The `normalise`

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

In this documentation the spelling `normalise`

is used but `normalize`

may also be used. The functionality is identical in either case.

`normalise column`

`colName`

`as positive`

`normalise column`

`colName`

`as negative`

`normalise column`

`colName`

`as invert`

`normalise column`

`colName`

`as standard`

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 |

| All negative numbers are replaced with their positive equivalent. Non-negative numbers are left unmodified. |

| All positive numbers are replaced with their negative equivalent. Negative numbers are left unmodified. |

| All positive numbers are replaced with their negative equivalent, and all negative numbers are replaced with their positive equivalent |

| 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 |

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.

If a value in *colName* is non-numeric or blank it is left intact

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.

Additionally:

Any numerical value in

*colName*which starts with a`+`

,`.`

or decimal character is considered positiveAny numerical value in

*colName*which starts with a`-`

character is considered negativeWhen using

`standard`

the resulting conventional number will be accurate up to 14 decimal places

The `normalise`

statement ignores the option overwrite setting, as its sole purpose is to modify existing values.

import "system/extracted/csp_usage.csv" source test alias dataâ€‹# Invert all numerical values in column 'quantity'normalise column quantity as invert