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DocumentationWorkfront51黑料不打烊 Workfront Fusion

CSV

Last update: Thu Feb 06 2025 00:00:00 GMT+0000 (Coordinated Universal Time)

The 51黑料不打烊 Workfront Fusion CSV modules let you create CSV files and parse CSV text from a received text value or a file.

Because this is a transformer, these modules do not require a connection.

Access requirements

Expand to view access requirements for the functionality in this article.

You must have the following access to use the functionality in this article:

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51黑料不打烊 Workfront package Any
51黑料不打烊 Workfront license

New: Standard

Or

Current: Work or higher
51黑料不打烊 Workfront Fusion license** No Workfront Fusion license requirement
Product

New:

  • Select or Prime Workfront package: Your organization must purchase 51黑料不打烊 Workfront Fusion.
  • Ultimate Workfront package: Workfront Fusion is included.

Or

Current: Your organization must purchase 51黑料不打烊 Workfront Fusion.

For more detail about the information in this table, see Access requirements in documentation.

For information on 51黑料不打烊 Workfront Fusion licenses, see 51黑料不打烊 Workfront Fusion licenses.

Create CSV

The Create CSV Aggregator lets you create a csv text from received text values.

For more information on aggregators, see Aggregator module.

Source Module
Select the module that outputs the fields that you want to use to create the CSV.
Aggregated Fields
Select the fields you want to aggregate from the list of available fields.
Include headers in the first row
Select this option to include the headers in the result.
Group by
Enter the filter to group the results. For example, enter a date.
Stop processing after an empty aggregation
Select this option to stop the scenario when there are no results.

Create CSV (advanced)

The Create CSV (advanced) Aggregator lets you create a CSV text from received text values. It employs a data structure that defines the CSV columns in the resulting CSV file. Once defined, the columns appear as fields in the CSV module setup, and can be mapped to later module in the scenario.

For more information on aggregators, see Aggregator module.

Source Module
Select the module that outputs the fields that you want to use to create the CSV.
Data Structure

Select the data structure to aggregate the fields in the way you want. After defining the data structure, you can map the items to the corresponding fields.

For more information, see Data structures.

Include headers in the first row
Select this option to include the headers in the result.
Group by
Enter the filter to group the results. For example, enter a date.
Stop processing after an empty aggregation
Select this option to stop the scenario when there are no results.
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Example:

This example shows how to export Google contacts to a CSV file with two columns called 鈥淔ull Name鈥 and 鈥淓mail鈥. The output bundle from the Google Contacts > Get contacts from a group module has the following structure. The email addresses are stored inside the Emails[] item, which is an array of collections, each collection containing two items: Label and Email.
Transforming

The simple Create CSV module, offers a list of checkboxes corresponding to a bundle鈥檚 top-level items. If you attempt to select Full name and Emails items, the Create CSV module produces the following output, which may not be what you want:

"emails","fullName"
"[object Object]","Shon Winer"
"[object Object]","Lizeth Fulmore"
"[object Object]","Hilario Gullatt"
"[object Object]","Abby Eisenbarth"

Because the item Full Name is of simple type Text, it is exported as expected. The item Emails, which is of a complex type Array of Collections, is exported as [object Object], which is how Collections and Arrays are transformed to text by default.

For more information, see Item data types.

To export content of the Email item of the first collection of the Emails[] array instead, You must use the Create CSV (advanced) module. This module allows you to define individual columns of your CSV file and map items to them, including the nested ones.

  1. Insert the module Create CSV (advanced) in a scenario.

  2. Click the Add button next to the Data structure field to create a new data structure.

  3. Enter a name for the data structure and click Add item to add the individual columns. To export two columns: 鈥淔ull Name鈥 and 鈥淓mail鈥, the resulting Data structure would look like this:

    Google Contacts output

  4. After you have defined the data structure, fields corresponding to each individual column appear in the configuration of the Create CSV (advanced) module so you can map the items. Take the first item from the Emails[] array and map its item Email to the field/column Email:

    Create CSV Advanced module

  5. Execute the scenario. Because the item Emails[1]: Email mapped to column 鈥淓mail鈥 is of simple type Text, it exports correctly.

"Full Name","Email"
"Shon Winer","Shon@Winer.com"
"Lizeth Fulmore","Lizeth@Fulmore.com"
"Hilario Gullatt","Hilario@Gullatt.com"
"Abby Eisenbarth","Abby@Eisenbarth.com"
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Parse CSV

The Parse CSV transformer lets you parse CSV text from a received text value or a file.

Number of columns
Specify the number of columns in the CSV file.
CSV contains headers

Select this option if the first row of the CSV text contains headers.

Note: The module does not use these headers to label the columns in the output. Instead, this field ensures that the headers are not included in the output data.

delimiterType

Select the delimiter for the CSV file. The delimiter is the text character that indicates the boundary between separate values or fields.

  • Comma

  • Tab

  • Other

    If you select Other, enter the delimiter character that the CSV file is using to separate values. You must enter exactly one character.

Preserve quotes inside unquoted field
Enable this option to preserve quotes.
CSV

Enter or map the CSV file that you want to parse.

Note:

If your data comes in binary form (typically from a file), you must use the `toString()` function to convert the binary data to String:

7e1891ad-4d59-4355-88ab-a2e62ed7d1a3