Transpose Advanced

Transpose Node

Type

transform

Class

fire.nodes.etl.NodeTransposeColumns

Fields

Name	Title	Description
transpose	Transpose
keyCols	Key Columns	Columns to keep fixed (identifiers)
dataCols	Data Columns	Columns to unpivot (convert to rows)
inputColumnPropagation	Select all fields as Data Columns	When enabled, automatically include all unspecified input columns as data cols leaving selected key cols.
schema	InferSchema
outputColNames	Column Names of the Table	Output Columns Names of the Table
outputColTypes	Column Types of the Table	Output Column Types of the Table
outputColFormats	Column Formats	Output Column Formats

Details

This node creates a DataFrame by transposing (unpivoting) the incoming DataFrame.

It converts selected data columns into rows, preserving key columns, and creates new columns for the original column names (Name) and their corresponding values (Value).

When Input Column Propagation is enabled (inputColumnPropagation = True), the node automatically treats all non-key columns as data columns.

This means any new or unknown columns appearing in the input will also be included in the transpose operation without requiring reconfiguration.

Examples

### Example 1: Standard Transpose

• Incoming DataFrame:*

DEPT         |    NEW JERSEY       |    NEW YORK
---------------------------------------------------
FRONT DESK   |    1                |    1
MARKETING    |    1                |    1
HR           |    0                |    1
SALES        |    1                |    0
MAINTENANCE  |    1                |    1

• Configuration:*

KEY COLUMNS : DEPT

DATA COLUMNS : NEW JERSEY, NEW YORK

INPUT COLUMN PROPAGATION : False

• Output DataFrame:*

DEPT         |    Name             |    Value
---------------------------------------------------
FRONT DESK   |    NEW JERSEY       |    1
FRONT DESK   |    NEW YORK         |    1
MARKETING    |    NEW JERSEY       |    1
MARKETING    |    NEW YORK         |    1
HR           |    NEW JERSEY       |    0
HR           |    NEW YORK         |    1
SALES        |    NEW JERSEY       |    1
SALES        |    NEW YORK         |    0
MAINTENANCE  |    NEW JERSEY       |    1
MAINTENANCE  |    NEW YORK         |    1

---

### Example 2: With Input Column Propagation Enabled

• Incoming DataFrame:*

DEPT         |    NEW JERSEY   |    NEW YORK   |    CALIFORNIA   |    TEXAS
---------------------------------------------------------------------------
FRONT DESK   |    1            |    1          |    0            |    1
MARKETING    |    1            |    1          |    1            |    1
HR           |    0            |    1          |    0            |    1
SALES        |    1            |    0          |    1            |    0

• Configuration:*

KEY COLUMNS : DEPT

DATA COLUMNS : (not explicitly selected — auto-inferred)

INPUT COLUMN PROPAGATION : True

• Explanation:*

Since propagation is enabled, all non-key columns (NEW JERSEY, NEW YORK, CALIFORNIA, TEXAS)

are automatically considered for transposition.

• Output DataFrame:*

DEPT         |    Name           |    Value
--------------------------------------------
FRONT DESK   |    NEW JERSEY     |    1
FRONT DESK   |    NEW YORK       |    1
FRONT DESK   |    CALIFORNIA     |    0
FRONT DESK   |    TEXAS          |    1
MARKETING    |    NEW JERSEY     |    1
MARKETING    |    NEW YORK       |    1
MARKETING    |    CALIFORNIA     |    1
MARKETING    |    TEXAS          |    1
HR           |    NEW JERSEY     |    0
HR           |    NEW YORK       |    1
HR           |    CALIFORNIA     |    0
HR           |    TEXAS          |    1
SALES        |    NEW JERSEY     |    1
SALES        |    NEW YORK       |    0
SALES        |    CALIFORNIA     |    1
SALES        |    TEXAS          |    0

• Result:*

The node automatically includes all new fields without requiring updates to the configuration.