normalise
Overview
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.
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.
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.
Example
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