Transformations > Python transformation > Example: Add an ID column to nonpartitioned data

Example: Add an ID column to nonpartitioned data

Your organization runs a solar thermal power system that uses sensors to monitor the health of the system. Currently, each sensor is identified by its location. Instead, you want to identify each sensor using an ID to simplify future analytics on the data.
You collect the following data on sensor readings:
Area A
7/9/2019 11:36:09
Area B
7/9/2019 16:43:42
Area C
7/9/2019 13:23:53
To add an ID column and assign ID values to each sensor, perform the following tasks:
Step 1. Create a Python transformation.
Create a Python transformation. On the Advanced tab, set the behavior to Passive.
Step 2. Pass data to the Python transformation.
Pass data from upstream transformations in the mapping to the Python transformation.
After you pass the data to the Python transformation, it contains the following incoming fields:
Step 3. Create output fields.
Use the Output Fields tab in the Python transformation to create the output field SensorID_out to represent the ID column.
Additionally, create the following output fields to pass incoming data to downstream transformations:
Step 4. Set the ID value for each row.
In the Main Python Code section, set the ID value for each row that is processed and write the data to the output fields using the following code:
SensorID="".join(str(x) for x in map(ord, SensorLocation))

SensorID_out = SensorID
SensorLocation_out = SensorLocation
LastReadingTime_out = LastReadingTime
Step 5. Run the mapping.
If the output fields in the Python transformation are linked directly to a Write transformation, the target contains the following data after you run the mapping:
Area A
7/9/2019 11:30:00
Area B
7/9/2019 11:35:00
Area C
7/9/2019 11:40:00