# normalise

## Overview

The `normalise` statement is used to update the values in a numerical column such that they are all positive, negative or inverted.

{% hint style="info" %}
In this documentation the spelling `normalise` is used but `normalize` may also be used. The functionality is identical in either case.
{% endhint %}

## Syntax

**`normalise column`***`colName`***`as positive`**

**`normalise column`***`colName`***`as negative`**

**`normalise column`***`colName`***`as invert`**

**`normalise column`***`colName`***`as standard`**

## Details

The `normalise` statement processes each value in the column called *colName* and applies the following logic based on the last argument shown above as follows:

| Argument   | Result                                                                                                                                                                                                                                                                        |
| ---------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `positive` | All negative numbers are replaced with their positive equivalent. Non-negative numbers are left unmodified.                                                                                                                                                                   |
| `negative` | All positive numbers are replaced with their negative equivalent. Negative numbers are left unmodified.                                                                                                                                                                       |
| `invert`   | All positive numbers are replaced with their negative equivalent, and all negative numbers are replaced with their positive equivalent                                                                                                                                        |
| `standard` | All non-blank values are assumed to be a decimal number and are replaced with that value in conventional notation. This functionality is intended to provide a means to convert numbers in scientific notation such as `2.1E-5` to conventional notation such as  `0.000021`. |

In order to be considered a number, a value in the *colName* column must start with any of the characters `+`, `-`, `.` or `0 to 9` and may contain a single `.` character which is interpreted as a decimal point.

{% hint style="info" %}
If a value in *colName* is non-numeric or blank it is left intact
{% endhint %}

{% hint style="warning" %}
When using `standard`all non-blank values are assumed to be numeric, and as such any non-numeric values will be changed to a numeric zero.
{% endhint %}

Additionally:

* Any numerical value in *colName* which starts with a `+`, `.` or decimal character is considered positive
* Any numerical value in *colName* which starts with a `-` character is considered negative
* When using `standard` the resulting conventional number will be accurate up to 14 decimal places

The `normalise` statement ignores the [option overwrite](https://olddocs.exivity.io/3.5.4/data-pipelines/transform/language/option) setting, as its sole purpose is to modify existing values.

## Example

```
import "system/extracted/csp_usage.csv" source test alias data

# Invert all numerical values in column 'quantity'
normalise column quantity as invert
```