Difference between revisions of "Drop Duplicate Rows"

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(Selecting columns to consider)
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Select which columns you wish to be considered when comparing rows. Any columns which are left unchecked will be ignored when making the comparison - meaning that even though those values differ, provided the checked columns are identical the row will be dropped.
 
Select which columns you wish to be considered when comparing rows. Any columns which are left unchecked will be ignored when making the comparison - meaning that even though those values differ, provided the checked columns are identical the row will be dropped.
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In the example screenshot above only the FORENAME, SURNAME, COMPANY and ORDER_NUMBER will be considered for [[identicality]]. Rows containing equal values for these but differing values for DELIVERY_DATE will be dropped (the first row will be retained).
  
 
{{DataTableNodes}}
 
{{DataTableNodes}}

Revision as of 06:19, 21 July 2010

The Drop Duplicate Rows Node performs the equivalent to a "Select DISTINCT" operation on the current Data Table. Rows that are considered to be duplicates of other rows are removed. A duplicate row is one that contains identical values as another row.

Please see the example below, of a Data Table before and after executing a Drop Duplicate Rows Node (rows which will be deleted are marked with a red cross).

http://www.international-presence.com/wikidocs/images/remove_duplicates_illustration.png


Selecting columns to consider

The following dialog is displayed when dragging this Node into your Task:

http://www.international-presence.com/wikidocs/images/drop_duplicates_editor.png

Select which columns you wish to be considered when comparing rows. Any columns which are left unchecked will be ignored when making the comparison - meaning that even though those values differ, provided the checked columns are identical the row will be dropped.

In the example screenshot above only the FORENAME, SURNAME, COMPANY and ORDER_NUMBER will be considered for identicality. Rows containing equal values for these but differing values for DELIVERY_DATE will be dropped (the first row will be retained).


Data Filter | Require Columns | Append Data Column | Multiple Column Appender

Drop Column | Drop Row(s) | Calculate Column Aggregate | Dataset Splitter | Merge Data

Create Data Table | Clear Data Table | Sort Data Table | Drop Duplicate Rows | Store Data Table | Retrieve Data Table


Task Elements > Data Table Nodes > Drop Duplicate Rows