Split Transform

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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.

Splits the specified column into separate columns of data based on the delimiters in the transform. Delimiters can be specified in a number of literal or pattern-based methods. Whitespace delimiters are supported.

This transform might be automatically applied as one of the first steps of your recipe.

Tip: When this transform appears in a suggestion card, the maximum number of suggested columns to split is 250, which may prevent the browser from crashing. If your dataset requires additional column splits, you can edit the transformation and increase the maximum number of splits. Avoid creating datasets that are wider than 1000 columns.

When the split transform is applied, the source column is dropped.

Before applying this transform, you can create a copy of the source column using the derive transform. See Derive Transform.
To retain the source column, you can use the extract transform and pattern-based matching. See Extract Transform.

Basic Usage

split col: MyValues on: ',' limit: 3

Output: Splits the source MyValues column into four separate columns. Values in the columns are determined based on the comma (,) delimiter. If a row only has two commas in it, then the final generated column is null.

Syntax and Parameters

split col:column_ref [quote:'quoted_string'] [ignoreCase:true|false] [limit:int_num] [after:start_point | from: start_point] [before:end_point | to:end_point] [on:'exact_match'] [at:(start_index,end_index)] [delimiters:'string1','string2', 'string3'] [positions: int1,int2,int3] [every:int_num]

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

The split transform supports the following general methods for specifying the delimiters by which to split the column. Depending on your use of the transform, different sets of parameters apply.

Delimiter Methods Description Operative Parameters

single-pattern delimiters

Column is split based on one of the following:

1) patterns used to describe the beginning and ending of the field delimiter(s),

2) single delimiter, which may be repeated,

3) index values of the start and end points of the delimiter

quote

At least one of the following parameters must be specified:

after , at , from , before , on , to

multi-pattern delimiters

Column is split based one of the following literal methods:

1) explicit sequence of delimiters,

2) explicit list of character index positions

3) every N characters

At least one of the following parameters must be specified:

delimiters, positions , every

Shared parameters:

The following parameters are shared between the operating modes:

Token	Required?	Data Type	Description
split	Y	transform	Name of the transform
col	Y	string	Source column name
ignoreCase	N	boolean	If `true`, matching is case-insensitive.
limit	N	integer (positive)	Specifies the maximum of columns to split from the source column

Single-pattern delimiter parameters:

Tip: For this method of matching, at least one of the following parameters must be used: at, before, from, on, or to.

Token	Required?	Data Type	Description
after	N	string	String literal or pattern that precedes the pattern to match
at	N	Array	Two-integer array identifying the character indexes of start and end characters to match
before	N	string	String literal or pattern that appears after the pattern to match
from	N	string	String literal or pattern that identifies the start of the pattern to match
on	N	string	String literal or pattern that identifies the pattern to match.
to	N	string	String literal or pattern that identifies the end of the pattern to match
quote	N	string	Specifies a quoted object that is omitted from pattern matching

Multi-pattern delimiter parameters:

Tip: Use one of the following parameters for this method of matching. Do not use combinations of them.

Token	Required?	Data Type	Description
delimiters	N	array	Array of strings that list the explicit field delimiters in the order to apply them to the column.
positions	N	array	Array of integers that identify the zero-based character index values where to split the column.
every	N	integer	String literal or pattern that appears after the pattern to match

col

Identifies the column to which to apply the transform. You can specify only one column.

split col: MyCol on: 'MyString'

Output: Splits the MyCol column into two separate columns whose values are to the left and right of the MyString value in each cell.

If a delimiter value is not detected, the cell value appears in the first of the new columns.
When the limit parameter is not specified, the default value of 1 is applied.

Usage Notes:

Required?	Data Type
Yes	String (column name)

ignoreCase

Indicates whether the match should ignore case or not.

Set to true to ignore case matching.
(Default) Set to false to perform case-sensitive matching.

split col: MyCol on: 'My String' ignoreCase: true

Output: Splits the MyCol column on case-insensitive versions of the on parameter value, if they appear in cell values: My String, my string, My string, etc.

Usage Notes:

Required?	Data Type
No	Boolean

limit

The limit parameter defines the maximum number of times that a pattern can be matched within a column.

NOTE: The limit parameter cannot be used with the following parameters: at, positions, or delimiters.

