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NOTE: Transforms are a part of the underlying language, which is not directly accessible to users. This content is maintained for reference purposes only. For more information on the user-accessible equivalent to transforms, see Transformation Reference.

Sets the data type of the specified column or columns. The column data is validated against the new data type, which can change the results of column profiling.

Type is specified as a string literal or comma-separated set of literals. For more information on valid string literals, see Valid Data Type Strings .

Tips:

Tip: You can use the settype transform to override the data type inferred for a column. However, if a new transformation step is added, the column data type is re-inferred, which may override your specific typing. You should consider applying setttype transforms as late as possible in your recipes.

  • When a column is set to a data type, all values in the column are validated against the new type, which might change the number of mismatched values. Some cleanup might be required. Some operations might cause the data type to be re-validated automatically.
  • It might be easier to set type using the column's drop-down. Selections of data type from the column drop-down are turned into recipe steps using the settype transform.
  • If you encounter a significant number of mismatches after you change the data type, you might find it helpful to change or revert the type to String. All data can be interpreted as a String or a list of string values. The transforms and functions for manipulating String data might be easier to use to clean up mismatched data before changing the data type to the preferred one.
  • Row values that do not match the new data type might be turned to null values during job execution.

Basic Usage

Single-column example:

settype col: Score type: 'Integer'

Output: Changes the data type for the Score column to Integer.

Multi-column example:

settype col: Score,studentId type: 'Integer'

Output: Changes the data type for the Score and studentId columns to Integer.

Parameters

settype col:col1,col2 type:'string_literal'

TokenRequired?Data TypeDescription
settypeYtransformName of the transform
colYstringComma-separated list of columns to which to apply the specified type.
typeYstringString literal identifying the data type to apply to the column(s). See Valid Data Type Strings.

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

col

Identifies the column(s) to which to apply the transform. You can specify one or more columns.

Usage Notes:

Required?Data Type
YesComma-separated strings (column name or names)

type

Defines the data type that is to be applied to the transform. Type is defined as a String literal. For a list of valid strings, see Valid Data Type Strings.

settype col: zips type:'Zipcode'

Output: Changes the data type of the zips column to Zip Code data type. All values are validated as U.S. Zip code.

Usage Notes:

Required?Data Type
YesString value

Examples

Example - Simple settype with date values

Source:

Here is a list of activities listed by date. Note the variation in date values, including what is clearly an invalid date. Here is the source data:

myDate, myAction
4/4/2016,Woke up at 6:30
4-4-2016,Got ready
9-9-9999,Drove kids to school
4-4-2016, Commuted to work


Transformation:

When this data is imported into the Transformer page, there are couple of immediate issues: no column headings and blank rows at the bottom. These two transformations fix that:

Transformation Name Rename column with row(s)
Parameter: Option Use row(s) as column names
Parameter: Type Use a single row to name columns
Parameter: Row number 1

Transformation Name Filter rows
Parameter: Condition Custom formula
Parameter: Type of formula Custom single
Parameter: Condition ismissing([myDate])
Parameter: Action Delete matching rows

For the invalid date, you can infer from the rows around it that it should be from the same date. You can make the following change to fix it:

Transformation Name Replace text or pattern
Parameter: Column myDate
Parameter: Find `9-9-9999`
Parameter: Replace with '4-4-2016'
Parameter: Match all occurrences true

Now that the dates look fairly consistent, you can set the data type of the column to a matching Datetime format:

Transformation Name Change column data type
Parameter: Columns myDate
Parameter: New type Custom or Date/Time
Parameter: Specify type 'mm-dd-yy','mm*dd*yyyy'

Note the syntax above for specifying Datetime types. In addition to the Datetime keyword, you must specify the format type, followed by the variation of that format.

Tip: A set of supported formats and variations for Datetime are available through the column data type selector. When you select your desired Datetime format, the setttype transform is added to your recipe.

Results:

myDatemyAction
4/4/2016Woke up at 6:30
4-4-2016Got ready
4-4-2016Drove kids to school
4-4-2016Commuted to work

Example - Use merge and settype to clean up numeric data that should be treated as other data types

This example illustrates how to clean up data that has been interpreted as numeric in nature, when it is actually String or a structured string type, such as Gender. This example uses:

Source:

The following example contains customer ID and Zip code information in two columns. When this data is loaded into the Transformer page, it is initially interpreted as numeric, since it contains all numerals.

The four-digit ZipCode values should have five digits, with a 0 in front.

CustIdZipCode
40201231234
201212194105
321201294101
13012122020

Transformation:

CustId column: This column needs to be retyped as String values. You can set the column data type to String through the column drop-down, which is rendered as the following transformation:

Transformation Name Change column data type
Parameter: Columns CustId
Parameter: New type String

While the column is now of String type, future transformations might cause it to be re-inferred as Integer values. To protect against this possibility, you might want to add a marker at the front of the string. This marker should be removed prior to execution.

The basic method is to create a new column containing the customer ID marker (C) and then merge this column and the existing CustId column together. It's useful to add such an indicator to the front in case the customer identifier is a numeric value that could be confused with other numeric values. Also, this merge step forces the value to be interpreted as a String value, which is more appropriate for an identifier.

Transformation Name Merge columns
Parameter: Columns 'C',CustId

You can now delete the CustId columns and rename the new column as CustId.

ZipCode column: This column needs to be converted to valid Zip Code values. For ease of use, this column should be of type String:

Transformation Name Change column data type
Parameter: Columns ZipCode
Parameter: New type Zipcode

The transformation below changes the value in the ZipCode column if the length of the value is four in any row. The new value is the original value prepended with the numeral 0:

Transformation Name Edit column with formula
Parameter: Columns ZipCode
Parameter: Formula if(len($col) == 4, merge(['0',$col]), $col)

This column might now be re-typed as Zipcode type.

Results:

CustIdZipCode
C402012301234
C201212194105
C321201294101
C130121202020

Remember to remove the C marker from the CustId column. Select the C value in the CustId column and choose the replace transform. You might need to re-type the cleaned data as String data.

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