiter.
to_number to_number(string) Returns the number represented by string, or null if the string cannot be converted.
to_string to_string(value) Returns the string representation of value, or an empty string if value is null.

Date functions

Date functions enable you to work with dates and times.

Functions for any Looker expression

Function Syntax Purpose
add_days add_days(number, date) Adds number days to date.
add_hours add_hours(number, date) Adds number hours to date.
add_minutes add_minutes(number, date) Adds number minutes to date.
add_months add_months(number, date) Adds number months to date.
add_seconds add_seconds(number, date) Adds number seconds to date.
add_years add_years(number, date) Adds number years to date.
date date(year, month, day) Returns "year-month-day" date or null if the date would be invalid.
date_time date_time(year, month, day, hours, minutes, seconds) Returns year-month-day hours:minutes:seconds date or null if the date would be invalid.
diff_days diff_days(start_date, end_date) Returns the number of days between start_date and end_date.

For an example, see the Using dates in table calculations Community post.
diff_hours diff_hours(start_date, end_date) Returns the number of hours between start_date and end_date.
diff_minutes diff_minutes(start_date, end_date) Returns the number of minutes between start_date and end_date.

For an example, see the Using dates in table calculations Community post.
diff_months diff_months(start_date, end_date) Returns the number of months between start_date and end_date.

For an example, see the Grouping by a dimension in table calculations Community post.
diff_seconds diff_seconds(start_date, end_date) Returns the number of seconds between start_date and end_date.
diff_years diff_years(start_date, end_date) Returns the number of years between start_date and end_date.
extract_days extract_days(date) Extracts the days from date.

For an example, see the Using dates in table calculations Community post.
extract_hours extract_hours(date) Extracts the hours from date.
extract_minutes extract_minutes(date) Extracts the minutes from date.
extract_months extract_months(date) Extracts the months from date.
extract_seconds extract_seconds(date) Extracts the seconds from date.
extract_years extract_years(date) Extracts the years from date.
now now() Returns the current date and time.

For examples using now, see the Now() Table Calculation Function Has Better Timezone Handling and Using dates in table calculations Community posts.
trunc_days trunc_days(date) Truncates date to days.
trunc_hours trunc_hours(date) Truncates date to hours.
trunc_minutes trunc_minutes(date) Truncates date to minutes.
trunc_months trunc_months(date) Truncates date to months.
trunc_years trunc_years(date) Truncates date to years.

Functions for table calculations only

Function Syntax Purpose
to_date to_date(string) Returns the date and time corresponding to string (YYYY, YYYY-MM, YYYY-MM-DD, YYYY-MM-DD hh, YYYY-MM-DD hh:mm, or YYYY-MM-DD hh:mm:ss).

Logical functions, operators, and constants

Logical functions and operators are used to assess whether something is true or false. Expressions using these elements take a value, evaluate it against some criteria, return Yes if the criteria are met, and No if the criteria are not met. There are also various logical operators for comparing values and combining logical expressions.

Functions for any Looker expression

Function Syntax Purpose
case case(when(yesno_arg, value_if_yes), when(yesno_arg, value_if_yes), ..., else_value) ADDED 21.10 Allows conditional logic with multiple conditions and outcomes. Returns value_if_yes for the first when case whose yesno_arg value is yes. Returns else_value if all when cases are no.
coalesce coalesce(value_1, value_2, ...) Returns the first non-null value in value_1, value_2, ..., value_n if found and null otherwise.

For examples using coalesce, see the Creating a running total across rows with table calculations, Creating a percent of total across rows with table calculations, and Using pivot_index in table calculations Community posts.
if if(yesno_expression, value_if_yes, value_if_no) If yesno_expression evaluates to Yes, returns the value_if_yes value. Otherwise, returns the value_if_no value.

For an example, see the Grouping by a dimension in table calculations Community post.
is_null is_null(value) Returns Yes if value is null, and No otherwise.

For an example, see the Creating Looker expressions documentation page. For another example that uses is_null with the NOT operator, see the Using table calculations documentation page.

Operators for any Looker expression

The following comparison operators can be used with any data type:

Operator Syntax Purpose
= value_1 = value_2 Returns Yes if value_1 is equal to value_2, and No otherwise.
!= value_1 != value_2 Returns Yes if value_1 is not equal to value_2, and No otherwise.

The following comparison operators can be used with numbers, dates, and strings:

Operator Syntax Purpose
> value_1 > value_2 Returns Yes if value_1 is greater than value_2, and No otherwise.
< value_1 < value_2 Returns Yes if value_1 is less than value_2, and No otherwise.
>= value_1 >= value_2 Returns Yes if value_1 is greater than or equal to value_2, and No otherwise.
<= value_1 <= value_2 Returns Yes if value_1 is less than or equal to value_2, and No otherwise.

