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Extract Values

Extracting one or more values from within a column of values can turn data into meaningful and discrete information. This section describes how to extract column data, the methods for which may vary depending on the data type.

Extract vs. Split

Extract and split transformations do not do the same thing:

  • A split transformation separates a single column into one or more separate columns based on one or more values in the source column that identify where the data should be split. These delimiters can be determined by the application or specified by the user when defining the transformation.

  • An extract transformation matches literal or pattern values from a source column and stores it in a separate column.

    Note

    The source column is untouched by extract transformations.

Extract methods

In the Transformer page, you can use the following methods to extract values:

Method

Description

By selection

Select part of a value in the data grid to prompt a series of suggestions on what to do with the data. Typically, extract options are near the top of the suggestions when you select part of a value.

By column menu

From the menu to the right of the column, select Extract and a sub-menu item to begin configuring a transformation.

By Transformer toolbar

At the top of the data grid, click the Extract icon in the Transformer toolbar to begin configuring extract transformations.

By Search panel

In the Search panel, enter extract to build a transformation from scratch.

Extract text or patterns

A primary use of extraction is to remove literal or patterned values of text from a column of values. Suppose your dataset included a column of LinkedIn updates. You can use one of the following methods to extract keywords from these values.

Extract single values

The following example transformation extracts the word #bigdata from the column msg_LinkedIn:

Transformation Name

Extract text or pattern

Parameter: Column to extract from

msg_LinkedIn

Parameter: Option

Custom text or pattern

Parameter: Text to extract

'#bigdata'

Parameter: Number of matches to extract

1

Notes:

  • The option parameter identifies that the pattern to match is a custom one specified by the user.

  • The Number of matches to extract parameter defaults to 1, meaning that the transformation extracts a maximum of one value from each cell. This value can be set from 1-50.

Extract values by example

You can generate a new column of values extracted from a source column by entering example values to match with source values. Values with similar patterns may also be matched based on your entered example value.

Tip

This method provides an easy way to build pattern-based matching for values in a source column.

For more information on transformation by example, see Overview of TBE.

Constrain matching

Within the extract transformation, you can specify literals or patterns before or after which the match is found. This method can be used to remove parts of each cell value from erroneously matching on the literal or pattern that is desired.

The following example extracts the second three-digit element of a phone number, skipping the area code:

Transformation Name

Extract text or pattern

Parameter: Column to extract from

phone_num

Parameter: Option

Custom text or pattern

Parameter: Text to extract

`{digit}`

Parameter: Number of matches to extract

1

Parameter: Ignore matches between

`{start}{digit}{3}\-`

Extract single patterns

You can also do pattern-based extractions using Alteryx patterns or regular expressions.

  • Regular expressions are a standards-based method of describing patterns of characters for matching purposes. Regular expressions are very powerful but can be difficult to use.

  • A Alteryx pattern is a proprietary method of describing patterns, which is much simpler to use than regular expressions.

  • For more information on both types of patterns, see Text Matching.

The following example extracts all words that begin with # in the msg_LinkedIn column:

Transformation Name

Extract text or pattern

Parameter: Column to extract from

msg_LinkedIn

Parameter: Option

Custom text or pattern

Parameter: Text to extract

`\#{alphanum-underscore}+`

Parameter: Number of matches to extract

50

Notes:

  • The Text to extract parameter has changed:

    Element

    Description

    Two back-ticks (`)

    Indicate that the expression between them represents a Alteryx pattern.

    \#

    The slash indicates that the character right after it should be interpreted as a character only; it should not be interpreted as any special character in the pattern.

    {alphanum-underscore}

    This Alteryx pattern element is used to indicate a single alphanumeric or underscore character.

    +

    Adding the plus sign after the above character signifies that the pattern can match on a sequence of alphanumeric or underscore characters of one or more length.

  • The Number of matches to extract parameter has been increased to grab up to 50 hashtags.

Advanced options

Option

Description

Number of patterns to extract

Set this value to the total number of patterns you wish to extract.

Note

This value determines the number of columns that are generated by the extraction. If no value is available, an empty value is written into the corresponding column.

The default is 1.

Ignore case

By default, pattern matching is case-sensitive. Select this checkbox to ignore case when matching.

Ignore matches between

You can enter a pattern here to describe any patterns that should not be part of any match. This option is useful if you have multiple instances of text but want to ignore the first one, for example.

Extract multiple values

In your pattern expressions, you can use the vertical pipe character (|) to define multiple patterns to find. The following example extracts any value from the myDate column that ends in 7 pr in 8:

Transformation Name

Extract text or pattern

Parameter: Column to extract from

myDate

Parameter: Text to extract

`{any}+7|{any}+8`

Parameter: End extracting before

`{end}`

You can use the vertical pipe in both Alteryx patterns and regular expressions.

Extract first or last characters

You can extract the first or last set of characters from a column into a new column. In the following example, the first five characters from the ProductName column are extracted into a new product identifier column:

Transformation Name

Extract by positions

Parameter: Column to extract from

ProductName

Parameter: Option

First characters

Parameter: Number of characters to extract

5

You can change the Option value to Last characters to extract from the right side of the column value.

Extract and remove

If you need to remove the characters that you extracted, you can use the following transformation. In this case, the first five characters, which were extracted in the previous transformation, are removed:

Transformation Name

Edit column with formula

Parameter: Columns

ProductName

Parameter: Formula

RIGHT(ProductName, LEN(ProductName)-5)

Extract by positions

You can extract values between specified index positions within a set of column values. In the following example, the text between the fifth and tenth characters in a column are extracted to a new column.

Tip

This extraction method is useful if the content before and after the match area is inconsistent and cannot be described using patterns. If it is consistent, you should use the Extract text or pattern transformation.

