Reshaping

Reshaping processors are used to change the « shape » (rows/columns) of the data.

DSS provides the following reshaping processors

Split and Fold

The Split and fold processor creates new lines by splitting the values of a column.

For example, with the following dataset:

customer_id events browser
1 login,product,buy Mozilla
2 login,product,logout Chrome

Applying “Split and Fold” on the “events” column with ”,” as the separator will generate the following result:

customer_id events browser
1 login Mozilla
1 product Mozilla
1 buy Mozilla
2 login Chrome
2 product Chrome
2 logout Chrome

More details are available in the reference

Fold multiple columns

The processors/fold-columns-by-names processor takes values from multiple columns and transforms them to one line per column.

For example, with the following dataset representing monthly scores:

person age 01/2014 02/2014 03/2014
John 24 3 4 3
Sidney 31   6 9
Bill 33 1   4

We would like to get one line per (month, person) couple with the score.

Applying the “Split multiple columns” processor with:

  • 3 columns in the “columns list”: 01/2014, 02/2014, 03/2014
  • “month” as the “fold name column”
  • “score” as the “fold value column”

will generate the following result:

person age month score
John 24 01/2014 3
John 24 02/2014 4
John 24 03/2014 6
Sidney 31 01/2014  
Sidney 31 02/2014 6
Sidney 31 03/2014 9
Bill 33 01/2014 1
Bill 33 02/2014  
Bill 33 03/2014 4

More details are available in the reference

Fold multiple columns by pattern

This processor is a variant of fold-multiple-label, where the columns to fold are specified by a pattern instead of a list. The processor only creates lines for non-empty columns.

For example, using “tag_(.*)” as column to fold pattern :

name n_connection tag_1 tag_2 tag_3
Florian 16570 bigdata python puns

becomes

name n_connection tag rank
Florian 16570 bigdata 1
Florian 16570 python 2
Florian 16570 puns 3

More details are available in the reference

Unfold

This processor transforms cell values into binary columns.

For example, with the following dataset:

id type
0 A
1 A
2 C
3 B

Applying the “Unfold” processor on the “type” column will generate the following result:

id type_A type_C type_B
0 1    
1 1    
2   1  
3     1

Each value of the unfolded column will create a new column. This new column:

  • contain the value “1” if the original column contained this value
  • remains empty else.

Unfolding is often used to find some correlations to a particular value, or for creating graphs.

Warning

Limitations

The Unfold processor dynamically creates new columns based on the actual data within the cells.

Due to the way the schema is handled when you create a preparation recipe, only the values that were found at least once in the sample will create columns in the output dataset.

Unfolding a column with a large number of values will create a large number of columns. This can cause performance issues. It is highly recommended not to unfold columns with more than 100 values.

More details are available in the reference

Split and Unfold

This processor splits multiple values in a cell and transforms them into columns.

For example, with the following dataset:

customer_id events
1 login, product, buy
2 login, product, logout

We get:

customer_id events_login events_product events_buy events_logout
1 1 1 1  
2 1 1   1

The unfolded column is deleted.

Warning

Limitations

The limitations that apply to the “Unfold” processor also apply to the “Split and Unfold” processor.

More details are available in the reference

Triggered Unfold

This processor is used to reassemble several rows when a specific value is encountered. It is useful for analysis of “interaction sessions” (a series of events with a specific event marking the beginning of a new interaction session). For example, while analyzing the logs of a web game, the “start game” event would be the beginning event.

More details are available in the reference