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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:

  • 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: 

MethodDescription
By selectionSelect 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 menuFrom the menu to the right of the column, select Extract and a sub-menu item to begin configuring a transformation. See Column Menus.
By Transformer toolbarAt the top of the data grid, click the Extract icon in the Transformer toolbar to begin configuring extract transformations. See Transformer Toolbar.
By Search panelIn the Search panel, enter extract to build a transformation from scratch. See Search Panel.

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:

extractpatterns type: custom col: msg_LinkedIn on: '#bigdata' limit: 1

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. 

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 Trifacta 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.  
  • Trifacta 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:

extractpatterns type: custom col: msg_LinkedIn on: `\#{alphanum-underscore}+` limit: 50

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:

    ElementDescription

    Two back-ticks (`)

    Indicate that the expression between them represents a Trifacta 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 Trifacta 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

OptionDescription
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 caseBy default, pattern matching is case-sensitive. Select this checkbox to ignore case when matching.
Ignore matches betweenYou 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 Trifacta 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:

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 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 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:

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

 

 

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