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Set Transform

Nota

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.

Replaces values in the specified column or columns with the specified value, which can be a literal or an expression. Expressions can use conditional functions to filter the set of rows.

The set transform is used to replace entire cell values. For replacement of partial cell values using literals or patterns, use the replace transform. See Replace Transform.

Basic Usage

Literal example:

set col: Country value: 'USA'

Output: Sets the values of all rows in the Country column to USA.

Multi-column Literal example:

set col: SSN,Phone value: '##REDACTED###'

Output: Sets the values of all rows in the SSN and Phone columns to ##REDACTED##.

Expression example:

set col: isAmerica value: IF(Country == 'USA', true', 'false')

Output: If the value in the Country column is USA, then the value in isAmerica is set to true.

Placeholder example:

You can substitute a placeholder value for the column name, which is useful if you are applying the same function across multiple columns. For example:

set col:score1,score2 value:IF ($col == 0, AVERAGE($col), $col)

Output: In the above transform, the values in score1 and score2 are set to the average of the column value when the value in the column is 0. Note that the computation of average is applied across all rows in the column, instead of just the filtered rows.

Window function example:

You can use window functions in your set transforms:

set col: avgSales value: ROLLINGAVERAGE(POS_Sales, 7, 0) group: saleDate order: saleDate

Output: Calculate the value in the column of avgSales to be the rolling average of the POS_Sales values for the preceding seven days, grouped and ordered by the saleDate column. For more information, see Window Functions.

Syntax and Parameters

set col:col1,[col2] value:(expression) [group: group_col]

Token

Required?

Data Type

Description

set

Y

transform

Name of the transform

col1

Y

string

Column name

col2

N

string

Column name

value

Y

string

Expression that generates the value to store in the column

group

N

string

If you are using aggregate or window functions, you can specify agroupexpression to identify the subset of records to apply thevalueexpression.

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

col1, col2

Identifies the column and optional additional columns to which to apply the transform.

set col: MyCol value: 'myNewString'

Output: Sets value in MyCol column to myNewString.

Usage Notes:

Required?

Data Type

Yes

String (column name)

value

Identifies the expression that is applied by the transform. The value parameter can be one of the following types:

  • test predicates that evaluate to Boolean values (value: myAge == '30' yields a true or false value), or

  • computational expressions ( value: abs(pow(myCol,3)) ).

The expected type of value expression is determined by the transform type. Each type of expression can contain combinations of the following:

  • literal values: value: 'Hello, world'

  • column references: value: amountOwed * 10

  • functions: value: left(myString, 4)

  • combinations: value: abs(pow(myCol,3))

The types of any generated values are re-inferred by the platform.

Usage Notes:

Required?

Data Type

Yes

String (literal, column name, or expression)

group

Identifies the column by which the dataset is grouped for purposes of applying the transform.

Nota

Transforms that use the group parameter can result in non-deterministic re-ordering in the data grid. However, you should apply the group parameter, particularly on larger datasets, or your job may run out of memory and fail. To enforce row ordering, you can use the sort transform. For more information, see Sort Transform.

If the value parameter contains aggregate or window functions, you can apply the group parameter to specify subsets of records across which the value computation is applied.

You can specify one or more columns by which to group using comma-separated column references.

Usage Notes:

Required?

Data Type

No

String (column name)

Examples

Suggerimento

For additional examples, see Common Tasks.

Example - Clean up marketing contact data with replace, set, and extract

This example illustrates the different uses of the replacement transformations to replace or extract cell data.

Source:

The following dataset contains contact information that has been gathered by your marketing platform from actions taken by visitors on your website. You must clean up this data and prepare it for use in an analytics platform.

LeadId

LastName

FirstName

Title

Phone

Request

LE160301001

Jones

Charles

Chief Technical Officer

415-555-1212

reg

LE160301002

Lyons

Edward

415-012-3456

download whitepaper

LE160301003

Martin

Mary

CEO

510-555-5555

delete account

LE160301004

Smith

Talia

Engineer

510-123-4567

free trial

Transformation:

Title column: For example, you first notice that some data is missing. Your analytics platform recognizes the string value, "#MISSING#" as an indicator of a missing value. So, you click the missing values bar in the Title column. Then, you select the Replace suggestion card. Note that the default replacement is a null value, so you click Edit and update it:

Transformation Name

Edit column with formula

Parameter: Columns

Title

Parameter: Formula

if(ismissing([Title]),'#MISSING#',Title)

Request column: In the Request column, you notice that the reg entry should be cleaned up. Add the following transformation, which replaces that value:

Transformation Name

Replace text or pattern

Parameter: Column

Request

Parameter: Find

`{start}reg{end}`

Parameter: Replace with

Registration

The above transformation uses a Wrangle as the expression of the on: parameter. This expression indicates to match from the start of the cell value, the string literal reg, and then the end of the cell value, which matches on complete cell values of reg only.

