Page tree

 

Support | BlogContact Us | 844.332.2821

 

Contents:

The cloud-based version of Trifacta Wrangler is now available! Read all about it, and register for your free account.

Contents:


This section describes techniques to standardize text values in your datasets using patterns. From the Column Details panel, you can review and select patterns in the column's data. These selections can be used as the basis for converting all applicable values to the selected format.

NOTE: Pattern-based conversions can be applied to any data type. 

In the Patterns tab, click a group of patterns and then review the Convert suggestion to define how the pattern matches can be converted to a single standardized format.

NOTE: The application does not suggest pattern-based conversions that add or remove alphanumeric characters.

 

Figure: Selecting Datetime patterns in the Patterns tab

In the above, the pattern block prompts suggestions for Convert tasks based on the selected patterns. 

  • Click Edit to modify the task.
  • Click Add to add the task as a step to your recipe.

Example - Phone number patterns

For columns containing phone number data, you can use the Patterns tab to standardize formatting options. Consider the following values, which are valid phone numbers. Next to each value is a pattern representing the value:

PhoneNum

Trifacta pattern

(415) 555-1212
\({{digit}{3})\) ({digit}{3})\-({digit}{4})
415-555-1212
({digit}{3})\-({digit}{3})\-({digit}{4})
415.555.1212
({digit}{3}).({digit}{3}).({digit}{3})
415 555-1212
({digit}{3}) ({digit}{3})\-({digit}{4})
1+415-555-1212
1\+{digit}{3}\-{digit}{3}\-{digit}{4}

In the Patterns tab, you can select the patterns to which you would like the other patterns in the same pattern group to be converted. Below, the selected target pattern becomes the pattern to which other patterns in the column values are converted:

 

NOTE: You may have to modify the phone number values before attempting the conversion, as they may contain extra alphanumeric values. For example, international country codes (such as 044) or a preceding 1+ required in long-distance numbers, may need to extracted or removed from the column values prior to conversion.

Generic Conversions

Below are types of conversions that are supported and not supported.

Supported:

Example Source ValueExample Target ValueNotes
123.456.7890123-456-7890Changing symbolic characters
(123) 456-7890123 456-7890Removing symbolic characters
(123)456-7890(123)-456-7890Adding symbolic characters
1234567890123-456-7890Splitting a long character group and adding symbolic characters
123-456-78901234567890Merging multiple character groups and removing symbolic characters


Not supported:

Example Source ValueExample Target ValueNotes
123.456.7890+1.123.456.7890Adding a new character group
+1.123.456.7890123.456.7890Deleting a character group (alphanumeric characters cannot be deleted through pattern standardization)
Adam WilsonA WilsonPartial deletion of data from a character group
+1 (123) 456-7890+001 (123) 456-7890Prepending or appending a character group with specified characters

Datetime Patterns

For columns of Datetime type, the available Convert mappings are based upon the supported date formats in the platform. Standardization of Datetime patterns is a specific implementation. 

Notes on Datetime patterns:

Two-digit years (YY) do not yield four-digit year (YYYY) suggestions due to ambiguity. For example, it is unclear if 50 should map to 1950 or 2050.

For performance reasons, a maximum of two semantic standardizations can be applied at once. Examples:

Source ValuePossible StandardizationSemantic MappingsStatus
Jan 1, 1981 01/01/1981
  • Jan01 
  • 101
ok (2 mappings)
Jan 1, 1981
01/01/81
  • Jan  01 
  • 1  01
  • 1981  81
Not suggested (3 mappings)

For more information on supported formats, see Datetime Data Type.

For more information on converting Datetime values to a different format, see DATEFORMAT Function.

Your Rating: Results: PatheticBadOKGoodOutstanding! 3 rates

This page has no comments.