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Extractlist Transform

Anmerkung

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.

Extracts a set of values based on a specified pattern from a source column of any data type. The generated column contains an array of occurrences of the specified pattern.

While the new column contains array data, the data type of the new column is sometimes inferred as String.

Basic Usage

Your source column (myWidgetInventory) is formatted in the following manner:

{ "red":"100","white":"1300","blue":"315","purple":"55"}

The following step extracts the raw inventory contents of each color:

extractlist col: myWidgetInventory on:`{digit}+`

Output: The generated column contains data that looks like the following array:

["100","1300","315","55"]

Syntax and Parameters

extractlist: col:column_ref on:string_literal_pattern delimiter:string_literal_pattern [quote:'quoted_string'] [as:'new_column_name']

Token

Required?

Data Type

Description

extractlist

Y

transform

Name of the transform

col

Y

string

Source column name

on

Y

string

String literal or pattern that identifies the values to extract from the source column

delimiter

Y

string

String literal or pattern that identifies the separator between the values to extract

quote

N

string

Specifies a quoted object that is omitted from matching delimiters

as

N

string

Name of the newly generated column

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

col

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

Usage Notes:

Required?

Data Type

Yes

String (column name)

on

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

Tipp

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.

For the extractlist tranform, all instances that match this pattern in the source column are extracted into the array list in the new column. Each occurrence in the generated array corresponds to an individual instance in the source; the new column can contain duplicate values.

To create array elements based only on the delimiter parameter, set the following regular expression:

on:`/+/`

Usage Notes:

Required?

Data Type

Yes

String (literal, regular expression, orAlteryx pattern )

delimiter

Specifies the character or pattern that defines the end of a key-value pair. This value can be specified as a String literal, regular expression, or Wrangle.

In the following:

{ key1=value1,key2=value2 }

The delimiter is the comma ( ','). The final key-value pair does not need a delimiter.

For this transform, this parameter defines the pattern that separates the values that you want to extract into the array.

Tipp

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

Yes

String (literal, regular expression, orAlteryx pattern )

quote

extractlist col: MySourceValues on:`{alpha}+` delimiter:';' quote:'\"'

Output: Extracts from the MySourceValues column each instance of a string value that occurs before the delimiter. Values between double-quotes are considered string literals and are not processed according to the delimiters defined in the transform.

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

Usage Notes:

Required?

Data Type

No

String

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

as

Name of the new column that is being generated. If the as parameter is not specified, a default name is used.

Usage Notes:

Required?

Data Type

No

String (column name)

Examples

Tipp

For additional examples, see Common Tasks.

Extract hashtag values

In this example, you extract one or more values from a source column and assemble them in an Array column.

Suppose you need to extract the hashtags from customer tweets to another column. In such cases, you can use the {hashtag} Alteryx pattern to extract all hashtag values from a customer's tweets into a new column.

Source:

The following dataset contains customer tweets across different locations.

User Name

Location

Customer tweets

James

U.K

Excited to announce that we’ve transitioned Wrangler from a hybrid desktop application to a completely cloud-based service! #dataprep #businessintelligence #CommitToCleanData # London

Mark

Berlin

Learnt more about the importance of identifying issues in your data—early and often #CommitToCleanData #predictivetransformations #realbusinessintelligence

Catherine

Paris

Clean data is the foundation of your analysis. Learn more about what we consider the five tenets of sound #dataprep, starting with #1a prioritizing and setting targets. #startwiththeuser #realbusinessintelligence #Paris

Dave

New York

Learn how #NewYorklife

onboarded as part of their #bigdata #dataprep initiative to unlock hidden insights and make them accessible across departments.

Christy

San Francisco

How can you quickly determine the number of times a user ID appears in your data?#dataprep #pivot #aggregation#machinelearning initiatives #SFO

Transformation:

The following transformation extracts the hashtag messages from customer tweets.

Transformation Name

Extract matches into Array

Parameter: Column

customer_tweets

Parameter: Pattern matching elements in the list

`{hashtag}`

Parameter: New column name

Hashtag tweets

Then, the source column can be deleted.

Results:

User Name

Location

Hashtag tweets

James

U.K

["#dataprep", "#businessintelligence", "#CommitToCleanData", " # London"]

Mark

Berlin

["#CommitToCleanData", "#predictivetransformations", "#realbusinessintelligence", "0"]

Catherine

Paris

["#dataprep", "#startwiththeuser","#realbusinessintelligence", "# Paris"]

Dave

New York

["#NewYorklife", "dataprep", "bigdata", "0"]

Christy

SanFrancisco

[ "dataprep", "#pivot", "#aggregation", "#machinelearning"]

Extract query parameters from URLs

Source:

In this example, a list of URLs identifies the items in the shopping carts of visitors to your web site. You want to extract the shopping cart information embedded in the query parameters of the URL.