A set of new columns is generated, as defined by the limit parameter. Each matched instance populates a separate column, until there are no more matches or all of the limit-generated new columns are filled.

split col: MyCol on: 'z' limit: 3

Output: Splits the MyCol column on each instance of the letter z, generating 4 new columns. If there are fewer than 3 instances of z in a cell, the corresponding columns after the split are blank.

NOTE: Avoid creating datasets that are wider than 2500 columns. Performance can degrade significantly on very wide datasets.

Usage Notes:

Required?	Data Type
No	Integer (positive)

Defines the number of columns that can be created by the split transform.
If not specified, exactly one column is created. See Pattern Clause Position Matching.

after

A pattern identifier that precedes the value or pattern to match. Define the after parameter value using string literals, regular expressions, or Patterns .

If this parameter is the only pattern describer:
- The column is split into two. The first column contains the part of the source column before the after matching value. The second column is blank.
- If the value appears more than once, no additional splitting is made, since there is no other pattern parameter.
  
  NOTE: For after, before, from, and to, matching occurs only one time at most. Additional instances of the parameter's value in the cell do not cause another column split. For more predictable results, you should specify another pattern parameter.
If the after value does not appear in the column, the original column value is written to the first split column.
This parameter is typically used with another to describe a field delimiting pattern. See below.

split col: MyCol after: '\' before:'|'

Output: Splits values in MyCol based on value between the two characters. The first column contains the part of the MyCol that appears before the backslash (\), and the second column contains the part of MyCol that appears after the pipe character (|). The content between the delimiting characters is dropped.

Usage Notes:

Required?	Data Type
No	String or pattern

The after and from parameters are very similar. from includes the matching value as part of the delimiter string.
after can be used with either to or before. See Pattern Clause Position Matching.

at

Identifies the start and end point of the pattern to interest.

Parameter inputs are in the form of x,y where x and y are positive integers indicating the starting character and ending character, respectively, of the pattern of interest.

x must be less than y.
If y is greater than the length of the value, the pattern is defined to the end of the value, and a match is made.

split col: MyCol at: 2,6

Output: Splits the MyCol column on the value that begins at the second character in the column and extends to the sixth character of the column. Contents before the value are in the first column, and contents after the value are in the second column.

Usage Notes:

Required?	Data Type
No	Array of two Integers ( `X,Y` )

The at parameter cannot be combined with any of the following: on, after, before, from, to, and quote. See Pattern Clause Position Matching.

before

A pattern identifier that occurs after the value or pattern to match. Define the pattern using string literals, regular expressions, or Patterns .

If this parameter is the only pattern describer:
- The column is split into two. The first column is blank. The second column contains the part of the source column after the before matching value.
- If the value appears more than once, no additional splitting is made, since there is no other pattern parameter.
  
  NOTE: For after, before, from, and to, matching occurs only one time at most. Additional instances of the parameter's value in the cell do not cause another column split. For more predictable results, you should specify another pattern parameter.
If the before value does not appear in the column, the original column value is written to the first split column.
This parameter is typically used with another to describe a field delimiting pattern. See below.

split col: MyCol before: '/' from:'Go:'

Output: Splits contents of MyCol into two columns. The first column contains the values that appear before the Go: string, and the second column contains the values after the backslash.

Usage Notes:

Required?	Data Type
No	String or pattern

The before and to parameters are very similar. to includes the matching value as part of the delimiter string.
before can be used with either from or after. See Pattern Clause Position Matching .

from

Identifies the pattern that marks the beginning of the value to match. It can be a string literal, Pattern , or regular expression. The from value is included in the match.

If this parameter is the only pattern describer:
- The column is split into two. The first column contains the part of the source column before the from matching value. The second column is blank.
- If the value appears more than once, no additional splitting is made, since there is no other pattern parameter.
  
  NOTE: For after, before, from, and to, matching occurs only one time at most. Additional instances of the parameter's value in the cell do not cause another column split. For more predictable results, you should specify another pattern parameter.
If the from value does not appear in the column, the output value is original column value.
This parameter is typically used with another to describe a field delimiting pattern. See below.

split col: MyCol from: 'go:' to:'stop:'

Output: Splits contents of MyCol from go:, including go: to stop:, including stop:. Contents before the string appear in the first column, contents after the string appear in the second one.

Usage Notes:

Required?	Data Type
No	String or pattern

The after and from parameters are very similar. from includes the matching value as part of the delimiter string.
from can be used with either to or before. See Pattern Clause Position Matching .

on

Identifies the pattern to match, which can be a string literal, Pattern , or regular expression.

