# Formula

Add new features to your dataset.

**Inputs**

- Data: input dataset

**Outputs**

- Data: dataset with additional features

**Formula** allows computing new columns by combining the existing ones with a user-defined expression. The resulting column can be categorical, numerical or textual.

For numeric variables, it sufices to provide a name and an expression.

- List of constructed variables
- Add or remove variables
- New feature name
- Expression in Python
- Select a feature
- Select a function
- Produce a report
- Press
*Send*to communicate changes

The following example shows construction of a categorical variable: its value is "lower" is "sepal length" is below 6, "mid" if it is at least 6 but below 7, and "higher" otherwise. Note that spaces need to be replaced by underscores (`sepal_length`

).

- List of variable definitions
- Add or remove variables
- New feature name
- Expression in Python
- If checked, the feature is put among meta attributes
- Select a feature to use in expression
- Select a function to use in expression
- Optional list of values, used to define their order
- Press
*Send*to compute and output data

## Hints

If you are unfamiliar with Python math language, here's a quick introduction.

Expressions can use the following operators:

`+`

,`-`

,`*`

,`/`

: addition, subtraction, multiplication, division`//`

: integer division`%`

: remainder after integer division`**`

: exponentiation (for square root square by 0.5)`<`

,`>`

,`<=`

,`>=`

less than, greater than, less or equal, greater or equal`==`

equal`!=`

not equal- if-else:
*value*`if`

*condition*else*other-value*(see the above example

See more here.