Username

cartURL

joe.robinson

http://example123.com/cart.asp?prodid=1001&qty=2

steph.schmidt

http://example123.com/cart.asp?prodid=1005&qty=4

jack.holmes

http://example123.com/cart.asp?prodid=2102&qty=1

tina.jones

http://example123.com/cart.asp?prodid=10412&qty=2

Transformation:

The following transformation extracts the list of query values from the URL. Note that the equals sign is included in the matching pattern so that you don't accidentally pick up numeric values from the non-parameter part of the URL:

Transformation Name

Extract matches into Array

Parameter: Column

cartURL

Parameter: Pattern matching elements in list

`=[digit]+`

The two query parameter values have been extracted into an array of values, including the equals sign, which must be removed:

Transformation Name

Replace text or pattern

Parameter: Column

cartURL

Parameter: Find

'='

Parameter: Replace with

''

Parameter: Match all occurrences

true

You can now unnest these values into separate columns:

Transformation Name

Unnest Objects into columns

Parameter: Column

extractlist_cartURL

Parameter: Paths to elements

'[0]','[1]'

After you rename the two columns to prodId and Qty, you can delete the column generated by the first transformation.

Results:

Username

cartURL

prodId

Qty

joe.robinson

http://example123.com/cart.asp?prodid=1001&qty=2

1001

2

steph.schmidt

http://example123.com/cart.asp?prodid=1005&qty=4

1005

4

jack.holmes

http://example123.com/cart.asp?prodid=2102&qty=1

2102

1

tina.jones

http://example123.com/cart.asp?prodid=10412&qty=2

10412

2

Extract counts from a ragged array using extractlist

This example illustrates how to extract values from a column.

Source:

The following dataset contains counts of support emails processed by each member of the support team for individual customers over a six-month period. In this case, you are interested in the total number of emails processed for each customer.

Unfortunately, the data is ragged, as there are no entries for a support team member if he or she has not answered an email for a customer.

custId

startDate

endDate

supportEmailCount

C001

7/15/2015

12/31/2015

["Max":"2","Ted":"0","Sally":"12","Jack":"6","Sue":"4"]

C002

7/15/2015

12/31/2015

["Sally":"4","Sue":"3"]

C003

7/15/2015

12/31/2015

["Ted":"12","Sally":"2"]

C004

7/15/2015

12/31/2015

["Jack":"7","Sue":"4","Ted":"5"]

If the data is imported from a CSV file, you might need to make some simple Replace Text or Pattern transformations to clean up the data to look like the above example.

Transformation:

Use the following transformation to extract just the numeric values from the supportEmailCount array:

Transformation Name

Extract matches into Array

Parameter: Column

supportEmailCount

Parameter: Pattern matching elements in list

`{digit}+`

You should now have a column extractlist_supportEmailCount containing a ragged array. You can use the following transformations to convert this data to a comma-separated list of values:

Transformation Name

Replace text or pattern

Parameter: Column

extractlist_supportEmailCount

Parameter: Find

`[`

Parameter: Replace with

''

Parameter: Match all occurrences

true

Transformation Name

Replace text or pattern

Parameter: Column

extractlist_supportEmailCount

Parameter: Find

`]`

Parameter: Replace with

''

Parameter: Match all occurrences

true

Transformation Name

Replace text or pattern

Parameter: Column

extractlist_supportEmailCount

Parameter: Find

`"`

Parameter: Replace with

''

Parameter: Match all occurrences

true

Convert the column to String data type.

You can now split out the column into separate columns containing individual values in the modified source. The limit parameter specifies the number of splits to create, resulting in 5 new columns, which is the maximum number of entries in the source arrays.

Transformation Name

Split by delimiter

Parameter: Column

extractlist_supportEmailCount

Parameter: Option

On pattern

Parameter: Match pattern

','

Parameter: Number of columns to create

4

You might have to set the type for each generated column to Integer. If you try to use a New Formula transformation to calculate the sum of all of the generated columns, it only returns values for the first row because the missing rows are null values.

In the columns containing null values, select the missing value bar in the data histogram. Select the Replace suggestion card, and modify the transformation to write a 0 in place of the null value, as follows:

Transformation Name

Edit column with formula

Parameter: Columns

extractlist_supportEmailCount3

Parameter: Formula

'0'

Parameter: Group rows by

ismissing([extractlist_supportEmailCount3])

Repeat this step for any other column containing null values.

You can now use the following to sum the values in the generated columns:

Transformation Name

New formula

Parameter: Formula type

Single row formula

Parameter: Formula

(extractlist_supportEmailCount1 + extractlist_supportEmailCount2 + extractlist_supportEmailCount3 + extractlist_supportEmailCount4 + extractlist_supportEmailCount5)

Results:

After renaming the generated column to totalSupportEmails and dropping the columns used to create it, your dataset should look like the following:

custId

startDate

endDate

supportEmailCount

totalSupportEmails

C001

7/15/2015

12/31/2015

["Max":"2","Ted":"0","Sally":"12","Jack":"6","Sue":"4"]

24

C002

7/15/2015

12/31/2015

["Sally":"4","Sue":"3"]

7

C003

7/15/2015

12/31/2015

["Ted":"12","Sally":"2"]

14

C004

7/15/2015

12/31/2015

["Jack":"7","Sue":"4","Ted":"5"]

16