If the value does not appear in the source column, the original value is written to the first column of the split columns.

split col: MyCol on: `###ERROR`

Output: Column into two columns. The first column contains values in the column appearing before ###ERROR, and the second column contains the values appearing after this string.

Tip: You can insert the Unicode equivalent character for this parameter value using a regular expression of the form /\uHHHH/. For example, /\u0013/ represents Unicode character 0013 (carriage return). For more information, see Supported Special Regular Expression Characters.

Usage Notes:

Required?	Data Type
No	String or pattern

to

Identifies the pattern that marks the ending of the value to match. Pattern can be a string literal, Patterns , or regular expression. The to value is included in the match.

If this parameter is the only pattern describer:
- The column is split into two. The first column is blank. The second column contains the part of the source column after the to matching value.
- If the value appears more than once, no additional splitting is made, since there is no other pattern parameter.
  
  NOTE: For after, before, from, and to, matching occurs only one time at most. Additional instances of the parameter's value in the cell do not cause another column split. For more predictable results, you should specify another pattern parameter.
If the to value does not appear in the column, the original column value is written to the first split column.
This parameter is typically used with another to describe a field delimiting pattern. See below.

split col:MyCol from:'note:' to: ` `

Output: Splits MyCol column all contents that appear before note: in the first column and all contents that appear after the first space after note: in the second column.

Usage Notes:

Required?	Data Type
No	String or pattern

The before and to parameters are very similar. to includes the matching value as part of the delimiter string.
to can be used with either from or after. See Pattern Clause Position Matching.

quote

Can be used to specify a string as a single quoted object. This parameter value can be one or more characters.

split col: MyLog on: `|` limit:10 quote: '"'

Output: Splits the MyLog column, on the pipe character (|), while ignoring any pipe characters that are found between double-quote characters in the column. Based on the value in the limit parameter, the transform is limited to creating a maximum of 10 splits.

Usage Notes:

Required?	Data Type
No	String

Parameter value is the quoted object.
The quote value can appear anywhere in the column value. It is not limited by the constraints of any other parameters.

delimiters

The delimiters parameter specifies a comma-separated list of string literals or patterns to identify the delimiters to use to split the data. Values can be string literals, regular expressions, or Patterns .

The sequence of values defines the order in which the delimiters are applied.
Values do not need to be the same.

split col:myCol delimiters:'|',' ','|'

Output: Splits the myCol column into four separate columns, as indicated by the sequence of delimiters.

Usage Notes:

NOTE: Do not use the limit or quote parameters with the delimiters parameter.

Required?	Data Type
No	Array of Strings (literal, regular expression, Pattern )

positions

The positions parameter specifies a comma-separated list of integers that identify zero-based character index values at which to split the column. Values must be Integers.

split col:myCol positions:20,55,80

Tip: Numeric values for positions do not need to be in sorted order.

Output: Splits the myCol column into four separate columns, where:

column1 = characters 0-20 from the source column,
column2 = characters 21-55
column3 = characters 56-80
column4 = characters 80 to the end of the cell value

Usage Notes:

NOTE: Do not use the limit or quote parameters with the positions parameter.

Required?	Data Type
No	Array of Integers (literal, regular expression, Pattern )

every

The every parameter can be used to specify fixed-width splitting of the source column. This Integer value defines the number of characters in each column of the split output.

If needed, you can use the every parameter with the limit parameter to define the maximum number of output columns:

split col:myCol every:20 limit:5

Output: Splits the myCol column every 20 characters, with a limit of five splits. The sixth column contains all characters after the 100th character in the cell value.

Usage Notes:

Required?	Data Type
No	Integer

Pattern Groups

When you build or edit a split transform step in the Transform Builder, you can select one of the following pattern groups to apply to your transform. A pattern group is a set of related patterns that define a method of matching in a cell's data. Some pattern groups apply to multiple transforms, and some apply to the split transform only.

Examples

Tip: For additional examples, see Common Tasks.

