# class - Script and chart function
The class function assigns the first parameter to a class interval. The result is a dual value with a<=x<b as the textual value, where a and b are the upper and lower limits of the bin, and the lower bound as numeric value.
Syntax:
class(expression, interval [ , label [ , offset ]])
Return data type: dual
Arguments
| Argument | Description |
| --- | --- |
| interval | A number that specifies the bin width. |
| label | An arbitrary string that can replace the 'x' in the result text. |
| offset | A number that can be used as offset from the default starting point of the classification. The default starting point is normally 0. |
Example: Chart expressions
| Example | Result |
| --- | --- |
| `class( var,10
)` with `var = 23` | Returns
`20<=x<30` |
| `class( var,5,'value'
)` with `var = 23` | Returns
`20<= value <25` |
| `class(
var,10,'x',5
)` with `var = 23` | Returns
`15<=x<25` |
Some of the examples in this topic use inline loads. For more information, see [Using inline loads to load data](https://help.qlik.com/en-US/sense/November2025/Subsystems/Hub/Content/Sense_Hub/Scripting/inline-loads-qs.htm).
## Example - class fundamentals
[](https://help.qlik.com/en-US/sense/Subsystems/Hub/Content/Sense_Hub/Scripting/ConditionalFunctions/class.htm)
#### Chart expression
Overview
A data table is loaded that contains names and ages. You want to classify the people by age groups (with a ten year interval) and provide a count of the number of people in each group.
Open the Data load editor and add the load script below to a new section.
The load script contains:
* A dataset which is loaded into a data table called `Example`.
* The following fields in the data table:
* `Age`
* `Name`
Load script
```
Example:
LOAD * INLINE [
Age, Name
25, John
42, Karen
53, Yoshi
43, Bob
27, Charles
];
```
Results
1. Load the data and open a sheet. Create two new tables.
2. In the first table, do the following:
1. Add these dimensions:
* `Age`
* `Name`
2. Create the following measure:
* `=class(Age, 10, 'age')`, to classify the data into age categories.
3. In the second table, do the following:
1. Add this expression as a dimension
* `=class(Age, 10, 'age')`
2. Create the following measure:
* `=Count(Age)`, to count the number of people age classification.
The first table shows the raw data for `Name` and `Age`, and which age category they are classified in.
Results table 1
| Name | Age | class(Age, 10, 'age') |
| --- | --- | --- |
| Bob | 43 | 40 <= age < 50 |
| Charles | 27 | 20 <= age < 30 |
| John | 25 | 20 <= age < 30 |
| Karen | 42 | 40 <= age < 50 |
| Yoshi | 53 | 50 <= age < 60 |
In the second table, you can see how the data is now classified and summarized. The results of the `class` function created 3 age categories in the first measure. The second measure counts the number of people in each age category.
Results table 2
| class(Age, 10, 'age') | Count(Age) |
| --- | --- |
| Totals | 5 |
| 20 <= age < 30 | 2 |
| 40 <= age < 50 | 2 |
| 50 <= age < 60 | 1 |
[](https://help.qlik.com/en-US/sense/Subsystems/Hub/Content/Sense_Hub/Scripting/ConditionalFunctions/class.htm)
#### Load script
Overview
A data table is loaded that contains names and ages. You want to add a field that classifies each person according to an age group with a ten year interval. The original source table looks like the following.
Original source tableOriginal source table
| Name | Age |
| --- | --- |
| John | 25 |
| Karen | 42 |
| Yoshi | 53 |
Open the Data load editor and add the load script below to a new section.
The load script contains:
* A preceding load statement that uses the class function to create a new field called `Agegroup`.
Load script
```
LOAD *,
class(Age, 10, 'age') As Agegroup;
LOAD * INLINE
[ Age, Name
25, John
42, Karen
53, Yoshi];
```
Results
Load the data and open a sheet. Create a new table and add these fields as dimensions:
* `Name`
* `Age`
* `Agegroup`
Results table table
| Name | Age | Agegroup |
| --- | --- | --- |
| John | 25 | 20 <= age < 30 |
| Karen | 42 | 40 <= age < 50 |
| Yoshi | 53 | 50 <= age < 60 |
The new field `Agegroup` classifies each person by age category.
## Example - class scenario
[](https://help.qlik.com/en-US/sense/Subsystems/Hub/Content/Sense_Hub/Scripting/ConditionalFunctions/class.htm)
#### Chart expression
Overview
A data table is loaded that contains customer data. You want to classify customers by the number of lifetime value points earned and provide a count of the number of customers in each category.
Open the Data load editor and add the load script below to a new section.
The load script contains:
* A dataset which is loaded into a data table called `Example`.
* The following fields in the data table:
* `CustomerID`
* `CustomerName`
* `LifetimeValue`
Load script
```
Example:
Load * Inline [
CustomerID, CustomerName, LifetimeValue
1, John Doe, 12500
2, Jane Smith, 36000
3, Sam Brown, 15000
4, Lisa White, 50000
5, Tom Harris, 22000
];
```
Results
1. Load the data and open a sheet. Create two new tables.
2. In the first table, do the following:
1. Add these dimensions:
* `CustomerID`
* `CustomerName`
* `LifetimeValue`
2. Create the following calculated dimension:
* `=Class(LifetimeValue, 10000, 'Lifetime Value Group')`, to classify the data based on the `LifetimeValue` field.
3. In the second table, do the following:
1. Add this expression as a dimension
* `=Class(LifetimeValue, 10000, 'Lifetime Value Group')`
2. Create the following measure:
* `=Count(CustomerID)`, to count the number of people age classification.
The first table shows the raw data that has been entered and the results of using the class function.
Results table 1
| CustomerID | CustomerName | LifetimeValue | Class(LifetimeValue, 10000, 'Lifetime Value Group') |
| --- | --- | --- | --- |
| 1 | John Doe | 12500 | 10000 <= Lifetime Value Group < 20000 |
| 2 | Jane Smith | 36000 | 30000 <= Lifetime Value Group < 40000 |
| 3 | Sam Brown | 15000 | 10000 <= Lifetime Value Group < 20000 |
| 4 | Lisa White | 50000 | 50000 <= Lifetime Value Group < 60000 |
| 5 | Tom Harris | 22000 | 20000 <= Lifetime Value Group < 30000 |
The second table shows how the data can be classified and summarized. The data is categorized into four groups using the `class` function, and then counted to show the number of customers in each group.
Results table 2
| Class(LifetimeValue, 10000, 'Lifetime Value Group') | Count(CustomerID) |
| --- | --- |
| Totals | 5 |
| 10000 <= Lifetime Value Group < 20000 | 2 |
| 20000 <= Lifetime Value Group < 30000 | 1 |
| 30000 <= Lifetime Value Group < 40000 | 1 |
| 50000 <= Lifetime Value Group < 60000 | 1 |