Transformation Name

Extract by positions

Parameter: Column to extract from

ProductName

Parameter: Option

Between two positions

Parameter: Starting position

5

Parameter: Ending position

10

Extract by Data Type

You can perform extractions that are specific to a data type or based on failures of the data to match a specified data type.

Extract date values

You can use functions to extract values from Datetime columns. The example below extracts the year value from the myDate column:

Transformation Name

New formula

Parameter: Formula type

Single row formula

Parameter: Formula

YEAR(myDate)

Parameter: New column name

myYear

The following functions can be used to extract values from a Datetime column, as long as the values are present in the formatted date:

Function Name

Description

DAY Function

Derives the numeric day value from a Datetime value. Source value can be a a reference to a column containing Datetime values or a literal.

MONTH Function

Derives the month integer value from a Datetime value. Source value can be a a reference to a column containing Datetime values or a literal.

YEAR Function

Derives the four-digit year value from a Datetime value. Source value can be a a reference to a column containing Datetime values or a literal.

HOUR Function

Derives the hour value from a Datetime value. Generated hours are expressed according to the 24-hour clock.

MINUTE Function

Derives the minutes value from a Datetime value. Minutes are expressed as integers from 0 to 59.

SECOND Function

Derives the seconds value from a Datetime value. Source value can be a a reference to a column containing Datetime values or a literal.

You can also reformat the whole Datetime column using the DATEFORMAT function. The following reformats the column to show only the two-digit year:

Transformation Name

Edit column with formula

Parameter: Columns

myDate

Parameter: Formula

DATEFORMAT(myDate, "yy")

Extract numeric values

You can extract numerical data from text values. In the following example, the first number is extracted from the address column, which would correspond to extracting the street number for the address:

Transformation Name

Extract patterns

Parameter: Column to extract from

address

Parameter: Option

Numbers

Parameter: Number of matches to extract

1

Empty values in this new column might indicate a formatting problem with the address.

Tip

If you set the number of patterns to extract to 2 for the address column, you might extract apartment or suite information.

Extract components of a URL

URL components

Using functions, you can extract specific elements of a valid URL. The following transformation pulls the domain values from the myURL column:

Transformation Name

New formula

Parameter: Formula type

Single row formula

Parameter: Formula

DOMAIN(myURL)

Parameter: New column name

myDomain

In some cases, the function may not return values. For example, the SUBDOMAIN function returns empty values if there is no sub-domain part of the URL.

The following functions can be used to extract values from a set of URLs:

Function Name

Description

HOST Function

Finds the host value from a valid URL. Input values must be of URL or String type and can be literals or column references.

DOMAIN Function

Finds the value for the domain from a valid URL. Input values must be of URL or String type.

SUBDOMAIN Function

Finds the value a subdomain value from a valid URL. Input values must be of URL or String type.

SUFFIX Function

Finds the suffix value after the domain from a valid URL. Input values must be of URL or String type.

URLPARAMS Function

Extracts the query parameters of a URL into an Object. The Object keys are the parameter's names, and its values are the parameter's values. Input values must be of URL or String type.

Query parameters

You can extract query parameter values from an URL. The following example extracts the store_id value from the storeURL field value:

Transformation Name

Extract patterns

Parameter: Column to extract from

storeURL

Parameter: Option

HTTP Query strings

Parameter: Fields to extract

store_id

Extract object values

If your data includes sets of arrays, you can extract array elements into columns for each key, with the values written to each key column.

Suppose your restaurant dataset includes a set of characteristics in the restFeatures column in the following JSON format:

{
  "Credit": "Y",
  "Accessible": "Y",
  "Restrooms": "Y",
  "EatIn": "Y",
  "ToGo": "N",
  "AlcoholBeer": "Y",
  "AlcoholHard": "N",
  "TotalTables": "10",
  "TotalTableSeats": "36",
  "Counter": "Y",
  "CounterSeats": "8"
}

You can use the following transformation to extract the values from TotalTableSeats and CounterSeats into separate columns:

Transformation Name

Unnest Objects into columns

Parameter: Column

restFeatures

Parameter: Paths to elements - 1

TotalTableSeats

Parameter: Paths to elements - 2

CounterSeats

Parameter: Include original column name

Selected

After the above is executed, you can perform a simple sum of the TotalTableSeats and CounterSeats columns to determine the total number of seats in the restaurant.

Extract array values

In some cases, your data may contain arrays of repeated key-value pairs, where each pair would exist on a separate line. Suppose you have a column called, Events, which contains date and time information about the musician described in the same row of data. The Events column might look like the following:

[{"Date":"2018-06-15","Time":"19:00"},{"Date":"2018-06-17","Time":"19:00"},{"Date":"2018-06-19","Time":"20:00"},{"Date":"2018-06-20","Time":"20:00"}]

The following transformation creates a separate row for each entry in the Events column, populating the other fields in the new rows with the data from the original row:

Note

This type of transformation can significantly increase the size of your dataset.

Transformation Name

Expand arrays into rows

Parameter: Column

Events

Extract Values into a List

You can also extract sets of values into an array list of values.

Tip

This transformation is useful for extracting types or patterns of information from a single column.

Extract matches into array

Using Alteryx patterns, you can extract the values of the column to form a new column of arrays. The following example shows the usage of {any} pattern to extract the cell values and form a new array column.

Transformation:

Transformation Name

Extract matches into Array

Parameter: Column

product

Parameter: Pattern matching elements in the list

`{any}`

Parameter: Delimiter separating each element

`,`

Results:

Before

After

socks, socks, socks

["socks", "socks", "socks"]

pants, pants

["pants", "pants"]

Extract hashtags

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"]