Example - Split with single pattern delimiters

Source:

ColA	ColB	ColC
This my String That	abXcdXefXgh	01AA001
my string This That	ijXklXmnXop	02BB002
This That My String	qrXstXuvXwy	03CC003

Transformation:

ColA: You can use the following transformation to split on the variations of My String: In this case, the ignoreCase parameter ensures that all variations on capitalization are matched:

Transformation Name	`Split column`
Parameter: Column	`ColA`
Parameter: Option	`On pattern`
Parameter: Match pattern	`'My String'`
Parameter: Ignore case	`true`

ColB: For this column, the letter x is the split marker, and the data is consistently formatted with three instances per row:

Transformation Name	`Split column`
Parameter: Column	`ColB`
Parameter: Option	`On pattern`
Parameter: Match pattern	`'X'`
Parameter: Number of matches	`3`

ColC: In this column, the double-letter marker varies between the rows. However, it is consistently in the same location in each row:

Transformation Name	`Split column`
Parameter: Column	`ColC`
Parameter: Option	`Sequence of positions`
Parameter: Positions	`2,4`

Results:

When the above transforms are added, the source columns are dropped, leaving the following columns:

ColA1	ColA2	ColB1	ColB2	ColB3	ColB4	ColC1	ColC2
This	That	ab	cd	ef	gh	01	001
	This That	ij	kl	mn	op	02	002
This That		qr	st	uv	wy	03	003

Example - Split with quoted values

This example demonstrates how to use quote parameter for more sophisticated splitting of columns of data using the split transform.

Source:

In this example, the following CSV data, which contains contact information, is imported into the application:

LastName,FirstName,Role,Company,Address,Status
Wagner,Melody,VP of Engineering,Example.com,"123 Main Street, Oakland, CA 94601",Prospect
Gruber,Hans,"Director, IT",Example.com,"456 Broadway, Burlingame, CA, 94401",Customer
Franks,Mandy,"Sr. Manager, Analytics",Tricorp,"789 Market Street, San Francisco, CA, 94105",Customer

Transformationn:

When this data is pulled into the application, some initial parsing is performed for you:

column2	column3	column4	column5	column6	column7
LastName	FirstName	Role	Company	Address	Status
Wagner	Melody	VP of Engineering	Example.com	"123 Main Street, Oakland, CA 94601"	Prospect
Gruber	Hans	"Director, IT"	Example.com	"456 Broadway, Burlingame, CA, 94401"	Customer
Franks	Mandy	"Sr. Manager, Analytics"	Tricorp	"789 Market Street, San Francisco, CA, 94105"	Customer

When you open the Recipe Panel, you should see the following transforms:

Transformation Name	`Split into rows`
Parameter: Column	`column1`
Parameter: Split on	`\n`
Parameter: Ignore matches between	`\"`
Parameter: Quote escape character	`\"`

Transformation Name	`Split column`
Parameter: Column	`column1`
Parameter: Option	`On pattern`
Parameter: Match pattern	`','`
Parameter: Number of Matches	`5`
Parameter: Ignore matches between	`\"`

The first transform splits the raw source data into separate rows in the carriage return character (\r), ignoring all values between the double-quote characters. Note that this value must be escaped. The double-quote character does not require escaping. While there are no carriage returns within the actual data, the application recognizes that these double-quotes are identifying single values and adds the quote value.

The second transform splits each row of data into separate columns. Since it is comma-separated data, the application recognizes that this value is the column delimiter, so the on value is set to the comma character (,). In this case, the quoting is necessary, as there are commas in the values in column4 and column6, which are easy to clean up.

To finish clean up of the dataset, you can promote the first row to be your column headers:

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`

You can remove the quotes now. Note that the following applies to two columns:

Transformation Name	`Replace text or patterns`
Parameter: Column	`Address,Role`
Parameter: Find	`'\"'`
Parameter: Replace	`''`
Parameter: Match all occurrences	`true`

Now, you can split up the Address column. You can highlight one of the commas and the space after it in the column, but make sure that your final statement looks like the following:

Transformation Name	`Split column`
Parameter: Column	`column1`
Parameter: Option	`On pattern`
Parameter: Match pattern	`','`
Parameter: Number of Matches	`2`

Notice that there is some dirtiness to the resulting Address3 column:

Address3
CA 94601
CA, 94401
CA, 94105

Use the following to remove the comma. In this case, it's important to leave the space between the two values in the column, so the on value should only be a comma. Below, the width value is two single quotes:

Transformation Name	`Replace text or patterns`
Parameter: Column	`Address3`
Parameter: Find	`','`
Parameter: Replace	`''`
Parameter: Match all occurrences	`true`

You can now split the Address3 column on the space delimiter:

Transformation Name	`Split by delimiter`
Parameter: Column	`Address3`
Parameter: Option	`by delimiter`
Parameter: Delimiter	`' '`
Parameter: Number of columns to create	`2`