This transformation works great on the sample, but what happens if the value is Reg with a capital R? That value might not be replaced. To improve the transformation, you can modify the transformation with the following Wrangle in the on parameter, which captures differences in capitalization:

Transformation Name

Replace text or pattern

Parameter: Column

Request

Parameter: Find

`{start}{[R|r]}eg{end}`

Parameter: Replace with

'Registration'

Add the above transformation to your recipe. Then, it occurs to you that all of the values in the Request column should be capitalized in title or proper case:

Transformation Name

Edit column with formula

Parameter: Columns

Request

Parameter: Formula

proper(Request)

Now, all values are capitalized as titles.

Phone column: You might have noticed some issues with the values in the Phone column. In the United States, the prefix 555 is only used for gathering information; these are invalid phone numbers.

In the data grid, you select the first instance of 555 in the column. However, it selects all instances of that pattern, including ones that you don't want to modify. In this case, continue your selection by selecting the similar instance of 555 in the other row. In the suggestion cards, you click the Replace Text or Pattern transformation.

Notice, however, that the default Replace Text or Pattern transformation has also highlighted the second 555 pattern in one instance, which could be a problem in other phone numbers not displayed in the sample. You must modify the selection pattern for this transformation. In the on: parameter below, the Wrangle has been modified to match only the instances of 555 that appear in the second segment in the phone number format:

Transformation Name

Replace text or pattern

Parameter: Column

Phone

Parameter: Find

`{start}%{3}-555-%*{end}`

Parameter: Replace with

'#INVALID#'

Parameter: Match all occurrences

true

Note the wildcard construct has been added (%*). While it might be possible to add a pattern that matches on the last four characters exactly (%{4}), that matching pattern would not capture the possibility of a phone number having an extension at the end of it. The above expression does.

Nota

The above transformation creates values that are mismatched with the Phone Number data type. In this example, however, these mismatches are understood to be for the benefit of the system consuming your Alteryx output.

LeadId column: You might have noticed that the lead identifier column (LeadId) contains some embedded information: a date value and an identifier for the instance within the day. The following steps can be used to break out this information. The first one creates a separate working column with this information, which allows us to preserve the original, unmodified column:

Transformation Name

New formula

Parameter: Formula type

Single row formula

Parameter: Formula

LeadId

Parameter: New column name

'LeadIdworking'

You can now work off of this column to create your new ones. First, you can use the following replace transformation to remove the leading two characters, which are not required for the new columns:

Transformation Name

Replace text or pattern

Parameter: Column

LeadIdworking

Parameter: Find

'LE'

Parameter: Replace with

''

Notice that the date information is now neatly contained in the first characters of the working column. Use the following to extract these values to a new column:

Transformation Name

Extract text or pattern

Parameter: Column to extract from

LeadIdworking

Parameter: Option

Custom text or pattern

Parameter: Text to extract

`{start}%{6}`

The new LeadIdworking2 column now contains only the date information. Cleaning up this column requires reformatting the data, retyping it as a Datetime type, and then applying the dateformat function to format it to your satisfaction. These steps are left as a separate exercise.

For now, let's just rename the column:

Transformation Name

Rename columns

Parameter: Option

Manual rename

Parameter: Column

LeadIdworking1

Parameter: New column name

'LeadIdDate'

In the first working column, you can now remove the date information using the following:

Transformation Name

Replace text or pattern

Parameter: Column

LeadIdworking

Parameter: Find

`{start}%{6}`

Parameter: Replace with

''

You can rename this column to indicate it is a daily identifier:

Transformation Name

Rename columns

Parameter: Option

Manual rename

Parameter: Column

LeadIdworking

Parameter: New column name

'LeadIdDaily'

Results:

LeadId

LeadIdDaily

LeadIdDate

LastName

FirstName

Title

Phone

Request

LE160301001

001

160301

Jones

Charles

Chief Technical Officer

#INVALID#

Registration

LE160301002

002

160301

Lyons

Edward

#MISSING#

415-012-3456

Download Whitepaper

LE160301003

003

160301

Martin

Mary

CEO

#INVALID#

Delete Account

LE160301004

004

160301

Smith

Talia

Engineer

510-123-4567

Free Trial

Example - Using $col placeholder

This example illustrates how to use the conditional calculation functions.

Functions:

Item

Description

AVERAGEIF Function

Generates the average value of rows in each group that meet a specific condition. Generated value is of Decimal type.

MINIF Function

Generates the minimum value of rows in each group that meet a specific condition. Inputs can be Integer, Decimal, or Datetime.

MAXIF Function

Generates the maximum value of rows in each group that meet a specific condition. Inputs can be Integer, Decimal, or Datetime.

VARIF Function

Generates the variance of values by group in a column that meet a specific condition.

STDEVIF Function

Generates the standard deviation of values by group in a column that meet a specific condition.

