NUMFORMAT Function

Formats a numeric set of values according to the specified number formatting. Source values can be a reference to a column containing Integer or Decimal values.
  • If the source value does not include a valid input for this function, a missing value is returned.
  • When a NUMFORMAT transform is applied, the column can be re-typed to a different data type. For example, if your format string (second parameter) is '#' (a single hash mark), then all values are rounded to the nearest integer, and the column is re-typed as Integer.

    Tip: In general, you should format your numeric data after you have completed your computations on it. In some cases, you might lose numeric precision in converting formats, or your data can be re-typed to a different data type (e.g. Decimal to Integer).

  • For more information on formatting date values, see DATEFORMAT Function.

Cloud Dataprep by TRIFACTA® supports a wide variety of number formats, following Java standards. For more information, please see Java's documentation.

Basic Usage

derive type:single value: NUMFORMAT(MyPrice, '$###,###.##') as: 'dateformat_MyPrice'

Output: Generates a column of values from the MyNum column converted to a price format.

Syntax

derive type:single value:NUMFORMAT(numeric_col, number_format_string)

ArgumentRequired?Data TypeDescription
numeric_colYstring, integer, or decimalName of Integer or Decimal column whose values are to be formatted
number_format_stringYstringLiteral value of the number formatting string to apply

For more information on syntax standards, see Language Documentation Syntax Notes.

numeric_col

Name of the column whose Integer or Decimal data is to be formatted.

  • Values with more than 20 digits after the decimal point are truncated by this function.

  • Missing values for this function in the source data result in missing values in the output.
  • Multiple columns and wildcards are not supported.

Usage Notes:

Required?Data TypeExample Value
YesString (column reference) or Integer or Decimal literalMyPrice

number_format_string

String value indicating the number format to apply to the input values.

NOTE: You cannot create number format strings in which a 0 value appears before a # value. The following example strings are not supported: #.#0, #.#0#, #.#00

Some key codes:

CodeDescriptionExample Format StringExample InputsExample Outputs
#Insert a digit if it is present in the data.

'###,###'

99

999

1000

10000

99

999

1,000

10,000

0Insert a digit even if it is not present in a data.'00.##'

20

7.1

20

07.1

$You can add constants values to the expression. Whitespace is respected.
For example, you can insert currency markers at the beginning of your expression.
'$ ##.##'20
2514.22
6.6666
$ 20
$ 2514.22
$ 6.67
%Percentage expressions can be at the back of the number formatting expression.'##.## %'20
2514.22
6.6666
20 %
2514.22 %
6.67%

Cloud Dataprep by TRIFACTA supports Java number formatting strings, with some exceptions.

Missing values for this function in the source data result in missing values in the output.

Usage Notes:

Required?Data TypeExample Value
YesString'#.#'

Examples

Example - formatting price and percentages

This example steps through how to manage number formatting for price and percentage data when you have to perform some computations on the data in the application.

Source:

In this case, you need to compute sub-total and totals columns.

OrderIdQtyUnitPriceDiscountTaxRate
10015$25.000%8.25%
100215$39.995%8.25%
10032$99.9915%8.25%
1004100$999.990%8.25%

When this data is first imported into the Transformer page, you might notice the following:

  • The data type for OrderId is an Integer, when it should be treated as String data.
  • The UnitPrice, Discount , and TaxRate columns are typed as String data because of the unit characters in the values.

NOTE: Where possible, remove currency and three-digit separators from your numeric data prior to import.

You can re-type the OrderId column to String without issue. If you retype the other three columns, all values are mismatched. You can use the following transforms to remove the currency and percentage notation. The first transform removes the trailing % sign from every value across all columns using a Cloud Dataprep pattern.

replace col: * on: `\%{end}` with: ''

You can use a similar one to remove the $ sign at the beginning of values:

replace col: * on: `{start}\$` with:''

When both are applied, you can see that the data types of each column is updated to a numeric type: Integer or Decimal. Now, you can perform the following computations:

derive type:single value:(Qty * UnitPrice) as:'SubTotal'

You can use the new SubTotal column as the basis for computing the DiscountedTotal column, which factors in discounts:

derive type:single value:(SubTotal - (SubTotal * (Discount / 100))) as:'DiscountedTotal'

The Total column applies the tax to the DiscountedTotal column:

derive type:single value: DiscountedTotal * (1 + (TaxRate / 100)) as: 'Total'

Because of the math operations that have been applied to the original data, your values might no longer look like dollar information. You can now apply price formatting to your columns. The following changes the number format for the SubTotal column:

set col:SubTotal value:NUMFORMAT(SubTotal, '#.00')

Note that the leading $ was not added back to the data, which changes the data type to String. You can apply this transform to the Price, DiscountedTotal, and Total columns.

NOTE: The data types for your columns should match the expected inputs for your downstream analytics system.

The Discount and TaxRate values should be converted to decimals. The following adjusts the Discount column:

set col:Discount value:(Discount / 100)

Results:

The output data should look like the following:

OrderIdQtyUnitPriceSubTotalDiscountDiscountedTotalTaxRateTotal
1001525.00125.000125.000.0825135.31
10021539.99599.850.05569.860.0825616.87
1003299.99199.980.15169.980.0825184.01
1004100999.9999999.00099999.000.0825108248.92

See Also

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