Results:

After you rename the columns, you should see the following:

LastName	FirstName	Role	Company	Address	City	State	Zipcode	Status
Wagner	Melody	VP of Engineering	Example.com	123 Main Street	Oakland	CA	94601	Prospect
Gruber	Hans	Director, IT	Example.com	456 Broadway	Burlingame	CA	94401	Customer
Franks	Mandy	Sr. Manager, Analytics	Tricorp	789 Market Street	San Francisco	CA	94105	Customer

See Also for EXAMPLE - Quote Parameter:

Example - Splitting with different delimiter types

This example shows how you can split data from a single column into multiple columns using delimiters.

single-pattern delimiter: One pattern is applied one or more times to the source column to define the delimiters for the output columns
multi-pattern delimiter: Multiple patterns, in the form of explicit strings, character index positions, or fixed-width fields, are used to split the column.

Source:

In this example, your CSV dataset contains status messages from a set of servers. In this case, the data about the server and the timestamp is contained in a single value within the CSV.

Server|Date Time,Status
admin.examplecom|2016-03-05 07:04:00,down
webapp.examplecom|2016-03-05 07:04:00,ok
admin.examplecom|2016-03-05 07:04:30,rebooting
webapp.examplecom|2016-03-05 07:04:00,ok
admin.examplecom|2016-03-05 07:05:00,ok
webapp.examplecom|2016-03-05 07:05:00,ok

Transformation:

When the data is first loaded into the Transformer page, the CSV data is split using the following two transformations:

Transformation Name	`Split into rows`
Parameter: Column	`column1`
Parameter: Split on	`\n`

Transformation Name	`Split column`
Parameter: Column	`column1`
Parameter: Option	`On pattern`
Parameter: Match pattern	`','`
Parameter: Ignore matches between	`\"`

You might need to add a header as the first step:

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`

At this point, your data should look like the following:

Server_Date_Time	Status
admin.example.com\|2016-03-05 07:04:00	down
webapp.example.com\|2016-03-05 07:04:00	ok
admin.example.com\|2016-03-05 07:04:30	rebooting
webapp.example.com\|2016-03-05 07:04:30	ok
admin.example.com\|2016-03-05 07:05:00	ok
webapp.example.com\|2016-03-05 07:05:00	ok

The first column contains three distinct sets of data: the server name, the date, and the time. Note that the delimiters between these fields are different, so you should use a multi-pattern delimiter to break them apart:

Transformation Name	`Split column`
Parameter: Column	`Server\|Date Time`
Parameter: Option	`Sequence of patterns`
Parameter: Pattern1	`','`
Parameter: Pattern2	`' '`

When the above is added, you should see three separate columns with the individual fields of information. Note that the source column has been automatically dropped.

Now, you decide that it would be useful to break apart the date information column into separate columns for year, month, and day. Since the column delimiter of this field is consistently a dash (-), you can use a single-pattern delimiter with the following transformation:

Transformation Name	`Split by delimiter`
Parameter: Column	`Server\|Date Time2`
Parameter: Option	`By delimiter`
Parameter: Delimiter	`'-'`
Parameter: Number of columns to create	`2`

Results:

After you rename the generated columns, your dataset should look like the following. Note that the source timestamp column has been automatically dropped.

server	year	month	day	time	Status
admin.example.com	2016	03	05	07:04:00	down
webapp.example.com	2016	03	05	07:04:00	ok
admin.example.com	2016	03	05	07:04:30	rebooting
webapp.example.com	2016	03	05	07:04:30	ok
admin.example.com	2016	03	05	07:05:00	ok
webapp.example.com	2016	03	05	07:05:00	ok

See Also for EXAMPLE - Splitting with Different Delimiter Types:

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Splitrows Transform
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Split Transform

See Also for Split Transform:

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EXAMPLE - Extractlist Transform
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EXAMPLE - Quote Parameter
Page:

EXAMPLE - Splitting with Different Delimiter Types
Page:

Split Column
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Initial Parsing Steps
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MATCHES Function
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Find Missing Data
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Analyze across Multiple Columns
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Compare Strings
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Reshaping Steps

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Split Transform

Basic Usage

Syntax and Parameters

col

ignoreCase

limit

after

at

before

from

on

to

quote

delimiters

positions

every

Pattern Groups

Examples

Example - Split with single pattern delimiters

Example - Split with quoted values

Example - Splitting with different delimiter types