Source:

Here is some example weather data:

date

city

rain

temp

wind

1/23/17

Valleyville

0.00

12.8

6.7

1/23/17

Center Town

0.31

9.4

5.3

1/23/17

Magic Mountain

0.00

0.0

7.3

1/24/17

Valleyville

0.25

17.2

3.3

1/24/17

Center Town

0.54

1.1

7.6

1/24/17

Magic Mountain

0.32

5.0

8.8

1/25/17

Valleyville

0.02

3.3

6.8

1/25/17

Center Town

0.83

3.3

5.1

1/25/17

Magic Mountain

0.59

-1.7

6.4

1/26/17

Valleyville

1.08

15.0

4.2

1/26/17

Center Town

0.96

6.1

7.6

1/26/17

Magic Mountain

0.77

-3.9

3.0

1/27/17

Valleyville

1.00

7.2

2.8

1/27/17

Center Town

1.32

20.0

0.2

1/27/17

Magic Mountain

0.77

5.6

5.2

1/28/17

Valleyville

0.12

-6.1

5.1

1/28/17

Center Town

0.14

5.0

4.9

1/28/17

Magic Mountain

1.50

1.1

0.4

1/29/17

Valleyville

0.36

13.3

7.3

1/29/17

Center Town

0.75

6.1

9.0

1/29/17

Magic Mountain

0.60

3.3

6.0

Transformation:

The following computes average temperature for rainy days by city:

Transformation Name

New formula

Parameter: Formula type

Single row formula

Parameter: Formula

AVERAGEIF(temp, rain > 0)

Parameter: Group rows by

city

Parameter: New column name

'avgTempWRain'

The following computes maximum wind for sub-zero days by city:

Transformation Name

New formula

Parameter: Formula type

Single row formula

Parameter: Formula

MAXIF(wind,temp < 0)

Parameter: Group rows by

city

Parameter: New column name

'maxWindSubZero'

This step calculates the minimum temp when the wind is less than 5 mph by city:

Transformation Name

New formula

Parameter: Formula type

Single row formula

Parameter: Formula

MINIF(temp,wind<5)

Parameter: Group rows by

city

Parameter: New column name

'minTempWind5'

This step computes the variance in temperature for rainy days by city:

Transformation Name

New formula

Parameter: Formula type

Single row formula

Parameter: Formula

VARIF(temp,rain >0)

Parameter: Group rows by

city

Parameter: New column name

'varTempWRain'

The following computes the standard deviation in rainfall for Center Town:

Transformation Name

New formula

Parameter: Formula type

Single row formula

Parameter: Formula

STDEVIF(rain,city=='Center Town')

Parameter: Group rows by

city

Parameter: New column name

'stDevRainCT'

You can use the following transforms to format the generated output. Note the $col placeholder value for the multi-column transforms:

Transformation Name

Edit column with formula

Parameter: Columns

stDevRainCenterTown,maxWindSubZero

Parameter: Formula

numformat($col,'##.##')

Since the following rely on data that has only one significant digit, you should format them differently:

Transformation Name

Edit column with formula

Parameter: Columns

varTempWRain,avgTempWRain,minTempWind5

Parameter: Formula

numformat($col,'##.#')

Results:

date

city

rain

temp

wind

avgTempWRain

maxWindSubZero

minTempWind5

varTempWRain

stDevRainCT

1/23/17

Valleyville

0.00

12.8

6.7

8.3

5.1

7.2

63.8

0.37

1/23/17

Center Town

0.31

9.4

5.3

7.3

5

32.6

0.37

1/23/17

Magic Mountain

0.00

0.0

7.3

1.6

6.43

-3.9

12

0.37

1/24/17

Valleyville

0.25

17.2

3.3

8.3

5.1

7.2

63.8

0.37

1/24/17

Center Town

0.54

1.1

7.6

7.3

5

32.6

0.37

1/24/17

Magic Mountain

0.32

5.0

8.8

1.6

6.43

-3.9

12

0.37

1/25/17

Valleyville

0.02

3.3

6.8

8.3

5.1

7.2

63.8

0.37

1/25/17

Center Town

0.83

3.3

5.1

7.3

5

32.6

0.37

1/25/17

Magic Mountain

0.59

-1.7

6.4

1.6

6.43

-3.9

12

0.37

1/26/17

Valleyville

1.08

15.0

4.2

8.3

5.1

7.2

63.8

0.37

1/26/17

Center Town

0.96

6.1

7.6

7.3

5

32.6

0.37

1/26/17

Magic Mountain

0.77

-3.9

3.0

1.6

6.43

-3.9

12

0.37

1/27/17

Valleyville

1.00

7.2

2.8

8.3

5.1

7.2

63.8

0.37

1/27/17

Center Town

1.32

20.0

0.2

7.3

5

32.6

0.37

1/27/17

Magic Mountain

0.77

5.6

5.2

1.6

6.43

-3.9

12

0.37

1/28/17

Valleyville

0.12

-6.1

5.1

8.3

5.1

7.2

63.8

0.37

1/28/17

Center Town

0.14

5.0

4.9

7.3

5

32.6

0.37

1/28/17

Magic Mountain

1.50

1.1

0.4

1.6

6.43

-3.9

12

0.37

1/29/17

Valleyville

0.36

13.3

7.3

8.3

5.1

7.2

63.8

0.37

1/29/17

Center Town

0.75

6.1

9.0

7.3

5

32.6

0.37

1/29/17

Magic Mountain

0.60

3.3

6.0

1.6

6.43

-3.9

12

0.37