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 Fold multiple columns 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 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, or to limit the number of created columns with the “Max nb. columns to create” option.

More details are available in the reference

Unfold an array

This processor transforms array values into occurence columns.

For example, with the following dataset:

id	words
0	[‘hello’, ‘hello’, ‘world’]
1	[‘hello’, ‘world’]
2	[‘hello’]
3	[‘world’, ‘world’]

Applying the “Unfold an array” processor on the “words” column will generate the following result:

id	words	words_hello	words_world
0	[‘hello’, ‘hello’, ‘world’]	2	1
1	[‘hello’, ‘world’]	1	1
2	[‘hello’]	1
3	[‘world’, ‘world’]		2

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

• contains the number of occurrences of the value found in the original column,

• remains empty if the original column does not contain this value.

Warning

Limitations

The limitations that apply to the Unfold processor also apply to the Unfold an array processor.

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