You also can combine Looker expressions with these logical operators:

Operator Syntax Purpose
AND value_1 AND value_2 Returns Yes if both value_1 and value_2 are Yes, and No otherwise.
OR value_1 OR value_2 Returns Yes if either value_1 or value_2 is Yes, and No otherwise.
NOT NOT value Returns Yes if value is No, and No otherwise.

These logical operators must be capitalized. Logical operators written in lowercase will not work.

Logical constants

You can use logical constants in Looker expressions. These constants are always written in lowercase and have the following meanings:

Constant Meaning
yes True
no False
null No value

Note that the constants yes and no are the special symbols that ​mean true or false in Looker expressions. In contrast, using quotes such as in "yes" and "no" creates literal strings with those values.

Logical expressions evaluate to true or false without requiring an if function. For example, this:

if(${field} > 100, yes, no)

is equivalent to this:

${field} > 100

You also can use null to indicate no value. For example, you may want to determine if a field is empty, or assign an empty value in a certain situation. This formula returns no value if the field is less than 1, or the value of the field if it is more than 1:

if(${field} < 1, null, ${field})

Combining AND and OR operators

AND operators are evaluated before OR operators, if you don't otherwise specify the order with parentheses. Thus, the following expression without additional parentheses:

if (
  ${order_items.days_to_process}>=4 OR
  ${order_items.shipping_time}>5 AND
  ${order_facts.is_first_purchase},
"review", "okay")

would be evaluated as:

if (
  ${order_items.days_to_process}>=4 OR
  (${order_items.shipping_time}>5 AND ${order_facts.is_first_purchase}),
"review", "okay")

Positional functions

When creating table calculations, you can use positional transformation functions to extract information about fields in different rows or pivot columns. You can also create lists and retrieve the current row or pivot column index.

Column and row totals for table calculations only

If your Explore contains totals, you can reference total values for columns and rows:

Function Syntax Purpose
:total ${field:total} Returns the column total of the field.
:row_total ${field:row_total} Returns the row total of the field.

Some of these functions use the relative positions of rows, so changing the sort order of the rows affects the results of the functions.

Function Syntax Purpose
index index(expression, n) Returns the value of the nth element of the column created by expression, unless expression defines a column of lists, in which case returns the nth element of each list.
list list(value_1, value_2, ...) Creates a list out of the given values.

For an example, see the Using lists in table calculations Community post.
lookup lookup(value, lookup_column, result_column) Returns the value in result_column that is in the same row as value is in lookup_column.
offset offset(column, row_offset) Returns the value of row (n + row_offset) in column, where n is the current row number.

For examples using offset, see the Calculating Percent of Previous and Percent Change with Table Calculations Best Practices page.
offset_list offset_list(column, row_offset, num_values) Returns a list of the num_values values starting at row (n + row_offset) in column, where n is the current row number.

For an example, see the Calculating Moving Averages Community post.
row row() Returns the current row number.

Some of these functions use the relative positions of pivot columns, so changing the sort order of the pivoted dimension affects the results of those functions.

Function Syntax Purpose
pivot_column pivot_column() Returns the index of the current pivot column.
pivot_index pivot_index(expression, pivot_index) Evaluates expression in the context of the pivot column at position pivot_index (1 for first pivot, 2 second pivot, etc.). Returns null for unpivoted results.

For examples using pivot_index, see the Using pivot_index in table calculations and Creating a percent of total across rows with table calculations Community posts.
pivot_offset pivot_offset(pivot_expression, col_offset) Returns the value of the pivot_expression in position (n + col_offset), where n is the current pivot column position. Returns null for unpivoted results.

For examples using pivot_offset, see the Creating a running total across rows with table calculations Community post and the Calculating Percent of Previous and Percent Change with Table Calculations Best Practices page.
pivot_offset_list pivot_offset_list(pivot_expression, col_offset, num_values) Returns a list of the num_values values in pivot_expression starting at position (n + col_offset), where n is the current pivot index. Returns null for unpivoted results.
pivot_row pivot_row(expression) Returns the pivoted values of expression as a list. Returns null for unpivoted results.

For examples using pivot_row, see the Aggregating Across Rows (Row Totals) in Table Calculations and How to Calculate Percent-of-Total Best Practices pages.
pivot_where pivot_where(select_expression, expression) Returns the value of expression for the pivot column that uniquely satisfies select_expression or null if such a unique column does not exist.

The specific pivot functions you use determine whether the table calculation is displayed next to each pivoted column, or is displayed as a single column at the end of the table.

Filter functions for custom filters and custom fields

Filter functions let you work with filter expressions to return values based on filtered data. Filter functions work in custom filters, filters on custom measures, and custom dimensions, but are not valid in table calculations.

Function Syntax Purpose
matches_filter matches_filter(field, filter_expression) Returns Yes if the value of the field matches the filter expression, No if not.